Journal Description
Technologies
Technologies
is an international, peer-reviewed, open access journal singularly focusing on emerging scientific and technological trends, published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within ESCI (Web of Science), Scopus, Inspec, Ei Compendex, INSPIRE, and other databases.
- Journal Rank: JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q1 (Computer Science (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.1 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the second half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Technologies.
- Journal Cluster of Mechanical Manufacturing and Automation Control: Aerospace, Automation, Drones, Journal of Manufacturing and Materials Processing, Machines, Robotics and Technologies.
Impact Factor:
3.6 (2024);
5-Year Impact Factor:
4.2 (2024)
Latest Articles
Green Plasma Process for Converting Natural Gas into Valuable Organic Products and Carbon with Preferential Ethane Adsorption
Technologies 2026, 14(5), 307; https://doi.org/10.3390/technologies14050307 - 18 May 2026
Abstract
To accelerate the transition to sustainable energy, efficient methods for CO2-free hydrogen production and carbon utilization are needed. This study presents a new, sustainable approach for the simultaneous production of hydrogen, valuable hydrocarbons, and functional carbon materials by converting methane in
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To accelerate the transition to sustainable energy, efficient methods for CO2-free hydrogen production and carbon utilization are needed. This study presents a new, sustainable approach for the simultaneous production of hydrogen, valuable hydrocarbons, and functional carbon materials by converting methane in low-pressure microwave plasma. Compared to traditional methane reforming methods (such as steam reforming), our plasma-based process operates at low temperatures, eliminates direct CO2 emissions, and enables the conversion of methane into three valuable products: (1) environmentally friendly hydrogen for fuel cells and energy storage systems, (2) a range of valuable organic products (C2H2, C2H4, C2H6), and (3) functional carbon films with self-improving catalytic properties. Optical emission spectroscopy (OES) and the Langmuir double probe method were used for plasma diagnostics, revealing an increase in the concentration of active species (CH, Hα, C2) and electron temperature upon argon addition. The structure, morphology, and impurity composition of the deposited films were investigated using X-ray diffraction (XRD), scanning electron microscopy (SEM), and inductively coupled plasma mass spectrometry (ICP-MS), respectively. Gas-phase byproducts were analyzed using gas chromatography–mass spectrometry (GC-MS). Argon addition at an Ar/CH4 ratio of 1 leads to the formation of carbon films with a more ordered structure, as confirmed by XRD data, and improved surface morphology. It was established that argon, by effectively participating in the excitation and dissociation processes of methane molecules through energy transfer from metastable states and increased electron temperature, optimizes plasma–chemical reactions, promoting the deposition of higher-quality carbon coatings.
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(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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Open AccessArticle
IISD-YOLO: Infrared Detection of Insulator Strings for Transmission Lines Based on Improved YOLOv11
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Chen-Hao Zhao, Yi-Feng Ren, Long-Kun Cao and Hong-Yu Wang
Technologies 2026, 14(5), 306; https://doi.org/10.3390/technologies14050306 - 18 May 2026
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In the area of transmission line inspection, one of the prominent areas of research has been to unite Unmanned Aerial Vehicles (UAVs) with neural network object detection algorithms. This area of research is challenging because of high computational resource consumption and poor infrared
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In the area of transmission line inspection, one of the prominent areas of research has been to unite Unmanned Aerial Vehicles (UAVs) with neural network object detection algorithms. This area of research is challenging because of high computational resource consumption and poor infrared detection capabilities. In this study we propose an infrared image detection algorithm, named IISD-YOLO, using a modified version of the YOLOv11 network, to detect infrared transmission line insulator strings. Firstly, the original object detection layer was removed and replaced with the ShuffleNetv2 network to achieve the goal of a lightweight model; subsequently, based on the original feature extraction module C3k2, the Manhattan Self-Attention (MaSA) mechanism was introduced to design a new feature extraction module, C3k2-MaSA, which enhances the feature extraction capability for infrared objects; finally, the bidirectional feature pyramid network (Bi-FPN) is used to replace the original feature fusion module, enhancing the network’s ability to process and fuse information at different scales. The comparative experiments show that compared with the mainstream YOLO models, IISD-YOLO has improved by 4.5, 6.1, and 4.8 percentage points respectively on mAP@50 over YOLOv5, YOLOv8, and YOLOv10; furthermore, this model outperforms advanced models including YOLO-CIR, FA-YOLO, YOFIR, and RT-DETR, with improvements of 2.9, 9.1, 5.0, and 1.1 percentage points respectively on mAP@50. The ablation study shows that each improvement effectively enhances the overall performance. Compared with the original YOLOv11, the IISD-YOLO has increased its mAP@50 by 3.5 percentage points, while reducing the number of Params by 1.1 million and the computational GFLOPs by 2 G. These results confirm the superior performance of IISD-YOLO in infrared insulator string detection.
