Technologies

19 pages, 5302 KiB

Open AccessArticle

Localized Ultrasonic Cleaning for Injection Mold Cavities: A Scalable In Situ Process with Surface Quality Monitoring

by Deviprasad Chalicheemalapalli Jayasankar, Thomas Tröster and Thorsten Marten

Technologies 2025, 13(8), 354; https://doi.org/10.3390/technologies13080354 - 11 Aug 2025

As global industries seek to reduce energy consumption and lower CO₂ emissions, the need for sustainable, efficient maintenance processes in manufacturing has become increasingly important. Traditional mold cleaning methods often require complete tool disassembly, extended downtime, and heavy use of solvents, resulting [...] Read more.

As global industries seek to reduce energy consumption and lower CO₂ emissions, the need for sustainable, efficient maintenance processes in manufacturing has become increasingly important. Traditional mold cleaning methods often require complete tool disassembly, extended downtime, and heavy use of solvents, resulting in high energy costs and environmental impact. This study presents a novel localized ultrasonic cleaning process for injection molding tools that enables targeted, in situ cleaning of mold cavities without removing the tool from the press. A precisely positioned ultrasonic transducer delivers cleaning energy directly to contaminated areas, eliminating the need for complete mold removal. Multiple cleaning agents, including alkaline and organic acid solutions, were evaluated for their effectiveness in combination with ultrasonic excitation. Surface roughness measurements were used to assess cleaning performance over repeated contamination and cleaning cycles. Although initial tests were performed manually in the lab, results indicate that the method can be scaled up and automated effectively. This process offers a promising path toward energy-efficient, low-emission tool maintenance across a wide range of injection molding applications. Full article

(This article belongs to the Section Manufacturing Technology)

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20 pages, 14906 KiB

Open AccessArticle

Dual-Channel ADCMix–BiLSTM Model with Attention Mechanisms for Multi-Dimensional Sentiment Analysis of Danmu

by Wenhao Ping, Zhihui Bai and Yubo Tao

Technologies 2025, 13(8), 353; https://doi.org/10.3390/technologies13080353 - 10 Aug 2025

Abstract

Sentiment analysis methods for interactive services such as Danmu in online videos are challenged by their colloquial style and diverse sentiment expressions. For instance, the existing methods cannot easily distinguish between similar sentiments. To address these limitations, this paper proposes a dual-channel model [...] Read more.

Sentiment analysis methods for interactive services such as Danmu in online videos are challenged by their colloquial style and diverse sentiment expressions. For instance, the existing methods cannot easily distinguish between similar sentiments. To address these limitations, this paper proposes a dual-channel model integrated with attention mechanisms for multi-dimensional sentiment analysis of Danmu. First, we replace word embeddings with character embeddings to better capture the colloquial nature of Danmu text. Second, the dual-channel multi-dimensional sentiment encoder extracts both the high-level semantic and raw contextual information. Channel I of the encoder learns the sentiment features from different perspectives through a mixed model that combines the benefits of self-Attention and Dilated CNN (ADCMix) and performs contextual modeling through bidirectional long short-term memory (BiLSTM) with attention mechanisms. Channel II mitigates potential biases and omissions in the sentiment features. The model combines the two channels to erase the fuzzy boundaries between similar sentiments. Third, a multi-dimensional sentiment decoder is designed to handle the diversity in sentiment expressions. The superior performance of the proposed model is experimentally demonstrated on two datasets. Our model outperformed the state-of-the-art methods on both datasets, with improvements of at least 2.05% in accuracy and 3.28% in F1-score. Full article

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38 pages, 6389 KiB

Open AccessReview

Mobility and Handover Management in 5G/6G Networks: Challenges, Innovations, and Sustainable Solutions

by Bilal Saoud, Ibraheem Shayea, Mohammad Ahmed Alnakhli and Hafizal Mohamad

Technologies 2025, 13(8), 352; https://doi.org/10.3390/technologies13080352 - 8 Aug 2025

Viewed by 418

Abstract

Compared to 4G long-term evolution (LTE) networks, 5G and 6G networks provide fast data transmission with little delay, larger base station capacity, enhanced quality of service (QoS), and extensive multiple-input-multiple-output (MIMO) channels. Nevertheless, the attainment of mobility and handover (HO) in 5/6G networks [...] Read more.

