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Technologies
Volume 13
Issue 10
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Technologies, Volume 13, Issue 10 (October 2025) – 53 articles

Cover Story (view full-size image): Real-time video systems—such as drone piloting and remote surveillance—demand extremely low delay. This work presents a lightweight encoder implemented on reconfigurable FPGA hardware to minimize end-to-end latency under tight power budgets. The architecture is deeply pipelined and parallelized so that streaming can begin while frames are still being processed, achieving sub-frame delay. Compression can adapt to regions with different levels of detail and uses a quantized differential scheme to cut data. On a 150 MHz FPGA, it delivers 1.9–2.1 ms latency at ~2.7 W—~6.6× lower than a parallel GPU version at about half the power—highlighting strong suitability for responsive embedded video control. View this paper
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17 pages, 1373 KB  
Article
TOXOS: Spinning Up Nonlinearity in On-Vehicle Inference with a RISC-V CORDIC Coprocessor
by Luigi Giuffrida, Guido Masera and Maurizio Martina
Technologies 2025, 13(10), 479; https://doi.org/10.3390/technologies13100479 - 21 Oct 2025
Viewed by 153
Abstract
The rapid advancement of artificial intelligence in automotive applications, particularly in Advanced Driver-Assistance Systems (ADAS) and smart battery management on electric vehicles, increases the demand for efficient near-sensor processing. While the problem of linear algebra in machine learning is well-addressed by existing accelerators, [...] Read more.
The rapid advancement of artificial intelligence in automotive applications, particularly in Advanced Driver-Assistance Systems (ADAS) and smart battery management on electric vehicles, increases the demand for efficient near-sensor processing. While the problem of linear algebra in machine learning is well-addressed by existing accelerators, the computation of nonlinear activation functions is usually delegated to the host CPU, resulting in energy inefficiency and high computational costs. This paper introduces TOXOS, a RISC-V-compliant coprocessor designed to address this challenge. TOXOS implements the COordinateRotation DIgital Computer (CORDIC) algorithm to efficiently compute nonlinear functions. Taking advantage of RISC-V modularity and extendability, TOXOS seamlessly integrates with existing computing architectures. The coprocessor’s configurability enables fine-tuning of the area-performance tradeoff by adjusting the internal parallelism, the CORDIC iteration count, and the overall latency. Our implementation on a 65nm technology demonstrates a significant improvement over CPU-based solutions, showcasing a considerable speedup compared to the glibc implementation of nonlinear functions. To validate TOXOS’s real-world impact, we integrated TOXOS in an actual RISC-V microcontroller targeting the on-vehicle execution of machine learning models. This work addresses a critical gap in transcendental function computation for AI, enabling real-time decision-making for autonomous driving systems, maintaining the power efficiency crucial for electric vehicles. Full article
(This article belongs to the Section Manufacturing Technology)
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16 pages, 4886 KB  
Article
Fibonacci Tessellation for Optimizing Planar Phased Arrays in Satellite Communications
by Juan L. Valle, Marco A. Panduro, Carlos A. Brizuela, Roberto Conte, Carlos del Río Bocio and David H. Covarrubias
Technologies 2025, 13(10), 478; https://doi.org/10.3390/technologies13100478 - 21 Oct 2025
Viewed by 122
Abstract
This article presents a novel strategy for the design of planar phased arrays using Fibonacci-based partitioning combined with a random multi-objective search. This approach intends to minimize the number of phase shifters used by the system while maintaining the radiation characteristics required for [...] Read more.
This article presents a novel strategy for the design of planar phased arrays using Fibonacci-based partitioning combined with a random multi-objective search. This approach intends to minimize the number of phase shifters used by the system while maintaining the radiation characteristics required for Ku-band user terminals in Low Earth Orbit (LEO) satellite communications. This methodology efficiently tessellates a 16×16 antenna array, reducing the solution search space size and improving algorithmic computational time. From a total of 409,600 possible configurations, an optimal candidate solution was obtained in 2 h. This configuration achieves a balanced trade-off between radiation performance metrics, including side lobe level (SLL), first null beamwidth (FNBW), and the number of phase shifters. This optimal design maintains a value of SLL below −15 dB across all the azimuth scanning angles, with a beam steering capability of θ=−40∘ and 0∘≤ϕ≤360∘. These results demonstrate the suitability of this novel approach regarding Ku-band satellite communications, providing efficient and practical solutions for high-demand internet services via LEO satellite systems. Full article
(This article belongs to the Special Issue Technologies Based on Antenna Arrays and Applications)
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38 pages, 766 KB  
Article
Sustainable Swarm Intelligence: Assessing Carbon-Aware Optimization in High-Performance AI Systems
by Vasileios Alevizos, Nikitas Gerolimos, Eleni Aikaterini Leligkou, Giorgos Hompis, Georgios Priniotakis and George A. Papakostas
Technologies 2025, 13(10), 477; https://doi.org/10.3390/technologies13100477 - 21 Oct 2025
Viewed by 291
Abstract
Carbon-aware AI demands clear links between algorithmic choices and verified emission outcomes. This study measures and steers the carbon footprint of swarm-based optimization in HPC by coupling a job-level Emission Impact Metric with sub-minute power and grid-intensity telemetry. Across 480 runs covering 41 [...] Read more.
Carbon-aware AI demands clear links between algorithmic choices and verified emission outcomes. This study measures and steers the carbon footprint of swarm-based optimization in HPC by coupling a job-level Emission Impact Metric with sub-minute power and grid-intensity telemetry. Across 480 runs covering 41 algorithms, we report grams CO2 per successful optimisation and an efficiency index η (objective gain per kg CO2). Results show faster swarms achieve lower integral energy: Particle Swarm emits 24.9 g CO2 per optimum versus 61.3 g for GridSearch on identical hardware; Whale and Cuckoo approach the best η frontier, while L-SHADE exhibits front-loaded power spikes. Conservative scale factor schedules and moderate populations reduce emissions without degrading fitness; idle-node suppression further cuts leakage. Agreement between CodeCarbon, MLCO2, and vendor telemetry is within 1.8%, supporting reproducibility. The framework offers auditable, runtime controls (throttle/hold/release) that embed carbon objectives without violating solution quality budgets. Full article
(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies)
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28 pages, 5234 KB  
Systematic Review
Intelligent Eco-Technologies for End-of-Life Photovoltaic Modules: A Systematic Review
by Valentina-Daniela Băjenaru, Roxana-Mariana Nechita and Simona-Elena Istrițeanu
Technologies 2025, 13(10), 476; https://doi.org/10.3390/technologies13100476 - 20 Oct 2025
Viewed by 255
Abstract
This paper explores the evolution of first-generation solar cells by analysing the selection and engineering of materials that led to innovations. It also addresses the potential of using materials other than silicon and issues related to innovative recycling technologies. The paper presents the [...] Read more.
