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Technologies, Volume 13, Issue 6 (June 2025) – 42 articles

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17 pages, 9296 KiB  
Article
Linear Average Yield Criterion and Its Application in Failure Pressure Evaluation of Defect-Free Pipelines
by Jian-Hong Ji, Ming-Ming Sun and Jie Zhang
Technologies 2025, 13(6), 252; https://doi.org/10.3390/technologies13060252 - 13 Jun 2025
Abstract
Analysis of internal pressure failure is a crucial aspect of assessing pipeline integrity. By combining the unified yield criterion with actual burst data, the applicability of different yield criteria is elucidated. Based on the distribution law of burst data, a linear average yield [...] Read more.
Analysis of internal pressure failure is a crucial aspect of assessing pipeline integrity. By combining the unified yield criterion with actual burst data, the applicability of different yield criteria is elucidated. Based on the distribution law of burst data, a linear average yield criterion is proposed. The results indicate that the yield function of the linear average yield criterion is a linear expression, and the yield path forms an equilateral non-equiangular inscribed dodecagon within the von Mises circle. For the evaluation of failure pressure, this yield criterion exhibits the highest level of applicability, followed by the ASSY and Tresca yield theories. The linear average yield criterion limits the failure pressure prediction error, with low strain-hardening (0 ≤ n ≤ 0.06) to within 3%. Full article
(This article belongs to the Section Construction Technologies)
47 pages, 915 KiB  
Review
Smart Textile Design: A Systematic Review of Materials and Technologies for Textile Interaction and User Experience Evaluation Methods
by Manoella Guennes, Joana Cunha and Isabel Cabral
Technologies 2025, 13(6), 251; https://doi.org/10.3390/technologies13060251 - 13 Jun 2025
Abstract
Creating meaningful interactions using smart textiles involves both a comprehensive understanding of relevant materials and technologies (M&T) and how users engage with this type of interface. Despite its relevance to design research, user experience (UX) evaluation remains limited within the smart textile field. [...] Read more.
Creating meaningful interactions using smart textiles involves both a comprehensive understanding of relevant materials and technologies (M&T) and how users engage with this type of interface. Despite its relevance to design research, user experience (UX) evaluation remains limited within the smart textile field. This research aims to systematize information regarding the main M&T used in recent smart textile design research and the evaluation methods (EMs) employed to assess the UX. For this purpose, a systematic literature review was conducted in the Scopus database. The search covered the period from 2018 to 2025 and yielded a total of 232 results. Of these, 56 full papers in English, available on the internet, and focusing on experimental research on smart textile interaction and experience evaluation were included. This review identifies the prevalent use of electronic components and conductive materials, emphasizing the importance of selecting materials that enable sensing, actuation, communication, and processing capabilities. UX evaluation focused on the pragmatic dimension, whereas the combination with the hedonic dimension was generally regarded as future work. The study led to the proposal of four key topics to support the creation of meaningful interactions and highlights the need for further research on evaluating users’ emotional experiences with smart textiles. Full article
(This article belongs to the Section Information and Communication Technologies)
23 pages, 4656 KiB  
Article
A Hybrid Intelligent Model for Olympic Medal Prediction Based on Data-Intelligence Fusion
by Ning Li, Junhao Li, Hejia Fang, Jian Wang, Qiao Yu and Yafei Shi
Technologies 2025, 13(6), 250; https://doi.org/10.3390/technologies13060250 - 13 Jun 2025
Abstract
This study presents a hybrid intelligent model for predicting Olympic medal distribution at the 2028 Los Angeles Games, based on data-intelligence fusion (DIF). By integrating historical medal records, athlete performance metrics, debut medal-winning countries, and coaching resources, the model aims to provide accurate [...] Read more.
This study presents a hybrid intelligent model for predicting Olympic medal distribution at the 2028 Los Angeles Games, based on data-intelligence fusion (DIF). By integrating historical medal records, athlete performance metrics, debut medal-winning countries, and coaching resources, the model aims to provide accurate national medal forecasts. The model introduces a Performance Score (PS) system combining a Traditional Advantage Index (TAI) via K-means clustering, an Athlete Strength Index (ASI) using a backpropagation neural network, and a Host effect factor. Sub-models include an autoregressive integrated moving average model for time-series forecasting, logistic regression for predicting debut medal-winning countries, and random forest regression to quantify the “Great Coach” effect. The results project America winning 44 gold and 124 total medals, and China 44 gold and 94 total medals. The model demonstrates strong accuracy with root mean square errors of 3.21 (gold) and 4.32 (total medals), and mean-relative errors of 17.6% and 8.04%. Compared to the 2024 Paris Olympics, the model projects a notable reshuffling in 2028, with the United States expected to strengthen its overall lead as host while countries like France are predicted to experience significant declines in medal counts. Findings highlight the nonlinear impact of coaching and event expansion’s role in medal growth. This model offers a strategic tool for Olympic planning, advancing medal prediction from simple extrapolation to intelligent decision support. Full article
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25 pages, 3966 KiB  
Article
Tribomechanical Analysis and Performance Optimization of Sustainable Basalt Fiber Polymer Composites for Engineering Applications
by Corina Birleanu, Razvan Udroiu, Mircea Cioaza, Paul Bere and Marius Pustan
Technologies 2025, 13(6), 249; https://doi.org/10.3390/technologies13060249 - 13 Jun 2025
Abstract
This study investigates the effect of fiber weight fraction on the tribomechanical behavior of basalt fiber-reinforced polymer (BFRP) composites under dry sliding conditions. Composite specimens with 50%, 65%, and 70% basalt fiber contents were manufactured and tested through tensile, flexural, and pin-on-disc tribological [...] Read more.
This study investigates the effect of fiber weight fraction on the tribomechanical behavior of basalt fiber-reinforced polymer (BFRP) composites under dry sliding conditions. Composite specimens with 50%, 65%, and 70% basalt fiber contents were manufactured and tested through tensile, flexural, and pin-on-disc tribological evaluations. Key tribological parameters, including the coefficient of friction (COF), specific wear rate (K), and contact temperature, were measured under various applied loads and sliding speeds. Statistical analysis was performed using a generalized linear model (GLM) to identify significant factors and their interactions. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) analyses indicated that abrasive wear, matrix cracking, and fiber–matrix interfacial failure were the dominant wear mechanisms. The experimental results revealed that the fiber weight fraction had the most significant influence on COF (42.78%), while the sliding speed had the predominant effect on the specific wear rate (77.69%) and contact temperature (32.79%). These findings highlight the potential of BFRP composites for applications requiring enhanced wear resistance and mechanical stability under varying loading conditions. Full article
(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)
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20 pages, 5754 KiB  
Article
Neck Functional Status Assessment Using Virtual Reality Simulation of Daily Activities
by José Angel Santos-Paz, Álvaro Sánchez-Picot, Elena Bocos-Corredor, Filippo Moggioli, Aitor Martin-Pintado-Zugasti, Rodrigo García-Carmona and Abraham Otero
Technologies 2025, 13(6), 248; https://doi.org/10.3390/technologies13060248 - 12 Jun 2025
Abstract
Neck pain is a significant global health concern and a leading cause of disability. Conventional clinical neck assessments often rely on maximal Cervical Range of Motion (CROM) measurements, which may not accurately reflect functional limitations experienced during activities of daily living (ADLs). This [...] Read more.
