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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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Technologies
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2227-7080
Published by MDPI Homepage  [258 journals]
  • Technologies, Vol. 12, Pages 93: Multi-Objective Optimisation of the
           Battery Box in a Racing Car

    • Authors: Chao Ma, Caiqi Xu, Mohammad Souri, Elham Hosseinzadeh, Masoud Jabbari
      First page: 93
      Abstract: The optimisation of electric vehicle battery boxes while preserving their structural performance presents a formidable challenge. Many studies typically involve fewer than 10 design variables in their optimisation processes, a deviation from the reality of battery box design scenarios. The present study, for the first time, attempts to use sensitivity analysis to screen the design variables and achieve an efficient optimisation design with a large number of original design variables. Specifically, the sensitivity analysis method was proposed to screen a certain number of optimisation variables, reducing the computational complexity while ensuring the efficiency of the optimisation process. A combination of the Generalised Regression Neural Network (GRNN) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was employed to construct surrogate models and solve the optimisation problem. The optimisation model integrates these techniques to balance structural performance and weight reduction. The optimisation results demonstrate a significant reduction in battery box weight while maintaining structural integrity. Therefore, the proposed approach in this study provides important insights for achieving high-efficiency multi-objective optimisation of battery box structures.
      Citation: Technologies
      PubDate: 2024-06-25
      DOI: 10.3390/technologies12070093
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 94: Transformer-Based Water Stress Estimation
           Using Leaf Wilting Computed from Leaf Images and Unsupervised Domain
           Adaptation for Tomato Crops

    • Authors: Makoto Koike, Riku Onuma, Ryo Adachi, Hiroshi Mineno
      First page: 94
      Abstract: Modern agriculture faces the dual challenge of ensuring sustainability while meeting the growing global demand for food. Smart agriculture, which uses data from the environment and plants to deliver water exactly when and how it is needed, has attracted significant attention. This approach requires precise water management and highly accurate real-time monitoring of crop water stress. Existing monitoring methods pose challenges such as the risk of plant damage, costly sensors, and the need for expert adjustments. Therefore, a low-cost, highly accurate water stress estimation model was developed that uses deep learning and commercially available sensors. The model uses the relative stem diameter as a water stress index and incorporates data from environmental sensors and an RGB camera, which are processed by the proposed daily normalization. In addition, domain adaptation in our Transformer model was implemented to enable robust learning in different areas. The accuracy of the model was evaluated using real cultivation data from tomato crops, achieving a coefficient of determination (R2) of 0.79 in water stress estimation. Furthermore, the model maintained a high level of accuracy when applied to different areas, with an R2 of 0.76, demonstrating its high adaptability under different conditions.
      Citation: Technologies
      PubDate: 2024-06-25
      DOI: 10.3390/technologies12070094
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 95: Integrating Artificial Intelligence to
           Biomedical Science: New Applications for Innovative Stem Cell Research and
           Drug Development

    • Authors: Minjae Kim, Sunghoi Hong
      First page: 95
      Abstract: Artificial intelligence (AI) is rapidly advancing, aiming to mimic human cognitive abilities, and is addressing complex medical challenges in the field of biological science. Over the past decade, AI has experienced exponential growth and proven its effectiveness in processing massive datasets and optimizing decision-making. The main content of this review paper emphasizes the active utilization of AI in the field of stem cells. Stem cell therapies use diverse stem cells for drug development, disease modeling, and medical treatment research. However, cultivating and differentiating stem cells, along with demonstrating cell efficacy, require significant time and labor. In this review paper, convolutional neural networks (CNNs) are widely used to overcome these limitations by analyzing stem cell images, predicting cell types and differentiation efficiency, and enhancing therapeutic outcomes. In the biomedical sciences field, AI algorithms are used to automatically screen large compound databases, identify potential molecular structures and characteristics, and evaluate the efficacy and safety of candidate drugs for specific diseases. Also, AI aids in predicting disease occurrence by analyzing patients’ genetic data, medical images, and physiological signals, facilitating early diagnosis. The stem cell field also actively utilizes AI. Artificial intelligence has the potential to make significant advances in disease risk prediction, diagnosis, prognosis, and treatment and to reshape the future of healthcare. This review summarizes the applications and advancements of AI technology in fields such as drug development, regenerative medicine, and stem cell research.
      Citation: Technologies
      PubDate: 2024-06-26
      DOI: 10.3390/technologies12070095
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 96: Deep Learning for Skeleton-Based Human
           Activity Segmentation: An Autoencoder Approach

    • Authors: Md Amran Hossen, Abdul Ghani Naim, Pg Emeroylariffion Abas
      First page: 96
      Abstract: Automatic segmentation is essential for enhancing human activity recognition, especially given the limitations of publicly available datasets that often lack diversity in daily activities. This study introduces a novel segmentation method that utilizes skeleton data for a more accurate and efficient analysis of human actions. By employing an autoencoder, this method extracts representative features and reconstructs the dataset, using the discrepancies between the original and reconstructed data to establish a segmentation threshold. This innovative approach allows for the automatic segmentation of activity datasets into distinct segments. Rigorous evaluations against ground truth across three publicly available datasets demonstrate the method’s effectiveness, achieving impressive average annotation error, precision, recall, and F1-score values of 3.6, 90%, 87%, and 88%, respectively. This illustrates the robustness of the proposed method in accurately identifying change points and segmenting continuous skeleton-based activities as compared to two other state-of-the-art techniques: one based on deep learning and another using the classical time-series segmentation algorithm. Additionally, the dynamic thresholding mechanism enhances the adaptability of the segmentation process to different activity dynamics improving overall segmentation accuracy. This performance highlights the potential of the proposed method to significantly advance the field of human activity recognition by improving the accuracy and efficiency of identifying and categorizing human movements.
      Citation: Technologies
      PubDate: 2024-06-27
      DOI: 10.3390/technologies12070096
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 97: Tongue Disease Prediction Based on
           Machine Learning Algorithms

    • Authors: Hassoon, Al-Naji, Khalid, Chahl
      First page: 97
      Abstract: The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.
      Citation: Technologies
      PubDate: 2024-06-28
      DOI: 10.3390/technologies12070097
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 98: Evaluating Factors Shaping Real-Time
           Internet-of-Things-Based License Plate Recognition Using Single-Board
           Computer Technology

    • Authors: Paniti Netinant, Siwakron Phonsawang, Meennapa Rukhiran
      First page: 98
      Abstract: Reliable and cost-efficient license plate recognition (LPR) systems enhance security, traffic management, and automated toll collection in real-world applications. This study addresses optimal unique configurations for enhancing LPR system accuracy and reliability by evaluating the impact of camera angle, object velocity, and distance on the efficacy of real-time LPR systems. The Internet of Things (IoT) LPR framework is proposed and utilized on single-board computer (SBC) technology, such as the Raspberry Pi 4 platform, with a high-resolution webcam using advanced OpenCV and OCR–Tesseract algorithms applied. The research endeavors to simulate common deployment scenarios of the real-time LPR system and perform thorough testing by leveraging SBC computational capabilities and the webcam’s imaging capabilities. The testing process is not just comprehensive, but also meticulous, ensuring the system’s reliability in various operational settings. We performed extensive experiments with a hundred repetitions at diverse angles, velocities, and distances. An assessment of the data’s precision, recall, and F1 score indicates the accuracy with which Thai license plates are identified. The results show that camera angles close to 180° significantly reduce perspective distortion, thus enhancing precision. Lower vehicle speeds (<10 km/h) and shorter distances (<10 m) also improve recognition accuracy by reducing motion blur and improving image clarity. Images captured from shorter distances (approximately less than 10 m) are more accurate for high-resolution character recognition. This study substantially contributes to SBC technology utilizing IoT-based real-time LPR systems for practical, accurate, and cost-effective implementations.
      Citation: Technologies
      PubDate: 2024-07-01
      DOI: 10.3390/technologies12070098
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 99: Analysis of the Use of Artificial
           Intelligence in Software-Defined Intelligent Networks: A Survey

    • Authors: Bayron Jesit Ospina Cifuentes, Álvaro Suárez, Vanessa García Pineda, Ricardo Alvarado Jaimes, Alber Oswaldo Montoya Benitez, Juan David Grajales Bustamante
      First page: 99
      Abstract: The distributed structure of traditional networks often fails to promptly and accurately provide the computational power required for artificial intelligence (AI), hindering its practical application and implementation. Consequently, this research aims to analyze the use of AI in software-defined networks (SDNs). To achieve this goal, a systematic literature review (SLR) is conducted based on the PRISMA 2020 statement. Through this review, it is found that, bottom-up, from the perspective of the data plane, control plane, and application plane of SDNs, the integration of various network planes with AI is feasible, giving rise to Intelligent Software Defined Networking (ISDN). As a primary conclusion, it was found that the application of AI-related algorithms in SDNs is extensive and faces numerous challenges. Nonetheless, these challenges are propelling the development of SDNs in a more promising direction through the adoption of novel methods and tools such as route optimization, software-defined routing, intelligent methods for network security, and AI-based traffic engineering, among others.
      Citation: Technologies
      PubDate: 2024-07-02
      DOI: 10.3390/technologies12070099
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 100: Effect of Physical Parameters on Fatigue
           Life of Materials and Alloys: A Critical Review

    • Authors: Amit Kaimkuriya, Balaguru Sethuraman, Manoj Gupta
      First page: 100
      Abstract: Fatigue refers to the progressive and localized structural damage that occurs when a material is subjected to repeated loading and unloading, typically at levels below its ultimate strength. Several failure mechanisms have been observed in practical scenarios, encompassing high-cycle, low-cycle, thermal, surface, corrosion, and fretting fatigue. Fatigue, connected to the failure of numerous engineered products, stands out as a prevalent cause of structural failure in service. Conducting research on the advancement and application of fatigue analysis technologies is crucial because fatigue analysis plays a critical role in determining the service life of components and mitigating the risk of failure. This study compiles data from a wide range of sources and offers a thorough summary of the state of fatigue analysis. It focuses on the effects of different parameters, including hardness, temperature, residual stresses, and hardfacing, on the fatigue life of different materials and their alloys. The fatigue life of alloys is typically high at low temperatures, but it is significantly reduced at high temperatures or under high-stress conditions. One of the main causes of lower fatigue life is residual stress. High-temperature conditions and hardfacing processes cause the development of tensile residual stresses, which in turn decreases fatigue life. But, if the hardness of the material significantly increases due to hardfacing, then the fatigue life also increases. This manuscript focuses on reviewing the research on fatigue-life prediction methods, shortcomings, and recommendations.
      Citation: Technologies
      PubDate: 2024-07-03
      DOI: 10.3390/technologies12070100
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 101: Smartphone-Based Citizen Science Tool
           

    • Authors: Panagiotis Christakakis, Garyfallia Papadopoulou, Georgios Mikos, Nikolaos Kalogiannidis, Dimosthenis Ioannidis, Dimitrios Tzovaras, Eleftheria Maria Pechlivani
      First page: 101
      Abstract: In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool’s applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices.
      Citation: Technologies
      PubDate: 2024-07-03
      DOI: 10.3390/technologies12070101
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 102: Technique of High-Field Electron
           Injection for Wafer-Level Testing of Gate Dielectrics of MIS Devices

    • Authors: Dmitrii V. Andreev, Vladimir V. Andreev, Marina Konuhova, Anatoli I. Popov
      First page: 102
      Abstract: We propose a technique for the wafer-level testing of the gate dielectrics of metal–insulator–semiconductor (MIS) devices by the high-field injection of electrons into the dielectric using a mode of increasing injection current density up to a set level. This method provides the capability to control a change in the charge state of the gate dielectric during all the testing. The proposed technique makes it possible to assess the integrity of the thin dielectric and at the same time to control the charge effects of its degradation. The method in particular can be used for manufacturing processes to control integrated circuits (ICs) based on MIS structures. In the paper, we propose an advanced algorithm of the Bounded J-Ramp testing of the gate dielectric and receive its approval when monitoring the quality of the gate dielectrics of production-manufactured MIS devices. We found that the maximum value of positive charge obtained when tested by the proposed method was a value close to that obtained when the charge was injected into the dielectric under a constant current with a Bounded J value despite large differences in the rate of degradation of the dielectric.
      Citation: Technologies
      PubDate: 2024-07-04
      DOI: 10.3390/technologies12070102
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 103: Defining a Metric-Driven Approach for
           Learning Hazardous Situations

    • Authors: Mario Fiorino, Muddasar Naeem, Mario Ciampi, Antonio Coronato
      First page: 103
      Abstract: Artificial intelligence has brought many innovations to our lives. At the same time, it is worth designing robust safety machine learning (ML) algorithms to obtain more benefits from technology. Reinforcement learning (RL) being an important ML method is largely applied in safety-centric scenarios. In such a situation, learning safety constraints are necessary to avoid undesired outcomes. Within the traditional RL paradigm, agents typically focus on identifying states associated with high rewards to maximize its long-term returns. This prioritization can lead to a neglect of potentially hazardous situations. Particularly, the exploration phase can pose significant risks, as it necessitates actions that may have unpredictable consequences. For instance, in autonomous driving applications, an RL agent might discover routes that yield high efficiency but fail to account for sudden hazardous conditions such as sharp turns or pedestrian crossings, potentially leading to catastrophic failures. Ensuring the safety of agents operating in unpredictable environments with potentially catastrophic failure states remains a critical challenge. This paper introduces a novel metric-driven approach aimed at containing risk in RL applications. Central to this approach are two developed indicators: the Hazard Indicator and the Risk Indicator. These metrics are designed to evaluate the safety of an environment by quantifying the likelihood of transitioning from safe states to failure states and assessing the associated risks. The fact that these indicators are characterized by a straightforward implementation, a highly generalizable probabilistic mathematical foundation, and a domain-independent nature makes them particularly interesting. To demonstrate their efficacy, we conducted experiments across various use cases, showcasing the feasibility of our proposed metrics. By enabling RL agents to effectively manage hazardous states, this approach paves the way for a more reliable and readily implementable RL in practical applications.
      Citation: Technologies
      PubDate: 2024-07-04
      DOI: 10.3390/technologies12070103
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 104: Adsorption of HFO-1234ze(E) onto
           Steam-Activated Carbon Derived from Sawmill Waste Wood

    • Authors: Huiyuan Bao, Md. Amirul Islam, Bidyut Baran Saha
      First page: 104
      Abstract: This study utilizes waste Albizia lebbeck wood from a sawmill to prepare activated carbon adsorbents and explores their potential application in adsorption cooling systems with a novel hydrofluoroolefin (HFO) refrigerant characterized by a low global warming potential. Activated carbon was synthesized through a simple and green steam activation method, and the optimal carbon shows a specific surface area of 946.8 m2/g and a pore volume of 0.843 cm3/g. The adsorption isotherms of HFO-1234ze(E) (Trans-1,3,3,3-tetrafluoropropene) on the activated carbon were examined at 30, 40, and 50 °C up to 400 kPa using a customized constant-volume variable-pressure system, and significant adsorption of 1.041 kg kg−1 was achieved at 30 °C and 400 kPa. The experimental data were fitted using both the Dubinin–Astakhov and Tóth models, and both models provided excellent fit results. The D−A adsorption model simulated the net adsorption capacity at possible operating temperatures. The isosteric of adsorption was determined using the Clausius–Clapeyron and modified Dubinin–Astakhov equations. In addition, the specific cooling effect and coefficient of performance were also studied.
      Citation: Technologies
      PubDate: 2024-07-05
      DOI: 10.3390/technologies12070104
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 105: Discovering Data Domains and Products in
           Data Meshes Using Semantic Blueprints

    • Authors: Michalis Pingos, Andreas S. Andreou
      First page: 105
      Abstract: Nowadays, one of the greatest challenges in data meshes revolves around detecting and creating data domains and data products for providing the ability to adapt easily and quickly to changing business needs. This requires a disciplined approach to identify, differentiate and prioritize distinct data sources according to their content and diversity. The current paper tackles this highly complicated issue and suggests a standardized approach that integrates the concept of data blueprints with data meshes. In essence, a novel standardization framework is proposed that creates data products using a metadata semantic enrichment mechanism, the latter also offering data domain readiness and alignment. The approach is demonstrated using real-world data produced by multiple sources in a poultry meat production factory. A set of functional attributes is used to qualitatively compare the proposed approach to existing data structures utilized in storage architectures, with quite promising results. Finally, experimentation with different scenarios varying in data product complexity and granularity suggests a successful performance.
      Citation: Technologies
      PubDate: 2024-07-07
      DOI: 10.3390/technologies12070105
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 106: Analysis and Development of an IoT
           System for an Agrivoltaics Plant