This paper explores the evolution of first-generation solar cells by analysing the selection and engineering of materials that led to innovations. It also addresses the potential of using materials other than silicon and issues related to innovative recycling technologies. The paper presents the evolution of the Romanian photovoltaic sector and assesses the life cycle of photovoltaic panels, focusing on the recovery of high-quality raw materials and their reintroduction into the production process to improve the circular economy in this field. As the number of installed panels grows exponentially, so does the need to manage waste efficiently at the end of their life cycle. Photovoltaic panel recycling is slowly but surely becoming a rapidly developing field that is essential for the sustainability of the solar industry. With the growth of production in the Romanian photovoltaic sector, it has been identified that the need for recycled raw materials will increase from 900 prosumers in 2019 to over 100,000 in 2024. In the future, it will be imperative to develop strategies for recovering, recycling and reintroducing materials, which will bring major benefits. This paper’s specific contributions include a bibliometric mapping of EoL-PV research trends, a technology-recycling matrix for modern cell architectures, and a perspective on the Romanian market contextualised within EU policies. Full article
(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies)
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18 pages, 2916 KB  
Article
A Study of Performance and Emission Characteristics of Diesel-Palm Oil Mill Effluent Gas on Dual-Fuel Diesel Engines Based on Energy Ratio
by Yanuandri Putrasari, Hafiziani Eka Putri, Achmad Praptijanto, Arifin Nur, Mulia Pratama, Ahmad Dimyani, Suherman, Bambang Wahono, Muhammad Khristamto Aditya Wardana, Ocktaeck Lim, Manida Tongroon and Sakda Thongchai
Technologies 2025, 13(10), 475; https://doi.org/10.3390/technologies13100475 - 20 Oct 2025
Viewed by 213
Abstract
Biogas from palm oil mill effluent (POME) is a promising fuel that has many advantages as an alternative fuel. The methane content in biogas derived from POME is up to 75% and can be used as an alternative fuel in an internal combustion [...] Read more.
Biogas from palm oil mill effluent (POME) is a promising fuel that has many advantages as an alternative fuel. The methane content in biogas derived from POME is up to 75% and can be used as an alternative fuel in an internal combustion engine. One of the technologies for utilizing biogas in compression ignition engines is the Diesel Dual-Fuel (DDF) technique due to the different characteristics of fuel and the impact on the environment due to significantly reducing emissions. This study aims to find the effect of biogas POME composition and energy ratio on the DDF engine’s performance and emissions. The simulations using AVL BOOST software were confirmed by experimental engine parameters. The modeling was conducted on the biogas energy ratio (20%, 40%, 60%, and 75% POME) and biogas POME composition (55% and 75% methane). The results showed that the fuel consumption of diesel fuel was reduced by up to 69%, and NOx and soot emissions were reduced by up to 92% and 80%, respectively, with dual-fuel mode operation. Meanwhile, the value of brake mean effective pressure (BMEP) and efficiency was reduced by up to 18%, volumetric efficiency decreased by up to 4%, the increase in brake specific energy consumption (BSEC) was up to 23%, and brake specific fuel consumption (BSFC) was up to 155%. The optimum of the engine’s performance and emission was 40% biogas ratio with 75% methane content. Full article
(This article belongs to the Section Environmental Technology)
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20 pages, 3698 KB  
Article
Lightweight Neural Network for Holographic Reconstruction of Pseudorandom Binary Data
by Mikhail K. Drozdov, Dmitry A. Rymov, Andrey S. Svistunov, Pavel A. Cheremkhin, Anna V. Shifrina, Semen A. Kiriy, Evgenii Yu. Zlokazov, Elizaveta K. Petrova, Vsevolod A. Nebavskiy, Nikolay N. Evtikhiev and Rostislav S. Starikov
Technologies 2025, 13(10), 474; https://doi.org/10.3390/technologies13100474 - 19 Oct 2025
Viewed by 270
Abstract
Neural networks are a state-of-the-art technology for fast and accurate holographic image reconstruction. However, at present, neural network-based reconstruction methods are predominantly applied to objects with simple, homogeneous spatial structures: blood cells, bacteria, microparticles in solutions, etc. However, in the case of objects [...] Read more.
Neural networks are a state-of-the-art technology for fast and accurate holographic image reconstruction. However, at present, neural network-based reconstruction methods are predominantly applied to objects with simple, homogeneous spatial structures: blood cells, bacteria, microparticles in solutions, etc. However, in the case of objects with high contrast details, the reconstruction needs to be as precise as possible to successfully extract details and parameters. In this paper we investigate the use of neural networks in holographic reconstruction of spatially inhomogeneous binary data containers (QR codes). Two modified lightweight convolutional neural networks (which we named HoloLightNet and HoloLightNet-Mini) with an encoder–decoder architecture have been used for image reconstruction. These neural networks enable high-quality reconstruction, guaranteeing the successful decoding of QR codes (both in demonstrated numerical and optical experiments). In addition, they perform reconstruction two orders of magnitude faster than more traditional architectures. In optical experiments with a liquid crystal spatial light modulator, the obtained bit error rate was equal to only 1.2%. These methods can be used for practical applications such as high-density data transmission in coherent systems, development of reliable digital information storage and memory techniques, secure optical information encryption and retrieval, and real-time precise reconstruction of complex objects. Full article
(This article belongs to the Section Information and Communication Technologies)
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24 pages, 2635 KB  
Review
Hailstorm Impact on Photovoltaic Modules: Damage Mechanisms, Testing Standards, and Diagnostic Techniques
by Marko Katinić and Mladen Bošnjaković
Technologies 2025, 13(10), 473; https://doi.org/10.3390/technologies13100473 - 18 Oct 2025
Viewed by 286
Abstract
This study examines the effects of hailstorms on photovoltaic (PV) modules, focussing on damage mechanisms, testing standards, numerical simulations, damage detection techniques, and mitigation strategies. A comprehensive review of the recent literature (2017–2025), experimental results, and case studies is complemented by advanced simulation [...] Read more.
This study examines the effects of hailstorms on photovoltaic (PV) modules, focussing on damage mechanisms, testing standards, numerical simulations, damage detection techniques, and mitigation strategies. A comprehensive review of the recent literature (2017–2025), experimental results, and case studies is complemented by advanced simulation methods such as finite element analysis (FEA) and smoothed particle hydrodynamics (SPH). The research emphasises the crucial role of protective glass thickness, cell type, number of busbars, and quality of lamination in improving hail resistance. While international standards such as IEC 61215 specify test protocols, actual hail events often exceed these conditions, leading to glass breakage, micro-cracks, and electrical faults. Numerical simulations confirm that thicker glass and optimised module designs significantly reduce damage and power loss. Detection methods, including visual inspection, thermal imaging, electroluminescence, and AI-driven imaging, enable rapid identification of both visible and hidden damage. The study also addresses the financial risks associated with hail damage and emphasises the importance of insurance and preventative measures. Recommendations include the use of certified, robust modules, protective covers, optimised installation angles, and regular inspections to mitigate the effects of hail. Future research should develop lightweight, impact-resistant materials, improve simulation modelling to better reflect real-world hail conditions, and improve AI-based damage detection in conjunction with drone inspections. This integrated approach aims to improve the durability and reliability of PV modules in hail-prone regions and support the sustainable use of solar energy amidst increasing climatic challenges. Full article
(This article belongs to the Special Issue Innovative Power System Technologies)
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23 pages, 8144 KB  
Article
Carbon Emission Reduction Capability Analysis of Electricity–Hydrogen Integrated Energy Storage Systems
by Rankai Zhu, Yuxi Li, Xu Huang, Yaoxuan Xia, Yunjin Tu, Bowen Zheng, Jing Qiu and Xiaoshun Zhang
Technologies 2025, 13(10), 472; https://doi.org/10.3390/technologies13100472 - 18 Oct 2025
Viewed by 187
Abstract
Against the dual backdrop of intensifying carbon emission constraints and the large-scale integration of renewable energy, integrated electricity–hydrogen energy systems (EH-ESs) have emerged as a crucial technological pathway for decarbonising energy systems, owing to their multi-energy complementarity and cross-scale regulation capabilities. This paper [...] Read more.