Neck pain is a significant global health concern and a leading cause of disability. Conventional clinical neck assessments often rely on maximal Cervical Range of Motion (CROM) measurements, which may not accurately reflect functional limitations experienced during activities of daily living (ADLs). This study introduces a novel approach to evaluate neck functional status by employing a virtual reality (VR) environment to simulate an apple-harvesting task. Three-dimensional head kinematics were continuously recorded in 60 participants (30 with clinically significant neck pain and 30 asymptomatic) as they performed the task. Spectral analysis of the data revealed that individuals with neck pain exhibited slower head rotation speed, particularly in the transverse and frontal planes, compared to the pain-free group, as evidenced by higher spectral power in the low-frequency band [0, 0.1] Hz and lower power in the [0.1, 0.5] Hz band. Furthermore, participants with neck pain required significantly more time to complete the apple-harvesting task. The VR system demonstrated high usability (SUS score = 84.21), and no adverse effects were reported. These findings suggest that VR-based assessment during simulated ADLs can provide valuable information about the functional impact of neck pain beyond traditional CROM measurements, potentially enabling remote evaluation and personalized telerehabilitation strategies. Full article
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29 pages, 3418 KiB  
Article
Green Ground: Construction and Demolition Waste Prediction Using a Deep Learning Algorithm
by Wadha N. Alsheddi, Shahad E. Aljayan, Asma Z. Alshehri, Manar F. Alenzi, Norah M. Alnaim, Maryam M. Alshammari, Nouf K. AL-Saleem and Abdulaziz I. Almulhim
Technologies 2025, 13(6), 247; https://doi.org/10.3390/technologies13060247 - 12 Jun 2025
Abstract
The waste management and recycling industry in Saudi Arabia is facing ongoing challenges in reducing the negative impact resulting from the recycling process. Different types of waste lack an efficient and accurate method for classification, especially in cases that require the rapid processing [...] Read more.
The waste management and recycling industry in Saudi Arabia is facing ongoing challenges in reducing the negative impact resulting from the recycling process. Different types of waste lack an efficient and accurate method for classification, especially in cases that require the rapid processing of materials. A deep learning prediction model based on a convolutional neural network algorithm was developed to classify and predict the types of construction and demolition waste (CDW). The CDW image dataset used contained 9273 images, including concrete, asphalt, ceramics, and autoclaved aerated concrete. The model obtained an overall accuracy of 97.12%. The Green Ground image prediction model is extremely useful in the construction and demolition industry for automating sorting processes. The model improves recycling rates by ensuring that materials are sorted correctly, thus reducing waste sent to landfills, by accurately identifying different types of materials in CDW images. As part of Saudi Arabia’s 2030 sustainability objectives, these steps contribute to achieving a greener future, complying with environmental regulations, and promoting sustainability. Full article
(This article belongs to the Section Environmental Technology)
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24 pages, 7065 KiB  
Article
Center of Mass Auto-Location in Space
by Lucas McLeland, Brian Erickson, Brendan Ruchlin, Eryn Daman, James Mejia, Benjamin Ho, Joshua Lewis, Bryan Mann, Connor Paw, James Ross, Christopher Reis, Scott Walter, Stefanie Coward, Thomas Post, Andrew Freeborn and Timothy Sands
Technologies 2025, 13(6), 246; https://doi.org/10.3390/technologies13060246 - 12 Jun 2025
Abstract
Maintaining a spacecraft’s center of mass at the origin of a body-fixed coordinate system is often key to precision trajectory tracking. Typically, the inertia matrix is estimated and verified with preliminary ground testing. This article presents groundbreaking preliminary results and significant findings from [...] Read more.
Maintaining a spacecraft’s center of mass at the origin of a body-fixed coordinate system is often key to precision trajectory tracking. Typically, the inertia matrix is estimated and verified with preliminary ground testing. This article presents groundbreaking preliminary results and significant findings from on-orbit space experiments validating recently proposed methods as part of a larger study over multiple years. Time-varying estimates of inertia moments and products are used to reveal time-varying estimates of the location of spacecraft center of mass using geosynchronous orbiting test satellites proposing a novel two-norm optimal projection learning method. Using the parallel axis theorem, the location of the mass center is parameterized using the cross products of inertia, and that information is extracted from spaceflight maneuver data validating modeling and simulation. Mass inertia properties are discerned, and the mass center is experimentally revealed to be over thirty centimeters away from the assumed locations in two of the three axes. Rotation about one axis is found to be very well balanced, with the center of gravity lying on that axis. Two-to-three orders of magnitude corrections to inertia identification are experimentally demonstrated. Combined-axis three-dimensional maneuvers are found to obscure identification compared with single-axis maneuvering as predicted by the sequel analytic study. Mass center location migrates 36–95% and subsequent validating experiments duplicate the results to within 0.1%. Full article
(This article belongs to the Special Issue Advanced Autonomous Systems and Artificial Intelligence Stage)
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42 pages, 9998 KiB  
Review
Routing Challenges and Enabling Technologies for 6G–Satellite Network Integration: Toward Seamless Global Connectivity
by Fatma Aktas, Ibraheem Shayea, Mustafa Ergen, Laura Aldasheva, Bilal Saoud, Akhmet Tussupov, Didar Yedilkhan and Saule Amanzholova
Technologies 2025, 13(6), 245; https://doi.org/10.3390/technologies13060245 - 12 Jun 2025
Abstract
The capabilities of 6G networks surpass those of existing networks, aiming to enable seamless connectivity between all entities and users at any given time. A critical aspect of achieving enhanced and ubiquitous mobile broadband, as promised by 6G networks, is merging satellite networks [...] Read more.
