Authors:Shuwen Yu, William P. Marnane, Geraldine B. Boylan, Gordon Lightbody First page: 151 Abstract: A deep learning classifier is proposed for grading hypoxic-ischemic encephalopathy (HIE) in neonates. Rather than using handcrafted features, this architecture can be fed with raw EEG. Fully convolutional layers were adopted both in the feature extraction and classification blocks, which makes this architecture simpler, and deeper, but with fewer parameters. Here, two large (335 h and 338 h, respectively) multi-center neonatal continuous EEG datasets were used for training and testing. The model was trained based on weak labels and channel independence. A majority vote method was used for the post-processing of the classifier results (across time and channels) to increase the robustness of the prediction. A dimension reduction tool, UMAP, was used to visualize the model classification effect. The proposed system achieved an accuracy of 86.09% (95% confidence interval: 82.41–89.78%), an MCC of 0.7691, and an AUC of 86.23% on the large unseen test set. Two convolutional neural network architectures which utilized time-frequency distribution features were selected as the baseline as they had been developed or tested on the same datasets. A relative improvement of 23.65% in test accuracy was obtained as compared with the best baseline. In addition, if only one channel was available, the test accuracy was only reduced by 2.63–5.91% compared with making decisions based on the eight channels. Citation: Technologies PubDate: 2023-10-25 DOI: 10.3390/technologies11060151 Issue No:Vol. 11, No. 6 (2023)
Authors:Yana Suchikova, Sergii Kovachov, Ihor Bohdanov, Artem L. Kozlovskiy, Maxim V. Zdorovets, Anatoli I. Popov First page: 152 Abstract: This article presents an enhanced method for synthesizing β-SiC on a silicon substrate, utilizing porous silicon as a buffer layer, followed by thermal carbide formation. This approach ensured strong adhesion of the SiC film to the substrate, facilitating the creation of a hybrid hetero-structure of SiC/por-Si/mono-Si. The surface morphology of the SiC film revealed islands measuring 2–6 μm in diameter, with detected micropores that were 70–80 nm in size. An XRD analysis confirmed the presence of spectra from crystalline silicon and crystalline silicon carbide in cubic symmetry. The observed shift in spectra to the low-frequency zone indicated the formation of nanostructures, correlating with our SEM analysis results. These research outcomes present prospects for the further utilization and optimization of β-SiC synthesis technology for electronic device development. Citation: Technologies PubDate: 2023-10-27 DOI: 10.3390/technologies11060152 Issue No:Vol. 11, No. 6 (2023)
Authors:Miklas Scholz First page: 153 Abstract: Activated carbon has many potential applications in both the liquid and gas phases. How activated carbon can help practitioners in industry is explained. This practical teaching article introduces the first part of the special issue on Recent Advances in Applied Activated Carbon Research by providing a handbook explaining the basic applications, technologies, processes, methods and material characteristics to readers from different backgrounds. The aim is to improve the knowledge and understanding of the subject of activated carbon for non-adsorption experts such as professionals in industry. Therefore, it is written in a comprehensible manner and dispenses with detailed explanations to complex processes and many background references. This handbook does not claim to be complete and concentrates only on the areas that are of practical relevance for most activated carbon applications. Activated carbon and its activation and reactivation are initially explained. Adsorption and relevant processes are outlined. The mechanical, chemical and adsorption properties of activated carbon are explained. The heart of the handbook outlines key application technologies. Other carbonaceous adsorbents are only introduced briefly. The content of the second part of the special issue is highlighted at the end. Citation: Technologies PubDate: 2023-11-01 DOI: 10.3390/technologies11060153 Issue No:Vol. 11, No. 6 (2023)
Authors:Lesia Mochurad, Pavlo Horun First page: 154 Abstract: Using existing software technologies for imputing missing genetic data (GD), such as Beagle, HPImpute, Impute, MACH, AlphaPlantImpute, MissForest, and LinkImputeR, has its advantages and disadvantages. The wide range of input parameters and their nonlinear dependence on the target results require a lot of time and effort to find optimal values in each specific case. Thus, optimizing resources for GD imputation and improving its quality is an important current issue for the quality analysis of digitized deoxyribonucleic acid (DNA) samples. This work provides a critical analysis of existing methods and approaches for obtaining high-quality imputed GD. We observed that most of them do not investigate the problem of time and resource costs, which play a significant role in a mass approach. It is also worth noting that the considered articles are often characterized by high development complexity and, at times, unclear (or missing) descriptions of the input parameters for the methods, algorithms, or models under consideration. As a result, two algorithms were developed in this work. The first one aims to optimize the imputation time, allowing for real-time solutions, while the second one aims to improve imputation accuracy by selecting the best results at each iteration. The success of the first algorithm in improving imputation speed ranges from 47% (for small files) to 87% of the time (for medium and larger files), depending on the available resources. For the second algorithm, the accuracy has been improved by about 0.1%. This, in turn, encourages continued research on the latest version of Beagle software, particularly in the selection of optimal input parameters and possibly other models with similar or higher imputation accuracy. Citation: Technologies PubDate: 2023-11-01 DOI: 10.3390/technologies11060154 Issue No:Vol. 11, No. 6 (2023)
Authors:Mithun Kanchan, Mohith Santhya, Ritesh Bhat, Nithesh Naik First page: 155 Abstract: Piezoelectric actuators find extensive application in delivering precision motion in the micrometer to nanometer range. The advantages of a broader range of motion, rapid response, higher stiffness, and large actuation force from piezoelectric actuators make them suitable for precision positioning applications. However, the inherent nonlinearity in the piezoelectric actuators under dynamic working conditions severely affects the accuracy of the generated motion. The nonlinearity in the piezoelectric actuators arises from hysteresis, creep, and vibration, which affect the performance of the piezoelectric actuator. Thus, there is a need for appropriate modeling and control approaches for piezoelectric actuators, which can model the nonlinearity phenomenon and provide adequate compensation to achieve higher motion accuracy. The present review covers different methods adopted for overcoming the nonlinearity issues in piezoelectric actuators. This review highlights the charge-based and voltage-based control methods that drive the piezoelectric actuators. The survey also includes different modeling approaches for the creep and hysteresis phenomenon of the piezoelectric actuators. In addition, the present review also highlights different control strategies and their applications in various types of piezoelectric actuators. An attempt is also made to compare the piezoelectric actuator’s different modeling and control approaches and highlight prospects. Citation: Technologies PubDate: 2023-11-01 DOI: 10.3390/technologies11060155 Issue No:Vol. 11, No. 6 (2023)
Authors:Bayan Kaidar, Gaukhar Smagulova, Aigerim Imash, Aruzhan Keneshbekova, Akram Ilyanov, Zulkhair Mansurov First page: 156 Abstract: This study investigates the synthesis and application of composite electrospun fibers incorporating coal tar pitch (CTP) and various nanomaterial additives, with a specific focus on their potential for eco-bio-applications. The research underscores the environmentally viable aspects of CTP following a thermal treatment process that eliminates volatile components and sulfur, rendering it amenable for fiber electrospinning and subsequent carbonization. Composite fibers were fabricated by integrating CTP with nanomaterials, including nickel oxide (NiO), titanium dioxide (TiO2), activated carbon (AC), and magnetite (Fe3O4). The C/NiO composite fibers exhibit notable acetone sensing capabilities, specifically displaying a rapid response time of 40.6 s to 100 ppm acetone at 220 °C. The C/TiO2 composite fibers exhibit a distinct “beads-on-a-string” structure and demonstrate a high efficiency of 96.13% in methylene blue decomposition, highlighting their potential for environmental remediation applications. Additionally, the C/AC composite fibers demonstrate effective adsorption properties, efficiently removing manganese (II) ions from aqueous solutions with an 88.62% efficiency, thereby suggesting their utility in water purification applications. This research employs an interdisciplinary approach by combining diverse methods, approaches, and materials, including the utilization of agricultural waste materials such as rice husks, to create composite materials with multifaceted applications. Beyond the immediate utility of the composite fibers, this study emphasizes the significance of deploying environmentally responsible materials and technologies to address pressing eco-bio-challenges. Citation: Technologies PubDate: 2023-11-07 DOI: 10.3390/technologies11060156 Issue No:Vol. 11, No. 6 (2023)
Authors:Mohomad Aqeel Abdhul Rahuman, Nipun Shantha Kahatapitiya, Viraj Niroshan Amarakoon, Udaya Wijenayake, Bhagya Nathali Silva, Mansik Jeon, Jeehyun Kim, Naresh Kumar Ravichandran, Ruchire Eranga Wijesinghe First page: 157 Abstract: Bio-mechatronics is an interdisciplinary scientific field that emphasizes the integration of biology and mechatronics to discover innovative solutions for numerous biomedical applications. The broad application spectrum of bio-mechatronics consists of minimally invasive surgeries, rehabilitation, development of prosthetics, and soft wearables to find engineering solutions for the human body. Fiber-optic-based sensors have recently become an indispensable part of bio-mechatronics systems, which are essential for position detection and control, monitoring measurements, compliance control, and various feedback applications. As a result, significant advancements have been introduced for designing and developing fiber-optic-based sensors in the past decade. This review discusses recent technological advancements in fiber-optical sensors, which have been potentially adapted for numerous bio-mechatronic applications. It also encompasses fundamental principles, different types of fiber-optical sensors based on recent development strategies, and characterizations of fiber Bragg gratings, optical fiber force myography, polymer optical fibers, optical tactile sensors, and Fabry–Perot interferometric applications. Hence, robust knowledge can be obtained regarding the technological enhancements in fiber-optical sensors for bio-mechatronics-based interdisciplinary developments. Therefore, this review offers a comprehensive exploration of recent technological advances in fiber-optical sensors for bio-mechatronics. It provides insights into their potential to revolutionize biomedical and bio-mechatronics applications, ultimately contributing to improved patient outcomes and healthcare innovation. Citation: Technologies PubDate: 2023-11-07 DOI: 10.3390/technologies11060157 Issue No:Vol. 11, No. 6 (2023)
Authors:Jun-Seong Kim, Kun-Woo Kim, Seong-Won Yang, Joong-Wha Chung, Seong-Yong Moon First page: 158 Abstract: Vein blood sampling is a method of mass blood sampling that involves drawing blood from a vein for blood type discrimination, confirmation of various physiological indicators, disease diagnosis, etc.; it is the most commonly used blood sampling method. An important aspect of vein blood sampling is the search for the exact location of the vein for insertion of the syringe to draw blood. This is influenced by obesity as well as skin and blood vessel conditions in the patient and the experience of the clinical technologist, nurse, and resident who performs the blood sampling. Frequent practice is required to effectively perform blood sampling techniques. However, due to the many limitations of the practice room or laboratory, there is a problem of using only a limited environment and model for clinical practice. As a result, many medical educational institutions have situations in which only fragmentary clinical practices are performed, and it is difficult to practice many blood sampling skills, so they do not provide enough experience to understand the actual skill field. In this paper, we propose a virtual-reality-based vein blood sampling simulator that allows the practice of blood sampling techniques without limitation. The proposed vein blood sampling simulator can operate a 3D model related to vein blood sampling using an HMD controller and a haptic device in a virtual space for vein blood sampling practice by wearing an HMD (head-mounted display). Vein blood sampling can also be practiced through interaction with the patient 3D model. In addition, the effectiveness of a simulator developed for dental students was verified, and as a result of the verification, the potential of the proposed vein blood sampling simulator was confirmed. Citation: Technologies PubDate: 2023-11-08 DOI: 10.3390/technologies11060158 Issue No:Vol. 11, No. 6 (2023)
Authors:Adina Giurgiuman, Marian Gliga, Adrian Bojita, Sergiu Andreica, Calin Munteanu, Vasile Topa, Claudia Constantinescu, Claudia Pacurar First page: 159 Abstract: The evaluation of human exposure to electric and magnetic fields represents a subject of great scientific and public interest due to the biological effects of electromagnetic fields (EMFs) on the human body and the risks caused by them to living organisms. In this context, this article proposes a software program designed by the authors for the evaluation of human exposure to electric and magnetic fields at low frequencies (EMF software program), an application that can also be accessed from a mobile phone. The analytical model on which the EMF program is based is synthetically presented, and the application is then described. The first example implemented in the EMF program is taken from the existing literature on this subject, thus confirming the correctness and calculation precision of the program. Next, a case study is proposed for an overhead transmission line of 400 KV from the Cluj-Napoca area, Romania, for which the electric and magnetic fields are first measured experimentally and then using the EMF program. The validation of the EMF software program is performed by comparing the obtained results with those measured experimentally and with those obtained with a commercial software program. Citation: Technologies PubDate: 2023-11-08 DOI: 10.3390/technologies11060159 Issue No:Vol. 11, No. 6 (2023)
Authors:Kilian Brunner, Stephen Dominiak, Martin Ostertag First page: 160 Abstract: Broadband powerline communication is a technology developed mainly with consumer applications and bulk data transmission in mind. Typical use cases include file download, streaming, or last-mile internet access for residential buildings. Applications gaining momentum are smart metering and grid automation, where response time requirements are relatively moderate compared to industrial (real-time) control. This work investigates to which extent G.hn technology, with existing, commercial off-the-shelf components, can be used for real-time control applications. Maximum packet rate and latency statistics are investigated for different G.hn profiles and MAC algorithms. An elevator control system serves as an example application to define the latency and throughput requirements. The results show that G.hn is a feasible technology candidate for industrial IoT-type applications if certain boundary conditions can be ensured. Citation: Technologies PubDate: 2023-11-10 DOI: 10.3390/technologies11060160 Issue No:Vol. 11, No. 6 (2023)
Authors:Abdullah M. Alnajim, Shabana Habib, Muhammad Islam, Su Myat Thwin, Faisal Alotaibi First page: 161 Abstract: The Industrial Internet of Things (IIoT) ecosystem faces increased risks and vulnerabilities due to adopting Industry 4.0 standards. Integrating data from various places and converging several systems have heightened the need for robust security measures beyond fundamental connection encryption. However, it is difficult to provide adequate security due to the IIoT ecosystem’s distributed hardware and software. The most effective countermeasures must be suggested together with the crucial vulnerabilities, linked threats, and hazards in order to protect industrial equipment and ensure the secure functioning of IIoT systems. This paper presents a thorough analysis of events that target IIoT systems to alleviate such concerns. It also offers a comprehensive analysis of the responses that have been advanced in the most recent research. This article examines several kinds of attacks and the possible consequences to understand the security landscape in the IIoT area. Additionally, we aim to encourage the development of effective defenses that will lessen the hazards detected and secure the privacy, accessibility, and reliability of IIoT systems. It is important to note that we examine the issues and solutions related to IIoT security using the most recent findings from research and the literature on this subject. This study organizes and evaluates recent research to provide significant insight into the present security situation in IIoT systems. Ultimately, we provide outlines for future research and projects in this field. Citation: Technologies PubDate: 2023-11-13 DOI: 10.3390/technologies11060161 Issue No:Vol. 11, No. 6 (2023)
Authors:Rugved Kore, Dorukalp Durmus First page: 162 Abstract: Solid-state lighting (SSL) devices are ubiquitous in several markets, including architectural, automotive, healthcare, heritage conservation, and entertainment lighting. Fine control of the LED light output is crucial for applications where spectral precision is required, but dimming LEDs can cause a nonlinear response in its output, shifting the chromaticity. The nonlinear response of a multi-color LEDs can be corrected by curve-fitting the measured data to input dimming controls. In this study, the spectral output of an RGB LED projector was corrected using polynomial curve fitting. The accuracy of four different measurement methods was compared in order to find the optimal correction approach in terms of the time and effort needed to perform measurements. The results suggest that the curve fitting of very high-resolution dimming steps (n = 125) significantly decreased the chromaticity shifts between measured (actual) and corrected spectra. The effect size between approaches indicates that the curve-fitting of the high-resolution approach (n = 23) performs equally well as at very high resolution (n = 125). The curve-fitting correction can be used as an alternative approach or in addition to existing methods, such as the closed-loop correction. The curve fitting method can be applied to any tunable multi-color LED lighting system to correct the nonlinear dimming response. Citation: Technologies PubDate: 2023-11-15 DOI: 10.3390/technologies11060162 Issue No:Vol. 11, No. 6 (2023)