Against the dual backdrop of intensifying carbon emission constraints and the large-scale integration of renewable energy, integrated electricity–hydrogen energy systems (EH-ESs) have emerged as a crucial technological pathway for decarbonising energy systems, owing to their multi-energy complementarity and cross-scale regulation capabilities. This paper proposes an operational optimisation and carbon reduction capability assessment framework for EH-ESs, focusing on revealing their operational response mechanisms and emission reduction potential under multi-disturbance conditions. A comprehensive model encompassing an electrolyser (EL), a fuel cell (FC), hydrogen storage tanks, and battery energy storage was constructed. Three optimisation objectives—cost minimisation, carbon emission minimisation, and energy loss minimisation—were introduced to systematically characterise the trade-offs between economic viability, environmental performance, and energy efficiency. Case study validation demonstrates the proposed model’s strong adaptability and robustness across varying output and load conditions. EL and FC efficiencies and costs emerge as critical bottlenecks influencing system carbon emissions and overall expenditure. Further analysis reveals that direct hydrogen utilisation outperforms the ‘electricity–hydrogen–electricity’ cycle in carbon reduction, providing data support and methodological foundations for low-carbon optimisation and widespread adoption of electricity–hydrogen systems. Full article
(This article belongs to the Special Issue AI for Smart Grid Optimization—Technological Advances and Future Perspectives)
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18 pages, 10816 KB  
Article
From Continuous Integer-Order to Fractional Discrete-Time: A New Computer Virus Model with Chaotic Dynamics
by Imane Zouak, Ahmad Alshanty, Adel Ouannas, Antonio Mongelli, Giovanni Ciccarese and Giuseppe Grassi
Technologies 2025, 13(10), 471; https://doi.org/10.3390/technologies13100471 - 17 Oct 2025
Viewed by 220
Abstract
Computer viruses remain a persistent technological challenge in information security. They require mathematical frameworks that realistically capture their propagation in digital networks. Classical continuous-time, integer-order models often overlook two key aspects of cyber environments: their inherently discrete nature and the memory-dependent effects of [...] Read more.
Computer viruses remain a persistent technological challenge in information security. They require mathematical frameworks that realistically capture their propagation in digital networks. Classical continuous-time, integer-order models often overlook two key aspects of cyber environments: their inherently discrete nature and the memory-dependent effects of networked interactions. In this work, we introduce a fractional-order discrete computer virus (FDCV) model, derived from a three-dimensional continuous integer-order formulation and reformulated into a two-dimensional fractional discrete framework. We analyze its rich dynamical behaviors under both commensurate and incommensurate fractional orders. Leveraging a comprehensive toolbox including bifurcation diagrams, Lyapunov spectra, phase portraits, the 0–1 test for chaos, spectral entropy, and C0 complexity measures, we demonstrate that the FDCV system exhibits persistent chaos and high dynamical complexity across broad parameter regimes. Our findings reveal that fractional-order discrete models not only enhance the dynamical richness compared to integer-order counterparts but also provide a more realistic representation of malware propagation. These insights advance the theoretical study of fractional discrete systems, supporting the development of potential technologies for cybersecurity modeling, detection, and prevention strategies. Full article
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20 pages, 719 KB  
Article
Quantum-Driven Chaos-Informed Deep Learning Framework for Efficient Feature Selection and Intrusion Detection in IoT Networks
by Padmasri Turaka and Saroj Kumar Panigrahy
Technologies 2025, 13(10), 470; https://doi.org/10.3390/technologies13100470 - 17 Oct 2025
Viewed by 307
Abstract
The rapid development of the Internet of Things (IoT) poses significant problems in securing heterogeneous, massive, and high-volume network traffic against cyber threats. Traditional intrusion detection systems (IDSs) are often found to be poorly scalable, or are ineffective computationally, because of the presence [...] Read more.
The rapid development of the Internet of Things (IoT) poses significant problems in securing heterogeneous, massive, and high-volume network traffic against cyber threats. Traditional intrusion detection systems (IDSs) are often found to be poorly scalable, or are ineffective computationally, because of the presence of redundant or irrelevant features, and they suffer from high false positive rates. Addressing these limitations, this study proposes a hybrid intelligent model that combines quantum computing, chaos theory, and deep learning to achieve efficient feature selection and effective intrusion classification. The proposed system offers four novel modules for feature optimization: chaotic swarm intelligence, quantum diffusion modeling, transformer-guided ranking, and multi-agent reinforcement learning, all of which work with a graph-based classifier enhanced with quantum attention mechanisms. This architecture allows as much as 75% feature reduction, while achieving 4% better classification accuracy and reducing computational overhead by 40% compared to the best-performing models. When evaluated on benchmark datasets (NSL-KDD, CICIDS2017, and UNSW-NB15), it shows superior performance in intrusion detection tasks, thereby marking it as a viable candidate for scalable and real-time IoT security analytics. Full article
(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)
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28 pages, 4794 KB  
Article
Aircraft Propeller Design Technology Based on CST Parameterization, Deep Learning Models, and Genetic Algorithm
by Evgenii I. Kurkin, Jose Gabriel Quijada Pioquinto, Oleg E. Lukyanov, Vladislava O. Chertykovtseva and Artem V. Nikonorov
Technologies 2025, 13(10), 469; https://doi.org/10.3390/technologies13100469 - 16 Oct 2025
Viewed by 263
Abstract
This article presents aircraft propeller optimal design technology; including an algorithm and OpenVINT 5 code. To achieve greater geometric flexibility, the proposed technique implements Class-Shape Transformation (CST) parameterization combined with Bézier curves, replacing the previous fully Bézier-based system. Performance improvements in the optimization [...] Read more.
This article presents aircraft propeller optimal design technology; including an algorithm and OpenVINT 5 code. To achieve greater geometric flexibility, the proposed technique implements Class-Shape Transformation (CST) parameterization combined with Bézier curves, replacing the previous fully Bézier-based system. Performance improvements in the optimization process are accomplished through deep learning models and a genetic algorithm, which substitute XFOIL and Differential Evolution-based approaches, respectively. The scientific novelty of the article lies in the application of a neural network to predict the aerodynamic characteristics of profiles in the form of contour diagrams, rather than scalar values, which execute the neural network repeatedly per ISM algorithm iteration and speed up the design time of propeller blades by 32 times as much. A propeller for an aircraft-type UAV was designed using the proposed methodology and OpenVINT 5. A comparison was made with the results to solve a similar problem using numerical mathematical models and experimental studies in a wind tunnel. Full article
(This article belongs to the Special Issue Aviation Science and Technology Applications)
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14 pages, 949 KB  
Article
Multi-Field Functional Electrical Stimulation with Fesia Grasp for Hand Rehabilitation in Multiple Sclerosis: A Randomized, Controlled Trial
by Olalla Saiz-Vázquez, Montserrat Santamaría-Vázquez, Aitor Martín-Odriozola, Tamara Martín-Pérez and Hilario Ortiz-Huerta
Technologies 2025, 13(10), 468; https://doi.org/10.3390/technologies13100468 - 15 Oct 2025
Viewed by 339
Abstract
This study investigates the use of multi-field electrostimulation with the Fesia Grasp device for hand rehabilitation in patients with Multiple Sclerosis (MS). This research aims to evaluate the effectiveness of this novel approach in improving hand function and dexterity in MS patients. A [...] Read more.
