Technologies

22 pages, 4488 KB

Open AccessArticle

Research on Dynamic Control Strategies for Intermittent Bus Lanes in Mixed Traffic Flow Environments

by Yuan Gao, Shiyao Cui and Yibing Yue

Technologies 2025, 13(11), 539; https://doi.org/10.3390/technologies13110539 - 20 Nov 2025

Viewed by 259

The traditional intermittent bus lane control struggles to achieve an effective balance between bus priority and lane utilization efficiency. To address this limitation, this study proposes a dynamic control strategy that enables the borrowing of intermittent bus lanes in mixed traffic flow environments [...] Read more.

The traditional intermittent bus lane control struggles to achieve an effective balance between bus priority and lane utilization efficiency. To address this limitation, this study proposes a dynamic control strategy that enables the borrowing of intermittent bus lanes in mixed traffic flow environments and constructs a connected vehicle control model encompassing both the target intersection and its upstream segment. First, a dynamic clearance framework is established on the dedicated lane based on the real-time speed of buses. Concurrently, the target connected and automated vehicle (CAV) predicts the traffic signal status upon its arrival at the stop line to determine its traversable zone at the bus lanes. Subsequently, a coordinated control strategy is designed for the dynamic clearance framework and the traversable zone, leading to the development of lane-changing decision models under four distinct scenarios. This approach allows CAVs to dynamically utilize residual lane resources without compromising bus operations. Finally, using average vehicle delay as the evaluation metric, a comparative simulation analysis is conducted against the traditional bus lane utilization method across four dimensions: connected vehicle penetration rate, traffic flow saturation, right-turn proportion, and bus departure frequency. The experimental results demonstrate that the proposed strategy significantly improves both bus priority and overall traffic efficiency. Full article

(This article belongs to the Special Issue Advanced Intelligent Driving Technology)

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26 pages, 3197 KB

Open AccessArticle

Design and Fabrication of a Compact Evaporator–Absorber Unit with Mechanical Enhancement for LiBr–H₂O Vertical Falling-Film Absorption, Part I: Experimental Validation

by Genis Díaz-Flórez, Carlos Alberto Olvera-Olvera, Santiago Villagrana-Barraza, Luis Octavio Solís-Sánchez, Héctor A. Guerrero-Osuna, Teodoro Ibarra-Pérez, Ramón Jaramillo-Martínez, Hans C. Correa-Aguado and Germán Díaz-Flórez

Technologies 2025, 13(11), 538; https://doi.org/10.3390/technologies13110538 - 19 Nov 2025

Viewed by 392

Abstract

Compact, low-power absorption cooling supports decentralized refrigeration needs and is positioned here as a sustainable approach within environmental technologies. This paper presents the design, fabrication, and experimental validation of a compact LiBr–H₂O evaporator–absorber, in which a low-energy fan assists in transporting [...] Read more.

Compact, low-power absorption cooling supports decentralized refrigeration needs and is positioned here as a sustainable approach within environmental technologies. This paper presents the design, fabrication, and experimental validation of a compact LiBr–H₂O evaporator–absorber, in which a low-energy fan assists in transporting refrigerant vapor from the evaporator to the absorber within a single vertical falling-film vessel. Twelve heat-load phases were tested with the fan OFF/ON, while temperatures, pressures, and flow rates were continuously monitored. The analysis focuses on temperature and pressure separation metrics, as well as a dimensionless separation index. Results show that fan assistance stabilizes thermal and pressure differentials and attenuates oscillations across grouped loads. The most significant benefits are observed at low to intermediate heat inputs, whereas the effect becomes marginal at higher loads, indicating the dominance of natural transport mechanisms. The compact unit remains thermally stable under all tested conditions. These findings indicate that a simple, low-power mechanical enhancement can improve controllability in an integrated evaporator–absorber without complex internal geometries. Protected under a Mexican utility model (IMPI, MX 4573 B), this prototype provides a replicable experimental basis for supporting compact, low-power solutions for sustainable, decentralized cooling in the field of environmental technologies. Full article

(This article belongs to the Section Manufacturing Technology)

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30 pages, 4723 KB

Open AccessArticle

Application of the Multi-Criteria Method FUCOM for Evaluating Technological Processes

by Tanya Avramova, Teodora Peneva and Aleksandar Ivanov

Technologies 2025, 13(11), 537; https://doi.org/10.3390/technologies13110537 - 19 Nov 2025

Viewed by 248

Abstract

In modern industrial production, the selection and evaluation of technological processes is a factor in achieving high quality, efficiency, and sustainability. Due to the existence of numerous and often contradictory criteria, the decision-making process requires the application of reliable multi-criteria methods. This article [...] Read more.

In modern industrial production, the selection and evaluation of technological processes is a factor in achieving high quality, efficiency, and sustainability. Due to the existence of numerous and often contradictory criteria, the decision-making process requires the application of reliable multi-criteria methods. This article demonstrates the application of MCDM (Multi-Criteria Decision-Making) methods, the FUCOM (Full Consistency Method), for evaluating and selecting a rational technological process under real production conditions. The research results presented in the article demonstrate that the FUCOM method ensures a high degree of consistency, transparency, and efficiency in the evaluation of technological processes. It allows, among a variety of alternative technological process for manufacturing a given product, for the clear identification of the most rational one according to specified requirements. The data obtained in a real production environment confirm the applicability of the method in the field of production engineering and provide a basis for future research and optimization of technological processes. Full article

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17 pages, 3875 KB

Open AccessArticle

Blockchain Gamification Solution for Regulation of Road Traffic Emissions

by Bogdan Cristian Florea, Alin Alexandru Șerban, Dragoș Daniel Țarălungă and Mădălin Corneliu Frunzete

Technologies 2025, 13(11), 536; https://doi.org/10.3390/technologies13110536 - 19 Nov 2025

Viewed by 246

Abstract

Over the past few decades, road traffic has grown significantly, bringing with it increasing safety concerns. These concerns range from short-term issues like accidents and congestion to long-term challenges such as deteriorating air quality and health problems caused by prolonged exposure to harmful [...] Read more.

Over the past few decades, road traffic has grown significantly, bringing with it increasing safety concerns. These concerns range from short-term issues like accidents and congestion to long-term challenges such as deteriorating air quality and health problems caused by prolonged exposure to harmful emissions. Various policies and regulations have been implemented to address these problems with varying degrees of success. While past efforts primarily focused on the industrial sector, private individuals are now the main contributors to road traffic challenges. In densely populated cities, vehicular traffic plays a major role in individuals’ emission footprint and heightens risks for both drivers and pedestrians. In this article, a new blockchain-driven gamification method for the improvement of road traffic safety and emission regulation is proposed, tested, and implemented. For this method, different types of driving sessions are recorded and analyzed, and based on the parameters received from an OBD-II (onboard diagnostic) device connected to the vehicle, data is collected through a smartphone app and recorded on a private Ethereum blockchain. The gamification component computes a score for each drive, based on the analysis of the data. For the regulation of emissions (CO₂, CO, and NOx), different methods of emission estimation are analyzed and compared, based on the OBD-II data. Full article

(This article belongs to the Special Issue Smart Transportation and Driving)

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35 pages, 4316 KB

Open AccessReview

Control Methods and AI Application for Grid-Connected PV Inverter: A Review

by Feng Wang, Ayiguzhali Tuluhong, Bao Luo and Ailitabaier Abudureyimu

Technologies 2025, 13(11), 535; https://doi.org/10.3390/technologies13110535 - 19 Nov 2025

Viewed by 628

Abstract

Grid-connected PV inverters (GCPI) are key components that enable photovoltaic (PV) power generation to interface with the grid. Their control performance directly influences system stability and grid connection quality. However, as PV penetration increases, conventional controllers encounter difficulties in managing nonlinear dynamics and [...] Read more.