Authors:Muhammad Talha, Maria Kyrarini, Ehsan Ali Buriro First page: 163 Abstract: In recent years, the usage of wearable systems in healthcare has gained much attention, as they can be easily worn by the subject and provide a continuous source of data required for the tracking and diagnosis of multiple kinds of abnormalities or diseases in the human body. Wearable systems can be made useful in improving a patient’s quality of life and at the same time reducing the overall cost of caring for individuals including the elderly. In this survey paper, the recent research in the development of intelligent wearable systems for the diagnosis of peripheral neuropathy is discussed. The paper provides detailed information about recent techniques based on different wearable sensors for the diagnosis of peripheral neuropathy including experimental protocols, biomarkers, and other specifications and parameters such as the type of signals and data processing methods, locations of sensors, the scales and tests used in the study, and the scope of the study. It also highlights challenges that are still present in order to make wearable devices more effective in the diagnosis of peripheral neuropathy in clinical settings. Citation: Technologies PubDate: 2023-11-17 DOI: 10.3390/technologies11060163 Issue No:Vol. 11, No. 6 (2023)
Authors:Nurgul Nalgozhina, Abdul Razaque, Uskenbayeva Raissa, Joon Yoo First page: 164 Abstract: Robotic process automation (RPA) is a popular process automation technology that leverages software to play the function of humans when employing graphical user interfaces. RPA’s scope is limited, and various requirements must be met for it to be applied efficiently. Business process management (BPM), on the other hand, is a well-established area of research that may provide favorable conditions for RPA to thrive. We provide an efficient technique for merging RPA with BPM (RPABPM) to synchronize the technology for efficient automated business processes. The problem formulation process is carried out to cut management-related expenditures. The proposed RPABPM strategy includes the five stages (design, modeling, execution, monitoring, and optimization) for optimal business automation and energy savings. Effective business process management is proved by employing an end-to-end process. Furthermore, findings have been obtained employing three empirical investigations that are performed to assess the practicality and precision of the proposed RPABPM approach. The first objective of the initial study is to confirm the practicality and precision of the approach employed to evaluate the acceptance, possibility, significance, and integration of RPA with BPM. The second study attempts to verify the method’s high-quality characteristics. The third study attempts to assess the approach’s effectiveness in analyzing and identifying BPM that are best suited for RPA. The proposed RPABPM is validated on the industrial robot manufactured by ABB with six-axis IRB140 and supported with a Windows CE-based Flex Pendant (teach pendant). An IRC5 controller is used to run RobotWare 5.13.10371. A pre-installed .NET Compact Framework 3.5 is used. Finally, the proposed method is compared with state-of-the-art methods from an efficiency and power consumption perspective. Citation: Technologies PubDate: 2023-11-18 DOI: 10.3390/technologies11060164 Issue No:Vol. 11, No. 6 (2023)
Authors:Jonathan Haase, Peter B. Walker, Olivia Berardi, Waldemar Karwowski First page: 165 Abstract: This paper discusses the “Get Real Get Better” (GRGB) approach to implementing agile program management in the U.S. Navy, supported by advanced data analytics and artificial intelligence (AI). GRGB was designed as a set of foundational principles to advance Navy culture and support its core values. This article identifies a need for a more informed and efficient approach to program management by highlighting the benefits of implementing comprehensive data analytics that leverage recent advances in cloud computing and machine learning. The Jupiter enclave within Advana implemented by the U.S. Navy, is also discussed. The presented approach represents a practical framework that cultivates a “Get Real Get Better” mindset for implementing agile program management in the U.S. Navy. Citation: Technologies PubDate: 2023-11-20 DOI: 10.3390/technologies11060165 Issue No:Vol. 11, No. 6 (2023)
Authors:José Pereira, Reinaldo Souza, António Moreira, Ana Moita First page: 166 Abstract: The current review offers a critical survey on published studies concerning the simultaneous use of PCMs and nanofluids for solar thermal energy storage and conversion processes. Also, the main thermophysical properties of PCMs and nanofluids are discussed in detail. On one hand, the properties of these types of nanofluids are analyzed, as well as those of the general types of nanofluids, like the thermal conductivity and latent heat capacity. On the other hand, there are specific characteristics of PCMs like, for instance, the phase-change duration and the phase-change temperature. Moreover, the main improvement techniques in order for PCMs and nanofluids to be used in solar thermal applications are described in detail, including the inclusion of highly thermal conductive nanoparticles and other nanostructures in nano-enhanced PCMs and PCMs with extended surfaces, among others. Regarding those improvement techniques, it was found that, for instance, nanofluids can enhance the thermal conductivity of the base fluids by up to 100%. In addition, it was also reported that the simultaneous use of PCMs and nanofluids enhances the overall, thermal, and electrical efficiencies of solar thermal energy storage systems and photovoltaic-nano-enhanced PCM systems. Finally, the main limitations and guidelines are summarized for future research in the technological and research fields of nanofluids and PCMs. Citation: Technologies PubDate: 2023-11-24 DOI: 10.3390/technologies11060166 Issue No:Vol. 11, No. 6 (2023)
Authors:Mehdi Imani, Hamid Reza Arabnia First page: 167 Abstract: This paper explores the application of various machine learning techniques for predicting customer churn in the telecommunications sector. We utilized a publicly accessible dataset and implemented several models, including Artificial Neural Networks, Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, and gradient boosting techniques (XGBoost, LightGBM, and CatBoost). To mitigate the challenges posed by imbalanced datasets, we adopted different data sampling strategies, namely SMOTE, SMOTE combined with Tomek Links, and SMOTE combined with Edited Nearest Neighbors. Moreover, hyperparameter tuning was employed to enhance model performance. Our evaluation employed standard metrics, such as Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). In terms of the F1-score metric, CatBoost demonstrates superior performance compared to other machine learning models, achieving an outstanding 93% following the application of Optuna hyperparameter optimization. In the context of the ROC AUC metric, both XGBoost and CatBoost exhibit exceptional performance, recording remarkable scores of 91%. This achievement for XGBoost is attained after implementing a combination of SMOTE with Tomek Links, while CatBoost reaches this level of performance after the application of Optuna hyperparameter optimization. Citation: Technologies PubDate: 2023-11-26 DOI: 10.3390/technologies11060167 Issue No:Vol. 11, No. 6 (2023)
Authors:Bismark Kweku Asiedu Asante, Hiroki Imamura First page: 168 Abstract: We propose a novel obstacle avoidance strategy implemented in a wearable assistive device, which serves as an electronic travel aid (ETA), designed to enhance the safety of visually impaired persons (VIPs) during navigation to their desired destinations. This method is grounded in the assumption that objects in close proximity and within a short distance from VIPs pose potential obstacles and hazards. Furthermore, objects that are farther away appear smaller in the camera’s field of view. To adapt this method for accurate obstacle selection, we employ an adaptable grid generated based on the apparent size of objects. These objects are detected using a custom lightweight YOLOv5 model. The grid helps select and prioritize the most immediate and dangerous obstacle within the user’s proximity. We also incorporate an audio feedback mechanism with an innovative neural perception system to alert the user. Experimental results demonstrate that our proposed system can detect obstacles within a range of 20 m and effectively prioritize obstacles within 2 m of the user. The system achieves an accuracy rate of 95% for both obstacle detection and prioritization of critical obstacles. Moreover, the ETA device provides real-time alerts, with a response time of just 5 s, preventing collisions with nearby objects. Citation: Technologies PubDate: 2023-11-28 DOI: 10.3390/technologies11060168 Issue No:Vol. 11, No. 6 (2023)
Authors:Cristóbal Araya, Francisco J. Peña, Ariel Norambuena, Bastián Castorene, Patricio Vargas First page: 169 Abstract: We studied the performance of a quantum magnetic Stirling cycle that uses a working substance composed of two entangled antiferromagnetic qubits (J) under the influence of an external magnetic field (Bz) and an uniaxial anisotropy field (K) along the total spin in the y-direction. The efficiency and work were calculated as a function of Bz and for different values of the anisotropy constant K given hot and cold reservoir temperatures. The anisotropy has been shown to extend the region of the external magnetic field in which the Stirling cycle is more efficient compared to the ideal case. Citation: Technologies PubDate: 2023-11-29 DOI: 10.3390/technologies11060169 Issue No:Vol. 11, No. 6 (2023)
Authors:Rodrigo Antunes, Martim Lima Aguiar, Pedro Dinis Gaspar First page: 170 Abstract: This study presents an innovative pedagogical approach aimed at enhancing the teaching of robotics within the broader context of STEM (science, technology, engineering, and mathematics) education across diverse academic levels. The integration of mobile robotics kits into a dynamic STEM-focused curriculum offers students an immersive and hands-on learning experience, fostering programming skills, advanced problem-solving, critical thinking, and spatial awareness. The motivation behind this research lies in improving the effectiveness of robotics education by addressing existing gaps in current strategies. It aims to better prepare students for this rapidly evolving field’s dynamic challenges and opportunities. To achieve this, detailed protocols were formulated that not only facilitate student learning but also cater to teacher training and involvement. These protocols encompass code documentation and examples, providing tangible representations of the practical outcomes of the course. In addition to the presented curriculum, this paper introduces the developed methodology that strategically leverages 3D-printing technology. The primary focus of this approach is to create captivating add-ons and establish a versatile workspace, actively promoting heightened engagement and facilitating the acquisition of knowledge among students. The research involves the development of tailored laboratory protocols suited to various academic levels, employing a systematic methodology aimed at deepening students’ comprehension of STEM concepts. Furthermore, an adaptable infrastructure for laboratory protocols and in-class testing was developed. The efficacy of this teaching/learning methodology is evaluated through student surveys, ensuring its continuous improvement. These protocols are to be integrated into both the robotics courses and teacher-training initiatives. This study aims to contribute to the field by using a dynamic STEM-driven approach based on mobile robotics. It outlines a strategic vision for better-preparing students and educators in the ever-evolving landscape of robotics education demanded by Industry 4.0 technologies. Citation: Technologies PubDate: 2023-12-01 DOI: 10.3390/technologies11060170 Issue No:Vol. 11, No. 6 (2023)
Authors:Luis A. Avila-Sánchez, Carlos Sánchez-López, Rocío Ochoa-Montiel, Fredy Montalvo-Galicia, Luis A. Sánchez-Gaspariano, Carlos Hernández-Mejía, Hugo G. González-Hernández First page: 171 Abstract: Advances in the development of collision-free path planning algorithms are the main need not only to solve mazes with robotic systems, but also for their use in modern product transportation or green logistics systems and planning merchandise deliveries inside or outside a factory. This challenge increases as the complexity of the task in its structure also increases. This paper deals with the development of a novel methodology for solving mazes with a mobile robot, using image processing techniques and graph theory. The novelty is that the mobile robot can find the shortest path from a start-point to the end-point into irregular mazes with abundant irregular obstacles, a situation that is not far from reality. Maze information is acquired from an image and depending on the size of the mobile robot, a grid of nodes with the same dimensions of the maze is built. Another contribution of this paper is that the size of the maze can be scaled from 1 m × 1 m to 66 m × 66 m, maintaining the essence of the proposed collision-free path planning methodology. Afterwards, graph theory is used to find the shortest path within the grid of reduced nodes due to the elimination of those nodes absorbed by the irregular obstacles. To avoid the mobile robot to travel through those nodes very close to obstacles and borders, resulting in a collision, each image of the obstacle and border is dilated taking into account the size of the mobile robot. The methodology was validated with two case studies with a mobile robot in different mazes. We emphasize that the maze solution is found in a single computational step, from the maze image as input until the generation of the Path vector. Experimental results show the usefulness of the proposed methodology, which can be used in applications such as intelligent traffic control, military, agriculture and so on. Citation: Technologies PubDate: 2023-12-05 DOI: 10.3390/technologies11060171 Issue No:Vol. 11, No. 6 (2023)
Authors:Simona Miclaus, Delia B. Deaconescu, David Vatamanu, Andreea M. Buda First page: 113 Abstract: To gain a deeper understanding of the hotly contested topic of the non-thermal biological effects of microwaves, new metrics and methodologies need to be adopted. The direction proposed in the current work, which includes peak exposure analysis and not just time-averaged analysis, aligns well with this objective. The proposed methodology is not intended to facilitate a comparison of the general characteristics between 4G and 5G mobile communication signals. Instead, its purpose is to provide a means for analyzing specific real-life exposure conditions that may vary based on multiple parameters. A differentiation based on amplitude-time features of the 4G versus 5G signals is followed, with the aim of describing the peculiarities of a user’s exposure when he runs four types of mobile applications on his mobile phone on either of the two mobile networks. To achieve the goals, we used signal and spectrum analyzers with adequate real-time analysis bandwidths and statistical descriptions provided by the amplitude probability density (APD) function, the complementary cumulative distribution function (CCDF), channel power measurements, and recorded spectrogram databases. We compared the exposimetric descriptors of emissions specific to file download, file upload, Internet video streaming, and video call usage in both 4G and 5G networks based on the specific modulation and coding schemes. The highest and lowest electric field strengths measured in the air at a 10 cm distance from the phone during emissions are indicated. The power distribution functions with the highest prevalence are highlighted and commented on. Afterwards, the capability of a convolutional neural network that belongs to the family of single-shot detectors is proven to recognize and classify the emissions with a very high degree of accuracy, enabling traceability of the dynamics of human exposure. Citation: Technologies PubDate: 2023-08-24 DOI: 10.3390/technologies11050113 Issue No:Vol. 11, No. 5 (2023)
Authors:Rongbin Yao, Peng Qi, Dezheng Hua, Xu Zhang, He Lu, Xinhua Liu First page: 114 Abstract: As one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and with improper human operation. In order to avoid major safety accidents caused by scratches, deviation, and the breakage of belts, a foreign object detection method is proposed for belt conveyors in this work. Firstly, a foreign object image dataset is collected and established, and an IAT image enhancement module and an attention mechanism for CBAM are introduced to enhance the image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine’s intelligent mining equipment laboratory, and different foreign objects are analyzed. The experimental results show that the accuracy, recall, and mAP50 of the proposed rotating frame foreign object detection method reach 93.87%, 93.69%, and 93.68%, respectively, and the average inference time for foreign object detection is 25 ms. Citation: Technologies PubDate: 2023-08-26 DOI: 10.3390/technologies11050114 Issue No:Vol. 11, No. 5 (2023)
Authors:Mohammad Shafiul Alam, Muhammad Mahbubur Rashid, Ahmed Rimaz Faizabadi, Hasan Firdaus Mohd Zaki, Tasfiq E. Alam, Md Shahin Ali, Kishor Datta Gupta, Md Manjurul Ahsan First page: 115 Abstract: The research describes an effective deep learning-based, data-centric approach for diagnosing autism spectrum disorder from facial images. To classify ASD and non-ASD subjects, this method requires training a convolutional neural network using the facial image dataset. As a part of the data-centric approach, this research applies pre-processing and synthesizing of the training dataset. The trained model is subsequently evaluated on an independent test set in order to assess the performance matrices of various data-centric approaches. The results reveal that the proposed method that simultaneously applies the pre-processing and augmentation approach on the training dataset outperforms the recent works, achieving excellent 98.9% prediction accuracy, sensitivity, and specificity while having 99.9% AUC. This work enhances the clarity and comprehensibility of the algorithm by integrating explainable AI techniques, providing clinicians with valuable and interpretable insights into the decision-making process of the ASD diagnosis model. Citation: Technologies PubDate: 2023-08-29 DOI: 10.3390/technologies11050115 Issue No:Vol. 11, No. 5 (2023)
Authors:Eric Hitimana, Omar Janvier Sinayobye, J. Chrisostome Ufitinema, Jane Mukamugema, Peter Rwibasira, Theoneste Murangira, Emmanuel Masabo, Lucy Cherono Chepkwony, Marie Cynthia Abijuru Kamikazi, Jeanne Aline Ukundiwabo Uwera, Simon Martin Mvuyekure, Gaurav Bajpai, Jackson Ngabonziza First page: 116 Abstract: Rwandan coffee holds significant importance and immense value within the realm of agriculture, serving as a vital and valuable commodity. Additionally, coffee plays a pivotal role in generating foreign exchange for numerous developing nations. However, the coffee plant is vulnerable to pests and diseases weakening production. Farmers in cooperation with experts use manual methods to detect diseases resulting in human errors. With the rapid improvements in deep learning methods, it is possible to detect and recognize plan diseases to support crop yield improvement. Therefore, it is an essential task to develop an efficient method for intelligently detecting, identifying, and predicting coffee leaf diseases. This study aims to build the Rwandan coffee plant dataset, with the occurrence of coffee rust, miner, and red spider mites identified to be the most popular due to their geographical situations. From the collected coffee leaves dataset of 37,939 images, the preprocessing, along with modeling used five deep learning models such as InceptionV3, ResNet50, Xception, VGG16, and DenseNet. The training, validation, and testing ratio is 80%, 10%, and 10%, respectively, with a maximum of 10 epochs. The comparative analysis of the models’ performances was investigated to select the best for future portable use. The experiment proved the DenseNet model to be the best with an accuracy of 99.57%. The efficiency of the suggested method is validated through an unbiased evaluation when compared to existing approaches with different metrics. Citation: Technologies PubDate: 2023-09-01 DOI: 10.3390/technologies11050116 Issue No:Vol. 11, No. 5 (2023)