This study investigates the use of multi-field electrostimulation with the Fesia Grasp device for hand rehabilitation in patients with Multiple Sclerosis (MS). This research aims to evaluate the effectiveness of this novel approach in improving hand function and dexterity in MS patients. A cohort of MS patients with varying degrees of hand impairment underwent a structured rehabilitation program using the Fesia Grasp device, which delivers targeted electrical stimulation to specific muscle groups. Outcome measures assessed multiple aspects of hand function, including gross and fine motor skills, strength, and functional independence, at baseline, post-intervention, and 1-month follow-up. The main finding was a sustained between-group improvement in gross manual dexterity, measured by the Box and Block Test, at 1-month follow-up (p = 0.008, η2ₚ = 0.429). Secondary analyses showed task-specific gains in the experimental group, with significant intragroup improvements in Jebsen–Taylor Hand Function Test items related to simulated feeding (p = 0.012) and lifting light objects (p = 0.036), and a trend toward better performance in stacking checkers (p = 0.069) and faster page-turning (p = 0.046) after the intervention. Other outcomes showed non-significant changes favoring the experimental group. This research contributes to the growing body of evidence supporting the use of advanced electrostimulation techniques in neurological rehabilitation and offers promising implications for enhancing the quality of life for individuals with MS-related hand dysfunction. Full article
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12 pages, 3838 KB  
Communication
Self-Powered Electrochemical Humidity Sensor Based on Hydroxylated Multi-Walled Carbon Nanotubes-Modified CeO2 Nanoparticles
by Zhen Yuan, Chong Tan, Zaihua Duan, Yadong Jiang and Huiling Tai
Technologies 2025, 13(10), 467; https://doi.org/10.3390/technologies13100467 - 15 Oct 2025
Viewed by 318
Abstract
Electrochemical humidity (ECH) sensors with self-generating capability have attracted widespread attention. In this work, a self-powered ECH sensor is developed using hydroxylated multi-walled carbon nanotubes (OH-MWCNTs)-modified CeO2 nanoparticles as the humidity sensing materials. The results show that the OH-MWCNTs are beneficial for [...] Read more.
Electrochemical humidity (ECH) sensors with self-generating capability have attracted widespread attention. In this work, a self-powered ECH sensor is developed using hydroxylated multi-walled carbon nanotubes (OH-MWCNTs)-modified CeO2 nanoparticles as the humidity sensing materials. The results show that the OH-MWCNTs are beneficial for improving the humidity sensing performances of the CeO2 nanoparticles. The optimized OH-MWCNTs/CeO2 ECH sensor exhibits a wide detection range (0–91.5% relative humidity (RH)) and fast response and recovery times (18.6 and 6.9 s), attributed to the synergistic effect of OH-MWCNTs and CeO2 nanoparticles. In addition, a single OH-MWCNTs/CeO2 ECH sensor can output a voltage of 0.711 V and a load power of 0.376 μW at 91.5% RH. When applied for respiratory rate monitoring, the OH-MWCNTs/CeO2 ECH sensor can accurately detect respiratory rate by converting exhaled humidity into voltage signal. This work demonstrates that the OH-MWCNTs-modified oxide material of CeO2 nanoparticles is a good candidate for fabricating self-powered ECH sensor. Full article
(This article belongs to the Special Issue New Technologies for Sensors)
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17 pages, 2559 KB  
Article
Multilayer Plasmonic Nanodisk Arrays for Enhanced Optical Hydrogen Sensing
by Junyi Jiang, Mingyu Cheng, Xinyi Chen and Bin Ai
Technologies 2025, 13(10), 466; https://doi.org/10.3390/technologies13100466 - 14 Oct 2025
Viewed by 219
Abstract
Plasmonic metasurfaces that convert hydrogen-induced dielectric changes into optical signals hold promise for next-generation hydrogen sensors. Here, we employ simulations and theoretical analysis to systematically assess single-layer, bilayer, and trilayer nanodisk arrays comprising magnesium, palladium, and noble metals. Although monolithic Mg nanodisks show [...] Read more.
Plasmonic metasurfaces that convert hydrogen-induced dielectric changes into optical signals hold promise for next-generation hydrogen sensors. Here, we employ simulations and theoretical analysis to systematically assess single-layer, bilayer, and trilayer nanodisk arrays comprising magnesium, palladium, and noble metals. Although monolithic Mg nanodisks show strong optical contrast after hydrogenation, the corresponding surface plasmon resonance disappears completely, preventing quantitative spectral tracking. In contrast, bilayer heterostructures, particularly those combining Mg and Au, achieve a resonance red-shift of Δλ = 62 nm, a narrowed full width at half maximum (FWHM) of 207 nm, and a figure of merit (FoM) of 0.30. Notably, the FoM is boosted by up to 15-fold when tuning both material choice and stacking sequence (from Mg-Ag to Au-Mg), underscoring the critical role of interface engineering. Trilayer “sandwich” architectures further amplify performance, achieving a max 10-fold and 13-fold enhancement in Δλ and FoM, respectively, relative to its bilayer counterpart. Particularly, the trilayer Mg-Au-Mg reaches Δλ = 120 nm and FoM = 0.41, outperforming most previous plasmonic hydrogen sensors. These enhancements arise from maximized electric-field overlap with dynamically changing dielectric regions at noble-metal–hydride interfaces, as confirmed by first-order perturbation theory. These results indicate that multilayer designs combining Mg and noble metals can simultaneously maximize hydrogen-induced spectral shifts and signal quality, providing a practical pathway toward high-performance all-optical hydrogen sensors. Full article
(This article belongs to the Special Issue New Technologies for Sensors)
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25 pages, 11220 KB  
Article
Industrial Internet of Things (IIoT)-Based Monitoring of Frictional, Vibration, and Sound Generation in Lubricated Automotive Chains
by Shubrajit Bhaumik, Krishnamoorthy Venkatsubramanian, Sharvani Varadharajan, Suruthi Meenachinathan, Shail Mavani, Vitalie Florea and Viorel Paleu
Technologies 2025, 13(10), 465; https://doi.org/10.3390/technologies13100465 - 14 Oct 2025
Viewed by 303
Abstract
This work assesses the frictional wear of lubricated transmission chains, correlating the coefficient of friction, root mean square (RMS) acoustic emissions, and vibrations induced by friction, incorporating Industrial Internet of Things (IIoT) components. The work is divided into two phases: understanding the frictional [...] Read more.
This work assesses the frictional wear of lubricated transmission chains, correlating the coefficient of friction, root mean square (RMS) acoustic emissions, and vibrations induced by friction, incorporating Industrial Internet of Things (IIoT) components. The work is divided into two phases: understanding the frictional interactions between the steel pins of commercial transmission chain and high chrome steel plate (mimicking the interaction between the pin and roller of the chain) using a reciprocating tribometer (20 N, 2.5 Hz, 15.1 stroke length) in the presence of three commercial lubricant aerosols (Grade A, Grade B, and Grade C) and analyzing the frictional wear, sound, and vibration signals generated during the tribo-tests. In the second phase, the findings from the laboratory scale are validated using a commercial transmission chain under aerosol lubrication. Results indicated that the coefficient of friction in the case of dry conditions was 41% higher than that of Grade A aerosol and Grade C aerosol and 28% higher than that of Grade B aerosol. However, the average wear scar diameter on the pin with Grade C (0.401 ± 0.129 mm) was higher than that on the pins with Grades A (0.209 ± 0.159 mm) and B (0.204 ± 0.165 mm). Grade A and Grade B aerosols exhibited similar frictional conditions, while the wear-scar diameter in Grade C was the highest among Grades A and B but still less than in dry conditions. Analyzing the sound and vibrations generated during the friction test, it can be seen that the dry condition produced approximately 60% more sound level than the Grade A and Grade B conditions, and 41% more sound than the Grade C condition. The laboratory results were validated with a real-time transmission chain using an in-house chain wear test rig. Results from the chain wear test rig indicated that the elongation of the chain with Grade B is the least amongst the aerosols and dry conditions. The surface characterizations of the steel pins also indicated intense deep grooves and surface damage in dry conditions, with Grade A exhibiting the most severe damage, followed by Grade C, and the least severe in Grade B. Additionally, dark patches were visually observed on the rollers of the lubricated commercial chains, indicating stressed areas on the rollers, while polished wear was observed on the rollers under dry conditions. Full article
(This article belongs to the Section Manufacturing Technology)
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19 pages, 748 KB  
Article
Patient Experiences of Remote Patient Monitoring: Implications for Health Literacy and Therapeutic Relationships
by Josephine Stevens, Amir Hossein Ghapanchi, Afrooz Purarjomandlangrudi and Stephanie Bruce
Technologies 2025, 13(10), 464; https://doi.org/10.3390/technologies13100464 - 13 Oct 2025
Viewed by 425
Abstract
This study explores patients’ experiences participating in a home-based remote patient monitoring program for chronic disease management. Using a mixed-methods approach, data was collected through semi-structured interviews and surveys from participants with Chronic Obstructive Pulmonary Disease (COPD) and diabetes. Two key themes emerged: [...] Read more.