The capabilities of 6G networks surpass those of existing networks, aiming to enable seamless connectivity between all entities and users at any given time. A critical aspect of achieving enhanced and ubiquitous mobile broadband, as promised by 6G networks, is merging satellite networks with land-based networks, which offers significant potential in terms of coverage area. Advanced routing techniques in next-generation network technologies, particularly when incorporating terrestrial and non-terrestrial networks, are essential for optimizing network efficiency and delivering promised services. However, the dynamic nature of the network, the heterogeneity and complexity of next-generation networks, and the relative distance and mobility of satellite networks all present challenges that traditional routing protocols struggle to address. This paper provides an in-depth analysis of 6G networks, addressing key enablers, technologies, commitments, satellite networks, and routing techniques in the context of 6G and satellite network integration. To ensure 6G fulfills its promises, the paper emphasizes necessary scenarios and investigates potential bottlenecks in routing techniques. Additionally, it explores satellite networks and identifies routing challenges within these systems. The paper highlights routing issues that may arise in the integration of 6G and satellite networks and offers a comprehensive examination of essential approaches, technologies, and visions required for future advancements in this area. 6G and satellite networks are associated with technical terms such as AI/ML, quantum computing, THz communication, beamforming, MIMO technology, ultra-wide band and multi-band antennas, hybrid channel models, and quantum encryption methods. These technologies will be utilized to enhance the performance, security, and sustainability of future networks. Full article
(This article belongs to the Section Information and Communication Technologies)
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26 pages, 2568 KiB  
Article
Unified Framework for RIS-Enhanced Wireless Communication and Ambient RF Energy Harvesting: Performance and Sustainability Analysis
by Sunday Enahoro, Sunday Ekpo, Yasir Al-Yasir, Mfonobong Uko, Fanuel Elias, Rahul Unnikrishnan and Stephen Alabi
Technologies 2025, 13(6), 244; https://doi.org/10.3390/technologies13060244 - 12 Jun 2025
Abstract
The increasing demand for high-capacity, energy-efficient wireless networks poses significant challenges in maintaining spectral efficiency, minimizing interference, and ensuring sustainability. Traditional direct-link communication suffers from signal degradation due to path loss, multipath fading, and interference, limiting overall performance. To mitigate these challenges, this [...] Read more.
The increasing demand for high-capacity, energy-efficient wireless networks poses significant challenges in maintaining spectral efficiency, minimizing interference, and ensuring sustainability. Traditional direct-link communication suffers from signal degradation due to path loss, multipath fading, and interference, limiting overall performance. To mitigate these challenges, this paper proposes a unified RIS framework that integrates passive and active Reconfigurable Intelligent Surfaces (RISs) for enhanced communication and ambient RF energy harvesting. Our methodology optimizes RIS-assisted beamforming using successive convex approximation (SCA) and adaptive phase shift tuning, maximizing desired signal reception while reducing interference. Passive RIS efficiently reflects signals without external power, whereas active RIS employs amplification-assisted reflection for superior performance. Evaluations using realistic urban macrocell and mmWave channel models reveal that, compared to direct links, passive RIS boosts SNR from 3.0 dB to 7.1 dB, and throughput from 2.6 Gbps to 4.6 Gbps, while active RIS further enhances the SNR to 10.0 dB and throughput to 6.8 Gbps. Energy efficiency increases from 0.44 to 0.67 (passive) and 0.82 (active), with latency reduced from 80 ms to 35 ms. These performance metrics validate the proposed approach and highlight its potential applications in urban 5G networks, IoT systems, high-mobility scenarios, and other next-generation wireless environments. Full article
(This article belongs to the Special Issue Microwave/Millimeter-Wave Future Trends and Technologies)
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23 pages, 3557 KiB  
Article
Analysis of Surface Roughness and Machine Learning-Based Modeling in Dry Turning of Super Duplex Stainless Steel Using Textured Tools
by Shailendra Pawanr and Kapil Gupta
Technologies 2025, 13(6), 243; https://doi.org/10.3390/technologies13060243 - 11 Jun 2025
Viewed by 37
Abstract
One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. The accurate prediction of surface roughness is vital for enhancing component quality [...] Read more.
One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. The accurate prediction of surface roughness is vital for enhancing component quality and machining efficiency. This study presents a machine learning-driven framework for modeling mean roughness depth (Rz) during the dry machining of super duplex stainless steel (SDSS 2507). SDSS 2507 is known for its exceptional mechanical strength and corrosion resistance, but it poses significant challenges in machinability. To address this, this study employs flank-face textured cutting tools to enhance machining performance. Experiments were designed using the L27 orthogonal array with three continuous factors, cutting speed, feed rate, and depth of cut, and one categorical factor, tool texture type (dimple, groove, and wave), along with surface roughness as an output parameter. Gaussian Data Augmentation (GDA) was employed to enrich data variability and strengthen model generalization, resulting in the improved predictive performance of the machine learning models. MATLAB R2021a was employed for preprocessing, the normalization of datasets, and model development. Two models, Least-Squares Support Vector Machine (LSSVM) and Multi-Gene Genetic Programming (MGGP), were trained and evaluated on various statistical metrics. The results showed that both LSSVM and MGGP models learned well from the training data and accurately predicted Rz on the testing data, demonstrating their reliability and strong performance. Of the two models, LSSVM demonstrated superior performance, achieving a training accuracy of 98.14%, a coefficient of determination (R2) of 0.9959, and a root mean squared error (RMSE) of 0.1528. It also maintained strong generalization on the testing data, with 94.36% accuracy and 0.9391 R2 and 0.6730 RMSE values. The high predictive accuracy of the LSSVM model highlights its potential for identifying optimal machining parameters and integrating into intelligent process control systems to enhance surface quality and efficiency in the complex machining of materials like SDSS. Full article
(This article belongs to the Section Innovations in Materials Processing)
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38 pages, 2828 KiB  
Article
An Approach to Business Continuity Self-Assessment
by Nelson Russo, Henrique São Mamede and Leonilde Reis
Technologies 2025, 13(6), 242; https://doi.org/10.3390/technologies13060242 - 11 Jun 2025
Viewed by 17
Abstract
Business Continuity Management (BCM) is critical for organizations to mitigate disruptions and maintain operations, yet many struggle with fragmented and non-standardized self-assessment tools. Existing frameworks often lack holistic integration, focusing narrowly on isolated components like cyber resilience or risk management, which limits their [...] Read more.
Business Continuity Management (BCM) is critical for organizations to mitigate disruptions and maintain operations, yet many struggle with fragmented and non-standardized self-assessment tools. Existing frameworks often lack holistic integration, focusing narrowly on isolated components like cyber resilience or risk management, which limits their ability to evaluate BCM maturity comprehensively. This research addresses this gap by proposing a structured Self-Assessment System designed to unify BCM components into an adaptable, standards-aligned methodology. Grounded in Design Science Research, the system integrates a BCM Model comprising eight components and 118 activities, each evaluated through weighted questions to quantify organizational preparedness. The methodology enables organizations to conduct rapid as-is assessments using a 0–100 scoring mechanism with visual indicators (red/yellow/green), benchmark progress over time and against peers, and align with international standards (e.g., ISO 22301, ITIL) while accommodating unique organizational constraints. Demonstrated via focus groups and semi-structured interviews with 10 organizations, the system proved effective in enhancing top management commitment, prioritizing resource allocation, and streamlining BCM implementation—particularly for SMEs with limited resources. Key contributions include a reusable self-assessment tool adaptable to any BCM framework, empirical validation of its utility in identifying weaknesses and guiding continuous improvement, and a pathway from initial assessment to advanced measurement via the Plan-Do-Check-Act cycle. By bridging the gap between theoretical standards and practical application, this research offers a scalable solution for organizations to systematically evaluate and improve BCM resilience. Full article
(This article belongs to the Section Information and Communication Technologies)
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18 pages, 605 KiB  
Article
A Novel Framework for Co-Expansion Planning of Transmission Lines and Energy Storage Devices Considering Unit Commitment
by Edimar José de Oliveira, Lucas Santiago Nepomuceno, Leonardo Willer de Oliveira and Arthur Neves de Paula
Technologies 2025, 13(6), 241; https://doi.org/10.3390/technologies13060241 - 11 Jun 2025
Viewed by 37
Abstract
This paper presents a methodology for the co-expansion planning of transmission lines and energy storage systems, considering unit commitment constraints and uncertainties in load demand and wind generation. The problem is formulated as a mixed-integer nonlinear program and solved using a decomposition-based approach [...] Read more.