Grid-connected PV inverters (GCPI) are key components that enable photovoltaic (PV) power generation to interface with the grid. Their control performance directly influences system stability and grid connection quality. However, as PV penetration increases, conventional controllers encounter difficulties in managing nonlinear dynamics and weak-grid conditions. This paper reviews both conventional and artificial intelligence (AI)-based control methods for GCPI. It compares their performance characteristics, application scenarios, and limitations and summarizes current research progress and remaining challenges. The potential and issues of applying AI to enhance system intelligence are also highlighted. Finally, future development trends are discussed, emphasizing high efficiency, strong adaptability, and intelligent integration in GCPI technologies. Full article

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27 pages, 2354 KB

Open AccessArticle

Unsupervised Real-Time Anomaly Detection in Hydropower Systems via Time Series Clustering and Autoencoders

by Ana I. Oviedo, John F. Vargas, Roberto C. Hincapie, Andres F. Molina, Edimerk A. Vergara and Diana M. Tello

Technologies 2025, 13(11), 534; https://doi.org/10.3390/technologies13110534 - 19 Nov 2025

Viewed by 486

Abstract

Hydropower plants generate large volumes of data with high-dimensional time series, making early anomaly detection essential for monitoring, preventive maintenance and cost reduction. This study addresses the challenge of detecting anomalies in real time without labeled failure data by proposing an unsupervised approach [...] Read more.

Hydropower plants generate large volumes of data with high-dimensional time series, making early anomaly detection essential for monitoring, preventive maintenance and cost reduction. This study addresses the challenge of detecting anomalies in real time without labeled failure data by proposing an unsupervised approach that combines time series clustering, autoencoder-based models, and an adaptative anomaly thresholding. Initially, a clustering process is applied to historical time series data from multiple sensors in a hydroelectric power plant to identify groups of variables with similar temporal dynamics. Subsequently, for each cluster, various unsupervised models are trained to learn the normal behavior of the variables, including ARIMA, Autoencoders, Variational Autoencoders, Long Short-Term Memory networks, Sliding-window Autoencoders and Sliding-window Variational Autoencoders. Among these, the Autoencoder model demonstrated superior performance and was selected for real-time deployment. Finally, anomalies were detected by comparing predicted and actual values, using an adaptative threshold based on prediction errors. The system was tested on a real hydropower plant with over 150 time-dependent variables. The results show that 97% of the variables achieved an R² score above 0.8, with low MAE values indicating high reconstruction accuracy. The proposed approach, deployed in a real-time system integrated with Grafana dashboards, demonstrates the system’s capability to detect anomalies. Full article

(This article belongs to the Section Information and Communication Technologies)

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19 pages, 1642 KB

Open AccessArticle

High-Voltage Overhead Power Line Fault Location Through Sequential Determination of Faulted Section

by Aleksandr Kulikov, Anton Loskutov, Pavel Ilyushin, Andrey Kurkin and Anastasia Sluzova

Technologies 2025, 13(11), 533; https://doi.org/10.3390/technologies13110533 - 18 Nov 2025

Viewed by 210

Abstract

Overhead power lines (OHPLs) represent the backbone of electric power systems and connect generation sources with consumers. The power supply reliability and maintenance costs of power grids largely depend on accurate fault location on OHPLs, as this significantly affects the speed of power [...] Read more.

Overhead power lines (OHPLs) represent the backbone of electric power systems and connect generation sources with consumers. The power supply reliability and maintenance costs of power grids largely depend on accurate fault location on OHPLs, as this significantly affects the speed of power supply restoration and reduces equipment downtime. This article proposes a new approach to fault location which includes the division of the OHPL bypass (inspection) zone into sections with subsequent implementation of a faulted section location procedure. This article substantiates the application of sequential multi-hypothesis analysis, which allows us to adapt the decision-making process regarding the OHPL faulted section to the peculiarities of emergency event oscillogram distortion and the conditions for estimating their parameters. According to the results of our calculations, it is noted that the application of sequential analysis practically does not affect the speed of OHPL fault location but does ensure unambiguity in decision making regarding the faulted section under the influence of random factors. Full article

(This article belongs to the Special Issue Innovative Power System Technologies)

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22 pages, 326 KB

Open AccessArticle

Integrating Advanced Neuro-Oncology Imaging into Guideline-Directed Multimodal Therapy for Brain Metastases: Evaluating Comparative Treatment Effectiveness

by Keren Rouvinov, Rashad Naamneh, Wenad Najjar, Mahmoud Abu Amna, Arina Soklakova, Ez El Din Abu Zeid, Fahmi Abu Ghalion, Ali Abu Juma’a, Mohnnad Asla, Alexander Yakobson and Walid Shalata

Technologies 2025, 13(11), 532; https://doi.org/10.3390/technologies13110532 - 18 Nov 2025

Viewed by 343

Abstract

Background: Brain metastases (BM) are a common and serious complication in cancer patients, particularly those with lung, breast, or melanoma primaries. As systemic therapies extend survival, the incidence of BM has increased, necessitating improved diagnostic and treatment strategies. Recent advances in neuroimaging and [...] Read more.

Background: Brain metastases (BM) are a common and serious complication in cancer patients, particularly those with lung, breast, or melanoma primaries. As systemic therapies extend survival, the incidence of BM has increased, necessitating improved diagnostic and treatment strategies. Recent advances in neuroimaging and therapy have significantly enhanced the ability to diagnose and manage these lesions with greater precision. Methods: This article summarizes current diagnostic imaging modalities—Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Perfusion-Weighted Imaging (PWI), and Magnetic Resonance Spectroscopy (MRS) and their roles in distinguishing tumor progression from treatment effects. It also compares the efficacy of therapeutic options including Whole-Brain Radiation Therapy (WBRT), Stereotactic Radiosurgery (SRS), and systemic therapies such as targeted drugs and immunotherapies. Outcomes were evaluated based on local tumor control and overall survival. Results: Advanced imaging techniques like PWI, MRS, and PET improve diagnostic accuracy by providing functional and metabolic information beyond standard MRI. Therapeutically, SRS offers better local control and fewer cognitive side effects than WBRT for patients with limited metastases. Targeted and immune-based therapies have shown improved survival in patients with specific genetic mutations, supporting a personalized treatment approach. Conclusions: The integration of advanced imaging and individualized therapies has improved diagnosis, treatment decisions, and outcomes in patients with brain metastases. Ongoing research is essential to refine these tools and approaches, further optimizing patient care and quality of life. Full article

30 pages, 25845 KB

Open AccessArticle

Benchmarking YOLOv8 to YOLOv11 Architectures for Real-Time Traffic Sign Recognition in Embedded 1:10 Scale Autonomous Vehicles

by Rafael Reveles-Martínez, Hamurabi Gamboa-Rosales, Erika Sánchez-Femat, Javier Saldívar-Pérez, Teodoro Ibarra-Pérez, Luis Carlos Reveles-Gómez, Omar A. Guirette-Barbosa, Jorge I. Galván-Tejada, Carlos E. Galván-Tejada, Huizilopoztli Luna-García and José M. Celaya-Padilla

Technologies 2025, 13(11), 531; https://doi.org/10.3390/technologies13110531 - 18 Nov 2025

Viewed by 933

Abstract

Traffic sign recognition is still one of the challenging aspects of intelligent vehicle systems, mainly when processor or memory resources are limited. In this work, real-time traffic sign detection was evaluated using five YOLO model variants—Nano, Small, Medium, Large, and XLarge—across versions 8 [...] Read more.