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Open AccessArticle
Usability and Feasibility of a Contrast Avoidance Model-Based Virtual Reality Protocol Designed for Generalized Anxiety Disorder
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Barbora Darmová, Iveta Fajnerová and Lora Appel
Technologies 2026, 14(5), 305; https://doi.org/10.3390/technologies14050305 - 16 May 2026
Abstract
Generalized anxiety disorder (GAD) is characterized by persistent, excessive, and difficult-to-control worry. The Contrast Avoidance Model (CAM) proposes that individuals with GAD use worry to sustain negative emotional arousal, thereby avoiding sharp negative emotional contrasts that would otherwise follow unexpected adverse events. A
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Generalized anxiety disorder (GAD) is characterized by persistent, excessive, and difficult-to-control worry. The Contrast Avoidance Model (CAM) proposes that individuals with GAD use worry to sustain negative emotional arousal, thereby avoiding sharp negative emotional contrasts that would otherwise follow unexpected adverse events. A virtual reality (VR) protocol was developed to simulate such contrasts by alternating guided relaxation with brief anxiety-inducing scenarios (skyline plank, crowded elevator, and loose dog encounter). This study evaluated the usability and feasibility of this protocol in 20 subclinical adults aged 18–45 who met a screening threshold of GAD-7 ≥ 5, using a Meta Quest 3 headset and Polar H10 heart rate sensor. Exposure segments produced a significant decrease in RMSSD (β = −0.185, p < 0.001), consistent with reduced parasympathetic activity during exposure, whereas heart rate did not differ significantly between conditions. Subjectively, exposure increased SUDS (β = 2.23, p < 0.001) and SAM arousal (β = 1.95, p < 0.001), and decreased SAM valence (β = −2.68, p < 0.001) and dominance (β = −1.70, p = 0.005). Presence scores, cybersickness ratings, and qualitative feedback supported the usability of the protocol and identified concrete design refinements. These results support the feasibility of the protocol and provide a foundation for future controlled clinical evaluation.
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(This article belongs to the Special Issue VR for Cognitive and Emotional Well-Being)
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Open AccessArticle
Digital Transformation and AI Readiness in Public Knowledge Ecosystems: Assessing Digital Maturity in European Public Libraries
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Ioana Cornelia Cristina Crihană and Josef Rebenda
Technologies 2026, 14(5), 304; https://doi.org/10.3390/technologies14050304 - 15 May 2026
Abstract
This paper discusses how digital transformation takes place in public knowledge institutions by examining public libraries as socio-technical service ecosystems, and conceptualizes digital maturity. Based on Service-Dominant Logic and the socio-technical systems theory, this study explores digital maturity as a natural product of
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This paper discusses how digital transformation takes place in public knowledge institutions by examining public libraries as socio-technical service ecosystems, and conceptualizes digital maturity. Based on Service-Dominant Logic and the socio-technical systems theory, this study explores digital maturity as a natural product of convergence in technological infrastructures, professional expertise, governance mechanisms, and community involvement. The data analysis is conducted on a structured 48-item questionnaire which, at its turn, is based on a sample of 101 members of library staff in public libraries in Romania. The Romanian dataset is contextualized by using a national comparative dataset comprising 363 respondents from France. We employ a mixed method of descriptive and inferential statistical analyses and thematic coding in order to investigate institutional adaptability, AI readiness, and service development trends. The results reveal the continuing movement from collection-centered models toward hybrid physical–digital service platforms and differences in digital maturity and overall strategic planning among institutions. The results demonstrate that digital maturity is sensitive to the organized coordination and the planning capability in institutions rather than to isolated technological adoption. Drawing from this evidence, the study proposes an analytical framework and a tempered analytical lens for interpreting digital transformation processes in public knowledge ecosystems, forming a solid foundation for more general investigations of institutional adaptation to digitally mediated environments.
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(This article belongs to the Topic Challenges and Opportunities of Integrating Service Science with Data Science and Artificial Intelligence)
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Open AccessArticle
A Comparative Framework for Formal Representation Strategies in Sign Language Avatar Systems
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Nurzada Amangeldy, Aigerim Yerimbetova, Marek Milosz, Akmaral Kassymova, Elmira Daiyrbayeva and Nazira Tursynova
Technologies 2026, 14(5), 303; https://doi.org/10.3390/technologies14050303 - 14 May 2026
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This paper proposes a unified methodological framework for evaluating heterogeneous approaches to avatar-based sign language visualization. The study introduces a four-dimensional analytical framework based on four independent criteria: (A1) pipeline architecture and degree of automation, (A2) data and annotation requirements, (A3) portability across
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This paper proposes a unified methodological framework for evaluating heterogeneous approaches to avatar-based sign language visualization. The study introduces a four-dimensional analytical framework based on four independent criteria: (A1) pipeline architecture and degree of automation, (A2) data and annotation requirements, (A3) portability across sign languages and domains, and (A4) integration and accessibility. The framework is applied to a comparative analysis of three dominant paradigms: (P1) notation → animation (e.g., HamNoSys), (P2) writing-based representation → animation (e.g., SignWriting), and (P3) keypoint-based animation and Artificial Intelligence (AI) methods. The comparative assessment shows that the differences between the paradigms are structural and reflect trade-offs among linguistic accuracy, automation level, scalability, and user accessibility, rather than the superiority of any one technology. Overall, the structured comparative framework (A1–A4) is applied for analyzing three paradigms of sign language avatar generation. It enables a systematic evaluation of architectural, data-related, and practical characteristics, highlighting key trade-offs between linguistic accuracy, scalability, and accessibility.