Authors:Memoona Sadaf, Zafar Iqbal, Abdul Rehman Javed, Irum Saba, Moez Krichen, Sajid Majeed, Arooj Raza First page: 117 Abstract: Autonomous vehicles (AV) are game-changing innovations that promise a safer, more convenient, and environmentally friendly mode of transportation than traditional vehicles. Therefore, understanding AV technologies and their impact on society is critical as we continue this revolutionary journey. Generally, there needs to be a detailed study available to assist a researcher in understanding AV and its challenges. This research presents a comprehensive survey encompassing various aspects of AVs, such as public adoption, driverless city planning, traffic management, environmental impact, public health, social implications, international standards, safety, and security. Furthermore, it presents emerging technologies such as artificial intelligence (AI), integration of cloud computing, and solar power usage in automated vehicles. It also presents forensics approaches, tools used, standards involved, and challenges associated with conducting digital forensics in the context of autonomous vehicles. Moreover, this research provides an overview of cyber attacks affecting autonomous vehicles, attack management, traditional security devices, threat modeling, authentication schemes, over-the-air updates, zero-trust architectures, data privacy, and the corresponding defensive strategies to mitigate such risks. It also presents international standards, guidelines, and best practices for AVs. Finally, it outlines the future directions of AVs and the challenges that must be addressed to achieve widespread adoption. Citation: Technologies PubDate: 2023-09-04 DOI: 10.3390/technologies11050117 Issue No:Vol. 11, No. 5 (2023)
Authors:Anatoly A. Olkhov, Polina M. Tyubaeva, Yulia N. Zernova, Valery S. Markin, Regina Kosenko, Anna G. Filatova, Kristina G. Gasparyan, Alexey L. Iordanskii First page: 118 Abstract: The article examines the regularities of structure formation of ultrafine fibers based on poly-3-hydroxybutyrat under the influence of technological (electrical conductivity, viscosity), molecular (molecular weight), and external factors (low-molecular and nanodispersed substances of different chemical natures). Systems with polar substances are characterized by the presence of intermolecular interactions and the formation of a more perfect crystalline fiber structure. Changes in technological and molecular characteristics affect the fiber formation process, resulting in alterations in the morphology of the nonwoven fabric, fiber geometry, and supramolecular fiber structure. Polymer molecular weight, electrical conductivity, and solution viscosity influence fiber formation and fiber diameter. The fiber structure is heterogeneous, consisting of both crystalline and non-equilibrium amorphous phases. This article shows that with an increase in the molecular weight and concentration of the polymer, the diameter of the fiber increases. At the same time, the increase in the productivity of the electrospinning process does not affect the fiber geometry. The chemical structure of the solvent and the concentration of polar substances play a decisive role in the formation of fibers of even geometry. As the polarity of the solvent increases, the intermolecular interaction with the polar groups of poly-3-hydroxybutyrate increases. As a result of this interaction, the crystallites are improved, and the amorphous phase of the polymer is compacted. The action of polar molecules on the polymer is similar to the action of polar nanoparticles. They increase crystallinity via a nucleation mechanism. This is significant in the development of matrix-fibrillar systems for drug delivery, bioactive substances, antiseptics, tissue engineering constructs, tissue engineering scaffolds, artificial biodegradable implants, sorbents, and other applications. Citation: Technologies PubDate: 2023-09-06 DOI: 10.3390/technologies11050118 Issue No:Vol. 11, No. 5 (2023)
Authors:Mohammad Ali Humayun, Junaid Shuja, Pg Emeroylariffion Abas First page: 119 Abstract: Speech samples can provide valuable information regarding speaker characteristics, including their social backgrounds. Accent variations with speaker backgrounds reflect corresponding acoustic features of speech, and these acoustic variations can be analyzed to assist in tracking down criminals from speech samples available as forensic evidence. Speech accent identification has recently received significant consideration in the speech forensics research community. However, most works have utilized long-term temporal modelling of acoustic features for accent classification and disregarded the stationary acoustic characteristics of particular phoneme articulations. This paper analyzes short-term acoustic features extracted from a central time window of English vowel speech segments for accent discrimination. Various feature computation techniques have been compared for the accent classification task. It has been found that using spectral features as an input gives better performance than using cepstral features, with the lower filters contributing more significantly to the classification task. Moreover, detailed analysis has been presented for time window durations and frequency bin resolution to compute short-term spectral features concerning accent discrimination. Using longer time durations generally requires higher frequency resolution to optimize classification performance. These results are significant, as they show the benefits of using spectral features for speaker profiling despite the popularity of cepstral features for other speech-related tasks. Citation: Technologies PubDate: 2023-09-07 DOI: 10.3390/technologies11050119 Issue No:Vol. 11, No. 5 (2023)
Authors:Giulio E. Lancioni, Gloria Alberti, Chiara Filippini, Valeria Chiariello, Nirbhay N. Singh, Mark F. O’Reilly, Jeff Sigafoos First page: 120 Abstract: The study assessed a new interactive technology system for helping six people with intellectual and visual disabilities exercise relevant physical responses embedded within a fairly straightforward activity (i.e., placing objects in containers). Activity responses consisted of the participants taking objects from the floor or a low shelf and placing those objects in a container high up in front of them (thus bending their body and legs and stretching their arms and hands). The technology involved a portable computer, a webcam, and three mini speakers whose basic functions included monitoring the participants’ responses, delivering preferred stimulation contingent on the responses and verbal encouragements/prompts for lack of responses, and assisting in data recording. The study was conducted following a non-concurrent multiple baseline design across participants. During baseline (i.e., when the system was used only for data recording), the participants’ mean frequency of responses per session varied between zero and nearly 12. During intervention (i.e., when the system was fully working), the participants’ mean frequency of responses per session increased to between about 34 and 59. Mean session duration varied between nearly 10 and over 14 min. The new system may be a valuable tool for supporting relevant physical activity engagement in people with intellectual and multiple disabilities. Citation: Technologies PubDate: 2023-09-07 DOI: 10.3390/technologies11050120 Issue No:Vol. 11, No. 5 (2023)
Authors:Aysha Bibi, Gabriel Avelino Sampedro, Ahmad Almadhor, Abdul Rehman Javed, Tai-hoon Kim First page: 121 Abstract: Given the increasing frequency of network attacks, there is an urgent need for more effective network security measures. While traditional approaches such as firewalls and data encryption have been implemented, there is still room for improvement in their effectiveness. To effectively address this concern, it is essential to integrate Artificial Intelligence (AI)-based solutions into historical methods. However, AI-driven approaches often encounter challenges, including lower detection rates and the complexity of feature engineering requirements. Finding solutions to overcome these hurdles is critical for enhancing the effectiveness of intrusion detection systems. This research paper introduces a deep learning-based approach for network intrusion detection to overcome these challenges. The proposed approach utilizes various classification algorithms, including the AutoEncoder (AE), Long-short-term-memory (LSTM), Multi-Layer Perceptron (MLP), Linear Support Vector Machine (L-SVM), Quantum Support Vector Machine (Q-SVM), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). To validate the effectiveness of the proposed approach, three datasets, namely IOT23, CICIDS2017, and NSL KDD, are used for experimentation. The results demonstrate impressive accuracy, particularly with the LSTM algorithm, achieving a 97.7% accuracy rate on the NSL KDD dataset, 99% accuracy rate on the CICIDS2017 dataset, and 98.7% accuracy on the IOT23 dataset. These findings highlight the potential of deep learning algorithms in enhancing network intrusion detection. By providing network administrators with robust security measures for accurate and timely intrusion detection, the proposed approach contributes to network safety and helps mitigate the impact of network attacks. Citation: Technologies PubDate: 2023-09-07 DOI: 10.3390/technologies11050121 Issue No:Vol. 11, No. 5 (2023)
Authors:Bihao Sun, Peizhi Yang, Zhiyuan Zhu First page: 122 Abstract: The thermal effect and heat dissipation have a significant impact on three-dimensional stacked chips, and the positional layout of the chip’s three-dimensional layout directly affects the internal temperature field. One effective way is to plan the overall layout of three-dimensional integrated circuits by considering the thermal effect and layout utilization. In this paper, an ant colony algorithm is used to search for the most planned paths and achieve the overall layout optimization by considering the effects of power, temperature, and location on the thermal layout and using feedback optimization of pheromone concentration. The simulation results show that the optimization of the thermal layout of 3D integrated circuits can be well realized by adjusting the algorithm parameters. The maximum temperature, temperature gradient, and layout scheme verify reliability and practicability. It improves the utilization rate of chips, optimizes the layout, realizes energy conservation, and reduces resource waste. Citation: Technologies PubDate: 2023-09-10 DOI: 10.3390/technologies11050122 Issue No:Vol. 11, No. 5 (2023)
Authors:Bilal Abu-Salih, Salihah Alotaibi First page: 123 Abstract: The rise of online social networks has revolutionized the way businesses and consumers interact, creating new opportunities for customer word-of-mouth (WoM) and brand advocacy. Understanding and managing customer advocacy in the online realm has become crucial for businesses aiming to cultivate a positive brand image and engage with their target audience effectively. In this study, we propose a framework that leverages the pre-trained XLNet- (bi-directional long-short term memory) BiLSTM- conditional random field (CRF) architecture to construct a Knowledge Graph (KG) for social customer advocacy in online customer engagement (CE). The XLNet-BiLSTM-CRF model combines the strengths of XLNet, a powerful language representation model, with BiLSTM-CRF, a sequence labeling model commonly used in natural language processing tasks. This architecture effectively captures contextual information and sequential dependencies in CE data. The XLNet-BiLSTM-CRF model is evaluated against several baseline architectures, including variations of BERT integrated with other models, to compare their performance in identifying brand advocates and capturing CE dynamics. Additionally, an ablation study is conducted to analyze the contributions of different components in the model. The evaluation metrics, including accuracy, precision, recall, and F1 score, demonstrate that the XLNet-BiLSTM-CRF model outperforms the baseline architectures, indicating its superior ability to accurately identify brand advocates and label customer advocacy entities. The findings highlight the significance of leveraging pre-trained contextual embeddings, sequential modeling, and sequence labeling techniques in constructing effective models for constructing a KG for customer advocacy in online engagement. The proposed framework contributes to the understanding and management of customer advocacy by facilitating meaningful customer-brand interactions and fostering brand loyalty. Citation: Technologies PubDate: 2023-09-11 DOI: 10.3390/technologies11050123 Issue No:Vol. 11, No. 5 (2023)
Authors:Giovanni Galeoto, Anna Berardi, Massimiliano Mangone, Leonardo Tufo, Martina Silvani, Jerónimo González-Bernal, Jesús Seco-Calvo First page: 125 Abstract: The use of robotics in rehabilitating motor functions has increased exponentially in recent decades. One of the most used robotic tools is undoubtedly the Armeo® Power, which has proved to have excellent qualities as a rehabilitation tool. However, none of these studies has investigated the ability of Armeo® Power to assess the upper limb by correlating the data resulting from the software with patient-reported outcome measures (PROMs). The present study aims to evaluate the variability between the standardized PROMs, Stroke Upper Limb Capacity Scale (SULCS), Fugl–Meyer upper limb assessment (FMA-UL), and the Armeo® Power measurements. To evaluate the correlation between SULCS and FMA-UL and the strength and joint assessments obtained with the Armeo® Power, Pearson’s correlation coefficient was used. A total of 102 stroke survivors were included in this cross-sectional study, and all participants finished the study. The results showed many statistically significant correlations between PROM items and Armeo® Power data. In conclusion, from this study, it can be stated that Armeo® Power, based on the analysis of the data collected, can be an objective evaluation tool, which can be combined with the operator-employee traditional evaluation techniques, especially when compared to a patient-reported outcome measures (PROMs). Citation: Technologies PubDate: 2023-09-13 DOI: 10.3390/technologies11050125 Issue No:Vol. 11, No. 5 (2023)
Authors:Padmanabhan Balasubramanian, Douglas L. Maskell First page: 126 Abstract: This article introduces a novel asynchronous full adder that operates in an input–output mode (IOM), displaying both monotonicity and an early output characteristic. In a monotonic asynchronous circuit, the intermediate and primary outputs exhibit similar signal transitions as the primary inputs during data and spacer application. The proposed asynchronous full adder ensures monotonicity for processing data and spacer, utilizing dual-rail encoding for inputs and outputs, and corresponds to return-to-zero (RtZ) and return-to-one (RtO) handshaking. The early output feature of the proposed full adder allows the production of sum and carry outputs based on the adder inputs regardless of the carry input when the spacer is supplied. When utilized in a ripple carry adder (RCA) architecture, the proposed full adder achieves significant reductions in design metrics, such as cycle time, area, and power, compared to existing IOM asynchronous full adders. For a 32-bit RCA implementation using a 28 nm CMOS technology, the proposed full adder outperforms an existing state-of-the-art high-speed asynchronous full adder by reducing the cycle time by 10.4% and the area by 15.8% for RtZ handshaking and reduces the cycle time by 9.8% and the area by 15.8% for RtO handshaking without incurring any power penalty. Further, in terms of the power-cycle time product, which serves as a representative measure of energy, the proposed full adder yields an 11.8% reduction for RtZ handshaking and an 11.2% reduction for RtO handshaking. Citation: Technologies PubDate: 2023-09-14 DOI: 10.3390/technologies11050126 Issue No:Vol. 11, No. 5 (2023)
Authors:Shatha Abu Rass, Omer Cohen, Eliav Bareli, Sigal Portnoy First page: 127 Abstract: Audio guidance is a common means of helping visually impaired individuals to navigate, thereby increasing their independence. However, the differences between different guidance modalities for locating objects in 3D space have yet to be investigated. The aim of this study was to compare the time, the hand’s path length, and the satisfaction levels of visually impaired individuals using three automatic cueing modalities: pitch sonification, verbal, and vibration. We recruited 30 visually impaired individuals (11 women, average age 39.6 ± 15.0), who were asked to locate a small cube, guided by one of three cueing modalities: sonification (a continuous beep that increases in frequency as the hand approaches the cube), verbal prompting (“right”, “forward”, etc.), and vibration (via five motors, attached to different locations on the hand). The three cueing modalities were automatically activated by computerized motion capture systems. The subjects separately answered satisfaction questions for each cueing modality. The main finding was that the time to find the cube was longer using the sonification cueing (p = 0.016). There were no significant differences in the hand path length or the subjects’ satisfaction. It can be concluded that verbal guidance may be the most effective for guiding people with visual impairment to locate an object in a 3D space. Citation: Technologies PubDate: 2023-09-16 DOI: 10.3390/technologies11050127 Issue No:Vol. 11, No. 5 (2023)
Authors:Usharani Bhimavarapu, Nalini Chintalapudi, Gopi Battineni First page: 128 Abstract: Lung disease is a respiratory disease that poses a high risk to people worldwide and includes pneumonia and COVID-19. As such, quick and precise identification of lung disease is vital in medical treatment. Early detection and diagnosis can significantly reduce the life-threatening nature of lung diseases and improve the quality of life of human beings. Chest X-ray and computed tomography (CT) scan images are currently the best techniques to detect and diagnose lung infection. The increase in the chest X-ray or CT scan images at the time of training addresses the overfitting dilemma, and multi-class classification of lung diseases will deal with meaningful information and overfitting. Overfitting deteriorates the performance of the model and gives inaccurate results. This study reduces the overfitting issue and computational complexity by proposing a new enhanced kernel convolution function. Alongside an enhanced kernel convolution function, this study used convolution neural network (CNN) models to determine pneumonia and COVID-19. Each CNN model was applied to the collected dataset to extract the features and later applied these features as input to the classification models. This study shows that extracting deep features from the common layers of the CNN models increased the performance of the classification procedure. The multi-class classification improves the diagnostic performance, and the evaluation metrics improved significantly with the improved support vector machine (SVM). The best results were obtained using the improved SVM classifier fed with the features provided by CNN, and the success rate of the improved SVM was 99.8%. Citation: Technologies PubDate: 2023-09-17 DOI: 10.3390/technologies11050128 Issue No:Vol. 11, No. 5 (2023)