Compared to 4G long-term evolution (LTE) networks, 5G and 6G networks provide fast data transmission with little delay, larger base station capacity, enhanced quality of service (QoS), and extensive multiple-input-multiple-output (MIMO) channels. Nevertheless, the attainment of mobility and handover (HO) in 5/6G networks has been hindered by substantial changes in intelligent devices and the high-definition applications of multimedia. Therefore, the existing cellular network is compared with difficulties in transmitting large amounts of data at a faster rate, ensuring high QoS, minimizing latency, and efficiently managing HOs and mobility. This paper primarily addresses the difficulties related to HO and mobility management in 5G/6G networks. The findings of this paper emphasize the importance of aligning mobility and HO strategies with sustainable development goals to reduce energy consumption and improve resource allocation. It focuses on integrating innovative technologies such as artificial intelligence and machine learning to enhance the sustainability and efficiency of mobility and HO management. The paper provides a comprehensive analysis of the current body of the literature and explores essential metrics for measuring performance (known as KPIs) and potential solutions for difficulties linked to HO and mobility. The analysis takes into account established standards in the field. Furthermore, it assesses the effectiveness of existing models in dealing with HO and mobility management problems, considering criteria such as energy efficiency, dependability, latency, and scalability. This survey concludes by highlighting key challenges associated with HO and mobility management in existing research models. It also offers comprehensive assessments of the proposed solutions, accompanied by suggestions for future research. Full article

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

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20 pages, 3290 KiB

Open AccessArticle

Sodium Alginate-Pomegranate Peel Hydrogels for the Remediation of Heavy Metals from Water

by Punita Lalchand, Nirusha Thavarajah and Xavier Fernando

Technologies 2025, 13(8), 351; https://doi.org/10.3390/technologies13080351 - 8 Aug 2025

Viewed by 218

Abstract

The use of agrochemicals in agriculture is widespread globally, as it enables increased crop yields. However, they also contain heavy metals such as copper and nickel, which can leach into the drinking water and harm the environment and human health. As such, it [...] Read more.

The use of agrochemicals in agriculture is widespread globally, as it enables increased crop yields. However, they also contain heavy metals such as copper and nickel, which can leach into the drinking water and harm the environment and human health. As such, it is imperative that they are removed from drinking water. One way to achieve this is through adsorption using biosorbents. This proof-of-concept study aimed to synthesize and characterize environmentally friendly hydrogels from sodium alginate (SA) and pomegranate peel powder (PPP). The gels were characterized using Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and water uptake tests. The FTIR analysis confirmed the presence of the expected functional groups, SEM revealed that incorporating PPP enhanced the roughness and porosity of the gels, and gels with PPP incorporation were able to absorb 1.58 times more water than SA-only gels. Moreover, their ability to remediate copper and nickel from contaminated water was tested. Here, the effects of contact time, pH, and adsorbent amount were tested for copper, demonstrating that the optimal contact time was 60 min, the optimal pH was ~5, and 0.01 g of adsorbent was needed for optimal adsorption. The effect of contact time was tested for nickel, and it was found that the optimal contact time was 5 min. Overall, these gels show promising results for the remediation of copper and nickel from contaminated water. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)

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15 pages, 562 KiB

Open AccessArticle

Predicting Disease Activity Score in Rheumatoid Arthritis Patients Treated with Biologic Disease-Modifying Antirheumatic Drugs Using Machine Learning Models

by Fatemeh Salehi, Sara Zarifi, Sara Bayat, Mahdis Habibpour, Amirreza Asemanrafat, Arnd Kleyer, Georg Schett, Ruth Fritsch-Stork and Bjoern M. Eskofier

Technologies 2025, 13(8), 350; https://doi.org/10.3390/technologies13080350 - 8 Aug 2025

Viewed by 203

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune disease marked by joint inflammation and progressive disability. While biological disease-modifying antirheumatic drugs (bDMARDs) have significantly improved disease control, predicting individual treatment response remains clinically challenging. This study presents a machine learning approach to predict 12-month [...] Read more.