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Open AccessReview
Non-Prosthetic Assistive Technologies for Persons with Hearing Losses: A Survey
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Reemas Alsubaiei, Farah AlHayek, Mariam Alsahhaf, Ghadah Alajmi, Aliah Almutairi, Karim Youssef, Ghina El Mir, Sherif Said, Taha Beyrouthy and Samer Al Kork
Technologies 2026, 14(5), 302; https://doi.org/10.3390/technologies14050302 - 13 May 2026
Abstract
Millions of persons worldwide experience varying degrees of hearing loss, traditionally addressed through prosthetic solutions such as hearing aids and cochlear implants. However, a significant proportion of individuals cannot benefit from these technologies, cannot access them, or choose not to use them. In
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Millions of persons worldwide experience varying degrees of hearing loss, traditionally addressed through prosthetic solutions such as hearing aids and cochlear implants. However, a significant proportion of individuals cannot benefit from these technologies, cannot access them, or choose not to use them. In this context, non-prosthetic assistive technologies have emerged as a complementary paradigm, leveraging advances in sensing, artificial intelligence, and wearable computing to transform acoustic information into alternative perceptual representations rather than restoring auditory function. This survey provides a review of such systems, focusing on technologies that enhance environmental awareness, communication, and social interaction. Existing approaches are categorized along two main dimensions: the tasks they perform and the platforms on which they operate. Task-oriented analysis includes sound recognition (speech and non-speech), sound source localization, emotion recognition, sign language recognition, and related emerging functionalities. Platform-based analysis emphasizes wearable devices and mobile solutions enabling real-time and context-aware assistance. The survey further highlights key research trends, including real-time auditory scene analysis, portable processing, and artificial intelligence. It shows that recent studies increasingly demonstrate that combining auditory, visual, and haptic modalities improves robustness and usability in real-world conditions, particularly in noisy and dynamic environments. Finally, open challenges such as energy efficiency, latency, evaluation methodologies, and user acceptance are discussed. By synthesizing existing work and identifying open research directions, this survey aims to provide a structured foundation for future developments in intelligent, non-prosthetic assistive systems that redefine how auditory information is accessed and interpreted.
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(This article belongs to the Section Assistive Technologies)
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Open AccessReview
From Acquisition to Validation: Methodological Dependencies and Reproducibility in EEG-Based Alzheimer’s Disease Detection
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Ruimin Wang, Takenao Sugi and Takao Yamasaki
Technologies 2026, 14(5), 301; https://doi.org/10.3390/technologies14050301 - 13 May 2026
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early detection and reliable monitoring remain major clinical challenges. Electroencephalography (EEG) combined with machine learning has attracted growing interest as a scalable and non-invasive approach to AD detection, yet reported classification accuracies vary
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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early detection and reliable monitoring remain major clinical challenges. Electroencephalography (EEG) combined with machine learning has attracted growing interest as a scalable and non-invasive approach to AD detection, yet reported classification accuracies vary widely across studies and are rarely comparable or clinically translatable. One important reason is that the analytical pipeline—from data acquisition to model validation—involves numerous methodological choices whose inter-stage dependencies and reproducibility implications are rarely made explicit. In this narrative review, we adopt a methodological chain framework to make these dependencies explicit, organizing EEG-based AD research into five sequential stages: data acquisition, preprocessing, feature representation, modeling, and validation. Choices at each stage can shape downstream analyses, inflate reported performance, and reduce cross-study comparability in ways that are difficult to detect when stages are assessed independently. These effects are particularly consequential in EEG-based AD research, where cohorts are typically small and biomarkers are subtle. We make three primary contributions: (1) we describe inter-stage methodological dependencies that may contribute to reproducibility problems and performance inflation; (2) we synthesize major sources of methodological variability across representative EEG–AD studies and evaluate their differential impact on spectral, connectivity, and complexity features; and (3) we provide practical, stage-aligned recommendations culminating in a minimum reporting checklist.