Authors:Alejandro Villanueva Cerón, Eduardo López Domínguez, Saúl Domínguez Isidro, María Auxilio Medina Nieto, Jorge De La Calleja, Saúl Eduardo Pomares Hernández First page: 129 Abstract: In the field of eHealth, several works have proposed telemonitoring systems focused on patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD) treatment. Nevertheless, no secondary study presents a comparative analysis of these works regarding the technology readiness level (TRL) framework. The TRL scale goes from 1 to 9, with 1 being the lowest level of readiness and 9 being the highest. This paper analyzes works that propose telemonitoring systems focused on patients with CKD undergoing PD treatment to determine their TRL. We also analyzed the requirements and parameters that the systems of the selected works provide to the users to perform telemonitoring of the patient’s treatment undergoing PD. Fourteen works were relevant to the present study. Of these works, eight were classified within TRL 9, two were categorized within TRL 7, three were identified within TRL 6, and one within TRL 4. The works reported with the highest TRL partially cover the requirements for appropriate telemonitoring of patients based on the specialized literature; in addition, those works are focused on the treatment of patients in the automated peritoneal dialysis (APD) modality, which limits the care of patients undergoing the continuous ambulatory peritoneal dialysis (CAPD) modality. Citation: Technologies PubDate: 2023-09-18 DOI: 10.3390/technologies11050129 Issue No:Vol. 11, No. 5 (2023)
Authors:Seetha S, Esther Daniel, S Durga, Jennifer Eunice R, Andrew J First page: 130 Abstract: The academic and research communities are showing significant interest in the modern and highly promising technology of wireless mesh networks (WMNs) due to their low-cost deployment, self-configuration, self-organization, robustness, scalability, and reliable service coverage. Multicasting is a broadcast technique in which the communication is started by an individual user and is shared by one or multiple groups of destinations concurrently as one-to-many allotments. The multicasting protocols are focused on building accurate paths with proper channel optimization techniques. The forwarder nodes of the multicast protocol may behave with certain malicious characteristics, such as dropping packets, and delayed transmissions that cause heavy packet loss in the network. This leads to a reduced packet delivery ratio and throughput of the network. Hence, the forwarder node validation is critical for building a secure network. This research paper presents a secure forwarder selection between a sender and the batch of receivers by utilizing the node’s communication behavior. The parameters of the malicious nodes are analyzed using orthogonal projection and statistical methods to distinguish malicious node behaviors from normal node behaviors based on node actions. The protocol then validates the malicious behaviors and subsequently eliminates them from the forwarder selection process using secure path finding strategies, which lead to dynamic and scalable multicast mesh networks for communication. Citation: Technologies PubDate: 2023-09-18 DOI: 10.3390/technologies11050130 Issue No:Vol. 11, No. 5 (2023)
Authors:Maha Gharaibeh, Wlla Abedalaziz, Noor Aldeen Alawad, Hasan Gharaibeh, Ahmad Nasayreh, Mwaffaq El-Heis, Maryam Altalhi, Agostino Forestiero, Laith Abualigah First page: 131 Abstract: The intricate neuroinflammatory diseases multiple sclerosis (MS) and neuromyelitis optica (NMO) often present similar clinical symptoms, creating challenges in their precise detection via magnetic resonance imaging (MRI). This challenge is further compounded when detecting the active and inactive states of MS. To address this diagnostic problem, we introduce an innovative framework that incorporates state-of-the-art machine learning algorithms applied to features culled from MRI scans by pre-trained deep learning models, VGG-NET and InceptionV3. To develop and test this methodology, we utilized a robust dataset obtained from the King Abdullah University Hospital in Jordan, encompassing cases diagnosed with both MS and NMO. We benchmarked thirteen distinct machine learning algorithms and discovered that support vector machine (SVM) and K-nearest neighbor (KNN) algorithms performed superiorly in our context. Our results demonstrated KNN’s exceptional performance in differentiating between MS and NMO, with precision, recall, F1-score, and accuracy values of 0.98, 0.99, 0.99, and 0.99, respectively, using leveraging features extracted from VGG16. In contrast, SVM excelled in classifying active versus inactive states of MS, achieving precision, recall, F1-score, and accuracy values of 0.99, 0.97, 0.98, and 0.98, respectively, using leveraging features extracted from VGG16 and VGG19. Our advanced methodology outshines previous studies, providing clinicians with a highly accurate, efficient tool for diagnosing these diseases. The immediate implication of our research is the potential to streamline treatment processes, thereby delivering timely, appropriate care to patients suffering from these complex diseases. Citation: Technologies PubDate: 2023-09-20 DOI: 10.3390/technologies11050131 Issue No:Vol. 11, No. 5 (2023)
Authors:Francesc Auli-Llinas First page: 132 Abstract: The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented—more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems. Citation: Technologies PubDate: 2023-09-26 DOI: 10.3390/technologies11050132 Issue No:Vol. 11, No. 5 (2023)
Authors:Marcos Severt, Roberto Casado-Vara, Angel Martín del Martín del Rey First page: 133 Abstract: Malware propagation is a growing concern due to its potential impact on the security and integrity of connected devices in Internet of Things (IoT) network environments. This study investigates parameter estimation for Susceptible-Infectious-Recovered (SIR) and Susceptible–Infectious–Recovered–Susceptible (SIRS) models modeling malware propagation in an IoT network. Synthetic data of malware propagation in the IoT network is generated and a comprehensive comparison is made between two approaches: algorithms based on Monte Carlo methods and Physics-Informed Neural Networks (PINNs). The results show that, based on the infection curve measured in the IoT network, both methods are able to provide accurate estimates of the parameters of the malware propagation model. Furthermore, the results show that the choice of the appropriate method depends on the dynamics of the spreading malware and computational constraints. This work highlights the importance of considering both classical and AI-based approaches and provides a basis for future research on parameter estimation in epidemiological models applied to malware propagation in IoT networks. Citation: Technologies PubDate: 2023-09-30 DOI: 10.3390/technologies11050133 Issue No:Vol. 11, No. 5 (2023)
Authors:Ovi Sarkar, Md. Robiul Islam, Md. Khalid Syfullah, Md. Tohidul Islam, Md. Faysal Ahamed, Mominul Ahsan, Julfikar Haider First page: 134 Abstract: Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest X-Ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, and more. In this comprehensive study, four publicly accessible datasets have been combined to create a robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal and six different lung-related diseases (namely, bacterial pneumonia, COVID-19, fibrosis, lung opacity, tuberculosis, and viral pneumonia), a Deep Learning (DL) architecture called a Multi-Scale Convolutional Neural Network (MS-CNN) is introduced. The model is adapted to classify multiple numbers of lung disease classes, which is considered to be a persistent challenge in the field. While prior studies have demonstrated high accuracy in binary and limited-class scenarios, the proposed framework maintains this accuracy across a diverse range of lung conditions. The innovative model harnesses the power of combining predictions from multiple feature maps at different resolution scales, significantly enhancing disease classification accuracy. The approach aims to shorten testing duration compared to the state-of-the-art models, offering a potential solution toward expediting medical interventions for patients with lung-related diseases and integrating explainable AI (XAI) for enhancing prediction capability. The results demonstrated an impressive accuracy of 96.05%, with average values for precision, recall, F1-score, and AUC at 0.97, 0.95, 0.95, and 0.94, respectively, for the seven-class classification. The model exhibited exceptional performance across multi-class classifications, achieving accuracy rates of 100%, 99.65%, 99.21%, 98.67%, and 97.47% for two, three, four, five, and six-class scenarios, respectively. The novel approach not only surpasses many pre-existing state-of-the-art (SOTA) methodologies but also sets a new standard for the diagnosis of lung-affected diseases using multi-class CXR data. Furthermore, the integration of XAI techniques such as SHAP and Grad-CAM enhanced the transparency and interpretability of the model’s predictions. The findings hold immense promise for accelerating and improving the accuracy and confidence of diagnostic decisions in the field of lung disease identification. Citation: Technologies PubDate: 2023-09-30 DOI: 10.3390/technologies11050134 Issue No:Vol. 11, No. 5 (2023)
Authors: Varughese, Raj, Joel, Gautam First page: 135 Abstract: The persistent threat posed by infectious pathogens remains a formidable challenge for humanity. Rapidly spreading infectious diseases caused by airborne microorganisms have far-reaching global consequences, imposing substantial costs on society. While various detection technologies have emerged, including biochemical, immunological, and molecular approaches, these methods still exhibit significant limitations such as time-intensive procedures, instability, and the need for specialized operators. This study presents an innovative solution that harnesses the potential of surface acoustic wave (SAW) sensors for the detection of airborne microorganisms. The research involves the establishment of a sensor model within the framework of COMSOL Multiphysics, utilizing a predefined piezoelectric multi-physics interface and employing a 2D modeling approach. Chitosan, selected as the sensing film for the model, interfaces with lithium niobate (LiNbO3), the chosen piezoelectric material responsible for detecting airborne pathogens. The analysis of microbe presence centers on solid displacement and electric potential frequencies, operating within the 850–900 MHz range. Notably, the first and second resonant frequencies are identified at 856 and 859 MHz, respectively. To enhance understanding, this study proposes a novel mathematical model grounded in Stokes’ Law and mass balance equations. This model serves to analyze microbe concentration, offering a fresh perspective on quantifying the presence of airborne pathogens. Through these endeavors, this research contributes to advancing the field of airborne microorganism detection, offering a promising avenue for addressing the challenges posed by infectious diseases. Citation: Technologies PubDate: 2023-10-02 DOI: 10.3390/technologies11050135 Issue No:Vol. 11, No. 5 (2023)
Authors:Zhen Pan, Shunqi Yuan, Xi Ren, Zhibin He, Zhenzhong Wang, Shujun Han, Yuexin Qi, Haifeng Yu, Jingang Liu First page: 136 Abstract: Nanotechnologies are being increasingly widely used in advanced energy fields. Triboelectric nanogenerators (TENGs) represent a class of new-type flexible energy-harvesting devices with promising application prospects in future human societies. As one of the most important parts of TENG devices, triboelectric materials play key roles in the achievement of high-efficiency power generation. Conventional polymer tribo-negative materials, such as polytetrafluoroethylene (PTFE), polyvinylidene difluoride (PVDF), and the standard polyimide (PI) film with the Kapton® trademark based on pyromellitic anhydride (PMDA) and 4,4′-oxydianiline (ODA), usually suffer from low output performance. In addition, the relationship between molecular structure and triboelectric properties remains a challenge in the search for novel triboelectric materials. In the current work, by incorporating functional groups of trifluoromethyl (–CF3) with strong electron withdrawal into the backbone, a series of fluorine-containing polyimide (FPI) negative friction layers have been designed and prepared. The derived FPI-1 (6FDA-6FODA), FPI-2 (6FDA-TFMB), and FPI-3 (6FDA-TFMDA) resins possessed good solubility in polar aprotic solvents, such as the N,N-dimethylacetamide (DMAc) and N-methyl-2-pyrrolidone (NMP). The PI films obtained via the solution-casting procedure showed glass transition temperatures (Tg) higher than 280 °C with differential scanning calorimetry (DSC) analyses. The TENG prototypes were successfully fabricated using the developed PI films as the tribo-negative layers. The electron-withdrawing trifluoromethyl (–CF3) units in the molecular backbones of the PI layers provided the devices with an apparently enhanced output performance. The FPI-3 (6FDA-TFMDA) layer-based TENG devices showcased an especially impressive open-circuit voltage and short-circuit current, measuring 277.8 V and 9.54 μA, respectively. These values were 4~5 times greater when compared to the TENGs manufactured using the readily accessible Kapton® film. Citation: Technologies PubDate: 2023-10-03 DOI: 10.3390/technologies11050136 Issue No:Vol. 11, No. 5 (2023)
Authors:Arsalan D. Badaraev, Tuan-Hoang Tran, Anastasia G. Drozd, Evgenii V. Plotnikov, Gleb E. Dubinenko, Anna I. Kozelskaya, Sven Rutkowski, Sergei I. Tverdokhlebov First page: 137 Abstract: In this work, the effects of weight concentration on the properties of poly(lactide-co-glycolide) polymeric scaffolds prepared by electrospinning are investigated, using four different weight concentrations of poly(lactide-co-glycolide) for the electrospinning solutions (2, 3, 4, 5 wt.%). With increasing concentration of poly(lactide-co-glycolide) in the electrospinning solutions, their viscosity increases significantly. The average fiber diameter of the scaffolds also increases with increasing concentration. Moreover, the tensile strength and maximum elongation at break of the scaffold increase with increasing electrospinning concentration. The prepared scaffolds have hydrophobic properties and their wetting angle does not change with the concentration of the electrospinning solution. All poly(lactide-co-glycolide) scaffolds are non-toxic toward fibroblasts of the cell line 3T3-L1, with the highest numbers of cells observed on the surface of scaffolds prepared from the 2-, 3- and 4-wt.% electrospinning solutions. The results of the analysis of mechanical and biological properties indicate that the poly(lactide-co-glycolide) scaffolds prepared from the 4 wt.% electrospinning solution have optimal properties for future applications in skin tissue engineering. This is due to the fact that the poly(lactide-co-glycolide) scaffolds prepared from the 2 wt.% and 3 wt.% electrospinning solution exhibit low mechanical properties, and 5 wt.% have the lowest porosity values, which might be the cause of their lowest biological properties. Citation: Technologies PubDate: 2023-10-05 DOI: 10.3390/technologies11050137 Issue No:Vol. 11, No. 5 (2023)
Authors:Musulmon Lolaev, Shraddha M. Naik, Anand Paul, Abdellah Chehri First page: 138 Abstract: The advent of Artificial Intelligence (AI) has had a broad impact on life to solve various tasks. Building AI models and integrating them with modern technologies is a central challenge for researchers. These technologies include wearables and implants in living beings, and their use is known as human augmentation, using technology to enhance human abilities. Combining human augmentation with artificial intelligence (AI), especially after the recent successes of the latter, is the most significant advancement in their applicability. In the first section, we briefly introduce these modern applications in health care and examples of their use cases. Then, we present a computationally efficient AI-driven method to diagnose heart failure events by leveraging actual heart failure data. The classifier model is designed without conventional models such as gradient descent. Instead, a heuristic is used to discover the optimal parameters of a linear model. An analysis of the proposed model shows that it achieves an accuracy of 84% and an F1 score of 0.72 with only one feature. With five features for diagnosis, the accuracy achieved is 83%, and the F1 score is 0.74. Moreover, the model is flexible, allowing experts to determine which variables are more important than others when implementing diagnostic systems. Citation: Technologies PubDate: 2023-10-06 DOI: 10.3390/technologies11050138 Issue No:Vol. 11, No. 5 (2023)
Authors:Yingxiu Du, Mingyue Hu, Xiaohua Tu, Chengping Miao, Yang Zhang, Jiayou Li First page: 139 Abstract: An environmentally friendly alkaline electrolyte of silicate and borate, which contained the addition of carbohydrates (lactose, starch, and dextrin), was applied to produce micro-arc oxidation (MAO) coatings on AZ31B magnesium alloy surfaces in constant current mode. The effects of the carbohydrates on the performance of the MAO coatings were investigated using a scanning electron microscope (SEM), an X-ray diffractometer (XRD), energy-dispersive spectroscopy (EDS), the salt spray test, potentiodynamic polarization curves, and electrochemical impedance spectroscopy (EIS). The results show that the carbohydrates effectively inhibited spark discharge, so the anodized growth process, surface morphology, composition, and corrosion resistance of the MAO coatings were strongly dependent on the carbohydrate concentration. This is ascribed to the surface adsorption layer formed on the surface of the magnesium alloy. When the carbohydrate concentration was 10 g/L, smooth, compact, and thick MAO coatings with excellent corrosion resistance on the magnesium alloy were obtained. Citation: Technologies PubDate: 2023-10-10 DOI: 10.3390/technologies11050139 Issue No:Vol. 11, No. 5 (2023)
Authors:Md Masum Reza, Jairo Gutierrez First page: 140 Abstract: With the rapid expansion of the Internet of Things (IoT), the necessity for lightweight communication is also increasing due to the constrained capabilities of IoT devices. This paper presents the design of a novel lightweight protocol called the Enhanced Lightweight Security Gateway Protocol (ELSGP) based on a distributed computation model of the IoT layer. This model introduces a new type of node called a sub-server to assist edge layer servers and IoT devices with computational tasks and act as a primary gateway for dependent IoT nodes. This paper then introduces six features of ELSGP with developed algorithms that include access token distribution and validation, authentication and dynamic interoperability, attribute-based access control, traffic filtering, secure tunneling, and dynamic load distribution and balancing. Considering the variability of system requirements, ELSGP also outlines how to adopt a system-defined policy framework. For fault resiliency, this paper also presents fault mitigation mechanisms, especially Trust and Priority Impact Relation for Byzantine, Cascading, and Transient faults. A simulation study was carried out to validate the protocol’s performance. Based on the findings from the performance evaluation, further analysis of the protocol and future research directions are outlined. Citation: Technologies PubDate: 2023-10-12 DOI: 10.3390/technologies11050140 Issue No:Vol. 11, No. 5 (2023)
Authors:Yue Hao Choong, Manickavasagam Krishnan, Manoj Gupta First page: 141 Abstract: Thermal management devices such as heat exchangers and heat pipes are integral to safe and efficient performance in multiple engineering applications, including lithium-ion batteries, electric vehicles, electronics, and renewable energy. However, the functional designs of these devices have until now been created around conventional manufacturing constraints, and thermal performance has plateaued as a result. While 3D printing offers the design freedom to address these limitations, there has been a notable lack in high thermal conductivity materials beyond aluminium alloys. Recently, the 3D printing of pure copper to sufficiently high densities has finally taken off, due to the emergence of commercial-grade printers which are now equipped with 1 kW high-power lasers or short-wavelength lasers. Although the capabilities of these new systems appear ideal for processing pure copper as a bulk material, the performance of advanced thermal management devices are strongly dependent on topology-optimised filigree structures, which can require a very different processing window. Hence, this article presents a broad overview of the state-of-the-art in various additive manufacturing technologies used to fabricate pure copper functional filigree geometries comprising thin walls, lattice structures, and porous foams, and identifies opportunities for future developments in the 3D printing of pure copper for advanced thermal management devices. Citation: Technologies PubDate: 2023-10-15 DOI: 10.3390/technologies11050141 Issue No:Vol. 11, No. 5 (2023)
