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- Technologies, Vol. 13, Pages 88: Comprehensive Analysis of Random Forest
and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying
Imbalance Levels
Authors: Mehdi Imani, Ali Beikmohammadi, Hamid Reza Arabnia
First page: 88
Abstract: This study examines the efficacy of Random Forest and XGBoost classifiers in conjunction with three upsampling techniques—SMOTE, ADASYN, and Gaussian noise upsampling (GNUS)—across datasets with varying class imbalance levels, ranging from moderate to extreme (15% to 1% churn rate). Employing metrics such as F1 score, ROC AUC, PR AUC, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa, this research provides a comprehensive evaluation of classifier performance under different imbalance scenarios, focusing on applications in the telecommunications domain. The findings highlight that tuned XGBoost paired with SMOTE (Tuned_XGB_SMOTE) consistently achieves the highest F1 score and robust performance across all imbalance levels. SMOTE emerged as the most effective upsampling method, particularly when used with XGBoost, whereas Random Forest performed poorly under severe imbalance. ADASYN showed moderate effectiveness with XGBoost but underperformed with Random Forest, and GNUS produced inconsistent results. This study underscores the impact of data imbalance, with MCC, Kappa, and F1 scores fluctuating significantly, whereas ROC AUC and PR AUC remained relatively stable. Moreover, rigorous statistical analyses employing the Friedman test and Nemenyi post hoc comparisons confirmed that the observed improvements in F1 score, PR-AUC, Kappa, and MCC were statistically significant (p < 0.05), with Tuned_XGB_SMOTE significantly outperforming Tuned_RF_GNUS. While differences in ROC-AUC were not significant, the consistency of these results across multiple performance metrics underscores the reliability of our framework, offering a statistically validated and attractive solution for model selection in imbalanced classification scenarios.
Citation: Technologies
PubDate: 2025-02-20
DOI: 10.3390/technologies13030088
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 89: Effect of Pore Characteristics of
Biomass-Derived Activated Carbon for Automobile Canisters via Chemical
Stabilization Method on Butane Adsorption Characteristics
Authors: Dong-Sin Jo, Ju-Hwan Kim, Byung-Joo Kim, Hye-Min Lee
First page: 89
Abstract: In this study, kenaf-derived activated carbons (AK-AC) was prepared for automobile canisters via chemical stabilization and physical activation methods. The thermogravimetric analysis and differential thermogravimetry revealed a crystallite change in the kenaf with chemical stabilization. The AK-AC texture properties were studied using the Brunauer–Emmett–Teller, Dubinin–Radushkevitch, and non-local density functional theory equations, with N2/77K isotherm adsorption–desorption curves. The AK-AC nanocrystallite characteristics were observed through X-ray diffraction and Raman spectroscopy. The AK-AC butane adsorption characteristics were analyzed via breakthrough curves and compared with those of commercial coconut-derived activated carbon (Coconut AC). As the activation time increased, the specific surface area and mesopore volume ratio of the AK-AC increased to 1080–1940 m2/g and 10.6–50.0%, respectively. The AK-AC also exhibited better mesoporous pore characteristics than the Coconut AC. The AK-AC butane adsorption capacity increased from 0.31 to 0.79 g/g. In particular, the AK-AC had an approximately 50% improved butane adsorption capacity compared to the Coconut AC. In addition, the butane adsorption characteristics of the AK-AC were determined using the mesopore volume, with a diameter of 3.0–4.0 nm. The results suggest that AK-AC may be proposed as an adsorbent to improve evaporative emissions from automotive canisters in the future.
Citation: Technologies
PubDate: 2025-02-21
DOI: 10.3390/technologies13030089
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 90: Electrospinning of
Chitosan–Halloysite Nanotube Biohybrid Mats for Clobetasol
Propionate Delivery
Authors: Natallia V. Dubashynskaya, Valentina A. Petrova, Igor V. Kudryavtsev, Andrey S. Trulioff, Artem A. Rubinstein, Alexey S. Golovkin, Alexander I. Mishanin, Anton A. Murav’ev, Iosif V. Gofman, Daria N. Poshina, Yury A. Skorik
First page: 90
Abstract: The application of electrospinning technologies for the preparation of mats based on mucoadhesive polysaccharides, such as chitosan (CS), is an attractive strategy for the development of biopolymeric delivery systems for topical corticosteroids. In this work, an electrospinning technique is described for the preparation of CS-based mats doped with halloysite nanotubes (HNTs) with modified release of clobetasol propionate (CP). The optimized composition of the electrospinning solution was determined: 2.4% solution of CS in 46% acetic acid with addition of PEO (10% of CS mass) and HNTs (5% of CS mass); CP was introduced as an ethanol solution at the rate of 2 mg CP per 1 g of the obtained nonwoven material. The process parameters (the electrospinning voltage of 50–65 kV, the rotation speed of the spinning electrode of 10 min−1, and the distance between the electrodes of 24 cm) were also optimized. The developed technology allowed us to obtain homogeneous nanofiber mats with excellent mechanical properties and biphasic drug release patterns (66% of CP released within 0.5 h and 88% of CP released within 6 h). The obtained nanofiber mats maintained the anti-inflammatory activity of corticosteroid at the level of free CP and showed no cytotoxicity.
Citation: Technologies
PubDate: 2025-02-21
DOI: 10.3390/technologies13030090
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 91: Field Data Retrieval for Electric
Vehicles and Estimating Equivalent Circuit Model Parameters via Particle
Swarm Optimization
Authors: Syed Adil Sardar, Shahzad Iqbal, Jeongju Park, Sekyung Han, Woo Young Kim
First page: 91
Abstract: Data retrieval techniques are crucial for developing an effective battery management system for an electric vehicle to accurately assess the battery’s health and performance by monitoring operating conditions such as voltage, current, time, temperature, and state of charge. This paper proposes an efficient approach to retrieve real-world field data (voltage, current, and time) under running vehicle conditions. In the first step, noise is removed from the field data using a moving-average filter. Then, first- and second-order derivations are applied to the filtered data to determine specific data set conditions. After that, a new approach based on zero-crossing is implemented to retrieve the field data. A second-order Randle circuit (SORC) is utilized in this study to analyze the selected field data. Further, a particle swarm optimization algorithm is adapted to estimate the parameters of the SORC. Our experiments indicate that the relative errors of the equivalent circuit model (ECM) are less than 2% compared to the model voltage and real voltage, which is consistent with the stable parameters of ECM.
Citation: Technologies
PubDate: 2025-03-01
DOI: 10.3390/technologies13030091
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 92: Design of Multi-Sourced MIMO Multiband
Hybrid Wireless RF-Perovskite Photovoltaic Energy Harvesting Subsystems
for Iots Applications in Smart Cities
Authors: Fanuel Elias, Sunday Ekpo, Stephen Alabi, Mfonobong Uko, Sunday Enahoro, Muhammad Ijaz, Helen Ji, Rahul Unnikrishnan, Nurudeen Olasunkanmi
First page: 92
Abstract: Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband hybrid wireless RF-perovskite photovoltaic energy harvesting subsystems for IoT application. The research findings evaluate the efficiency and power output of different RF configurations (1 to 16 antennas) within MIMO RF subsystems. A Delon quadruple rectifier in the RF energy harvesting system demonstrates a system-level power conversion efficiency of 51%. The research also explores the I-V and P-V characteristics of the adopted perovskite tandem cell. This results in an impressive array capable of producing 6.4 V and generating a maximum power of 650 mW. For the first time, the combined mathematical modelling of the system architecture is presented. The achieved efficiency of the combined system is 90% (for 8 MIMO) and 98% (for 16 MIMO) at 0 dBm input RF power. This novel study holds great promise for next-generation 5G/6G smart IoT passive electronics. Additionally, it establishes the hybrid RF-perovskite energy harvester as a promising, compact, and eco-friendly solution for efficiently powering IoT devices in smart cities. This work contributes to the development of sustainable, scalable, and smart energy solutions for IoT integration into smart city infrastructures.
Citation: Technologies
PubDate: 2025-03-01
DOI: 10.3390/technologies13030092
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 93: A Systematic Literature Review of the
Latest Advancements in XAI
Authors: Zaid M. Altukhi, Sojen Pradhan, Nasser Aljohani
First page: 93
Abstract: This systematic review details recent advancements in the field of Explainable Artificial Intelligence (XAI) from 2014 to 2024. XAI utilises a wide range of frameworks, techniques, and methods used to interpret machine learning (ML) black-box models. We aim to understand the technical advancements in the field and future directions. We followed the PRISMA methodology and selected 30 relevant publications from three main databases: IEEE Xplore, ACM, and ScienceDirect. Through comprehensive thematic analysis, we categorised the research into three main topics: ‘model developments’, ‘evaluation metrics and methods’, and ‘user-centred and XAI system design’. Our results uncover ‘What’, ‘How’, and ‘Why’ these advancements were developed. We found that 13 papers focused on model developments, 8 studies focused on the XAI evaluation metrics, and 12 papers focused on user-centred and XAI system design. Moreover, it was found that these advancements aimed to bridge the gap between technical model outputs and user understanding.
Citation: Technologies
PubDate: 2025-03-01
DOI: 10.3390/technologies13030093
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 94: A Comprehensive Review of Quality Control
and Reliability Research in Micro–Nano Technology
Authors: Nowshin Sharmile, Risat Rimi Chowdhury, Salil Desai
First page: 94
Abstract: This paper presents a comprehensive review of quality control (QC) and reliability research in micro–nano technology, which is vital for advancing microelectronics, biomedical engineering, and manufacturing. Micro- and nanotechnologies operate at different scales, yet both require precise control to ensure the performance and durability of small-scale systems. This review synthesizes key quality control methodologies, including statistical quality control methods, machine learning and AI-driven methods, and advanced techniques emphasizing their relevance to nanotechnology applications. The paper also discusses the application of micro/nanotechnology in quality control in other technological areas. The discussion extends to the unique reliability challenges posed by micro–nano systems, such as failure modes related to stiction, material fatigue, and environmental factors. Advanced reliability testing and modeling approaches are highlighted for their effectiveness in predicting performance and mitigating risks. Additionally, the paper explores the integration of emerging technologies to enhance and improve reliability in micro–nano manufacturing. By examining both established and novel techniques, this review underscores the evolving nature of quality control and reliability research in the field. It identifies key areas for future investigation, particularly in the adaptation of these methods to the increasing complexity of micro–nano systems. The paper concludes by proposing research directions that can further optimize quality control and reliability to ensure the continued advancement and industrial application of micro–nano technologies.
Citation: Technologies
PubDate: 2025-03-01
DOI: 10.3390/technologies13030094
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 95: The Transition to an Eco-Friendly City as
a First Step Toward Climate Neutrality with Green Hydrogen
Authors: Lăzăroiu Gheorghe, Mihăescu Lucian, Stoica Dorel, Năstasă (Băcăran) Florentina-Cătălina
First page: 95
Abstract: A city of the future will need to be eco-friendly while meeting general social and economic requirements. Hydrogen-based technologies provide solutions for initially limiting CO2 emissions, with prospects indicating complete decarbonization in the future. Cities will need to adopt and integrate these technologies to avoid a gap between the development of hydrogen production and its urban application. Achievable results are analyzed by injecting hydrogen into the urban methane gas network, initially in small proportions, but gradually increasing over time. This paper also presents a numerical application pertaining to the city of Bucharest, Romania—a metropolis with a population of 2.1 million inhabitants. Although the use of fuel cells is less advantageous for urban transport compared to electric battery-based solutions, the heat generated by hydrogen-based technologies, such as fuel cells, can be efficiently utilized for residential heating. However, storage solutions are required for residential consumption, separate from that of urban transport, along with advancements in electric transport using existing batteries, which necessitate a detailed economic assessment. For electricity generation, including cogeneration, gas turbines have proven to be the most suitable solution. Based on the analyzed data, the paper synthesizes the opportunities offered by hydrogen-based technologies for a city of the future.
Citation: Technologies
PubDate: 2025-03-01
DOI: 10.3390/technologies13030095
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 96: Training Cognitive Functions Using
DUAL-REHAB, a New Dual-Task Application in MCI and SMC: A Study Protocol
of a Randomized Control Trial
Authors: Elisa Pedroli, Francesca Bruni, Valentina Mancuso, Silvia Cavedoni, Francesco Bigotto, Jonathan Panigada, Monica Rossi, Lorenzo Boilini, Karine Goulene, Marco Stramba-Badiale, Silvia Serino
First page: 96
Abstract: Background: Current research on Alzheimer’s Disease has progressively focused on Mild Cognitive Impairment (MCI) as a pre-dementia state, as well as on Subjective Memory Complaint (SMC), as a potential early indicator of cognitive change. Consequently, timely interventions to prevent cognitive decline are essential and are most effective when combined with motor training. Nevertheless, motor-cognitive dual-task training often employs non-ecological tasks and is confined to clinical contexts lacking generalizability to daily life. The integration of 360° media could overcome these limitations. Therefore, the aim of the current work is twofold: (a) to present a dual-task training using 360° technology for its interactivity, versatility, and ecological validity, and (b) to propose a protocol to test its efficacy through a randomized clinical trial. Methods: This study will recruit 90 older adults (MCI and SMC). Participants will follow two phases of training: in-hospital rehabilitation and at-home rehabilitation. The experimental design will follow a 2 × 3 × 2 structure with 3 factors: type of treatment (360° training vs. traditional rehabilitation), time (baseline, post in-hospital training, and post at-home training), and group (SMC vs. MCI). Results: The expected outcome is an improvement in cognitive and motor functioning after the experimental training. Conclusion: This study will advance the literature on non-pharmacological interventions and innovative technological tools for cognitive trainings in the early stages of cognitive decline.
Citation: Technologies
PubDate: 2025-03-01
DOI: 10.3390/technologies13030096
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 97: Scientific Molding and Adaptive Process
Quality Control with External Sensors for Injection Molding Process
Authors: Chen-Hsiang Chang, Chien-Hung Wen, Ren-Ho Tseng, Chieh-Hsun Tsai, Yu-Hao Chen, Sheng-Jye Hwang, Hsin-Shu Peng
First page: 97
Abstract: This study established a real-time measurement system to monitor the melt quality in an injection molding process using a pressure sensor installed on the nozzle and a strain gauge installed on the tie bar. Based on the sensing curves from these two external sensors, the characteristic values of nozzle pressure and clamping force were used to optimize parameters. This study defined product weight as a quality indicator and developed a scientific molding parameter setup process. The optimization sequence of parameters is injection speed, V/P switchover point, packing pressure, packing time, and clamping force. Finally, an adaptive process control system was established based on the online quality characteristic values to maintain product quality consistency. Continuous production experiments were conducted at two sites to verify the system’s effectiveness. The results revealed that the optimized process parameters can ensure product weight stability during long-term production. Furthermore, using the adaptive process control system further enhanced product weight stability at both sites, reducing the standard deviation of product weight to 0.0289 g and 0.0148 g, and the coefficient of variation to 0.065% and 0.035%, respectively.
Citation: Technologies
PubDate: 2025-03-01
DOI: 10.3390/technologies13030097
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 98: “The Foot Can Do It”:
Controlling the “Persistence” Prosthetic Arm Using the
“Infinity-2” Foot Controller
Authors: Peter L. Bishay, Gerbert Funes Alfaro, Ian Sherrill, Isaiah Reoyo, Elihu McMahon, Camron Carter, Cristian Valdez, Naweeth M. Riyaz, Sara Ali, Adrian Lima, Abel Nieto, Jared Tirone
First page: 98
Abstract: The “Infinity” foot controller for controlling prosthetic arms has been improved in this paper in several ways, including a foot sleeve that enables barefoot use, an improved sensor-controller unit design, and a more intuitive control scheme that allows gradual control of finger actuation. Furthermore, the “Persistence Arm”, a novel transradial prosthetic arm prototype, is introduced. This below-the-elbow arm has a direct-drive wrist actuation system, a thumb design with two degrees of freedom, and carbon fiber tendons for actuating the four forefingers. The manufactured prototype arm and foot controller underwent various tests to verify their efficacy. Wireless transmission speed tests showed that the maximum time delay is less than 165 ms, giving almost instantaneous response from the arm to any user’s foot control signal. Gripping tests quantified the grip and pulling forces of the arm prototype as 2.8 and 12.7 kg, respectively. The arm successfully gripped various household items of different shapes, weights, and sizes. These results highlight the potential of foot control as an alternative prosthetic arm control method and the possibility of new 3D-printed prosthetic arm designs to replace costly prostheses in the market, which could potentially reduce the high rejection rates of upper limb prostheses.
Citation: Technologies
PubDate: 2025-03-01
DOI: 10.3390/technologies13030098
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 99: Hand Dexterity Evaluation Grounded on
Cursor Trajectory Investigation in Children with ADHD Using a Mouse and a
Joystick
Authors: Alexandros Pino, Nikolaos Papatheodorou, Georgios Kouroupetroglou, Panagiotis-Alexios Giannopoulos, Gerasimos Makris, Charalambos Papageorgiou
First page: 99
Abstract: This study investigates disparities in upper limb motor skills between children with and without Attention Deficit Hyperactivity Disorder (ADHD), employing one-dimensional (1D) and two-dimensional (2D) point-and-click experiments using a mouse and a joystick and introducing one new metric for mouse cursor trajectory analysis. The participant pool comprised 46 children with combined type ADHD and an equivalent number of children without ADHD. The Input Device Evaluation Application (IDEA) system monitored the mouse pointer’s trajectory. Ten trajectory parameters were computed, including Index of Difficulty, Movement Time, Throughput, Missed Clicks, Target Re-Entry, Task Axis Crossing, Movement Direction Change, Movement Variability, Movement Error, Movement Offset, and Sample Entropy. The 2D joystick experiment trajectory parameters analysis conducted using a hierarchical logistic regression model achieved a 78% success rate in identifying children with ADHD. This research sheds light on the motor skill differences associated with ADHD in the context of computer-based tasks, providing valuable insights into potential diagnostic applications and intervention strategies and introducing one new metric makes for a deeper cursor trajectory analysis.