This study explores patients’ experiences participating in a home-based remote patient monitoring program for chronic disease management. Using a mixed-methods approach, data was collected through semi-structured interviews and surveys from participants with Chronic Obstructive Pulmonary Disease (COPD) and diabetes. Two key themes emerged: “knowing” and “relationship.” The “knowing” theme encompassed data-driven awareness and contextualized education that empowered patients in their health management. The “relationship” theme highlighted the importance of interpersonal connections with healthcare providers and the sense of security from clinical oversight. Technology served as a communication platform supporting patient-clinician interactions rather than replacing them. The findings demonstrate that remote monitoring programs enhance chronic disease self-management through two interconnected mechanisms: the development of ‘situated health literacy’ through real-time, personalized data interpretation, and strengthened therapeutic relationships enabled by technology-mediated clinical oversight. Rather than replacing human interaction, technology serves as a platform for meaningful patient-provider communication that supports both immediate health management and long-term self-management capability development. These exploratory findings suggest potential design considerations for patient-centered telehealth services that integrate health literacy enhancement with relationship-centered care. Full article
(This article belongs to the Special Issue Advanced Technologies for Enhancing Safety, Health, and Well-Being)
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21 pages, 5787 KB  
Article
Design and Validation of a Walking Exoskeleton for Gait Rehabilitation Using a Dual Eight-Bar Mechanism
by Fidel Chávez, Juan A. Cabrera, Alex Bataller and Javier Pérez
Technologies 2025, 13(10), 463; https://doi.org/10.3390/technologies13100463 - 13 Oct 2025
Viewed by 365
Abstract
Improvements in exoskeletons and robotic systems are gaining increasing attention because of their potential to improve neuromuscular rehabilitation and assist people in their daily activities, significantly improving their quality of life. However, the high cost and complexity of current devices limit their accessibility [...] Read more.
Improvements in exoskeletons and robotic systems are gaining increasing attention because of their potential to improve neuromuscular rehabilitation and assist people in their daily activities, significantly improving their quality of life. However, the high cost and complexity of current devices limit their accessibility to many patients and rehabilitation centers. This work presents the design and development of a low-cost walking exoskeleton, conceived to offer an affordable and simple alternative. The system uses a compact eight-bar mechanism with only one degree of freedom per leg, drastically simplifying motorization and control. The exoskeleton is customized for each patient using a synthesis process based on evolutionary algorithms to replicate a predefined gait. Despite the reduced number of degrees of freedom, the resulting mechanism perfectly matches the desired ankle and knee trajectories. The device is designed to be lightweight and affordable, with components fabricated using 3D printing, standard aluminum bars, and one actuator per leg. A working prototype was fabricated, and its functionality and gait accuracy were confirmed. Although limited to a predefined gait pattern and requiring crutches for balance and steering, this exoskeleton represents a promising solution for rehabilitation centers with limited resources, offering accessible and effective gait assistance to a wider population. Full article
(This article belongs to the Special Issue Advanced Technologies for Enhancing Safety, Health, and Well-Being)
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23 pages, 13104 KB  
Article
A Hierarchical Distributed Control System Design for Lower Limb Rehabilitation Robot
by Aihui Wang, Jinkang Dong, Rui Teng, Ping Liu, Xuebin Yue and Xiang Zhang
Technologies 2025, 13(10), 462; https://doi.org/10.3390/technologies13100462 - 13 Oct 2025
Viewed by 357
Abstract
With the acceleration of global aging and the rising incidence of stroke, the demand for lower limb rehabilitation has been steadily increasing. Traditional therapeutic methods can no longer meet the growing needs, which has led to the widespread application of robotic solutions to [...] Read more.
With the acceleration of global aging and the rising incidence of stroke, the demand for lower limb rehabilitation has been steadily increasing. Traditional therapeutic methods can no longer meet the growing needs, which has led to the widespread application of robotic solutions to address labor shortages. In this context, this paper presents a hierarchical and distributed control system based on ROS 2 and Micro-ROS. The distributed architecture decouples functional modules, improving system maintainability and supporting modular upgrades. The control system consists of a three-layer structure, including a high-level controller, Jetson Nano, for gait data processing and advanced command generation; a middle-layer controller, ESP32-S3, for sensor data fusion and inter-layer communication bridging; and a low-level controller, STM32F405, for field-oriented control to drive the motors along a predefined trajectory. Experimental validation in both early and late rehabilitation stages demonstrates the system’s ability to achieve accurate trajectory tracking. In the early rehabilitation stage, the maximum root mean square error of the joint motors is 1.143°; in the later rehabilitation stage, the maximum root mean square error of the joint motors is 1.833°, confirming the robustness of the control system. Additionally, the hierarchical and distributed architecture ensures maintainability and facilitates future upgrades. This paper provides a feasible control scheme for the next generation of lower limb rehabilitation robots. Full article
(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)
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18 pages, 7473 KB  
Article
Modeling the Soil Surface Temperature–Wind Speed–Evaporation Relationship Using a Feedforward Backpropagation ANN in Al Medina, Saudi Arabia
by Samyah Salem Refadah, Sultan AlAbadi, Mansour Almazroui, Mohammad Ayaz Khan, Mohamed ElKashouty and Mohd Yawar Ali Khan
Technologies 2025, 13(10), 461; https://doi.org/10.3390/technologies13100461 - 12 Oct 2025
Viewed by 440
Abstract
Artificial neural networks (ANNs) offer considerable advantages in predicting evaporation (EVAP), particularly in handling nonlinear relationships and complex interactions among factors like soil surface temperature (SST) and wind speed (WS). In Al Medina, Saudi Arabia, the connections [...] Read more.
Artificial neural networks (ANNs) offer considerable advantages in predicting evaporation (EVAP), particularly in handling nonlinear relationships and complex interactions among factors like soil surface temperature (SST) and wind speed (WS). In Al Medina, Saudi Arabia, the connections among WS, SST at 5 cm, SST at 10 cm, and EVAP have been modeled using an ANN. This study demonstrates the practical effectiveness and applicability of the approach in simulating complex nonlinear dynamics in real-life systems. The modeling process employs time series data for WS, SST at both 5 cm and 10 cm, and EVAP, gathered from January to December (2002–2010). Four ANNs labeled T1–T4 were developed and trained with the feedforward backpropagation (FFBP) algorithm using MATLAB routines, each featuring a distinct configuration. The networks were further refined through the enumeration technique, ultimately selecting the most efficient network for forecasting EVAP values. The results from the ANN model are compared with the actual measured EVAP values. The mean square error (MSE) values for the optimal network topology are 0.00343, 0.00394, 0.00309, and 0.00306 for T1, T2, T3, and T4, respectively. Full article
(This article belongs to the Special Issue Sustainable Water and Environmental Technologies of Global Relevance)
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20 pages, 1029 KB  
Article
Image-Type Data Security via Dynamic Cipher Composition from Method Libraries
by Saadia Drissi, Faiq Gmira, Jamal Belkadid, Meriyem Chergui and Mohamed El Kamili
Technologies 2025, 13(10), 460; https://doi.org/10.3390/technologies13100460 - 10 Oct 2025
Viewed by 388
Abstract
In this paper, we propose a novel Dynamic Cipher Composition (DCC) based on multi-algorithm approach using the Library of Image Encryption Methods (LIEM). Unlike conventional static encryption schemes, the proposed DCC randomly selects and applies different encryption algorithms to spatially segmented regions of [...] Read more.