    • Authors: Francesco Zito, Nicola Ivan Giannoccaro, Roberto Serio, Sergio Strazzella
      First page: 106
      Abstract: This article illustrates the development of SolarFertigation (SF), an IoT (Internet of Things) solution for precision agriculture. Contrary to similar systems on the market, SolarFertigation can monitor and optimize fertigation autonomously, based on the analysis of data collected through the cloud. The system is made up of two main components: the central unit, which enables the precise deployment and distribution of water and fertilizers in different areas of the agricultural field, and the sensor node, which oversees collecting environmental and soil data. This article delves into the evolution of the system, focusing on structural and architectural changes to develop an infrastructure suitable for implementing a predictive model based on artificial intelligence and big data. Aspects concerning both the sensor node, such as energy management, accuracy of solar radiation readings, and qualitative soil moisture measurements, as well as implementations to the hydraulic system and the control and monitoring system of the central unit, are explored. This article provides an overview of the results obtained from solar radiation and soil moisture measurements. In addition, the results of an experimental campaign, in which 300 salad plants were grown using the SolarFertigation system in a photovoltaic field, are presented. This study demonstrated the effectiveness and applicability of the system under real-world conditions and highlighted its potential in optimizing resources and increasing agricultural productivity, especially in agrivoltaic settings.
      Citation: Technologies
      PubDate: 2024-07-07
      DOI: 10.3390/technologies12070106
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 107: Numerical Simulation and Design of a
           Mechanical Structure of an Ankle Exoskeleton for Elderly People

    • Authors: Ammir Rojas, Julio Ronceros, Carlos Raymundo, Gianpierre Zapata, Leonardo Vinces, Gustavo Ronceros
      First page: 107
      Abstract: This article presents the numerical simulation and design of an ankle exoskeleton oriented to elderly users. For the design, anatomical measurements were taken from a user of this age group to obtain an ergonomic, resistant, and exceptionally reliable mechanical structure. In addition, the design was validated to support a “weight range” of users between 50 and 80 kg in order to evaluate the reaction of the mechanism within the range of loads generated in relation to the first principal stress, the safety coefficient, the Von Mises stress, and principal deformations, for which the 3D CAD software Autodesk Inventor and theoretical correlations were used to calculate the displacement and rotation angles of the ankle in the structure. Likewise, two types of materials were evaluated: ABS (acrylonitrile butadiene styrene) and a polymer reinforced with carbon fiber. Finally, the designed pieces were assembled with the guarantee that the mobility of the system had been validated through the numerical simulation environment, highlighting that by being generated through 3D printing, manufacturing costs are reduced, allowing them to be accessible and ensuring that more people can benefit from this ankle exoskeleton.
      Citation: Technologies
      PubDate: 2024-07-09
      DOI: 10.3390/technologies12070107
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 108: The Measurement of Contrast Sensitivity
           in Near Vision: The Use of a Digital System vs. a Conventional Printed
           Test

    • Authors: Kevin J. Mena-Guevara, David P. Piñero, María José Luque, Dolores de Fez
      First page: 108
      Abstract: In recent years, there has been intense development of digital diagnostic tests for vision. All of these tests must be validated for clinical use. The current study enrolled 51 healthy individuals (age 19–72 years) in which achromatic contrast sensitivity function (CSF) in near vision was measured with the printed Vistech VCTS test (Stereo Optical Co., Inc., Chicago, IL, USA) and the Optopad-CSF (developed by our research group to be used on an iPad). Likewise, chromatic CSF was evaluated with a digital test. Statistically significant differences between tests were only found for the two higher spatial frequencies evaluated (p = 0.012 and <0.001, respectively). The mean achromatic index of contrast sensitivity (ICS) was 0.02 ± 1.07 and −0.76 ± 1.63 for the Vistech VCTS and Optopad tests, respectively (p < 0.001). The ranges of agreement between tests were 0.55, 0.76, 0.78, and 0.69 log units for the spatial frequencies of 1.5, 3, 6, and 12 cpd, respectively. The mean chromatic ICS values were −20.56 ± 0.96 and −0.16 ± 0.99 for the CSF-T and CSF-D plates, respectively (p < 0.001). Furthermore, better achromatic, red–green, and blue–yellow CSF values were found in the youngest groups. The digital test allows the fast measurement of near-achromatic and chromatic CSF using a colorimetrically calibrated iPad, but the achromatic measures cannot be used interchangeably with those obtained with a conventional printed test.
      Citation: Technologies
      PubDate: 2024-07-09
      DOI: 10.3390/technologies12070108
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 109: HUB3D: Intelligent Manufacturing HUB
           System

    • Authors: Antonio Trejo-Morales, Edgar Adrián Franco-Urquiza, Hansell David Devilet-Castellanos, Dario Bringas-Posadas
      First page: 109
      Abstract: HUB3D represents a cutting-edge solution for managing and operating a 3D printer farm through the integration of advanced hardware and software. It features intuitive, responsive interfaces that support seamless interaction across various devices. Leveraging cloud services ensures the system’s stability, security, and scalability, enabling users from diverse locations to effortlessly upload and manage their 3D printing projects. The hardware component includes a purpose-built rack capable of housing up to four 3D printers, each synchronized and managed by a manipulator arm controlled via Raspberry Pi technology. This setup facilitates continuous operation and high automation, optimizing production efficiency and reducing downtime significantly. This integrated approach positions HUB3D at the forefront of additive manufacturing management. By combining robust hardware capabilities with sophisticated software functionalities and cloud integration, the system offers unparalleled advantages. It supports continuous manufacturing processes, enhances workflow efficiency, and enables remote monitoring and management of printing operations. Overall, HUB3D’s innovative design and comprehensive features cater to both individual users and businesses seeking to streamline 3D printing workflows. With scalability, automation, and remote accessibility at its core, HUB3D represents a pivotal advancement in modern manufacturing technology, promising increased productivity and operational flexibility in the realm of additive manufacturing.
      Citation: Technologies
      PubDate: 2024-07-09
      DOI: 10.3390/technologies12070109
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 110: Securing Blockchain-Based Supply Chain
           Management: Textual Data Encryption and Access Control

    • Authors: Imran Khan, Qazi Ejaz Ali, Hassan Jalil Hadi, Naveed Ahmad, Gauhar Ali, Yue Cao, Mohammed Ali Alshara
      First page: 110
      Abstract: A supply chain (SC) encompasses a network of businesses, individuals, events, data, and resources orchestrating the movement of goods or services from suppliers to customers. Leveraging a blockchain-based platform, smart contracts play a pivotal role in aligning business logic and tracking progress within supply chain activities. Employing two distinct ledgers, namely Hyperledger and Ethereum, introduces challenges in handling the escalating volume of data and addressing the technical expertise gap related to supply chain management (SCM) tools in blockchain technology. Within the domain of blockchain-based SCM, the growing volume of data activities introduces challenges in the efficient regulation of data flow and the assurance of privacy. To tackle these challenges, a straightforward approach is recommended to manage data growth and thwart unauthorized entries or spam attempts within blockchain ledgers. The proposed technique focuses on validating hashes to ensure blockchain integrity. Emphasizing the authentication of sensitive data on the blockchain to bolster SCM, this approach compels applications to shoulder increased accountability. The suggested technique involves converting all data into textual format, implementing code encryption, and establishing permission-based access control. This strategy aims to address inherent weaknesses in blockchain within SCM. The results demonstrate the efficacy of the proposed technique in providing security and privacy for various types of data within SCM. Overall, the approach enhances the robustness of blockchain-based SCM, offering a comprehensive solution to navigate evolving challenges in data management and privacy assurance.
      Citation: Technologies
      PubDate: 2024-07-09
      DOI: 10.3390/technologies12070110
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 111: Optimizing Speech Emotion Recognition
           with Machine Learning Based Advanced Audio Cue Analysis

    • Authors: Nuwan Pallewela, Damminda Alahakoon, Achini Adikari, John E. Pierce, Miranda L. Rose
      First page: 111
      Abstract: In today’s fast-paced and interconnected world, where human–computer interaction is an integral component of daily life, the ability to recognize and understand human emotions has emerged as a crucial facet of technological advancement. However, human emotion, a complex interplay of physiological, psychological, and social factors, poses a formidable challenge even for other humans to comprehend accurately. With the emergence of voice assistants and other speech-based applications, it has become essential to improve audio-based emotion expression. However, there is a lack of specificity and agreement in current emotion annotation practice, as evidenced by conflicting labels in many human-annotated emotional datasets for the same speech segments. Previous studies have had to filter out these conflicts and, therefore, a large portion of the collected data has been considered unusable. In this study, we aimed to improve the accuracy of computational prediction of uncertain emotion labels by utilizing high-confidence emotion labelled speech segments from the IEMOCAP emotion dataset. We implemented an audio-based emotion recognition model using bag of audio word encoding (BoAW) to obtain a representation of audio aspects of emotion in speech with state-of-the-art recurrent neural network models. Our approach improved the state-of-the-art audio-based emotion recognition with a 61.09% accuracy rate, an improvement of 1.02% over the BiDialogueRNN model and 1.72% over the EmoCaps multi-modal emotion recognition models. In comparison to human annotation, our approach achieved similar results in identifying positive and negative emotions. Furthermore, it has proven effective in accurately recognizing the sentiment of uncertain emotion segments that were previously considered unusable in other studies. Improvements in audio emotion recognition could have implications in voice-based assistants, healthcare, and other industrial applications that benefit from automated communication.
      Citation: Technologies
      PubDate: 2024-07-11
      DOI: 10.3390/technologies12070111
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 112: Improvement of the ANN-Based Prediction
           Technology for Extremely Small Biomedical Data Analysis

    • Authors: Ivan Izonin, Roman Tkachenko, Oleh Berezsky, Iurii Krak, Michal Kováč, Maksym Fedorchuk
      First page: 112
      Abstract: Today, the field of biomedical engineering spans numerous areas of scientific research that grapple with the challenges of intelligent analysis of small datasets. Analyzing such datasets with existing artificial intelligence tools is a complex task, often complicated by issues like overfitting and other challenges inherent to machine learning methods and artificial neural networks. These challenges impose significant constraints on the practical application of these tools to the problem at hand. While data augmentation can offer some mitigation, existing methods often introduce their own set of limitations, reducing their overall effectiveness in solving the problem. In this paper, the authors present an improved neural network-based technology for predicting outcomes when analyzing small and extremely small datasets. This approach builds on the input doubling method, leveraging response surface linearization principles to improve performance. Detailed flowcharts of the improved technology’s operations are provided, alongside descriptions of new preparation and application algorithms for the proposed solution. The modeling, conducted using two biomedical datasets with optimal parameters selected via differential evolution, demonstrated high prediction accuracy. A comparison with several existing methods revealed a significant reduction in various errors, underscoring the advantages of the improved neural network technology, which does not require training, for the analysis of extremely small biomedical datasets.
      Citation: Technologies
      PubDate: 2024-07-12
      DOI: 10.3390/technologies12070112
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 113: Probabilistic Confusion Matrix: A Novel
           Method for Machine Learning Algorithm Generalized Performance Analysis

    • Authors: Ioannis Markoulidakis, Georgios Markoulidakis
      First page: 113
      Abstract: The paper addresses the issue of classification machine learning algorithm performance based on a novel probabilistic confusion matrix concept. The paper develops a theoretical framework which associates the proposed confusion matrix and the resulting performance metrics with the regular confusion matrix. The theoretical results are verified based on a wide variety of real-world classification problems and state-of-the-art machine learning algorithms. Based on the properties of the probabilistic confusion matrix, the paper then highlights the benefits of using the proposed concept both during the training phase and the application phase of a classification machine learning algorithm.
      Citation: Technologies
      PubDate: 2024-07-13
      DOI: 10.3390/technologies12070113
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 114: Development and Evaluation of an mHealth
           App That Promotes Access to 3D Printable Assistive Devices

    • Authors: Jeffrey Bush, Sara Benham, Monica Kaniamattam
      First page: 114
      Abstract: Three-dimensional printing is an emerging service delivery method for on-demand access to customized assistive technology devices. However, barriers exist in locating and designing appropriate models and having the devices printed. The purpose of this work is to outline the development of an app, 3DAdapt, which allows users to overcome these issues by searching within a curated list of 3D printable assistive devices, customizing models that support it, and ordering the device to be printed by manufacturers linked within the app or shared with local 3D printing operators. The app integrates searching and filters based on the International Classification of Functioning, Disability, and Health, with the available devices including those developed from fieldwork collaborations with multiple professionals and students within clinical, community, and educational settings. It provides users the ability to customize select models to meet their needs. The model can then be shared, downloaded, or ordered from a third-party 3D printing service. This development and expert testing phase to assess feasibility and modify the app based on identified themes then prepared the team for the next phases of beta testing to reach the overall aim of 3DAdapt to connect individuals to affordable and customizable devices to increase independence and quality of life.
      Citation: Technologies
      PubDate: 2024-07-13
      DOI: 10.3390/technologies12070114
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 115: Oxygen Measurement in Cuprate
           Superconductors Using the Dissolved Oxygen/Chlorine Method

    • Authors: Yuliang Wei, Chengcheng Yan, Shiro Kambe
      First page: 115
      Abstract: We have developed a dissolved oxygen (DO) method with differential equation (DE) correction. We measured the oxygen content in La-based and Y-based superconductors, and succeeded in measuring the oxygen content simply in one-third of the time required by the iodometric titration method. However, there was a problem with Bi-based superconductors where the measured oxygen content was smaller compared to the iodometric titration method. We hypothesized that not only O2 but also Cl2 gas is generated when dissolving Bi-based superconductors and developed a dissolved oxygen/chlorine (DO/Cl) method with DE correction. This method uses only a dissolved oxygen sensor and a dissolved chlorine sensor to measure the dissolved oxygen and dissolved chlorine content in Bi2Sr2−xLaxCuOy, allowing for the calculation of copper valence and oxygen content. The results from the DO/Cl method with DE correction show that the measured copper valence and oxygen content differ very little from those obtained using the iodometric titration method, with discrepancies within 0.016 and 0.008, respectively. Additionally, this method reduces the measurement time by one-third compared to the iodometric titration method. The results demonstrate that the DO/Cl method with DE correction can effectively measure the copper valence and oxygen content in cuprate superconductors, and using hydrochloric acid as the experimental solution is superior to sulfuric acid and nitric acid.
      Citation: Technologies
      PubDate: 2024-07-16
      DOI: 10.3390/technologies12070115
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 116: Development of a New Prototype
           Paediatric Central Sleep Apnoea Monitor

    • Authors: Saatchi, Elphick, Rowson, Wesseler, Marris, Shortland, Thomas
      First page: 116
      Abstract: A new prototype device to monitor breathing in children diagnosed with central sleep apnoea (CSA) was developed. CSA is caused by the failure of central nervous system signals to the respiratory muscles and results in intermittent breathing pauses during sleep. Children diagnosed with CSA require home respiration monitoring during sleep. Apnoea monitors initiate an audio alarm when the breath-to-breath respiration interval exceeds a preset time. This allows the child’s parents to attend to the child to ensure safety. The article describes the development of the monitor’s hardware, software, and evaluation. Features of the device include the detection of abnormal respiratory pauses and the generation of an associated alarm, the ability to record the respiratory signal and its storage using an on-board disk, miniaturised hardware, child-friendliness, cost-effectiveness, and ease of use. The device was evaluated on 10 healthy adult volunteers with a mean age of 46.6 years (and a standard deviation of 14.4 years). The participants randomly intentionally paused their breathing during the recording. The device detected and provided an alarm when the respiratory pauses exceeded the preset time. The respiration rates determined from the device closely matched the values from a commercial respiration monitor. The study indicated the peak-detection method of the respiration rate measurement is more robust than the zero-crossing method.
      Citation: Technologies
      PubDate: 2024-07-17
      DOI: 10.3390/technologies12070116
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 117: Nano-Level Additive Manufacturing:
           Condensed Review of Processes, Materials, and Industrial Applications

    • Authors: Ismail Fidan, Mohammad Alshaikh Ali, Vivekanand Naikwadi, Shamil Gudavasov, Mushfig Mahmudov, Mahdi Mohammadizadeh, Zhicheng Zhang, Ankit Sharma
      First page: 117
      Abstract: Additive manufacturing, commonly known as 3D printing, represents the forefront of modern manufacturing technology. Its growing popularity spans across research and development, material science, design, processes, and everyday applications. This review paper presents a crucial review of nano-level 3D printing, examining it from the perspectives of processes, materials, industrial applications, and future trends. The authors have synthesized the latest insights from a wide range of archival articles and source books, highlighting the key findings. The primary contribution of this study is a condensed review report that consolidates the newest research on nano-level 3D printing, offering a broad overview of this innovative technology for researchers, inventors, educators, and technologists. It is anticipated that this review study will significantly advance research in nanotechnology, additive manufacturing, and related technological fields.
      Citation: Technologies
      PubDate: 2024-07-18
      DOI: 10.3390/technologies12070117
      Issue No: Vol. 12, No. 7 (2024)
       
  • Technologies, Vol. 12, Pages 73: Gamified VR Storytelling for Cultural
           Tourism Using 3D Reconstructions, Virtual Humans, and 360° Videos

    • Authors: Kontogiorgakis, Zidianakis, Kontaki, Partarakis, Manoli, Ntoa, Stephanidis
      First page: 73
      Abstract: This work addresses the lack of methodologies for the seamless integration of 360° videos, 3D digitized artifacts, and virtual human agents within a virtual reality environment. The proposed methodology is showcased in the context of a tour guide application and centers around the innovative use of a central hub, metaphorically linking users to various historical locations. Leveraging a treasure hunt metaphor and a storytelling approach, this combination of digital structures is capable of building an exploratory learning experience. Virtual human agents contribute to the scenario by offering personalized narratives and educational content, contributing to an enriched cultural heritage journey. Key contributions of this research include the exploration of the symbolic use of the central hub, the application of a gamified approach through the treasure hunt metaphor, and the seamless integration of various technologies to enhance user engagement. This work contributes to the understanding of context-specific cultural heritage applications and their potential impact on cultural tourism. The output of this research work is the reusable methodology and its demonstration in the implemented showcase application that was assessed by a heuristic evaluation.
      Citation: Technologies
      PubDate: 2024-05-22
      DOI: 10.3390/technologies12060073
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 74: Effect of Oscillating Area on Generating
           Microbubbles from Hollow Ultrasonic Horn

