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Volume 13
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Technologies, Volume 13, Issue 2 (February 2025) – 46 articles

Cover Story (view full-size image): Solar energy offers an alternative for powering electrolysis for green hydrogen production as well as wastewater treatment. High costs and logistical challenges of electrolysis have limited widespread investigation and implementation of electrochemistry in wastewater plants. To overcome these challenges, researchers from the University of Western Ontario have designed and tested a new approach to chemical experiments and wastewater treatment tests, using a portable stand-alone open-source PV-powered station that can be located onsite with unreliable electrical power. Equipped with six wheels, 1.2 kW of solar panels, an energy-monitoring data acquisition system and sensors to enable real-time monitoring of gases, the unique setup demonstrates its potential and adaptability to different applications and scenarios. View this paper
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17 pages, 973 KiB  
Article
Using AI and NLP for Tacit Knowledge Conversion in Knowledge Management Systems: A Comparative Analysis
by Ouissale Zaoui Seghroucheni, Mohamed Lazaar and Mohammed Al Achhab
Technologies 2025, 13(2), 87; https://doi.org/10.3390/technologies13020087 - 19 Feb 2025
Viewed by 1695
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Artificial Intelligence and Smart Information Systems: Trends and Innovations)
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13 pages, 3281 KiB  
Article
Compliant Parallel Asymmetrical Gripper System
by Andrea Deaconescu and Tudor Deaconescu
Technologies 2025, 13(2), 86; https://doi.org/10.3390/technologies13020086 - 19 Feb 2025
Viewed by 712
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)
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19 pages, 7109 KiB  
Article
Exploration and Deconstruction of Correlation Cycles in Multidimensional Datasets
by Adam Dudáš, Emil Kršák and Miroslav Kvaššay
Technologies 2025, 13(2), 85; https://doi.org/10.3390/technologies13020085 - 18 Feb 2025
Cited by 1 | Viewed by 740
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 [...] Read more.
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. Full article
(This article belongs to the Section Information and Communication Technologies)
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15 pages, 2779 KiB  
Article
Systematic Generation and Evaluation of Synthetic Production Data for Industry 5.0 Optimization
by Solomiia Liaskovska, Sviatoslav Tyskyi, Yevgen Martyn, Andy T. Augousti and Volodymyr Kulyk
Technologies 2025, 13(2), 84; https://doi.org/10.3390/technologies13020084 - 18 Feb 2025
Viewed by 900
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 [...] Read more.
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. Full article
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18 pages, 2925 KiB  
Article
Instrumentation and Evaluation of a Sensing System with Signal Conditioning Using Fuzzy Logic for a Rotary Dryer
by Juan Manuel Tabares-Martinez, Adriana Guzmán-López, Micael Gerardo Bravo-Sánchez, Alejandro Israel Barranco-Gutierrez, Juan José Martínez-Nolasco and Francisco Villaseñor-Ortega
Technologies 2025, 13(2), 83; https://doi.org/10.3390/technologies13020083 - 18 Feb 2025
Viewed by 928
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 [...] Read more.
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. Full article
(This article belongs to the Section Assistive Technologies)
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18 pages, 9690 KiB  
Article
Reducing Energy Consumption in Embedded Systems Applications
by Ioannis Sofianidis, Vasileios Konstantakos and Spyridon Nikolaidis
Technologies 2025, 13(2), 82; https://doi.org/10.3390/technologies13020082 - 16 Feb 2025
Viewed by 1042
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 [...] Read more.
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. Full article
(This article belongs to the Collection Modern Circuits, System Technologies, and Selected Papers from MOCAST)
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22 pages, 9768 KiB  
Article
Research on Circuit Partitioning Algorithm Based on Partition Connectivity Clustering and Tabu Search
by Linzi Yin, Hao Hu and Changgeng Li
Technologies 2025, 13(2), 81; https://doi.org/10.3390/technologies13020081 - 14 Feb 2025
Viewed by 961
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, [...] Read more.
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. Full article
(This article belongs to the Section Information and Communication Technologies)
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19 pages, 12626 KiB  
Article
Nanostructured TiNi Wires for Textile Implants: Optimization of Drawing Process by Means of Mechano-Chemical Treatment
by Nadezhda V. Artyukhova, Anastasiia V. Shabalina, Sergey G. Anikeev, Helmut-Takahiro Uchida and Sergei A. Kulinich
Technologies 2025, 13(2), 80; https://doi.org/10.3390/technologies13020080 - 13 Feb 2025
Viewed by 1117
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 [...] Read more.
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. Full article
(This article belongs to the Section Manufacturing Technology)
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28 pages, 12512 KiB  
Article
The Design, Simulation, and Construction of an O2, C3H8, and CO2 Gas Detection System Based on the Electrical Response of MgSb2O6 Oxide
by José Trinidad Guillen Bonilla, Maricela Jiménez Rodríguez, Héctor Guillen Bonilla, Alex Guillen Bonilla, Emilio Huízar Padilla, María Eugenia Sánchez Morales, Ariadna Berenice Flores Jiménez and Juan Carlos Estrada Gutiérrez
Technologies 2025, 13(2), 79; https://doi.org/10.3390/technologies13020079 - 13 Feb 2025
Viewed by 1005
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 [...] Read more.