In this paper, we propose a novel Dynamic Cipher Composition (DCC) based on multi-algorithm approach using the Library of Image Encryption Methods (LIEM). Unlike conventional static encryption schemes, the proposed DCC randomly selects and applies different encryption algorithms to spatially segmented regions of an image during each execution. To manage this process efficiently, the system employs two lightweight registers: one for configuration management and another for region-specific modality assignment, both indexed for streamlined storage and retrieval. Experimental evaluations conducted on standard test images demonstrate that the DCC achieves a near-optimal Shannon entropy, high values of Net Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI), and negligible pixel correlation coefficients. These results confirm the scheme’s strong resistance to statistical, differential, and structural attacks, while preserving computational efficiency suitable for real-time applications such as telemedicine, cloud storage, and video surveillance systems. Full article
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35 pages, 13290 KB  
Article
Blockchain-Enabled Secure Energy Transactions for Scalable and Decentralized Peer-to-Peer Solar Energy Trading with Dynamic Pricing
by Jovika Nithyanantham Balamurugan, Devineni Poojitha, Ramu Jahna Bindu, Archana Pallakonda, Rayappa David Amar Raj, Rama Muni Reddy Yanamala, Christian Napoli and Cristian Randieri
Technologies 2025, 13(10), 459; https://doi.org/10.3390/technologies13100459 - 10 Oct 2025
Viewed by 357
Abstract
Decentralized energy trading has been designed as a scalable substitute for traditional electricity markets. While blockchain technology facilitates efficient transparency and automation for peer-to-peer energy trading, the majority of current proposals lack real-time intelligence and adaptability concerning pricing strategies. This paper presents an [...] Read more.
Decentralized energy trading has been designed as a scalable substitute for traditional electricity markets. While blockchain technology facilitates efficient transparency and automation for peer-to-peer energy trading, the majority of current proposals lack real-time intelligence and adaptability concerning pricing strategies. This paper presents an innovative machine learning-driven solar energy trading platform on the Ethereum blockchain that uniquely integrates Bayesian-optimized XGBoost models with dynamic pricing mechanisms inherently incorporated within smart contracts. The principal innovation resides in the real-time amalgamation of meteorological data via Chainlink oracles with machine learning-enhanced price optimization, thereby establishing an adaptive system that autonomously responds to fluctuations in supply and demand. In contrast to existing static pricing methodologies, our framework introduces a multi-faceted dynamic pricing model that encompasses peak-hour adjustments, prediction confidence weighting, and weather-influenced corrections. The system dynamically establishes energy prices predicated on real-time supply–demand forecasts through the implementation of role-based access control, cryptographic hash functions, and ongoing integration of meteorological and machine learning data. Utilizing real-world meteorological data from La Trobe University’s UNISOLAR dataset, the Bayesian-optimized XGBoost model attains a remarkable prediction accuracy of 97.45% while facilitating low-latency price updates at 30 min intervals. The proposed system delivers robust transaction validation, secure offer creation, and scalable dynamic pricing through the seamless amalgamation of off-chain machine learning inference with on-chain smart contract execution, thereby providing a validated platform for trustless, real-time, and intelligent decentralized energy markets that effectively address the disparity between theoretical blockchain energy trading and practical implementation needs. Full article
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21 pages, 23370 KB  
Article
Green Methodology for Producing Bioactive Nanocomposites of Mesoporous Silica Support for Silver and Gold Nanoparticles Against E. coli and S. aureus
by Una Stamenović, Dijana Mašojević, Maja Kokunešoski, Mojca Otoničar, Slađana Davidović, Srečo Škapin, Tanja Barudžija, Dejan Pjević, Tamara Minović Arsić and Vesna Vodnik
Technologies 2025, 13(10), 458; https://doi.org/10.3390/technologies13100458 - 9 Oct 2025
Viewed by 235
Abstract
This study considered and compared silver, gold, and their combination of nanoparticles (AgNPs, AuNPs, and Au-AgNPs) with biocompatible material mesoporous silica SBA-15 as potential antibacterial agents. A facile, one-pot “green” methodology, utilizing L-histidine as a reducing agent and bridge between components, was employed [...] Read more.
This study considered and compared silver, gold, and their combination of nanoparticles (AgNPs, AuNPs, and Au-AgNPs) with biocompatible material mesoporous silica SBA-15 as potential antibacterial agents. A facile, one-pot “green” methodology, utilizing L-histidine as a reducing agent and bridge between components, was employed to obtain Ag@SBA-15, Au@SBA-15, and Au-Ag@SBA-15 nanocomposites without the use of external additives. Various physicochemical tools (UV-Vis, TEM, SAED, FESEM, XPS, BET, XRD, and FTIR) presented SBA-15 as a good carrier for spherical AgNPs, AuNPs, and Au-AgNPs with average diameters of 8.5, 16, and 9 nm, respectively. Antibacterial evaluations of Escherichia coli and Staphylococcus aureus showed that only Ag@SBA-15, at a very low Ag concentration (1 ppm) during 2 h of contact, completely reduced the growth (99.99%) of both strains, while the Au@SBA-15 nanocomposite required higher concentrations (5 ppm) and time (4 h) to reduce 99.98% E. coli and 94.54% S. aureus. However, Au introduction in Ag@SBA-15 to form Au-Ag@SBA-15 negatively affected its antibacterial potential, lowering it due to the galvanic replacement reaction. Nevertheless, the rapid and effective combating of two bacteria at low NPs concentrations, through the synergistic effects of mesoporous silica and AgNPs or AuNPs, in Ag@SBA-15 and Au@SBA-15 nanocomposites, provides a potential substitute for existing bacterial disinfectants. Full article
(This article belongs to the Section Environmental Technology)
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26 pages, 5350 KB  
Review
Comprehensive Review of Smart Water Enhanced Oil Recovery Based on Patents and Articles
by Cristina M. Quintella, Pamela D. Rodrigues, Jorge L. Nicoleti and Samira A. Hanna
Technologies 2025, 13(10), 457; https://doi.org/10.3390/technologies13100457 - 9 Oct 2025
Viewed by 495
Abstract
The transition to a sustainable energy mix is essential to mitigate climate change. Enhanced Oil Recovery (EOR) using low-salinity water (smart water) has emerged as a promising strategy for reducing environmental impacts in the petroleum industry, producing a highly valuable energy source due [...] Read more.