    • Authors: Kodai Hasegawa, Nobuhiro Yabuki, Toshinori Makuta
      First page: 74
      Abstract: Microbubbles, which are tiny bubbles with a diameter of less than 100 µm, have been attracting attention in recent years. Conventional methods of microbubble generation using porous material and swirling flows have problems such as large equipment size and non-uniform bubble generation. Therefore, we have been developing a hollow ultrasonic horn with an internal flow path as a microbubble-generating device. By supplying gas and ultrasonic waves simultaneously, the gas–liquid interface is violently disturbed to generate microbubbles. Although this device can generate microbubbles even in highly viscous fluids and high-temperature fluids such as molten metals, it has the problem of generating many relatively large bubbles of 1 mm or more. Since the generation of a large amount of microbubbles in a short period of time is required to realize actual applications in agriculture, aquaculture, and medicine, conventional research has tried to solve this problem by increasing the amplitude of the ultrasonic oscillation. However, it is difficult to further increase the amplitude due to the structural reasons of the horn and the behavior of bubbles at the horn tip; therefore, the oscillating area of the tip of the horn, which had not received attention before, was enlarged by a factor of 2.94 times to facilitate the ultrasonic wave transmission to the bubbles, and the effect of this was investigated. As a result, a large number of gases were miniaturized, especially at high gas flow rates, leading to an increase in the amount of microbubbles generated.
      Citation: Technologies
      PubDate: 2024-05-25
      DOI: 10.3390/technologies12060074
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 75: Intelligent Cane for Assisting the
           Visually Impaired

    • Authors: Claudiu-Eugen Panazan, Eva-Henrietta Dulf
      First page: 75
      Abstract: Those with visual impairments, including complete blindness or partial sight loss, constitute a significant global population. According to estimates by the World Health Organization (WHO), there are at least 2.2 billion people worldwide who have near or distance vision disorders. Addressing their needs is crucial. Introducing a smart cane tailored for the blind can greatly improve their daily lives. This paper introduces a significant technical innovation, presenting a smart cane equipped with dual ultrasonic sensors for obstacle detection, catering to the visually impaired. The primary focus is on developing a versatile device capable of operating in diverse conditions, ensuring efficient obstacle alerts. The strategic placement of ultrasonic sensors facilitates the emission and measurement of high-frequency sound waves, calculating obstacle distances and assessing potential threats to the user. Addressing various obstacle types, two ultrasonic sensors handle overhead and ground-level barriers, ensuring precise warnings. With a detection range spanning 2 to 400 cm, the device provides timely information for user reaction. Dual alert methods, including vibrations and audio signals, offer flexibility to users, controlled through intuitive switches. Additionally, a Bluetooth-connected mobile app enhances functionality, activating audio alerts if the cane is misplaced or too distant. Cost-effective implementation enhances accessibility, supporting a broader user base. This innovative smart cane not only represents a technical achievement but also significantly improves the quality of life for visually impaired individuals, emphasizing the social impact of technology. The research underscores the importance of technological research in addressing societal challenges and highlights the need for solutions that positively impact vulnerable communities, shaping future directions in research and technological development.
      Citation: Technologies
      PubDate: 2024-05-27
      DOI: 10.3390/technologies12060075
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 76: Vertical Balance of an Autonomous
           Two-Wheeled Single-Track Electric Vehicle

    • Authors: David Rodríguez-Rosa, Andrea Martín-Parra, Andrés García-Vanegas, Francisco Moya-Fernández, Ismael Payo-Gutiérrez, Fernando J. Castillo-García
      First page: 76
      Abstract: In the dynamic landscape of autonomous transport, the integration of intelligent transport systems and embedded control technology is pivotal. While strides have been made in the development of autonomous agents and multi-agent systems, the unique challenges posed by two-wheeled vehicles remain largely unaddressed. Dedicated control strategies for these vehicles have yet to be developed. The vertical balance of an autonomous two-wheeled single-track vehicle is a challenge for engineering. This type of vehicle is unstable and its dynamic behaviour changes with the forward velocity. We designed a scheduled-gain proportional–integral controller that adapts its gains to the forward velocity, maintaining the vertical balance of the vehicle by means of the steering front-wheel angle. The control law was tested with a prototype designed by the authors under different scenarios, smooth and uneven floors, maintaining the vertical balance in all cases.
      Citation: Technologies
      PubDate: 2024-05-28
      DOI: 10.3390/technologies12060076
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 77: Deep Learning Approaches for Water Stress
           Forecasting in Arboriculture Using Time Series of Remote Sensing Images:
           Comparative Study between ConvLSTM and CNN-LSTM Models

    • Authors: Ismail Bounoua, Youssef Saidi, Reda Yaagoubi, Mourad Bouziani
      First page: 77
      Abstract: Irrigation is crucial for crop cultivation and productivity. However, traditional methods often waste water and energy due to neglecting soil and crop variations, leading to inefficient water distribution and potential crop water stress. The crop water stress index (CWSI) has become a widely accepted index for assessing plant water status. However, it is necessary to forecast the plant water stress to estimate the quantity of water to irrigate. Deep learning (DL) models for water stress forecasting have gained prominence in irrigation management to address these needs. In this paper, we present a comparative study between two deep learning models, ConvLSTM and CNN-LSTM, for water stress forecasting using remote sensing data. While these DL architectures have been previously proposed and studied in various applications, our novelty lies in studying their effectiveness in the field of water stress forecasting using time series of remote sensing images. The proposed methodology involves meticulous preparation of time series data, where we calculate the crop water stress index (CWSI) using Landsat 8 satellite imagery through Google Earth Engine. Subsequently, we implemented and fine-tuned the hyperparameters of the ConvLSTM and CNN-LSTM models. The same processes of model compilation, optimization of hyperparameters, and model training were applied for the two architectures. A citrus farm in Morocco was chosen as a case study. The analysis of the results reveals that the CNN-LSTM model excels over the ConvLSTM model for long sequences (nine images) with an RMSE of 0.119 and 0.123, respectively, while ConvLSTM provides better results for short sequences (three images) than CNN-LSTM with an RMSE of 0.153 and 0.187, respectively.
      Citation: Technologies
      PubDate: 2024-06-01
      DOI: 10.3390/technologies12060077
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 78: Smart Energy Systems Based on
           Next-Generation Power Electronic Devices

    • Authors: Nikolay Hinov
      First page: 78
      Abstract: Power electronics plays a key role in the management and conversion of electrical energy in a variety of applications, including the use of renewable energy sources such as solar, wind and hydrogen energy, as well as in electric vehicles, industrial technologies, homes and smart grids. These technologies are essential for the successful implementation of the green transition, as they help reduce carbon emissions and promote the production and consumption of cleaner and more sustainable energy. The present work presents a new generation of power electronic devices and systems, which includes the following main aspects: advances in semiconductor technologies, such as the use of silicon carbide (SiC) and gallium nitride (GaN); nanomaterials for the realization of magnetic components; using a modular principle to construct power electronic devices; applying artificial intelligence techniques to device lifecycle design; and the environmental aspects of design. The new materials allow the devices to operate at higher voltages, temperatures and frequencies, making them ideal for high-power applications and high-frequency operation. In addition, the development of integrated and modular power electronic systems that combine energy management, diagnostics and communication capabilities contributes to the more intelligent and efficient management of energy resources. This includes integration with the Internet of Things (IoT) and artificial intelligence (AI) for automated task solving and work optimization.
      Citation: Technologies
      PubDate: 2024-06-01
      DOI: 10.3390/technologies12060078
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 79: Comparison of a Custom-Made Inexpensive
           Air Permeability Tester with a Standardized Measurement Instrument

    • Authors: Dietrich Spädt, Niclas Richter, Cornelia Golle, Andrea Ehrmann, Lilia Sabantina
      First page: 79
      Abstract: The air permeability of a textile fabric belongs to the parameters which characterize its potential applications as garments, filters, airbags, etc. Calculating the air permeability is complicated due to its dependence on many other fabric parameters, such as porosity, thickness, weaving parameters and others, which is why the air permeability is usually measured. Standardized measurement instruments according to EN ISO 9237, however, are expensive and complex, prohibiting small companies or many universities from using them. This is why a simpler and inexpensive test instrument was suggested in a previous paper. Here, we show correlations between the results of the standardized and the custom-made instrument and verify this correlation using fluid dynamics calculations.
      Citation: Technologies
      PubDate: 2024-06-02
      DOI: 10.3390/technologies12060079
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 80: Applications of Brain Wave Classification
           for Controlling an Intelligent Wheelchair

    • Authors: Maria Carolina Avelar, Patricia Almeida, Brigida Monica Faria, Luis Paulo Reis
      First page: 80
      Abstract: The independence and autonomy of both elderly and disabled people have been a growing concern in today’s society. Therefore, wheelchairs have proven to be fundamental for the movement of these people with physical disabilities in the lower limbs, paralysis, or other type of restrictive diseases. Various adapted sensors can be employed in order to facilitate the wheelchair’s driving experience. This work develops the proof concept of a brain–computer interface (BCI), whose ultimate final goal will be to control an intelligent wheelchair. An event-related (de)synchronization neuro-mechanism will be used, since it corresponds to a synchronization, or desynchronization, in the mu and beta brain rhythms, during the execution, preparation, or imagination of motor actions. Two datasets were used for algorithm development: one from the IV competition of BCIs (A), acquired through twenty-two Ag/AgCl electrodes and encompassing motor imagery of the right and left hands, and feet; and the other (B) was obtained in the laboratory using an Emotiv EPOC headset, also with the same motor imaginary. Regarding feature extraction, several approaches were tested: namely, two versions of the signal’s power spectral density, followed by a filter bank version; the use of respective frequency coefficients; and, finally, two versions of the known method filter bank common spatial pattern (FBCSP). Concerning the results from the second version of FBCSP, dataset A presented an F1-score of 0.797 and a rather low false positive rate of 0.150. Moreover, the correspondent average kappa score reached the value of 0.693, which is in the same order of magnitude as 0.57, obtained by the competition. Regarding dataset B, the average value of the F1-score was 0.651, followed by a kappa score of 0.447, and a false positive rate of 0.471. However, it should be noted that some subjects from this dataset presented F1-scores of 0.747 and 0.911, suggesting that the movement imagery (MI) aptness of different users may influence their performance. In conclusion, it is possible to obtain promising results, using an architecture for a real-time application.
      Citation: Technologies
      PubDate: 2024-06-03
      DOI: 10.3390/technologies12060080
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 81: A Survey of Machine Learning in Edge
           Computing: Techniques, Frameworks, Applications, Issues, and Research
           Directions

    • Authors: Oumayma Jouini, Kaouthar Sethom, Abdallah Namoun, Nasser Aljohani, Meshari Huwaytim Alanazi, Mohammad N. Alanazi
      First page: 81
      Abstract: Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.
      Citation: Technologies
      PubDate: 2024-06-03
      DOI: 10.3390/technologies12060081
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 82: Path Planning for Autonomous Mobile Robot
           Using Intelligent Algorithms

    • Authors: Jorge Galarza-Falfan, Enrique Efrén García-Guerrero, Oscar Adrian Aguirre-Castro, Oscar Roberto López-Bonilla, Ulises Jesús Tamayo-Pérez, José Ricardo Cárdenas-Valdez, Carlos Hernández-Mejía, Susana Borrego-Dominguez, Everardo Inzunza-Gonzalez
      First page: 82
      Abstract: Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs.
      Citation: Technologies
      PubDate: 2024-06-03
      DOI: 10.3390/technologies12060082
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 83: Data Readout Techniques on FPGA for the
           ATLAS RPC-BIS78 Detectors

    • Authors: Andreas Vgenopoulos, Kostas Kordas, Federico Lasagni, Sabrina Perrella, Alessandro Polini, Riccardo Vari
      First page: 83
      Abstract: The firmware developed for the readout and trigger processing of the information emerging from the BIS78-RPC Muon Spectrometer chambers in the ATLAS experiment at CERN is presented here, together with data processing techniques, data acquisition software, and tests of the readout chain system, which represent efforts to make these chambers operational in the ATLAS experiment. This work is performed in the context of the BIS78-RPC project, which deals with the pilot deployment of a new generation of sMDT+RPCs in the experiment. Such chambers are planned to be fully deployed in the whole barrel inner layer of the Muon Spectrometer during the Phase II upgrade of the ATLAS experiment. On-chamber front-ends include an amplifier, a discriminator ASIC, and an LVDS transmitter. The signal is digitized by CERN HPTDC chips and then processed by an FPGA, which is the heart of the readout and trigger processing, using various techniques.
      Citation: Technologies
      PubDate: 2024-06-04
      DOI: 10.3390/technologies12060083
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 84: Dual-Band Antenna at 28 and 38 GHz Using
           Internal Stubs and Slot Perturbations

    • Authors: Parveez Shariff Bhadravathi Ghouse, Pradeep Kumar, Pallavi R. Mane, Sameena Pathan, Tanweer Ali, Alexandros-Apostolos A. Boulogeorgos, Jaume Anguera
      First page: 84
      Abstract: A double-stub matching technique is used to design a dual-band monopole antenna at 28 and 38 GHz. The transmission line stubs represent the matching elements. The first matching network comprises series capacitive and inductive stubs, causing impedance matching at the 28 GHz band with a wide bandwidth. On the other hand, the second matching network has two shunt inductive stubs, generating resonance at 38 GHz. A Smith chart is utilized to predict the stub lengths. While incorporating their dimensions physically, some of the stub lengths are fine-tuned. The proposed antenna is compact with a profile of 0.75λ1×0.66λ1 (where λ1 is the free-space wavelength at 28 GHz). The measured bandwidths are 27–28.75 GHz and 36.20–42.43 GHz. Although the physical series capacitance of the first matching network is a slot in the ground plane, the antenna is able to achieve a good gain of 7 dBi in both bands. The proposed antenna has a compact design, good bandwidth and gain, making it a candidate for 5G wireless applications.
      Citation: Technologies
      PubDate: 2024-06-06
      DOI: 10.3390/technologies12060084
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 85: Behind the Door: Practical
           Parameterization of Propagation Parameters for IEEE 802.11ad Use Cases

    • Authors: Luciano Ahumada, Erick Carreño, Albert Anglès, Diego Dujovne, Pablo Palacios Játiva
      First page: 85
      Abstract: The integration of the 60 GHz band into the IEEE 802.11 standard has revolutionized indoor wireless services. However, this band presents unique challenges to indoor wireless communication infrastructure, originally designed to handle data traffic in residential and office environments. Estimating 60 GHz signal propagation in indoor settings is particularly complicated due to dynamic contextual factors, making it essential to ensure adequate coverage for all connected devices. Consequently, empirical channel modeling plays a pivotal role in understanding real-world behavior, which is characterized by a complex interplay of stationary and mobile elements. Given the highly directional nature of 60 GHz propagation, this study addresses a seemingly simple but important question: what is the impact of employing highly directive antennas when deviating from the line of sight' To address this question, we conducted an empirical measurement campaign of wireless channels within an office environment. Our assessment focused on power losses and distribution within an angular range while an indoor base station served indoor users, simulating the operation of an IEEE 802.11ad high-speed WLAN at 60 GHz. Additionally, we explored scenarios with and without pedestrian movement in the vicinity of wireless terminals. Our observations reveal the presence of significant antenna lobes even in obstructed links, indicating potential opportunities to use angular combiners or beamformers to enhance link availability and the data rate. This empirical study provides valuable information and channel parameters to simulate 60 GHz millimeter wave (mm-wave) links in indoor environments, paving the way for more efficient and robust wireless communication systems.
      Citation: Technologies
      PubDate: 2024-06-07
      DOI: 10.3390/technologies12060085
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 86: Advancements in 3D Printing: Directed
           Energy Deposition Techniques, Defect Analysis, and Quality Monitoring

    • Authors: Muhammad Mu’az Imran, Azam Che Idris, Liyanage Chandratilak De Silva, Yun-Bae Kim, Pg Emeroylariffion Abas
      First page: 86
      Abstract: This paper provides a comprehensive analysis of recent advancements in additive manufacturing, a transformative approach to industrial production that allows for the layer-by-layer construction of complex parts directly from digital models. Focusing specifically on Directed Energy Deposition, it begins by clarifying the fundamental principles of metal additive manufacturing as defined by International Organization of Standardization and American Society for Testing and Materials standards, with an emphasis on laser- and powder-based methods that are pivotal to Directed Energy Deposition. It explores the critical process mechanisms that can lead to defect formation in the manufactured parts, offering in-depth insights into the factors that influence these outcomes. Additionally, the unique mechanisms of defect formation inherent to Directed Energy Deposition are examined in detail. The review also covers the current landscape of process evaluation and non-destructive testing methods essential for quality assurance, including both traditional and contemporary in situ monitoring techniques, with a particular focus given to advanced machine-vision-based methods for geometric analysis. Furthermore, the integration of process monitoring, multiphysics simulation models, and data analytics is discussed, charting a forward-looking roadmap for the development of Digital Twins in Laser–Powder-based Directed Energy Deposition. Finally, this review highlights critical research gaps and proposes directions for future research to enhance the accuracy and efficiency of Directed Energy Deposition systems.
      Citation: Technologies
      PubDate: 2024-06-07
      DOI: 10.3390/technologies12060086
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 87: Electron Energy-Loss Spectroscopy Method
           for Thin-Film Thickness Calculations with a Low Incident Energy Electron
           Beam