This paper presents a methodology for the co-expansion planning of transmission lines and energy storage systems, considering unit commitment constraints and uncertainties in load demand and wind generation. The problem is formulated as a mixed-integer nonlinear program and solved using a decomposition-based approach that combines a genetic algorithm with mixed-integer linear programming. Uncertainties are modeled through representative day scenarios obtained via clustering. The methodology is validated on a modified IEEE 24-bus system. The results show that co-planning reduces total expansion costs by 14.69%, annual operating costs by 26.19%, and wind curtailment by 91.99% compared to transmission only expansion. These improvements are due to the flexibility introduced by energy storage systems, which enables more efficient thermal dispatch, reduces fuel consumption, and minimizes renewable energy curtailment. Full article
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25 pages, 2627 KiB  
Article
Photovoltaic Power Estimation for Energy Management Systems Addressing NMOT Removal with Simplified Thermal Models
by Juan G. Marroquín-Pimentel, Manuel Madrigal-Martínez, Juan C. Olivares-Galvan and Alma L. Núñez-González
Technologies 2025, 13(6), 240; https://doi.org/10.3390/technologies13060240 - 11 Jun 2025
Viewed by 56
Abstract
For energy management systems, it is crucial to determine, in advance, the available energy from renewable sources to be dispatched in the next hours or days, in order to meet their generation and consumption goals. Predicting the photovoltaic power output strongly depends on [...] Read more.
For energy management systems, it is crucial to determine, in advance, the available energy from renewable sources to be dispatched in the next hours or days, in order to meet their generation and consumption goals. Predicting the photovoltaic power output strongly depends on accurate weather forecasting data and properly photovoltaic panel models. In this context, several traditional thermal models are expected to become obsolete due to the removal of the widely used Nominal Module Operating Temperature parameter, stated in the IEC 61215-2:2021 standard, according to reports of longer time periods in test data processing. The main contribution of the photovoltaic power estimation algorithm developed in this paper is the integration of an accurate procedure to calculate the hourly day-ahead power output of a photovoltaic plant, based on three simplified thermal models in steady state. These models are proposed and evaluated as remedial alternatives to the removal of the Nominal Module Operating Temperature parameter—a subject that has not been widely addressed in the related literature. The proposed estimation algorithm converts specific Numerical Weather Prediction data and solar module specifications into photovoltaic power output, which can be used in energy management applications to provide economic and ecological benefits. This approach focuses on rooftop-mounted mono-crystalline silicon photovoltaic panel arrays and incorporates a nonlinear translation of Standard Test Conditions parameters to real operating conditions. All necessary input data are provided for the analysis, and the accuracy of experimental results is validated using appropriate error metrics. Full article
(This article belongs to the Section Environmental Technology)
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14 pages, 3134 KiB  
Article
Development of a Low-Cost Multi-Physiological Signal Simulation System for Multimodal Wearable Device Calibration
by Tumenkhuslen Delgerkhaan, Qun Wei, Jiwoo Jung, Sangwon Lee, Gangoh Na, Bongjo Kim, In-Cheol Kim and Heejoon Park
Technologies 2025, 13(6), 239; https://doi.org/10.3390/technologies13060239 - 10 Jun 2025
Viewed by 132
Abstract
Using multimodal wearable devices to diagnose cardiovascular diseases early is essential for providing timely medical assistance, particularly in remote areas. This approach helps prevent risks and reduce mortality rates. However, prolonged use of medical devices can lead to measurement inaccuracies, necessitating calibration to [...] Read more.
Using multimodal wearable devices to diagnose cardiovascular diseases early is essential for providing timely medical assistance, particularly in remote areas. This approach helps prevent risks and reduce mortality rates. However, prolonged use of medical devices can lead to measurement inaccuracies, necessitating calibration to maintain precision. Unfortunately, wearable devices often lack affordable calibrators that are suitable for personal use. This study introduces a low-cost simulation system for phonocardiography (PCG) and photoplethysmography (PPG) signals designed for a multimodal smart stethoscope calibration. The proposed system was developed using a multicore microprocessor (MCU), two digital-to-analog converters (DACs), an LED light, and a speaker. It synchronizes dual signals by assigning tasks based on a multitasking function. A designed time adjustment algorithm controls the pulse transit time (PTT) to simulate various cardiovascular conditions. The simulation signals are generated from preprocessed PCG and PPG signals collected during in vivo experiments. A prototype device was manufactured to evaluate performance by measuring the generated signal using an oscilloscope and a multimodal smart stethoscope. The preprocessed signals, generated signals, and measurements by the smart stethoscope were compared and evaluated through correlation analysis. The experimental results confirm that the proposed system accurately generates the features of the physiological signals and remains in phase with the original signals. Full article
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36 pages, 667 KiB  
Article
Transition to a Circular Bioeconomy in the Sugar Agro-Industry: Predictive Modeling to Estimate the Energy Potential of By-Products
by Yoisdel Castillo Alvarez, Reinier Jiménez Borges, Gendry Alfonso-Francia, Berlan Rodríguez Pérez, Carlos Diego Patiño Vidal, Luis Angel Iturralde Carrera and Juvenal Rodríguez-Reséndiz
Technologies 2025, 13(6), 238; https://doi.org/10.3390/technologies13060238 - 10 Jun 2025
Viewed by 318
Abstract
The linear economy model in the sugar agroindustry has generated multiple impacts due to the underutilization of by-products and reliance on fossil fuels. Through predictive modeling and anaerobic digestion, the circular bioeconomy of sugarcane biomass enables the generation of biogas and electricity in [...] Read more.