Rheumatoid arthritis (RA) is a chronic autoimmune disease marked by joint inflammation and progressive disability. While biological disease-modifying antirheumatic drugs (bDMARDs) have significantly improved disease control, predicting individual treatment response remains clinically challenging. This study presents a machine learning approach to predict 12-month disease activity, measured by DAS28-CRP, in RA patients beginning bDMARD therapy. We trained and evaluated eight regression models, including Ridge, Lasso, Support Vector Regression, and XGBoost, using baseline clinical features from 154 RA patients treated at University Hospital Erlangen. A rigorous nested cross-validation strategy was applied for internal model selection and validation. Importantly, model generalizability was assessed using an independent external dataset from the Austrian BioReg registry, which includes a more diverse, real-world RA patient population from across multiple clinical sites. The Ridge regression model achieved the best internal performance (MAE: 0.633, R²: 0.542) and showed strong external validity when applied to unseen BioReg data (MAE: 0.678, R²: 0.491). These results indicate robust cross-cohort generalization. By predicting continuous DAS28-CRP scores instead of binary remission labels, our approach supports flexible, individualized treatment planning based on local or evolving clinical thresholds. This work demonstrates the feasibility and clinical value of externally validated, data-driven tools for precision treatment planning in RA. Full article

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

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20 pages, 1737 KiB

Open AccessReview

A Systematic Review on Assistive Technology Terminologies, Concepts, and Definitions

by Jordam Wilson Lourenço, Paulo Alexandre Correia de Jesus, Franciele Lourenço, Osiris Canciglieri Junior and Jones Luís Schaefer

Technologies 2025, 13(8), 349; https://doi.org/10.3390/technologies13080349 - 7 Aug 2025

Viewed by 307

Abstract

This study examines the diversity of terminologies associated with assistive technology (AT), a crucial field that promotes autonomy and inclusion for people with disabilities. Although the wide use of assistive technology is observed in the literature, a variety of terms are often used [...] Read more.

This study examines the diversity of terminologies associated with assistive technology (AT), a crucial field that promotes autonomy and inclusion for people with disabilities. Although the wide use of assistive technology is observed in the literature, a variety of terms are often used interchangeably, which hinders research, technological development, and the formulation of public policies. In this sense, this systematic review aimed to identify, categorise, and analyse the diversity of terms used to describe AT in the scientific literature, contributing to greater conceptual clarity and supporting structured and interdisciplinary development in the field. A comprehensive search was conducted in July 2024 across the Scopus, Web of Science, and PubMed databases, covering publications from 1989 to 2024. Eligible studies were peer-reviewed journal articles in English that conceptually defined at least one AT-related term. The selection process followed the PRISMA 2020 guidelines and included studies from Q1 and Q2 journals to ensure academic rigour. A total of 117 studies were included out of 11,941 initial records. Sixteen distinct terms were identified and grouped into five clusters based on semantic and functional similarities: Cluster 1—Technologies for assistance and inclusion. Cluster 2—Functional assistive devices. Cluster 3—Assistive interaction interfaces. Cluster 4—Assistive environmental technologies. Cluster 5—Assistive systems. A complementary meta-analysis revealed geographic and temporal trends, indicating that terms such as “assistive technology” and “assistive device” are globally dominant. In contrast, others, like “enabling technology,” are more context-specific and emerging. The findings contribute theoretically by providing a structured framework for understanding AT terminology and practically by supporting the design of public policy and interdisciplinary communication. Full article

(This article belongs to the Special Issue Innovations in Design, Development and Evaluation of Assistive Technologies)

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33 pages, 2475 KiB

Open AccessArticle

Real-Time Detection and Response to Wormhole and Sinkhole Attacks in Wireless Sensor Networks

by Tamara Zhukabayeva, Lazzat Zholshiyeva, Yerik Mardenov, Atdhe Buja, Shafiullah Khan and Noha Alnazzawi

Technologies 2025, 13(8), 348; https://doi.org/10.3390/technologies13080348 - 7 Aug 2025

Viewed by 118

Abstract

Wireless sensor networks have become a vital technology that is extensively applied across multiple industries, including agriculture, industrial operations, and smart cities, as well as residential smart homes and environmental monitoring systems. Security threats emerge in these systems through hidden routing-level attacks such [...] Read more.