    • Authors: Ahmad M. D. (Assa’d) Jaber, Ammar Alsoud, Saleh R. Al-Bashaish, Hmoud Al Dmour, Marwan S. Mousa, Tomáš Trčka, Vladimír Holcman, Dinara Sobola
      First page: 87
      Abstract: In this study, the thickness of a thin film (tc) at a low primary electron energy of less than or equal to 10 keV was calculated using electron energy-loss spectroscopy. This method uses the ratio of the intensity of the transmitted background spectrum to the intensity of the transmission electrons with zero-loss energy (elastic) in the presence of an accurate average inelastic free path length (λ). The Monte Carlo model was used to simulate the interaction between the electron beam and the tested thin films. The total background of the transmitted electrons is considered to be the electron transmitting the film with an energy above 50 eV to eliminate the effect of the secondary electrons. The method was used at low primary electron energy to measure the thickness (t) of C, Si, Cr, Cu, Ag, and Au films below 12 nm. For the C and Si films, the accuracy of the thickness calculation increased as the energy of the primary electrons and thickness of the film increased. However, for heavy elements, the accuracy of the film thickness calculations increased as the primary electron energy increased and the film thickness decreased. High accuracy (with 2% uncertainty) in the measurement of C and Si thin films was observed at large thicknesses and 10 keV, where . However, in the case of heavy-element films, the highest accuracy (with an uncertainty below 8%) was found for thin thicknesses and 10 keV, where . The present results show that an accurate film thickness measurement can be obtained at primary electron energy equal to or less than 10 keV and a ratio of . This method demonstrates the potential of low-loss electron energy-loss spectroscopy in transmission electron microscopy as a fast and straightforward method for determining the thin-film thickness of the material under investigation at low primary electron energies.
      Citation: Technologies
      PubDate: 2024-06-07
      DOI: 10.3390/technologies12060087
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 88: Accurate Surge Arrester Modeling for
           Optimal Risk-Aware Lightning Protection Utilizing a Hybrid Monte
           Carlo–Particle Swarm Optimization Algorithm

    • Authors: Amir Hossein Kimiai Asadi, Mohsen Eskandari, Hadi Delavari
      First page: 88
      Abstract: The application of arresters is critical for the safe operation of electric grids against lightning. Arresters limit the consequences of lightning-induced over-voltages. However, surge arrester protection in electric grids is challenging due to the intrinsic complexities of distribution grids, including overhead lines and power components such as transformers. In this paper, an optimal arrester placement technique is developed by proposing a multi-objective function that includes technical, safety and risk, and economic indices. However, an effective placement model demands a comprehensive and accurate modeling of an electric grid’s components. In this light, appropriate models of a grid’s components including an arrester, the earth, an oil-immersed transformer, overhead lines, and lightning-induced voltage are developed. To achieve accurate models, high-frequency transient mathematical models are developed for the grid’s components. Notably, to have an accurate model of the arrester, which critically impacts the performance of the arrester placement technique, a new arrester model is developed and evaluated based on real technical data from manufacturers such as Pars, Tridelta, and Siemens. Then, the proposed model is compared with the IEEE, Fernandez, and Pinceti models. The arrester model is incorporated in an optimization problem considering the performance of the over-voltage protection and the risk, technical, and economic indices, and it is solved using the particle swarm optimization (PSO) and Monte Carlo (MC) techniques. To validate the proposed arrester model and the placement technique, real data from the Chopoghloo feeder in Bahar, Hamedan, Iran, are simulated. The feeder is expanded over three different geographical areas, including rural, agricultural plain, and mountainous areas.
      Citation: Technologies
      PubDate: 2024-06-08
      DOI: 10.3390/technologies12060088
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 89: A New LCL Filter Design Method for
           Single-Phase Photovoltaic Systems Connected to the Grid via
           Micro-Inverters

    • Authors: Heriberto Adamas-Pérez, Mario Ponce-Silva, Jesús Darío Mina-Antonio, Abraham Claudio-Sánchez, Omar Rodríguez-Benítez, Oscar Miguel Rodríguez-Benítez
      First page: 89
      Abstract: This paper aims to propose a new sizing approach to reduce the footprint and optimize the performance of an LCL filter implemented in photovoltaic systems using grid-connected single-phase microinverters. In particular, the analysis is carried out on a single-phase full-bridge inverter, assuming the following two conditions: (1) a unit power factor at the connection point between the AC grid and the LCL filter; (2) a control circuit based on unipolar sinusoidal pulse width modulation (SPWM). In particular, the ripple and harmonics of the LCL filter input current and the current injected into the grid are analyzed. The results of the Simulink simulation and the experimental tests carried out confirm that it is possible to considerably reduce filter volume by optimizing each passive component compared with what is already available in the literature while guaranteeing excellent filtering performance. Specifically, the inductance values were reduced by almost 40% and the capacitor value by almost 100%. The main applications of this new design methodology are for use in single-phase microinverters connected to the grid and for research purposes in power electronics and optimization.
      Citation: Technologies
      PubDate: 2024-06-12
      DOI: 10.3390/technologies12060089
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 90: A Modified Criss-Cross-Based T-Type MLI
           with Reduced Power Components

    • Authors: Kailash Kumar Mahto, Bidyut Mahato, Bikramaditya Chandan, Durbanjali Das, Priyanath Das, Swati Kumari, Vasiliki Vita, Christos Pavlatos, Georgios Fotis
      First page: 90
      Abstract: Significant advancements in the field of power electronics have created an ideal opportunity to introduce various topologies of multilevel inverters. These multilevel inverter topologies comprise different notable characteristics, such as staircase sinusoidal output voltage with high quality, a lowered number of power switches, no filter requirement, etc. In this literature, a new asymmetrical MLI topology is proposed to reduce the number of components of the inverter with admirable voltage-step creation. The proposed topology provides a 17-level, staircase-type, nearly sinusoidal output voltage waveform. The number of switches required for the proposed multilevel inverter topology is fewer compared to the existing topology for the same level. A carrier-based sinusoidal pulse-width modulation technique is used for the proposed topology at a switching frequency of 3 kHz. The functioning of the proposed inverter topology is thoroughly examined. A 17-level asymmetrical inverter is executed; both the MATLAB/SIMULINK as well as the experimental results using dSPACE-1103 controller. The simulation results are verified using the experimental results for the proposed 17-level multilevel inverter for modulation indexes of 1 and 0.6.
      Citation: Technologies
      PubDate: 2024-06-18
      DOI: 10.3390/technologies12060090
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 91: A Computational Framework for Enhancing
           Industrial Operations and Electric Network Management: A Case Study

    • Authors: Pedroso, Silva, Campilho, Sales-Contini, Pinto, Moreira
      First page: 91
      Abstract: Automotive industries require constant technological development and the capacity to adapt to market needs. Hence, component suppliers must be able to adapt to persistent trend changes and technical improvements, acting in response to customers’ expectations and developing their manufacturing methods to be as flexible as possible. Concepts such as layout flexibility, management of industrial facilities, and building information modeling (BIM) are becoming ever more addressed within the automotive industry in order to envision and select the necessary information exchanges. Given this question and based on the gap in the literature regarding this subject, this work proposes a solution, developing a novel tool that allows the monitoring and assignment of newer/relocated equipment to the switchboards within a given industrial plant. The solution intends to increase the flexibility of production lines through the assessment, analysis, improvement, and reorganization of the electrical load distribution to develop projects accurately implying layout changes. The tool is validated with an automotive manufacturer. With the implementation of this open-source tool, a detailed electrical flow management system is accomplished, and it has proven successful and essential in raising levels of organizational flexibility. This has guaranteed the company’s competitiveness with effective integrated administration methods and tools, such as a much easier study upon inserting new/relocated equipment without production line breaks.
      Citation: Technologies
      PubDate: 2024-06-19
      DOI: 10.3390/technologies12060091
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 92: A Review of Automatic Pain Assessment
           from Facial Information Using Machine Learning

    • Authors: Najib Ben Aoun
      First page: 92
      Abstract: Pain assessment has become an important component in modern healthcare systems. It aids medical professionals in patient diagnosis and providing the appropriate care and therapy. Conventionally, patients are asked to provide their pain level verbally. However, this subjective method is generally inaccurate, not possible for non-communicative people, can be affected by physiological and environmental factors and is time-consuming, which renders it inefficient in healthcare settings. So, there has been a growing need to build objective, reliable and automatic pain assessment alternatives. In fact, due to the efficiency of facial expressions as pain biomarkers that accurately expand the pain intensity and the power of machine learning methods to effectively learn the subtle nuances of pain expressions and accurately predict pain intensity, automatic pain assessment methods have evolved rapidly. This paper reviews recent spatial facial expressions and machine learning-based pain assessment methods. Moreover, we highlight the pain intensity scales, datasets and method performance evaluation criteria. In addition, these methods’ contributions, strengths and limitations will be reported and discussed. Additionally, the review lays the groundwork for further study and improvement for more accurate automatic pain assessment.
      Citation: Technologies
      PubDate: 2024-06-20
      DOI: 10.3390/technologies12060092
      Issue No: Vol. 12, No. 6 (2024)
       
  • Technologies, Vol. 12, Pages 57: Miniaturized Microstrip Dual-Channel
           Diplexer Based on Modified Meander Line Resonators for Wireless and
           Computer Communication Technologies

    • Authors: Yaqeen Sabah Mezaal, Shahad K. Khaleel, Ban M. Alameri, Kadhum Al-Majdi, Aqeel A. Al-Hilali
      First page: 57
      Abstract: There has been a lot of interest in microstrip diplexers lately due to their potential use in numerous wireless and computer communication technologies, including radio broadcasts, mobile phones, broadband wireless, and satellite-based communication systems. It can do this because it has a communication channel that can combine two distinct filters into one. This article presents a narrow-band microstrip diplexer that uses a stepped impedance resonator, a uniform impedance resonator, tiny square patches, and a meander line resonator. The projected diplexer might be made smaller than its initial dimensions by utilizing the winding construction. To model the microstrip diplexer topology for WiMAX and WIFI/WLAN at 1.66 GHz and 2.52 GHz, the Advanced Wave Research (AWR) solver was employed. It exhibited an insertion loss of 3.2 dB and a return loss of 16 dB for the first channel, while the insertion loss and return loss were 2.88 dB and 21 dB, respectively, for the second channel. When both filters were simulated, the band isolation was 31 dB. The projected microstrip diplexer has been fabricated using an FR4 epoxy laminate with dimensions of 32 × 26 mm2. The simulated S-parameters phase and group delay closely matched the measurements.
      Citation: Technologies
      PubDate: 2024-04-24
      DOI: 10.3390/technologies12050057
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 58: RFID Tags for On-Metal Applications: A
           Brief Survey

    • Authors: Emanuel Pereira, Sandoval Júnior, Luís Felipe Vieira Silva, Mateus Batista, Eliel Santos, Ícaro Araújo, Jobson Araújo, Erick Barboza, Francisco Gomes, Ismael Trindade Fraga, Daniel Oliveira Dos Dos Santos, Roger Davanso
      First page: 58
      Abstract: Radio-frequency identification technology finds extensive use in various industrial applications, including those involving metallic surfaces. The integration of radio-frequency identification systems with metal surfaces, such as those found in the automotive sector, presents distinct challenges that can notably affect system efficacy due to metal’s tendency to reflect electromagnetic waves, thus degrading the functionality of conventional radio-frequency identification tags. This highlights the importance of conducting research into academic publications and patents to grasp the current advancements and challenges in this field, aiming to improve the applications of radio-frequency identification tags technology on metal. Consequently, this research undertakes a concise review of both the literature and patents exploring radio-frequency identification technology’s use for on-metal tags, utilizing resources like Google Scholar and Google Patents. The research categorized crucial aspects such as tag flexibility, operating frequency, and geographic origins of the research. Findings highlight China’s prominent role in contributing to metal-focused radio-frequency identification tag research, with a considerable volume of articles and patents. In particular, flexible tags and the Ultra-High Frequency range are dominant in both scholarly and patent documents, reflecting their significance in radio-frequency identification technology applications. The research underscores a vibrant area of development within radio-frequency identification technology, with continued innovation driven by specific industrial needs. Despite the noted advances, the presence of a significant percentage of no longer valid patents suggests substantial opportunities for further research and innovation in radio-frequency identification technology for on-metal applications, especially considering the demand for flexible tags and for solutions in systems that offer specialized characteristics or are tailored for specific uses.
      Citation: Technologies
      PubDate: 2024-04-27
      DOI: 10.3390/technologies12050058
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 59: Neural Network-Based Body Weight
           Prediction in Pelibuey Sheep through Biometric Measurements

    • Authors: Alfonso J. Chay-Canul, Enrique Camacho-Pérez, Fernando Casanova-Lugo, Omar Rodríguez-Abreo, Mayra Cruz-Fernández, Juvenal Rodríguez-Reséndiz
      First page: 59
      Abstract: This paper presents an intelligent system for the dynamic estimation of sheep body weight (BW). The methodology used to estimate body weight is based on measuring seven biometric parameters: height at withers, rump height, body length, body diagonal length, total body length, semicircumference of the abdomen, and semicircumference of the girth. A biometric parameter acquisition system was developed using a Kinect as a sensor. The results were contrasted with measurements obtained manually with a flexometer. The comparison gives an average root mean square error (RMSE) of 9.91 and a mean R2 of 0.81. Subsequently, the parameters were used as input in a back-propagation artificial neural network. Performance tests were performed with different combinations to make the best choice of architecture. In this way, an intelligent body weight estimation system was obtained from biometric parameters, with a 5.8% RMSE in the weight estimations for the best architecture. This approach represents an innovative, feasible, and economical alternative to contribute to decision-making in livestock production systems.
      Citation: Technologies
      PubDate: 2024-04-30
      DOI: 10.3390/technologies12050059
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 60: A Cyber–Physical System Based on
           Digital Twin and 3D SCADA for Real-Time Monitoring of Olive Oil Mills

    • Authors: Cristina Martinez-Ruedas, Jose-Maria Flores-Arias, Isabel M. Moreno-Garcia, Matias Linan-Reyes, Francisco Jose Bellido-Outeiriño
      First page: 60
      Abstract: Cyber–physical systems involve the creation, continuous updating, and monitoring of virtual replicas that closely mirror their physical counterparts. These virtual representations are fed by real-time data from sensors, Internet of Things (IoT) devices, and other sources, enabling a dynamic and accurate reflection of the state of the physical system. This emphasizes the importance of data synchronization, visualization, and interaction within virtual environments as a means to improve decision-making, training, maintenance, and overall operational efficiency. This paper presents a novel approach to a cyber–physical system that integrates virtual reality (VR)-based digital twins and 3D SCADA in the context of Industry 4.0 for the monitoring and optimization of an olive mill. The methodology leverages virtual reality to create a digital twin that enables immersive data-driven simulations for olive mill monitoring. The proposed CPS takes data from the physical environment through the existing sensors and measurement elements in the olive mill, concentrates them, and exposes them to the virtual environment through the Open Platform Communication United Architecture (OPC-UA) protocol, thus establishing bidirectional and real-time communication. Furthermore, in the proposed virtual environment, the digital twin is interfaced with the 3D SCADA system, allowing it to create virtual models of the process. This innovative approach has the potential to revolutionize the olive oil industry by improving operational efficiency, product quality, and sustainability while optimizing maintenance practices.
      Citation: Technologies
      PubDate: 2024-04-30
      DOI: 10.3390/technologies12050060
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 61: New Upgrade to Improve Operation of
           Conventional Grid-Connected Photovoltaic Systems

    • Authors: Manuel Cáceres, Alexis Raúl González González Mayans, Andrés Firman, Luis Vera, Juan de la Casa de la Casa Higueras
      First page: 61
      Abstract: The incorporation of distributed generation with photovoltaic systems entails a drawback associated with intermittency in the generation capacity due to variations in the solar resource. In general, this aspect limits the level of penetration that this resource can have without producing an appreciable impact on the quality of the electrical supply. With the intention of reducing its intermittency, this paper presents the characterization of a methodology for maximizing grid-connected PV system operation under low-solar-radiation conditions. A new concept of a hybrid system based on a constant current source and capable of integrating different sources into a conventional grid-connected PV system is presented. Results of an experimental characterization of a low-voltage grid–PV system connection with a DC/DC converter for constant-current source application are shown in zero and non-zero radiation conditions. The results obtained demonstrate that the proposed integration method works efficiently without causing appreciable effects on the parameters that define the quality of the electrical supply. In this way, it is possible to efficiently incorporate another source of energy, taking advantage of the characteristics of the GCPVS without further interventions in the system. It is expected that this topology could help to integrate other generation and/or storage technologies into already existing PV systems, opening a wide field of research in the PV systems area.
      Citation: Technologies
      PubDate: 2024-05-02
      DOI: 10.3390/technologies12050061
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 62: Inference Analysis of Video Quality of
           Experience in Relation with Face Emotion, Video Advertisement, and ITU-T
           P.1203