Authors:Poonam Tiwari, Vishant Gahlaut, Meenu Kaushik, Anshuman Shastri, Vivek Arya, Issa Elfergani, Chemseddine Zebiri, Jonathan Rodriguez First page: 142 Abstract: An approach is presented to enhance the isolation of a two-port Multiple Input Multiple Output (MIMO) antenna using a decoupling structure and a common defected ground structure (DGS) that physically separates the antennas from each other. The antenna operates in the 24 to 40 GHz frequency range. The innovation in the presented MIMO antenna design involves the novel integration of two arc-shaped symmetrical elements with dimensions of 35 × 35 × 1.6 mm3 placed perpendicular to each other. The benefits of employing an antenna with elements arranged perpendicularly are exemplified by the enhancement of its overall performance metrics. These elements incorporate a microstrip feed featuring a quarter-wave transformer (QWT). This concept synergizes with decoupling techniques and a defected ground structure to significantly enhance isolation in a millimeter wave (mm wave) MIMO antenna. These methods collectively achieve an impressively wide bandwidth. Efficient decoupling methodologies have been implemented, yielding a notable increase of 5 dB in isolation performance. The antenna exhibits 10 dB impedance matching, with a 15 GHz (46.87%) wide bandwidth, excellent isolation of more than 28 dB, and a desirable gain of 4.6 dB. Antennas have been analyzed to improve their performance in mm wave applications by evaluating diversity parameters such as envelope correlation coefficient (ECC) and diversity gain (DG), with achieved values of 0.0016 and 9.992 dB, respectively. The simulation is conducted using CST software. To validate the findings, experimental investigations have been conducted, affirming the accuracy of the simulations. Citation: Technologies PubDate: 2023-10-16 DOI: 10.3390/technologies11050142 Issue No:Vol. 11, No. 5 (2023)
Authors:Koji Nakano, Shunsuke Tsukiyama, Yasuaki Ito, Takashi Yazane, Junko Yano, Takumi Kato, Shiro Ozaki, Rie Mori, Ryota Katsuki First page: 143 Abstract: The Ising model is defined by an objective function using a quadratic formula of qubit variables. The problem of an Ising model aims to determine the qubit values of the variables that minimize the objective function, and many optimization problems can be reduced to this problem. In this paper, we focus on optimization problems related to permutations, where the goal is to find the optimal permutation out of the n! possible permutations of n elements. To represent these problems as Ising models, a commonly employed approach is to use a kernel that applies one-hot encoding to find any one of the n! permutations as the optimal solution. However, this kernel contains a large number of quadratic terms and high absolute coefficient values. The main contribution of this paper is the introduction of a novel permutation encoding technique called the dual-matrix domain wall, which significantly reduces the number of quadratic terms and the maximum absolute coefficient values in the kernel. Surprisingly, our dual-matrix domain-wall encoding reduces the quadratic term count and maximum absolute coefficient values from n3−n2 and 2n−4 to 6n2−12n+4 and 2, respectively. We also demonstrate the applicability of our encoding technique to partial permutations and Quadratic Unconstrained Binary Optimization (QUBO) models. Furthermore, we discuss a family of permutation problems that can be efficiently implemented using Ising/QUBO models with our dual-matrix domain-wall encoding. Citation: Technologies PubDate: 2023-10-17 DOI: 10.3390/technologies11050143 Issue No:Vol. 11, No. 5 (2023)
Authors:Pramita Sen, Praneel Bhattacharya, Gargi Mukherjee, Jumasri Ganguly, Berochan Marik, Devyani Thapliyal, Sarojini Verma, Geroge D. Verros, Manvendra Singh Chauhan, Raj Kumar Arya First page: 144 Abstract: Environmental pollution poses a pressing global challenge, demanding innovative solutions for effective pollutant removal.. Photocatalysts, particularly titanium dioxide (TiO2), are renowned for their catalytic prowess; however, they often require ultraviolet light for activation. Researchers had turned to doping with metals and non-metals to extend their utility into the visible spectrum. While this approach shows promise, it also presents challenges such as material stability and dopant leaching. Co-doping, involving both metals and non-metals, has emerged as a viable strategy to mitigate these limitations. Inthe fieldof adsorbents, carbon-based materials doped with nitrogen are gaining attention for their improved adsorption capabilities and CO2/N2 selectivity. Nitrogen doping enhances surface area and fosters interactions between acidic CO2 molecules and basic nitrogen functionalities. The optimal combination of an ultramicroporous surface area and specific nitrogen functional groups is key to achievehigh CO2 uptake values and selectivity. The integration of photocatalysis and adsorption processes in doped materials has shown synergistic pollutant removal efficiency. Various synthesis methods, including sol–gel, co-precipitation, and hydrothermal approaches had been employed to create hybrid units of doped photocatalysts and adsorbents. While progress has been made in enhancing the performance of doped materials at the laboratory scale, challenges persist in transitioning these technologies to large-scale industrial applications. Rigorous studies are needed to investigate the impact of doping on material structure and stability, optimize process parameters, and assess performance in real-world industrial reactors. These advancements are promising foraddressing environmental pollution challenges, promoting sustainability, and paving the way for a cleaner and healthier future. This manuscript provides a comprehensive overview of recent developments in doping strategies for photocatalysts and adsorbents, offering insights into the potential of these materials to revolutionize environmental remediation technologies. Citation: Technologies PubDate: 2023-10-17 DOI: 10.3390/technologies11050144 Issue No:Vol. 11, No. 5 (2023)
Authors:Gilyana K. Kazakova, Victoria S. Presniakova, Yuri M. Efremov, Svetlana L. Kotova, Anastasia A. Frolova, Sergei V. Kostjuk, Yury A. Rochev, Peter S. Timashev First page: 145 Abstract: In the realm of scaffold-free cell therapies, there is a questto develop organotypic three-dimensional (3D) tissue surrogates in vitro, capitalizing on the inherent ability of cells to create tissues with an efficiency and sophistication that still remains unmatched by human-made devices. In this study, we explored the properties of scaffolds obtained by the electrospinning of a thermosensitive copolymer, poly(N-isopropylacrylamide-co-N-tert-butylacrylamide) (P(NIPAM-co-NtBA)), intended for use in such therapies. Two copolymers with molecular weights of 123 and 137 kDa and a content of N-tert-butylacrylamide of ca. 15 mol% were utilized to generate 3D scaffolds via electrospinning. We examined the morphology, solution viscosity, porosity, and thickness of the spun matrices as well as the mechanical properties and hydrophobic–hydrophilic characteristics of the scaffolds. Particular attention was paid to studying the influence of the thermosensitive polymer’s molecular weight and dispersity on the resultant scaffolds’ properties and the role of electroforming parameters on the morphology and mechanical characteristics of the scaffolds. The cytotoxicity of the copolymers and interaction of cells with the scaffolds were also studied. Our findings provide significant insight into approaches to optimizing scaffolds for specific cell cultures, thereby offering new opportunities for scaffold-free cell therapies. Citation: Technologies PubDate: 2023-10-18 DOI: 10.3390/technologies11050145 Issue No:Vol. 11, No. 5 (2023)
Authors:Nataliya Kildeeva, Nikita Sazhnev, Maria Drozdova, Vasilina Zakharova, Evgeniya Svidchenko, Nikolay Surin, Elena Markvicheva First page: 146 Abstract: Silk fibroin (SF) holds promise for the preparation of matrices for tissue engineering and regenerative medicine or for the development of drug delivery systems. Regenerated fibroin from Bombyx mori cocoons is water-soluble and can be processed into scaffolds of various forms, such as fibrous matrices, using the electrospinning method. In the current study, we studied the correlation between concentrations of fibroin aqueous solutions and their properties, in order to obtain electrospun mats for tissue engineering. Two methods were used to prevent solubility in fibroin-based matrices: The conversion of fibroin to the β-conformation via treatment with an ethanol solution and chemical cross-linking with genipin (Gp). The interaction of Gp with SF led to the appearance of a characteristic blue color but did not lead to the gelation of solutions. To speed up the cross-linking reaction with Gp, we propose using chitosan-containing systems and modifying fibrous materials via treatment with a solution of Gp in 80% ethanol. It was shown that the composition of fibroin with chitosan contributes to an improved water resistance, reduces defective material, and leads to a decrease in the diameter of the fibers. The electrospun fiber matrices based on regenerated fibroin modified by cross-linking with genipin in water–alcohol solutions were shown to promote cell adhesion, spreading, and growth and, therefore, could hold promise for tissue engineering. Citation: Technologies PubDate: 2023-10-19 DOI: 10.3390/technologies11050146 Issue No:Vol. 11, No. 5 (2023)
Authors:Hsin-Tsung Lin, Wei-Han Pan, Pi-Chung Wang First page: 147 Abstract: Packet classification based on rules of packet header fields is the key technology for enabling software-defined networking (SDN). Ternary content addressable memory (TCAM) is a widely used hardware for packet classification; however, commercially available TCAM chips have only limited storage. As the number of supported header fields in SDN increases, the number of supported rules in a TCAM chip is reduced. In this work, we present a novel scheme to enable packet classification using TCAM with entries that are narrower than rules by storing the most representative field of a ruleset in TCAM. Due to the fact that not all rules can be distinguished using one field, our scheme employs a TCAM-based multimatch packet classification technique to ensure correctness. We further develop approaches to reduce redundant TCAM accesses for multimatch packet classification. Although our scheme requires additional TCAM accesses, it supports packet classification upon long rules with narrow TCAM entries, and drastically reduces the required TCAM storage. Our experimental results show that our scheme requires a moderate number of additional TCAM accesses and consumes much less storage compared to the basic TCAM-based packet classification. Thus, it can provide the required scalability for long rules required by potential applications of SDN. Citation: Technologies PubDate: 2023-10-19 DOI: 10.3390/technologies11050147 Issue No:Vol. 11, No. 5 (2023)
Authors:Tianyi Zhang , Yuan Ke First page: 148 Abstract: In this article, we introduce an innovative hybrid quantum search algorithm, the Robust Non-oracle Quantum Search (RNQS), which is specifically designed to efficiently identify the minimum value within a large set of random numbers. Distinct from the Grover’s algorithm, the proposed RNQS algorithm circumvents the need for an oracle function that describes the true solution state, a feature often impractical for data science applications. Building on existing non-oracular quantum search algorithms, RNQS enhances robustness while substantially reducing running time. The superior properties of RNQS have been demonstrated through careful analysis and extensive empirical experiments. Our findings underscore the potential of the RNQS algorithm as an effective and efficient solution to combinatorial optimization problems in the realm of quantum computing. Citation: Technologies PubDate: 2023-10-19 DOI: 10.3390/technologies11050148 Issue No:Vol. 11, No. 5 (2023)
Authors:Sofia Paschou, Georgios Papaioannou First page: 149 Abstract: This paper contributes to the field of museum and visitor experience in terms of atmosphere by discussing the “museum digital atmosphere” or MDA, a notion that has been introduced and found across museums in Greece. Research on museum atmospherics has tended to focus on physical museum spaces and exhibits. By “atmosphere”, we mean the emotional state that is a result of public response adding to the overall museum experience. The MDA is therefore studied as the specific emotional state caused by the use of digital applications and technologies. The stimulus–organism–response or SOR model is used to define the MDA, so as to confirm and reinforce the concept. To that end, a qualitative methodological approach is used; we conduct semi-structured interviews and evaluate findings via content analysis. The sample consists of 17 specialists and professionals from the field, namely museologists, museographers, museum managers, and digital application developers working in Greek museums. Ultimately, this research uses the SOR model to reveal the effect of digital tools on the digital atmosphere in Greek museums. It also enriches the SOR model with additional concepts and emotions taken from real-life situations, adding new categories of variables. This research provides the initial data and knowledge regarding the concept of the MDA, along with its importance. Citation: Technologies PubDate: 2023-10-22 DOI: 10.3390/technologies11050149 Issue No:Vol. 11, No. 5 (2023)
Authors:Elyor Berdimurodov, Omar Dagdag, Khasan Berdimuradov, Wan Mohd Norsani Wan Nik, Ilyos Eliboev, Mansur Ashirov, Sherzod Niyozkulov, Muslum Demir, Chinmurot Yodgorov, Nizomiddin Aliev First page: 150 Abstract: Green electrospinning harnesses the potential of renewable biomaterials to craft biodegradable nanofiber structures, expanding their utility across a spectrum of applications. In this comprehensive review, we summarize the production, characterization and application of electrospun cellulose, collagen, gelatin and other biopolymer nanofibers in tissue engineering, drug delivery, biosensing, environmental remediation, agriculture and synthetic biology. These applications span diverse fields, including tissue engineering, drug delivery, biosensing, environmental remediation, agriculture, and synthetic biology. In the realm of tissue engineering, nanofibers emerge as key players, adept at mimicking the intricacies of the extracellular matrix. These fibers serve as scaffolds and vascular grafts, showcasing their potential to regenerate and repair tissues. Moreover, they facilitate controlled drug and gene delivery, ensuring sustained therapeutic levels essential for optimized wound healing and cancer treatment. Biosensing platforms, another prominent arena, leverage nanofibers by immobilizing enzymes and antibodies onto their surfaces. This enables precise glucose monitoring, pathogen detection, and immunodiagnostics. In the environmental sector, these fibers prove invaluable, purifying water through efficient adsorption and filtration, while also serving as potent air filtration agents against pollutants and pathogens. Agricultural applications see the deployment of nanofibers in controlled release fertilizers and pesticides, enhancing crop management, and extending antimicrobial food packaging coatings to prolong shelf life. In the realm of synthetic biology, these fibers play a pivotal role by encapsulating cells and facilitating bacteria-mediated prodrug activation strategies. Across this multifaceted landscape, nanofibers offer tunable topographies and surface functionalities that tightly regulate cellular behavior and molecular interactions. Importantly, their biodegradable nature aligns with sustainability goals, positioning them as promising alternatives to synthetic polymer-based technologies. As research and development continue to refine and expand the capabilities of green electrospun nanofibers, their versatility promises to advance numerous applications in the realms of biomedicine and biotechnology, contributing to a more sustainable and environmentally conscious future. Citation: Technologies PubDate: 2023-10-23 DOI: 10.3390/technologies11050150 Issue No:Vol. 11, No. 5 (2023)
Authors:Alma E. Guerrero-Sánchez, Edgar A. Rivas-Araiza, Mariano Garduño-Aparicio, Saul Tovar-Arriaga, Juvenal Rodriguez-Resendiz, Manuel Toledano-Ayala First page: 82 Abstract: Electrical power quality is one of the main elements in power generation systems. At the same time, it is one of the most significant challenges regarding stability and reliability. Due to different switching devices in this type of architecture, different kinds of power generators as well as non-linear loads are used for different industrial processes. A result of this is the need to classify and analyze Power Quality Disturbance (PQD) to prevent and analyze the degradation of the system reliability affected by the non-linear and non-stationary oscillatory nature. This paper presents a novel Multitasking Deep Neural Network (MDL) for the classification and analysis of multiple electrical disturbances. The characteristics are extracted using a specialized and adaptive methodology for non-stationary signals, namely, Empirical Mode Decomposition (EMD). The methodology’s design, development, and various performance tests are carried out with 28 different difficulties levels, such as severity, disturbance duration time, and noise in the 20 dB to 60 dB signal range. MDL was developed with a diverse data set in difficulty and noise, with a quantity of 4500 records of different samples of multiple electrical disturbances. The analysis and classification methodology has an average accuracy percentage of 95% with multiple disturbances. In addition, it has an average accuracy percentage of 90% in analyzing important signal aspects for studying electrical power quality such as the crest factor, per unit voltage analysis, Short-term Flicker Perceptibility (Pst), and Total Harmonic Distortion (THD), among others. Citation: Technologies PubDate: 2023-06-21 DOI: 10.3390/technologies11040082 Issue No:Vol. 11, No. 4 (2023)
Authors:Maria Papatsimouli, Panos Sarigiannidis, George F. Fragulis First page: 83 Abstract: Real-time sign language translation systems are of paramount importance in enabling communication for deaf and hard-of-hearing individuals. This population relies on various communication methods, including sign languages and visual techniques, to interact with others. While assistive technologies, such as hearing aids and captioning, have improved their communication capabilities, a significant communication gap still exists between sign language users and non-users. In order to bridge this gap, numerous sign language translation systems have been developed, encompassing sign language recognition and gesture-based controls. Our research aimed to analyze the advancements in real-time sign language translators developed over the past five years and their integration with IoT technology. By closely examining these technologies, we aimed to attain a deeper comprehension of their practical applications and evolution in the domain of sign language translation. We analyzed the current literature, technical reports, and conference papers on real-time sign language translation systems. Our results offer insights into the current state of the art in real-time sign language translation systems and their integration with IoT technology. We also provide a deep understanding of the recent developments in sign language translation technology and the potential for their fusion with Internet of Things technology to improve communication and promote inclusivity for the deaf and hard-of-hearing population. Citation: Technologies PubDate: 2023-06-22 DOI: 10.3390/technologies11040083 Issue No:Vol. 11, No. 4 (2023)