Wireless sensor networks have become a vital technology that is extensively applied across multiple industries, including agriculture, industrial operations, and smart cities, as well as residential smart homes and environmental monitoring systems. Security threats emerge in these systems through hidden routing-level attacks such as Wormhole and Sinkhole attacks. The aim of this research was to develop a methodology for detecting security incidents in WSNs by conducting real-time analysis of Wormhole and Sinkhole attacks. Furthermore, the paper proposes a novel detection methodology combined with architectural enhancements to improve network robustness, measured by hop counts, delays, false data ratios, and route integrity. A real-time WSN infrastructure was developed using ZigBee and Global System for Mobile Communications/General Packet Radio Service (GSM/GPRS) technologies. To realistically simulate Wormhole and Sinkhole attack scenarios and conduct evaluations, we developed a modular cyber–physical architecture that supports real-time monitoring, repeatability, and integration of ZigBee- and GSM/GPRS-based attacker nodes. During the experimentation, Wormhole attacks caused the hop count to decrease from 4 to 3, while the average delay increased by 40%, and false sensor readings were introduced in over 30% of cases. Additionally, Sinkhole attacks led to a 27% increase in traffic concentration at the malicious node, disrupting load balancing and route integrity. The proposed multi-stage methodology includes data collection, preprocessing, anomaly detection using the 3-sigma rule, and risk-based decision making. Simulation results demonstrated that the methodology successfully detected route shortening, packet loss, and data manipulation in real time. Thus, the integration of anomaly-based detection with ZigBee and GSM/GPRS enables a timely response to security threats in critical WSN deployments. Full article

(This article belongs to the Special Issue New Technologies for Sensors)

18 pages, 5296 KiB

Open AccessArticle

Grid-Search-Optimized, Gated Recurrent Unit-Based Prediction Model for Ionospheric Total Electron Content

by Shuo Zhou, Ziyi Yang, Qiao Yu and Jian Wang

Technologies 2025, 13(8), 347; https://doi.org/10.3390/technologies13080347 - 7 Aug 2025

Viewed by 178

Abstract

Accurately predicting the ionosphere’s Total Electron Content (TEC) is significant for ensuring the regular operation of satellite navigation and communication systems and space weather prediction. To further improve the accuracy of TEC prediction, this paper proposes a TEC prediction model based on the [...] Read more.

Accurately predicting the ionosphere’s Total Electron Content (TEC) is significant for ensuring the regular operation of satellite navigation and communication systems and space weather prediction. To further improve the accuracy of TEC prediction, this paper proposes a TEC prediction model based on the grid-optimized Gate Recurrent Unit (GRU). This model has the following main features: (1) it uses statistical learning methods to interpolate the missing data of TEC observations; (2) it constructs a sliding time window by using the multi-dimensional time series features of two types of solar activity indices to support modeling; (3) It adopts grid search combined with optimization of network depth, time step length, and other hyperparameters to significantly enhance the model’s ability to extract the characteristics of the ionospheric 11-year cycle and seasonal variations. Taking the EGLIN station as an example, the proposed model is verified. The experimental results show that the root mean square error of the GRU model during the period from 2019 to 2020 was 0.78 TECU, which was significantly lower than those of the CCIR, URSI, and statistical machine learning models. Compared with the other three models, the RMSE error of the GRU model was reduced by 72.73%, 72.64%, and 57.38%, respectively. The above research verifies the advantages of the proposed model in predicting TEC and provides a new idea for ionospheric modeling. Full article

(This article belongs to the Section Environmental Technology)

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24 pages, 2199 KiB

Open AccessReview

Smart Walking Aids with Sensor Technology for Gait Support and Health Monitoring: A Scoping Review

by Stefan Resch, Aya Zirari, Thi Diem Quynh Tran, Luca Marco Bauer and Daniel Sanchez-Morillo

Technologies 2025, 13(8), 346; https://doi.org/10.3390/technologies13080346 - 7 Aug 2025

Viewed by 218

Abstract

Smart walking aids represent a growing trend in assistive technologies designed to support individuals with mobility impairments in their daily lives and rehabilitation. Previous research has introduced sensor-integrated systems that provide user feedback to enhance safety and functional mobility. However, a comprehensive overview [...] Read more.