    • Authors: Tisa Selma, Mohammad Mehedy Masud, Abdelhak Bentaleb, Saad Harous
      First page: 62
      Abstract: This study introduces an FER-based machine learning framework for real-time QoE assessment in video streaming. This study’s aim is to address the challenges posed by end-to-end encryption and video advertisement while enhancing user QoE. Our proposed framework significantly outperforms the base reference, ITU-T P.1203, by up to 37.1% in terms of accuracy and 21.74% after attribute selection. Our study contributes to the field in two ways. First, we offer a promising solution to enhance user satisfaction in video streaming services via real-time user emotion and user feedback integration, providing a more holistic understanding of user experience. Second, high-quality data collection and insights are offered by collecting real data from diverse regions to minimize any potential biases and provide advertisement placement suggestions.
      Citation: Technologies
      PubDate: 2024-05-03
      DOI: 10.3390/technologies12050062
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 63: Hunting Search Algorithm-Based Adaptive
           Fuzzy Tracking Controller for an Aero-Pendulum

    • Authors: Ricardo Rojas-Galván, José R. García-Martínez, Edson E. Cruz-Miguel, Omar A. Barra-Vázquez, Luis F. Olmedo-García, Juvenal Rodríguez-Reséndiz
      First page: 63
      Abstract: The aero-pendulum is a non-linear system used broadly to develop and test new controller strategies. This paper presents a new methodology for an adaptive PID fuzzy-based tracking controller using a Hunting Search (HuS) algorithm. The HuS algorithm computes the parameters of the membership functions of the fuzzification stage. As a novelty, the algorithm guarantees the overlap of the membership functions to ensure that all the functions are interconnected, generating new hunters to search for better solutions in the overlapping area. For the defuzzification stage, the HuS algorithm sets the singletons in optimal positions to evaluate the controller response using the centroid method. To probe the robustness of the methodology, the PID fuzzy controller algorithm is implemented in an embedded system to track the angular position of an aero-pendulum test bench. The results show that the adaptive PID fuzzy controller proposed presents root mean square error values of 0.42, 0.40, and 0.49 for 80, 90, and 100 degrees, respectively.
      Citation: Technologies
      PubDate: 2024-05-04
      DOI: 10.3390/technologies12050063
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 64: Atomic Quantum Technologies for Quantum
           Matter and Fundamental Physics Applications

    • Authors: Jorge Yago Malo, Luca Lepori, Laura Gentini, Maria Luisa Chiofalo
      First page: 64
      Abstract: Physics is living an era of unprecedented cross-fertilization among the different areas of science. In this perspective review, we discuss the manifold impact that state-of-the-art cold and ultracold-atomic platforms can have in fundamental and applied science through the development of platforms for quantum simulation, computation, metrology and sensing. We illustrate how the engineering of table-top experiments with atom technologies is engendering applications to understand problems in condensed matter and fundamental physics, cosmology and astrophysics, unveil foundational aspects of quantum mechanics, and advance quantum chemistry and the emerging field of quantum biology. In this journey, we take the perspective of two main approaches, i.e., creating quantum analogues and building quantum simulators, highlighting that independently of the ultimate goal of a universal quantum computer to be met, the remarkable transformative effects of these achievements remain unchanged. We wish to convey three main messages. First, this atom-based quantum technology enterprise is signing a new era in the way quantum technologies are used for fundamental science, even beyond the advancement of knowledge, which is characterised by truly cross-disciplinary research, extended interplay between theoretical and experimental thinking, and intersectoral approach. Second, quantum many-body physics is unavoidably taking center stage in frontier’s science. Third, quantum science and technology progress will have capillary impact on society, meaning this effect is not confined to isolated or highly specialized areas of knowledge, but is expected to reach and have a pervasive influence on a broad range of society aspects: while this happens, the adoption of a responsible research and innovation approach to quantum technologies is mandatory, to accompany citizens in building awareness and future scaffolding. Following on all the above reflections, this perspective review is thus aimed at scientists active or interested in interdisciplinary research, providing the reader with an overview of the current status of these wide fields of research where cold and ultracold-atomic platforms play a vital role in their description and simulation.
      Citation: Technologies
      PubDate: 2024-05-07
      DOI: 10.3390/technologies12050064
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 65: Fluorine-Free Single-Component
           Polyelectrolyte of Poly(ethylene glycol) Bearing Lithium
           Methanesulfonylsulfonimide Terminal Groups: Effect of Structural Variance
           on Ionic Conductivity

    • Authors: Bungo Ochiai, Koki Hirabayashi, Yudai Fujii, Yoshimasa Matsumura
      First page: 65
      Abstract: Fluorine-free single-component polyelectrolytes were developed via the hybridization of lithium methanesulfonylsulfonimide (LiMSSI) moieties to poly(ethylene glycol) (PEG) derivatives with different morphologies, and the relationship between the structure and its ionic conductivity was investigated. The PEG-LiMSSI derivatives with one, two, and three LiMSSI end groups were prepared via the concomitant Michael-type addition and lithiation of PEGs and N-methanesulfonylvinylsulfonimide. The ionic conductivity at 60 °C ranged from 1.8 × 10−7 to 2.0 × 10−4 S/cm. PEG-LiMSSI derivatives with one LiMSSI terminus and with two LiMSSI termini at both ends show higher ionic conductivity, that is as good as fluorine-free single-component polyelectrolytes, than that with two LiMSSI termini at one end and that with three LiMSSI termini.
      Citation: Technologies
      PubDate: 2024-05-09
      DOI: 10.3390/technologies12050065
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 66: Converging Artificial Intelligence and
           Quantum Technologies: Accelerated Growth Effects in Technological
           Evolution

    • Authors: Coccia
      First page: 66
      Abstract: One of the fundamental problems in the field of technological studies is to clarify the drivers and dynamics of technological evolution for sustaining industrial and economic change. This study confronts the problem by analyzing the converging technologies to explain effects on the evolutionary dynamics over time. This paper focuses on technological interaction between artificial intelligence and quantum technologies using a technometric model of technological evolution based on scientific and technological information (publications and patents). Findings show that quantum technology has a growth rate of 1.07, artificial intelligence technology has a rate of growth of 1.37, whereas the technological interaction of converging quantum and artificial intelligence technologies has an accelerated rate of growth of 1.58, higher than trends of these technologies taken individually. These findings suggest that technological interaction is one of the fundamental determinants in the rapid evolution of path-breaking technologies and disruptive innovations. The deductive implications of results about the effects of converging technologies are: (a) accelerated evolutionary growth; (b) a disproportionate (allometric) growth of patents driven by publications supporting a fast technological evolution. Our results support policy and managerial implications for the decision making of policymakers, technology analysts, and R&D managers that can direct R&D investments towards fruitful inter-relationships between radical technologies to foster scientific and technological change with positive societal and economic impacts.
      Citation: Technologies
      PubDate: 2024-05-10
      DOI: 10.3390/technologies12050066
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 67: Study of an LLC Converter for
           Thermoelectric Waste Heat Recovery Integration in Shipboard Microgrids

    • Authors: Nick Rigogiannis, Ioannis Roussos, Christos Pechlivanis, Ioannis Bogatsis, Anastasios Kyritsis, Nick Papanikolaou, Michael Loupis
      First page: 67
      Abstract: Static waste heat recovery, by means of thermoelectric generator (TEG) modules, constitutes a fast-growing energy harvesting technology on the way towards greener transportation. Many commercial solutions are already available for small internal combustion engine (ICE) vehicles, whereas further development and cost reductions of TEG devices expand their applicability at higher-power transportation means (i.e., ships and aircrafts). In this light, the integration of waste heat recovery based on TEG modules in a shipboard distribution network is studied in this work. Several voltage step-up techniques are considered, whereas the most suitable ones are assessed via the LTspice simulation platform. The design procedure of the selected LLC resonant converter is presented and analyzed in detail. Furthermore, a flexible control strategy is proposed, capable of either output voltage regulation (constant voltage) or maximum power point tracking (MPPT), according to the application demands. Finally, both simulations and experiments (on a suitable laboratory testbench) are performed. The obtained measurements indicate the high efficiency that can be achieved with the LLC converter for a wide operating area as well as the functionality and adequate performance of the control scheme in both operating conditions.
      Citation: Technologies
      PubDate: 2024-05-11
      DOI: 10.3390/technologies12050067
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 68: Application and Challenges of the
           Technology Acceptance Model in Elderly Healthcare: Insights from ChatGPT

    • Authors: Sang Dol Kim
      First page: 68
      Abstract: The Technology Acceptance Model (TAM) plays a pivotal role in elderly healthcare, serving as a theoretical framework. This study aimed to identify TAM’s core components, practical applications, challenges arising from its applications, and propose countermeasures in elderly healthcare. This descriptive study was conducted by utilizing OpenAI’s ChatGPT, with an access date of 10 January 2024. The three open-ended questions administered to ChatGPT and its responses were collected and qualitatively evaluated for reliability through previous studies. The core components of TAMs were identified as perceived usefulness, perceived ease of use, attitude toward use, behavioral intention to use, subjective norms, image, and facilitating conditions. TAM’s application areas span various technologies in elderly healthcare, such as telehealth, wearable devices, mobile health apps, and more. Challenges arising from TAM applications include technological literacy barriers, digital divide concerns, privacy and security apprehensions, resistance to change, limited awareness and information, health conditions and cognitive impairment, trust and reliability concerns, a lack of tailored interventions, overcoming age stereotypes, and integration with traditional healthcare. In conclusion, customized interventions are crucial for successful tech acceptance among the elderly population. The findings of this study are expected to enhance understanding of elderly healthcare and technology adoption, with insights gained through natural language processing models like ChatGPT anticipated to provide a fresh perspective.
      Citation: Technologies
      PubDate: 2024-05-13
      DOI: 10.3390/technologies12050068
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 69: Evaluating a Controlled Electromagnetic
           Launcher for Safe Remote Drug Delivery

    • Authors: John LaRocco, Qudsia Tahmina, John Simonis
      First page: 69
      Abstract: Biologists and veterinarians rely on dart projectors to inject animals with drugs, take biopsies from specimens, or inject tracking chips. Firearms, air guns, and other launchers are limited in their ability to precisely control the kinetic energy of a projectile, which can injure the animal if too high. In order to improve the safety of remote drug delivery, a lidar-modulated electromagnetic launcher and a soft drug delivery dart were prototyped. A single-stage revolver coilgun and soft dart were designed and tested at distances up to 8 m. With a coil efficiency of 2.25%, the launcher could consistently deliver a projectile at a controlled kinetic energy of 1.00 ± 0.006 J and an uncontrolled kinetic energy of 2.66 ± 0.076 J. Although modifications to charging time, sensors, and electronics could improve performance, our launcher performed at the required level at the necessary distances. The precision achieved with commercial components enables many other applications, from law enforcement to manufacturing.
      Citation: Technologies
      PubDate: 2024-05-17
      DOI: 10.3390/technologies12050069
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 70: Speckle Plethysmograph-Based Blood
           Pressure Assessment

    • Authors: Floranne T. Ellington, Anh Nguyen, Mao-Hsiang Huang, Tai Le, Bernard Choi, Hung Cao
      First page: 70
      Abstract: Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse arrival time (PAT), the time difference between the proximal and distal signal peaks. The most widely employed pairing involves electrocardiography (ECG) and photoplethysmography (PPG). Possessing similar characteristics in terms of measuring blood flow changes, a recently investigated optical signal known as speckleplethysmography (SPG) showed its stability and high signal-to-noise ratio compared with PPG. Thus, SPG is a potential surrogate to pair with ECG for CNBP estimation. The present study aims to unlock the untapped potential of SPG as a signal for non-invasive blood pressure monitoring based on PAT. To ascertain SPG’s capabilities, eight subjects were enrolled in multiple recording sessions. A third-party device was employed for ECG and PPG measurements, while a commercial device served as the reference for arterial blood pressure (ABP). SPG measurements were obtained using a prototype smartphone-based system. Following the completion of three scenarios—sitting, walking, and running—the subjects’ signals and ABP were recorded to investigate the predictive capacity of systolic blood pressure. The collected data were processed and prepared for machine learning models, including support vector regression and decision tree regression. The models’ effectiveness was evaluated using root-mean-square error and mean absolute percentage error. In most instances, predictions utilizing PATSPG exhibited comparable or superior performance to PATPPG (i.e., SPG Rest ± 12.4 mmHg vs. PPG Rest ± 13.7 mmHg for RSME, and SPG 8% vs. PPG 9% for MAPE). Furthermore, incorporating an additional feature, namely the previous SBP value, resulted in reduced prediction errors for both signals in multiple model configurations (i.e., SPG Rest ± 12.4 mmHg to ±3.7 mmHg for RSME, and SPG Rest 8% to 3% for MAPE). These preliminary tests of SPG underscore the remarkable potential of this novel signal in PAT-based blood pressure predictions. Subsequent studies involving a larger cohort of test subjects and advancements in the SPG acquisition system hold promise for further improving the effectiveness of this newly explored signal in blood pressure monitoring.
      Citation: Technologies
      PubDate: 2024-05-18
      DOI: 10.3390/technologies12050070
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 71: Analysis, Evaluation, and Future
           Directions on Multimodal Deception Detection

    • Authors: Arianna D’Ulizia, Alessia D’Andrea, Patrizia Grifoni, Fernando Ferri
      First page: 71
      Abstract: Multimodal deception detection has received increasing attention from the scientific community in recent years, mainly due to growing ethical and security issues, as well as the growing use of digital media. A great number of deception detection methods have been proposed in several domains, such as political elections, security contexts, and job interviews. However, a systematic analysis of the current situation and the evaluation and future directions of deception detection based on cues coming from multiple modalities seems to be lacking. This paper, starting from a description of methods and metrics used for the analysis and evaluation of multimodal deception detection on video, provides a vision of future directions in this field. For the analysis, the PRISMA recommendations are followed, which allow the collection and synthesis of all the available research on the topic and the extraction of information on the multimodal features, the fusion methods, the classification approaches, the evaluation datasets, and metrics. The results of this analysis contribute to the assessment of the state of the art and the evaluation of evidence on important research questions in multimodal deceptive deception. Moreover, they provide guidance on future research in the field.
      Citation: Technologies
      PubDate: 2024-05-18
      DOI: 10.3390/technologies12050071
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 72: A Comprehensive Survey on the
           Investigation of Machine-Learning-Powered Augmented Reality Applications
           in Education

    • Authors: Haseeb Ali Khan, Sonain Jamil, Md. Jalil Piran, Oh-Jin Kwon, Jong-Weon Lee
      First page: 72
      Abstract: Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and gestures from kindergarten through university. The purpose of this survey is to provide an overview of various ways in which ML techniques can be applied within the field of AR within education. The first step is to describe the background of AR. In the next step, we discuss the ML models that are used in AR education applications. Additionally, we discuss how ML is used in AR. Each subgroup’s challenges and solutions can be identified by analyzing these frameworks. In addition, we outline several research gaps and future research directions in ML-based AR frameworks for education.
      Citation: Technologies
      PubDate: 2024-05-19
      DOI: 10.3390/technologies12050072
      Issue No: Vol. 12, No. 5 (2024)
       
  • Technologies, Vol. 12, Pages 43: Applied Deep Learning-Based Crop Yield
           Prediction: A Systematic Analysis of Current Developments and Potential
           Challenges

    • Authors: Khadija Meghraoui, Imane Sebari, Juergen Pilz, Kenza Ait El Kadi, Saloua Bensiali
      First page: 43
      Abstract: Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field.
      Citation: Technologies
      PubDate: 2024-03-24
      DOI: 10.3390/technologies12040043
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 44: Blood Pressure Measurement Device
           Accuracy Evaluation: Statistical Considerations with an Implementation in
           R

    • Authors: Tanvi Chandel, Victor Miranda, Andrew Lowe, Tet Chuan Lee
      First page: 44
      Abstract: Inaccuracies from devices for non-invasive blood pressure measurements have been well reported with clinical consequences. International standards, such as ISO 81060-2 and the seminal AAMI/ANSI SP10, define protocols and acceptance criteria for these devices. Prior to applying these standards, a sample size of N >= 85 is mandatory, that is, the number of distinct subcjects used to calculate device inaccuracies. Often, it is not possible to gather such a large sample. Many studies apply these standards with a smaller sample. The objective of the paper is to introduce a methodology that broadens the method first developed by the AAMI Sphygmomanometer Committee for accepting a blood pressure measurement device. We study changes in the acceptance region for various sample sizes using the sampling distribution for proportions and introduce a methodology for estimating the exact probability of the acceptance of a device. This enables the comparison of the accuracies of existing device development techniques even if they were studied with a smaller sample size. The study is useful in assisting BP measurement device manufacturers. To assist clinicians, we present a newly developed “bpAcc” package in R to evaluate acceptance statistics for various sample sizes.
      Citation: Technologies
      PubDate: 2024-03-25
      DOI: 10.3390/technologies12040044
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 45: An Artificial Bee Colony Algorithm for
           Coordinated Scheduling of Production Jobs and Flexible Maintenance in
           Permutation Flowshops