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(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives—Second Edition)
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Open AccessArticle
Micro-Attention CNN Hybrid Architecture for Real-Time Stress Detection Using Minimalistic Bio-Signals
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Chaymae Yahyati, Ismail Lamaakal, Yassine Maleh, Khalid El Makkaoui and Ibrahim Ouahbi
Technologies 2026, 14(5), 300; https://doi.org/10.3390/technologies14050300 - 13 May 2026
Abstract
Real-time psychological stress detection on wearable and edge devices requires models that are accurate, computationally efficient, and small enough for on-device deployment. This paper proposes a Micro-Attention CNN Hybrid Architecture for stress recognition using wearable bio-signals. The model uses six sensor channels, namely
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Real-time psychological stress detection on wearable and edge devices requires models that are accurate, computationally efficient, and small enough for on-device deployment. This paper proposes a Micro-Attention CNN Hybrid Architecture for stress recognition using wearable bio-signals. The model uses six sensor channels, namely tri-axial acceleration, electrodermal activity, heart rate, and skin temperature, and classifies three stress levels: no stress, low stress, and high stress. This study is conducted on a public wearable sensor dataset collected from 15 nurses during hospital work, providing a realistic benchmark for continuous stress monitoring under practical conditions. The proposed architecture combines one-dimensional and depthwise separable convolutions with a lightweight attention module to emphasize the most informative temporal patterns in short multivariate signal segments. To support deployment on resource-constrained devices, we further apply structured pruning, selective quantization-aware training, and post-training quantization. The full-precision model achieves a Macro-F1 score of 99.63%, while the final compressed model retains 98.03% Macro-F1 with a model size of 1.76 kilobytes and a CPU inference latency of 0.40 ms. Additional analyses show that most residual errors occur near the boundary between low stress and neighboring classes, while simple post-compression calibration improves reliability. These results demonstrate that accurate and low-latency stress detection using wearable bio-signals is feasible on compact edge hardware without transmitting raw sensor streams off-device.
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(This article belongs to the Special Issue AI-Enabled Smart Healthcare Systems)
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Open AccessArticle
Moving from the Paschen Law to More Accurate Electrical Discharge Models for the Design of Insulation Systems Under Variable Pressure
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Gian Carlo Montanari and Sukesh Babu Myneni
Technologies 2026, 14(5), 299; https://doi.org/10.3390/technologies14050299 - 13 May 2026
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The Paschen law, especially in its linear approximation, is said to be useful for predicting the partial discharge inception voltage (PDIV) in insulation systems when considering different defect sizes and pressure values. Hence, it is often used for designing electrical insulation systems in
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The Paschen law, especially in its linear approximation, is said to be useful for predicting the partial discharge inception voltage (PDIV) in insulation systems when considering different defect sizes and pressure values. Hence, it is often used for designing electrical insulation systems in aerospace applications. This paper presents a comparison between PDIV estimates provided by the Paschen law and a new model applicable to internal and surface discharges in electrical insulation systems under varying pressure and defect size or creepage distance. It is shown that the Paschen law estimates can often be very far from the measured PDIV values for both surface and internal defects and at pressures above and below standard atmospheric pressure (SAP), which can negatively affect the design and reliability of insulation systems. On the contrary, the proposed model provides accurate and consistent PDIV estimates, which are very close to those measured, for both internal and surface discharges. The lower limit of the model application/validation is 50 mbar from SAP.
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Open AccessArticle
Low-Cost Active Cell Balancing Battery Management System for Electric Vehicles with Cell Charger as Cell Balancer
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Amin Amin, Feri Yusivar, Faiz Husnayain and Aam Muharam
Technologies 2026, 14(5), 298; https://doi.org/10.3390/technologies14050298 - 12 May 2026
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Cell imbalance in battery packs can cause premature termination during battery discharge and recharge processes. This condition can decrease the usable energy of the battery. The cost of batteries can reach 30–40% of the price of an electric vehicle, so battery cell balancing
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Cell imbalance in battery packs can cause premature termination during battery discharge and recharge processes. This condition can decrease the usable energy of the battery. The cost of batteries can reach 30–40% of the price of an electric vehicle, so battery cell balancing in a battery management system (BMS) and a battery thermal management system (BTMS) is very important to maximize battery capacity, safety, and life. In conventional active balancing studies, the cell-balancing process draws energy from the cells or battery pack, resulting in a reduction in battery pack energy due to power losses during the balancing process. This condition can reduce the range of electric vehicles. In this paper, a battery balancing system with a reduced number of switches and low cost, as well as the use of a cell charger, is proposed. The cell charger will draw energy from the electrical grid so that it can maximize the energy in the battery pack. A balancing current of 3 A from the cell charger is used in the balancing process. A 23S1P 100 Ah LiFePO4 battery pack, consisting of 23 cells, is used for validation. Test results show that the proposed battery balancing system can balance the voltage of 23 battery cells for 40 minutes from the highest and lowest voltage difference of 116.7 mV to 11.8 mV.