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. Full article
(This article belongs to the Section Innovations in Materials Processing)
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27 pages, 4085 KiB  
Article
Fuzzy Guiding of Roulette Selection in Evolutionary Algorithms
by Krzysztof Pytel
Technologies 2025, 13(2), 78; https://doi.org/10.3390/technologies13020078 - 12 Feb 2025
Viewed by 1058
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 [...] Read more.
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. Full article
(This article belongs to the Section Information and Communication Technologies)
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27 pages, 799 KiB  
Article
The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting Applications
by Luis F. F. M. Santos, Miguel Ángel Sánchez-Tena, Cristina Alvarez-Peregrina, José-María Sánchez-González and Clara Martinez-Perez
Technologies 2025, 13(2), 77; https://doi.org/10.3390/technologies13020077 - 12 Feb 2025
Viewed by 1755
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, [...] Read more.
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. Full article
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18 pages, 641 KiB  
Review
Exploring the Combination of Serious Games, Social Interactions, and Virtual Reality in Adolescents with ASD: A Scoping Review
by Fabrizio Stasolla, Enza Curcio, Anna Passaro, Mariacarla Di Gioia, Antonio Zullo and Elvira Martini
Technologies 2025, 13(2), 76; https://doi.org/10.3390/technologies13020076 - 12 Feb 2025
Cited by 1 | Viewed by 1483
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 [...] Read more.
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. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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35 pages, 8667 KiB  
Article
Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures
by Valerio Zippo, Elisa Robotti, Daniele Maestri, Pietro Fossati, David Valenza, Stefano Maggi, Gennaro Papallo, Masho Hilawie Belay, Simone Cerruti, Giorgio Porcu and Emilio Marengo
Technologies 2025, 13(2), 75; https://doi.org/10.3390/technologies13020075 - 11 Feb 2025
Viewed by 1520
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 [...] Read more.
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. Full article
(This article belongs to the Section Manufacturing Technology)
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40 pages, 8054 KiB  
Review
Solid State Transformers: A Review—Part I: Stages of Conversion and Topologies
by Dragoș-Mihail Predescu and Ștefan-George Roșu
Technologies 2025, 13(2), 74; https://doi.org/10.3390/technologies13020074 - 10 Feb 2025
Viewed by 1724
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Next-Generation Distribution System Planning, Operation, and Control)
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22 pages, 5140 KiB  
Article
Effect of Anti-Bending Bars on Vertical Vibrations of Passenger Carriage Body
by Ioana-Izabela Apostol, Traian Mazilu and Mădălina Dumitriu
Technologies 2025, 13(2), 73; https://doi.org/10.3390/technologies13020073 - 10 Feb 2025
Viewed by 949
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 [...] Read more.
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. Full article
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20 pages, 661 KiB  
Article
Dynamic Surgical Prioritization: A Machine Learning and XAI-Based Strategy
by Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan, Muhammad Ehsan Rana and Jimmy H. Gutiérrez-Bahamondes
Technologies 2025, 13(2), 72; https://doi.org/10.3390/technologies13020072 - 8 Feb 2025
Viewed by 1159
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)
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31 pages, 10395 KiB  
Article
Dynamic Controller Design for Maximum Power Point Tracking Control for Solar Energy Systems
by M. A. Fkirin, Zeinab M. Gowaly and Emad A. Elsheikh
Technologies 2025, 13(2), 71; https://doi.org/10.3390/technologies13020071 - 8 Feb 2025
Cited by 1 | Viewed by 1313
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 [...] Read more.
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. Full article
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23 pages, 11170 KiB  
Article
Automatic Robotic Ultrasound for 3D Musculoskeletal Reconstruction: A Comprehensive Framework
by Dezhi Sun, Alessandro Cappellari, Bangyu Lan, Momen Abayazid, Stefano Stramigioli and Kenan Niu
Technologies 2025, 13(2), 70; https://doi.org/10.3390/technologies13020070 - 8 Feb 2025
Viewed by 1470
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Innovations in Design, Development and Evaluation of Assistive Technologies)
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22 pages, 14692 KiB  
Review
A Systematic Review of Locomotion Assistance Exoskeletons: Prototype Development and Technical Challenges
by Weiqi Lin, Hui Dong, Yongzhuo Gao, Wenda Wang, Yi Long, Long He, Xiwang Mao, Dongmei Wu and Wei Dong
Technologies 2025, 13(2), 69; https://doi.org/10.3390/technologies13020069 - 5 Feb 2025
Cited by 2 | Viewed by 2116
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 [...] Read more.