Smart walking aids represent a growing trend in assistive technologies designed to support individuals with mobility impairments in their daily lives and rehabilitation. Previous research has introduced sensor-integrated systems that provide user feedback to enhance safety and functional mobility. However, a comprehensive overview of their technological and functional characteristics is lacking. To address this gap, this scoping review systematically mapped the current state of research in sensor-based walking aids, focusing on device types, sensor technologies, application contexts, target populations, and reported outcomes. In addition, integrated artificial intelligence (AI)-based approaches for functional support and health monitoring were examined. Following PRISMA-ScR guidelines, 35 peer-reviewed articles were identified from three databases: ACM Digital Library, IEEE Xplore, and Web of Science. Extracted data were thematically analyzed and synthesized across device types (e.g., walking canes, crutches, walkers, rollators) and use cases, including gait training, fall prevention, and daily support. Findings show that, while many prototypes show promising features, few have been evaluated in clinical settings or over extended periods. A lack of standardized methods for sensor location assessment, often the superficial implementation of feedback modalities, and limited integration with other assistive technologies were identified. In addition, system validation and user testing lack consensus, with few long-term studies and often incomplete demographic data. Diversity in data communication approaches and the heterogeneous use of AI algorithms were also notable. The review highlights key challenges and research opportunities to guide the future development of intelligent, user-centered mobility systems. Full article

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14 pages, 7196 KiB

Open AccessArticle

Touch to Speak: Real-Time Tactile Pronunciation Feedback for Individuals with Speech and Hearing Impairments

by Anat Sharon, Roi Yozevitch and Eldad Holdengreber

Technologies 2025, 13(8), 345; https://doi.org/10.3390/technologies13080345 - 7 Aug 2025

Viewed by 239

Abstract

This study presents a wearable haptic feedback system designed to support speech training for individuals with speech and hearing impairments. The system provides real-time tactile cues based on detected phonemes, helping users correct their pronunciation independently. Unlike prior approaches focused on passive reception [...] Read more.

This study presents a wearable haptic feedback system designed to support speech training for individuals with speech and hearing impairments. The system provides real-time tactile cues based on detected phonemes, helping users correct their pronunciation independently. Unlike prior approaches focused on passive reception or therapist-led instruction, our method enables active, phoneme-level feedback using a multimodal interface combining audio input, visual reference, and spatially mapped vibrotactile output. We validated the system through three user studies measuring pronunciation accuracy, phoneme discrimination, and learning over time. The results show a significant improvement in word articulation accuracy and user engagement. These findings highlight the potential of real-time haptic pronunciation tools as accessible, scalable aids for speech rehabilitation and second-language learning. Full article

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

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15 pages, 1111 KiB

Open AccessArticle

A Novel Methodology for Data Augmentation in Cognitive Impairment Subjects Using Semantic and Pragmatic Features Through Large Language Models

by Luis Roberto García-Noguez, Sebastián Salazar-Colores, Siddhartha Mondragón-Rodríguez and Saúl Tovar-Arriaga

Technologies 2025, 13(8), 344; https://doi.org/10.3390/technologies13080344 - 7 Aug 2025

Viewed by 151

Abstract

In recent years, researchers have become increasingly interested in identifying traits of cognitive impairment using audio from neuropsychological tests. Unfortunately, there is no universally accepted terminology system that can be used to describe language impairment, and considerable variability exists between clinicians, making detection [...] Read more.

In recent years, researchers have become increasingly interested in identifying traits of cognitive impairment using audio from neuropsychological tests. Unfortunately, there is no universally accepted terminology system that can be used to describe language impairment, and considerable variability exists between clinicians, making detection particularly challenging. Furthermore, databases commonly used by the scientific community present sparse or unbalanced data, which hinders the optimal performance of machine learning models. Therefore, this study aims to test a new methodology for augmenting text data from neuropsychological tests in the Pitt Corpus database to increase classification and interpretability results. The proposed method involves augmenting text data with symptoms commonly present in subjects with cognitive impairment. This innovative approach has enabled us to differentiate between two groups in the database better than widely used text augmentation techniques. The proposed method yielded an increase in the metrics, achieving 0.8742 accuracy, 0.8744 F1-score, 0.8736 precision, and 0.8781 recall. It is shown that implementing large language models with commonly observed symptoms in the language of patients with cognitive impairment in text augmentation can improve the results in low-resource scenarios. Full article