Citation: Technologies
PubDate: 2025-03-03
DOI: 10.3390/technologies13030099
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 100: Low-Temperature Slow Pyrolysis:
Exploring Biomass-Specific Biochar Characteristics and Potential for Soil
Applications
Authors: Matheus Antonio da Silva, Adibe Luiz Abdalla Filho, Ruan Carnier, Juliana de Oliveira Santos Marcatto, Marcelo Saldanha, Aline Renee Coscione, Thaís Alves de Carvalho, Gabriel Rodrigo Merlotto, Cristiano Alberto de Andrade
First page: 100
Abstract: The pyrolysis process of residues has emerged as a sustainable method for managing organic waste, producing biochars that offer significant benefits for agriculture and the environment. These benefits depend on the properties of the raw biomass and the pyrolysis conditions, such as washing and drying. This study investigated biochar production through slow pyrolysis at 300 °C, using eight biomass types, four being plant residues (PBR)—sugarcane bagasse, filter cake, sawdust, and stranded algae—and four non-plant-based residues (NPBR)—poultry litter, sheep manure, layer chicken manure, and sewage sludge. The physicochemical properties assessed included yield, carbon (C) and nitrogen (N) content, electrical conductivity, pH, macro- and micronutrients, and potentially toxic metals. Pyrolysis generally increased pH and concentrated C, N, phosphorus (P), and other nutrients while reducing electrical conductivity, C/N ratio, potassium (K), and sulfur (S) contents. The increases in the pH of the biochars in relation to the respective biomasses were between 0.3 and 1.9, with the greatest differences observed for the NPBR biochars. Biochars from sugarcane bagasse and sawdust exhibited high C content (74.57–77.67%), highlighting their potential use for C sequestration. Filter cake biochar excelled in P (14.28 g kg⁻1) and micronutrients, while algae biochar showed elevated N, calcium (Ca), and boron (B) levels. NPBR biochars were rich in N (2.28–3.67%) and P (20.7–43.4 g kg⁻1), making them ideal fertilizers. Although sewage sludge biochar contained higher levels of potentially toxic metals, these remained within regulatory limits. This research highlights variations in the composition of biochars depending on the characteristics of the original biomass and the pyrolysis process, to contribute to the production of customized biochars for the purposes of their application in the soil. Biochars derived from exclusively plant biomasses showed important aspects related to the recovery of carbon from biomass and can be preferred as biochar used to sequester carbon in the soil. On the other hand, biochars obtained from residues with some animal contributions are more enriched in nutrients and should be directed to the management of soil fertility.
Citation: Technologies
PubDate: 2025-03-03
DOI: 10.3390/technologies13030100
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 101: Technology for Improving the Accuracy of
Predicting the Position and Speed of Human Movement Based on Machine
Learning Models
Authors: Artem Obukhov, Denis Dedov, Andrey Volkov, Maksim Rybachok
First page: 101
Abstract: The solution to the problem of insufficient accuracy in determining the position and speed of human movement during interaction with a treadmill-based training complex is considered. Control command generation based on the training complex user’s actions may be performed with a delay, may not take into account the specificity of movements, or be inaccurate due to the error of the initial data. The article introduces a technology for improving the accuracy of predicting a person’s position and speed on a running platform using machine learning and computer vision methods. The proposed technology includes analysing and processing data from the tracking system, developing machine learning models to improve the quality of the raw data, predicting the position and speed of human movement, and implementing and integrating neural network methods into the running platform control system. Experimental results demonstrate that the decision tree (DT) model provides better accuracy and performance in solving the problem of positioning key points of a human model in complex conditions with overlapping limbs. For speed prediction, the linear regression (LR) model showed the best results when the analysed window length was 10 frames. Prediction of the person’s position (based on 10 previous frames) is performed using the DT model, which is optimal in terms of accuracy and computation time relative to other options. The comparison of the control methods of the running platform based on machine learning models showed the advantage of the combined method (linear control function combined with the speed prediction model), which provides an average absolute error value of 0.116 m/s. The results of the research confirmed the achievement of the primary objective (increasing the accuracy of human position and speed prediction), making the proposed technology promising for application in human-machine systems.
Citation: Technologies
PubDate: 2025-03-03
DOI: 10.3390/technologies13030101
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 102: Robust Intrusion Detection System Using
an Improved Hybrid Deep Learning Model for Binary and Multi-Class
Classification in IoT Networks
Authors: Hesham Kamal, Maggie Mashaly
First page: 102
Abstract: The rapid expansion of internet of things (IoT) applications has significantly boosted productivity and streamlined daily activities. However, this widespread adoption has also introduced considerable security challenges, making IoT environments vulnerable to large-scale botnet attacks. These attacks have often succeeded in achieving their malicious goals, highlighting the urgent need for robust detection strategies to secure IoT networks. To overcome these obstacles, this research presents an innovative anomaly-driven intrusion detection approach specifically tailored for IoT networks. The proposed model employs an advanced hybrid architecture that seamlessly integrates convolutional neural networks (CNN) with multilayer perceptron (MLP), enabling precise detection and classification of both binary and multi-class IoT network traffic. The CNN component is responsible for extracting and enhancing features from network traffic data and preparing these features for effective classification by the MLP, which handles the final classification task. To further manage class imbalance, the model incorporates the enhanced hybrid adaptive synthetic sampling-synthetic minority oversampling technique (ADASYN-SMOTE) for binary classification, advanced ADASYN for multiclass classification, and employs edited nearest neighbors (ENN) alongside class weights. The CNN-MLP architecture is meticulously crafted to minimize erroneous classifications, enhance instantaneous threat detection, and precisely recognize previously unseen cyber intrusions. The model’s effectiveness was rigorously tested using the IoT-23 and NF-BoT-IoT-v2 datasets. On the IoT-23 dataset, the model achieved 99.94% accuracy in two-stage binary classification, 99.99% accuracy in multiclass classification excluding the normal class, and 99.91% accuracy in single-phase multiclass classification including the normal class. Utilizing the NF-BoT-IoT-v2 dataset, the model attained an exceptional 99.96% accuracy in the dual-phase binary classification paradigm, 98.02% accuracy in multiclass classification excluding the normal class, and 98.11% accuracy in single-phase multiclass classification including the normal class. The results demonstrate that our model consistently delivers high levels of accuracy, precision, recall, and F1 score across both binary and multiclass classifications, establishing it as a robust solution for securing IoT networks.
Citation: Technologies
PubDate: 2025-03-04
DOI: 10.3390/technologies13030102
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 103: Recent Development of Corrosion
Inhibitors: Types, Mechanisms, Electrochemical Behavior, Efficiency, and
Environmental Impact
Authors: Denisa-Ioana (Gheorghe) Răuță, Ecaterina Matei, Sorin-Marius Avramescu
First page: 103
Abstract: This review examines recent advances in corrosion inhibitor technologies, with a focus on sustainable and environmentally friendly solutions that address both industrial efficiency and environmental safety. Corrosion is a ubiquitous problem, contributing to massive economic losses globally, with costs estimated between 1 and 5% of GDP in different countries. Traditional inorganic corrosion inhibitors, while effective, are often based on toxic compounds, necessitating the development of more environmentally friendly and non-toxic alternatives. The present work highlights innovative eco-friendly corrosion inhibitors derived from natural sources, including plant extracts and oils, biopolymers, etc., being biodegradable substances that provide effective corrosion resistance with minimal environmental impact. In addition, this review explores organic–inorganic hybrid inhibitors and nanotechnology-enhanced coatings that demonstrate improved efficiency, durability, and adaptability across industries. Key considerations, such as application techniques, mechanisms of action, and the impact of environmental factors on inhibitor performance, are discussed. This comprehensive presentation aims to contribute to updating the data on the development of advanced corrosion inhibitors capable of meeting the requirements of modern industries while promoting sustainable and safe practices in corrosion management.
Citation: Technologies
PubDate: 2025-03-05
DOI: 10.3390/technologies13030103
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 104: A Hybrid Solar–Thermoelectric
Authors: Mahmoud Z. Mistarihi, Ghazi M. Magableh, Saba M. Abu Dalu
First page: 104
Abstract: Green sustainable energy, especially renewable energy, is gaining huge popularity and is considered a vital energy in addressing energy conservation and global climate change. One of the most significant renewable energy sources in the UAE is solar energy, due to the country’s high solar radiation levels. This paper focuses on advanced technology that integrates parabolic trough mirrors, molten salt storage, and thermoelectric generators (TEGs) to provide a reliable and effective solar system in the UAE. Furthermore, the new system can be manufactured in different sizes suitable for consumption whether in ordinary houses or commercial establishments and businesses. The proposed design theoretically achieves the target electrical energy of 2.067 kWh/day with 90% thermal efficiency, 90.2% optical efficiency, and 8% TEG efficiency that can be elevated to higher values reaching 149% using the liquid-saturated porous medium, ensuring the operation of the system throughout the day. This makes it a suitable solar system in off-grid areas. Moreover, this system is a cost-effective, carbon-free, and day-and-night energy source that can be dispatched on the electric grid like any fossil fuel plant under the proposed method, with less maintenance, thus contributing to the UAE’s renewable energy strategy.
Citation: Technologies
PubDate: 2025-03-05
DOI: 10.3390/technologies13030104
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 105: Scalable Container-Based Time
Synchronization for Smart Grid Data Center Networks
Authors: Kennedy Chinedu Okafor, Wisdom Onyema Okafor, Omowunmi Mary Longe, Ikechukwu Ignatius Ayogu, Kelvin Anoh, Bamidele Adebisi
First page: 105
Abstract: The integration of edge-to-cloud infrastructures in smart grid (SG) data center networks requires scalable, efficient, and secure architecture. Traditional server-based SG data center architectures face high computational loads and delays. To address this problem, a lightweight data center network (DCN) with low-cost, and fast-converging optimization is required. This paper introduces a container-based time synchronization model (CTSM) within a spine–leaf virtual private cloud (SL-VPC), deployed via AWS CloudFormation stack as a practical use case. The CTSM optimizes resource utilization, security, and traffic management while reducing computational overhead. The model was benchmarked against five DCN topologies—DCell, Mesh, Skywalk, Dahu, and Ficonn—using Mininet simulations and a software-defined CloudFormation stack on an Amazon EC2 HPC testbed under realistic SG traffic patterns. The results show that CTSM achieved near-100% reliability, with the highest received energy data (29.87%), lowest packetization delay (13.11%), and highest traffic availability (70.85%). Stateless container engines improved resource allocation, reducing administrative overhead and enhancing grid stability. Software-defined Network (SDN)-driven adaptive routing and load balancing further optimized performance under dynamic demand conditions. These findings position CTSM-SL-VPC as a secure, scalable, and efficient solution for next-generation smart grid automation.
Citation: Technologies
PubDate: 2025-03-05
DOI: 10.3390/technologies13030105
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 106: A Realistic Breast Phantom for
Investigating the Features of the Microwave Radiometry Method Using
Mathematical and Physical Modelling
Authors: Maxim V. Polyakov, Danila S. Sirotin
First page: 106
Abstract: This article presents the development of an anatomical breast phantom for investigating the capabilities of microwave radiometry in assessing thermal processes in biological tissues. The phantom accounts for the heterogeneous tissue structure and haemodynamics, enabling realistic heat transfer modelling. Numerical simulation software was developed, accurately reproducing experimental results and allowing the study of thermal anomalies. Experimental validation demonstrated that the temperature in the subcutaneous layer differed on average by 0.3 °C from deeper tissues, confirming the method’s effectiveness. The presence of a tumour in the model resulted in a local temperature increase of up to 0.77 °C, highlighting the sensitivity of microwave radiometry to tumour-induced thermal anomalies. These findings contribute to enhancing non-invasive techniques for early breast disease detection.
Citation: Technologies
PubDate: 2025-03-06
DOI: 10.3390/technologies13030106
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 107: Harnessing Metacognition for Safe and
Responsible AI
Authors: Peter B. Walker, Jonathan J. Haase, Melissa L. Mehalick, Christopher T. Steele, Dale W. Russell, Ian N. Davidson
First page: 107
Abstract: The rapid advancement of artificial intelligence (AI) technologies has transformed various sectors, significantly enhancing processes and augmenting human capabilities. However, these advancements have also introduced critical concerns related to the safety, ethics, and responsibility of AI systems. To address these challenges, the principles of the robustness, interpretability, controllability, and ethical alignment framework are essential. This paper explores the integration of metacognition—defined as “thinking about thinking”—into AI systems as a promising approach to meeting these requirements. Metacognition enables AI systems to monitor, control, and regulate the system’s cognitive processes, thereby enhancing their ability to self-assess, correct errors, and adapt to changing environments. By embedding metacognitive processes within AI, this paper proposes a framework that enhances the transparency, accountability, and adaptability of AI systems, fostering trust and mitigating risks associated with autonomous decision-making. Additionally, the paper examines the current state of AI safety and responsibility, discusses the applicability of metacognition to AI, and outlines a mathematical framework for incorporating metacognitive strategies into active learning processes. The findings aim to contribute to the development of safe, responsible, and ethically aligned AI systems.
Citation: Technologies
PubDate: 2025-03-06
DOI: 10.3390/technologies13030107
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 108: A Hardware-in-the-Loop Simulation
Authors: Linzi Yin, Cong Luo, Ling Liu, Junfeng Cui, Zhiming Liu, Guoying Sun
First page: 108
Abstract: In order to solve the testing and verification problems at the early development stage of a high-speed Maglev positioning and speed measurement system (MPSS), a hardware-in-the-loop (HIL) simulation platform is presented, which includes induction loops, transmitting antennas, a power driver unit, a simulator based on a field-programmable gate array (FPGA), a host computer, etc. This HIL simulation platform simulates the operation of a high-speed Maglev train and generates the related loop-induced signals to test the performance of a real ground signal processing unit (GSPU). Furthermore, an absolute position detection method based on Gray-coded loops is proposed to identify which Gray-coded period the train is in. A relative position detection method based on height compensation is also proposed to calculate the exact position of the train in a Gray-coded period. The experimental results show that the positioning error is only 2.58 mm, and the speed error is 6.34 km/h even in the 600 km/h condition. The proposed HIL platform also effectively simulates the three kinds of operation modes of high-speed Maglev trains, which verifies the effectiveness and practicality of the HIL simulation strategy. This provides favorable conditions for the development and early validation of high-speed MPSS.
Citation: Technologies
PubDate: 2025-03-06
DOI: 10.3390/technologies13030108
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 109: Enhancing Computational Efficiency of
Network Reliability with a New Prime Shortest Path Algorithm
Authors: Wei-Chang Yeh, Yunzhi Jiang, Chia-Ling Huang
First page: 109
Abstract: To address the increasing demands of modern networks, evaluating computational efficiency of modified network reliability is essential, with minimal paths (MPs) serving as a critical factor. However, traditional approaches to assessing computational efficiency of network reliability often struggle with challenges such as duplicate MPs and sub-path identification, resulting in exponential computational time. In this study, we present a novel algorithm based on the Prime Shortest Path (PSP) approach, which efficiently resolves these challenges by self-detecting and eliminating duplication in polynomial time. This marks a significant improvement over existing methods. The algorithm’s correctness is rigorously validated, and its superior performance is confirmed through a detailed time complexity analysis and comparisons with the leading state-of-the-art algorithms.
Citation: Technologies
PubDate: 2025-03-07
DOI: 10.3390/technologies13030109
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 110: Comparative Evaluation of Commercial,
Freely Available, and Open-Source Tools for Single-Cell Analysis Within
Freehand-Defined Histological Brightfield Image Regions of Interest
Authors: Filippo Piccinini, Marcella Tazzari, Maria Maddalena Tumedei, Nicola Normanno, Gastone Castellani, Antonella Carbonaro
First page: 110
Abstract: In the field of histological analysis, one of the typical issues is the analysis of single cells contained in regions of interest (i.e., ROIs). Today, several commercial, freely available, and open-source software options are accessible for this task. However, the literature lacks recent extensive reviews that summarise the functionalities of the opportunities currently available and provide guidance on selecting the most suitable option for analysing specific cases, for instance, irregular freehand-defined ROIs on brightfield images. In this work, we reviewed and compared 14 software tools tailored for single-cell analysis within a 2D histological freehand-defined image ROI. Precisely, six open-source tools (i.e., CellProfiler, Cytomine, Digital Slide Archive, Icy, ImageJ/Fiji, QuPath), four freely available tools (i.e., Aperio ImageScope, NIS Elements Viewer, Sedeen, SlideViewer), and four commercial tools (i.e., Amira, Arivis, HALO, Imaris) were considered. We focused on three key aspects: (a) the capacity to handle large file formats such as SVS, DICOM, and TIFF, ensuring compatibility with diverse datasets; (b) the flexibility in defining irregular ROIs, whether through automated extraction or manual delineation, encompassing square, circular, polygonal, and freehand shapes to accommodate varied research needs; and (c) the capability to classify single cells within selected ROIs on brightfield images, ranging from fully automated to semi-automated or manual approaches, requiring different levels of user involvement. Thanks to this work, a deeper understanding of the strengths and limitations of different software platforms emerges, facilitating informed decision making for researchers looking for a tool to analyse histological brightfield images.
Citation: Technologies
PubDate: 2025-03-07
DOI: 10.3390/technologies13030110
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 111: RANFIS-Based Sensor System with Low-Cost
Multi-Sensors for Reliable Measurement of VOCs
Authors: Keunyoung Kim, Woosung Yang
First page: 111
Abstract: This study describes a sensor system for continuous monitoring of volatile organic compounds (VOCs) emitted from small industrial facilities in urban centers, such as automobile paint facilities and printing facilities. Previously, intermittent measurements were made using expensive flame ionization detector (FID)-type instruments that were impossible to install, resulting in a lack of continuous management. This paper develops a low-cost sensor system for full-time management and consists of multi-sensor systems to increase the spatial resolution in the pipe. To improve the accuracy and reliability of this system, a new reinforced adaptive neuro fuzzy inference system (RANFIS) model with enhanced preprocessing based on the adaptive neuro fuzzy inference system (ANFIS) model is proposed. For this purpose, a smart sensor module consisting of low-cost metal oxide semiconductors (MOSs) and photo-ionization detectors (PIDs) is fabricated, and an operating controller is configured for real-time data acquisition, analysis, and evaluation. In the front part of the RANFIS, interquartile range (IQR) is used to remove outliers, and gradient analysis is used to detect and correct data with abnormal change rates to solve nonlinearities and outliers in sensor data. In the latter stage, the complex nonlinear relationship of the data was modeled using the ANFIS to reliably handle data uncertainty and noise. For practical verification, a toluene evaporation chamber with a sensor system for monitoring was built, and the results of real-time data sensing after training based on real data were compared and evaluated. As a result of applying the RANFIS model, the RMSE of the MQ135, MQ138, and PID-A15 sensors were 3.578, 11.594, and 4.837, respectively, which improved the performance by 87.1%, 25.9%, and 35.8% compared to the existing ANFIS. Therefore, the precision within 5% of the measurement results of the two experimentally verified sensors shows that the proposed RANFIS-based sensor system can be sufficiently applied in the field.