The linear economy model in the sugar agroindustry has generated multiple impacts due to the underutilization of by-products and reliance on fossil fuels. Through predictive modeling and anaerobic digestion, the circular bioeconomy of sugarcane biomass enables the generation of biogas and electricity in an environmentally sustainable manner. This theoretical-applied research proposes a predictive model to estimate the energy potential of by-products such as bagasse, vinasse, molasses, and filter cake, based on historical production data and validated technical coefficients. The model uses milled sugarcane as a baseline and projects its energy conversion under three scenarios through 2030. In its most favorable configuration, the model estimates energy production of up to 15.5 billion Nm3 of biogas in Cuba and 9.9 billion in Peru. The model’s architecture includes four residual biomass flows and bioenergy conversion factors applicable to electricity generation. It is validated using national statistical series from 2000 to 2018 and presents relative errors below 5%. Cuba, with a peak of over 13,000 GWh of electricity from bagasse, and Peru, with a stable output between 6500 and 7500 GWh, reflect the highest and lowest projected energy utilization, respectively. Bagasse accounts for over 60% of the total estimated energy contribution. This modeling tool is fundamental for advancing a transition toward a circular economy, as it helps mitigate environmental impacts, improve agroindustrial waste management, and guide sustainable policies in sugarcane-based contexts. Full article
(This article belongs to the Section Environmental Technology)
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29 pages, 3636 KiB  
Article
Design, Development, and Evaluation of a Contactless Respiration Rate Measurement Device Utilizing a Self-Heating Thermistor
by Reza Saatchi, Alan Holloway, Johnathan Travis, Heather Elphick, William Daw, Ruth N. Kingshott, Ben Hughes, Derek Burke, Anthony Jones and Robert L. Evans
Technologies 2025, 13(6), 237; https://doi.org/10.3390/technologies13060237 - 9 Jun 2025
Viewed by 72
Abstract
The respiration rate (RR) is an important vital sign for early detection of health deterioration in critically unwell patients. Its current measurement has limitations, relying on visual counting of chest movements. The design of a new RR measurement device utilizing a self-heating thermistor [...] Read more.
The respiration rate (RR) is an important vital sign for early detection of health deterioration in critically unwell patients. Its current measurement has limitations, relying on visual counting of chest movements. The design of a new RR measurement device utilizing a self-heating thermistor is described. The thermistor is integrated into a hand-held air chamber with a funnel attachment to sensitively detect respiratory airflow. The exhaled respiratory airflow reduces the temperature of the thermistor that is kept at a preset temperature, and its temperature recovers during inhalation. A microcontroller provides signal processing, while its display screen shows the respiratory signal and RR. The device was evaluated on 27 healthy adult volunteers, with a mean age of 32.8 years (standard deviation of 8.6 years). The RR measurements from the device were compared with the visual counting of chest movements, and the contact method of inductance plethysmography that was implemented using a commercial device (SOMNOtouch™ RESP). Statistical analysis, e.g., correlations were performed. The RR measurements from the new device and SOMNOtouch™ RESP, averaged across the 27 participants, were 14.6 breaths per minute (bpm) and 14.0 bpm, respectively. The device has a robust operation, is easy to use, and provides an objective measure of the RR in a noncontact manner. Full article
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18 pages, 3130 KiB  
Article
Mechatronic Test Bench Used to Simulate Wind Power Conversion to Thermal Power by Means of a Hydraulic Transmission
by Victor Constantin, Ionela Popescu and Mihai Avram
Technologies 2025, 13(6), 236; https://doi.org/10.3390/technologies13060236 - 6 Jun 2025
Viewed by 323
Abstract
The work presented in this paper discusses the steps taken to design, implement, and test a mechatronic test stand that uses historical wind power data to generate thermal power that could be used by small-to-medium consumers. The work also pertains to usage in [...] Read more.
The work presented in this paper discusses the steps taken to design, implement, and test a mechatronic test stand that uses historical wind power data to generate thermal power that could be used by small-to-medium consumers. The work also pertains to usage in areas where large wind turbines could not be installed due to space restrictions, such as highly populated areas. A rotor flux control (RFC) speed-controlled 2.2 kW AC motor was used to simulate the action of a wind turbine on a 6 cm3 hydraulic pump. The setup allows for a small form factor and a much lighter turbine to be installed. The paper describes the schematic, installation, usage, and initial results obtained using a hydraulic test stand developed by the authors. The initial work allowed us to obtain different temperatures of the hydraulic oil, up to 60 °C, over a period of 30 min, for various pressures and flow rates, thus confirming that the system is functional overall. Further work will elaborate on the effect of different wind patterns on the setup, as well as provide an in-depth study on a use case for the system. Full article
(This article belongs to the Section Environmental Technology)
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24 pages, 2229 KiB  
Article
Mathematical Modeling of Optimal Drone Flight Trajectories for Enhanced Object Detection in Video Streams Using Kolmogorov–Arnold Networks
by Aida Issembayeva, Oleksandr Kuznetsov, Anargul Shaushenova, Ardak Nurpeisova, Gabit Shuitenov and Maral Ongarbayeva
Technologies 2025, 13(6), 235; https://doi.org/10.3390/technologies13060235 - 6 Jun 2025
Viewed by 207
Abstract
This study addresses the critical challenge of optimizing drone flight parameters for enhanced object detection in video streams. While most research focuses on improving detection algorithms, the relationship between flight parameters and detection performance remains poorly understood. We present a novel approach using [...] Read more.
This study addresses the critical challenge of optimizing drone flight parameters for enhanced object detection in video streams. While most research focuses on improving detection algorithms, the relationship between flight parameters and detection performance remains poorly understood. We present a novel approach using Kolmogorov–Arnold Networks (KANs) to model complex, non-linear relationships between altitude, pitch angle, speed, and object detection performance. Our main contributions include the following: (1) the systematic analysis of flight parameters’ effects on detection performance using the AU-AIR dataset, (2) development of a KAN-based mathematical model achieving R2 = 0.99, (3) identification of optimal flight parameters through multi-start optimization, and (4) creation of a flexible implementation framework adaptable to different UAV platforms. Sensitivity analysis confirms the solution’s robustness with only 7.3% performance degradation under ±10% parameter variations. This research bridges flight operations and detection algorithms, offering practical guidelines that enhance the detection capability by optimizing image acquisition rather than modifying detection algorithms. Full article
(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)
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16 pages, 1874 KiB  
Article
Computationally Efficient Transfer Learning Pipeline for Oil Palm Fresh Fruit Bunch Defect Detection
by Yang Luo, Anwar P. P. Abdul Majeed, Zaid Omar, Saad Aslam and Yi Chen
Technologies 2025, 13(6), 234; https://doi.org/10.3390/technologies13060234 - 6 Jun 2025
Viewed by 197
Abstract
The present study addresses the inefficiencies of the manual classification of oil palm fresh fruit bunches (FFBs) by introducing a computationally efficient alternative to traditional deep learning approaches that require extensive retraining and large datasets. Using feature-based transfer learning, where pre-trained Convolutional Neural [...] Read more.
The present study addresses the inefficiencies of the manual classification of oil palm fresh fruit bunches (FFBs) by introducing a computationally efficient alternative to traditional deep learning approaches that require extensive retraining and large datasets. Using feature-based transfer learning, where pre-trained Convolutional Neural Network architectures, namely EfficientNet_B0, EfficientNet_B4, ResNet152, and VGG16, serve as fixed feature extractors coupled with the Logistic Regression classifier, this research evaluated the performance on a dataset of 466 images categorized as defective or non-defective. The results demonstrate a robust classification performance across all architectures, with the EfficientNet_B4–LR pipeline achieving an exceptional accuracy value of 96.81%, which was further enhanced through hyperparameter optimization. This confirms that feature-based transfer learning offers a reliable, resource-efficient, and practical solution for automated FFB defect detection that can significantly benefit the palm oil industry by providing a scalable alternative to subjective manual-grading methods. Full article
(This article belongs to the Section Manufacturing Technology)
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20 pages, 2342 KiB  
Article
Comparing Strategies for Optimal Pumps as Turbines Selection in Pressurised Irrigation Networks Using Particle Swarm Optimisation: Application in Canal del Zújar Irrigation District, Spain
by Mariana Akemi Ikegawa Bernabé, Miguel Crespo Chacón, Juan Antonio Rodríguez Díaz, Pilar Montesinos and Jorge García Morillo
Technologies 2025, 13(6), 233; https://doi.org/10.3390/technologies13060233 - 5 Jun 2025
Viewed by 288
Abstract
The modernisation of irrigation networks has enhanced water use efficiency but increased energy demand and costs in agriculture. Energy recovery (ER) is possible by utilising excess pressure to generate electricity with pumps as turbines (PATs), offering a cost-effective alternative to traditional turbines. This [...] Read more.