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Open AccessSystematic Review
Robot Performance Evaluation for Engineering Applications: A Systematic Review of Metrics, Methods and Practices
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Xiang Wei, Songjie Peng and Baosheng Zhao
Technologies 2026, 14(5), 297; https://doi.org/10.3390/technologies14050297 - 12 May 2026
Abstract
Robotics integration across manufacturing, healthcare, and hazardous environments demands robust performance evaluation. This study proposes a comprehensive Task–Environment–System–Metric (TESM) framework to link operational tasks and environmental constraints with quantifiable metrics. Based on TESM, a multi-level evaluation system is established, covering kinematic/dynamic performance, perception,
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Robotics integration across manufacturing, healthcare, and hazardous environments demands robust performance evaluation. This study proposes a comprehensive Task–Environment–System–Metric (TESM) framework to link operational tasks and environmental constraints with quantifiable metrics. Based on TESM, a multi-level evaluation system is established, covering kinematic/dynamic performance, perception, human–robot interaction (HRI), reliability, and lifecycle economics. We systematically review key evaluation methodologies, including mechanistic modeling, digital twin simulation, physical testing, and multi-criteria decision-making (MCDM). Furthermore, typical engineering applications—ranging from industrial manipulators and mobile robots to collaborative and field systems are analyzed to demonstrate practical implementation. Despite significant progress, challenges persist regarding unified standards, testing fidelity, and the “black box” nature of data-driven assessments in safety-critical scenarios. This review concludes by identifying future research directions, such as establishing benchmark testing platforms, improving lifecycle assessment schemes, and developing modular evaluation tools. These advancements aim to ensure the scalable and reliable deployment of robotic systems in complex engineering environments.
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Open AccessArticle
AIoT Ecosystem for Intelligent Water Quality Monitoring Through Edge Processing and Generative Artificial Intelligence
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Giovanni Rafael Caicedo Escorcia, Liliana Vera-Londoño and Jaime Andres Perez-Taborda
Technologies 2026, 14(5), 296; https://doi.org/10.3390/technologies14050296 - 12 May 2026
Abstract
Water quality monitoring remains a critical challenge for achieving Sustainable Development Goal 6, particularly in rural and resource-constrained environments where conventional laboratory-based methods are costly and slow. This study presents the development and field validation of an Artificial Intelligence of Things (AIoT) ecosystem
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Water quality monitoring remains a critical challenge for achieving Sustainable Development Goal 6, particularly in rural and resource-constrained environments where conventional laboratory-based methods are costly and slow. This study presents the development and field validation of an Artificial Intelligence of Things (AIoT) ecosystem for intelligent, low-cost, and real-time water quality assessment using edge computing and generative artificial intelligence. The system integrates a laboratory-developed multiparameter probe measuring temperature, pH, dissolved oxygen, and electrical conductivity with a mobile application and a cloud-based backend. Field validation was conducted in riverine environments in the municipality of Pueblo Bello (Cesar, Colombia), where the system was deployed for in situ data acquisition and real-time inference. A supervised Artificial Neural Network (ANN) was trained to classify water quality based on a Water Quality Index (WQI) ground truth derived from a public dataset, employing KNN-based missing data imputation, interquartile range outlier filtering, stratified balancing, and grid search hyperparameter optimization. The best-performing model achieved 85.1% accuracy and an AUC of 0.87 using only four physical parameters and was successfully deployed in TensorFlow Lite format on both the embedded probe and the mobile application with sub-millisecond inference time. Integration with a generative AI backend provides contextual natural-language interpretations of measurements. These results demonstrate that reduced-parameter edge AI systems can provide reliable environmental diagnostics while enhancing accessibility and citizen engagement for participatory water monitoring.
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(This article belongs to the Special Issue Sustainable Water and Environmental Technologies of Global Relevance)
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Open AccessArticle
Robot-Assisted Omnidirectional Gait Training: Control System Design and Fall Prediction
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Shuoyu Wang and Taiki Miyaji
Technologies 2026, 14(5), 295; https://doi.org/10.3390/technologies14050295 - 12 May 2026
Abstract
The number of patients with lower-limb dysfunction is increasing each year due to aging, illness, accidents, and other factors. Without timely rehabilitation and rapid recovery of walking function, further physical and mental deterioration may be accelerated, potentially leading to long-term bedriddenness. This study
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The number of patients with lower-limb dysfunction is increasing each year due to aging, illness, accidents, and other factors. Without timely rehabilitation and rapid recovery of walking function, further physical and mental deterioration may be accelerated, potentially leading to long-term bedriddenness. This study discusses gait training in rehabilitation therapy from the perspectives of kinesiology, cognitive science, walking function, and safety, and an omnidirectional gait training robot was developed. This study proposed a control system construction method for an omnidirectional gait training robot based on both prescription-based training and autonomous training. In the prescription-based training system, the target values are derived from the training prescription, and the control objective is to guide the patient to walk along the robot’s prescribed path and speed. In the autonomous training system, the target values are automatically generated based on the patient’s walking intentions, and the control objective is for the robot to safely follow the patient’s movement. A necessary condition for robot-assisted autonomous gait training is effective fall prevention. A fall prediction strategy based on foot position information and handrail pressure data was developed. Using this strategy, the robot can predict falls immediately before they occur, similar to a physical therapist, thereby reducing the risk of falls during gait training. Experimental results demonstrate the feasibility of implementing this strategy.