Traffic sign recognition is still one of the challenging aspects of intelligent vehicle systems, mainly when processor or memory resources are limited. In this work, real-time traffic sign detection was evaluated using five YOLO model variants—Nano, Small, Medium, Large, and XLarge—across versions 8 to 11. All models were trained and validated with a custom dataset collected in a simulated urban environment designed to replicate FIRA competition tracks. The models were then deployed and tested on a 1:10 scale autonomous vehicle equipped with a mini PC running the detector in real time. Performance was compared using mAP@50–95, F1-score, inference latency, and preprocessing and postprocessing times. The authors also analyzed training behavior, focusing on convergence speed and stopping criteria. The experiments showed that YOLOv10 B achieved the highest performance across varying conditions, while YOLOv8 M provided a better balance between speed and accuracy. These results can help practitioners select appropriate YOLO architectures for embedded traffic sign recognition systems that must operate in real time on resource-constrained autonomous vehicles. Full article

(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies)

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24 pages, 2551 KB

Open AccessArticle

Towards Intelligent Virtual Clerks: AI-Driven Automation for Clinical Data Entry in Dialysis Care

by Perasuk Worragin, Suepphong Chernbumroong, Kitti Puritat, Phichete Julrode and Kannikar Intawong

Technologies 2025, 13(11), 530; https://doi.org/10.3390/technologies13110530 - 17 Nov 2025

Viewed by 450

Abstract

Manual data entry in dialysis centers is time-consuming, error-prone, and increases the administrative burden on healthcare professionals. Traditional optical character recognition (OCR) systems partially automate this process but lack the ability to handle complex data anomalies and ensure reliable clinical documentation. This study [...] Read more.

Manual data entry in dialysis centers is time-consuming, error-prone, and increases the administrative burden on healthcare professionals. Traditional optical character recognition (OCR) systems partially automate this process but lack the ability to handle complex data anomalies and ensure reliable clinical documentation. This study presents the design and evaluation of an AI-enhanced OCR system that integrates advanced image processing, rule-based validation, and large language model-driven anomaly detection to improve data accuracy, workflow efficiency, and user experience. A total of 65 laboratory reports, each containing approximately 35 fields, were processed and compared under two configurations: a basic OCR system and the AI-enhanced OCR system. System performance was evaluated using three key metrics: error detection accuracy across three error categories (Missing Values, Out-of-Range, and Typo/Free-text), workflow efficiency measured by average processing time per record and total completion time, and user acceptance measured using the System Usability Scale (SUS). The AI-enhanced OCR system outperformed the basic OCR system in all metrics, particularly in detecting and correcting Out-of-Range errors, such as decimal placement issues, achieving near-perfect precision and recall. It reduced the average processing time per record by almost 50% (85.2 to 42.1 s) and improved usability, scoring 81.0 (Excellent) compared to 75.0 (Good). These results demonstrate the potential of AI-driven OCR to reduce clerical workload, improve healthcare data quality, and streamline clinical workflows, while maintaining a human-in-the-loop verification process to ensure patient safety and data integrity. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare and Information Processing)

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22 pages, 4412 KB

Open AccessArticle

Deep Learning-Based Aerodynamic Analysis for Diverse Aircraft Configurations

by Oleg Lukyanov, Van Hung Hoang, Damian Josue Guerra Guerra, Jose Gabriel Quijada Pioquinto, Evgenii Kurkin and Artem Nikonorov

Technologies 2025, 13(11), 529; https://doi.org/10.3390/technologies13110529 - 15 Nov 2025

Viewed by 287

Abstract

In this study, a neural network was developed to predict the aerodynamic characteristics of fixed-wing aircraft with two lifting surfaces of various aerodynamic configurations. The proposed neural network model can incorporate 23 parameters to describe the aerodynamic configuration of an aircraft. A methodology [...] Read more.

In this study, a neural network was developed to predict the aerodynamic characteristics of fixed-wing aircraft with two lifting surfaces of various aerodynamic configurations. The proposed neural network model can incorporate 23 parameters to describe the aerodynamic configuration of an aircraft. A methodology for discrete geometric parameterization of aerodynamic configurations is introduced, enabling coverage of various combinations of relative positions of aircraft components. This study presents an approach to database construction and automated sample generation for neural network training. Furthermore, a procedure is provided for data preprocessing and correlation analysis of the input variables. The optimization process of the hyperparameters of the multilayer perceptron (MLP) architecture is described. The neural network models are validated through comparison with numerical simulations. Finally, several aerodynamic design problems are addressed, and the key advantages of the developed MLP-based surrogate aerodynamic models are demonstrated. Full article

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28 pages, 18703 KB

Open AccessArticle

Bidirectional Effects of Acceleration on Rotor–SFD System: Dynamic Analysis Based on Imbalance Condition Differences

by Zhongyu Yang, Jiaqi Li, Yihang Shi and Yinli Feng

Technologies 2025, 13(11), 528; https://doi.org/10.3390/technologies13110528 - 14 Nov 2025

Viewed by 411

Abstract

The rotor is a crucial component in rotating machinery, where its stability directly impacts performance and safety. Imbalance-induced vibrations can cause severe component wear, resonance instability, and even catastrophic failures, especially in high-speed systems like aero-engines. While the squeeze film damper (SFD) is [...] Read more.

The rotor is a crucial component in rotating machinery, where its stability directly impacts performance and safety. Imbalance-induced vibrations can cause severe component wear, resonance instability, and even catastrophic failures, especially in high-speed systems like aero-engines. While the squeeze film damper (SFD) is widely used for vibration suppression, the effects of imbalance (manifested as SFD eccentricity) on its dynamic performance are not well understood. Additionally, the combined impact of imbalance and acceleration on rotor–SFD system stability has not been systematically investigated. This study uses numerical simulations to explore the influence of SFD eccentricity, caused by imbalance, on its dynamic characteristics. Experimental tests are conducted to examine the effects of imbalance and acceleration on rotor–SFD dynamics. Results show that increasing imbalance raises SFD eccentricity, reducing the effective oil film bearing area. This results in a rapid increase in the oil film’s stiffness and slower growth in damping, enhancing nonlinearity and reducing stability. Under small imbalance conditions, increasing acceleration improves stability by facilitating critical speed crossing and reducing vibration amplitude. However, excessive imbalance renders acceleration control ineffective, exacerbating system instability. This study provides valuable insights into the interaction between imbalance, acceleration, and SFD performance, offering guidance for optimizing rotor–SFD system parameters and ensuring stable operation. Full article

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19 pages, 2999 KB

Open AccessArticle

Energy Storage Systems in Micro-Grid of Hybrid Renewable Energy Solutions

by Helena M. Ramos, Oscar E. Coronado-Hernández, Mohsen Besharat, Armando Carravetta, Oreste Fecarotta and Modesto Pérez-Sánchez

Technologies 2025, 13(11), 527; https://doi.org/10.3390/technologies13110527 - 14 Nov 2025

Viewed by 586

Abstract

This research evaluates Battery Energy Storage Systems (BESS) and Compressed Air Vessels (CAV) as complementary solutions for enhancing micro-grid resilience, flexibility, and sustainability. BESS units ranging from 5 to 400 kWh were modeled using a Nonlinear Autoregressive Neural Network with Exogenous Inputs (NARX) [...] Read more.