Authors:Abdulaziz Aljaloud, Abdul Razzaq First page: 84 Abstract: The use of blockchain technology is expanding in various industries, including finance, supply chain management, food, energy, IoT, and healthcare. The article aims to address the challenges of complex medical procedures, large-scale medical data management, and cost optimization in the healthcare industry. By employing blockchain technology, the article aims to enhance data security and privacy while ensuring the integrity and efficiency of the healthcare system. This article focuses on the application of blockchain technology in the healthcare system by reviewing the existing literature and proposing multiple workflows for better data management. These workflows were implemented using the Ethereum blockchain platform and involve complex medical procedures such as surgery and clinical trials, as well as managing a large amount of medical data. The feasibility of the proposed system is analyzed in terms of associated costs, and a model-driven engineering approach is used to recover the architecture of traditional healthcare systems. The aim is to provide stakeholders in the healthcare system with better healthcare services and cost optimization. The solution being proposed automates interactions between different parties involved. Smart contracts were created using Solidity language, and their functions were tested using the Remix IDE. This paper illustrates that our smart contract code was designed to avoid common security vulnerabilities and attacks. To test the framework, a prototype of the smart contract was deployed on an Ethereum TESTNET blockchain in a Windows environment. This study found that the proposed approach is both practical and efficient. Citation: Technologies PubDate: 2023-06-29 DOI: 10.3390/technologies11040084 Issue No:Vol. 11, No. 4 (2023)
Authors:Alireza Ebrahimi First page: 85 Abstract: Self-directed learning and self-design became unexpectedly popular and common during the COVID-19 era. Learners are encouraged to take charge of their learning and, often the opportunity to independently design their learning experience. This research illustrates the use of technology in teaching and learning technology with a central theme of promoting self-directed learning with engaging self-design for both educators and learners. The technology used includes existing tools such as web page design, Learning Management Systems (LMS), project management tools, and basic programming foundations and concepts of big data and databases. In addition, end-users and developers can create their own tools with simple coding. Planning techniques, such as Visual Plan Construct Language with its embedded AI, are used to integrate course material and rubrics with time management. Educators may use project management tools instead. The research proposes a self-directed paradigm with self-designed resources using the existing technology with LMS modules, discussions, and self-tests. The research establishes its criteria for ensuring the quality of content and design, known as 7x2C. Additionally, other criteria for analysis, such as Design Thinking, are included. The approach is examined for a technology-based business course in creating an experiential learning system for COVID-19 awareness. Likewise, among other projects, an environment for educating learners about diabetes and obesity has been designed. The project is known as Sunchoke, which has a theme of Grow, Eat, and Heal. Educators can use their own content and rubrics to adapt this approach to their own customized teaching methods. Citation: Technologies PubDate: 2023-06-29 DOI: 10.3390/technologies11040085 Issue No:Vol. 11, No. 4 (2023)
Authors:Taishu Kumagai, Yoshimune Nonomura First page: 86 Abstract: “Handshaking parties,” where pop idols shake hands with fans, can be exciting. The multimodal stimulation of tactile, visual, and auditory sensations can be captivating. In this study, we presented subjects with stimuli eliciting three sensory responses: tactile, visual, and auditory sensations. We found that the attraction scores of subjects increased because they felt the smoothness and obtained a human-like sensory experience grasping a grip handle covered with artificial skin, faux fur, and abrasive cloth with their dominant hand as they looked at a picture of a pop idol or listened to a song. When no pictures or songs were presented, a simple feeling of slight warmth was correlated with the attraction score. Results suggest that multimodal stimuli alter tactile sensations and the feelings evoked. This finding may be useful for designing materials that activate the human mind through tactile sensation and for developing humanoid robots and virtual reality systems. Citation: Technologies PubDate: 2023-07-01 DOI: 10.3390/technologies11040086 Issue No:Vol. 11, No. 4 (2023)
Authors:Marcos Aviles, Juvenal Rodríguez-Reséndiz, Danjela Ibrahimi First page: 87 Abstract: This work proposes a metaheuristic-based approach to hyperparameter selection in a multilayer perceptron to classify EMG signals. The main goal of the study is to improve the performance of the model by optimizing four important hyperparameters: the number of neurons, the learning rate, the epochs, and the training batches. The approach proposed in this work shows that hyperparameter optimization using particle swarm optimization and the gray wolf optimizer significantly improves the performance of a multilayer perceptron in classifying EMG motion signals. The final model achieves an average classification rate of 93% for the validation phase. The results obtained are promising and suggest that the proposed approach may be helpful for the optimization of deep learning models in other signal processing applications. Citation: Technologies PubDate: 2023-07-02 DOI: 10.3390/technologies11040087 Issue No:Vol. 11, No. 4 (2023)
Authors:María Gonzalez-Moreno, Carlos Monfort-Vinuesa, Antonio Piñas-Mesa, Esther Rincon First page: 88 Abstract: Objectives: The need to incentivize the humanization of healthcare providers coincides with the development of a more technological approach to medicine, which gives rise to depersonalization when treating patients. Currently, there is a culture of humanization that reflects the awareness of health professionals, patients, and policy makers, although it is unknown if there are university curricula incorporating specific skills in humanization, or what these may include. Therefore, the objectives of this study are as follows: (1) to identify what type of education in humanization is provided to university students of Health Sciences using digital technologies; and (2) determine the strengths and weaknesses of this education. The authors propose a curriculum focusing on undergraduate students to strengthen the humanization skills of future health professionals, including digital health strategies. Methods: A systematic review, based on the scientific literature published in EBSCO, Ovid, PubMed, Scopus, and Web of Science, over the last decade (2012–2022), was carried out in November 2022. The keywords used were “humanization of care” and “humanization of healthcare” combined both with and without “students”. Results: A total of 475 articles were retrieved, of which 6 met the inclusion criteria and were subsequently analyzed, involving a total of 295 students. Three of them (50%) were qualitative studies, while the other three (50%) involved mixed methods. Only one of the studies (16.7%) included digital health strategies to train humanization. Meanwhile, another study (16.7%) measured the level of humanization after training. Conclusions: There is a clear lack of empirically tested university curricula that combine education in humanization and digital technology for future health professionals. Greater focus on the training of future health professionals is needed, in order to guarantee that they begin their professional careers with the precept of medical humanities as a basis. Citation: Technologies PubDate: 2023-07-05 DOI: 10.3390/technologies11040088 Issue No:Vol. 11, No. 4 (2023)
Authors:Viktor Shamakhov, Sergey Slipchenko, Dmitriy Nikolaev, Ilya Soshnikov, Alexander Smirnov, Ilya Eliseyev, Artyom Grishin, Matvei Kondratov, Artem Rizaev, Nikita Pikhtin, Peter Kop’ev First page: 89 Abstract: AlzGa1−zAs layers of various compositions were grown using metalorganic chemical vapor deposition on a GaAs substrate with a pattern of alternating SiO2 mask/window stripes, each 100 µm wide. Microphotoluminescence maps and thickness profiles of AlzGa1−zAs layers that demonstrated the distribution of the growth rate and z in the window were experimentally studied. It was shown that the layer growth rate and the AlAs mole fraction increased continuously from the center to the edge of the window. It was experimentally shown that for a fixed growth time of 10 min, as z increased from 0 to 0.3, the layer thickness difference between the center of the window and the edge increased from 700 Å to 1100 Å, and the maximum change in z between the center of the window and the edge reached Δz 0.016, respectively. Within the framework of the vapor -phase diffusion model, simulations of the spatial distribution of the layer thickness and z across the window were carried out. It was shown that the simulation results were in good agreement with the experimental results for the effective diffusion length D/k: Ga—85 µm, Al—50 µm. Citation: Technologies PubDate: 2023-07-07 DOI: 10.3390/technologies11040089 Issue No:Vol. 11, No. 4 (2023)
Authors:Iouri E. Borissevitch, Pablo J. Gonçalves, Lucimara P. Ferreira, Alexey A. Kostyukov, Vladimir A. Kuzmin First page: 90 Abstract: Quantum yields (φT) and energies (ET) of the first triplet state T1 for four molecules of cyanine dyes with two chromophores (BCDs), promising photoactive compounds for various applications, for example, as photosensitizers in photodynamic therapy (PDT) and fluorescence diagnostics (FD), were studied in 1-propanol solutions by steady-state and time-resolved optical absorption techniques. BCDs differ by the structure of the central heterocycle, connecting the chromophores. The heterocycle structure is responsible for electron tunneling between chromophores, for which efficiency can be characterized by splitting of the BCD triplet energy levels. It was shown that the increase in the tunneling efficiency reduces ET values and increases φT values. This aspect is very promising for the synthesis of new effective photosensitizers based on cyanine dyes with two interacting chromophores for various applications, including photodynamic therapy. Citation: Technologies PubDate: 2023-07-08 DOI: 10.3390/technologies11040090 Issue No:Vol. 11, No. 4 (2023)
Authors:Muhammad Faizan, Ioannis Intzes, Ioana Cretu, Hongying Meng First page: 91 Abstract: Deep neutral networks (DNNs) are complex machine learning models designed for decision-making tasks with high accuracy. However, DNNs require high computational power and memory, which limits such models to fitting on edge devices, resulting in unnecessary processing delays and high energy consumption. Graphical processing units (GPUs) offer reliable hardware acceleration, but their bulky sizes prevent their utilization in portable equipment. System-on-chip field programmable gated arrays (SoC-FPGAs) provide considerable computational power with low energy consumption, making them ideal for edge computing applications, owing to their innovative, flexible, and small design. In this paper, we implement a deep-learning-based music genre classification system on a SoC-FPGA board, evaluate the model’s performance, and provide a comparative analysis across different platforms. Specifically, we compare the performance of long short-term memory (LSTM), convolutional neural networks (CNNs), and a hybrid model (CNN-LSTM) on an Intel Core i7-8550U by Intel Cooperation. The models are fed an acoustic feature called the Mel-frequency cepstral coefficient (MFCC) for training and testing (inference). Then, by using the advanced Vitis AI tool, a deployable version of the model is generated. The experimental results show that the execution speed is increased by 80%, and the throughput rises four times when the CNN-based music genre classification system is implemented on SoC-FPGA. Citation: Technologies PubDate: 2023-07-10 DOI: 10.3390/technologies11040091 Issue No:Vol. 11, No. 4 (2023)
Authors:Sundarapandian Vaidyanathan, Esteban Tlelo-Cuautle, Khaled Benkouider, Aceng Sambas, Brisbane Ovilla-Martínez First page: 92 Abstract: Mechanical jerk systems have applications in several areas, such as oscillators, microcontrollers, circuits, memristors, encryption, etc. This research manuscript reports a new 3-D chaotic jerk system with two unstable balance points. It is shown that the proposed mechanical jerk system exhibits multistability with coexisting chaotic attractors for the same set of system constants but for different initial states. A bifurcation analysis of the proposed mechanical jerk system is presented to highlight the special properties of the system with respect to the variation of system constants. A field-programmable gate array (FPGA) implementation of the proposed mechanical jerk system is given by synthesizing the discrete equations that are obtained by applying one-step numerical methods. The hardware resources are reduced by performing pipeline operations, and, finally, the paper concludes that the experimental results of the proposed mechanical jerk system using FPGA-based design show good agreement with the MATLAB simulations of the same system. Citation: Technologies PubDate: 2023-07-11 DOI: 10.3390/technologies11040092 Issue No:Vol. 11, No. 4 (2023)
Authors:Konstantin Baranov, Ivan Reznik, Sofia Karamysheva, Jacobus W. Swart, Stanislav Moshkalev, Anna Orlova First page: 93 Abstract: Colloidal nanoparticles, and quantum dots in particular, are a new class of materials that can significantly improve the functionality of photonics, electronics, sensor devices, etc. The main challenge addressed in the article is modification of the syntheses of colloidal NP to launch them into mass production. It is proposed to use an additive printing method of chips for microfluidic synthesis, and it is shown that our approach allows to offer a cheap, easily scalable and automated synthesis method which allows to increase the product yield up to 60% with improved optical properties of AgInS2 quantum dots. Citation: Technologies PubDate: 2023-07-12 DOI: 10.3390/technologies11040093 Issue No:Vol. 11, No. 4 (2023)
Authors:Ilaria Ceccarelli, Luca Filoni, Massimiliano Poli, Ciro Apollonio, Andrea Petroselli First page: 94 Abstract: Of all the substances that can be present in water intended for human consumption, arsenic (As) is one of the most toxic. Many treatment technologies can be used for removing As from water, for instance, adsorption onto iron media, where commercially available adsorbents are removed and replaced with new media when they are exhausted. Since this is an expensive operation, in this work, a novel and portable plant for regenerating iron media has been developed and tested in four real case studies in Central Italy. The obtained results highlight the good efficiency of the system, which was able, from 2019 to 2023, to regenerate the iron media and to restore its capability to adsorb the As from water almost entirely. Indeed, when the legal threshold value of 10 μg/L is exceeded, the regeneration process is performed and, after that, the As concentration in the water effluent is at the minimum level in all the investigated case studies. Citation: Technologies PubDate: 2023-07-12 DOI: 10.3390/technologies11040094 Issue No:Vol. 11, No. 4 (2023)
Authors:Mikhail Vasiliev, Victor Rosenberg, Jamie Lyford, David Goodfield First page: 95 Abstract: Currently, there are strong and sustained growth trends observed in multi-disciplinary industrial technologies such as building-integrated photovoltaics and agrivoltaics, where renewable energy production is featured in building envelopes of varying degrees of transparency. Novel glass products can provide a combination of thermal energy savings and solar energy harvesting, enabled by either patterned-semiconductor thin-film energy converters on glass substrates, or by using luminescent concentrator-type approaches to achieve high transparency. Significant progress has been demonstrated recently in building integrated solar windows featuring visible light transmission of up to 70%, with electric power outputs of up to Pmax ~ 30–33 Wp/m2. Several slightly different designs were tested during 2021–2023 in a greenhouse installation at Murdoch University in Perth, Western Australia; their long-term energy harvesting performance differences were found to be on the scale of ~10% in wall-mounted locations. Solar greenhouse generated electricity at rates of up to 19 kWh/day, offsetting nearly 40% of energy costs. The objective of this paper is to report on the field performance of these PV windows in the context of agrivoltaics and to provide some detail of the performance differences measured in several solar window designs related to their glazing structure materials. Methods for the identification and quantification of long-term field performance differences and energy generation trends in solar windows of marginally different design types are reported. The paper also aims to outline the practical application potential of these transparent construction materials in built environments, focusing on the measured renewable energy figures and seasonal trends observed during the long-term study. Citation: Technologies PubDate: 2023-07-12 DOI: 10.3390/technologies11040095 Issue No:Vol. 11, No. 4 (2023)
Authors:Faa-Jeng Lin, Chao-Fu Chang, Yu-Cheng Huang, Tzu-Ming Su First page: 96 Abstract: This paper focuses on the economic power dispatch (EPD) operation of a microgrid in an OPAL-RT environment. First, a long short-term memory (LSTM) network is proposed to forecast the load information of a microgrid to determine the output of a power generator and the charging/discharging control strategy of a battery energy storage system (BESS). Then, a deep reinforcement learning method, the deep deterministic policy gradient (DDPG), is utilized to develop the power dispatch of a microgrid to minimize the total energy expense while considering power constraints, load uncertainties and electricity price. Moreover, a microgrid built in Cimei Island of Penghu Archipelago, Taiwan, is investigated to examine the compliance with the requirements of equality and inequality constraints and the performance of the deep reinforcement learning method. Furthermore, a comparison of the proposed method with the experience-based energy management system (EMS), Newton particle swarm optimization (Newton-PSO) and the deep Q-learning network (DQN) is provided to evaluate the obtained solutions. In this study, the average deviation of the LSTM forecast accuracy is less than 5%. In addition, the daily operating cost of the proposed method obtains a 3.8% to 7.4% lower electricity cost compared to that of the other methods. Finally, a detailed emulation in the OPAL-RT environment is carried out to validate the effectiveness of the proposed method. Citation: Technologies PubDate: 2023-07-12 DOI: 10.3390/technologies11040096 Issue No:Vol. 11, No. 4 (2023)