The modernisation of irrigation networks has enhanced water use efficiency but increased energy demand and costs in agriculture. Energy recovery (ER) is possible by utilising excess pressure to generate electricity with pumps as turbines (PATs), offering a cost-effective alternative to traditional turbines. This study assesses the use of PATs in pressurised irrigation networks for recovering wasted hydraulic energy, employing the particle swarm optimisation (PSO) algorithm for PAT sizing based on two single-objective functions. The analysis focuses on minimising the payback period (MPP) and maximising energy recovery (MER) at specific excess pressure points (EPPs). A comparative analysis of values for each EPP and objective function is conducted independently in Sector II of the Canal del Zújar Irrigation District (CZID) in Extremadura, Spain. A sensitivity analysis on energy prices and installation costs is also performed to assess socioeconomic trends and volatility, examining their effects on both objective functions. The optimisation process predicts an annual ER for an average irrigation season using 2015 data ranging from 9554.86 kWh to 43,992.15 kWh per PATs from the MER function, and payback periods (PPs) from 12.92 years to 3.01 years for the MPP function. The sensitivity analysis replicated the optimisation for the years 2022 and 2023, showing potential annual ER of up to 54,963.21 kWh and PPs ranging from 0.88 to 5.96 years for the year 2022. Full article
(This article belongs to the Special Issue Sustainable Water and Environmental Technologies of Global Relevance)
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19 pages, 16547 KiB  
Article
A New Method for Camera Auto White Balance for Portrait
by Sicong Zhou, Kaida Xiao, Changjun Li, Peihua Lai, Hong Luo and Wenjun Sun
Technologies 2025, 13(6), 232; https://doi.org/10.3390/technologies13060232 - 5 Jun 2025
Viewed by 272
Abstract
Accurate skin color reproduction under varying CCT remains a critical challenge in the graphic arts, impacting applications such as face recognition, portrait photography, and human–computer interaction. Traditional AWB methods like gray-world or max-RGB often rely on statistical assumptions, which limit their accuracy under [...] Read more.
Accurate skin color reproduction under varying CCT remains a critical challenge in the graphic arts, impacting applications such as face recognition, portrait photography, and human–computer interaction. Traditional AWB methods like gray-world or max-RGB often rely on statistical assumptions, which limit their accuracy under complex or extreme lighting. We propose SCR-AWB, a novel algorithm that leverages real skin reflectance data to estimate the scene illuminant’s SPD and CCT, enabling accurate skin tone reproduction. The method integrates prior knowledge of human skin reflectance, basis vectors, and camera sensitivity to perform pixel-wise spectral estimation. Experimental results on difficult skin color reproduction task demonstrate that SCR-AWB significantly outperforms traditional AWB algorithms. It achieves lower reproduction angle errors and more accurate CCT predictions, with deviations below 300 K in most cases. These findings validate SCR-AWB as an effective and computationally efficient solution for robust skin color correction. Full article
(This article belongs to the Special Issue Image Analysis and Processing)
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19 pages, 8696 KiB  
Article
In Situ Ceramic Phase Reinforcement via Short-Pulsed Laser Cladding for Enhanced Tribo-Mechanical Behavior of Metal Matrix Composite FeNiCr-B4C (5 and 7 wt.%) Coatings
by Artem Okulov, Olga Iusupova, Alexander Stepchenkov, Vladimir Zavalishin, Elena Marchenkova, Kun Liu, Jie Li, Tushar Sonar, Aleksey Makarov, Yury Korobov, Evgeny Kharanzhevskiy, Ivan Zhidkov, Yulia Korkh, Tatyana Kuznetsova, Pei Wang and Yuefei Jia
Technologies 2025, 13(6), 231; https://doi.org/10.3390/technologies13060231 - 4 Jun 2025
Viewed by 212
Abstract
This study elucidates the dynamic tribo-mechanical response of laser-cladded FeNiCr-B4C metal matrix composite (MMC) coatings on AISI 1040 steel substrate, unraveling the intricate interplay between microstructural features and phase transformations. A multi-faceted approach, employing high-resolution scanning electron microscopy (SEM) and advanced [...] Read more.
This study elucidates the dynamic tribo-mechanical response of laser-cladded FeNiCr-B4C metal matrix composite (MMC) coatings on AISI 1040 steel substrate, unraveling the intricate interplay between microstructural features and phase transformations. A multi-faceted approach, employing high-resolution scanning electron microscopy (SEM) and advanced X-ray diffraction/Raman spectroscopy techniques, provided a comprehensive characterization of the coatings’ behavior under mechanical and scratch testing, shedding light on the mechanisms governing their wear resistance. Specifically, microstructural analysis revealed uniform coatings with a columnar structure and controlled defect density, showcasing an average thickness of 250 ± 20 μm and a transition zone of 80 ± 10 μm. X-ray diffraction and Raman spectroscopy confirmed the presence of α-Fe (Im-3m), γ-FeNiCr (Fm-3m), Fe2B (I-42m), and B4C (R-3m) phases, highlighting the successful incorporation of B4C reinforcement. The addition of 5 and 7 wt.% B4C significantly increased microhardness, showing enhancements up to 201% compared to the B4C-free FeNiCr coating and up to 351% relative to the AISI 1040 steel substrate, respectively. Boron carbide addition promoted a synergistic strengthening effect between the in situ formed Fe2B and the retained B4C phases. Furthermore, scratch test analysis clarified improved wear resistance, excellent adhesion, and a tailored hardness gradient. These findings demonstrated that optimized short-pulsed laser cladding, combined with moderate B4C reinforcement, is a promising route for creating robust, high-strength FeNiCr-B4C MMC coatings suitable for demanding engineering applications. Full article
(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)
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21 pages, 8188 KiB  
Article
New Approach to Dominant and Prominent Color Extraction in Images with a Wide Range of Hues
by Yurii Kynash and Mariia Semeniv
Technologies 2025, 13(6), 230; https://doi.org/10.3390/technologies13060230 - 4 Jun 2025
Viewed by 257
Abstract
Dominant colors significantly influence visual image perception and are widely used in computer vision and design. Traditional extraction methods often neglect visually salient colors that occupy small areas yet possess high aesthetic relevance. This study introduces a method for detecting both dominant and [...] Read more.