This research evaluates Battery Energy Storage Systems (BESS) and Compressed Air Vessels (CAV) as complementary solutions for enhancing micro-grid resilience, flexibility, and sustainability. BESS units ranging from 5 to 400 kWh were modeled using a Nonlinear Autoregressive Neural Network with Exogenous Inputs (NARX) neural network, achieving high SOC prediction accuracy with R² > 0.98 and MSE as low as 0.13 kWh². Larger batteries (400–800 kWh) effectively reduced grid purchases and redistributed surplus energy, improving system efficiency. CAVs were tested in pumped-storage mode, achieving 33.9–57.1% efficiency under 0.5–2 bar and high head conditions, offering long-duration, low-degradation storage. Waterhammer-induced CAV storage demonstrated reliable pressure capture when Reynolds number ≤ 75,000 and Volume Fraction Ratio, VFR > 11%, with a prototype reaching 6142 kW and 170 kWh at 50% air volume. CAVs proved modular, scalable, and environmentally robust, suitable for both energy and water management. Hybrid systems combining BESS and CAVs offer strategic advantages in balancing renewable intermittency. Machine learning and hydraulic modeling support intelligent control and adaptive dispatch. Together, these technologies enable future-ready micro-grids aligned with sustainability and grid stability goals. Full article

(This article belongs to the Special Issue Innovative Power System Technologies)

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13 pages, 1330 KB

Open AccessArticle

Integrating Fieldbus and Data-Centric Middleware: An STM32 Modbus Master Gateway for DDS-Based IIoT Systems

by Ioan Ungurean

Technologies 2025, 13(11), 526; https://doi.org/10.3390/technologies13110526 - 13 Nov 2025

Viewed by 401

Abstract

This paper presents an embedded gateway architecture that enables the seamless integration of Modbus-based industrial devices into Data Distribution Service (DDS) middleware for Industrial Internet of Things (IIoT) applications. The gateway, implemented on an STM32 microcontroller, acts as both a Modbus master and [...] Read more.

This paper presents an embedded gateway architecture that enables the seamless integration of Modbus-based industrial devices into Data Distribution Service (DDS) middleware for Industrial Internet of Things (IIoT) applications. The gateway, implemented on an STM32 microcontroller, acts as both a Modbus master and DDS-XRCE client, mapping Modbus registers directly to DDS topics with a configurable Quality of Service (QoS). Experimental validation demonstrates median latencies below 15 ms in four out of five scenarios, a throughput of up to 80 messages/s, and stable scalability to 160 subscribers with moderate resource usage. The results confirmed the feasibility and efficiency of Modbus–DDS integration on resource-constrained platforms. Full article

(This article belongs to the Section Information and Communication Technologies)

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20 pages, 16078 KB

Open AccessArticle

Shielding Gas Effect on Dendrite-Reinforced Composite Bronze Coatings via WAAM Cladding: Minimizing Defects and Intergranular Bronze Penetration into 09G2S Steel

by Artem Okulov, Yulia Khlebnikova, Olga Iusupova, Lada Egorova, Teona Suaridze, Yury Korobov, Boris Potekhin, Michael Sholokhov, Tushar Sonar, Majid Naseri, Tao He and Zaijiu Li

Technologies 2025, 13(11), 525; https://doi.org/10.3390/technologies13110525 - 13 Nov 2025

Viewed by 312

Abstract

Bronze materials are indispensable across numerous industries for enhancing the durability and performance of components, primarily due to their excellent tribological properties, corrosion resistance, and machinability. This study investigates the impact of different atmospheric conditions on the properties of WAAM (wire arc additive [...] Read more.

Bronze materials are indispensable across numerous industries for enhancing the durability and performance of components, primarily due to their excellent tribological properties, corrosion resistance, and machinability. This study investigates the impact of different atmospheric conditions on the properties of WAAM (wire arc additive manufacturing) cladded bronze coatings on 09G2S steel substrate. Specifically, the research examines how varying atmospheres—including ambient air (N₂/O₂, no shielding gas), pure argon (Ar), carbon dioxide (CO₂), and 82% Ar + 18% CO₂ (Ar/CO₂) mixture—influence coating defectiveness (porosity, cracks, non-uniformity), wettability (manifested as uniform layer formation and strong adhesion), and the extent of intergranular penetration (IGP), leading to the formation of characteristic infiltrated cracks or “bronze whiskers”. Modern investigative techniques such as optical microscopy (OM), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD) were employed for comprehensive material characterization. Microhardness testing was also carried out to evaluate and confirm the homogeneity of the coating structure. The findings revealed that the bronze coatings primarily consisted of a dominant, highly textured FCC α-Cu phase and a minor BCC α-Fe phase, with Rietveld refinement quantifying a α-Fe volume fraction of ~5%, lattice parameters of a = 0.3616 nm for α-Cu and a = 0.2869 nm for α-Fe, and a modest microstrain of 0.001. The bronze coating deposited under a pure Ar atmosphere exhibited superior performance, characterized by excellent wettability, a uniform, near-defect-free structure with minimal porosity and cracks, and significantly suppressed formation of bronze whiskers, both in quantity and size. Conversely, the coating deposited without a protective atmosphere demonstrated the highest degree of defectiveness, including agglomerated pores and cracks, leading to an uneven interface and extensive whisker growth of varied morphologies. Microhardness tests confirmed that while the Ar-atmosphere coating displayed the lowest hardness (~130 HV_0.1), it maintained consistent values across the entire analyzed area, indicating structural homogeneity. These results underscore the critical role of atmosphere selection in WAAM processing for achieving high-quality bronze coatings with enhanced interfacial integrity and functional performance. Full article

(This article belongs to the Special Issue Wire Arc Additive Manufacturing: Pioneering Techniques, Materials, and Applications)

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26 pages, 1227 KB

Open AccessArticle

Automated Sleep Spindle Analysis in Epilepsy EEG Using Deep Learning

by Nikolay V. Gromov, Albina V. Lebedeva, Artem A. Sharkov, Anna D. Grebenyukova, Anton E. Malkov, Svetlana A. Gerasimova, Lev A. Smirnov, Tatiana A. Levanova and Alexander N. Pisarchik

Technologies 2025, 13(11), 524; https://doi.org/10.3390/technologies13110524 - 13 Nov 2025

Viewed by 664

Abstract

Sleep spindles, together with K-complexes, are the distinctive patterns of neuronal activity in EEG recordings during stage 2 sleep. When the mechanisms of sleep spindle generation are impaired, e.g., in epilepsy, their quantitative parameters change. The analysis of these changes can provide valuable [...] Read more.