    • Authors: Asma Ladj, Fatima Benbouzid-Si Tayeb, Alaeddine Dahamni, Mohamed Benbouzid
      First page: 45
      Abstract: This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed for each machine on the shopfloor. With such a flexible nature of maintenance activities, the resulting joint schedule is more practical and representative of real-world scenarios. Our goal is to determine the best job permutation in which flexible maintenance activities are properly incorporated. To tackle the NP-hard nature of this problem, an artificial bee colony (ABC) algorithm is developed to minimize the total production time (Makespan). Experiments are conducted utilizing well-known Taillard’s benchmarks, enriched with maintenance data, to compare the proposed algorithm performance against the variable neighbourhood search (VNS) method from the literature. Computational results demonstrate the effectiveness of the proposed algorithm in terms of both solution quality and computational times.
      Citation: Technologies
      PubDate: 2024-03-25
      DOI: 10.3390/technologies12040045
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 46: Enhancing Patient Care in Radiotherapy:
           Proof-of-Concept of a Monitoring Tool

    • Authors: Guillaume Beldjoudi, Rémi Eugène, Vincent Grégoire, Ronan Tanguy
      First page: 46
      Abstract: Introduction: A monitoring tool, named Oncology Data Management (ODM), was developed in radiotherapy to generate structured information based on data contained in an Oncology Information System (OIS). This study presents the proof-of-concept of the ODM tool and highlights its applications to enhance patient care in radiotherapy. Material & Methods: ODM is a sophisticated SQL query which extracts specific features from the Mosaiq OIS (Elekta, UK) database into an independent structured database. Data from 2016 to 2022 was extracted to enable monitoring of treatment units and evaluation of the quality of patient care. Results: A total of 25,259 treatments were extracted. Treatment machine monitoring revealed a daily 11-treatement difference between two units. ODM showed that the unit with fewer daily treatments performed more complex treatments on diverse locations. In 2019, the implementation of ODM led to the definition of quality indicators and in organizational changes that improved the quality of care. As consequences, for palliative treatments, there was an improvement in the proportion of treatments prepared within 7 calendar days between the scanner and the first treatment session (29.1% before 2020, 40.4% in 2020 and 46.4% after 2020). The study of fractionation in breast treatments exhibited decreased prescription variability after 2019, with distinct patient age categories. Bi-fractionation once a week for larynx prescriptions of 35 x 2.0Gy achieved an overall treatment duration of 47.0 ± 3.0 calendar days in 2022. Conclusions: ODM enables data extraction from the OIS and provides quantitative tools for improving organization of a department and the quality of patient care in radiotherapy.
      Citation: Technologies
      PubDate: 2024-03-29
      DOI: 10.3390/technologies12040046
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 47: Impact Localization for Haptic Input
           Devices Using Hybrid Laminates with Sensoric Function

    • Authors: René Schmidt, Alexander Graf, Ricardo Decker, Stephan Lede, Verena Kräusel, Lothar Kroll, Wolfram Hardt
      First page: 47
      Abstract: The required energy savings can be achieved in all automotive domains through weight savings and the merging of manufacturing processes in production. This fact is taken into account through functional integration in lightweight materials and manufacturing in a process close to large-scale production. In previous work, separate steps of a process chain for manufacturing a center console cover utilizing a sensoric hybrid laminate have been developed and evaluated. This includes the process steps of joining, forming and inline polarization as well as connecting to an embedded system. This work continues the research process by evaluating impact localization methods to use the center console as a haptic input device. For this purpose, different deep learning methods are derived from the state of the art and analyzed for their applicability in two consecutive studies. The results show that MLPs, LSTMs, GRUs and CNNs are suitable to localize impacts on the novel laminate with high localization rates of up to 99 %, and thus the usability of the developed laminate as a haptic input device has been proven.
      Citation: Technologies
      PubDate: 2024-04-01
      DOI: 10.3390/technologies12040047
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 48: Carbon Fiber Polymer Reinforced 3D
           Printed Composites for Centrifugal Pump Impeller Manufacturing

    • Authors: Gabriel Mansour, Vasileios Papageorgiou, Dimitrios Tzetzis
      First page: 48
      Abstract: Centrifugal pumps are used extensively in various everyday applications. The occurrence of corrosion phenomena during operation often leads to the failure of a pump’s operating components, such as the impeller. The present research study examines the utilization of composite materials for fabricating centrifugal pump components using additive manufacturing as an effort to fabricate corrosion resistant parts. To achieve the latter two nanocomposite materials, carbon fiber reinforced polyamide and carbon fiber reinforced polyphenylene sulfide were compared with two metal alloys, cast iron and brass, which are currently used in pump impeller manufacturing. The mechanical properties of the materials are extracted by performing a series of experiments, such as uniaxial tensile tests, nanoindentation and scanning electron microscope (SEM) examination of the specimen’s fracture area. Then, computational fluid dynamics (CFD) analysis is performed using various impeller designs to determine the fluid pressure exerted on the impeller’s geometry during its operation. Finally, the maximum power rating of an impeller that can be made from such composites will be determined using a static finite element model (FEM). The FEM static model is developed by integrating the data collected from the experiments with the results obtained from the CFD analysis. The current research work shows that nanocomposites can potentially be used for developing impellers with rated power of up to 9.41 kW.
      Citation: Technologies
      PubDate: 2024-04-03
      DOI: 10.3390/technologies12040048
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 49: Numerical Study of the Influence of the
           Structural Parameters on the Stress Dissipation of 3D Orthogonal Woven
           Composites under Low-Velocity Impact

    • Authors: Wang Xu, Mohammed Zikry, Abdel-Fattah M. Seyam
      First page: 49
      Abstract: This study investigates the effects of the number of layers, x-yarn (weft) density, and z-yarn (binder) path on the mechanical behavior of E-glass 3D orthogonal woven (3DOW) composites during low-velocity impacts. Meso-level finite element (FE) models were developed and validated for 3DOW composites with different yarn densities and z-yarn paths, providing analyses of stress distribution within reinforcement fibers and matrix, energy absorption, and failure time. Our findings revealed that lower x-yarn densities led to accumulations of stress concentrations. Furthermore, changing the z-yarn path, such as transitioning from plain weaves to twill or basket weaves had a noticeable impact on stress distributions. The research highlights the significance of designing more resilient 3DOW composites for impact applications by choosing appropriate parameters in weaving composite designs.
      Citation: Technologies
      PubDate: 2024-04-05
      DOI: 10.3390/technologies12040049
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 50: Past, Present, and Future of New
           Applications in Utilization of Eddy Currents

    • Authors: Nestor O. Romero-Arismendi, Juan C. Olivares-Galvan, Jose L. Hernandez-Avila, Rafael Escarela-Perez, Victor M. Jimenez-Mondragon, Felipe Gonzalez-Montañez
      First page: 50
      Abstract: Eddy currents are an electromagnetic phenomenon that represent an inexhaustible source of inspiration for technological innovations in the 21st century. Throughout history, these currents have been a subject of research and technological development in multiple fields. This article delves into the fascinating world of eddy currents, revealing their physical foundations and highlighting their impact on a wide range of applications, ranging from non-destructive evaluation of materials to levitation phenomena, as well as their influence on fields as diverse as medicine, the automotive industry, and aerospace. The nature of eddy currents has stimulated the imaginations of scientists and engineers, driving the creation of revolutionary technologies that are transforming our society. As we progress through this article, we will cover the main aspects of eddy currents, their practical applications, and challenges for future works.
      Citation: Technologies
      PubDate: 2024-04-09
      DOI: 10.3390/technologies12040050
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 51: Monitoring of Hip Joint Forces and
           Physical Activity after Total Hip Replacement by an Integrated
           Piezoelectric Element

    • Authors: Franziska Geiger, Henning Bathel, Sascha Spors, Rainer Bader, Daniel Kluess
      First page: 51
      Abstract: Resultant hip joint forces can currently only be recorded in situ in a laboratory setting using instrumented total hip replacements (THRs) equipped with strain gauges. However, permanent recording is important for monitoring the structural condition of the implant, for therapeutic purposes, for self-reflection, and for research into managing the predicted increasing number of THRs worldwide. Therefore, this study aims to investigate whether a recently proposed THR with an integrated piezoelectric element represents a new possibility for the permanent recording of hip joint forces and the physical activities of the patient. Hip joint forces from nine different daily activities were obtained from the OrthoLoad database and applied to a total hip stem equipped with a piezoelectric element using a uniaxial testing machine. The forces acting on the piezoelectric element were calculated from the generated voltages. The correlation between the calculated forces on the piezoelectric element and the applied forces was investigated, and the regression equations were determined. In addition, the voltage outputs were used to predict the activity with a random forest classifier. The coefficient of determination between the applied maximum forces on the implant and the calculated maximum forces on the piezoelectric element was R2 = 0.97 (p < 0.01). The maximum forces on the THR could be determined via activity-independent determinations with a deviation of 2.49 ± 13.16% and activity-dependent calculation with 0.87 ± 7.28% deviation. The activities could be correctly predicted using the classification model with 95% accuracy. Hence, piezoelectric elements integrated into a total hip stem represent a promising sensor option for the energy-autonomous detection of joint forces and physical activities.
      Citation: Technologies
      PubDate: 2024-04-09
      DOI: 10.3390/technologies12040051
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 52: A Comparison of Machine Learning-Based
           and Conventional Technologies for Video Compression

    • Authors: Lesia Mochurad
      First page: 52
      Abstract: The growing demand for high-quality video transmission over bandwidth-constrained networks and the increasing availability of video content have led to the need for efficient storage and distribution of large video files. To improve the latter, this article offers a comparison of six video compression methods without loss of quality. Particularly, H.255, VP9, AV1, convolutional neural network (CNN), recurrent neural network (RNN), and deep autoencoder (DAE). The proposed decision is to use a dataset of high-quality videos to implement and compare the performance of classical compression algorithms and algorithms based on machine learning. Evaluations of the compression efficiency and the quality of the received images were made on the basis of two metrics: PSNR and SSIM. This comparison revealed the strengths and weaknesses of each approach and provided insights into how machine learning algorithms can be optimized in future research. In general, it contributed to the development of more efficient and effective video compression algorithms that can be useful for a wide range of applications.
      Citation: Technologies
      PubDate: 2024-04-15
      DOI: 10.3390/technologies12040052
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 53: Developing a Performance Evaluation
           Framework Structural Model for Educational Metaverse

    • Authors: Elena Tsappi, Ioannis Deliyannis, George Nathaniel Papageorgiou
      First page: 53
      Abstract: In response to the transformative impact of digital technology on education, this study introduces a novel performance management framework for virtual learning environments suitable for the metaverse era. Based on the Structural Equation Modeling (SEM) approach, this paper proposes a comprehensive evaluative model, anchored on the integration of the Theory of Planned Behavior (TPB), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Community of Inquiry Framework (CoI). The model synthesizes five Key Performance Indicators (KPIs)—content delivery, student engagement, metaverse tool utilization, student performance, and adaptability—to intricately assess academic avatar performances in virtual educational settings. This theoretical approach marks a significant stride in understanding and enhancing avatar efficacy in the metaverse environment. It enriches the discourse on performance management in digital education and sets a foundation for future empirical studies. As virtual online environments gain prominence in education and training, this research study establishes the basic principles and highlights the key points for further empirical research in the new era of the metaverse educational environment.
      Citation: Technologies
      PubDate: 2024-04-16
      DOI: 10.3390/technologies12040053
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 54: Experimental and Numerical Analysis of a
           Novel Cycloid-Type Rotor versus S-Type Rotor for Vertical-Axis Wind
           Turbine

    • Authors: José Eli Eduardo González-Durán, Juan Manuel Olivares-Ramírez, María Angélica Luján-Vega, Juan Emigdio Soto-Osornio, Juan Manuel García-Guendulain, Juvenal Rodriguez-Resendiz
      First page: 54
      Abstract: The performance of a new vertical-axis wind turbine rotor based on the mathematical equation of the cycloid is analyzed and compared through simulation and experimental testing against a semicircular or S-type rotor, which is widely used. The study examines three cases: equalizing the diameter, chord length and the area under the curve. Computational Fluid Dynamics (CFD) was used to simulate these cases and evaluate moment, angular velocity and power. Experimental validation was carried out in a wind tunnel that was designed and optimized with the support of CFD. The rotors for all three cases were 3D printed in resin to analyze their experimental performance as a function of wind speed. The moment and Maximum Power Point (MPP) were determined in each case. The simulation results indicate that the cycloid-type rotor outperforms the semicircular or S-type rotor by 15%. Additionally, experimental evidence confirms that the cycloid-type rotor performs better in all three cases. In the MPP analysis, the cycloid-type rotor achieved an efficiency of 10.8% which was 38% better than the S-type rotor.
      Citation: Technologies
      PubDate: 2024-04-17
      DOI: 10.3390/technologies12040054
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 55: Digital Twin Models for Personalised and
           Predictive Medicine in Ophthalmology

    • Authors: Miruna-Elena Iliuţă, Mihnea-Alexandru Moisescu, Simona-Iuliana Caramihai, Alexandra Cernian, Eugen Pop, Daniel-Ioan Chiş, Traian-Costin Mitulescu
      First page: 55
      Abstract: This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured architectural framework comprising five levels has been proposed, integrating Digital Twin, Systems Medicine, and Predictive Medicine for managing eye diseases. Based on demographic parameters, statistics were performed to identify potential correlations that may contribute to predispositions to glaucoma. With the aid of a dataset, a neural network was trained with the goal of identifying glaucoma. This comprehensive approach, based on statistical analysis and Machine Learning, is a promising method to enhance diagnostic accuracy and provide personalized treatment approaches.
      Citation: Technologies
      PubDate: 2024-04-18
      DOI: 10.3390/technologies12040055
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 56: An End-to-End Lightweight Multi-Scale CNN
           for the Classification of Lung and Colon Cancer with XAI Integration

    • Authors: Mohammad Asif Hasan, Fariha Haque, Saifur Rahman Sabuj, Hasan Sarker, Md. Omaer Faruq Goni, Fahmida Rahman, Md Mamunur Rashid
      First page: 56
      Abstract: To effectively treat lung and colon cancer and save lives, early and accurate identification is essential. Conventional diagnosis takes a long time and requires the manual expertise of radiologists. The rising number of new cancer cases makes it challenging to process massive volumes of data quickly. Different machine learning approaches to the classification and detection of lung and colon cancer have been proposed by multiple research studies. However, when it comes to self-learning classification and detection tasks, deep learning (DL) excels. This paper suggests a novel DL convolutional neural network (CNN) model for detecting lung and colon cancer. The proposed model is lightweight and multi-scale since it uses only 1.1 million parameters, making it appropriate for real-time applications as it provides an end-to-end solution. By incorporating features extracted at multiple scales, the model can effectively capture both local and global patterns within the input data. The explainability tools such as gradient-weighted class activation mapping and Shapley additive explanation can identify potential problems by highlighting the specific input data areas that have an impact on the model’s choice. The experimental findings demonstrate that for lung and colon cancer detection, the proposed model was outperformed by the competition and accuracy rates of 99.20% have been achieved for multi-class (containing five classes) predictions.
      Citation: Technologies
      PubDate: 2024-04-21
      DOI: 10.3390/technologies12040056
      Issue No: Vol. 12, No. 4 (2024)
       
  • Technologies, Vol. 12, Pages 28: Nested Contrastive Boundary Learning:
           Point Transformer Self-Attention Regularization for 3D Intracranial
           Aneurysm Segmentation

    • Authors: Luis Felipe Estrella-Ibarra, Alejandro de León-Cuevas, Saul Tovar-Arriaga
      First page: 28
      Abstract: In 3D segmentation, point-based models excel but face difficulties in precise class delineation at class intersections, an inherent challenge in segmentation models. This is particularly critical in medical applications, influencing patient care and surgical planning, where accurate 3D boundary identification is essential for assisting surgery and enhancing medical training through advanced simulations. This study introduces the Nested Contrastive Boundary Learning Point Transformer (NCBL-PT), specially designed for 3D point cloud segmentation. NCBL-PT employs contrastive learning to improve boundary point representation by enhancing feature similarity within the same class. NCBL-PT incorporates a border-aware distinction within the same class points, allowing the model to distinctly learn from both points in proximity to the class intersection and from those beyond. This reduces semantic confusion among the points of different classes in the ambiguous class intersection zone, where similarity in features due to proximity could lead to incorrect associations. The model operates within subsampled point clouds at each encoder block stage of the point transformer architecture. It applies self-attention with k = 16 nearest neighbors to local neighborhoods, aligning with NCBL calculations for consistent self-attention regularization in local contexts. NCBL-PT improves 3D segmentation at class intersections, as evidenced by a 3.31% increase in Intersection over Union (IOU) for aneurysm segmentation compared to the base point transformer model.
      Citation: Technologies
      PubDate: 2024-02-21
      DOI: 10.3390/technologies12030028
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 29: A Kinetic Study of a Photo-Oxidation
           Reaction between α-Terpinene and Singlet Oxygen in a Novel
           Oscillatory Baffled Photo Reactor