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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Open AccessArticle
Modeling and Implementation of a Practical Methodology to Size LCL Filter in a Photovoltaic Park
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Judith Gálvez-García, Vicente Torres-García, Juan Ramón Rodríguez, José Ángel Barrios and Alberto Cavazos
Technologies 2026, 14(5), 294; https://doi.org/10.3390/technologies14050294 - 12 May 2026
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This paper presents a sizing and optimization methodology for LCL filters tailored to high-capacity modular power systems. The approach prioritizes the strategic selection of the resonance frequency, an asymmetric inductance design, and strict harmonic current limits. The methodology is validated through a case
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This paper presents a sizing and optimization methodology for LCL filters tailored to high-capacity modular power systems. The approach prioritizes the strategic selection of the resonance frequency, an asymmetric inductance design, and strict harmonic current limits. The methodology is validated through a case study simulation of a 126 MW photovoltaic plant in a region of Mexico, analyzing its 2.34 MW inverter architecture. The simulations show that precise capacitor sizing for reactive power management, combined with a passive resistive damping strategy, ensures compliance with grid interconnection standards (IEEE 1547) and power quality standards (IEC 61000). This approach simplifies practical implementation by eliminating the need for complex active damping control algorithms. Additionally, dynamic decoupling is validated through time-domain step responses, and frequency-domain sensitivity analysis confirms robust stability margins even under ±20% variations in passive parameters. Ultimately, the system achieves voltage total harmonic distortion (THD) levels below 0.18%, demonstrating a scalable solution for maintaining grid stability.
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Open AccessArticle
Adaptive L-Wigner Initialization for Sparse Time–Frequency Distribution Reconstruction
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Vedran Jurdana
Technologies 2026, 14(5), 293; https://doi.org/10.3390/technologies14050293 - 11 May 2026
Abstract
Compressed sensing (CS) applied in the ambiguity domain offers an effective approach for recovering time–frequency distributions (TFDs) of non-stationary signals from sparse representations. Existing methods predominantly rely on the Wigner–Ville distribution (WVD) as the initial representation due to its simplicity and high auto-term
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Compressed sensing (CS) applied in the ambiguity domain offers an effective approach for recovering time–frequency distributions (TFDs) of non-stationary signals from sparse representations. Existing methods predominantly rely on the Wigner–Ville distribution (WVD) as the initial representation due to its simplicity and high auto-term concentration. However, the WVD performs poorly for signals with higher-order frequency-modulated (FM) components and is highly sensitive to interference and noise, which then propagate into the reconstruction. This paper introduces the systematic use of the L-Wigner distribution (LWD) as the initial representation for CS-based reconstruction, providing front-end adaptability to signal characteristics. By generating a controllable family of TFDs ranging from the spectrogram to cross-term-free polynomial WVDs, the LWD enables effective suppression of interference and noise while simultaneously enhancing auto-term localization for nonlinear FM components. The proposed LWD-based reconstruction framework is evaluated against the conventional WVD-based method using several state-of-the-art reconstruction algorithms, whose parameters are jointly optimized through a multi-objective meta-heuristic framework to ensure a fair comparison. Experiments on noisy synthetic signals and real-world gravitational-wave data demonstrate consistent performance gains. On synthetic signals, the proposed approach reduces the average reconstruction error index by up to 36.6%, improves the -reconstruction error by up to 75.8%, and achieves complete elimination of cross-term energy. In addition, robustness analysis under additive white Gaussian noise shows up to a 75% improvement in performance. For real gravitational-wave data, the method reduces the mean reconstruction index by up to 5.8% while maintaining auto-term preservation and eliminating cross-term artifacts. These results establish the LWD-based initialization as an effective and general strategy for TFD reconstruction in complex signal environments.