Sleep spindles, together with K-complexes, are the distinctive patterns of neuronal activity in EEG recordings during stage 2 sleep. When the mechanisms of sleep spindle generation are impaired, e.g., in epilepsy, their quantitative parameters change. The analysis of these changes can provide valuable insights into the formation of epileptiform activity patterns and help to develop an additional tool for more accurate medical diagnosis. Despite the central role of EEG in the diagnosis of epilepsy, disorders of consciousness, and neurological research, resources specifically dedicated to large-scale EEG data analysis are under-represented. In our study, we collect a specialized database of clinical EEG recordings from epilepsy patients and controls during N2 sleep, characterized by rhythmic spindle activity in frontocentral and vertex regions, and manually annotate them. We then quantify four key sleep spindle characteristics using a comparison of manual annotation by a clinician and artificial intelligence technologies. A thorough evaluation of state-of-the-art deep learning architectures for detecting and characterizing sleep spindles in EEG recordings from epilepsy patients is conducted. The results show that the 1D U-Net and SEED architectures achieve competitive overall performance, but their precision-to-recall ratios differ markedly in clinical settings. This suggests that different approaches may be appropriate for each clinical situation. Furthermore, our results demonstrate that epilepsy is associated with significant and quantifiable changes in sleep spindle morphology and frequency. Automated analysis of these characteristics using artificial intelligence provides a reliable biomarker that provides a detailed picture of thalamocortical dysfunction in epilepsy. This approach has great potential for accelerated diagnosis and the development of targeted therapeutic strategies for epilepsy. Full article

(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)

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51 pages, 13018 KB

Open AccessReview

Advances in Magnesia–Dolomite Refractory Materials: Properties, Emerging Technologies, and Industrial Applications: A Review

by Leonel Díaz-Tato, Luis Angel Iturralde Carrera, Jesús Fernando López-Perales, Marcos Aviles, Edén Amaral Rodríguez-Castellanos and Juvenal Rodríguez-Resendiz

Technologies 2025, 13(11), 523; https://doi.org/10.3390/technologies13110523 - 13 Nov 2025

Viewed by 791

Abstract

Magnesia-dolomite refractories have emerged as sustainable alternatives to traditional carbon- or chromium-containing linings in steelmaking and cement industries. Their outstanding thermochemical stability, high refractoriness, and strong basic slag compatibility make them suitable for converters, electric arc furnaces (EAF), and argon–oxygen decarburization (AOD) units. [...] Read more.

Magnesia-dolomite refractories have emerged as sustainable alternatives to traditional carbon- or chromium-containing linings in steelmaking and cement industries. Their outstanding thermochemical stability, high refractoriness, and strong basic slag compatibility make them suitable for converters, electric arc furnaces (EAF), and argon–oxygen decarburization (AOD) units. However, their practical application has long been constrained by hydration and thermal shock sensitivity associated with free CaO and open porosity. Recent advances, including optimized raw material purity, fused co-clinker synthesis, nano-additive incorporation (TiO₂, MgAl₂O₄ spinel, FeAl₂O₄), and improved sintering strategies, have significantly enhanced density, mechanical strength, and hydration resistance. Emerging technologies such as co-sintered magnesia–dolomite composites and additive-assisted microstructural tailoring have enabled superior corrosion resistance and extended service life. This review provides a comprehensive analysis of physicochemical mechanisms, processing routes, and industrial performance of magnesia–dolomite refractories, with special emphasis on their contribution to technological innovation, decarbonization, and circular economy strategies in high-temperature industries. Full article

(This article belongs to the Section Innovations in Materials Science and Materials Processing)

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22 pages, 26125 KB

Open AccessArticle

A Parkinson’s Disease Recognition Method Based on Plantar Pressure Feature Fusion

by Lan Ma and Hua Huo

Technologies 2025, 13(11), 522; https://doi.org/10.3390/technologies13110522 - 13 Nov 2025

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Abstract

With the increasing number of patients with Parkinson’s disease, the detection of Parkinson’s disease is crucial for the early intervention and treatment of this condition. The motor characteristics of Parkinson’s disease primarily include typical motor features. Flexible pressure sensor arrays, due to their [...] Read more.

With the increasing number of patients with Parkinson’s disease, the detection of Parkinson’s disease is crucial for the early intervention and treatment of this condition. The motor characteristics of Parkinson’s disease primarily include typical motor features. Flexible pressure sensor arrays, due to their unique mechanical properties and biocompatibility, have shown great potential for capturing movement characteristics. This research aims to develop a deep learning model based on foot pressure data for the detection of Parkinson’s disease. By collecting the pressure data of patients during walking and analyzing the distribution of foot pressure, the model can capture the unique biomechanical characteristics of Parkinson’s disease patients. To address the core challenges of spatial irregularity and data disorder in footprint data, we propose an innovative approach that leverages the Transformer-based attention mechanism and tensor fusion technique to enable accurate identification of Parkinson’s disease. This attention mechanism has inherent permutation invariance, which is highly suitable for the feature learning of footprint data. The tensor fusion technique can effectively integrate the foot features at different levels. A large-scale dataset of foot pressure data was used for training and validation. The experimental results show that the model achieves a high accuracy of 87.03% and good stability in Parkinson’s disease detection, enabling effective differentiation between patients and healthy individuals. On the one hand, our work is critical for analyzing pressure data and fusion features from large-area flexible force-sensitive sensors, which enables the accurate identification of foot data. On the other hand, it greatly facilitates gait analysis, gait evaluation, and the diagnosis of Parkinson’s disease. Full article

(This article belongs to the Section Information and Communication Technologies)

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17 pages, 2779 KB

Open AccessArticle

Image Restoration Based on Semantic Prior Aware Hierarchical Network and Multi-Scale Fusion Generator

by Yapei Feng, Yuxiang Tang and Hua Zhong

Technologies 2025, 13(11), 521; https://doi.org/10.3390/technologies13110521 - 13 Nov 2025

Viewed by 349

Abstract

As a fundamental low-level vision task, image restoration plays a pivotal role in reconstructing authentic visual information from corrupted inputs, directly impacting the performance of downstream high-level vision systems. Current approaches frequently exhibit two critical limitations: (1) Progressive texture degradation and blurring during [...] Read more.

As a fundamental low-level vision task, image restoration plays a pivotal role in reconstructing authentic visual information from corrupted inputs, directly impacting the performance of downstream high-level vision systems. Current approaches frequently exhibit two critical limitations: (1) Progressive texture degradation and blurring during iterative refinement, particularly in irregular damage patterns. (2) Structural incoherence when handling cross-domain artifacts. To address these challenges, we present a semantic-aware hierarchical network (SAHN) that synergistically integrates multi-scale semantic guidance with structural consistency constraints. Firstly, we construct a Dual-Stream Feature Extractor. Based on a modified U-Net backbone with dilated residual blocks, this skip-connected encoder–decoder module simultaneously captures hierarchical semantic contexts and fine-grained texture details. Secondly, we propose the semantic prior mapper by establishing spatial–semantic correspondences between damaged areas and multi-scale features through predefined semantic prototypes through adaptive attention pooling. Additionally, we construct a multi-scale fusion generator, by employing cascaded association blocks with structural similarity constraints. This unit progressively aggregates features from different semantic levels using deformable convolution kernels, effectively bridging the gap between global structure and local texture reconstruction. Compared to existing methods, our algorithm attains the highest overall PSNR of 34.99 with the best visual authenticity (with the lowest FID of 11.56). Comprehensive evaluations of three datasets demonstrate its leading performance in restoring visual realism. Full article

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23 pages, 346 KB

Open AccessArticle

CPU-Only Self Enhancing Authoring Copilot Design-Based Markov Decision Processes Orchestration and Qwen 3 Local Large Language Model

by Smail Tigani

Technologies 2025, 13(11), 520; https://doi.org/10.3390/technologies13110520 - 13 Nov 2025

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Abstract

We introduce a novel, privacy-preserving AI authoring copilot designed for educational content creation, which uniquely combines a Markov Decision Process (MDP) as a reinforcement learning orchestrator with a locally deployed Qwen3-1.7B-ONNX large language model to iteratively refine text for clarity, unity, and engagement—all [...] Read more.