Citation: Technologies
PubDate: 2025-03-07
DOI: 10.3390/technologies13030111
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 112: Power Tracking and Performance Analysis
of Hybrid Perturb–Observe, Particle Swarm Optimization, and Fuzzy
Logic-Based Improved MPPT Control for Standalone PV System
Authors: Ali Abbas, Muhammad Farhan, Muhammad Shahzad, Rehan Liaqat, Umer Ijaz
First page: 112
Abstract: The increasing energy demand and initiatives to lower carbon emissions have elevated the significance of renewable energy sources. Photovoltaic (PV) systems are pivotal in converting solar energy into electricity and have a significant role in sustainable energy production. Therefore, it is critical to implement maximum power point tracking (MPPT) controllers to optimize the efficiency of PV systems by extracting accessible maximum power. This research investigates the performance and comparison of various MPPT control algorithms for a standalone PV system. Several cases involving individual MPPT controllers, as well as hybrid combinations using two and three controllers, have been simulated in MATLAB/SIMULINK. The sensed parameters, i.e., output power, voltage, and current, specify that though individual controllers effectively track the maximum power point, hybrid controllers achieve superior performance by utilizing the combined strengths of each algorithm. The results indicate that individual MPPT controllers, such as perturb and observe (P&O), particle swarm optimization (PSO), and fuzzy logic (FL), achieved tracking efficiencies of 97.6%, 90.3%, and 90.1%, respectively. In contrast, hybrid dual controllers such as P&O-PSO, PSO-FL, and P&O-FL demonstrated improved performance, with tracking efficiencies of 96.8%, 96.4%, and 96.5%, respectively. This research also proposes a new hybrid triple-MPPT controller combining P&O-PSO-FL, which surpassed both individual and dual-hybrid controllers, achieving an impressive efficiency of 99.5%. Finally, a comparison of all seven cases of MPPT control algorithms is presented, highlighting the advantages and disadvantages of individual as well as hybrid approaches.
Citation: Technologies
PubDate: 2025-03-08
DOI: 10.3390/technologies13030112
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 113: Using Serious Games and Digital Games to
Improve Students’ Computational Thinking and Programming Skills in
K-12 Education: A Systematic Literature Review
Authors: Sindre Wennevold Gundersen, Georgios Lampropoulos
First page: 113
Abstract: Computational thinking and problem-solving skills have become vital for students to develop. Digital games and serious games are increasingly being used in educational settings and present great potential to aid students’ learning. This study aims to explore the role and impact of serious games and digital games on students’ computational thinking and programming skills in primary, secondary, and K-12 education through a systematic review of the existing literature. Four research questions were set to be examined. Following the PRISMA framework, 78 studies deriving from IEEE, Scopus, and Web of Science over the period of 2011–2024 are examined. The studies are categorized into Theoretical and Review studies, Proposal and Showcase studies, and Experimental and Case studies. Based on the results, serious games and digital games arose as meaningful educational tools that are positively viewed by education stakeholders and that can effectively support and improve K-12 education students’ computational thinking and programming skills. Among the benefits identified, it was revealed that serious games offer enjoyable and interactive learning experiences that can improve students’ learning performance, engagement, and motivation, enhance students’ confidence and focus, and promote self-regulated learning and personalized learning. Additionally, serious games emerged as an educational means that can effectively support social learning and provide real-time feedback. The challenges identified were related to the selection of games and the game-related design elements, decisions, and approaches. Hence, the study highlights the significance of the design of serious games and the need to cultivate students’ computational thinking, problem-solving, and social skills from a young age. Finally, the study reveals key design principles and aspects to consider when developing serious games and digital games and highlights the need to involve education stakeholders throughout the design and development process.
Citation: Technologies
PubDate: 2025-03-11
DOI: 10.3390/technologies13030113
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 114: Portable DNA Probe Detector and a New
Dry-QCM Approach for SARS-CoV-2 Detection
Authors: Dhanunjaya Munthala, Thita Sonklin, Narong Chanlek, Ashish Mathur, Souradeep Roy, Devash Kumar Avasthi, Sanong Suksaweang, Soodkhet Pojprapai
First page: 114
Abstract: This work demonstrates the preliminary results of rapid and direct detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) using the quartz crystal microbalance (QCM) method. Coronavirus Disease 2019 (COVID-19)-specific RNA-dependent RNA polymerase (RdRP) gene-dependent probe DNA was used as a selective agent toward target DNA, the inactivated SARS-CoV-2 virus, and RNAs extracted from clinical samples. This study developed and utilised a unique dry-QCM approach with a mitigated experimental procedure. Contact angle measurements, Atomic Force Microscopy (AFM) and X-ray Photoelectron Spectroscopy (XPS) measurements were employed to investigate the surface during probe immobilisation and target hybridisation. This study also investigates the effect of temperature on probe immobilisation and target hybridisation. The estimated probe density was 0.51 × 1012 probes/cm2, which is below the critical limit. The estimated hybridisation efficiency was about 58.9%. The linear detection range with a Limit of Detection (LoD) was about ~1.22 nM with high selectivity toward SARS-CoV-2 target DNA. The sensor shelf-life was found to be extended to 25 days. The novelty of using a new dry-QCM approach for SARS-CoV-2 detection was proven with the results.
Citation: Technologies
PubDate: 2025-03-12
DOI: 10.3390/technologies13030114
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 115: Optimizing Last-Mile Deliveries:
Addressing Customer Absence Through Genetic Algorithm
Authors: Javier Sánchez-Soriano, Guillermo Verdín-Urgal, Natalia Gordo-Herrera
First page: 115
Abstract: Last-mile delivery logistics face significant challenges, particularly regarding customer absences during scheduled delivery times. This issue not only frustrates customers but also imposes substantial economic costs on delivery companies, estimated at up to 15 euros per failed delivery. This research aims to address this problem by optimizing last-mile delivery processes using a genetic algorithm (GA) designed to minimize rerouting costs while respecting customer time preferences. The study compares the performance of the proposed GA with a Simulated Annealing (SA) algorithm, assessing their efficiency in route optimization. Through detailed simulations, GA reduces operational costs by over 35,000 euros annually by considering customer preferences. It significantly outperforms the SA algorithm in scenarios with high customer variability, highlighting its potential for cost-efficient last-mile delivery solutions. Additionally, the GA consistently respected 4–7 more customer preferences per route compared to traditional methods, leading to enhanced customer satisfaction. This work contributes to the field by providing a robust methodology for balancing cost efficiency and user satisfaction in last-mile deliveries, offering actionable insights for logistics optimization.
Citation: Technologies
PubDate: 2025-03-12
DOI: 10.3390/technologies13030115
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 116: Implementation of Machine Vision Methods
for Cattle Detection and Activity Monitoring
Authors: Roman Bumbálek, Tomáš Zoubek, Jean de Dieu Marcel Ufitikirezi, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš, Petr Bartoš
First page: 116
Abstract: The goal of this research was to implement machine vision algorithms in a cattle stable to detect cattle in stalls and determine their activities. It also focused on finding the optimal hyperparameter settings for training the model, as balancing prediction accuracy, training time, and computational demands is crucial for real-world implementation. The investigation of suitable parameters was carried out on the YOLOv5 convolutional neural network (CNN). The types of the YOLOv5 network (v5x, v5l, v5m, v5s, and v5n), the effect of the learning rate (0.1, 0.01, and 0.001), the batch size (4, 8, 16, and 32), and the effect of the optimizer used (SGD and Adam) were compared in a step-by-step process. The main focus was on mAP 0.5 and mAP 0.5:0.95 metrics and total training time, and we came to the following conclusions: In terms of optimization between time and accuracy, the YOLOv5m performed the best, with a mAP 0.5:0.95 of 0.8969 (compared to 0.9070 for YOLOv5x). The training time for YOLOv5m was 7:48:19, while YOLOv5x took 16:53:27. When comparing learning rates, the variations in accuracy and training time were minimal. The highest accuracy (0.9028) occurred with a learning rate of 0.001, and the lowest (0.8897) with a learning rate of 0.1. For training time, the fastest was 7:47:17, with a difference of 1:02:00 between the fastest and slowest times. When comparing the effect of batch size, model accuracy showed only minimal differences (in tenths of a percentage), but there were significant time savings. When using a batch size of 8, the training time was 12:50:48, while increasing the batch size to 32 reduced the training time to 6:07:13, thus speeding up the training process by 6:43:35. The last parameter compared was the optimizer. SGD and Adam optimizers were compared. The choice of optimizer had a minimal impact on the training time, with differences only in seconds. However, the accuracy of the trained model was 6 per cent higher (0.8969) when using the SGD optimizer.
Citation: Technologies
PubDate: 2025-03-12
DOI: 10.3390/technologies13030116
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 117: Towards a Holistic Approach for
UAV-Based Large-Scale Photovoltaic Inspection: A Review on Deep Learning
and Image Processing Techniques
Authors: Zoubir Barraz, Imane Sebari, Kenza Ait El Kadi, Ibtihal Ait Abdelmoula
First page: 117
Abstract: This paper provides an in-depth literature review on image processing techniques, focusing on deep learning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based PV inspection, including data acquisition protocols, panel segmentation and geolocation, anomaly classification, and optimizations for model generalization. Furthermore, challenges related to domain adaptation, dataset limitations, and multimodal fusion of RGB and thermal data are also discussed. Finally, research gaps and opportunities are analyzed to create a holistic, scalable, and real-time inspection workflow for large-scale installation. This review serves as a reference for researchers and industry professionals to advance UAV-based PV inspection.
Citation: Technologies
PubDate: 2025-03-14
DOI: 10.3390/technologies13030117
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 118: Comparative Analysis of Different
Display Technologies for Defect Detection in 3D Objects
Authors: Kozov, Minev, Andreeva, Vassilev, Rusev
First page: 118
Abstract: This paper starts with an overview of current methods of displaying 3D objects. Two different technologies are compared—a glasses-free 3D laptop that uses stereoscopy, and one that uses front projection on a silver impregnated fabric screen that diffracts light to achieve a holographic effect. The research question is defined—which one is suitable for use by specialists. A methodology for an experiment is designed. A scenario for finding the solution to the problem during the experiment is created. An experiment environment with different workstations for each technology has been set up. An additional reference workstation with a standard screen has been created. Three-dimensional CAD models from the field of mechanical engineering were chosen. Different categories of defects were introduced to make the models usable for the scenario—finding the defects in each of the different workstations. A survey for participant feedback, using several categories of questions, was created, improved, and used during the experiment. The experiment was completed, short discussions were held with each participant, and their feedback was analyzed. The categories of the participants were discussed. The results from the experiment were discussed and analyzed. Statistical analysis was performed on the survey results. The applicability of the experiment in other fields was discussed. Conclusions were made, and the comparative advantages and specifics of each technology were discussed based on the analysis results and the experience gained during the experiment.
Citation: Technologies
PubDate: 2025-03-14
DOI: 10.3390/technologies13030118
Issue No: Vol. 13, No. 3 (2025)
- Technologies, Vol. 13, Pages 42: Time-to-Fault Prediction Framework for
Automated Manufacturing in Humanoid Robotics Using Deep Learning
Authors: Amir R. Ali, Hossam Kamal
First page: 42
Abstract: Industry 4.0 is transforming predictive failure management by utilizing deep learning to enhance maintenance strategies and automate production processes. Traditional methods often fail to predict failures in time. This research addresses this issue by developing a time-to-fault prediction framework that utilizes an enhanced long short-term memory (LSTM) model to predict machine faults. The proposed method integrates real-time sensor data, including current, voltage, and temperature calibrated via ultra-sensitive optical sensing technologies based on the typical whispering gallery optical mode (WGM) to create a robust dataset. Due to the high-quality factor that these sensors exhibit, any minute change on the surrounding medium will makes a significant change on its transmission spectrum. The LSTM model trained on these data demonstrated rapid and stable convergence, outperforming other deep learning techniques with a mean absolute error (MAE) of 0.83, a root mean squared error (RMSE) of 1.62, and a coefficient of determination (R2) of 0.99. The results show the superior performance of LSTM in predicting machine failures early in real-world environments within 10 min lead time, improving productivity and reducing downtime. This framework advances smart industries by improving fault prediction in manufacturing precision robotics components, demonstrated through two humanoid robots, GUCnoid 1.0 and ARAtronica.
Citation: Technologies
PubDate: 2025-01-21
DOI: 10.3390/technologies13020042
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 43: Enhancing Decision-Making and Data
Management in Healthcare: A Hybrid Ensemble Learning and Blockchain
Approach
Authors: Geetanjali Rathee, Razi Iqbal
First page: 43
Abstract: Currently, big data is considered one of the most significant areas of research and development. The advancement in technologies along with the involvement of intelligent and automated devices in each field of development leads to huge generation, analysis, and the recording of information in the network. Though a number of schemes have been proposed for providing accurate decision-making while analyzing the records, however, the existing methods lead to massive delays and difficulty in the management of stored information. Furthermore, the excessive delays in information processing pose a critical challenge to making accurate decisions in the context of big data. The aim of this paper is to propose an effective approach for accurate decision-making and analysis of the vast volumes of data generated by intelligent devices in the healthcare sector. The processed and managed records can be stored and accessed in a systematic and efficient manner. The proposed mechanism uses the hybrid of ensemble learning along with blockchain for fast and continuous recording and surveillance of information. The recorded information is analyzed using several existing methods focusing on various measurement outcomes. The results of the proposed technique are compared with existing techniques through various experiments that demonstrate the efficiency and accuracy of this technique.
Citation: Technologies
PubDate: 2025-01-23
DOI: 10.3390/technologies13020043
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 44: Transforming Telemedicine: Strategic
Lessons and Metrics from Italy’s Telemechron Project (Telemechron
Study)
Authors: Sara Jayousi, Martina Cinelli, Roberto Bigazzi, Stefano Bianchi, Simonetta Scalvini, Gabriella Borghi, Palmira Bernocchi, Sandro Inchiostro, Alexia Giovanazzi, Marina Mastellaro, Maria Adalgisa Gentilini, Lorenzo Gios, Mauro Grigioni, Carla Daniele, Giuseppe D’Avenio, Sandra Morelli, Daniele Giansanti
First page: 44
Abstract: Background: The Telemechron project addresses critical needs in telemedicine by evaluating technology assessment frameworks and key performance indicators (KPIs), among other objectives. Effective frameworks and KPIs are crucial for assessing telemedicine services, especially in terms of their impact on patient outcomes and service efficiency. Methods: This study adopted a dual approach to assess the contributions of the Telemechron project. Firstly, it applied a technology assessment framework to quantitatively evaluate telemedicine services, focusing on measurable improvements and systematic analysis. Secondly, it investigated a set of predefined KPIs using specific metrics and parameters to evaluate their applicability and limitations in telemedicine settings. Results and Discussion: The technology assessment framework demonstrated significant utility by providing structured, quantifiable improvements across key aspects of telemedicine services. It was effective in evaluating the alignment of services with strategic goals and their integration with existing healthcare systems. The predefined KPIs, although not developed within this study and not directly used by the different operational units (OUs)—which established their own context-specific KPIs—still provided valuable insights into telemedicine performance. However, the study revealed that these KPIs highlighted a need for further customization to enhance their relevance across various contexts. While the KPIs may offer useful performance indicators, their general applicability necessitated adjustments to better address the specific needs of telemedicine applications. The analysis model for the KPI set, in terms of metrics and parameters, is exportable and generalizable to other national and international telemedicine contexts. Conclusions: The study confirms the effectiveness of the framework in delivering measurable improvements in telemedicine services and underscores the importance of adapting KPIs for specific contexts. Future research should focus on further applying and expanding the framework and metrics, refining KPI models, integrating new technologies, and conducting cross-national comparisons to enhance the applicability and effectiveness of telemedicine evaluations.
Citation: Technologies
PubDate: 2025-01-23
DOI: 10.3390/technologies13020044
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 45: An Intelligent Technique for Android
Malware Identification Using Fuzzy Rank-Based Fusion
Authors: Altyeb Taha, Ahmed Hamza Osman, Yakubu Suleiman Baguda
First page: 45
Abstract: Android’s open-source nature, combined with its large market share, has made it a primary target for malware developers. Consequently, there is a dramatic need for effective Android malware detection methods. This paper suggests a novel fuzzy rank-based fusion approach for Android malware detection (ANDFRF). The suggested ANDFRF primarily consists of two steps: in the first step, five machine learning algorithms, comprising K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), XGbooost (XGB) and Light Gradient Boosting Machine (LightGBM), were utilized as base classifiers for the initial identification of Android Apps either as goodware or malware apps. Second, the fuzzy rank-based fusion approach was employed to adaptively integrate the classification results obtained from the base machine learning algorithms. By leveraging rankings instead of explicit class labels, the proposed ANDFRF method reduces the impact of anomalies and noisy predictions, leading to more accurate ensemble outcomes. Furthermore, the rankings reflect the relative importance or acceptance of each class across multiple classifiers, providing deeper insights into the ensemble’s decision-making process. The proposed framework was validated on two publicly accessible datasets, CICAndMal2020 and DREBIN, with a 5-fold cross-validation technique. The proposed ensemble framework achieves a classification accuracy of 95.51% and an AUC of 95.40% on the DREBIN dataset. On the CICAndMal2020 LBC dataset, it attains an accuracy of 95.31% and an AUC of 95.30%. Experimental results demonstrate that the proposed scheme is both efficient and effective for Android malware detection.
Citation: Technologies
PubDate: 2025-01-23
DOI: 10.3390/technologies13020045
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 46: Deep Learning Framework Using Spatial
Attention Mechanisms for Adaptable Angle Estimation Across Diverse Array
Configurations
Authors: Constantinos M. Mylonakis, Pantelis Velanas, Pavlos I. Lazaridis, Panagiotis Sarigiannidis, Sotirios K. Goudos, Zaharias D. Zaharis
First page: 46
Abstract: Rapid advancement of wireless communication systems and the increasing need for accurate, real-time signal processing have driven innovations in direction-of-arrival (DoA) estimation techniques. This paper introduces a novel convolutional neural network (CNN) architecture that combines spatial attention mechanisms with a transfer learning framework to enhance both accuracy and versatility in DoA estimation. The model integrates spatial attention layers to dynamically prioritize signal regions with the highest information value, allowing it to isolate relevant signals and suppress interference in noisy or crowded signal environments. In addition, we utilize a transfer learning framework that enables the model to generalize across various antenna array configurations (i.e., planar, linear, and circular arrays) with minimal additional training. Extensive simulation results benchmark the proposed model against existing state-of-the-art methods for DoA estimation, achieving improved absolute error across diverse conditions. This hybrid approach not only enhances DoA estimation precision, but also significantly reduces retraining requirements when adapting to new array configurations, positioning it as a robust, scalable tool for next-generation wireless communication systems.