    • Authors: Jianhan Chen, Rohen Prinsloo, Xiongwei Ni
      First page: 29
      Abstract: By planting LEDs on the surfaces of orifice baffles, a novel batch oscillatory baffled photoreactor (OBPR) together with polymer-supported Rose Bengal (Ps-RB) beads are here used to investigate the reaction kinetics of a photo-oxidation reaction between α-terpinene and singlet oxygen (1O2). In the mode of NMR data analysis that is widely used for this reaction, α-terpinene and ascaridole are treated as a reaction pair, assuming kinetically singlet oxygen is in excess or constant. We have, for the first time, here examined the validity of the method, discovered that increasing α-terpinene initially leads to an increase in ascaridole, indicating that the supply of singlet oxygen is in excess. Applying a kinetic analysis, a pseudo-first-order reaction kinetics is confirmed, supporting this assumption. We have subsequently initiated a methodology of estimating the 1O2 concentrations based on the proportionality of ascaridole concentrations with respect to its maximum under these conditions. With the help of the estimated singlet oxygen data, the efficiency of 1O2 utilization and the photo efficiency of converting molecular oxygen to 1O2 are further proposed and evaluated. We have also identified conditions under which a further increase in α-terpinene has caused decreases in ascaridole, implying kinetically that 1O2 has now become a limiting reagent, and the method of treating α-terpinene and ascaridole as a reaction pair in the data analysis would no longer be valid under those conditions.
      Citation: Technologies
      PubDate: 2024-02-21
      DOI: 10.3390/technologies12030029
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 30: Mapping Acoustic Frictional Properties of
           Self-Lubricating Epoxy-Coated Bearing Steel with Acoustic Emissions during
           Friction Test

    • Authors: Venkatasubramanian Krishnamoorthy, Ashvita Anitha John, Shubrajit Bhaumik, Viorel Paleu
      First page: 30
      Abstract: This work investigates the stick–slip phenomenon during sliding motion between solid lubricant-impregnated epoxy polymer-coated steel bars and AISI 52,100 steel balls. An acoustic sensor detected the stick–slip phenomenon during the tribo-pair interaction. The wear characteristics of the workpiece coated with different epoxy coatings were observed and scrutinized. The RMS values of the acoustic sensor were correlated with the frictional coefficient to develop a standard based on the acoustic sensor, leading to the detection of the stick–slip phenomenon. As per the findings, the acoustic waveform remained relatively similar to the friction coefficient observed during the study and can be used effectively in detecting the stick–slip phenomenon between steel and polymer interaction. This work will be highly beneficial in industrial and automotive applications with a significant interaction of polymer and steel surfaces.
      Citation: Technologies
      PubDate: 2024-02-24
      DOI: 10.3390/technologies12030030
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 31: Visualization of Spatial–Temporal
           Epidemiological Data: A Scoping Review

    • Authors: Denisse Kim, Bernardo Cánovas-Segura, Manuel Campos, Jose M. Juarez
      First page: 31
      Abstract: In recent years, the proliferation of health data sources due to computer technologies has prompted the use of visualization techniques to tackle epidemiological challenges. However, existing reviews lack a specific focus on the spatial and temporal analysis of epidemiological data using visualization tools. This study aims to address this gap by conducting a scoping review following the PRISMA-ScR guidelines, examining the literature from 2000 to 2024 on spatial–temporal visualization techniques when applied to epidemics, across five databases: PubMed, IEEE Xplore, Scopus, Google Scholar, and ACM Digital Library until 24 January 2024. Among 1312 papers reviewed, 114 were selected, emphasizing aggregate measures, web platform tools, and geospatial data representation, particularly favoring choropleth maps and extended charts. Visualization techniques were predominantly utilized for real-time data presentation, trend analysis, and predictions. Evaluation methods, categorized into standard methodology, user experience, task efficiency, and accuracy, were observed. Although various open-access datasets were available, only a few were commonly used, mainly those related to COVID-19. This study sheds light on the current trends in visualizing epidemiological data over the past 24 years, highlighting the gaps in standardized evaluation methodologies and the limited exploration of individual epidemiological data and diseases acquired in hospitals during epidemics.
      Citation: Technologies
      PubDate: 2024-02-28
      DOI: 10.3390/technologies12030031
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 32: Measurement of Light-Duty Vehicle Exhaust
           Emissions with Light Absorption Spectrometers

    • Authors: Barouch Giechaskiel, Anastasios Melas, Jacopo Franzetti, Victor Valverde, Michaël Clairotte, Ricardo Suarez-Bertoa
      First page: 32
      Abstract: Light-duty vehicle emission regulations worldwide set limits for the following gaseous pollutants: carbon monoxide (CO), nitric oxides (NOX), hydrocarbons (HCs), and/or non-methane hydrocarbons (NMHCs). Carbon dioxide (CO2) is indirectly limited by fleet CO2 or fuel consumption targets. Measurements are carried out at the dilution tunnel with “standard” laboratory-grade instruments following well-defined principles of operation: non-dispersive infrared (NDIR) analyzers for CO and CO2, flame ionization detectors (FIDs) for hydrocarbons, and chemiluminescence analyzers (CLAs) or non-dispersive ultraviolet detectors (NDUVs) for NOX. In the United States in 2012 and in China in 2020, with Stage 6, nitrous oxide (N2O) was also included. Brazil is phasing in NH3 in its regulation. Alternative instruments that can measure some or all these pollutants include Fourier transform infrared (FTIR)- and laser absorption spectroscopy (LAS)-based instruments. In the second category, quantum cascade laser (QCL) spectroscopy in the mid-infrared area or laser diode spectroscopy (LDS) in the near-infrared area, such as tunable diode laser absorption spectroscopy (TDLAS), are included. According to current regulations and technical specifications, NH3 is the only component that has to be measured at the tailpipe to avoid ammonia losses due to its hydrophilic properties and adsorption on the transfer lines. There are not many studies that have evaluated such instruments, in particular those for “non-regulated” worldwide pollutants. For this reason, we compared laboratory-grade “standard” analyzers with FTIR- and TDLAS-based instruments measuring NH3. One diesel and two gasoline vehicles at different ambient temperatures and with different test cycles produced emissions in a wide range. In general, the agreement among the instruments was very good (in most cases, within ±10%), confirming their suitability for the measurement of pollutants.
      Citation: Technologies
      PubDate: 2024-02-28
      DOI: 10.3390/technologies12030032
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 33: A Comparison between Kinematic Models for
           Robotic Needle Insertion with Application into Transperineal Prostate
           Biopsy

    • Authors: Chiara Zandonà, Andrea Roberti, Davide Costanzi, Burçin Gül, Özge Akbulut, Paolo Fiorini, Andrea Calanca
      First page: 33
      Abstract: Transperineal prostate biopsy is the most reliable technique for detecting prostate cancer, and robot-assisted needle insertion has the potential to improve the accuracy of this procedure. Modeling the interaction between a bevel-tip needle and the tissue, considering tissue heterogeneity, needle bending, and tissue/organ deformation and movement is a required step to enable robotic needle insertion. Even if several models exist, they have never been compared on experimental grounds. Based on this motivation, this paper proposes an experimental comparison for kinematic models of needle insertion, considering different needle insertion speeds and different degrees of tissue stiffness. The experimental comparison considers automated insertions of needles into transparent silicone phantoms under stereo-image guidance. The comparison evaluates the accuracy of existing models in predicting needle deformation.
      Citation: Technologies
      PubDate: 2024-03-01
      DOI: 10.3390/technologies12030033
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 34: Reinforcement Learning as an Approach to
           Train Multiplayer First-Person Shooter Game Agents

    • Authors: Pedro Almeida, Vítor Carvalho, Alberto Simões
      First page: 34
      Abstract: Artificial Intelligence bots are extensively used in multiplayer First-Person Shooter (FPS) games. By using Machine Learning techniques, we can improve their performance and bring them to human skill levels. In this work, we focused on comparing and combining two Reinforcement Learning training architectures, Curriculum Learning and Behaviour Cloning, applied to an FPS developed in the Unity Engine. We have created four teams of three agents each: one team for Curriculum Learning, one for Behaviour Cloning, and another two for two different methods of combining Curriculum Learning and Behaviour Cloning. After completing the training, each agent was matched to battle against another agent of a different team until each pairing had five wins or ten time-outs. In the end, results showed that the agents trained with Curriculum Learning achieved better performance than the ones trained with Behaviour Cloning by a matter of 23.67% more average victories in one case. In terms of the combination attempts, not only did the agents trained with both devised methods had problems during training, but they also achieved insufficient results in the battle, with an average of 0 wins.
      Citation: Technologies
      PubDate: 2024-03-05
      DOI: 10.3390/technologies12030034
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 35: A 28 GHz Highly Linear Up-Conversion
           Mixer for 5G Cellular Communications

    • Authors: Chul-Woo Byeon
      First page: 35
      Abstract: In this paper, we present a highly linear direct in-phase/quadrature (I/Q) up-conversion mixer for 5G millimeter-wave applications. To enhance the linearity of the mixer, we propose a complementary derivative superposition technique with pre-distortion. The proposed up-conversion mixer consists of a quadrature generator, LO buffer amplifiers, and an I/Q up-conversion mixer core and achieves an output third-order intercept point of 15.7 dBm and an output 1 dB compression point of 2 dBm at 27.6 GHz, while it consumes 15 mW at a supply voltage of 1 V. The conversion gain is 11.4 dB and the LO leakage and image rejection ratio are −56 dBc and 61 dB, respectively, in the measurement. The proposed I/Q up-conversion mixer is suitable for 5G cellular communication systems.
      Citation: Technologies
      PubDate: 2024-03-07
      DOI: 10.3390/technologies12030035
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 36: Energy Sustainability Indicators for the
           Use of Biomass as Fuel for the Sugar Industry

    • Authors: Reinier Jiménez Borges, Luis Angel Iturralde Carrera, Eduardo Julio Lopez Bastida, José R. García-Martínez, Roberto V. Carrillo-Serrano, Juvenal Rodríguez-Reséndiz
      First page: 36
      Abstract: There are numerous analytical and/or computational tools for evaluating the energetic sustainability of biomass in the sugar industry. However, the simultaneous integration of the energetic–exergetic and emergetic criteria for such evaluation is still insufficient. The objective of the present work is to propose a range of indicators to evaluate the sustainability of the use of biomass as fuel in the sugar industry. For this purpose, energy, exergy, and emergy evaluation tools were theoretically used as sustainability indicators. They were validated in five variants of different biomass and their mixtures in two studies of technologies used in Cuba for the sugar industry. As a result, the energy method showed, for all variants, an increase in efficiency of about 5% in the VU-40 technology compared to the Retal technology. There is an increase in energy efficiency when considering AHRs of 2.8% or Marabu (Dichrostachys cinerea) (5.3%) compared to the V1 variant. Through the study of the exergetic efficiency, an increase of 2% was determined in both technologies for the case of the V1 variant, and an increase in efficiency is observed in the V2 variant of 5% and the V3 variant (5.6%) over the V1 variant. The emergetic method showed superior results for the VU-40 technology over the Retal technology due to higher fuel utilization. In the case of the V1 variant, there was a 7% increase in the renewability ratio and an 11.07% increase in the sustainability index. This is because more energy is produced per unit of environmental load.
      Citation: Technologies
      PubDate: 2024-03-08
      DOI: 10.3390/technologies12030036
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 37: Pioneering a Framework for Robust
           Telemedicine Technology Assessment (Telemechron Study)

    • Authors: Sandra Morelli, Carla Daniele, Giuseppe D’Avenio, Mauro Grigioni, Daniele Giansanti
      First page: 37
      Abstract: The field of technology assessment in telemedicine is garnering increasing attention due to the widespread adoption of this discipline and its complex and heterogeneous system characteristics, making its application complex. As part of a national telemedicine project, the National Center for Innovative Technologies in Public Health at the Italian National Institute of Health played the role of promoting and utilizing technology assessment tools within partnership projects. This study aims to outline the design, development, and application of assessment methodologies within the telemedicine project proposed by the ISS team, utilizing a specific framework developed within the project. The sub-objectives include evaluating the proposed methodology’s effectiveness and feasibility, gathering feedback for improvement, and assessing its impact on various project components. The study emphasizes the multifaceted nature of action domains and underscores the crucial role of technology assessments in telemedicine, highlighting its impact across diverse realms through iterative interaction cycles with project partners. Both the impact and the acceptance of the methodology have been assessed by means of specific computer-aided web interviewing (CAWI) tools. The proposed methodology received significant acceptance, providing valuable insights for refining future frameworks. The impact assessment revealed a consistent quality improvement trend in the project’s products, evident in methodological consolidations. The overall message encourages similar initiatives in this domain, shedding light on the intricacies of technology assessment implementation. In conclusion, the study serves as a comprehensive outcome of the national telemedicine project, witnessing the success and adaptability of the technology assessment methodology and advocating for further exploration and implementation in analogous contexts.
      Citation: Technologies
      PubDate: 2024-03-08
      DOI: 10.3390/technologies12030037
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 38: Examining the Landscape of Cognitive
           Fatigue Detection: A Comprehensive Survey

    • Authors: Enamul Karim, Hamza Reza Pavel, Sama Nikanfar, Aref Hebri, Ayon Roy, Harish Ram Nambiappan, Ashish Jaiswal, Glenn R. Wylie, Fillia Makedon
      First page: 38
      Abstract: Cognitive fatigue, a state of reduced mental capacity arising from prolonged cognitive activity, poses significant challenges in various domains, from road safety to workplace productivity. Accurately detecting and mitigating cognitive fatigue is crucial for ensuring optimal performance and minimizing potential risks. This paper presents a comprehensive survey of the current landscape in cognitive fatigue detection. We systematically review various approaches, encompassing physiological, behavioral, and performance-based measures, for robust and objective fatigue detection. The paper further analyzes different challenges, including the lack of standardized ground truth and the need for context-aware fatigue assessment. This survey aims to serve as a valuable resource for researchers and practitioners seeking to understand and address the multifaceted challenge of cognitive fatigue detection.
      Citation: Technologies
      PubDate: 2024-03-11
      DOI: 10.3390/technologies12030038
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 39: Reinforcement-Learning-Based Virtual
           Inertia Controller for Frequency Support in Islanded Microgrids

    • Authors: Mohamed A. Afifi, Mostafa I. Marei, Ahmed M. I. Mohamad
      First page: 39
      Abstract: As the world grapples with the energy crisis, integrating renewable energy sources into the power grid has become increasingly crucial. Microgrids have emerged as a vital solution to this challenge. However, the reliance on renewable energy sources in microgrids often leads to low inertia. Renewable energy sources interfaced with the network through interlinking converters lack the inertia of conventional synchronous generators, and hence, need to provide frequency support through virtual inertia techniques. This paper presents a new control algorithm that utilizes the reinforcement learning agents Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG) to support the frequency in low-inertia microgrids. The RL agents are trained using the system-linearized model and then extended to the nonlinear model to reduce the computational burden. The proposed system consists of an AC–DC microgrid comprising a renewable energy source on the DC microgrid, along with constant and resistive loads. On the AC microgrid side, a synchronous generator is utilized to represent the low inertia of the grid, which is accompanied by dynamic and static loads. The model of the system is developed and verified using Matlab/Simulink and the reinforcement learning toolbox. The system performance with the proposed AI-based methods is compared to conventional low-pass and high-pass filter (LPF and HPF) controllers.
      Citation: Technologies
      PubDate: 2024-03-15
      DOI: 10.3390/technologies12030039
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 40: Applications of 3D Reconstruction in
           Virtual Reality-Based Teleoperation: A Review in the Mining Industry

    • Authors: Alireza Kamran-Pishhesari, Amin Moniri-Morad, Javad Sattarvand
      First page: 40
      Abstract: Although multiview platforms have enhanced work efficiency in mining teleoperation systems, they also induce “cognitive tunneling” and depth-detection issues for operators. These issues inadvertently focus their attention on a restricted central view. Fully immersive virtual reality (VR) has recently attracted the attention of specialists in the mining industry to address these issues. Nevertheless, developing VR teleoperation systems remains a formidable challenge, particularly in achieving a realistic 3D model of the environment. This study investigates the existing gap in fully immersive teleoperation systems within the mining industry, aiming to identify the most optimal methods for their development and ensure operator’s safety. To achieve this purpose, a literature search is employed to identify and extract information from the most relevant sources. The most advanced teleoperation systems are examined by focusing on their visualization types. Then, various 3D reconstruction techniques applicable to mining VR teleoperation are investigated, and their data acquisition methods, sensor technologies, and algorithms are analyzed. Ultimately, the study discusses challenges associated with 3D reconstruction techniques for mining teleoperation. The findings demonstrated that the real-time 3D reconstruction of underground mining environments primarily involves depth-based techniques. In contrast, point cloud generation techniques can mostly be employed for 3D reconstruction in open-pit mining operations.
      Citation: Technologies
      PubDate: 2024-03-15
      DOI: 10.3390/technologies12030040
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 41: Implementation of a Wireless Sensor
           Network for Environmental Measurements