Dominant colors significantly influence visual image perception and are widely used in computer vision and design. Traditional extraction methods often neglect visually salient colors that occupy small areas yet possess high aesthetic relevance. This study introduces a method for detecting both dominant and visually prominent colors in a wide range of hues and images. We analyzed the color gamut of images in the CIE L*a*b* color space and concluded that it is difficult to identify the dominant and prominent colors due to high color variability. To address these challenges, the proposed approach transforms images into the orthogonal ICaS color space, integrating the properties of RGB and CMYK models, followed by K-means clustering. A spectral residual saliency map is applied to exclude background regions and emphasize perceptually significant objects. Experimental evaluation on an image database shows that the proposed method yields color palettes with broader gamut coverage, preserved luminance, and visually balanced combinations. A comparative analysis was conducted using the ΔE00 metric, which accounts not only for differences in lightness, chroma, and hue but also for the perceptual interactions between colors, based on their proximity in the color space. The results confirm that the proposed method exhibits greater color stability and aesthetic coherence than existing approaches. These findings highlight the effectiveness of the orthogonal saliency mean method for delivering a more perceptually accurate and visually consistent representation of the dominant colors in an image. This outcome validates the method’s applicability for image analysis and design. Full article
(This article belongs to the Special Issue Image Analysis and Processing)
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18 pages, 43879 KiB  
Article
Using AI to Reconstruct and Preserve 3D Temple Art with Old Images
by Naai-Jung Shih
Technologies 2025, 13(6), 229; https://doi.org/10.3390/technologies13060229 - 3 Jun 2025
Viewed by 220
Abstract
How can AI help us connect to the past in terms of conservation? How can 17-year-old photos be helpful in renewed preservation efforts? This research aims to use AI to connect both in a seamless 3D reconstruction of heritage from images taken of [...] Read more.
How can AI help us connect to the past in terms of conservation? How can 17-year-old photos be helpful in renewed preservation efforts? This research aims to use AI to connect both in a seamless 3D reconstruction of heritage from images taken of Gongfan Palace, Yunlin, Taiwan. AI-assisted 3D modeling was used to reconstruct the details of these images across different 3D platforms of the 3DGS or NeRF models generated by Postshot®, RODIN®, and KIRI Engine®. Mesh and point models created using Zephyr® were referred to and assessed in three sets. The consistent and inconsistent reconstructed results also included AI-assisted modeling outcomes in Stable Diffusion®- and Postshot®-based animations, followed by a 3D assessment and section-based composition analysis. The AI-assisted environment concluded with a recursive reconstruction involving 3D models and 2D images. AI assisted the 3D modeling process in an alternative approach, producing extraordinary structural and visual details. AI-trained models can be assessed and their use extended to composition analysis by section. Evolved documentation and interpretation using AI enables new structures and the management of resources, formats, and interfaces as part of continuous preservation efforts. Full article
(This article belongs to the Section Construction Technologies)
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27 pages, 5926 KiB  
Article
Evaluation of Machine Learning Models for Enhancing Sustainability in Additive Manufacturing
by Waqar Shehbaz and Qingjin Peng
Technologies 2025, 13(6), 228; https://doi.org/10.3390/technologies13060228 - 3 Jun 2025
Viewed by 299
Abstract
Additive manufacturing (AM) presents significant opportunities for advancing sustainability through optimized process control and material utilization. This research investigates the application of machine learning (ML) models to directly associate AM process parameters with sustainability metrics, which is often a challenge by experimental methods [...] Read more.
Additive manufacturing (AM) presents significant opportunities for advancing sustainability through optimized process control and material utilization. This research investigates the application of machine learning (ML) models to directly associate AM process parameters with sustainability metrics, which is often a challenge by experimental methods alone. Initially, experimental data are generated by systematically varying key AM parameters, layer height, infill density, infill pattern, build orientation, and number of shells. Subsequently, four ML models, Linear Regression, Decision Trees, Random Forest, and Gradient Boosting, are trained and evaluated. Hyperparameter tuning is conducted using the Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Box constraints (L-BFGS-B) algorithm, which demonstrates the superior computational efficiency compared to traditional approaches such as grid and random search. Among the models, Random Forest yields the highest predictive accuracy and lowest mean squared error across all target sustainability indicators: energy consumption, part weight, scrap weight, and production time. The results confirm the efficacy of ML in predicting sustainability outcomes when supported by robust experimental data. This research offers a scalable and computationally efficient approach to enhancing sustainability in AM processes and contributes to data-driven decision-making in sustainable manufacturing. Full article
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26 pages, 8159 KiB  
Article
A Combined Mirror–EMG Robot-Assisted Therapy System for Lower Limb Rehabilitation
by Florin Covaciu, Bogdan Gherman, Calin Vaida, Adrian Pisla, Paul Tucan, Andrei Caprariu and Doina Pisla
Technologies 2025, 13(6), 227; https://doi.org/10.3390/technologies13060227 - 3 Jun 2025
Viewed by 827
Abstract
This paper presents the development and initial evaluation of a novel protocol for robot-assisted lower limb rehabilitation. It integrates dual-modal patient interaction, employing mirror therapy and an auto-adaptive EMG-driven control system, designed to enhance lower limb rehabilitation in patients with hemiparesis impairments. The [...] Read more.
This paper presents the development and initial evaluation of a novel protocol for robot-assisted lower limb rehabilitation. It integrates dual-modal patient interaction, employing mirror therapy and an auto-adaptive EMG-driven control system, designed to enhance lower limb rehabilitation in patients with hemiparesis impairments. The system features a robotic platform specifically engineered for lower limb rehabilitation, which operates in conjunction with a virtual reality (VR) environment. This immersive environment comprises a digital twin of the robotic system alongside a human avatar representing the patient and a set of virtual targets to be reached by the patient. To implement mirror therapy, the proposed protocol utilizes a set of inertial sensors placed on the patient’s healthy limb to capture real-time motion data. The auto-adaptive protocol takes as input the EMG signals (if any) from sensors placed on the impaired limb and performs the required motions to reach the virtual targets in the VR application. By synchronizing the motions of the healthy limb with the digital twin in the VR space, the system aims to promote neuroplasticity, reduce pain perception, and encourage engagement in rehabilitation exercises. Initial laboratory trials demonstrate promising outcomes in terms of improved motor function and subject motivation. This research not only underscores the efficacy of integrating robotics and virtual reality in rehabilitation but also opens avenues for advanced personalized therapies in clinical settings. Future work will investigate the efficiency of the proposed solution using patients, thus demonstrating clinical usability, and explore the potential integration of additional feedback mechanisms to further enhance the therapeutic efficacy of the system. Full article
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23 pages, 2955 KiB  
Article
Numerical Simulations of Scaling of the Chamber Dimensions of the Liquid Piston Compressor for Hydrogen Applications
by Marina Konuhova, Valerijs Bezrukovs, Vladislavs Bezrukovs, Deniss Bezrukovs, Maksym Buryi, Nikita Gorbunovs and Anatoli I. Popov
Technologies 2025, 13(6), 226; https://doi.org/10.3390/technologies13060226 - 3 Jun 2025
Viewed by 635
Abstract
Hydrogen compression is a critical process in hydrogen storage and distribution, particularly for energy infrastructure and transportation. As hydrogen technologies expand beyond limited industrial applications, they are increasingly supporting the green economy, including offshore energy systems, smart ports, and sustainable marine industries. Efficient [...] Read more.