Citation: Technologies
PubDate: 2025-01-24
DOI: 10.3390/technologies13020046
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 47: Hybrid System for Fault Tolerance in
Selective Compliance Assembly Robot Arm: Integration of Differential Gears
and Coordination Algorithms
Authors: Claudio Urrea, Pablo Sari, John Kern
First page: 47
Abstract: This study presents a fault-tolerant control system for Selective Compliance Assembly Robot Arm (SCARA) robots, ensuring operational continuity in cooperative tasks. It is evaluated in five scenarios: normal operation, failures without reconfiguration, and with active reconfiguration. The system employs redundant actuators, differential gears, torque limiters, and rapid detection and reconfiguration algorithms. Simulations in MATLAB R2024a demonstrated reconfiguration times of 0.5 s and reduced trajectory errors (0.0042 m on the X-axis for Robot 1), achieving efficiency above 99%. Nonlinear Model Predictive Controllers (NLMPCs) and Adaptive Sliding Mode Control (ASMC) were compared, with NLMPC excelling in stability and ASMC in precision. The system showcased high productivity in pick-and-place tasks, even under critical failures, establishing itself as a robust solution for industrial environments requiring high reliability and advanced automation.
Citation: Technologies
PubDate: 2025-01-24
DOI: 10.3390/technologies13020047
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 48: Challenges and Ethical Considerations in
Implementing Assistive Technologies in Healthcare
Authors: Eleni Gkiolnta, Debopriyo Roy, George F. Fragulis
First page: 48
Abstract: Assistive technologies are becoming an increasingly important aspect of healthcare, particularly for people with physical or cognitive problems. While earlier research has investigated the ethical, legal, and societal implications of AI and assistive technologies, many studies have failed to address real-world obstacles such as data privacy, algorithm bias, and regulatory issues. To further understand these issues, we conducted a thorough analysis of the current literature and analyzed real-world case studies. As AI-powered solutions become more widely used, we discovered that stronger legal frameworks and robust data security standards are required. Furthermore, privacy-preserving procedures and transparent accountability are critical for retaining patient trust and guaranteeing the effective use of these technologies in healthcare. This research provides important insights into the ethical and practical challenges that must be tackled for the successful integration of assistive technologies.
Citation: Technologies
PubDate: 2025-01-27
DOI: 10.3390/technologies13020048
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 49: A New Bundling and Packaging Method Using
Nonwoven Polylactide Based on Polymer Shrinkage by Carbon Dioxide
Authors: Takafumi Aizawa
First page: 49
Abstract: This study proposes the exposure of nonwoven fabrics to carbon dioxide for bundling and packaging purposes. The proposed process, which utilizes the shrinking property of the nonwoven fabric during carbon dioxide exposure, is demonstrated on a polylactic acid (PLA) nonwoven fabric produced by the melt-blown method. Evaluating the shrinkage induced by carbon dioxide in PLA nonwoven fabrics with varying degrees of crystallinity, it was found that increasing the crystallinity decreases both the speed and amount of shrinkage. This process is potentially applicable as a simple, inexpensive, and environmentally friendly approach for packaging food and drug products.
Citation: Technologies
PubDate: 2025-01-28
DOI: 10.3390/technologies13020049
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 50: Solid-State Transformers: A
Review—Part II: Modularity and Applications
Authors: Dragoș-Mihail Predescu, Ștefan-George Roșu
First page: 50
Abstract: The Solid-State Transformer (SST) is a complex conversion device that intends to replace the Low-Frequency Transformers (LFTs) used in various power applications with Medium- or High-Frequency Transformers (MFTs/HFTs) that integrate modular converter structures as their input and output stages. The purpose is to obtain additional capabilities, such as power factor correction, voltage control, and interconnection of distributed supplies, among others, while reducing the overall volume. Given the expansive research conducted in this area in the past years, the volume of information available is large, so the main contribution of this paper is a new method of classification based on the modular construction of the SST derived from its applications and available constructive degrees of freedom. This paper can be considered the second part of a broader review in which the first part presented the fundamental converter roles and topologies. As a continuation, this paper aims to expand the definition of modularity to the entire SST structure and analyze how the converters can be combined in order to achieve the desired SST functionality. Three areas of interest are chosen: partitioning of power, phase modularity, and port configuration. The partitioning of power analyzes the fundamental switching cells and the arrangement of the converters across stages. Phase modularity details the construction of multiphase-system SSTs. Finally, the types of input/output ports, their placements, and roles are discussed. These characteristics are presented together with the applications in which they were suggested to give a broader context.
Citation: Technologies
PubDate: 2025-01-28
DOI: 10.3390/technologies13020050
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 51: From Google Gemini to OpenAI Q (Q-Star):
A Survey on Reshaping the Generative Artificial Intelligence (AI) Research
Landscape*
Authors: Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul Watters, Dan Xu, Dongwei Liu, Malka N. Halgamuge
First page: 51
Abstract: This comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the recent technological breakthroughs and the gathering advancements toward possible Artificial General Intelligence (AGI). It critically examined the current state and future trajectory of generative AI, exploring how innovations in developing actionable and multimodal AI agents with the ability scale their “thinking” in solving complex reasoning tasks are reshaping research priorities and applications across various domains, while the survey also offers an impact analysis on the generative AI research taxonomy. This work has assessed the computational challenges, scalability, and real-world implications of these technologies while highlighting their potential in driving significant progress in fields like healthcare, finance, and education. Our study also addressed the emerging academic challenges posed by the proliferation of both AI-themed and AI-generated preprints, examining their impact on the peer-review process and scholarly communication. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare, and outlined a strategy for future AI research that focuses on a balanced and conscientious use of generative AI as its capabilities continue to scale.
Citation: Technologies
PubDate: 2025-01-30
DOI: 10.3390/technologies13020051
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 52: The Role of Surface {010} Facets in
Improving the NOx Depolluting Activity of TiO2 and Its Application on
Building Materials
Authors: Luna, Cruces, Gatica, Cruceira, Cifredo, Vidal, Mosquera
First page: 52
Abstract: Air pollution, a major health concern, necessitates innovative solutions such as TiO2-based photocatalytic building materials to combat its harmful effects. This study focuses on developing high-performance TiO2 photocatalysts for NOx removal in building applications, aiming to overcome the limitations of commercial TiO2. These photocatalysts were synthesized via a hydrothermal method, with parameters such as synthesis time and post-treatment investigated to optimize their properties. Hydrothermal synthesis yielded TiO2 nanoparticles with reduced aggregation and a high proportion of elongated particles with exposed {010} facets. This resulted in significantly enhanced photocatalytic activity compared to commercial P25 in methylene blue degradation and NOx depollution. Subsequently, the optimized hydrothermal TiO2 was successfully integrated into a silica sol–gel coating for application on building materials. The coated concrete demonstrated significantly higher NOx removal efficiency and lower NO2 release, achieving a 1.7-fold improvement in overall NOx removal and significantly higher depolluting effectiveness compared to its P25 counterpart. These findings highlight the potential of hydrothermally synthesized TiO2 with controlled morphology for the development of high-performance, environmentally friendly building materials with enhanced air purification capabilities.
Citation: Technologies
PubDate: 2025-01-31
DOI: 10.3390/technologies13020052
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 53: A New Efficient Hybrid Technique for
Human Action Recognition Using 2D Conv-RBM and LSTM with Optimized Frame
Selection
Authors: Majid Joudaki, Mehdi Imani, Hamid R. Arabnia
First page: 53
Abstract: Recognizing human actions through video analysis has gained significant attention in applications like surveillance, sports analytics, and human–computer interaction. While deep learning models such as 3D convolutional neural networks (CNNs) and recurrent neural networks (RNNs) deliver promising results, they often struggle with computational inefficiencies and inadequate spatial–temporal feature extraction, hindering scalability to larger datasets or high-resolution videos. To address these limitations, we propose a novel model combining a two-dimensional convolutional restricted Boltzmann machine (2D Conv-RBM) with a long short-term memory (LSTM) network. The 2D Conv-RBM efficiently extracts spatial features such as edges, textures, and motion patterns while preserving spatial relationships and reducing parameters via weight sharing. These features are subsequently processed by the LSTM to capture temporal dependencies across frames, enabling effective recognition of both short- and long-term action patterns. Additionally, a smart frame selection mechanism minimizes frame redundancy, significantly lowering computational costs without compromising accuracy. Evaluation on the KTH, UCF Sports, and HMDB51 datasets demonstrated superior performance, achieving accuracies of 97.3%, 94.8%, and 81.5%, respectively. Compared to traditional approaches like 2D RBM and 3D CNN, our method offers notable improvements in both accuracy and computational efficiency, presenting a scalable solution for real-time applications in surveillance, video security, and sports analytics.
Citation: Technologies
PubDate: 2025-02-01
DOI: 10.3390/technologies13020053
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 54: Lung and Colon Cancer Classification
Using Multiscale Deep Features Integration of Compact Convolutional Neural
Networks and Feature Selection
Authors: Omneya Attallah
First page: 54
Abstract: The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and their ineffectiveness in utilising multiscale features. To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. Initially, it extracts deep attributes from two separate layers (pooling and fully connected) of three pre-trained CNNs (MobileNet, ResNet-18, and EfficientNetB0). Second, the system uses the benefits of canonical correlation analysis for dimensionality reduction in pooling layer attributes to reduce complexity. In addition, it integrates the dual-layer features to encapsulate both high- and low-level representations. Finally, to benefit from multiple deep network architectures while reducing classification complexity, the proposed CAD merges dual deep layer variables of the three CNNs and then applies the analysis of variance (ANOVA) and Chi-Squared for the selection of the most discriminative features from the integrated CNN architectures. The CAD is assessed on the LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, and k-nearest neighbours. The experimental results exhibited outstanding performance, attaining 99.8% classification accuracy with cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding the performance of individual CNNs while markedly diminishing computational complexity. The framework’s capacity to sustain exceptional accuracy with a limited feature set renders it especially advantageous for clinical applications where diagnostic precision and efficiency are critical. These findings confirm the efficacy of the multi-CNN, multi-layer methodology in enhancing cancer classification precision while mitigating the computational constraints of current systems.
Citation: Technologies
PubDate: 2025-02-01
DOI: 10.3390/technologies13020054
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 55: PictureGuard: Enhancing Software-Defined
Networking–Internet of Things Security with Novel Image-Based
Authentication and Artificial Intelligence-Powered Two-Stage Intrusion
Detection
Authors: Hazem (Moh’d Said) Hatamleh, As’ad Mahmoud As’ad Alnaser, Said S. Saloum, Ahmed Sharadqeh, Jawdat S. Alkasassbeh
First page: 55
Abstract: Software-defined networking (SDN) represents a transformative approach to network management, enabling the centralized and programmable control of network infrastructure. This paradigm facilitates enhanced scalability, flexibility, and security in managing complex systems. When integrated with the Internet of Things (IoT), SDN addresses critical challenges such as security and efficient network management, positioning the SDN-IoT paradigm as an emerging and impactful technology in modern networking. The rapid proliferation of IoT applications has led to a significant increase in security threats, posing challenges to the safe operation of IoT systems. Consequently, SDN-IoT-based applications and services have been widely adopted to address these issues and challenges. However, this platform faces critical limitations in ensuring scalability, optimizing energy consumption, and addressing persistent security vulnerabilities. To overcome these issues, we proposed a secure SDN-IoT environment for intrusion detection and prevention using virtual blockchain (V-Block). Initially, IoT users are registered and authenticated to the shadow blockchain nodes using a picture-based authentication mechanism. After that, authenticated user flows validation was provided by considering effective metrics utilizing the Trading-based Evolutionary Game Theory (TEGT) approach. Then, we performed a local risk assessment based on evaluated malicious flows severity and then the attack graph was constructed using an Isomorphism-based Graph Neural Network (IGNN) model. Further, multi-controllers were placed optimally using fox optimization algorithm. The generated global paths were securely stored in the virtual blockchain Finally, the two agents in the multi-controllers were responsible for validating and classifying the incoming suspicious flow packets into normal and malicious packets by considering the operative metrics using the Dueling Deep Q Network (DDQN) algorithm. The presented work was conducted by Network Simulator-3.26 and the different performance matrices were used to itemize the suggested V-Block model based on its malicious traffic, attack detection rate, link failure rate, anomaly detection rate, and scalability.
Citation: Technologies
PubDate: 2025-02-01
DOI: 10.3390/technologies13020055
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 56: Analysis and Optimization of DC-DC
Converters Through Sensitivity to Parametric Variations
Authors: Nikolay Hinov, Plamen Stanchev, Gergana Vacheva
First page: 56
Abstract: The optimization of DC-DC converters is crucial for enhancing their performance and efficiency in various applications. This study focuses on the sensitivity analysis of DC-DC converters to parametric variations, which plays a key role in designing robust and efficient systems. The methodology involves developing a simulation model that describes the behavior of converters under different conditions and analyzing the effects of parameter variations through simulation tools. Sensitivity analysis of DC-DC converters involves understanding the sources of harmonics, modeling the converter, analyzing the harmonic content, and implementing mitigation techniques. By combining theoretical analysis with practical design modifications, engineers can optimize DC-DC converters for improved performance, efficiency, and compliance with electromagnetic compatibility standards. Examples of harmonic analysis of the main types of DC-DC converters—Buck, Boost, and Buck-Boost—are discussed in the manuscript. Based on a study of the influence of harmonics in the operating modes, ratios have been derived to be applied during design. In this respect, the research presented is useful for designers and for use in power electronics education.
Citation: Technologies
PubDate: 2025-02-01
DOI: 10.3390/technologies13020056
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 57: Portable Solar-Integrated Open-Source
Chemistry Lab for Water Treatment with Electrolysis
Authors: Giorgio Antonini, Md Motakabbir Rahman, Cameron Brooks, Domenico Santoro, Christopher Muller, Ahmed Al-Omari, Katherine Bell, Joshua M. Pearce
First page: 57
Abstract: Harnessing solar energy offers a sustainable alternative for powering electrolysis for green hydrogen production as well as wastewater treatment. The high costs and logistical challenges of electrolysis have resulted in limited widespread investigation and implementation of electrochemical technologies on an industrial scale. To overcome these challenges, this study designs and tests a new approach to chemical experiments and wastewater treatment research using a portable standalone open-source solar photovoltaic (PV)-powered station that can be located onsite at a wastewater treatment plant with unreliable electrical power. The experimental system is equipped with an energy monitoring data acquisition system. In addition, sensors enable real-time monitoring of gases—CO, CO2, CH4, H2, H2S, and NH3—along with temperature, humidity, and volatile organic compounds, enhancing safety during electrochemical experiments on wastewater, which may release hazardous gases. SAMA software was used to evaluate energy-sharing scenarios under different grid-connected conditions, and the system can operate off the power grid for 98% of the year in Ontario, Canada. The complete system was tested utilizing a laboratory-scale electrolyzer (electrodes of SS316L, Duplex 2205, titanium grade II and graphite) with electrolyte solutions of potassium hydroxide, sulfuric acid, and secondary wastewater effluent. The electrolytic cell specifically developed for testing electrode materials and wastewater showed a Faraday efficiency up to 95% and an energy efficiency of 55% at STP, demonstrating the potential for use of this technology in future work.
Citation: Technologies
PubDate: 2025-02-01
DOI: 10.3390/technologies13020057
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 58: Distributed Ledger Technology in
Healthcare: Enhancing Governance and Performance in a Decentralized
Ecosystem
Authors: Juan Minango, Henry Carvajal Mora, Marcelo Zambrano, Nathaly Orozco Garzón, Francisco Pérez
First page: 58
Abstract: This paper evaluates the technical feasibility of Distributed Ledger Technology (DLT) within the healthcare ecosystem, with a focus on the use of Corda DLT to enhance governance and performance in a decentralized ecosystem, ensuring data integrity, security, and trustworthiness. Key attributes examined include the guarantee of data integrity, ensuring that transmitted data remain unaltered; authenticity through the implementation of digital signatures and certificates; confidentiality achieved via secure peer-to-peer communication accessible only to authorized parties; and traceability and auditing mechanisms that enable tracking of information changes and accountability. To validate these features, a Corda Distributed Application (CorDapp) was developed to manage the core logic of the healthcare ecosystem. The CorDapp was deployed across nodes and executed within the Corda network. Its performance was assessed using metrics such as throughput, latency, CPU usage, and memory consumption in both local and cloud network environments. Results demonstrate the feasibility of using Corda DLT technology in healthcare, effectively addressing critical requirements such as integrity, authenticity, confidentiality, traceability, and auditing while maintaining satisfactory performance across diverse deployment scenarios.
Citation: Technologies
PubDate: 2025-02-01
DOI: 10.3390/technologies13020058
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 59: Enhancing Electricity Load Forecasting
with Machine Learning and Deep Learning
Authors: Arbër Perçuku, Daniela Minkovska, Nikolay Hinov
First page: 59
Abstract: The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the reliability of power systems and is a key component in the operational planning and efficient market. For many years, a conventional method has been used by using historical data as input parameters. With swift progress and improvement in technology, which shows more potential due to its accuracy, different methods can be applied depending on the identified model. To enhance the forecast of load, this paper introduces and proposes a framework developed on graph database technology to archive large amounts of data, which collects measured data from electrical substations in Pristina, Kosovo. The data includes electrical and weather parameters collected over a four-year timeframe. The proposed framework is designed to handle short-term load forecasting. Machine learning Linear Regression and deep learning Long Short-Term Memory algorithms are applied to multiple datasets and mean absolute error and root mean square error are calculated. The results show the promising performance and effectiveness of the proposed model, with high accuracy in load forecasting.
Citation: Technologies
PubDate: 2025-02-01
DOI: 10.3390/technologies13020059
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 60: Backfill for Advanced Potash Ore Mining
Technologies
Authors: Evgeny Kovalsky, Cheynesh Kongar-Syuryun, Angelika Morgoeva, Roman Klyuev, Marat Khayrutdinov
First page: 60
Abstract: In today’s world, advanced technologies are indispensable. In the field of mining, the use of machine-learning techniques is a reliable and productive way to solve various problems. This article touches upon the issues of increasing the recovery rate at potash mines, using the technology of backfilling with hardening materials. The compositions of backfills with increased strength are developed. The results of laboratory studies are given. To reduce the labor intensity of the experimental work, as well as to develop and validate methodological approaches to machine-learning introduction in the fields of mining and geomechanical research, this paper also presents the results of the predicted calculated values of the multi-component backfill strength, obtained with the help of neural networks.