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Open AccessArticle
Research on Key Evaluation Indicators and A Measurability Framework for the Development Level of Chinese Manufacturing Industry 6.0
by
Bin Li and Wai Yie Leong
Technologies 2026, 14(5), 292; https://doi.org/10.3390/technologies14050292 - 11 May 2026
Abstract
The evolution from Industry 4.0 to Industry 6.0 represents a paradigm shift—moving from automation toward an integrated model that incorporates intelligentization, sustainability, and human-centric resilience. While numerous conceptual frameworks have been put forward, empirical research remains scarce, primarily because of the absence of
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The evolution from Industry 4.0 to Industry 6.0 represents a paradigm shift—moving from automation toward an integrated model that incorporates intelligentization, sustainability, and human-centric resilience. While numerous conceptual frameworks have been put forward, empirical research remains scarce, primarily because of the absence of standardized indicators derived from verifiable corporate disclosures. To fill this research gap, the present study develops three quantifiable indices—Intelligence (INT), Sustainability (SUS), and Resilience & Human-centric (RES)—by extracting data from the annual reports and ESG disclosures of 100 Chinese A-share manufacturing enterprises (covering 2022–2024). Fixed-effects panel regression models are employed to assess the impact of these indices on financial performance (ROA, ROE, EPS), market valuation (Tobin’s Q), and sustainability outcomes (ESG ratings). Our findings reveal that INT is the most significant predictor of profitability, with statistically significant positive effects on ROA and ROE—effects that are particularly pronounced among high-tech enterprises. This supports the view that digital capabilities serve as strategic assets. SUS also demonstrates a positive influence on performance, especially in non-high-tech enterprises, where eco-efficiency, regulatory compliance, and ESG-linked financing help offset technological disadvantages. RES contributes to operational and financial stability by enhancing human capital, safety protocols, and organizational practices that reduce performance volatility. Collectively, these results indicate that different types of enterprises follow distinct yet converging pathways toward Industry 6.0: high-tech enterprises capitalize on intelligence to generate innovation rents, while non-high-tech enterprises increasingly rely on sustainability and resilience as strategies to build legitimacy. This study makes significant contributions in three aspects: Methodologically, it differs from previous research that relies on questionnaires and interviews. Instead, it quantifies Industry 6.0 through auditable large-sample key indicators, enhancing the objectivity and operability of the indicators. Empirically, it provides the first empirical evidence on the development path of Industry 6.0 based on data from Chinese manufacturing enterprises. In practical terms, it offers clear references for enterprises and policymakers on the core indicators and their construction framework that should be prioritized during the transformation to Industry 6.0. By linking the index derived from enterprise disclosures with quantifiable performance results, this study effectively bridges the gap between theoretical conceptions and practical applications. It further emphasizes that Industry 6.0 is not merely a technological upgrade but a systematic transformation driven by digitalization, sustainability, and resilience aimed at enhancing enterprise performance and achieving sustainable industrial development.
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(This article belongs to the Topic Industrial Big Data and Artificial Intelligence)
Open AccessArticle
Study on the Effect of Microbial/Enzyme-Induced Calcium Carbonate Precipitation Combined with Fiber Reinforcement on the Mechanical Properties and Permeability Resistance of Sand
by
Shuquan Peng, Yilin Qi, Ling Fan, Wanqi Huang and Yan Zhou
Technologies 2026, 14(5), 291; https://doi.org/10.3390/technologies14050291 - 11 May 2026
Abstract
Against the backdrop of growing demand for environmentally friendly reinforcement in geotechnical engineering, natural fiber reinforcement combined with microbial-induced calcium carbonate (MICP) and enzyme-induced calcium carbonate (EICP) technologies has garnered significant attention due to their eco-friendly and efficient advantages. However, few studies have
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Against the backdrop of growing demand for environmentally friendly reinforcement in geotechnical engineering, natural fiber reinforcement combined with microbial-induced calcium carbonate (MICP) and enzyme-induced calcium carbonate (EICP) technologies has garnered significant attention due to their eco-friendly and efficient advantages. However, few studies have reported the combined application of these three techniques for sand consolidation. This study employs a combined MICP-EICP approach with natural fiber reinforcement to enhance the overall strength of sandy soils and investigate related rock fracture permeability phenomena. Tests conducted include calcium carbonate content, unconfined compressive strength, permeability coefficient, and permeability flow rate. Results indicate that when brown fiber length is 6 mm and dosage is 0.8%, the unconfined compressive strength of MICP-EICP composite specimens reaches a maximum of 0.61 MPa, calcium carbonate content peaks at 7.07%, and permeability coefficient drops to a minimum of 0.0044 cm/s. This composite method offers a highly promising and sustainable improvement solution for geotechnical engineering applications such as sand consolidation, crack sealing, and cultural relic restoration. It not only optimizes mechanical properties but also enhances the utilization rate of waste materials.