We introduce a novel, privacy-preserving AI authoring copilot designed for educational content creation, which uniquely combines a Markov Decision Process (MDP) as a reinforcement learning orchestrator with a locally deployed Qwen3-1.7B-ONNX large language model to iteratively refine text for clarity, unity, and engagement—all running on a modest CPU-only system (Intel i7, 16 GB RAM). Unlike cloud-dependent models, our agent treats writing as a sequential decision problem, selecting refinement actions (e.g., simplification, elaboration) based on real-time LLM and sentiment feedback, ensuring pedagogically sound outputs without internet dependency. Evaluated across five diverse topics, our MDP-orchestrated agent achieved an overall average quality score of 4.23 (on a 0–5 scale), statistically equivalent to leading cloud-based LLMs like ChatGPT and DeepSeek. This performance was validated through blind evaluations by four independent LLMs and human raters, supported by statistical consistency analysis. Our work demonstrates that lightweight local LLMs, when guided by principled MDP policies, can deliver high-quality, context-aware educational content, bridging the gap between powerful AI generation and ethical, on-device deployment. This advancement empowers educators, researchers, and curriculum designers with a trustworthy, accessible tool for intelligent content augmentation aligning with the Quality Education Sustainable Development Goal through innovations in educational technology, inclusive education, equity in education, and lifelong learning. Full article

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15 pages, 1115 KB

Open AccessArticle

AI-Driven Cognitive Digital Twin for Optimizing Energy Efficiency in Industrial Air Compressors

by Mawande Sikibi, Thokozani Justin Kunene and Lagouge Tartibu

Technologies 2025, 13(11), 519; https://doi.org/10.3390/technologies13110519 - 12 Nov 2025

Viewed by 395

Abstract

Energy efficiency is widely recognized as a critical strategy for reducing energy consumption in industrial systems. Improving energy efficiency has become a central point in industrial systems aiming to reduce energy consumption and operational costs. Industrial air compressors are among the most energy-intensive [...] Read more.

Energy efficiency is widely recognized as a critical strategy for reducing energy consumption in industrial systems. Improving energy efficiency has become a central point in industrial systems aiming to reduce energy consumption and operational costs. Industrial air compressors are among the most energy-intensive assets and often operate under static control policies that fail to adapt to real-time dynamics. This paper proposes a cognitive digital twin (CDT) framework that integrates reinforcement learning as, especially, a Proximal Policy Optimization (PPO) agent into the virtual replica of the air compressor system. CDT learns continuous from multidimensional telemetry which includes power, outlet pressure, air flow, and intake temperature, enabling autonomous decision-making, fault adaptation, and dynamic energy optimization. Simulation results demonstrate that PPO strategy reduces average SEC by 12.4%, yielding annual energy savings of approximately 70,800 kWh and a projected payback period of one year. These findings highlight the CDT potential to transform industrial asset management by bridging intelligent control. Full article

(This article belongs to the Special Issue AI for Smart Engineering Systems)

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16 pages, 354 KB

Open AccessArticle

AI-Based Intelligent System for Personalized Examination Scheduling

by Marco Barone, Muddasar Naeem, Matteo Ciaschi, Giancarlo Tretola and Antonio Coronato

Technologies 2025, 13(11), 518; https://doi.org/10.3390/technologies13110518 - 12 Nov 2025

Viewed by 469

Abstract

Artificial Intelligence (AI) has brought a revolution in many areas, including the education sector. It has the potential to improve learning practices, innovate teaching, and accelerate the path towards personalized learning. This work introduces Reinforcement Learning (RL) methods to develop a personalized examination [...] Read more.

Artificial Intelligence (AI) has brought a revolution in many areas, including the education sector. It has the potential to improve learning practices, innovate teaching, and accelerate the path towards personalized learning. This work introduces Reinforcement Learning (RL) methods to develop a personalized examination scheduling system at a university level. We use two widely established RL algorithms, Q-Learning and Proximal Policy Optimization (PPO), for the task of personalized exam scheduling. We consider several key points, including learning efficiency, the quality of the personalized educational path, adaptability to changes in student performance, scalability with increasing numbers of students and courses, and implementation complexity. Experimental results, based on case studies conducted within a single degree program at a university, demonstrate that, while Q-Learning offers simplicity and greater interpretability, PPO offers superior performance in handling the complex and stochastic nature of students’ learning trajectories. Experimental results, conducted on a dataset of 391 students and 5700 exam records from a single degree program, demonstrate that PPO achieved a 42.0% success rate in improving student scheduling compared to Q-Learning’s 26.3%, with particularly strong performance on problematic students (41.3% vs 18.0% improvement rate). The average delay reduction was 5.5 months per student with PPO versus 3.0 months with Q-Learning, highlighting the critical role of algorithmic design in shaping educational outcomes. This work contributes to the growing field of AI-based instructional support systems and offers practical guidance for the implementation of intelligent tutoring systems. Full article

(This article belongs to the Topic AI Trends in Teacher and Student Training)

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25 pages, 2563 KB

Open AccessArticle

LungVisionNet: A Hybrid Deep Learning Model for Chest X-Ray Classification—A Case Study at King Hussein Cancer Center (KHCC)

by Iyad Sultan, Hasan Gharaibeh, Azza Gharaibeh, Belal Lahham, Mais Al-Tarawneh, Rula Al-Qawabah and Ahmad Nasayreh

Technologies 2025, 13(11), 517; https://doi.org/10.3390/technologies13110517 - 12 Nov 2025

Viewed by 670

Abstract

Early diagnosis and rapid treatment of respiratory abnormalities such as many lung diseases including pneumonia, TB, cancer, and other pulmonary problems depend on accurate and fast classification of chest X-ray images. Delayed diagnosis and insufficient treatment lead to the subjective, labour-intensive, error-prone features [...] Read more.

Early diagnosis and rapid treatment of respiratory abnormalities such as many lung diseases including pneumonia, TB, cancer, and other pulmonary problems depend on accurate and fast classification of chest X-ray images. Delayed diagnosis and insufficient treatment lead to the subjective, labour-intensive, error-prone features of current manual diagnosis systems. To tackle this pressing healthcare issue, this work investigates many deep convolutional neural network (CNN) architectures including VGG16, VGG19, ResNet50, InceptionV3, Xception, DenseNet121, NASNetMobile, and NASNet Large. LungVisionNet (LVNet) is an innovative hybrid model proposed here that combines MobileNetV2 with multilayer perceptron (MLP) layers in a unique way. LungVisionNet outperformed previous models in accuracy 96.91%, recall 97.59%, precision, specificity, F1-score 97.01%, and area under the curve (AUC) measurements according to thorough examination on two publicly available datasets including various chest abnormalities and normal cases exhibited. Comprehensive evaluation with an independent, real-world clinical dataset from King Hussein Cancer Centre (KHCC), which achieved 95.3% accuracy, 95.3% precision, 78.8% recall, 99.1% specificity, and 86.4% F1-score, confirmed the model’s robustness, generalizability, and clinical usefulness. We also created a simple mobile application that lets doctors quickly classify and evaluate chest X-ray images in hospitals, so enhancing clinical integration and practical application and supporting fast decision-making and better patient outcomes. Full article

(This article belongs to the Section Assistive Technologies)

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13 pages, 3175 KB

Open AccessArticle

Method of Topological Skeletonization for Evaluation of Effectiveness of Medical Rehabilitation Based on Upper Limb Exoskeletons

by Artem Obukhov, Anton Potlov, Mikhail Krasnyanskiy, Denis Dedov and Dmitry Sudakov

Technologies 2025, 13(11), 516; https://doi.org/10.3390/technologies13110516 - 11 Nov 2025

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Abstract

An important aspect of medical rehabilitation using exoskeletons is objective monitoring of the effectiveness of the exercise program. This control is most often manual and relies on the attention of a rehabilitation physician, but advanced rehabilitation systems also use computer vision technology. Topological [...] Read more.