Citation: Technologies
PubDate: 2025-02-02
DOI: 10.3390/technologies13020060
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 61: Intent-Bert and Universal Context
Encoders: A Framework for Workload and Sensor Agnostic Human Intention
Prediction
Authors: Maximillian Panoff, Joshua Acevedo, Honggang Yu, Peter Forcha, Shuo Wang, Christophe Bobda
First page: 61
Abstract: Determining human intention is a challenging task. Many existing techniques seek to address it by combining many forms of data, such as images, point clouds, poses, and others, creating multi-modal models. However, these techniques still often require significant foreknowledge in the form of known potential activities and objects in the environment, as well as specific types of data to collect. To address these limitations, we propose Intent-BERT and Universal Context Encoders, which combine to form workload-agnostic framework that can be used to predict the next activity that a human performs as an Open Vocabulary Problem and the time until that switch, along with the time the current activity ends. Universal Context Encoders utilize the distances between the embeddings of words to extract relationships between Human-Readable English descriptions of both the current task and the origin of various multi-modal inputs to determine how to weigh the values themselves. We examine the effectiveness of this approach by creating a multi-modal model using it and training it on the InHARD dataset. It is able to return a completely accurate description of the next Action performed by a human working alongside a robot in a manufacturing task in ∼42% of test cases and has a 95% Top-3 accuracy, all from a single time point, outperforming multi-modal gpt4o by about 50% on a token by token basis.
Citation: Technologies
PubDate: 2025-02-02
DOI: 10.3390/technologies13020061
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 62: Barriers to the Adoption of Augmented
Reality Technologies for Education and Training in the Built Environment:
A Developing Country Context
Authors: Opeoluwa Akinradewo, Mohamed Hafez, John Aliu, Ayodeji Oke, Clinton Aigbavboa, Samuel Adekunle
First page: 62
Abstract: The construction industry has been tasked to adapt to technological advancements that other industries have implemented to grow and remain relevant. One of these technological advancements is augmented reality technologies. ART combines real and virtual worlds without completely immersing the individual in a virtual simulation. The use of ART can significantly improve education and training, especially in the construction industry, by analysing real-world environments while training in a controlled setting. This study, therefore, sets out to identify the factors that hinder the use of ART in the built environment. To achieve this, a quantitative research approach was adopted, and questionnaires were distributed to professionals in the built environment using South Africa as the research location. Retrieved data were analysed using both descriptive and inferential statistics. Findings revealed that investment cost is the major hindrance stakeholders face in implementing ART for education and training in the built environment. The exploratory factor analysis result clustered the identified barriers as internal organisation-related, culture-related, knowledge-related, and educator-related barriers. The study concluded that stakeholders in the built environment still have major responsibilities to ensure there is proper awareness of the benefits of adopting ART for education and training.
Citation: Technologies
PubDate: 2025-02-03
DOI: 10.3390/technologies13020062
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 63: Solid-State Kinetic Modeling and
Experimental Validation of Cu-Fe Bimetallic Catalyst Synthesis and Its
Application to Furfural Hydrogenation
Authors: Bárbara Jazmín Lino Galarza, Javier Rivera De la Rosa, Eduardo Maximino Sánchez Cervantes, Carlos J. Lucio-Ortiz, Marco Antonio Garza-Navarro, Carolina Solís Maldonado, Ramón Moreno-Tost, Juan Antonio Cecilia-Buenestado, Antonia Infantes Molina
First page: 63
Abstract: In this work, combined experimental and modeling techniques were used to understand the bimetallic catalyst formation of Cu and Fe. The first part of this study aims to address this gap by employing analytical techniques such as X-ray diffraction (XRD), thermal and gravimetric (TGA), thermoprogrammed oxidation and reduction. These were used to track the evolution of the different crystalline phases formed for CuFe-Bulk and CuFe/Al2O3 catalysts, as well as hydrogen thermoprogrammed reduction (H2-TPR), to evaluate the reducibility of the oxide phases. Both bulk and supported catalysts were also studied in the hydrogenation of furfural at 170 °C, and 4 MPa of H2. The research provides insights into the thermal events and structural transformations that occur during oxidation and reduction processes, revealing the formation of multiple oxide and metallic phases. The proposed reaction mechanism obtained from XRD analysis and TG-based mathematical modeling provides valuable information about the chemical reaction and the diffusion control mechanisms. Furthermore, a catalytic test using furfural, a biomass-derived molecule, was conducted. This interconnects with the initial section of the study, in which we found that active Cu4Fe sites have superior performance in the CuFe/Al2O3 catalyst in the hydrogenation batch test.
Citation: Technologies
PubDate: 2025-02-03
DOI: 10.3390/technologies13020063
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 64: A Review of Academic and Patent Progress
on Internet of Things (IoT) Technologies for Enhanced Environmental
Solutions
Authors: Usharani Hareesh Govindarajan, Chuyi Zhang, Rakesh D. Raut, Gagan Narang, Alessandro Galdelli
First page: 64
Abstract: Environmental pollution is a pressing global issue, and the Internet of Things (IoT) offers transformative potential for its management through its application in advanced real-time monitoring and analytics. However, the heterogeneous and fragmented nature of IoT technologies poses challenges to seamless integration, limiting the efficacy of these solutions in addressing environmental impacts. This paper addresses these challenges by reviewing recent developments in IoT technologies, encompassing sensor networks, computing frameworks, and application layers for enhanced pollution management. A comprehensive analysis of 74,604 academic publications and 35,000 patent documents spanning from 2008 to 2024 is conducted using a textual analysis that combines quantitative bibliometric methods along with a qualitative analysis based on both scholarly research and patent innovations. This approach allows us to identify key challenges in IoT implementation for environmental monitoring—including integration, interoperability, and scalability issues—and to highlight corresponding architectural solutions. Our findings reveal emerging technology trends that aim to overcome a few of these challenges, and we present a scalable IoT architecture as key discussions that enhances system interoperability and efficiency for pollution monitoring. This framework provides targeted solutions for specific tasks in pollution monitoring while guiding decision-makers to adopt solutions effectively.
Citation: Technologies
PubDate: 2025-02-03
DOI: 10.3390/technologies13020064
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 65: The Role of BIM 6D and 7D in Enhancing
Sustainable Construction Practices: A Qualitative Study
Authors: Hanan Al-Raqeb, Seyed Hamidreza Ghaffar
First page: 65
Abstract: The construction industry in Kuwait is experiencing a transformative shift with the adoption of Building Information Modeling (BIM) technologies, particularly BIM 6D for sustainability analysis and 7D for facility management. This study investigates the integration of these dimensions to address sustainability challenges in Kuwait’s construction sector, aligning practices with the United Nations’ Sustainable Development Goals (SDGs). Through qualitative interviews with 15 stakeholders—including architects, engineers, and contractors—and analysis of industry reports, policies, and case studies, the research identifies both opportunities for and barriers to BIM adoption. While BIM offers significant potential for lifecycle analysis, waste reduction, and energy efficiency, its adoption remains limited, with only 27% of construction waste recycled. Challenges include high initial costs, a shortage of skilled personnel, and resistance to change. The study highlights actionable strategies, including enhanced regulatory frameworks, university curriculum integration, and professional training programs led by the Kuwait Society of Engineers, to address these barriers. It also emphasizes the critical role of collaboration among government bodies, industry leaders, and institutions like the Kuwait Institute for Scientific Research. Drawing from successful international BIM projects, the findings offer a practical framework for improving sustainability in arid regions, positioning Kuwait’s experience as a model for other Middle Eastern and North African countries. This research underscores the transformative role of BIM technologies in advancing global sustainable construction practices and achieving a more efficient and eco-friendly future.
Citation: Technologies
PubDate: 2025-02-03
DOI: 10.3390/technologies13020065
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 66: High-Gain Miniaturized Multi-Band MIMO
SSPP LWA for Vehicular Communications
Authors: Tale Saeidi, Sahar Saleh, Nick Timmons, Christopher McDaid, Ahmed Jamal Abdullah Al-Gburi, Faroq Razzaz, Saeid Karamzadeh
First page: 66
Abstract: This paper introduces a novel miniaturized, four-mode, semi-flexible leaky wave Multiple-Input Multiple-Output (MIMO) antenna specifically designed to advance vehicular communication systems. The proposed antenna addresses key challenges in 5G low- and high-frequency bands, including millimeter-wave communication, by integrating innovative features such as a periodic Spoof Surface Plasmon Polariton Transmission Line (SSPP-TL) and logarithmic-spiral-like semi-circular strip patches parasitically fed via orthogonal ports. These design elements facilitate stable impedance matching and wide impedance bandwidths across operating bands, which is essential for vehicular networks. The hybrid combination of leaky wave and SSPP structures, along with a defected wide-slot ground structure and backside meander lines, enhances radiation characteristics by reducing back and bidirectional radiation. Additionally, a naturalization network incorporating chamfered-edge meander lines minimizes mutual coupling and introduces a fourth radiation mode at 80 GHz. Compact in size (14 × 12 × 0.25 mm3), the antenna achieves high-performance metrics, including S11 < −18.34 dB, dual-polarization, peak directive gains of 11.6 dBi (free space) and 14.6 dBi (on vehicles), isolation > 27 dB, Channel Capacity Loss (CCL) < 3, Envelope Correlation Coefficient (ECC) < 0.001, axial ratio < 2.25, and diversity gain (DG) > 9.85 dB. Extensive testing across various vehicular scenarios confirms the antenna’s robustness for Vehicle-to-Vehicle (V2V), Vehicle-to-Pedestrian (V2P), and Vehicle-to-Infrastructure (V2I) communication. Its exceptional performance ensures seamless connectivity with mobile networks and enhances safety through Specific Absorption Rate (SAR) compliance. This compact, high-performance antenna is a transformative solution for connected and autonomous vehicles, addressing critical challenges in modern automotive communication networks and paving the way for reliable and efficient vehicular communication systems.
Citation: Technologies
PubDate: 2025-02-04
DOI: 10.3390/technologies13020066
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 67: A Survey on Data Mining for Data-Driven
Industrial Assets Maintenance
Authors: Eduardo Coronel, Benjamín Barán, Pedro Gardel
First page: 67
Abstract: This survey presents a comprehensive review of data-driven approaches for industrial asset maintenance, emphasizing the use of data mining and machine learning techniques, including deep learning, for condition-based and predictive maintenance. It examines 534 references from 1995 to 2023, along with three additional articles from 2024 on natural language processing and large language models in industrial maintenance. The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance needs for industrial assets. It details the data types utilized in the industrial sector, with the most frequently mentioned being time series data, event timestamp data, and image data. The survey also highlights the most frequently referenced data mining algorithms, such as the proportional hazard model, expert systems, support vector machines, random forest, autoencoder, and convolutional neural networks. Additionally, the survey proposes four level classes of asset complexity and studies five asset types, including mechanical, electromechanical, electrical, electronic, and computing assets. The growing adoption of deep learning is highlighted alongside the continued relevance of traditional approaches such as shallow machine learning and rule-based and model-based techniques. Furthermore, the survey explores emerging trends in machine learning and related technologies, identifies future research directions, and underscores their critical role in advancing condition-based and predictive maintenance frameworks.
Citation: Technologies
PubDate: 2025-02-04
DOI: 10.3390/technologies13020067
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 68: Integrated Dynamic Power Management
Strategy with a Field Programmable Gate Array-Based Cryptoprocessor System
for Secured Internet-of-Medical Things Networks
Authors: Javier Vázquez-Castillo, Daniel Visairo, Ramón Atoche-Enseñat, Alejandro Castillo-Atoche, Renán Quijano-Cetina, Carolina Del-Valle-Soto, Jaime Ortegón-Aguilar, Johan J. Estrada-López
First page: 68
Abstract: Advancements in electronics and sensor technologies are driving the deployment of ubiquitous sensor networks across various applications, including asset monitoring, security, and networking. At the same time, ensuring the integrity and confidentiality of data collected by sensor nodes is crucial to prevent unauthorized access or modification. However, the limited resources f low-power sensor networks present significant challenges for securing innovative Internet-of-Medical Things (IoMT) applications in complex environments. These miniature sensing systems, essential for diverse healthcare applications, grapple with constrained computational power and energy budgets. To address this challenge, this study proposes a dynamic power management strategy within a resource-constrained FPGA-based cryptoprocessor core for secure IoMT networks. The sensor node design comprises two main modules: an 8-bit reduced instruction set computer (RISC) and a cryptographic engine. These modules collaboratively manage their power consumption during the operational stages of data acquisition, encryption, transmission, and sleep mode activation. The cryptographic engine employs a pseudorandom number generator to generate a keystream for data encryption, utilizing direct sequence spread spectrum (DSSS) encoding to ensure secure communication. The experimental results demonstrate the effectiveness of the proposed dynamic power management strategy within the resource-constrained cryptoprocessor core. The sensor node achieves an average power consumption of 0.1 mW while utilizing 2414 logic cells and 5292 registers. A comparative analysis with other state-of-the-art lightweight sensor nodes highlights the advantages of our dynamic power management approach within the cryptoprocessor sensing system.
Citation: Technologies
PubDate: 2025-02-04
DOI: 10.3390/technologies13020068
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 69: A Systematic Review of Locomotion
Assistance Exoskeletons: Prototype Development and Technical Challenges
Authors: Weiqi Lin, Hui Dong, Yongzhuo Gao, Wenda Wang, Yi Long, Long He, Xiwang Mao, Dongmei Wu, Wei Dong
First page: 69
Abstract: Exoskeletons can track the wearer’s movements in real time, thereby enhancing physical performance or restoring mobility for individuals with gait impairments. These wearable assistive devices have demonstrated significant potential in both rehabilitation and industrial applications. This review focuses on the major advancements in exoskeleton technology published since 2020, with particular emphasis on the development of structural designs for lower-limb exoskeletons employed in locomotion assistance. We employed a systematic literature review methodology, categorizing the included studies into three main types: rigid exoskeleton, soft exoskeleton, and tethered platform. The current development status of robotic exoskeletons is analyzed based on publication year, system weight, target assistive joints, and main effects. Furthermore, we examine the factors driving these advancements and their implications for the field. The key challenges and opportunities that may influence the future development of exoskeleton technologies are also highlighted in this review.
Citation: Technologies
PubDate: 2025-02-05
DOI: 10.3390/technologies13020069
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 70: Automatic Robotic Ultrasound for 3D
Musculoskeletal Reconstruction: A Comprehensive Framework
Authors: Dezhi Sun, Alessandro Cappellari, Bangyu Lan, Momen Abayazid, Stefano Stramigioli, Kenan Niu
First page: 70
Abstract: Musculoskeletal ultrasound (US) imaging faces challenges such as operator experience, limited spatial flexibility, and high personnel costs. This study introduces an Automated Robotic Ultrasound Scanning (ARUS) system that integrates key technological advancements to automate the ultrasound scanning procedure with the robot, including anatomical target localization, automatic trajectory generation, deep-learning-based segmentation, and 3D reconstruction of musculoskeletal structures. The ARUS system consists of a robotic arm, ultrasound imaging, and stereo vision for precise anatomical area detection. A Graphical User Interface (GUI) facilitates a flexible selection of scanning trajectories, improving user interaction and enabling customized US scans. To handle complex and dynamic curvatures on the skin, together with anatomical area detection, the system employs a hybrid position–force control strategy based on the generated trajectory, ensuring stability and accuracy. Additionally, the utilized RA-UNet model offers multi-label segmentation on the bone and muscle tissues simultaneously, which incorporates residual blocks and attention mechanisms to enhance segmentation accuracy and robustness. A custom musculoskeletal phantom was used for validation. Compared to the reference 3D reconstruction result derived from the MRI scan, ARUS achieved a 3D reconstruction root mean square error (RMSE) of 1.22 mm, with a mean error of 0.94 mm and a standard deviation of 0.77 mm. The ARUS system extends 3D musculoskeletal imaging capacity by enabling both bones and muscles to be segmented and reconstructed into 3D shapes in real time and simultaneously. These features suggest significant potential as a cost-effective and reliable option for musculoskeletal examination and diagnosis in real-time applications.
Citation: Technologies
PubDate: 2025-02-08
DOI: 10.3390/technologies13020070
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 71: Dynamic Controller Design for Maximum
Power Point Tracking Control for Solar Energy Systems
Authors: M. A. Fkirin, Zeinab M. Gowaly, Emad A. Elsheikh
First page: 71
Abstract: The demand for efficient renewable energy solutions has spurred the development of advanced maximum power point tracking (MPPT) algorithms for photovoltaic (PV) systems, especially under variable atmospheric conditions. This study proposes a dynamic MPPT controller utilizing a combination of Long Short-Term Memory (LSTM)-based Artificial Neural Networks (ANNs) and Fuzzy Logic Control (FLC) to optimize power extraction in solar energy systems across diverse irradiance and temperature conditions. The study focuses on designing and implementing these two dynamic MPPT algorithms, LSTM-ANN and LSTM-FLC, to effectively manage the inherent variability in solar energy generation due to fluctuating atmospheric conditions, ensuring that the PV system consistently operates at its optimal power point. The proposed controllers are evaluated and compared to LSTM–Proportional Integral (PI) and traditional MPPT methods, including ANNs, Fuzzy Logic, and hybrid ANN–Fuzzy. The performance metrics used in the evaluation include tracking efficiency, response time, and system stability. The simulation results with real-time data demonstrate that the LSTM-optimized controllers significantly outperform conventional methods, particularly in adapting to sudden changes in irradiance and temperature.