    • Authors: Rosa M. Woo-García, José M. Pérez-Vista, Adrián Sánchez-Vidal, Agustín L. Herrera-May, Edith Osorio-de-la-Rosa, Felipe Caballero-Briones, Francisco López-Huerta
      First page: 41
      Abstract: Nowadays, the need to monitor different physical variables is constantly increasing and can be used in different applications, from humidity monitoring to disease detection in living beings, using a local or wireless sensor network (WSN). The Internet of Things has become a valuable approach to climate monitoring, daily parcel monitoring, early disease detection, crop plant counting, and risk assessment. Herein, an autonomous energy wireless sensor network for monitoring environmental variables is proposed. The network’s tree topology configuration, which involves master and slave modules, is managed by microcontrollers embedded with sensors, constituting a key part of the WSN architecture. The system’s slave modules are equipped with sensors for temperature, humidity, gas, and light detection, along with a photovoltaic cell to energize the system, and a WiFi module for data transmission. The receiver incorporates a user interface and the necessary computing components for efficient data handling. In an open-field configuration, the transceiver range of the proposed system reaches up to 750 m per module. The advantages of this approach are its scalability, energy efficiency, and the system’s ability to provide real-time environmental monitoring over a large area, which is particularly beneficial for applications in precision agriculture and environmental management.
      Citation: Technologies
      PubDate: 2024-03-16
      DOI: 10.3390/technologies12030041
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 42: Performance Assessment of Different
           Sustainable Energy Systems Using Multiple-Criteria Decision-Making Model
           and Self-Organizing Maps

    • Authors: Satyabrata Dash, Sujata Chakravarty, Nimay Chandra Giri, Umashankar Ghugar, Georgios Fotis
      First page: 42
      Abstract: The surging demand for electricity, propelled by the widespread adoption of intelligent grids and heightened consumer interaction with electricity demand and pricing, underscores the imperative for precise prognostication of optimal power plant utilization. To confront this challenge, a dataset centered on issue-centric power plans is meticulously crafted. This dataset encapsulates pivotal facets indispensable for attaining sustainable power generation, including meager gas emissions, installation cost, low maintenance cost, elevated power generation, and copious resource availability. The selection of an optimal power plant entails a multifaceted decision-making process, demanding a systematic approach. Our research advocates the amalgamation of multiple-criteria decision-making (MCDM) models with self-organizing maps to gauge the efficacy of diverse sustainable energy systems. The examination discerns solar energy as the preeminent MCDM criterion, securing the apex position with a score of 83.4%, attributable to its ample resource availability, considerable energy generation, nil greenhouse gas emissions, and commendable efficiency. Wind and hydroelectric power closely trail, registering scores of 75.3% and 74.5%, respectively, along with other energy sources. The analysis underscores the supremacy of the renewable energy sources, particularly solar and wind, in fulfilling sustainability objectives and scrutinizing factors such as cost, resource availability, and the environmental impact. The proposed methodology empowers stakeholders to make judicious decisions, accentuating facets that are required for more sustainable and resilient power infrastructure.
      Citation: Technologies
      PubDate: 2024-03-19
      DOI: 10.3390/technologies12030042
      Issue No: Vol. 12, No. 3 (2024)
       
  • Technologies, Vol. 12, Pages 18: A Comprehensive Performance Analysis of a
           48-Watt Transformerless DC-DC Boost Converter Using a
           Proportional–Integral–Derivative Controller with Special
           Attention to Inductor Design and Components Reliability

    • Authors: Kuldeep Jayaswal, D. K. Palwalia, Josep M. Guerrero
      First page: 18
      Abstract: In this research paper, a comprehensive performance analysis was carried out for a 48-watt transformerless DC-DC boost converter using a Proportional–Integral–Derivative (PID) controller through dynamic modeling. In a boost converter, the optimal design of the magnetic element plays an important role in efficient energy transfer. This research paper emphasizes the design of an inductor using the Area Product Technique (APT) to analyze factors such as area product, window area, number of turns, and wire size. Observations were made by examining its response to changes in load current, supply voltage, and load resistance at frequency levels of 100 and 500 kHz. Moreover, this paper extended its investigation by analyzing the failure rates and reliability of active and passive components in a 48-watt boost converter, providing valuable insights about failure behavior and reliability. Frequency domain analysis was conducted to assess the controller’s stability and robustness. The results conclusively underscore the benefits of incorporating the designed PID controller in terms of achieving the desired regulation and rapid response to disturbances at 100 and 500 kHz. The findings emphasize the outstanding reliability of the inductor, evident from the significantly low failure rates in comparison to other circuit components. Conversely, the research also reveals the inherent vulnerability of the switching device (MOSFET), characterized by a higher failure rate and lower reliability. The MATLAB® Simulink platform was utilized to investigate the results.
      Citation: Technologies
      PubDate: 2024-01-30
      DOI: 10.3390/technologies12020018
      Issue No: Vol. 12, No. 2 (2024)
       
  • Technologies, Vol. 12, Pages 19: An Optimum Load Forecasting Strategy
           (OLFS) for Smart Grids Based on Artificial Intelligence

    • Authors: Asmaa Hamdy Rabie, Ahmed I. Saleh, Said H. Abd Elkhalik, Ali E. Takieldeen
      First page: 19
      Abstract: Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces an Optimum Load Forecasting Strategy (OLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed OLFS consists of two sequential phases, which are: Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, an input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selection is carried out using Advanced Leopard Seal Optimization (ALSO) as a new nature-inspired optimization technique, while outlier rejection is accomplished through the Interquartile Range (IQR) as a measure of statistical dispersion. On the other hand, actual load forecasting takes place in LFP using a new predictor called the Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed OLFS has been tested through extensive experiments. Results have shown that OLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.
      Citation: Technologies
      PubDate: 2024-02-01
      DOI: 10.3390/technologies12020019
      Issue No: Vol. 12, No. 2 (2024)
       
  • Technologies, Vol. 12, Pages 20: Parametric Metamodeling Based on Optimal
           Transport Applied to Uncertainty Evaluation

    • Authors: Sergio Torregrosa, David Muñoz, Vincent Herbert, Francisco Chinesta
      First page: 20
      Abstract: When training a parametric surrogate to represent a real-world complex system in real time, there is a common assumption that the values of the parameters defining the system are known with absolute confidence. Consequently, during the training process, our focus is directed exclusively towards optimizing the accuracy of the surrogate’s output. However, real physics is characterized by increased complexity and unpredictability. Notably, a certain degree of uncertainty may exist in determining the system’s parameters. Therefore, in this paper, we account for the propagation of these uncertainties through the surrogate using a standard Monte Carlo methodology. Subsequently, we propose a novel regression technique based on optimal transport to infer the impact of the uncertainty of the surrogate’s input on its output precision in real time. The OT-based regression allows for the inference of fields emulating physical reality more accurately than classical regression techniques, including advanced ones.
      Citation: Technologies
      PubDate: 2024-02-02
      DOI: 10.3390/technologies12020020
      Issue No: Vol. 12, No. 2 (2024)
       
  • Technologies, Vol. 12, Pages 21: Energy Efficiency in Additive
           Manufacturing: Condensed Review

    • Authors: Ismail Fidan, Vivekanand Naikwadi, Suhas Alkunte, Roshan Mishra, Khalid Tantawi
      First page: 21
      Abstract: Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the cost of production, fabricate light weight and complex parts in a short period of time, and respond to the manufacturing needs of customers. It is clear that the AM technologies consume energy to complete the production tasks of each part. Therefore, it is imperative to know the impact of energy efficiency in order to economically and properly use these advancing technologies. This paper provides a holistic review of this important concept from the perspectives of process, materials science, industry, and initiatives. The goal of this research study is to collect and present the latest knowledge blocks related to the energy consumption of AM technologies from a number of recent technical resources. Overall, they are the collection of surveys, observations, experimentations, case studies, content analyses, and archival research studies. The study highlights the current trends and technologies associated with energy efficiency and their influence on the AM community.
      Citation: Technologies
      PubDate: 2024-02-05
      DOI: 10.3390/technologies12020021
      Issue No: Vol. 12, No. 2 (2024)
       
  • Technologies, Vol. 12, Pages 22: Angle Calculus-Based Thrust Force
           Determination on the Blades of a 10 kW Wind Turbine

    • Authors: José Rafael Dorrego-Portela, Adriana Eneida Ponce-Martínez, Eduardo Pérez-Chaltell, Jaime Peña-Antonio, Carlos Alberto Mateos-Mendoza, José Billerman Robles-Ocampo, Perla Yazmin Sevilla-Camacho, Marcos Aviles, Juvenal Rodríguez-Reséndiz
      First page: 22
      Abstract: In this article, the behavior of the thrust force on the blades of a 10 kW wind turbine was obtained by considering the characteristic wind speed of the Isthmus of Tehuantepec. Analyzing mechanical forces is essential to efficiently and safely design the different elements that make up the wind turbine because the thrust forces are related to the location point and the blade rotation. For this reason, the thrust force generated in each of the three blades of a low-power wind turbine was analyzed. The angular position (θ) of each blade varied from 0° to 120°, the blades were segmented (r), and different wind speeds were tested, such as cutting, design, average, and maximum. The results demonstrate that the thrust force increases proportionally with increasing wind speed and height, but it behaves differently on each blade segment and each angular position. This method determines the angular position and the exact blade segment where the smallest and the most considerable thrust force occurred. Blade 1, positioned at an angular position of 90°, is the blade most affected by the thrust force on P15. When the blade rotates 180°, the thrust force decreases by 9.09 N; this represents a 66.74% decrease. In addition, this study allows the designers to know the blade deflection caused by the thrust force. This information can be used to avoid collision with the tower. The thrust forces caused blade deflections of 10% to 13% concerning the rotor radius used in this study. These results guarantee the operation of the tested generator under their working conditions.
      Citation: Technologies
      PubDate: 2024-02-05
      DOI: 10.3390/technologies12020022
      Issue No: Vol. 12, No. 2 (2024)
       
  • Technologies, Vol. 12, Pages 23: Multistage Malware Detection Method for
           Backup Systems

    • Authors: Pavel Novak, Vaclav Oujezsky, Patrik Kaura, Tomas Horvath, Martin Holik
      First page: 23
      Abstract: This paper proposes an innovative solution to address the challenge of detecting latent malware in backup systems. The proposed detection system utilizes a multifaceted approach that combines similarity analysis with machine learning algorithms to improve malware detection. The results demonstrate the potential of advanced similarity search techniques, powered by the Faiss model, in strengthening malware discovery within system backups and network traffic. Implementing these techniques will lead to more resilient cybersecurity practices, protecting essential systems from hidden malware threats. This paper’s findings underscore the potential of advanced similarity search techniques to enhance malware discovery in system backups and network traffic, and the implications of implementing these techniques include more resilient cybersecurity practices and protecting essential systems from malicious threats hidden within backup archives and network data. The integration of AI methods improves the system’s efficiency and speed, making the proposed system more practical for real-world cybersecurity. This paper’s contribution is a novel and comprehensive solution designed to detect latent malware in backups, preventing the backup of compromised systems. The system comprises multiple analytical components, including a system file change detector, an agent to monitor network traffic, and a firewall, all integrated into a central decision-making unit. The current progress of the research and future steps are discussed, highlighting the contributions of this project and potential enhancements to improve cybersecurity practices.
      Citation: Technologies
      PubDate: 2024-02-05
      DOI: 10.3390/technologies12020023
      Issue No: Vol. 12, No. 2 (2024)
       
  • Technologies, Vol. 12, Pages 24: High Affinity of Nanoparticles and
           Matrices Based on Acid-Base Interaction for Nanoparticle-Filled Membrane

    • Authors: Tsutomu Makino, Keisuke Tabata, Takaaki Saito, Yosimasa Matsuo, Akito Masuhara
      First page: 24
      Abstract: The introduction of nanoparticles into the polymer matrix is a useful technique for creating highly functional composite membranes. Our research focuses on the development of nanoparticle-filled proton exchange membranes (PEMs). PEMs play a crucial role in efficiently controlling the electrical energy conversion process by facilitating the movement of specific ions. This is achieved by creating functionalized nanoparticles with polymer coatings on their surfaces, which are then combined with resins to create proton-conducting membranes. In this study, we prepared PEMs by coating the surfaces of silica nanoparticles with acidic polymers and integrating them into a basic matrix. This process resulted in the formation of a direct bond between the nanoparticles and the matrix, leading to composite membranes with a high dispersion and densely packed nanoparticles. This fabrication technique significantly improved mechanical strength and retention stability, resulting in high-performance membranes. Moreover, the proton conductivity of these membranes showed a remarkable enhancement of more than two orders of magnitude compared to the pristine basic matrix, reaching 4.2 × 10−4 S/cm at 80 °C and 95% relative humidity.
      Citation: Technologies
      PubDate: 2024-02-07
      DOI: 10.3390/technologies12020024
      Issue No: Vol. 12, No. 2 (2024)
       
  • Technologies, Vol. 12, Pages 25: Exploiting PlanetScope Imagery for
           Volcanic Deposits Mapping

    • Authors: Maddalena Dozzo, Gaetana Ganci, Federico Lucchi, Simona Scollo
      First page: 25
      Abstract: During explosive eruptions, tephra fallout represents one of the main volcanic hazards and can be extremely dangerous for air traffic, infrastructures, and human health. Here, we present a new technique aimed at identifying the area covered by tephra after an explosive event, based on processing PlanetScope imagery. We estimate the mean reflectance values of the visible (RGB) and near infrared (NIR) bands, analyzing pre- and post-eruptive data in specific areas and introducing a new index, which we call the ‘Tephra Fallout Index (TFI)’. We use the Google Earth Engine computing platform and define a threshold for the TFI of different eruptive events to distinguish the areas affected by the tephra fallout and quantify the surface coverage density. We apply our technique to the eruptive events occurring in 2021 at Mt. Etna (Italy), which mainly involved the eastern flank of the volcano, sometimes two or three times within a day, making field surveys difficult. Whenever possible, we compare our results with field data and find an optimal match. This work could have important implications for the identification and quantification of short-term volcanic hazard assessments in near real-time during a volcanic eruption, but also for the mapping of other hazardous events worldwide.
      Citation: Technologies
      PubDate: 2024-02-08
      DOI: 10.3390/technologies12020025
      Issue No: Vol. 12, No. 2 (2024)
       
  • Technologies, Vol. 12, Pages 26: A National Innovation System
           Concept-Based Analysis of Autonomous Vehicles’ Potential in Reaching
           Zero-Emission Fleets

    • Authors: Nalina Hamsaiyni Venkatesh, Laurencas Raslavičius
      First page: 26
      Abstract: Change management for technology adoption in the transportation sector is often used to address long-term challenges characterized by complexity, uncertainty, and ambiguity. Especially when technology is still evolving, an analysis of these challenges can help explore different alternative future pathways. Therefore, the analysis of development trajectories, correlations between key system variables, and the rate of change within the entire road transportation system can guide action toward sustainability. By adopting the National Innovation System concept, we evaluated the possibilities of an autonomous vehicle option to reach a zero-emission fleet. A case-specific analysis was conducted to evaluate the industry capacities, performance of R&D organizations, main objectives of future market-oriented reforms in the power sector, policy implications, and other aspects to gain insightful perspectives. Environmental insights for transportation sector scenarios in 2021, 2030, and 2050 were explored and analyzed using the COPERT v5.5.1 software program. This study offers a new perspective for road transport decarbonization research and adds new insights to the obtained correlation between the NIS dynamics and achievement of sustainability goals. In 2050, it is expected to achieve 100% carbon neutrality in the PC segment and ~85% in the HDV segment. Finally, four broad conclusions emerged from this research as a consequence of the analysis.
      Citation: Technologies
      PubDate: 2024-02-08
      DOI: 10.3390/technologies12020026
      Issue No: Vol. 12, No. 2 (2024)
       
  • Technologies, Vol. 12, Pages 27: ARSIP: Automated Robotic System for
           Industrial Painting

    • Authors: Hossam A. Gabbar, Muhammad Idrees
      First page: 27
      Abstract: This manuscript addresses the critical need for precise paint application to ensure product durability and aesthetics. While manual work carries risks, robotic systems promise accuracy, yet programming diverse product trajectories remains a challenge. This study aims to develop an autonomous system capable of generating paint trajectories based on object geometries for user-defined spraying processes. By emphasizing energy efficiency, process time, and coating thickness on complex surfaces, a hybrid optimization technique enhances overall efficiency. Extensive hardware and software development results in a robust robotic system leveraging the Robot Operating System (ROS). Integrating a low-cost 3D scanner, calibrator, and trajectory optimizer creates an autonomous painting system. Hardware components, including sensors, motors, and actuators, are seamlessly integrated with a Python and ROS-based software framework, enabling the desired automation. A web-based GUI, powered by JavaScript, allows user control over two robots, facilitating trajectory dispatch, 3D scanning, and optimization. Specific nodes manage calibration, validation, process settings, and real-time video feeds. The use of open-source software and an ROS ecosystem makes it a good choice for industrial-scale implementation. The results indicate that the proposed system can achieve the desired automation, contingent upon surface geometries, spraying processes, and robot dynamics.
      Citation: Technologies
      PubDate: 2024-02-19
      DOI: 10.3390/technologies12020027
      Issue No: Vol. 12, No. 2 (2024)
       
 
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