Hydrogen compression is a critical process in hydrogen storage and distribution, particularly for energy infrastructure and transportation. As hydrogen technologies expand beyond limited industrial applications, they are increasingly supporting the green economy, including offshore energy systems, smart ports, and sustainable marine industries. Efficient compression technologies are essential for ensuring reliable hydrogen storage and distribution across these sectors. This study focuses on optimizing hydrogen compression using a Liquid Piston Hydrogen Compressor through numerical simulations and scaling analysis. The research examines the influence of compression chamber geometry, including variations in radius and height, on thermal behavior and energy efficiency. A computational model was developed using COMSOL Multiphysics® 6.0, incorporating Computational Fluid Dynamics (CFD) and heat transfer modules to analyze thermodynamic processes. The results highlight temperature distribution in hydrogen, working fluid, and chamber walls at different initial pressures (3.0 MPa and 20.0 MPa) and compression stroke durations. Larger chamber volumes lead to higher temperature increases but reach thermal stabilization. Increasing the chamber volume allows for a significant increase in the performance of the hydraulic compression system with a moderate increase in the temperature of hydrogen. These findings provide insights into optimizing hydrogen compression for enhanced production and broader applications. Full article
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21 pages, 2822 KiB  
Article
Non-Contact Platform for the Assessment of Physical Function in Older Adults: A Pilot Study
by Ana Sobrino-Santos, Pedro Anuarbe, Carlos Fernandez-Viadero, Roberto García-García, José Miguel López-Higuera, Luis Rodríguez-Cobo and Adolfo Cobo
Technologies 2025, 13(6), 225; https://doi.org/10.3390/technologies13060225 - 2 Jun 2025
Viewed by 277
Abstract
In the context of global population aging, identifying reliable, objective tools to assess physical function and postural stability in older adults is increasingly important to mitigate fall risk. This study presents a non-contact platform that uses a Microsoft Azure Kinect depth camera to [...] Read more.
In the context of global population aging, identifying reliable, objective tools to assess physical function and postural stability in older adults is increasingly important to mitigate fall risk. This study presents a non-contact platform that uses a Microsoft Azure Kinect depth camera to evaluate functional performance related to lower-limb muscular capacity and static balance through self-selected depth squats and four progressively challenging stances (feet apart, feet together, semitandem, and tandem). By applying markerless motion capture algorithms, the system provides key biomechanical parameters such as center of mass displacement, knee angles, and sway trajectories. A comparison of older and younger individuals showed that the older group tended to perform shallower squats and exhibit greater mediolateral and anteroposterior sway, aligning with age-related declines in strength and postural control. Longitudinal tracking also illustrated how performance varied following a fall, indicating potential for ongoing risk assessment. Notably, in 30 s balance trials, the first 10 s often captured meaningful differences in stability, suggesting that short-duration stance tests can reliably detect early signs of imbalance. These findings highlight the feasibility of low-cost, user-friendly depth-camera technologies to complement traditional clinical measures and guide targeted fall-prevention strategies in older populations. Full article
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20 pages, 1984 KiB  
Article
The Use of Perlite and Rhyolite in Concrete Mix Design: Influence on Physical-Mechanical and Environmental Performance
by Giovanna Concu, Marco Zucca, Flavio Stochino, Monica Valdes and Francesca Maltinti
Technologies 2025, 13(6), 224; https://doi.org/10.3390/technologies13060224 - 29 May 2025
Viewed by 357
Abstract
During the last decades, the ever-growing evolution of the construction industry has led to a significant increase in demand for increasingly high-performing construction materials both in terms of mechanical characteristics and sustainability. Focusing on concrete, several researchers have designed different mixes to improve [...] Read more.
During the last decades, the ever-growing evolution of the construction industry has led to a significant increase in demand for increasingly high-performing construction materials both in terms of mechanical characteristics and sustainability. Focusing on concrete, several researchers have designed different mixes to improve mechanical properties such as compressive strength, workability and durability, and in many of the proposed mixes, the use of industrial waste stands out both for their ability to improve the mechanical properties of concrete and for the importance of their reuse from a sustainability point of view. In this paper, the use of two waste materials, perlite and rhyolite, in concrete mix design was studied in detail, considering their influence on the compressive strength at 7 and 28 days of curing. The waste materials were introduced in the mix design as substitutes for cement in percentages of 15% and 30% in weight. In addition, perlite was micronized to two different particle sizes, 20 μm and 63 μm, respectively, according to what is already used in concrete within perlite in the mix design. The behavior of the structural concrete containing perlite and rhyolite was compared in terms of compressive strength, Young modulus and produced equivalent CO2 with that of a standard C25/30 reference concrete, and with that of a mix design created using other waste materials, namely fly ash, metakaolin and silica fume, considering cement replacements that are always at 15% and 30% by weight. Moreover, ultrasonic testing and rebound hammer tests were run to evaluate a possible relationship between the physical-mechanical properties of the design mixes and their volumetric and surface characteristics. Full article
(This article belongs to the Section Construction Technologies)
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26 pages, 2438 KiB  
Article
A Hybrid KAN-BiLSTM Transformer with Multi-Domain Dynamic Attention Model for Cybersecurity
by Aleksandr Chechkin, Ekaterina Pleshakova and Sergey Gataullin
Technologies 2025, 13(6), 223; https://doi.org/10.3390/technologies13060223 - 29 May 2025
Viewed by 559
Abstract
With the exponential growth of cyberbullying cases on social media, there is a growing need to develop effective mechanisms for its detection and prediction, which can create a safer and more comfortable digital environment. One of the areas with such potential is the [...] Read more.
With the exponential growth of cyberbullying cases on social media, there is a growing need to develop effective mechanisms for its detection and prediction, which can create a safer and more comfortable digital environment. One of the areas with such potential is the application of natural language processing (NLP) and artificial intelligence (AI). This study applies a novel hybrid-structure Hybrid Transformer–Enriched Attention with Multi-Domain Dynamic Attention Network (Hyb-KAN), which combines a transformer-based architecture, an attention mechanism, and BiLSTM recurrent neural networks. In this study, a multi-class classification method is used to identify comments containing cyberbullying features. For better verification, we compared the proposed method with baseline methods. The Hyb-KAN model demonstrated high results on the multi-class classification dataset, achieving an accuracy of 95.25%. The synergy of BiLSTM, Transformer, MD-DAN, and KAN components provides flexibility and accuracy of text analysis. The study used explainable visualization techniques, including SHAP and LIME, to analyze the interpretability of the Hyb-KAN model, providing a deeper understanding of the decision-making mechanisms. In the final stage of the study, the results were compared with current research data to confirm their relevance to current trends. Full article
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