Citation: Technologies
PubDate: 2025-02-08
DOI: 10.3390/technologies13020071
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 72: Dynamic Surgical Prioritization: A
Machine Learning and XAI-Based Strategy
Authors: Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan, Muhammad Ehsan Rana, Jimmy H. Gutiérrez-Bahamondes
First page: 72
Abstract: Surgical waiting lists present significant challenges to healthcare systems, particularly in resource-constrained settings where equitable prioritization and efficient resource allocation are critical. We aim to address these issues by developing a novel, dynamic, and interpretable framework for prioritizing surgical patients. Our methodology integrates machine learning (ML), stochastic simulations, and explainable AI (XAI) to capture the temporal evolution of dynamic prioritization scores, qp(t), while ensuring transparency in decision making. Specifically, we employ the Light Gradient Boosting Machine (LightGBM) for predictive modeling, stochastic simulations to account for dynamic variables and competitive interactions, and SHapley Additive Explanations (SHAPs) to interpret model outputs at both the global and patient-specific levels. Our hybrid approach demonstrates strong predictive performance using a dataset of 205 patients from an otorhinolaryngology (ENT) unit of a high-complexity hospital in Chile. The LightGBM model achieved a mean squared error (MSE) of 0.00018 and a coefficient of determination (R2) value of 0.96282, underscoring its high accuracy in estimating qp(t). Stochastic simulations effectively captured temporal changes, illustrating that Patient 1’s qp(t) increased from 0.50 (at t=0) to 1.026 (at t=10) due to the significant growth of dynamic variables such as severity and urgency. SHAP analyses identified severity (Sever) as the most influential variable, contributing substantially to qp(t), while non-clinical factors, such as the capacity to participate in family activities (Lfam), exerted a moderating influence. Additionally, our methodology achieves a reduction in waiting times by up to 26%, demonstrating its effectiveness in optimizing surgical prioritization. Finally, our strategy effectively combines adaptability and interpretability, ensuring dynamic and transparent prioritization that aligns with evolving patient needs and resource constraints.
Citation: Technologies
PubDate: 2025-02-08
DOI: 10.3390/technologies13020072
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 73: Effect of Anti-Bending Bars on Vertical
Vibrations of Passenger Carriage Body
Authors: Ioana-Izabela Apostol, Traian Mazilu, Mădălina Dumitriu
First page: 73
Abstract: High-speed passenger carriages with a long and light carriage body are sensitive to vertical vibration because the bending mode eigenfrequency falls within the most sensible frequency interval for the human being. Anti-bending bars (ABBs) are a passive means to raise the eigenfrequency of the bending mode of the carriage body beyond the sensitive limit, ameliorating ride comfort. ABBs are two bars fixed via vertical supports under the carriage chassis on the longitudinal beams. ABBs resist the bending of the carriage body and can, therefore, increase the bending eigenfrequency beyond the sensitive limit, as necessary. In this paper, a new model for the ABBs, which takes into account the longitudinal stiffness of the ABBs, the three-direction stiffness of the fastening between the ABBs and the vertical supports and the vertical vibration modes of the ABBs via the Euler–Bernoulli beam theory and modal analysis, is incorporated in the 10 degrees of freedom model of a passenger carriage; this is to study the effect of the ABBs upon the running behaviour and ride comfort according to the specific regulations in the field. First, the frequency response functions (FRFs) of the passenger carriage with an ABB system are calculated and analysed, and then, the root mean square (r.m.s.) acceleration and the comfort index are evaluated in the carriage body centre in the context of a parametric study. The longitudinal stiffness of the fastening is critical to ensure the effectiveness of the ABB system. However, the effect of decreasing in the longitudinal stiffness of the fastening can be compensated by adopting longer ABBs.
Citation: Technologies
PubDate: 2025-02-10
DOI: 10.3390/technologies13020073
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 74: Solid State Transformers: A
Review—Part I: Stages of Conversion and Topologies
Authors: Dragoș-Mihail Predescu, Ștefan-George Roșu
First page: 74
Abstract: Solid State Transformers (SSTs) represent an emerging technology that seeks to improve upon traditional Low-Frequency Transformers (LFTs) with Medium-Frequency Transformers (MFTs) of reduced core size while incorporating modular converter structures as their input and output stages. In addition to magnetic circuit reduction, SSTs provide enhanced functionalities such as power factor correction, voltage regulation, and the capability to interface with various sources and loads. However, owing to the novelty of SSTs and the various proposed implementations, a general review would difficult to follow and might not be able to adequately analyze each aspect of SST structures. This complexity underscores the need for a new division of information and classification based on the number of conversion stages, which is the main contribution of this study. Converter functionalities are derived based on the number of stages. Utilizing these functionalities along with existing and proposed implementations, converter topologies are identified and then detailed in terms of their respective functionalities, advantages, disadvantages, and control schemes. The subsequent chapters provide a comparative analysis of the different topologies and present existing SST implementations. For this analysis, metrics such as the number of SST stages, power flow, voltage control, power quality, and component count are used. Based on the resulting analysis, single-stage SSTs are a promising solution that emphasize economy and high power density, while multi-stage SSTs are also a viable solution thanks to their ease of control and flexible design. This paper constitutes the first part of a two-part review. The second part will focus on the degrees of design freedom (such as multilevel structures/cells) and provide a generalized approach to modularity within SST systems.
Citation: Technologies
PubDate: 2025-02-10
DOI: 10.3390/technologies13020074
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 75: Development of a Self-Updating System for
the Prediction of Steel Mechanical Properties in a Steel Company by
Machine Learning Procedures
Authors: Valerio Zippo, Elisa Robotti, Daniele Maestri, Pietro Fossati, David Valenza, Stefano Maggi, Gennaro Papallo, Masho Hilawie Belay, Simone Cerruti, Giorgio Porcu, Emilio Marengo
First page: 75
Abstract: This study is focused on the implementation of statistical learning methods for the prediction of the mechanical properties of steel products from the chemical profile of the raw material and the process parameters. The integration of this model into the production process allows a large-scale steel industry to predict steel properties with heightened accuracy, optimizing the manufacturing process for minimal waste and improved consistency. A workflow for process data analysis has been developed, based on the use of machine learning algorithms to build an interface for data treatment to be directly used online. The proposed approach has a comprehensive connotation, starting from data pre-treatment and cleaning, to model building and prediction. Different machine learning algorithms are compared (Polynomial Regression, LASSO, Random Forests and Gradient Boosting, ANN, SVM, and k-NN), to provide the best predictive ability, also exploiting human reinforcement. The results proved to be very promising for all the types of steel investigated, with very good RMSE and R2 values both in fitting and in prediction. The application here presented is being integrated into Total Quality Tutor (TQT) software, developed in-house in C# language, for predicting the mechanical properties of steel.
Citation: Technologies
PubDate: 2025-02-11
DOI: 10.3390/technologies13020075
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 76: Exploring the Combination of Serious
Games, Social Interactions, and Virtual Reality in Adolescents with ASD: A
Scoping Review
Authors: Fabrizio Stasolla, Enza Curcio, Anna Passaro, Mariacarla Di Gioia, Antonio Zullo, Elvira Martini
First page: 76
Abstract: Autism spectrum disorder (ASD) often presents significant challenges for adolescents in developing social interaction skills. Emerging technologies such as Serious Games (SGs) and Virtual Reality (VR) offer promising solutions by providing immersive, interactive learning environments. This scoping review evaluates the potential of VR-based SGs to enhance social skills in adolescents with ASD by identifying current applications, benefits, limitations, and research gaps. A systematic search of the literature was conducted on Scopus, focusing on empirical studies published between 2013 and 2024. Studies were included based on their relevance to the use of SGs and VR in promoting social interactions in children and adolescents with ASD. The review highlights that VR-based SGs can effectively support the development of social skills, such as communication and collaboration, by providing structured, safe environments for children and adolescents to practice and refine their abilities. However, challenges remain, including the high cost of VR equipment, the need for greater customization, and the limited scope of long-term efficacy studies. While VR-based SGs show considerable promise, further research is needed to explore their long-term impacts and improve accessibility. Addressing these challenges could solidify VR’s role in ASD interventions, enhancing social skill development and improving the quality of life for children and adolescents with ASD.
Citation: Technologies
PubDate: 2025-02-12
DOI: 10.3390/technologies13020076
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 77: The Role of Artificial Intelligence in
Optometric Diagnostics and Research: Deep Learning and Time-Series
Forecasting Applications
Authors: Luis F. F. M. Santos, Miguel Ángel Sánchez-Tena, Cristina Alvarez-Peregrina, José-María Sánchez-González, Clara Martinez-Perez
First page: 77
Abstract: This study introduces an Artificial Intelligence framework based on the Deep Learning model Bidirectional Encoder Representations from Transformers framework trained on a dataset from 2000–2023. The AI tool categorizes articles into six classes: Contactology, Low Vision, Refractive Surgery, Pediatrics, Myopia, and Dry Eye, with supervised learning enhancing classification accuracy, achieving F1-Scores averaging 86.4%, AUC at 0.98, Precision at 87%, and Accuracy at 86.8% via one-shot training, while Epoch training showed 85.9% Accuracy and 92.8% Precision. Utilizing the Artificial Intelligence model outputs, the Autoregressive Integrated Moving Average model provides forecasts from all classes through 2030, predicting decreases in research interest for Contactology, Low Vision, and Refractive Surgery but increases for Myopia and Dry Eye due to rising prevalence and lifestyle changes. Stability is expected in pediatric research, highlighting its focus on early detection and intervention. This study demonstrates the effectiveness of AI in enhancing diagnostic precision and strategic planning in optometry, with potential implications for broader clinical applications and improved accessibility to eye care.
Citation: Technologies
PubDate: 2025-02-12
DOI: 10.3390/technologies13020077
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 78: Fuzzy Guiding of Roulette Selection in
Evolutionary Algorithms
Authors: Krzysztof Pytel
First page: 78
Abstract: This paper presents, discusses, and tests a novel method for guiding roulette selection in evolutionary algorithms. The new method uses fuzzy logic and incorporates information from both current and historical generations to predict the best scheme for the selection process. Fuzzy logic controls the probability of selecting individuals to the parent pool, based on historical data from the evolution process and the relationship between an individual’s fitness and the average fitness of the population. The new algorithm outperforms existing solutions by ensuring a proper balance between exploring new regions of the search space and exploiting previously found ones. The proposed system enhances the performance, efficiency, and robustness of evolutionary algorithms while reducing the risk of stagnation in suboptimal solutions. Results of experiments demonstrate that the newly developed algorithm is more efficient and resistant to premature convergence than standard evolutionary algorithms. Tests on both function optimization problems and real-world connected facility localization problems confirm the robustness of the newly developed algorithm. The algorithm can be an effective tool in solving a wide range of optimization problems, for example, optimization of computer network infrastructure.
Citation: Technologies
PubDate: 2025-02-12
DOI: 10.3390/technologies13020078
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 79: The Design, Simulation, and Construction
of an O2, C3H8, and CO2 Gas Detection System Based on the Electrical
Response of MgSb2O6 Oxide
Authors: José Trinidad Guillen Bonilla, Maricela Jiménez Rodríguez, Héctor Guillen Bonilla, Alex Guillen Bonilla, Emilio Huízar Padilla, María Eugenia Sánchez Morales, Ariadna Berenice Flores Jiménez, Juan Carlos Estrada Gutiérrez
First page: 79
Abstract: In this paper, the prototype of a gas detector based on the electrical response of MgSb2O6 oxide at 400 °C and with a concentration of 560 ppm was designed, simulated, and fabricated. This design considers a PIC18F4550 microcontroller and a response time of 3 s for the sensor. It is worth noting that the response system can be reduced in concordance with the mathematical model of the sensor’s electrical response. The proposed device is capable of detecting one to three gases: O2, C3H8, and CO2. The configuration is achieved through three switches. In programming the prototype, factors such as the gas sensor signals, device configuration, corrective gas signals, and indicator signals were carefully considered. The characteristic of the gas detector is an operational temperature of 400 °C, which is ideal for industrial processing. This can be configured to detect a single gas or all three of them O2,C3H8,and CO2. Each gas type has its corresponding corrective signal and an indicator-led diode. The operation concentration is 560 ppm, the device is scalable, and its programming can be extended to cover industrial networks.
Citation: Technologies
PubDate: 2025-02-13
DOI: 10.3390/technologies13020079
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 80: Nanostructured TiNi Wires for Textile
Implants: Optimization of Drawing Process by Means of Mechano-Chemical
Treatment
Authors: Nadezhda V. Artyukhova, Anastasiia V. Shabalina, Sergey G. Anikeev, Helmut-Takahiro Uchida, Sergei A. Kulinich
First page: 80
Abstract: TiNi-based alloys are widely utilized in various engineering and medical applications. This study presents a newly developed and optimized technology for producing TiNi wires with a diameter of 40 μm utilizing a combined mechano-chemical treatment and drawing process. The resulting thin wires were tested and characterized using multiple methods to determine their structural, phase, and mechanical properties. The structure of the TiNi wires, designed for use as textile implants in reconstructive medicine, features a TiNi metal matrix (B2 and B19′ phases) at the core and a surface oxide layer. A key structural characteristic of these wires is the presence of fine nanograins averaging 15–17 nm in size. No texturizing of the metallic material was observed during repeated plastic deformations throughout the drawing process. The applied mechano-chemical treatment aimed to modify the structure of the wires’ surface oxide layer. Specifically, reducing the thickness and roughness of this layer decreased the friction coefficient of the alloy during drawing, thus significantly reducing the number of breaks during production. At the same time, the cryogenic treatment of the final product was found to stabilize the martensitic phase B19′, which reduces the Young’s modulus by 10 GPa. Consequently, this newly developed methodology enhances the material’s quality and reduces labor costs during production.
Citation: Technologies
PubDate: 2025-02-13
DOI: 10.3390/technologies13020080
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 81: Research on Circuit Partitioning
Algorithm Based on Partition Connectivity Clustering and Tabu Search
Authors: Linzi Yin, Hao Hu, Changgeng Li
First page: 81
Abstract: In this paper, a circuit-partitioning method is proposed based on partition connectivity clustering and tabu search. It includes four phases: coarsening, initial partitioning, uncoarsening, and refinement. In the initial partitioning phase, the concept of partition connectivity is introduced to optimize the vertex-clustering process, which clusters vertices with high connectivity in advance to provide an optimal initial solution. In the refinement phase, an improved tabu search algorithm is proposed, which combines two flexible neighborhood search rules and a candidate solution-selection strategy based on vertex-exchange frequency to further optimize load balancing. Additionally, a random perturbation method is suggested to increase the diversity of the search space and improve both the depth and breadth of global search. The experimental results based on the ISCAS-89 and ISCAS-85 benchmark circuits show that the average cut size of the proposed circuit-partitioning method is 0.91 times that of METIS and 0.86 times that of the KL algorithm, with better performance for medium- and small-scale circuits.
Citation: Technologies
PubDate: 2025-02-14
DOI: 10.3390/technologies13020081
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 82: Reducing Energy Consumption in Embedded
Systems Applications
Authors: Ioannis Sofianidis, Vasileios Konstantakos, Spyridon Nikolaidis
First page: 82
Abstract: One of the most important challenges in modern digital systems, especially regarding autonomous embedded systems, is energy efficiency. This work studies an energy consumption optimization approach on a microcontroller that implements IoT-like applications, featuring Dynamic Voltage and Frequency Scaling (DVFS) capabilities, by dynamically changing the supply voltage and clock frequency. The proposed approach categorizes tasks according to their demands on timing requirements and analyzes speed–energy efficiency trade-offs. Results strongly indicate that energy performance is improved due to the proper adjustment of configurations towards required tasks. The findings are verified within a set of scenarios that highlight the potential balance between energy economy and operational demands for specialized IoT contexts.
Citation: Technologies
PubDate: 2025-02-16
DOI: 10.3390/technologies13020082
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 83: Instrumentation and Evaluation of a
Sensing System with Signal Conditioning Using Fuzzy Logic for a Rotary
Dryer
Authors: Juan Manuel Tabares-Martinez, Adriana Guzmán-López, Micael Gerardo Bravo-Sánchez, Alejandro Israel Barranco-Gutierrez, Juan José Martínez-Nolasco, Francisco Villaseñor-Ortega
First page: 83
Abstract: The growing demand for innovative solutions to accurately measure variables in dewatering processes has driven the development of advanced technologies. This study focuses on the evaluation of a measurement system in a rotary dryer used to dehydrate carrots at an operating temperature of 70 °C. The system uses the Arduino platform, strain gauges, and LM35 temperature sensors. Experimental tests were designed to evaluate the performance of the dryer, using initial quantities of carrots of 1.5 kg, 1.0 kg, and 0.5 kg. The novelty of this study lies in the application of fuzzy logic for signal conditioning in real time, in order to improve the precision of measurements, designed in MATLAB (version 9.5) and programmed in Arduino. The dryer reduces the water content of the product to a final average of 10%. The research offers a novel solution for the integration of an intelligent measurement system that optimizes dewatering efficiency. The manuscript is organized as follows: in the methodology section, the design of the measurement system is described; subsequently, the experimental results and the analysis of the dryer efficiency are presented, and finally, in the conclusions, the implications of the system and its possible applications in other processes are discussed.
Citation: Technologies
PubDate: 2025-02-18
DOI: 10.3390/technologies13020083
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 84: Systematic Generation and Evaluation of
Synthetic Production Data for Industry 5.0 Optimization
Authors: Solomiia Liaskovska, Sviatoslav Tyskyi, Yevgen Martyn, Andy T. Augousti, Volodymyr Kulyk
First page: 84
Abstract: Our research focused on analyzing and advancing information technologies to identify ecological parameters in production. The primary goals were to enhance efficiency, reduce waste, and minimize the environmental impact of manufacturing processes. By incorporating the results of the study, we observed and systematized changes occurring in the transition from Industry 4.0 to Industry 5.0. Special attention was given to studying processes and technologies related to the generation of synthetic data and analyzing the implementation of cutting-edge technologies. The research object includes new parameters introduced within the framework of Industry 5.0, encompassing automation and cognitive technologies. Our scientific interests also extended to synthetic data used in modeling various production processes, including optimizing device performance in manufacturing and forecasting abnormal situations in industrial equipment operations. The subject of the research involves algorithms for generating synthetic data and methods for validating them to ensure their statistical similarity to real-world data. During the study, we also analyzed the impact of artificial intelligence implementation on improving the efficiency and adaptability of manufacturing systems.
Citation: Technologies
PubDate: 2025-02-18
DOI: 10.3390/technologies13020084
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 85: Exploration and Deconstruction of
Correlation Cycles in Multidimensional Datasets
Authors: Adam Dudáš, Emil Kršák, Miroslav Kvaššay
First page: 85
Abstract: Correlation analysis is one of the most prolific statistical methods used in data analysis problems, mining of knowledge focused on relationships of attributes in large datasets, and in various predictive tasks utilizing statistical, machine learning, and deep learning models. This approach to the analysis of functional relationships in multidimensional datasets is commonly used in conjunction with visual analysis approaches, which offer novel context for the relationships in data and clarify the results presented in large correlation matrices. One of such visualization methods uses graphical models called correlation graphs and chains, which visualize individual direct and indirect relationships between pairs of attributes in a dataset of interest as a graph structure, where vertices of the graph represent attributes of the dataset and edges between vertices represent the correlation of these attributes. This work focuses on the definition, identification, and exploration of so-called correlation cycles, which can be—through their deconstruction—used as an approach to lower error values in regression tasks. After the implementation of the correlation cycle identification and deconstruction, the proposed concept is evaluated on predictive analysis tasks in the context of three benchmarking datasets from the engineering field—the Sensor dataset, Superconductivity dataset, and Energy Farm dataset. The results obtained in this study show that when using simple, explainable regressors, the method utilizing deconstructed correlation cycles reaches a lower error rate in 83.3% of regression cases compared to the same regression models without the cycle incorporation.