Authors:Rafael Ortiz-Feregrino, Saul Tovar-Arriaga, Jesus Carlos Pedraza-Ortega, Juvenal Rodriguez-Resendiz First page: 97 Abstract: Retinal vein segmentation is a crucial task that helps in the early detection of health problems, making it an essential area of research. With recent advancements in artificial intelligence, we can now develop highly reliable and efficient models for this task. CNN has been the traditional choice for image analysis tasks. However, the emergence of visual transformers with their unique attention mechanism has proved to be a game-changer. However, visual transformers require a large amount of data and computational power, making them unsuitable for tasks with limited data and resources. To deal with this constraint, we adapted the attention module of visual transformers and integrated it into a CNN-based UNET network, achieving superior performance compared to other models. The model achieved a 0.89 recall, 0.98 AUC, 0.97 accuracy, and 0.97 sensitivity on various datasets, including HRF, Drive, LES-AV, CHASE-DB1, Aria-A, Aria-D, Aria-C, IOSTAR, STARE and DRGAHIS. Moreover, the model can recognize blood vessels accurately, regardless of camera type or the original image resolution, ensuring that it generalizes well. This breakthrough in retinal vein segmentation could improve the early diagnosis of several health conditions. Citation: Technologies PubDate: 2023-07-13 DOI: 10.3390/technologies11040097 Issue No:Vol. 11, No. 4 (2023)
Authors: Chiarelli First page: 98 Abstract: This paper proposes a theoretical study that investigates quantum effects on the gravity of black holes. This study utilizes a gravitational model that incorporates quantum mechanics derived from the classical-like quantum hydrodynamic representation. This research calculates the mass density distribution of quantum black holes, specifically in the case of central symmetry. The gravity of a quantum black hole shows contributions coming from quantum potential energy, which is also sensitive to the presence of a background of gravitational noise. The additional energy, stored in quantum potential fluctuations and constituting a form of dark energy, leads to a repulsive gravity in the weak gravity limit. This repulsive gravity overcomes the attractive classical Newtonian force at large distances of order of the intergalactic length. Citation: Technologies PubDate: 2023-07-14 DOI: 10.3390/technologies11040098 Issue No:Vol. 11, No. 4 (2023)
Authors:Benjamin Svendsen, Seifedine Kadry First page: 99 Abstract: Communication is integral to every human’s life, allowing individuals to express themselves and understand each other. This process can be challenging for the hearing-impaired population, who rely on sign language for communication due to the limited number of individuals proficient in sign language. Image classification models can be used to create assistive systems to address this communication barrier. This paper conducts a comprehensive literature review and experiments to find the state of the art in sign language recognition. It identifies a lack of research in Norwegian Sign Language (NSL). To address this gap, we created a dataset from scratch containing 24,300 images of 27 NSL alphabet signs and performed a comparative analysis of various machine learning models, including the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Convolutional Neural Network (CNN) on the dataset. The evaluation of these models was based on accuracy and computational efficiency. Based on these metrics, our findings indicate that SVM and CNN were the most effective models, achieving accuracies of 99.9% with high computational efficiency. Consequently, the research conducted in this report aims to contribute to the field of NSL recognition and serve as a foundation for future studies in this area. Citation: Technologies PubDate: 2023-07-15 DOI: 10.3390/technologies11040099 Issue No:Vol. 11, No. 4 (2023)
Authors:Silvia Gaiani, Elisa Ferrari, Marica Gozzi, Maria Teresa Di Giovanni, Magdalena Lassinantti Gualtieri, Elena Colombini, Paolo Veronesi First page: 100 Abstract: Additive manufacturing technology has emerged over the past decade as one of the best solutions for building prototypes and components with complex geometries and reduced thicknesses. Its application has rapidly spread to various industries, such as motorsport, automotive, aerospace, and biomedical. In particular, titanium alloy Ti-6Al-4V, due to its exceptional mechanical properties, low density, and excellent corrosion resistance, turns out to be one of the most popular for the production of parts with additive manufacturing technology across all the market segments listed above. However, when producing components using Laser Powder Bed Fusion (LPBF) technology, it is always necessary to perform appropriate heat treatments whose main purpose is to reduce the residual stresses typically generated during the manufacturing process. Post-process heat treatments on Ti6Al4V components obtained by way of additive technology have been extensively studied in the literature, with the aim of identifying optimal thermal cycles, which may allow for the effective reduction of residual stresses combined with proper microstructural conditions. However, despite the usual target of maximizing relevant mechanical properties, it is mandatory for industrial production to achieve a robust process, i.e., minimizing the sensitivity to noise-induced variation. Therefore, the aim of the present work is to compare several post-process heat treatment strategies by performing different thermal cycles in the temperature range of 750–955 °C and investigating how these affect the average mechanical properties and their variance. The treated samples are then analyzed running a complete mechanical and microstructural characterization, and the latter particularly focused on the determination of the typical microstructure present in the treated samples by using the XRD technique. Citation: Technologies PubDate: 2023-07-15 DOI: 10.3390/technologies11040100 Issue No:Vol. 11, No. 4 (2023)
Authors:Obaid Alotaibi, Eric Pardede, Sarath Tomy First page: 101 Abstract: In today’s big data era, cleaning big data streams has become a challenging task because of the different formats of big data and the massive amount of big data which is being generated. Many studies have proposed different techniques to overcome these challenges, such as cleaning big data in real time. This systematic literature review presents recently developed techniques that have been used for the cleaning process and for each data cleaning issue. Following the PRISMA framework, four databases are searched, namely IEEE Xplore, ACM Library, Scopus, and Science Direct, to select relevant studies. After selecting the relevant studies, we identify the techniques that have been utilized to clean big data streams and the evaluation methods that have been used to examine their efficiency. Also, we define the cleaning issues that may appear during the cleaning process, namely missing values, duplicated data, outliers, and irrelevant data. Based on our study, the future directions of cleaning big data streams are identified. Citation: Technologies PubDate: 2023-07-26 DOI: 10.3390/technologies11040101 Issue No:Vol. 11, No. 4 (2023)
Authors:Anupama Ganguly, Pabitra Kumar Biswas, Chiranjit Sain, Taha Selim Ustun First page: 102 Abstract: Sustainable energy exhibited immense growth in the last few years. As compared to other sustainable sources, solar power is proved to be the most feasible source due to some unanticipated characteristics, such as being clean, noiseless, ecofriendly, etc. The output from the solar power is entirely unpredictable since solar power generation is dependent on the intensity of solar irradiation and solar panel temperature. Further, these parameters are weather dependent and thus intermittent in nature. To conquer intermittency, power converters play an important role in solar power generation. Generally, photovoltaic systems will eventually suffer from a decrease in energy conversion efficiency along with improper stability and intermittent properties. As a result, the maximum power point tracking (MPPT) algorithm must be incorporated to cultivate maximum power from solar power. To make solar power generation reliable, a proper control technique must be added to the DC–DC power converter topologies. Furthermore, this study reviewed the progress of the maximum power point tracking algorithm and included an in-depth discussion on modern and both unidirectional and bidirectional DC–DC power converter topologies for harvesting electric power. Lastly, for the reliability and continuity of the power demand and to allow for distributed generation, this article also established the possibility of integrating solar PV systems into nanogrids and picogrids in a sustainable environment. The outcome of this comprehensive survey would be of strong interest to the researchers, technologists, and the industry in the relevant field to carry out future research. Citation: Technologies PubDate: 2023-08-01 DOI: 10.3390/technologies11040102 Issue No:Vol. 11, No. 4 (2023)
Authors:Yair Wiseman First page: 103 Abstract: We suggest two steps of reducing the amount of data transmitted on Internet of Vehicle networks. The first step shifts the image from a full-color resolution to only an 8-color resolution. The reduction of the color numbers is noticeable; however, the 8-color images are enough for the requirements of common vehicles’ applications. The second step suggests modifying the quantization tables employed by H.264 to different tables that will be more suitable to an image with only 8 colors. The first step usually reduces the size of the image by more than 30%, and when continuing and performing the second step, the size of the image decreases by more than 40%. That is to say, the combination of the two steps can provide a significant reduction in the amount of data required to be transferred on vehicular networks. Citation: Technologies PubDate: 2023-08-03 DOI: 10.3390/technologies11040103 Issue No:Vol. 11, No. 4 (2023)
Authors:Lu Liu, Runlei Ma, Peter M. A. van Ooijen, Matthijs Oudkerk, Rozemarijn Vliegenthart, Raymond N. J. Veldhuis, Christoph Brune First page: 104 Abstract: Epicardial adipose tissue (EAT) is located between the visceral pericardium and myocardium, and EAT volume is correlated with cardiovascular risk. Nowadays, many deep learning-based automated EAT segmentation and quantification methods in the U-net family have been developed to reduce the workload for radiologists. The automatic assessment of EAT on non-contrast low-dose CT calcium score images poses a greater challenge compared to the automatic assessment on coronary CT angiography, which requires a higher radiation dose to capture the intricate details of the coronary arteries. This study comprehensively examined and evaluated state-of-the-art segmentation methods while outlining future research directions. Our dataset consisted of 154 non-contrast low-dose CT scans from the ROBINSCA study, with two types of labels: (a) region inside the pericardium and (b) pixel-wise EAT labels. We selected four advanced methods from the U-net family: 3D U-net, 3D attention U-net, an extended 3D attention U-net, and U-net++. For evaluation, we performed both four-fold cross-validation and hold-out tests. Agreement between the automatic segmentation/quantification and the manual quantification was evaluated with the Pearson correlation and the Bland–Altman analysis. Generally, the models trained with label type (a) showed better performance compared to models trained with label type (b). The U-net++ model trained with label type (a) showed the best performance for segmentation and quantification. The U-net++ model trained with label type (a) efficiently provided better EAT segmentation results (hold-out test: DCS = 80.18±0.20%, mIoU = 67.13±0.39%, sensitivity = 81.47±0.43%, specificity = 99.64±0.00%, Pearson correlation = 0.9405) and EAT volume compared to the other U-net-based networks and the recent EAT segmentation method. Interestingly, our findings indicate that 3D convolutional neural networks do not consistently outperform 2D networks in EAT segmentation and quantification. Moreover, utilizing labels representing the region inside the pericardium proved advantageous in training more accurate EAT segmentation models. These insights highlight the potential of deep learning-based methods for achieving robust EAT segmentation and quantification outcomes. Citation: Technologies PubDate: 2023-08-05 DOI: 10.3390/technologies11040104 Issue No:Vol. 11, No. 4 (2023)
Authors:Sivaramakrishnan Vinothini, Te-Wei Chiu, Subramanian Sakthinathan First page: 105 Abstract: Furaltadone (FLD) is an antibiotic drug that is widely treated for coccidiosis, intestinal infection, and turkey blackhead. Moreover, excessive use of FLD may have some negative consequences for humans and domestic animals. Therefore, practical, sensitive, selective, and facile detection of FLD is still needed. In this exploration, a Eu2(WO4)3-nanoparticles-modified screen-printed carbon electrode was developed for the low-level detection of FLD. Hydrothermal techniques were used effectively to prepare the Eu2(WO4)3 complex. Scanning electron microscopy and X-ray diffraction investigations were used to confirm the Eu2(WO4)3. The results revealed that the Eu2(WO4)3 was well formed, crystalline, and uniformly distributed. Furthermore, the electrochemical behavior of the SPCE/Eu2(WO4) electrode was examined by differential pulse voltammetry and cyclic voltammetry studies. The SPCE/Eu2(WO4) electrode demonstrated improved electrocatalytic activity in the detection of FLD with a detection limit of 97 µM (S/N = 3), linear range of 10 nM to 300 µM, and sensitivity of 2.1335 µA µM−1 cm−2. The SPCE/Eu2(WO4) electrode detected FLD in the presence of 500-fold excess concentrations of other interfering pollutant ions. The practical feasibility of the SPCE/Eu2(WO4) electrode was tested on different antibiotic medicines and showed adequate recovery. Moreover, the SPCE/Eu2(WO4) electrode shows appreciable repeatability, high stability, and reproducibility. Citation: Technologies PubDate: 2023-08-06 DOI: 10.3390/technologies11040105 Issue No:Vol. 11, No. 4 (2023)
Authors:Tatiana G. Volova, Aleksey V. Demidenko, Anastasiya V. Murueva, Alexey E. Dudaev, Ivan Nemtsev, Ekaterina I. Shishatskaya First page: 106 Abstract: Biodegradable polyhydroxyalkanoates, biopolymers of microbiological origin, formed by 3- and 4-hydroxybutyrate monomers P(3HB-co-4HB), were used to obtain nanomembranes loaded with drugs as cell carriers by electrospinning. Resorbable non-woven membranes from P(3HB-co-4HB) loaded with ceftazidime, doripinem, and actovegin have been obtained. The loading of membranes with drugs differently affected the size of fibers and the structure of membranes, and in all cases increased the hydrophilicity of the surface. The release of drugs in vitro was gradual, which corresponded to the Higuchi and Korsmeyer-Peppas models. Antibiotic-loaded membranes showed antibacterial activity against S. aureus and E. coli, in which growth inhibition zones were 41.7 ± 1.1 and 38.6 ± 1.7 mm for ceftazidime and doripinem, respectively. The study of the biological activity of membranes in the NIH 3T3 mouse fibroblast culture based on the results of DAPI and FITC staining of cells, as well as the MTT test, did not reveal a negative effect despite the presence of antibiotics in them. Samples containing actovegin exhibit a stimulating effect on fibroblasts. Biodegradable polyhydroxyalkanoates formed by 3-hydroxybutyrate and 4-hydroxybutyrate monomers provide electrospinning non-woven membranes suitable for long-term delivery of drugs and cultivation of eukaryotic cells, and are promising for the treatment of wound defects complicated by infection. Citation: Technologies PubDate: 2023-08-07 DOI: 10.3390/technologies11040106 Issue No:Vol. 11, No. 4 (2023)
Authors:Abdu Salam, Faizan Ullah, Farhan Amin, Mohammad Abrar First page: 107 Abstract: As the manufacturing industry advances towards Industry 5.0, which heavily integrates advanced technologies such as cyber-physical systems, artificial intelligence, and the Internet of Things (IoT), the potential for web-based attacks increases. Cybersecurity concerns remain a crucial challenge for Industry 5.0 environments, where cyber-attacks can cause devastating consequences, including production downtime, data breaches, and even physical harm. To address this challenge, this research proposes an innovative deep-learning methodology for detecting web-based attacks in Industry 5.0. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models are examples of deep learning techniques that are investigated in this study for their potential to effectively classify attacks and identify anomalous behavior. The proposed transformer-based system outperforms traditional machine learning methods and existing deep learning approaches in terms of accuracy, precision, and recall, demonstrating the effectiveness of deep learning for intrusion detection in Industry 5.0. The study’s findings showcased the superiority of the proposed transformer-based system, outperforming previous approaches in accuracy, precision, and recall. This highlights the significant contribution of deep learning in addressing cybersecurity challenges in Industry 5.0 environments. This study contributes to advancing cybersecurity in Industry 5.0, ensuring the protection of critical infrastructure and sensitive data. Citation: Technologies PubDate: 2023-08-08 DOI: 10.3390/technologies11040107 Issue No:Vol. 11, No. 4 (2023)
Authors:Woo Sik Yoo, Kitaek Kang, Jung Gon Kim, Yeongsik Yoo First page: 108 Abstract: Color fading naturally occurs with time under light illumination. It is triggered by the high photon energy of light. The rate of color fading and darkening depends on the substance, lighting condition, and storage conditions. Color fading is only observed after some time has passed. The current color of objects of interest can only be compared with old photographs or the observer’s perception at the time of reference. Color fading and color darkening rates between two or more points in time in the past can only be determined using photographic images from the past. For objective characterization of color difference between two or more different times, quantification of color in either digital or printed photographs is required. A newly developed image analysis and comparison software (PicMan) has been used for color quantification and pixel-by-pixel color difference mapping in this study. Images of two copies of Japanese wood-block prints with and without color fading have been selected for the exemplary study of quantitative characterization of color fading and color darkening. The fading occurred during a long period of exposure to light. Pixel-by-pixel, line-by-line, and area-by-area comparisons of color fading and darkening between two images were very effective in quantifying color change and visualization of the phenomena. RGB, HSV, CIE L*a*b* values between images and their differences of a single pixel to areas of interest in any shape can be quantified. Color fading and darkening analysis results were presented in numerical, graphical, and image formats for completeness. All formats have their own advantages and disadvantages over the other formats in terms of data size, complexity, readability, and communication among parties of interest. This paper demonstrates various display options for color analysis, a summary of color fading, or color difference among images of interest for practical artistic, cultural heritage conservation, and museum applications. Color simulation for various moments in time was proposed and demonstrated by interpolation or extrapolation of color change between images, with and without color fading, using PicMan. The degree of color fading and color darkening over the various moments in time (past and future) can be simulated and visualized for decision-making in public display, storage, and restoration planning. Citation: Technologies PubDate: 2023-08-08 DOI: 10.3390/technologies11040108 Issue No:Vol. 11, No. 4 (2023)