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. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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15 pages, 1868 KiB  
Article
Integrated Dynamic Power Management Strategy with a Field Programmable Gate Array-Based Cryptoprocessor System for Secured Internet-of-Medical Things Networks
by Javier Vázquez-Castillo, Daniel Visairo, Ramón Atoche-Enseñat, Alejandro Castillo-Atoche, Renán Quijano-Cetina, Carolina Del-Valle-Soto, Jaime Ortegón-Aguilar and Johan J. Estrada-López
Technologies 2025, 13(2), 68; https://doi.org/10.3390/technologies13020068 - 4 Feb 2025
Viewed by 1445
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Perpetual Sensor Nodes for Sustainable Wireless Network Applications)
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83 pages, 6612 KiB  
Review
A Survey on Data Mining for Data-Driven Industrial Assets Maintenance
by Eduardo Coronel, Benjamín Barán and Pedro Gardel
Technologies 2025, 13(2), 67; https://doi.org/10.3390/technologies13020067 - 4 Feb 2025
Viewed by 1497
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 [...] Read more.
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. Full article
(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)
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36 pages, 55356 KiB  
Article
High-Gain Miniaturized Multi-Band MIMO SSPP LWA for Vehicular Communications
by Tale Saeidi, Sahar Saleh, Nick Timmons, Christopher McDaid, Ahmed Jamal Abdullah Al-Gburi, Faroq Razzaz and Saeid Karamzadeh
Technologies 2025, 13(2), 66; https://doi.org/10.3390/technologies13020066 - 4 Feb 2025
Viewed by 1270
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 [...] Read more.
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. Full article
(This article belongs to the Collection Electrical Technologies)
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21 pages, 909 KiB  
Article
The Role of BIM 6D and 7D in Enhancing Sustainable Construction Practices: A Qualitative Study
by Hanan Al-Raqeb and Seyed Hamidreza Ghaffar
Technologies 2025, 13(2), 65; https://doi.org/10.3390/technologies13020065 - 3 Feb 2025
Viewed by 6107
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 [...] Read more.
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. Full article
(This article belongs to the Section Construction Technologies)
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25 pages, 5932 KiB  
Review
A Review of Academic and Patent Progress on Internet of Things (IoT) Technologies for Enhanced Environmental Solutions
by Usharani Hareesh Govindarajan, Chuyi Zhang, Rakesh D. Raut, Gagan Narang and Alessandro Galdelli
Technologies 2025, 13(2), 64; https://doi.org/10.3390/technologies13020064 - 3 Feb 2025
Viewed by 2365
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)
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18 pages, 2655 KiB  
Article
Solid-State Kinetic Modeling and Experimental Validation of Cu-Fe Bimetallic Catalyst Synthesis and Its Application to Furfural Hydrogenation
by 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 and Antonia Infantes Molina
Technologies 2025, 13(2), 63; https://doi.org/10.3390/technologies13020063 - 3 Feb 2025
Viewed by 1619
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 [...] Read more.
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. Full article
(This article belongs to the Section Innovations in Materials Processing)
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15 pages, 256 KiB  
Article
Barriers to the Adoption of Augmented Reality Technologies for Education and Training in the Built Environment: A Developing Country Context
by Opeoluwa Akinradewo, Mohamed Hafez, John Aliu, Ayodeji Oke, Clinton Aigbavboa and Samuel Adekunle
Technologies 2025, 13(2), 62; https://doi.org/10.3390/technologies13020062 - 3 Feb 2025
Cited by 2 | Viewed by 2823
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 [...] Read more.
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. Full article
(This article belongs to the Collection Technology Advances in IoT Learning and Teaching)
15 pages, 3013 KiB  
Article
Intent-Bert and Universal Context Encoders: A Framework for Workload and Sensor Agnostic Human Intention Prediction
by Maximillian Panoff, Joshua Acevedo, Honggang Yu, Peter Forcha, Shuo Wang and Christophe Bobda
Technologies 2025, 13(2), 61; https://doi.org/10.3390/technologies13020061 - 2 Feb 2025
Viewed by 1491
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 [...] Read more.
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. Full article
(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing, 2nd Volume)
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19 pages, 3018 KiB  
Article
Backfill for Advanced Potash Ore Mining Technologies
by Evgeny Kovalsky, Cheynesh Kongar-Syuryun, Angelika Morgoeva, Roman Klyuev and Marat Khayrutdinov
Technologies 2025, 13(2), 60; https://doi.org/10.3390/technologies13020060 - 2 Feb 2025
Cited by 1 | Viewed by 1294
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 [...] Read more.
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. Full article
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21 pages, 7254 KiB  
Article
Enhancing Electricity Load Forecasting with Machine Learning and Deep Learning
by Arbër Perçuku, Daniela Minkovska and Nikolay Hinov
Technologies 2025, 13(2), 59; https://doi.org/10.3390/technologies13020059 - 1 Feb 2025
Cited by 2 | Viewed by 2681
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 [...] Read more.
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. Full article
(This article belongs to the Collection Modern Circuits, System Technologies, and Selected Papers from MOCAST)
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27 pages, 1115 KiB  
Article
Distributed Ledger Technology in Healthcare: Enhancing Governance and Performance in a Decentralized Ecosystem
by Juan Minango, Henry Carvajal Mora, Marcelo Zambrano, Nathaly Orozco Garzón and Francisco Pérez
Technologies 2025, 13(2), 58; https://doi.org/10.3390/technologies13020058 - 1 Feb 2025
Viewed by 1671
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 [...] Read more.
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. Full article
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