Citation: Technologies
PubDate: 2025-02-18
DOI: 10.3390/technologies13020085
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 86: Compliant Parallel Asymmetrical Gripper
System
Authors: Andrea Deaconescu, Tudor Deaconescu
First page: 86
Abstract: The paper presents an innovative soft gripper system designed for automated assembling operations. The novel robotic soft gripper utilizes a linear pneumatic muscle as its motor, due to its inherently compliant behavior. This renders redundant the deployment of sensors or complex controllers, due to its mechanical system that ensures the desired adaptive behavior. Adaptivity is attained by adjusting the air pressure in the pneumatic muscle, monitored and controlled in a closed loop by means of a proportional pressure regulator. The kinematic diagram and the functional and constructive models of the gripper system are presented. The developed forces were measured followed by the calculation of stiffness and compliance. The paper concludes with recommendations for the operation of the gripper.
Citation: Technologies
PubDate: 2025-02-19
DOI: 10.3390/technologies13020086
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 87: Using AI and NLP for Tacit Knowledge
Conversion in Knowledge Management Systems: A Comparative Analysis
Authors: Ouissale Zaoui Seghroucheni, Mohamed Lazaar, Mohammed Al Achhab
First page: 87
Abstract: Tacit knowledge, often implicit and deeply embedded within individuals and organizational practices, is critical for fostering innovation and decision-making in knowledge management systems (KMS). Converting tacit knowledge into explicit forms enhances organizational effectiveness by making this knowledge accessible and reusable. This paper presents a comparative analysis of natural language processing (NLP) algorithms used for document and report mining to facilitate tacit knowledge conversion. This study focuses on algorithms that extract insights from semi-structured and document-based natural language representations, commonly found in organizational knowledge artifacts. Key NLP strategies, including text mining, information extraction, sentiment analysis, clustering, classification, recommendation systems, and affective computing, are evaluated for their effectiveness in identifying and externalizing tacit knowledge. The findings highlight the relative strengths and limitations of these techniques, offering practical guidance for selecting suitable algorithms based on organizational needs. Additionally, this paper identifies challenges and emerging opportunities for advancing NLP-driven tacit knowledge conversion, providing actionable insights for researchers and practitioners aiming to enhance KMS capabilities.
Citation: Technologies
PubDate: 2025-02-19
DOI: 10.3390/technologies13020087
Issue No: Vol. 13, No. 2 (2025)
- Technologies, Vol. 13, Pages 19: Optimization of Energy Consumption in
Voice Assistants Through AI-Enabled Cache Implementation: Development and
Evaluation of a Metric
Authors: Alber Oswaldo Montoya Benitez, Álvaro Suárez Sarmiento, Elsa María Macías López, Jorge Herrera-Ramirez
First page: 19
Abstract: Intelligent systems developed under the Internet of Things (IoT) paradigm offer solutions for various social and productive scenarios. Voice assistants (VAs), as part of IoT-based systems, facilitate task execution in a simple and automated manner, from entertainment to critical activities. Lithium batteries often power these devices. However, their energy consumption can be high due to the need to remain in continuous listening mode and the time it takes to search for and deliver responses from the Internet. This work proposes the implementation of a VA through Artificial Intelligence (AI) training and using cache memory to minimize response time and reduce energy consumption. First, the difference in energy consumption between VAs in active and passive states is experimentally verified. Subsequently, a communication architecture and a model representing the behavior of VAs are presented, from which a metric is developed to evaluate the energy consumption of these devices. The cache-enabled prototype shows a reduction in response time and energy expenditure (comparing the results of cloud-based VA and cache-based VA), several times lower according to the developed metric, demonstrating the effectiveness of the proposed system. This development could be a viable solution for areas with limited power sources, low coverage, and mobility situations that affect internet connectivity.
Citation: Technologies
PubDate: 2025-01-02
DOI: 10.3390/technologies13010019
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 20: Organic–Inorganic Hybrid Dielectric
Layers for Low-Temperature Thin-Film Transistors Applications: Recent
Developments and Perspectives
Authors: Javier Meza-Arroyo, Rafael Ramírez-Bon
First page: 20
Abstract: This paper reviews the recent development of organic–inorganic hybrid dielectric materials for application as gate dielectrics in thin-film transistors (TFTs). These hybrid materials consist of the blending of high-k inorganic dielectrics with polymers, and their resulting properties depend on the amount and type of interactions between the organic and inorganic phases. The resulting amorphous networks, characterized by crosslinked organic and inorganic phases, can be tailored for specific applications, including gate dielectrics in TFTs. As dielectric materials, they offer a synergistic combination of high dielectric constants, low leakage currents, and mechanical flexibility, crucial for next-generation flexible electronics. Furthermore, organic–inorganic hybrid materials are easily processed in solution, allowing for low-temperature deposition compatible with flexible substrates. Various configurations of these hybrid gate dielectrics, such as bilayer structures and polymer nanocomposites, are discussed, with an emphasis on their potential to enhance device performance. Despite the significant advancements, challenges remain in optimizing the performance and stability of these hybrid materials. This review summarizes recent progress and highlights the advantages and emerging applications of low-temperature, solution-processed hybrid dielectrics, with a focus on their integration into flexible, stretchable, and wearable electronic devices.
Citation: Technologies
PubDate: 2025-01-02
DOI: 10.3390/technologies13010020
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 21: Prosthetic Hand Based on Human Hand
Anatomy Controlled by Surface Electromyography and Artificial Neural
Network
Authors: Larisa Dunai, Isabel Seguí Verdú, Dinu Turcanu, Viorel Bostan
First page: 21
Abstract: Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then, the data have been segmented, feature extracted and classified for each gesture. To validate the method, 100 signals for three gestures with 64 samples each signal have been recorded from 2 users with OYMotion sensors and 100 signals for three gestures from 4 users with the MyWare sensors. These signals were used for feature extraction and classification using an artificial neuronal network. The model converges after 10 sessions, achieving 98% accuracy. As a result, an algorithm was developed that aimed to recognize two specific gestures (handling a bottle and pointing with the index finger) in real time with 95% accuracy.
Citation: Technologies
PubDate: 2025-01-02
DOI: 10.3390/technologies13010021
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 22: Bridging the Maturity Gaps in Industrial
Data Science: Navigating Challenges in IoT-Driven Manufacturing
Authors: Amruta Awasthi, Lenka Krpalkova, Joseph Walsh
First page: 22
Abstract: This narrative review evaluates the curtail components of data maturity in manufacturing industries, the associated challenges, and the application of industrial data science (IDS) to improve organisational decision-making. As data availability grows larger, manufacturing organisations face difficulties comprehending heterogeneous datasets of varying quality, which may lead to inefficient decision-making and other operational inefficiencies. It underlines that data appropriate for its intended application is considered quality data. The importance of including stakeholders in the data review process to enhance the data quality is accentuated in this paper, specifically when big data analysis is to be integrated into corporate strategies. Manufacturing industries leveraging their data thoughtfully can optimise efficiency and facilitate informed and productive decision-making by establishing a robust technical infrastructure and developing intuitive platforms and solutions. This study highlights the significance of IDS in revolutionising manufacturing sectors within the framework of Industry 4.0 and the Industrial Internet of Things (IIoT), demonstrating that big data can substantially improve efficiency, reduce costs, and guide strategic decision-making. The gaps or maturity levels among industries show a substantial discrepancy in this analysis, which is classified into three types: Industry 4.0 maturity levels, data maturity or readiness condition index, and industrial data science and analytics maturity. The emphasis is given to the pressing need for resilient data science frameworks enabling organisations to evaluate their digital readiness and execute their data-driven plans efficiently and effortlessly. Simultaneously, future work will focus on pragmatic applications to enhance industrial competitiveness within the heavy machinery sector.
Citation: Technologies
PubDate: 2025-01-06
DOI: 10.3390/technologies13010022
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 23: Blockchain Applications in the Military
Domain: A Systematic Review
Authors: Nikos Kostopoulos, Yannis C. Stamatiou, Constantinos Halkiopoulos, Hera Antonopoulou
First page: 23
Abstract: Background: Blockchain technology can transform military operations, increasing security and transparency and gaining efficiency. It addresses many problems related to data security, privacy, communication, and supply chain management. The most researched aspects are its integration with emerging technologies, such as artificial intelligence, the IoT, application in uncrewed aerial vehicles, and secure communications. Methods: A systematic review of 43 peer-reviewed articles was performed to discover the applications of blockchain in defense. Key areas analyzed include the role of blockchain in securing communications, fostering transparency, promoting real-time data sharing, and using smart contracts for maintenance management. Challenges were assessed, including scalability, interoperability, and integration with the legacy system, alongside possible solutions, such as sharding and optimized consensus mechanisms. Results: In the case of blockchain, great potential benefits were shown in enhancing military operations, including secure communication, immutable record keeping, and real-time integration of data with the IoT and AI. Smart contracts optimized resource allocation and reduced maintenance procedures. However, challenges remain, such as scalability, interoperability, and high energy requirements. Proposed solutions, like sharding and hybrid architecture, show promise to address these issues. Conclusions: Blockchain is set to revolutionize the efficiency and security of the military. Its potential is enormous, but it must overcome scalability, interoperability, and integration issues. Further research and strategic adoption will thus allow blockchain to become one of the cornerstones of future military operations.
Citation: Technologies
PubDate: 2025-01-06
DOI: 10.3390/technologies13010023
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 24: Smart Sport Watch Usage: The Dominant
Role of Technology Readiness over Exercise Motivation and Sensation
Seeking
Authors: Gershon Tenenbaum, Tomer Ben-Zion, Yair Amichai-Hamburger, Yair Galily, Assaf Lev
First page: 24
Abstract: The study examines the link between technology readiness/acceptance, motivation for exercising, and sensation seeking and using or avoiding Smart Sport Watches (SSW). A sample of 315 adolescents, Mage = 29.6 (SD = 11.01) and healthy male (n = 95, 30.2%) and female (n = 179, 56.85%), completed all the measures of these variables’ dimensions via the internet. Multiple followed by univariate analyses of variance (MANOVA, ANOVA) were performed for each of the study’s psychological dimensions and single variables. The two categorical factors (e.g., BS factors) were the use of SSW (yes/no) and walk/run (yes/no). Results revealed that adolescents using SSW rated themselves significantly (p < 0.05) and substantially higher than their non-SSW users on positive readiness for technology (d = 0.47), and specifically on optimism (d = 0.34) and innovation (d = 0.51). Moreover, users of SSW reported significantly (p < 0.05) and substantially lower negative readiness for technology than their non-SSW users’ counterparts (d = −0.49), and specifically on discomfort (d = −0.38) and distrust (d = −50), but neither on the overall motivation for exercise dimensions nor on sensation-seeking. Moreover, adolescents who walk/run reported being more internally motivated (d = 0.38), integrated (d = 0.61), and identified (d = 0.34) than their sedentary counterparts. Discussion centers on the important role of readiness/acceptance in using technological devices and the need to use technology-specific motivation and personality measures to further explore this phenomenon.
Citation: Technologies
PubDate: 2025-01-07
DOI: 10.3390/technologies13010024
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 25: A Reinforcement Learning-Based Dynamic
Clustering of Sleep Scheduling Algorithm (RLDCSSA-CDG) for Compressive
Data Gathering in Wireless Sensor Networks
Authors: Alaa N. El-Shenhabi, Ehab H. Abdelhay, Mohamed A. Mohamed, Ibrahim F. Moawad
First page: 25
Abstract: Energy plays a major role in wireless sensor networks (WSNs), and measurements demonstrate that transmission consumes more energy than processing. Hence, organizing the transmission process and managing energy usage throughout the network are the main goals for maximizing the network’s lifetime. This paper proposes an algorithm called RLDCSSA-CDG, which is processed through the 3F phases: foundation, formation, and forwarding phases. Firstly, the network architecture is founded, and the cluster heads (CHs) are determined in the foundation phase. Secondly, sensor nodes are dynamically gathered into clusters for better communication in the formation phase. Finally, the transmitting process will be adequately organized based on an adaptive wake-up/sleep scheduling algorithm to transmit the data at the “right time” in the forwarding phase. The MATLAB platform was utilized to conduct simulation studies to validate the proposed RLDCSSA-CDG’s effectiveness. Compared to a very recent work called RLSSA and RLDCA for CDG, the proposed RLDCSSA-CDG reduces total data transmissions by 22.7% and 63.3% and energy consumption by 8.93% and 38.8%, respectively. It also achieves the lowest latency compared to the two contrastive algorithms. Furthermore, the proposed algorithm increases the whole network lifetime by 77.3% and promotes data recovery accuracy by 91.1% relative to the compared algorithms.
Citation: Technologies
PubDate: 2025-01-08
DOI: 10.3390/technologies13010025
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 26: Cross-Attention Fusion of Visual and
Geometric Features for Large-Vocabulary Arabic Lipreading
Authors: Samar Daou, Achraf Ben-Hamadou, Ahmed Rekik, Abdelaziz Kallel
First page: 26
Abstract: Lipreading involves recognizing spoken words by analyzing the movements of the lips and surrounding area using visual data. It is an emerging research topic with many potential applications, such as human–machine interaction and enhancing audio-based speech recognition. Recent deep learning approaches integrate visual features from the mouth region and lip contours. However, simple methods such as concatenation may not effectively optimize the feature vector. In this article, we propose extracting optimal visual features using 3D convolution blocks followed by a ResNet-18, while employing a graph neural network to extract geometric features from tracked lip landmarks. To fuse these complementary features, we introduce a cross-attention mechanism that combines visual and geometric information to obtain an optimal representation of lip movements for lipreading tasks. To validate our approach for Arabic, we introduce the first large-scale Lipreading in the Wild for Arabic (LRW-AR) dataset, consisting of 20,000 videos across 100 word classes, spoken by 36 speakers. Experimental results on both the LRW-AR and LRW datasets demonstrate the effectiveness of our approach, achieving accuracies of 85.85% and 89.41%, respectively.
Citation: Technologies
PubDate: 2025-01-09
DOI: 10.3390/technologies13010026
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 27: Implementing Computer Vision in Android
Apps and Presenting the Background Technology with Mathematical
Demonstrations
Authors: Roland Szabo
First page: 27
Abstract: The aim of this paper is to create image-processing Android apps to launch on the Google Play Store. Three apps with different usages will be presented for different situations. The first app is a night-vision app on an Android phone that uses OpenCV. The second app is a tooth-brushing assistant application. The app is made for mobile phones and uses advanced image-processing techniques to detect when the tooth is brushed correctly or incorrectly. The main focus is on the direction of the toothbrush movement because this is one of the key aspects of correctly brushing teeth. The direction of movement of the brush is detected using movement vectors. The third app is a lane-detection app on the smartphone. Lane detection is carried out using OpenCV and TensorFlow libraries. The mobile app was implemented on the Android operating system. The app has a live video feed of the surroundings. When in the area of view, there will be a road with a lane. The system detects the lane and draws a green line over it.
Citation: Technologies
PubDate: 2025-01-09
DOI: 10.3390/technologies13010027
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 28: Enhancing Thyroid Nodule Detection in
Ultrasound Images: A Novel YOLOv8 Architecture with a C2fA Module and
Optimized Loss Functions
Authors: Shidan Wang, Zi-An Zhao, Yuze Chen, Ye-Jiao Mao, James Chung-Wai Cheung
First page: 28
Abstract: Thyroid-related diseases, particularly thyroid cancer, are rising globally, emphasizing the critical need for the early detection and accurate screening of thyroid nodules. Ultrasound imaging has inherent limitations—high noise, low contrast, and blurred boundaries—that make manual interpretation subjective and error-prone. To address these challenges, YOLO-Thyroid, an improved model for the automatic detection of thyroid nodules in ultrasound images, is presented herein. Building upon the YOLOv8 architecture, YOLO-Thyroid introduces the C2fA module—an extension of C2f that incorporates Coordinate Attention (CA)—to enhance feature extraction. Additionally, loss functions were incorporated, including class-weighted binary cross-entropy to alleviate class imbalance and SCYLLA-IoU (SIoU) to improve localization accuracy during boundary regression. A publicly available thyroid ultrasound image dataset was optimized using format conversion and data augmentation. The experimental results demonstrate that YOLO-Thyroid outperforms mainstream object detection models across multiple metrics, achieving a higher detection precision of 54%. The recall, calculated based on the detection of nodules containing at least one feature suspected of being malignant, reaches 58.2%, while the model maintains a lightweight structure. The proposed method significantly advances ultrasound nodule detection, providing an effective and practical solution for enhancing diagnostic accuracy in medical imaging.
Citation: Technologies
PubDate: 2025-01-09
DOI: 10.3390/technologies13010028
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 29: Real-Time Deployment of Ultrasound Image
Interpretation AI Models for Emergency Medicine Triage Using a Swine Model
Authors: Sofia I. Hernandez Torres, Lawrence Holland, Theodore Winter, Ryan Ortiz, Krysta-Lynn Amezcua, Austin Ruiz, Catherine R. Thorpe, Eric J. Snider
First page: 29
Abstract: Ultrasound imaging is commonly used for medical triage in both civilian and military emergency medicine sectors. One specific application is the eFAST, or the extended focused assessment with sonography in trauma exam, where pneumothorax, hemothorax, or abdominal hemorrhage injuries are identified. However, the diagnostic accuracy of an eFAST exam depends on obtaining proper scans and making quick interpretation decisions to evacuate casualties or administer necessary interventions. To improve ultrasound interpretation, we developed AI models to identify key anatomical structures at eFAST scan sites, simplifying image acquisition by assisting with proper probe placement. These models plus image interpretation diagnostic models were paired with two real-time eFAST implementations. The first implementation was a manual AI-driven ultrasound eFAST tool that used guidance models to select correct frames prior to making any diagnostic predictions. The second implementation was a robotic imaging platform capable of providing semi-autonomous image acquisition combined with diagnostic image interpretation. We highlight the use of both real-time approaches in a swine injury model and compare their performance of this emergency medicine application. In conclusion, AI can be deployed in real time to provide rapid triage decisions, lowering the skill threshold for ultrasound imaging at or near the point of injury.