An important aspect of medical rehabilitation using exoskeletons is objective monitoring of the effectiveness of the exercise program. This control is most often manual and relies on the attention of a rehabilitation physician, but advanced rehabilitation systems also use computer vision technology. Topological skeletons generalize large areas of digital images, representing a virtual internal framework of the analyzed object. The patient and the exoskeleton are described either as a set of spatially disparate (but not explicitly related to either the patient or the exoskeleton) topological skeletons, or as branches of a single topological skeleton which does not allow for objective monitoring of joint displacements. A method to solve this problem for medical rehabilitation using an upper-limb exoskeleton is proposed. It includes the following stages: (I) identifying the exoskeleton, as well as upper and lower parts of the patient’s body; (II) independent construction of three topological skeletons (separately for the exoskeleton and for the upper and lower parts of the patient’s body); (III) their integration. This approach allows for accurate, real-time analysis of movements in the upper-limb joints and prompt notification to the rehabilitation physician of any significant deviations in the technique of performing prescribed exercises. Full article

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43 pages, 2371 KB

Open AccessReview

SHEAB: A Novel Automated Benchmarking Framework for Edge AI

by Mustafa Abdulkadhim and Sandor R. Repas

Technologies 2025, 13(11), 515; https://doi.org/10.3390/technologies13110515 - 11 Nov 2025

Viewed by 671

Abstract

Edge computing is characterized by heterogeneous hardware, distributed deployment, and a need for on-site processing, which makes performance benchmarking challenging. This paper presents SHEAB (Scalable Heterogeneous Edge Automation Benchmarking), a novel framework designed to securely automate the benchmarking of Edge AI devices at [...] Read more.

Edge computing is characterized by heterogeneous hardware, distributed deployment, and a need for on-site processing, which makes performance benchmarking challenging. This paper presents SHEAB (Scalable Heterogeneous Edge Automation Benchmarking), a novel framework designed to securely automate the benchmarking of Edge AI devices at scale. The proposed framework enables concurrent performance evaluation of multiple edge nodes, drastically reducing the time-to-deploy (TTD) for benchmarking tasks compared to traditional sequential methods. SHEAB’s architecture leverages containerized microservices for orchestration and result aggregation, integrated with multi-layer security (firewalls, VPN tunneling, and SSH) to ensure safe operation in untrusted network environments. We provide a detailed system design and workflow, including algorithmic pseudocode for the SHEAB process. A comprehensive comparative review of related work highlights how SHEAB advances the state-of-the-art in edge benchmarking through its combination of secure automation and scalability. We detail a real-world implementation on eleven heterogeneous edge devices, using a centralized 48-core server to coordinate benchmarks. Statistical analysis of the experimental results demonstrates a 43.74% reduction in total benchmarking time and a 1.78× speedup in benchmarking throughput using SHEAB, relative to conventional one-by-one benchmarking. We also present mathematical formulations for performance gain and discuss the implications of our results. The framework’s effectiveness is validated through the concurrent execution of standard benchmarking workloads on distributed edge nodes, with results stored in a central database for analysis. SHEAB thus represents a significant step toward efficient and reproducible Edge AI performance evaluation. Future work will extend the framework to broader workloads and further improve parallel efficiency. Full article

(This article belongs to the Section Information and Communication Technologies)

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21 pages, 2679 KB

Open AccessArticle

Intelligent Feature Extraction and Event Classification in Distributed Acoustic Sensing Using Wavelet Packet Decomposition

by Artem Kozmin, Pavel Borozdin, Alexey Chernenko, Sergei Gostilovich, Oleg Kalashev and Alexey Redyuk

Technologies 2025, 13(11), 514; https://doi.org/10.3390/technologies13110514 - 11 Nov 2025

Viewed by 290

Abstract

Distributed acoustic sensing (DAS) systems enable real-time monitoring of physical events across extended areas using optical fiber that detects vibrations through changes in backscattered light patterns. In perimeter security applications, these systems must accurately distinguish between legitimate activities and potential security threats by [...] Read more.

Distributed acoustic sensing (DAS) systems enable real-time monitoring of physical events across extended areas using optical fiber that detects vibrations through changes in backscattered light patterns. In perimeter security applications, these systems must accurately distinguish between legitimate activities and potential security threats by analyzing complex spatio-temporal data patterns. However, the high dimensionality and noise content of raw DAS data presents significant challenges for effective feature extraction and event classification, particularly when computational efficiency is required for real-time deployment. Traditional approaches or current machine learning methods often struggle with the balance between information preservation and computational complexity. This study addresses the critical need for efficient and accurate feature extraction methods that can identify informative signal components while maintaining real-time processing capabilities in DAS-based security systems. Here we show that wavelet packet decomposition (WPD) combined with a cascaded machine learning approach achieves 98% classification accuracy while reducing computational load through intelligent channel selection and preliminary filtering. Our modified peak signal-to-noise ratio metric successfully identifies the most informative frequency bands, which we validate through comprehensive neural network experiments across all possible WPD channels. The integration of principal component analysis with logistic regression as a preprocessing filter eliminates a substantial portion of non-target events while maintaining high recall level, significantly improving upon methods that processed all available data. These findings establish WPD as a powerful preprocessing technique for distributed sensing applications, with immediate applications in critical infrastructure protection. The demonstrated gains in computational efficiency and accuracy improvements suggest broad applicability to other pattern recognition challenges in large-scale sensor networks, seismic monitoring, and structural health monitoring systems, where real-time processing of high-dimensional acoustic data is essential. Full article

(This article belongs to the Special Issue Application and Development of Distributed Acoustic Sensing (DAS) Technology)

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34 pages, 1179 KB

Open AccessArticle

Adapting the Smart Village Index as a Technological Tool for Rural Digitalization and Tourism Development in Emerging Economies

by Tamara Gajić, Ivana Blešić, Dragan Vukolić, Milan Ivkov, Milan M. Radovanović, Slavica Malinović-Milićević and Olgica Miljković

Technologies 2025, 13(11), 513; https://doi.org/10.3390/technologies13110513 - 10 Nov 2025

Viewed by 529

Abstract

This research adapts and tests the Smart Village Index (SVI) as a multidimensional technological model designed to assess the digital readiness, institutional maturity, and infrastructural connectivity of rural areas in Serbia. The research was undertaken in 10 rural municipalities that are representative of [...] Read more.