The transition to a sustainable energy mix is essential to mitigate climate change. Enhanced Oil Recovery (EOR) using low-salinity water (smart water) has emerged as a promising strategy for reducing environmental impacts in the petroleum industry, producing a highly valuable energy source due to both its energy density and market value. This study critically reviews intermediate technological readiness levels (TRL), applying a patent-based approach (TRL 4–5) and a review of articles (TRL 3) to analyze various aspects of smart water for EOR, including its composition. A total of 23 patents from the European Patent Office (Questel Orbit) and 1395 articles from Elsevier’s Scopus database were analyzed, considering annual trends, country distribution, international collaborations, author and applicant affiliations, citation dependencies, and factorial analyses. Both patents and articles show exponential growth; however, international collaboration is more frequent in the scientific literature, while patents remain concentrated in a few countries aligned with their markets. Technologies are focused on wettability, surface complexation, CO2 interactions, emulsification, aerogels, reinjection water treatment, carbonate reservoirs, effluent treatment, nanofluidics, and ASP fluids. Recent topics include CO2 associations, permeability, fractured reservoirs, gels, reservoir water, wettability alteration, and reservoir/oil heterogeneity. The findings indicate the need for multivariated development of customized smart waters to address complex interfacial synergistic mechanisms. International Joint Industry Projects and global regulations on the safe use and composition of hybrid injections are recommended to accelerate development, reduce environmental impacts, and enhance the efficient use of existing fields, alleviating the challenges of finding new reservoirs. Full article
(This article belongs to the Special Issue Sustainable Water and Environmental Technologies of Global Relevance)
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22 pages, 4621 KB  
Article
Determination of the Mechanical Tensile Characteristics of Some 3D-Printed Specimens from NYLON 12 CARBON Fiber Material
by Claudiu Babiș, Andrei Dimitrescu, Sorin Alexandru Fica, Ovidiu Antonescu, Daniel Vlăsceanu and Constantin Stochioiu
Technologies 2025, 13(10), 456; https://doi.org/10.3390/technologies13100456 - 8 Oct 2025
Viewed by 339
Abstract
This study investigates the mechanical behavior of Nylon 12 Carbon Fiber specimens manufactured through fused filament fabrication (FFF) for potential integration into light water well drilling rigs. Fifteen tensile test samples were 3D-printed on a MakerBot Method X printer in three orientations: horizontal, [...] Read more.
This study investigates the mechanical behavior of Nylon 12 Carbon Fiber specimens manufactured through fused filament fabrication (FFF) for potential integration into light water well drilling rigs. Fifteen tensile test samples were 3D-printed on a MakerBot Method X printer in three orientations: horizontal, vertical, and lateral. Each specimen was printed with a soluble SR-30 support material, which was subsequently dissolved in an SCA 1200-HT wash station using heated alkaline solution. Following support removal, all samples underwent thermal annealing at 80 °C for 5 h in the printer’s controlled chamber to eliminate residual moisture and improve structural integrity. The annealed specimens were subjected to uniaxial tensile testing using an Instron 8875 electrohydraulic machine, with strain measured by digital image correlation (DIC) on a speckle-patterned gauge section. Key mechanical properties, including Young’s modulus, Poisson’s ratio, yield strength, and ultimate tensile strength, were determined. Finally, a finite element analysis (FEA) was performed using MSC Visual Nastran for Windows to simulate the tensile loading conditions and assess internal stress distributions for each print orientation. The combined experimental and numerical results confirm the feasibility of using additively manufactured parts in demanding engineering applications. Full article
(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies)
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31 pages, 2573 KB  
Article
Hardware Design of DRAM Memory Prefetching Engine for General-Purpose GPUs
by Freddy Gabbay, Benjamin Salomon, Idan Golan and Dolev Shema
Technologies 2025, 13(10), 455; https://doi.org/10.3390/technologies13100455 - 8 Oct 2025
Viewed by 440
Abstract
General-purpose graphics computing on processing units (GPGPUs) face significant performance limitations due to memory access latencies, particularly when traditional memory hierarchies and thread-switching mechanisms prove insufficient for complex access patterns in data-intensive applications such as machine learning (ML) and scientific computing. This paper [...] Read more.
General-purpose graphics computing on processing units (GPGPUs) face significant performance limitations due to memory access latencies, particularly when traditional memory hierarchies and thread-switching mechanisms prove insufficient for complex access patterns in data-intensive applications such as machine learning (ML) and scientific computing. This paper presents a novel hardware design for a memory prefetching subsystem targeted at DDR (Double Data Rate) memory in GPGPU architectures. The proposed prefetching subsystem features a modular architecture comprising multiple parallel prefetching engines, each handling distinct memory address ranges with dedicated data buffers and adaptive stride detection algorithms that dynamically identify recurring memory access patterns. The design incorporates robust system integration features, including context flushing, watchdog timers, and flexible configuration interfaces, for runtime optimization. Comprehensive experimental validation using real-world workloads examined critical design parameters, including block sizes, prefetch outstanding limits, and throttling rates, across diverse memory access patterns. Results demonstrate significant performance improvements with average memory access latency reductions of up to 82% compared to no-prefetch baselines, and speedups in the range of 1.240–1.794. The proposed prefetching subsystem successfully enhances memory hierarchy efficiency and provides practical design guidelines for deployment in production GPGPU systems, establishing clear parameter optimization strategies for different workload characteristics. Full article
(This article belongs to the Topic Advances in Microelectronics and Semiconductor Engineering)
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22 pages, 7199 KB  
Article
Injection Mold Design Technology to Locate Weld Lines Away from Highly Loaded Structural Areas
by Vladislava O. Chertykovtseva, Evgenii A. Kishov and Evgenii I. Kurkin
Technologies 2025, 13(10), 454; https://doi.org/10.3390/technologies13100454 - 7 Oct 2025
Viewed by 438
Abstract
This article presents the technology of automated placement of an injection molding gate based on a parametric optimization algorithm with technological constraints consideration. The algorithm is based on the modification of the genetic algorithm using the criterion of maximum equivalent stresses on the [...] Read more.
This article presents the technology of automated placement of an injection molding gate based on a parametric optimization algorithm with technological constraints consideration. The algorithm is based on the modification of the genetic algorithm using the criterion of maximum equivalent stresses on the weld line as an optimization criterion. The proposed software’s modular structure combines the authors’ modules that implement a new optimization algorithm with the ANSYS 2022R1 and Moldflow calculation kernels called via API interfaces. This structure provides an opportunity to implement developed technology to solve industrial problems using standard mesh generation tools and complex geometric models due to the flexibility of modules and computing kernel scalability. The consideration of the technological constraints allows us to reduce the population size and optimization problem solution computational time to 1.9 times. The developed algorithms are used to solve the gate location optimization problem using the example of an aerospace bracket made of short-reinforced composite material with a nonzero genus surface and a weld line. The use of the proposed technology made it possible to increase the strength of the studied structure by two times. Full article
(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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25 pages, 3199 KB  
Article
Challenges in Aquaculture Hybrid Energy Management: Optimization Tools, New Solutions, and Comparative Evaluations
by Helena M. Ramos, Nicolas Soehlemann, Eyup Bekci, Oscar E. Coronado-Hernández, Modesto Pérez-Sánchez, Aonghus McNabola and John Gallagher
Technologies 2025, 13(10), 453; https://doi.org/10.3390/technologies13100453 - 7 Oct 2025
Viewed by 272
Abstract
A novel methodology for hybrid energy management in aquaculture is introduced, aimed at enhancing self-sufficiency and optimizing grid-related cash flows. Wind and solar energy generation are modeled using calibrated turbine performance curves and PVGIS data, respectively, with a photovoltaic capacity of 120 kWp. [...] Read more.