Authors:Edgar Rafael Ponce de Leon-Sanchez, Jorge Domingo Mendiola-Santibañez, Omar Arturo Dominguez-Ramirez, Ana Marcela Herrera-Navarro, Alberto Vazquez-Cervantes, Hugo Jimenez-Hernandez, Horacio Senties-Madrid First page: 109 Abstract: Interferon-beta is one of the most widely prescribed disease-modifying therapies for multiple sclerosis patients. However, this treatment is only partially effective, and a significant proportion of patients do not respond to this drug. This paper proposes an alternative fuzzy logic system, based on the opinion of a neurology expert, to classify relapsing–remitting multiple sclerosis patients as high, medium, or low responders to interferon-beta. Also, a pipeline prediction model trained with biomarkers associated with interferon-beta responses is proposed, for predicting whether patients are potential candidates to be treated with this drug, in order to avoid ineffective therapies. The classification results showed that the fuzzy system presented 100% efficiency, compared to an unsupervised hierarchical clustering method (52%). So, the performance of the prediction model was evaluated, and 0.8 testing accuracy was achieved. Hence, a pipeline model, including data standardization, data compression, and a learning algorithm, could be a useful tool for getting reliable predictions about responses to interferon-beta. Citation: Technologies PubDate: 2023-08-09 DOI: 10.3390/technologies11040109 Issue No:Vol. 11, No. 4 (2023)
Authors:Alexander Pastukh, Valery Tikhvinskiy, Svetlana Dymkova, Oleg Varlamov First page: 110 Abstract: This article presents a comprehensive study of the potential utilization of the L-band and S-band frequency ranges for satellite non-terrestrial network (NTN) technologies. This study encompasses an interference analysis in the S-band, investigating the coexistence of NTN satellite systems with mobile satellite networks such as Omnispace and Lyra, and an interference analysis in the L-band between NTN satellites and the mobile satellite network Inmarsat. This study simulates an NTN satellite network with typical characteristics defined by 3GPP and ITU-R for the n255 and n256 bands. Furthermore, it provides calculations illustrating the signal-to-noise ratio degradation of low-Earth-orbit (LEO), medium-Earth-orbit (MEO), and geostationary-Earth-orbit (GEO) satellite networks operating in the L-band and S-band when exposed to interference from NTN satellites. Citation: Technologies PubDate: 2023-08-10 DOI: 10.3390/technologies11040110 Issue No:Vol. 11, No. 4 (2023)
Authors:Rusul Sabah Jebur, Mohd Hazli Bin Mohamed Zabil, Dalal Abdulmohsin Hammood, Lim Kok Cheng, Ali Al-Naji First page: 111 Abstract: Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising. Leveraging Bidirectional Long Short-Term Memory (Bi-LSTM) and optimized Convolutional Neural Networks (CNN), the hybrid model aims to enhance denoising performance. The CNN’s weights are optimized using SI-OPA, resulting in improved denoising accuracy. Extensive comparisons against state-of-the-art denoising methods, including traditional algorithms and deep learning-based techniques, are conducted, focusing on denoising effectiveness, computational efficiency, and preservation of image details. The proposed approach demonstrates superior performance in all aspects, highlighting its potential as a promising solution for image-denoising tasks. Implemented in Python, the hybrid model showcases the benefits of combining Bi-LSTM, optimized CNN, and SI-OPA for advanced image-denoising applications. Citation: Technologies PubDate: 2023-08-12 DOI: 10.3390/technologies11040111 Issue No:Vol. 11, No. 4 (2023)
Authors:Victor Hugo Silva-Blancas, José Manuel Álvarez-Alvarado, Ana Marcela Herrera-Navarro, Juvenal Rodríguez-Reséndiz First page: 112 Abstract: With the fact that new server technologies are coming to market, it is necessary to update or create new methodologies for data analysis and exploitation. Applied methodologies go from decision tree categorization to artificial neural networks (ANN) usage, which implement artificial intelligence (AI) for decision making. One of the least used strategies is drill-down analysis (DD), belonging to the decision trees subcategory, which because of not having AI resources has lost interest among researchers. However, its easy implementation makes it a suitable tool for database processing systems. This research has developed a systematic review to understand the prospective of DD analysis on scientific literature in order to establish a knowledge platform and establish if it is convenient to drive it to integration with superior methodologies, as it would be those based on ANN, and produce a better diagnosis in future works. A total of 80 scientific articles were reviewed from 1997 to 2023, showing a high frequency in 2021 and experimental as the predominant methodology. From a total of 100 problems solved, 42% were using the experimental methodology, 34% descriptive, 17% comparative, and just 7% post facto. We detected 14 unsolved problems, from which 50% fall in the experimental area. At the same time, by study type, methodologies included correlation studies, processes, decision trees, plain queries, granularity, and labeling. It was observed that just one work focuses on mathematics, which reduces new knowledge production expectations. Additionally, just one work manifested ANN usage. Citation: Technologies PubDate: 2023-08-13 DOI: 10.3390/technologies11040112 Issue No:Vol. 11, No. 4 (2023)
Authors:Oleg Sergiyenko, Alexey Zhirabok, Paolo Mercorelli, Alexander Zuev, Vladimir Filaretov, Vera Tyrsa First page: 72 Abstract: The suggested methods for solving fault diagnosis and estimation problems are based on the use of the Jordan canonical form. The diagnostic observer, virtual sensor, interval, and sliding mode observer design problems are considered. Algorithms have been developed to solve these problems for both linear and nonlinear systems, considering the presence of external disturbances and measurement noise. It has been shown that the Jordan canonical form allows reducing the dimensions of interval observers and virtual sensors, thus simplifying the design process in comparison to the identification canonical form. The theoretical results are illustrated through examples. Citation: Technologies PubDate: 2023-06-03 DOI: 10.3390/technologies11030072 Issue No:Vol. 11, No. 3 (2023)
Authors:Abdel Nasser Soumana Hamadou, Ciira wa Maina, Moussa Moindze Soidridine First page: 73 Abstract: With the development of the next generation of mobile networks, new research challenges have emerged, and new technologies have been proposed to address them. On the other hand, reconfigurable intelligent surface (RIS) technology is being investigated for partially controlling wireless channels. RIS is a promising technology for improving signal quality by controlling the scattering of electromagnetic waves in a nearly passive manner. Heterogeneous networks (HetNets) are another promising technology that is designed to meet the capacity requirements of the network. RIS technology can be used to improve system performance in the context of HetNets. This study investigates the applications of reconfigurable intelligent surfaces (RISs) in heterogeneous downlink networks (HetNets). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUEs). In this paper, we utilise RIS to mitigate cross-tier interference in a HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider RIS-assisted heterogeneous networks consisting of multiple SBS nodes and MUEs that utilise both direct paths and reflected paths. Therefore, the aim of this study is to maximise the sum rate of all MUEs by jointly optimising the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficient-based optimisation (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts. Citation: Technologies PubDate: 2023-06-09 DOI: 10.3390/technologies11030073 Issue No:Vol. 11, No. 3 (2023)
Authors:Reem Alotaibi, Felwa Abukhodair First page: 74 Abstract: Radiation dose tracking is becoming very important due to the popularity of computerized tomography (CT) scans. One of the challenges of radiation dose tracking is that there are several variables that affect the dose from the patient side, machine side, and procedures side. Although some tracking software programs exists, they are based on static analysis and cause integration errors due to the heterogeneity of Hospital Information Systems (HISs) and prevent users from obtaining accurate answers to their questions. In this paper, a visual analytic approach is utilized to track radiation dose data from computed tomography (CT) through the use of Tableau data visualization software. The web solution is evaluated in real-life scenarios by domain experts. The results show that the visual analytics approach improves the tracking process, as users completed the tasks with a 100% success rate. The process increased user satisfaction and also provided invaluable insight into the analytical process. Citation: Technologies PubDate: 2023-06-10 DOI: 10.3390/technologies11030074 Issue No:Vol. 11, No. 3 (2023)
Authors:James Gerrans, Parastou Donyai, Katherine Finlay, R. Simon Sherratt First page: 75 Abstract: Medicine waste is a global issue, with economic, environmental, and social consequences that are only predicted to worsen. A structured review of the literature on medicine reuse revealed that there is a lack of technological applications addressing the key concerns raised by pharmaceutical stakeholders on the safety and feasibility of redispensing medication. A basis and guidelines for solutions aiming at enabling medicine reuse were devised by exploring a conceptual model of a Circular Pharmaceutical Supply Chain (CPSC), discussing concerns raised within the literature and identifying methods to influence the public and pharmaceutical companies. SPaRAS, a novel system to validate the storage conditions and streamline the assessment of returned medicines, is proposed. The Smart Packaging System (SPS) will record the storage conditions of medication while in patient care. The companion Returns Assessment System (RAS) will efficiently communicate with the SPS through RFID, configure the sensors within the SPS to the needs of its assigned medicine and assess the returns against tailored eligibility criteria. The increased safety and efficiency provided by SPaRAS addresses the concerns of large pharmaceutical companies and the public, offering a method to reuse previously owned medication and reduce the effects of unnecessary medicine waste. Citation: Technologies PubDate: 2023-06-10 DOI: 10.3390/technologies11030075 Issue No:Vol. 11, No. 3 (2023)
Authors:Zia Muhammad, Zahid Anwar, Abdul Rehman Javed, Bilal Saleem, Sidra Abbas, Thippa Reddy Gadekallu First page: 76 Abstract: There is an exponential rise in the use of smartphones in government and private institutions due to business dependencies such as communication, virtual meetings, and access to global information. These smartphones are an attractive target for cybercriminals and are one of the leading causes of cyber espionage and sabotage. A large number of sophisticated malware attacks as well as advanced persistent threats (APTs) have been launched on smartphone users. These attacks are becoming significantly more complex, sophisticated, persistent, and undetected for extended periods. Traditionally, devices are targeted by exploiting a vulnerability in the operating system (OS) or device sensors. Nevertheless, there is a rise in APTs, side-channel attacks, sensor-based attacks, and attacks launched through the Google Play Store. Previous research contributions have lacked contemporary threats, and some have proven ineffective against the latest variants of the mobile operating system. In this paper, we conducted an extensive survey of papers over the last 15 years (2009–2023), covering vulnerabilities, contemporary threats, and corresponding defenses. The research highlights APTs, classifies malware variants, defines how sensors are exploited, visualizes multiple ways that side-channel attacks are launched, and provides a comprehensive list of malware families that spread through the Google Play Store. In addition, the research provides details on threat defense solutions, such as malware detection tools and techniques presented in the last decade. Finally, it highlights open issues and identifies the research gap that needs to be addressed to meet the challenges of next-generation smartphones. Citation: Technologies PubDate: 2023-06-12 DOI: 10.3390/technologies11030076 Issue No:Vol. 11, No. 3 (2023)
Authors:Anastasia Yu. Teterina, Vladislav V. Minaychev, Polina V. Smirnova, Margarita I. Kobiakova, Igor V. Smirnov, Roman S. Fadeev, Alexey A. Egorov, Artem A. Ashmarin, Kira V. Pyatina, Anatoliy S. Senotov, Irina S. Fadeeva, Vladimir S. Komlev First page: 77 Abstract: The injectable hydrated calcium phosphate bone-like paste (hCPP) was developed with suitable rheological characteristics, enabling unhindered injection through standard 23G needles. In vitro assays showed the cytocompatibility of hCPP with mesenchymal embryonic C3H10T1/2 cell cultures. The hCPP was composed of aggregated micro-sized particles with sphere-like shapes and low crystallinity. The ability of hCPP particles to adsorb serum proteins (FBS) was investigated. The hCPP demonstrated high protein adsorption capacity, indicating its potential in various biomedical applications. The results of the in vivo assay upon subcutaneous injection in Wistar rats indicated nontoxicity and biocompatibility of experimental hCPP, as well as gradual resorption of hCPP, comparable to the period of bone regeneration. The data obtained are of great interest for the development of commercial highly effective osteoplastic materials for bone tissue regeneration and augmentation. Citation: Technologies PubDate: 2023-06-15 DOI: 10.3390/technologies11030077 Issue No:Vol. 11, No. 3 (2023)
Authors:Jason Daza, Wael Ben Mbarek, Lluisa Escoda, Joan Saurina, Joan-Josep Suñol First page: 78 Abstract: Fe-rich soft magnetic alloys are candidates for applications as magnetic sensors and actuators. Spring magnets can be obtained when these alloys are added to hard magnetic compounds. In this work, two nanocrystalline Fe-Zr-B-Cu alloys are produced by mechanical alloying, MA. The increase in boron content favours the reduction of the crystalline size. Thermal analysis (by differential scanning calorimetry) shows that, in the temperature range compressed between 450 and 650 K, wide exothermic processes take place, which are associated with the relaxation of the tensions of the alloys produced by MA. At high temperatures, a main crystallisation peak is found. A Kissinger and an isoconversional method were used to determine the apparent activation of the exothermic processes. The values are compared with those found in the scientific literature. Likewise, adapted thermogravimetry allowed for the determination of the Curie temperature. The functional response has been analysed by hysteresis loop cycles. According to the composition, the decrease of the Fe/B ratio diminishes the soft magnetic behaviour. Citation: Technologies PubDate: 2023-06-16 DOI: 10.3390/technologies11030078 Issue No:Vol. 11, No. 3 (2023)
Authors:Clinton Gardner, James W. Navalta, Bryson Carrier, Charli Aguilar, Jorge Perdomo Rodriguez First page: 79 Abstract: Methods: Training impulse (TRIMP) is obtained through wearable technology and plays a direct role on the load management of soccer players. It is important to understand TRIMP to best prepare athletes for competition. A systematic search for articles was conducted using Google Scholar, with papers screened and extracted by five reviewers. The inclusion criteria were: the study was focused on collegiate or professional soccer, the use of training impulse (TRIMP), and the use of wearable technology to measure TRIMP. Of 10,100 papers, 10,090 articles were excluded through the systematic review process. Ten papers were selected for final review and grouped based on (1) training vs. match (N = 8/10), (2) preseason vs. in-season (N = 3/10), and (3) positional comparison (N = 3/10). Wearable technologies mainly track physical metrics (N = 10/10). Higher TRIMP data were noted in starters than reserves throughout the season in matches and slightly lower TRIMP for starters vs. reserves during training. TRIMP data change throughout the season, being higher in preseason phases compared to early-season, mid-season, and late-season phases. These findings help highlight the benefits of TRIMP in managing internal player load in soccer. Future research should focus on utilizing wearable-derived TRIMP and the impact on player performance metrics, and how TRIMP data vary across different positions in soccer. Citation: Technologies PubDate: 2023-06-17 DOI: 10.3390/technologies11030079 Issue No:Vol. 11, No. 3 (2023)
Authors:Padmanabhan Balasubramanian First page: 80 Abstract: Electronic circuits/systems operating in harsh environments such as space are likely to experience faults or failures due to the impact of high-energy radiation. Given this, to overcome any faults or failures, redundancy is usually employed as a hardening-by-design approach. Moreover, low power and a small silicon footprint are also important considerations for space electronics since these translate into better energy efficiency, less system weight, and less cost. Therefore, the fault-tolerant design of electronic circuits and systems should go hand in hand with the optimization of design metrics, especially for resource-constrained electronics such as those used in space systems. A single circuit or system (also called a simplex implementation) is not fault-tolerant as it may become a single point of failure and is not used for a space application. As an alternative, a triple modular redundancy (TMR) implementation, which uses three identical copies of a circuit or system and a voter to perform majority voting of the circuits and systems outputs, may be used. However, in comparison with a simplex implementation, a TMR implementation consumes about 200% more area and dissipates 200% more power when circuits or systems are triplicated. To mitigate the area and power overheads of a TMR implementation compared to a simplex implementation, researchers have suggested alternative redundancy approaches such as selective TMR (STMR) insertion, partially approximate TMR (PATMR), fully approximate TMR (FATMR), and majority voting-based reduced precision redundancy (VRPR). Among these, VRPR appears to be promising, especially for inherently error-tolerant applications such as digital image/video/audio processing, which is relevant to space systems. However, the alternative redundancy approaches mentioned are unlikely to be suitable for the implementation of control logic. In this work, we analyze various redundancy approaches and evaluate the performance of TMR and VRPR for a digital image processing application. We provide MATLAB-based image processing results corresponding to TMR and VRPR and physical implementation results of functional units based on TMR and VRPR using a 28-nm CMOS technology. Citation: Technologies PubDate: 2023-06-19 DOI: 10.3390/technologies11030080 Issue No:Vol. 11, No. 3 (2023)
Authors:Sonam Dorji, Albert Alexander Stonier, Geno Peter, Ramya Kuppusamy, Yuvaraja Teekaraman First page: 81 Abstract: Given the various aspects of climate change and the growing demand for energy, energy efficiency and environmental protection have become major concerns worldwide. If not taken care of, energy demand will become unmanageable due to technological growth in cities and nations. The solution to the global energy crisis could be an advanced two-way digital power flow system that is capable of self-healing, interoperability, and predicting conditions under various uncertainties and is equipped with cyber protections against malicious attacks. The smart grid enables the integration of renewable energy sources such as solar, wind, and energy storage into the grid. Therefore, the perception of the smart grid and the weight given to it by researchers and policymakers are of utmost importance. In this paper, the studies of many researchers on smart grids are examined in detail. Based on the literature review, various principles of smart grids, the development of smart grids, functionality of smart grids, technologies of smart grids with their characteristics, communication of smart grids, problems in the implementation of smart grids, and possible future studies proposed by various researchers have been presented. Citation: Technologies PubDate: 2023-06-19 DOI: 10.3390/technologies11030081 Issue No:Vol. 11, No. 3 (2023)
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