Technologies

12 pages, 3838 KB

Open AccessCommunication

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

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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18 pages, 2559 KB

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessReview

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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

Open AccessArticle

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

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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17 pages, 2365 KB

Open AccessArticle

Temporal Segmentation of Urban Water Consumption Patterns Based on Non-Parametric Density Clustering

by Aliaksey A. Kapanski, Roman V. Klyuev, Vladimir S. Brigida and Nadezeya V. Hruntovich

Technologies 2025, 13(10), 449; https://doi.org/10.3390/technologies13100449 - 3 Oct 2025

Abstract

The management of modern water supply systems requires a detailed analysis of consumption patterns in order to optimize pump operation schedules, reduce energy costs, and support the development of intelligent management systems. Traditional clustering algorithms are applied for these tasks; however, their limitation [...] Read more.

The management of modern water supply systems requires a detailed analysis of consumption patterns in order to optimize pump operation schedules, reduce energy costs, and support the development of intelligent management systems. Traditional clustering algorithms are applied for these tasks; however, their limitation lies in the need to predefine the number of clusters. The aim of this study was to develop and validate a non-parametric method for clustering daily water consumption profiles based on a modified DBSCAN algorithm. The proposed approach includes the automatic optimization of neighborhood radius and the minimum number of points required to form a cluster. The input data consisted of half-hourly water supply and electricity consumption values for the water supply system of Gomel (Republic of Belarus), supplemented with the time-of-day factor. As a result of the multidimensional clustering, two stable regimes were identified: a high-demand regime (6:30–22:30), covering about 46% of the data and accounting for more than half of the total water supply and electricity consumption, and a low-demand regime (0:30–6:00), representing about 21% of the data and forming around 15% of the resources. The remaining regimes reflect transitional states in morning and evening periods. The obtained results make it possible to define the temporal boundaries of the regimes and to use them for data labeling in the development of predictive water consumption models. Full article

(This article belongs to the Special Issue Sustainable Water and Environmental Technologies of Global Relevance)

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