A novel methodology for hybrid energy management in aquaculture is introduced, aimed at enhancing self-sufficiency and optimizing grid-related cash flows. Wind and solar energy generation are modeled using calibrated turbine performance curves and PVGIS data, respectively, with a photovoltaic capacity of 120 kWp. The system also incorporates a 250 kW small hydroelectric plant and a wood drying kiln that utilizes surplus wind energy. This study conducts a comparative analysis between HY4RES, a research-oriented simulation model, and HOMER Pro, a commercially available optimization tool, across multiple hybrid energy scenarios at two aquaculture sites. For grid-connected configurations at the Primary site (base case, Scenarios 1, 2, and 6), both models demonstrate strong concordance in terms of energy balance and overall performance. In Scenario 1, a peak power demand exceeding 1000 kW is observed in both models, attributed to the biomass kiln load. Scenario 2 reveals a 3.1% improvement in self-sufficiency with the integration of photovoltaic generation, as reported by HY4RES. In the off-grid Scenario 3, HY4RES supplies an additional 96,634 kWh of annual load compared to HOMER Pro. However, HOMER Pro indicates a 3.6% higher electricity deficit, primarily due to battery energy storage system (BESS) losses. Scenario 4 yields comparable generation outputs, with HY4RES enabling 6% more wood-drying capacity through the inclusion of photovoltaic energy. Scenario 5, which features a large-scale BESS, highlights a 4.7% unmet demand in HY4RES, whereas HOMER Pro successfully meets the entire load. In Scenario 6, both models exhibit similar load profiles; however, HY4RES reports a self-sufficiency rate that is 1.3% lower than in Scenario 1. At the Secondary site, financial outcomes are closely aligned. For instance, in the base case, HY4RES projects a cash flow of 54,154 EUR, while HOMER Pro estimates 55,532 EUR. Scenario 1 presents nearly identical financial results, and Scenario 2 underscores HOMER Pro’s superior BESS modeling capabilities during periods of reduced hydroelectric output. In conclusion, HY4RES demonstrates robust performance across all scenarios. When provided with harmonized input parameters, its simulation results are consistent with those of HOMER Pro, thereby validating its reliability for hybrid energy management in aquaculture applications. Full article
(This article belongs to the Special Issue Innovative Power System Technologies)
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23 pages, 4731 KB  
Article
Advancing Urban Roof Segmentation: Transformative Deep Learning Models from CNNs to Transformers for Scalable and Accurate Urban Imaging Solutions—A Case Study in Ben Guerir City, Morocco
by Hachem Saadaoui, Saad Farah, Hatim Lechgar, Abdellatif Ghennioui and Hassan Rhinane
Technologies 2025, 13(10), 452; https://doi.org/10.3390/technologies13100452 - 6 Oct 2025
Viewed by 608
Abstract
Urban roof segmentation plays a pivotal role in applications such as urban planning, infrastructure management, and renewable energy deployment. This study explores the evolution of deep learning techniques from traditional Convolutional Neural Networks (CNNs) to cutting-edge transformer-based models in the context of roof [...] Read more.
Urban roof segmentation plays a pivotal role in applications such as urban planning, infrastructure management, and renewable energy deployment. This study explores the evolution of deep learning techniques from traditional Convolutional Neural Networks (CNNs) to cutting-edge transformer-based models in the context of roof segmentation from satellite imagery. We highlight the limitations of conventional methods when applied to urban environments, including resolution constraints and the complexity of roof structures. To address these challenges, we evaluate two advanced deep learning models, Mask R-CNN and MaskFormer, which have shown significant promise in accurately segmenting roofs, even in dense urban settings with diverse roof geometries. These models, especially the one based on transformers, offer improved segmentation accuracy by capturing both global and local image features, enhancing their performance in tasks where fine detail and contextual awareness are critical. A case study on Ben Guerir City in Morocco, an urban area experiencing rapid development, serves as the foundation for testing these models. Using high-resolution satellite imagery, the segmentation results offer a deeper understanding of the accuracy and effectiveness of these models, particularly in optimizing urban planning and renewable energy assessments. Quantitative metrics such as Intersection over Union (IoU), precision, recall, and F1-score are used to benchmark model performance. Mask R-CNN achieved a mean IoU of 74.6%, precision of 81.3%, recall of 78.9%, and F1-score of 80.1%, while MaskFormer reached a mean IoU of 79.8%, precision of 85.6%, recall of 82.7%, and F1-score of 84.1% (pixel-level, micro-averaged at IoU = 0.50 on the held-out test set), highlighting the transformative potential of transformer-based architectures for scalable and precise urban imaging. The study also outlines future work in 3D modeling and height estimation, positioning these advancements as critical tools for sustainable urban development. Full article
(This article belongs to the Section Information and Communication Technologies)
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22 pages, 5333 KB  
Article
Research on Key Technologies and Integrated Solutions for Intelligent Mine Ventilation Systems
by Deyun Zhong, Lixue Wen, Yulong Liu, Zhaohao Wu, Liguan Wang and Xianwei Ji
Technologies 2025, 13(10), 451; https://doi.org/10.3390/technologies13100451 - 6 Oct 2025
Viewed by 291
Abstract
Intelligent ventilation systems can optimize airflow regulation to enhance mining safety and reduce energy consumption, driving green development in mineral resource extraction. This paper systematically elaborates on the overall architecture, cutting-edge advances, and core technologies of current intelligent mining ventilation. Building upon this [...] Read more.
Intelligent ventilation systems can optimize airflow regulation to enhance mining safety and reduce energy consumption, driving green development in mineral resource extraction. This paper systematically elaborates on the overall architecture, cutting-edge advances, and core technologies of current intelligent mining ventilation. Building upon this foundation, a comprehensive intelligent mine ventilation solution encompassing the entire process of ventilation design, optimization, and operation is constructed based on a five-layer architecture, integrating key technologies such as intelligent sensing, real-time solving, airflow regulation, and remote control, providing an overarching framework for smart mine ventilation development. To address the computational efficiency bottleneck of traditional methods, an improved loop-solving method based on minimal independent closed loops is realized, achieving near real-time analysis of ventilation networks. Furthermore, a multi-level airflow regulation strategy is realized, including the methods of optimization control based on mixed integer linear programming and equipment-driven demand-based regulation, effectively resolving the challenges of calculating nonlinear programming models. Case studies indicate that the intelligent ventilation system significantly enhances mine safety and efficiency, leading to approximately 10–20% energy saving, a 40–60% quicker emergency response, and an average increase of about 20% in the utilization of fresh air at working faces through its remote and real-time control capabilities. Full article
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25 pages, 3263 KB  
Article
Combining MTCNN and Enhanced FaceNet with Adaptive Feature Fusion for Robust Face Recognition
by Sasan Karamizadeh, Saman Shojae Chaeikar and Hamidreza Salarian
Technologies 2025, 13(10), 450; https://doi.org/10.3390/technologies13100450 - 3 Oct 2025
Viewed by 484
Abstract
Face recognition systems typically face actual challenges like facial pose, illumination, occlusion, and ageing that significantly impact the recognition accuracy. In this paper, a robust face recognition system that uses Multi-task Cascaded Convolutional Networks (MTCNN) for face detection and face alignment with an [...] Read more.
Face recognition systems typically face actual challenges like facial pose, illumination, occlusion, and ageing that significantly impact the recognition accuracy. In this paper, a robust face recognition system that uses Multi-task Cascaded Convolutional Networks (MTCNN) for face detection and face alignment with an enhanced FaceNet for facial embedding extraction is presented. The enhanced FaceNet uses attention mechanisms to achieve more discriminative facial embeddings, especially in challenging scenarios. In addition, an Adaptive Feature Fusion module synthetically combines identity-specific embeddings with context information such as pose, lighting, and presence of masks, hence enhancing robustness and accuracy. Training takes place using the CelebA dataset, and the test is conducted independently on LFW and IJB-C to enable subject-disjoint evaluation. CelebA has over 200,000 faces of 10,177 individuals, LFW consists of 13,000+ faces of 5749 individuals in unconstrained conditions, and IJB-C has 31,000 faces and 117,000 video frames with extreme pose and occlusion changes. The system introduced here achieves 99.6% on CelebA, 94.2% on LFW, and 91.5% on IJB-C and outperforms baselines such as simple MTCNN-FaceNet, AFF-Net, and state-of-the-art models such as ArcFace, CosFace, and AdaCos. These findings demonstrate that the proposed framework generalizes effectively between datasets and is resilient in real-world scenarios. Full article
(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)
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