Citation: Technologies
PubDate: 2025-01-11
DOI: 10.3390/technologies13010029
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 30: Increasing the Wear Resistance of
Stamping Tools for Coordinate Punching of Sheet Steel Using CrAlSiN and
DLC:Si Coatings
Authors: Sergey N. Grigoriev, Marina A. Volosova, Ilya A. Korotkov, Vladimir D. Gurin, Artem P. Mitrofanov, Sergey V. Fedorov, Anna A. Okunkova
First page: 30
Abstract: The punching of holes or recesses on computer numerical control coordinate presses occurs in sheets at high speeds (up to 1200 strokes/min) with an accuracy of ~0.05 mm. One of the most effective approaches to the wear rate reduction of stamping tools is the use of solid lubricants, such as wear-resistant coatings, where the bulk properties of the tool are combined with high microhardness and lubricating ability to eliminate waste disposal and remove oil contaminants from liquid lubricants. This work describes the efficiency of complex CrAlSiN/DLC:Si coatings deposited using a hybrid unit combining physical vapor deposition and plasma-assisted chemical vapor deposition technologies to increase the wear resistance of a punch tool made of X165CrMoV12 die steel during coordinate punching of 4.0 mm thick 41Cr4 carbon structural steel sheets. The antifriction layer of DLC:Si allows for minimizing the wear under thermal exposure of 200 °C. The wear criterion of the lateral surface was 250 μm. The tribological tests allow us to consider the CrAlSiN/DLC:Si coatings as effective in increasing the wear resistance of stamping tools (21,000 strokes for the uncoated tool and 48,000 strokes for the coated one) when solving a wide range of technological problems in sheet stamping of structural steels.
Citation: Technologies
PubDate: 2025-01-12
DOI: 10.3390/technologies13010030
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 31: Study on the Dynamic Response of the
Carbody–Anti-Bending Bars System
Authors: Ioana-Izabela Apostol, Traian Mazilu, Mădălina Dumitriu
First page: 31
Abstract: Ride comfort is an important requirement that passenger rail vehicles must meet. Carbody–anti-bending system is a relatively new passive method to enhance the ride comfort in passenger rail vehicles with long and light carbody. The resonance frequency of the first bending mode (FBM) of such vehicle is within the most sensitive frequency range that affects ride comfort. Anti-bending bars consist of two bars that are mounted under the longitudinal beams of the carbody chassis using vertical supports. When the carbody bends, the anti-bending bars develop moments in the neutral axis of the carbody opposing the bending of the carbody. In this way, the carbody structure becomes stiffer and the resonance frequency of the FBM can be increased beyond the upper limit of the discomfort range of frequency, improving the ride comfort. The theoretical principle of this method has been demonstrated employing a passenger rail vehicle model that includes the carbody as a free–free Euler–Bernoulli beam and the anti-bending bars as longitudinal springs jointed to the vertical supports. Also, the method feasibility has been verified in the past using an experimental scale demonstrator system. In this paper, a new model of the carbody–anti-bending bar system is proposed by including three-directional elastic elements (vertical and longitudinal direction and rotation in the vertical–longitudinal plane) to model the fastening of the anti-bending bars to the supports and the vertical motion of the anti-bending bars modelled as free–free Euler–Bernoulli beams connected to the elastic elements of the fastening. In the longitudinal direction, the anti-bending bars work as springs connected to the longitudinal elastic elements of the fastening. The modal analysis method is applied to point out the basic properties of the frequency response functions (FRFs) of the carbody–anti-bending bars system, considering the bounce and FBMs of both the carbody and the anti-bending bars. A parametric study of the FRF of the carbody shows that the vertical stiffness of the fastening should be sufficiently high enough to eliminate the influence of the modes of the anti-bending bars upon the carbody response and to reduce the anti-bending bars vibration in the frequency range of interest. Longitudinal stiffness of the elastic elements of the fastening is critical to increase the bending resonance frequency of the carbody out of the sensitive range. Longer anti-bending bars can improve the capability of the anti-bending bars to increase the bending resonance without the risk of interference effects caused by the bounce and bending modes of the anti-bending bars.
Citation: Technologies
PubDate: 2025-01-12
DOI: 10.3390/technologies13010031
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 32: Vision Transformers for Image
Classification: A Comparative Survey
Authors: Yaoli Wang, Yaojun Deng, Yuanjin Zheng, Pratik Chattopadhyay, Lipo Wang
First page: 32
Abstract: Transformers were initially introduced for natural language processing, leveraging the self-attention mechanism. They require minimal inductive biases in their design and can function effectively as set-based architectures. Additionally, transformers excel at capturing long-range dependencies and enabling parallel processing, which allows them to outperform traditional models, such as long short-term memory (LSTM) networks, on sequence-based tasks. In recent years, transformers have been widely adopted in computer vision, driving remarkable advancements in the field. Previous surveys have provided overviews of transformer applications across various computer vision tasks, such as object detection, activity recognition, and image enhancement. In this survey, we focus specifically on image classification. We begin with an introduction to the fundamental concepts of transformers and highlight the first successful Vision Transformer (ViT). Building on the ViT, we review subsequent improvements and optimizations introduced for image classification tasks. We then compare the strengths and limitations of these transformer-based models against classic convolutional neural networks (CNNs) through experiments. Finally, we explore key challenges and potential future directions for image classification transformers.
Citation: Technologies
PubDate: 2025-01-12
DOI: 10.3390/technologies13010032
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 33: Application of Graph Theory and Variants
of Greedy Graph Coloring Algorithms for Optimization of Distributed
Peer-to-Peer Blockchain Networks
Authors: Miljenko Švarcmajer, Denis Ivanović, Tomislav Rudec, Ivica Lukić
First page: 33
Abstract: This paper investigates the application of graph theory and variants of greedy graph coloring algorithms for the optimization of distributed peer-to-peer networks, with a special focus on private blockchain networks. The graph coloring problem, as an NP-hard problem, presents a challenge in determining the minimum number of colors needed to efficiently allocate resources within the network. The paper deals with the influence of different graph density, i.e., the number of links, on the efficiency of greedy algorithms such as DSATUR, Descending, and Ascending. Experimental results show that increasing the number of links in the network contributes to a more uniform distribution of colors and increases the resistance of the network, whereby the DSATUR algorithm achieves the most uniform color saturation. The optimal configuration for a 100-node network has been identified at around 2000 to 2500 links, which achieves stability without excessive redundancy. These results are applied in the context of a private blockchain network that uses optimal connectivity to achieve high resilience and efficient resource allocation. The research findings suggest that adapting network configuration using greedy algorithms can contribute to the optimization of distributed systems, making them more stable and resilient to loads.
Citation: Technologies
PubDate: 2025-01-13
DOI: 10.3390/technologies13010033
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 34: Towards Transparent AI in Medicine:
ECG-Based Arrhythmia Detection with Explainable Deep Learning
Authors: Oleksii Kovalchuk, Oleksandr Barmak, Pavlo Radiuk, Liliana Klymenko, Iurii Krak
First page: 34
Abstract: Cardiovascular diseases are the leading cause of death globally, highlighting the need for accurate diagnostic tools. To address this issue, we introduce a novel approach for arrhythmia detection based on electrocardiogram (ECG) that incorporates explainable artificial intelligence through three key methods. First, we developed an enhanced R peak detection method that integrates domain-specific knowledge into the ECG, improving peak identification accuracy by accounting for the characteristic features of R peaks. Second, we proposed an arrhythmia classification method utilizing a modified convolutional neural network (CNN) architecture with additional convolutional and batch normalization layers. This model processes a triad of cardio cycles—the preceding, current, and following cycles—to capture temporal dependencies and hidden features related to arrhythmias. Third, we implemented an interpretation method that explains CNN’s decisions using clinically relevant features, making the results understandable to clinicians. Using the MIT-BIH database, our approach achieved an accuracy of 99.43%, with F1-scores approaching 100% for major arrhythmia classes. The integration of these methods enhances both the performance and transparency of arrhythmia detection systems.
Citation: Technologies
PubDate: 2025-01-14
DOI: 10.3390/technologies13010034
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 35: A Dual-Stage Processing Architecture for
Unmanned Aerial Vehicle Object Detection and Tracking Using Lightweight
Onboard and Ground Server Computations
Authors: Odysseas Ntousis, Evangelos Makris, Panayiotis Tsanakas, Christos Pavlatos
First page: 35
Abstract: UAVs are widely used for multiple tasks, which in many cases require autonomous processing and decision making. This autonomous function often requires significant computational capabilities that cannot be integrated into the UAV due to weight or cost limitations, making the distribution of the workload and the combination of the results produced necessary. In this paper, a dual-stage processing architecture for object detection and tracking in Unmanned Aerial Vehicles (UAVs) is presented, focusing on efficient resource utilization and real-time performance. The proposed system delegates lightweight detection tasks to onboard hardware while offloading computationally intensive processes to a ground server. The UAV is equipped with a Raspberry Pi for onboard data processing, utilizing an Intel Neural Compute Stick 2 (NCS2) for accelerated object detection. Specifically, YOLOv5n is selected as the onboard model. The UAV transmits selected frames to the ground server, which handles advanced tracking, trajectory prediction, and target repositioning using state-of-the-art deep learning models. Communication between the UAV and the server is maintained through a high-speed Wi-Fi link, with a fallback to a 4G connection when needed. The ground server, equipped with an NVIDIA A40 GPU, employs YOLOv8x for object detection and DeepSORT for multi-object tracking. The proposed architecture ensures real-time tracking with minimal latency, making it suitable for mission-critical UAV applications such as surveillance and search and rescue. The results demonstrate the system’s robustness in various environments, highlighting its potential for effective object tracking under limited onboard computational resources. The system achieves recall and accuracy scores as high as 0.53 and 0.74, respectively, using the remote server, and is capable of re-identifying a significant portion of objects of interest lost by the onboard system, measured at approximately 70%.
Citation: Technologies
PubDate: 2025-01-16
DOI: 10.3390/technologies13010035
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 36: Implementing a Wide-Area Network and Low
Power Solution Using Long-Range Wide-Area Network Technology
Authors: Floarea Pitu, Nicoleta Cristina Gaitan
First page: 36
Abstract: In recent decades, technology has undergone significant transformations, aimed at optimizing and enhancing the quality of human life. A prime example of this progress is the Internet of Things (IoT) technology. Today, the IoT is widely applied across diverse sectors, including logistics, communications, agriculture, education, and infrastructure, demonstrating its versatility and profound relevance in various domains. Agriculture has historically been a fundamental sector for meeting humanity’s basic needs, and it is indispensable for survival and development. A critical factor in this regard is climatic and meteorological conditions directly influencing agricultural productivity. Therefore, real-time monitoring and analysis of these variables becomes imperative for optimizing production and reducing vulnerability to climate change. This paper presents the development and implementation of a low-power wide-area network (LPWAN) solution using LoRaWAN (long-range wide-area network) technology, designed for real-time environmental monitoring in agricultural applications. The system consists of energy-efficient end nodes and a custom-configured gateway, designed to optimize data transmission and power consumption. The end nodes integrate advanced sensors for temperature, humidity, and pressure, ensuring accurate data collection.
Citation: Technologies
PubDate: 2025-01-16
DOI: 10.3390/technologies13010036
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 37: Robotic Systems for Hand
Rehabilitation—Past, Present and Future
Authors: Bogdan Gherman, Ionut Zima, Calin Vaida, Paul Tucan, Adrian Pisla, Iosif Birlescu, Jose Machado, Doina Pisla
First page: 37
Abstract: Background: Cerebrovascular accident, commonly known as stroke, Parkinson’s disease, and multiple sclerosis represent significant neurological conditions affecting millions globally. Stroke remains the third leading cause of death worldwide and significantly impacts patients’ hand functionality, making hand rehabilitation crucial for improving quality of life. Methods: A comprehensive literature review was conducted analyzing over 300 papers, and categorizing them based on mechanical design, mobility, and actuation systems. To evaluate each device, a database with 45 distinct criteria was developed to systematically assess their characteristics. Results: The analysis revealed three main categories of devices: rigid exoskeletons, soft exoskeletons, and hybrid devices. Electric actuation represents the most common source of power. The dorsal placement of the mechanism is predominant, followed by glove-based, lateral, and palmar configurations. A correlation between mass and functionality was observed during the analysis; an increase in the number of actuated fingers or in functionality automatically increases the mass of the device. The research shows significant technological evolution with considerable variation in design complexity, with 29.4% of devices using five or more actuators while 24.8% employ one or two actuators. Conclusions: While substantial progress has been made in recent years, several challenges persist, including missing information or incomplete data from source papers and a limited number of clinical studies to evaluate device effectiveness. Significant opportunities remain to improve device functionality, usability, and therapeutic effectiveness, as well as to implement advanced power systems for portable devices.
Citation: Technologies
PubDate: 2025-01-16
DOI: 10.3390/technologies13010037
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 38: Advanced Autonomous System for Monitoring
Soil Parameters
Authors: Băjenaru Valentina-Daniela, Istrițeanu Simona-Elena, Paul-Nicolae Ancuța
First page: 38
Abstract: Context: This research investigates the advantages of real-time monitoring of soil quality for various land management practices. It also highlights the significance of spatio-temporal soil modeling and mapping in providing a clear and visual understanding of how aridity changes over time and across different locations. Aims: This paper aims to provide a comprehensive guide to the key processes required for the development of a laboratory-based soil quality monitoring system. Methods: The applied methodologies involved the processes of sensor deployment, data acquisition infrastructure establishment, and sensor calibration. These procedures culminated in the development of a soil quality assessment model that was subsequently subjected to two months of laboratory testing using three distinct soil types. The analysis yielded a strong positive linear correlation between the measured and predicted soil quality values. Key Results: As expected, the assimilation of prior soil quality estimates within the modeling framework demonstrated a significant enhancement in the accuracy of real-time soil quality estimations. Conclusions: This research promotes the importance of iterative improvements of the soil quality monitoring system. The need for a long-term perspective and a plan for maintenance and continuous improvement of such systems in the ecosystem is important to improve the ease of making predictions to avoid soil aridization. The results of this research will be useful for researchers and practitioners involved in the design and implementation of soil monitoring systems.
Citation: Technologies
PubDate: 2025-01-16
DOI: 10.3390/technologies13010038
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 39: Pneumatically Actuated Rehabilitation
Equipment for the Sagittal and Frontal Plane Movements of the Neck Joint
Authors: Sarah Mareş, Andrea Deaconescu, Tudor Deaconescu
First page: 39
Abstract: The timely reintegration into their daily routine of patients suffering from work-related musculoskeletal disorders is a priority in medical rehabilitation. This can be accomplished by means of certain procedures and adequate medical rehabilitation equipment. Starting from these considerations this paper proposes an original constructive solution of a rehabilitation device designed for the passive mobilization of the neck joint in the sagittal and frontal plane. The constructive solution that is put forward uses a pneumatic muscle as the actuation element, ensuring the adaptability of the equipment to the particular pain tolerance of each patient. The construction and dimensioning calculations of the equipment are presented, followed by the determination of the torsional rigidity and compliance permitted by the system. Based on the results the paper concludes with recommendations for the optimum deployment of the rehabilitation equipment.
Citation: Technologies
PubDate: 2025-01-16
DOI: 10.3390/technologies13010039
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 40: An Investigation of Infrared Small Target
Detection by Using the SPT–YOLO Technique
Authors: Yongjun Qi, Shaohua Yang, Zhengzheng Jia, Yuanmeng Song, Jie Zhu, Xin Liu, Hongxing Zheng
First page: 40
Abstract: To detect and recognize small-size and submerged complex background targets in infrared images, we combine a dynamic receptive field fusion strategy and a multi-scale feature fusion mechanism to improve the detection performance of small targets significantly. The space-to-depth convolution module is introduced as a downsampling layer in the backbone first and achieves the same sampling effect. More detailed information is retained at the same time. Thus, the model’s detection capability for small targets has been enhanced. Then, the pyramid level 2 feature map with minimum receptive field and maximum resolution is added to the neck, which reduces the loss of positional information during feature sampling. Furthermore, x-small detection heads are added, the understanding of the overall characteristics and structure of the target is enhanced much more, and the representation and localization of small targets have been improved. Finally, the cross-entropy loss function in the original network model is replaced by an adaptive threshold focal loss function, forcing the model to allocate more attention to target features. The above methods are based on a public tool, the eighth version of You Only Look Once (YOLO) improved, it is named SPT–YOLO (SPDConv + P2 + Adaptive Threshold + YOLOV8s) in this paper. Some experiments on datasets such as infrared small object detection (IR-SOD) and infrared small target detection 1K(IRSTD-1K), etc. have been executed to verify the proposed algorithm; and the mean average precision of 94.0% and 69% under the condition of threshold at 0.5 and over a range from 0.5 to 0.95 is obtained, respectively. The results show that the proposed method achieves the best performance of infrared small target detection compared to existing methods.
Citation: Technologies
PubDate: 2025-01-17
DOI: 10.3390/technologies13010040
Issue No: Vol. 13, No. 1 (2025)
- Technologies, Vol. 13, Pages 41: Nu—A Marine Life Monitoring and
Exploration Submarine System
Authors: Ali A. M. R. Behiry, Tarek Dafar, Ahmed E. M. Hassan, Faisal Hassan, Abdullah AlGohary, Mounib Khanafer
First page: 41
Abstract: Marine life exploration is constrained by factors such as limited scuba diving time, depth restrictions for divers, costly expeditions, safety risks to divers’ health, and minimizing harm to marine ecosystems, where traditional diving often risks disturbing marine life. This paper introduces Nu (named after an ancient Egyptian deity), a 3D-printed Remotely Operated Underwater Vehicle (ROUV) designed in an attempt to address these challenges. Nu employs Long Range (LoRa), a low-power and long-range communication technology, enabling wireless operation via a manual controller. The vehicle features an onboard live-feed camera with a separate communication system that transmits video to an external real-time machine learning (ML) pipeline for fish species classification, reducing human error by taxonomists. It uses Brushless Direct Current (BLDC) motors for long-distance movement and water pump motors for precise navigation, minimizing disturbance, and reducing damage to surrounding species. Nu’s functionality was evaluated in a controlled 2.5-m-deep body of water, focusing on connectivity, maneuverability, and fish identification accuracy. The fish detection algorithm achieved an average precision of 60% in identifying fish presence, while the classification model achieved 97% precision in assigning species labels, with unknown species flagged correctly. The testing of Nu in a controlled environment has met the system design expectations.
Citation: Technologies
PubDate: 2025-01-20
DOI: 10.3390/technologies13010041
Issue No: Vol. 13, No. 1 (2025)
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