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(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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Open AccessArticle
Design Insights for Exploring Identity Bubbles with Alternate Reality Gameplay
by
Guilherme Almeida, Mariana Seiça and Licínio Roque
Technologies 2026, 14(5), 290; https://doi.org/10.3390/technologies14050290 - 10 May 2026
Abstract
To activate conscious reflection regarding personal identity and identity-building processes in our daily lives is an increasing social concern. With this aim, we designed an Alternate Reality Game that invites participants to collectively explore these themes. Participants played with a prototype, evoking themes
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To activate conscious reflection regarding personal identity and identity-building processes in our daily lives is an increasing social concern. With this aim, we designed an Alternate Reality Game that invites participants to collectively explore these themes. Participants played with a prototype, evoking themes of identity through emergent dynamics from gameplay and interpersonal interactions. We analyzed participants’ appropriation of the prototype through logged activity, direct observation and interviews. The identified dynamics enabled iterative redesign and further exploration of the players’ interaction and behaviors. From this process, we synthesized four design insights as our main findings that may guide further research in the field: (1) how to explore design–play–reflect as a co-design process supported on individual appropriation, (2) how ARGs generate reflective social phenomena, such as varied social identity and power narratives, (3) how ARG design can open doors to balance power dynamics, and (4) how ARG designs can become generative social theories. Our main contributions, alongside the designed prototype, are these four insights, and their potential scalability to other ARG designs that seek to provoke social phenomena and collaborative interventions.
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(This article belongs to the Special Issue Disruptive Technologies: Big Data, AI, IoT, Games, and Mixed Reality)
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Open AccessArticle
Automated Information Extraction from Safety and Material Data Sheets—A Domain-Specific NLP Pipeline for Structured Material Data Management in Battery Cell Production
by
Simon Otte, Felix Bayer, Sebastian Schabel and Jürgen Fleischer
Technologies 2026, 14(5), 289; https://doi.org/10.3390/technologies14050289 - 9 May 2026
Abstract
The performance of lithium-ion batteries is strongly determined by material properties, which are provided in technical data sheets but often in inconsistent formats and terminology. Automated extraction of these parameters could enable downstream applications such as process optimization, traceability, and hazard assessment. However,
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The performance of lithium-ion batteries is strongly determined by material properties, which are provided in technical data sheets but often in inconsistent formats and terminology. Automated extraction of these parameters could enable downstream applications such as process optimization, traceability, and hazard assessment. However, current approaches are unsuitable for industrial use. This work presents a prototype NLP-based extraction pipeline for material and safety data sheets. Using fine-tuned SpaCy models, F1-scores above 0.7 are achieved for key parameters such as CAS number, molecular mass, and density. The resulting structured material database provides a foundation for data-driven applications in battery cell production. The feasibility of domain-specific NLP for automated material information extraction is demonstrated and potential pathways for integration with process control and optimization workflows are discussed.
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(This article belongs to the Section Information and Communication Technologies)
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Open AccessArticle
Assessment of Geometric Scaling Factors and Anisotropic Phase Formation in GMAW-Additively Manufactured Duplex Stainless Steel (ER2209) Components
by
Uhamir Patrick, Stefanija Klaric and Sara Havrlisan
Technologies 2026, 14(5), 288; https://doi.org/10.3390/technologies14050288 - 8 May 2026
Abstract
Duplex stainless steel (DSS) blends impressive mechanical and chemical characteristics to withstand aggressive environments. Its fabrication by Gas Metal Arc Welding-Additive Manufacturing is an emerging research topic. However, its sensitive grain structure and alloy composition are prone to deterioration by repeated thermal shocks.
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Duplex stainless steel (DSS) blends impressive mechanical and chemical characteristics to withstand aggressive environments. Its fabrication by Gas Metal Arc Welding-Additive Manufacturing is an emerging research topic. However, its sensitive grain structure and alloy composition are prone to deterioration by repeated thermal shocks. Whether optimal weld parameters can resolve these challenges without additional costs from special fillers, gases, or mechanisms is a valid question. In this study, how different wire feed speeds, travel speeds, and weld voltages, chosen from a set of preliminary beads, translate into wall dimensions, phase formation and distribution, morphological transformation, and elemental segregation is investigated. The unique DSS microstructures were characterised using scanning electron microscopy and energy-dispersive spectroscopy to reveal differences in microstructural evolution and ferrite-austenite (α-γ) structure. The deposited walls exhibited satisfactory geometric quality with negligible distortions. However, the height suppression was noticeable at the deposition energy (DE) of 755 J/mm. Metallographic analysis revealed low γ phase formation (<30%) at low DE (227 J/mm) and excessive γ formation (>70%) in the high DE wall (755 J/mm). The parameters WFS:TS = 15, TS = 35 cm/min, WFS = 525 cm/min, and V = 20.804 volts suppressed the elemental segregation while maintaining a suitable phase balance without post-processing.
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(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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