This research adapts and tests the Smart Village Index (SVI) as a multidimensional technological model designed to assess the digital readiness, institutional maturity, and infrastructural connectivity of rural areas in Serbia. The research was undertaken in 10 rural municipalities that are representative of various phases of digital transformation and development typologies. The dimensions included in the analysis were six, which are information and communication technologies, digital governance, leadership and local competences, community participation, a sustainable economy, and infrastructure. The results indicated significant regional differences: About 30% of the municipalities, including Aranđelovac, Kanjiža, and Arilje, fall into the group of smart villages with developed infrastructure and high institutional readiness. About 40% of the municipalities, such as Titel, Knjazevac, and Despotovac, are in the phase of transiting to digital, while the remaining 30% (Knić, Rekovac, Žabari, and Crna Trava) still present a low level of digital connectivity, with limited capacities in their institutions. This research supports the fact that the successful digital transformation of rural communities requires a balance between technological development, institutional support, and social inclusion. The Smart Village Index (SVI) proposed is a robust way to evaluate the digital readiness of villages and to inform targeted policies on achieving sustainable rural development in Serbia. In addition to its analytical and evaluative role, the Smart Village Index (SVI) is a digital–technological innovation and a computational tool that unites data modeling, algorithmic standardization, and digital analytics in order to measure the level of digital readiness of a rural community. It therefore crosses over the thresholds of the conventional social scientist construct and gives a technological implementation that is within the threshold of technology being a reproducible and data-driven instrument for the real-life planning of digital governance and rural development. Full article

(This article belongs to the Special Issue Smart Technologies Shaping the Future of Tourism and Hospitality)

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14 pages, 7255 KB

Open AccessArticle

Cu-Assisted Corrosion Conquers Irregularities in Mesoporous Si

by Hanna V. Bandarenka, Anastasiya Shapel, Diana Laputsko, Alma Dauletbekova, Abdirash Akilbekov, Zhuldyz Nurlan, Diana Junisbekova, Uladzislau Shapel, Alise Podelinska, Elina Neilande, Anatoli I. Popov and Dmitry Bocharov

Technologies 2025, 13(11), 512; https://doi.org/10.3390/technologies13110512 - 9 Nov 2025

Viewed by 350

Abstract

Metal-coated mesoporous PSi (mesoPSi) opens up disruptive perspectives for biosensing, which is primarily enabled by surface-enhanced Raman scattering (SERS). Although the unique performance of SERS-active substrates based on metal-coated mesoPSi has already been praised, influence of defects in silicon wafer on its morphology [...] Read more.

Metal-coated mesoporous PSi (mesoPSi) opens up disruptive perspectives for biosensing, which is primarily enabled by surface-enhanced Raman scattering (SERS). Although the unique performance of SERS-active substrates based on metal-coated mesoPSi has already been praised, influence of defects in silicon wafer on its morphology has not been revealed. Defects lead to formation of spiral regions in mesoPSi with varying porosity, which affects SERS activity of the overlying metallic nanostructures. It limits the reliability of SERS analysis. Here, we investigate repeatability of morphology and SERS activity of silver particles on mesoPSi as a function of defects in parent silicon, which are induced by irregular dopant levels. We propose an original corrosion approach that has not yet been applied to control the morphology of silicon nanostructures in general and mesoPSi in particular. By replacing silicon nanocrystallites with sacrificial copper nanoparticles, we were able to eliminate the surface irreproducibility of mesoPSi. The copper-corrosion-modified porous silicon surface was shown to be a suitable substrate for reliable SERS-active substrates. In more detail, SERS-active substrate based on mesoPSi without a defective surface layer allowed for a more than 40% increase in the SERS-active surface area with a signal deviation of only 10 % compared to that with a defective layer. Full article

(This article belongs to the Section Innovations in Materials Science and Materials Processing)

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19 pages, 901 KB

Open AccessArticle

End-Users’ Perspectives on Implementation Outcomes of Digital Voice Assistants Delivering a Home-Based Lifestyle Intervention in Older Obese Adults with Type 2 Diabetes Mellitus: A Qualitative Analysis

by Costas Glavas, Jiani Ma, Surbhi Sood, Elena S. George, Robin M. Daly, Eugene Gvozdenko, Barbora de Courten, David Scott and Paul Jansons

Technologies 2025, 13(11), 511; https://doi.org/10.3390/technologies13110511 - 9 Nov 2025

Viewed by 602

Abstract

Managing blood glucose levels and adhering to exercise is challenging for older adults with obesity and type 2 diabetes mellitus (T2DM). Digital voice assistants (DVAs) utilising conversation-based interactions and natural language may overcome barriers to accessing home-based lifestyle programs, but end-user perspectives are [...] Read more.

Managing blood glucose levels and adhering to exercise is challenging for older adults with obesity and type 2 diabetes mellitus (T2DM). Digital voice assistants (DVAs) utilising conversation-based interactions and natural language may overcome barriers to accessing home-based lifestyle programs, but end-user perspectives are essential for implementation. This analysis investigated end-user perspectives on implementation outcomes of a DVA-delivered lifestyle program nested within a randomised controlled trial of 50 older adults (aged 50–75 years) with obesity and T2DM (DVA n = 25; control n = 25). Following trial completion, 10 DVA participants (mean ± SD age 67 ± 4 years) completed semi-structured interviews guided by the Practical Planning for Implementations and Scale-up guide and Proctor’s implementation outcome taxonomy. Over half (60%) were willing to pay for the DVA-delivered program, indicating perceived value. DVA audiovisual and conversation-based modalities enhanced engagement and acceptability. Most end-users found the DVA program feasible as a modality for delivering lifestyle programs, but suggested greater personalisation to bolster sustainability. Overall, the intervention was identified as acceptable and appropriate, suggesting digitally delivered programs may be feasible and sustainable for long-term use. Findings should be interpreted cautiously, given the small sample size and short intervention period. Nevertheless, end-users’ suggestions could inform the implementation of digital health interventions into healthcare systems. Full article

(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives—Second Edition)

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21 pages, 6004 KB

Open AccessArticle

A Frequency Regulation Strategy for Thermostatically Controlled Loads Combining Differentiated Deadband and Dynamic Droop Coefficients

by Meng Liu, Song Gao, Na Li, Yudun Li and Yuntao Sun

Technologies 2025, 13(11), 510; https://doi.org/10.3390/technologies13110510 - 8 Nov 2025

Viewed by 325

Abstract

With a large number of traditional thermal power units being replaced by inverter-based resources, the system inertia and regulation capability have significantly decreased in certain countries, exposing a critical gap in traditional generation-side-dominated frequency regulation strategies. The decline in system inertia deteriorates frequency [...] Read more.

With a large number of traditional thermal power units being replaced by inverter-based resources, the system inertia and regulation capability have significantly decreased in certain countries, exposing a critical gap in traditional generation-side-dominated frequency regulation strategies. The decline in system inertia deteriorates frequency dynamics, creating a critical need for load-side regulation. To enhance frequency stability in low-inertia power systems, this paper proposes a frequency regulation strategy for thermostatically controlled loads (TCLs). The strategy incorporates a differential deadband that adjusts response thresholds based on frequency deviation, along with dynamic droop coefficients that self-adapt according to real-time TCL capacity. First, the operational principles of TCLs and the frequency response characteristics of thermal power units are analyzed to establish the foundation for load-side frequency regulation. Second, building upon the spatiotemporal distribution characteristics of system frequency, the nodal frequency under high renewable energy penetration is derived, and a differential dead zone setting method for TCLs is proposed. Then, a dynamic droop coefficient tuning method is developed to enable adaptive parameter adjustment according to the real-time regulation capacity of TCLs. Finally, these key elements are integrated within a hybrid control framework to formulate the complete TCL frequency regulation strategy. Simulation results demonstrate a 0.342% improvement in frequency nadir and 0.253% reduction in settling time compared to conventional methods, while ensuring reliable TCL operation. This work presents a validated solution for enhancing frequency stability in renewable-rich power systems, where the proposed framework with nodal frequency-based deadbands and adaptive droop coefficients demonstrates effective regulation capability under low-inertia conditions. Full article

(This article belongs to the Special Issue Next-Generation Distribution System Planning, Operation, and Control)

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