(This article belongs to the Special Issue Advances in Biomedical Engineering and Artificial Intelligence for Neurological Health)

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24 pages, 1246 KiB

Open AccessSystematic Review

Exploring the Management Models and Strategies for Hospital in the Home Initiatives

by Amir Hossein Ghapanchi, Afrooz Purarjomandlangrudi, Navid Ahmadi Eftekhari, Josephine Stevens and Kirsty Barnes

Technologies 2025, 13(8), 343; https://doi.org/10.3390/technologies13080343 - 7 Aug 2025

Viewed by 159

Abstract

Hospital in the Home (HITH) programs are emerging as a key pillar of smart city healthcare infrastructure, leveraging technology to extend care beyond traditional hospital walls. The global healthcare sector has been conceptualizing the notion of a care without walls hospital, also called [...] Read more.

Hospital in the Home (HITH) programs are emerging as a key pillar of smart city healthcare infrastructure, leveraging technology to extend care beyond traditional hospital walls. The global healthcare sector has been conceptualizing the notion of a care without walls hospital, also called HITH, where virtual care takes precedence to address the multifaceted needs of an increasingly aging population grappling with a substantial burden of chronic disease. HITH programs have the potential to significantly reduce hospital bed occupancy, enabling hospitals to better manage the ever-increasing demand for inpatient care. Although many health providers and hospitals have established their own HITH programs, there is a lack of research that provides healthcare executives and HITH program managers with management models and frameworks for such initiatives. There is also a lack of research that provides strategies for improving HITH management in the health sector. To fill this gap, the current study ran a systematic literature review to explore state-of-the-art with regard to this topic. Out of 2631 articles in the pool of this systematic review, 20 articles were deemed to meet the eligibility criteria for the study. After analyzing these studies, nine management models were extracted, which were then categorized into three categories, namely, governance models, general models, and virtual models. Moreover, this study found 23 strategies and categorized them into five groups, namely, referral support, external support, care model support, technical support, and clinical team support. Finally, implications of findings for practitioners are carefully provided. These findings provide healthcare executives and HITH managers with practical frameworks for selecting appropriate management models and implementing evidence-based strategies to optimize program effectiveness, reduce costs, and improve patient outcomes while addressing the growing demand for home-based care. Full article

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

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16 pages, 655 KiB

Open AccessReview

Seeing Opportunity in Virtual Reality: A Rapid Review of the Use of VR as a Tool in Vision Care

by Kiana Masoudi, Madeline Wong, Danielle Tchao, Ani Orchanian-Cheff, Michael Reber and Lora Appel

Technologies 2025, 13(8), 342; https://doi.org/10.3390/technologies13080342 - 6 Aug 2025

Viewed by 268

Abstract

(1) Virtual reality (VR) technologies have shown significant potential for diagnosing and treating vision-related impairments. This rapid review evaluates and characterizes the existing literature on VR technologies for diagnosing and treating vision-based diseases. (2) Methods: A systematic search was conducted across Ovid MEDLINE, [...] Read more.

(1) Virtual reality (VR) technologies have shown significant potential for diagnosing and treating vision-related impairments. This rapid review evaluates and characterizes the existing literature on VR technologies for diagnosing and treating vision-based diseases. (2) Methods: A systematic search was conducted across Ovid MEDLINE, Ovid Embase, the Cochrane Database of Systematic Reviews (Ovid), and the Cochrane Central Register of Controlled Trials (Ovid). Abstracts were screened using Rayyan QCRI, followed by full-text screening and data extraction. Eligible studies were published in peer-reviewed journals, written in English, focused on human participants, used immersive and portable VR devices as the primary intervention, and reported on the clinical effectiveness of VR for therapeutic, diagnostic, or screening purposes for vision or auditory–visual impairments. Various study characteristics, including design and participant details, were extracted, and the MMAT assessment tool was used to evaluate study quality. (3) Results: Seventy-six studies met the inclusion criteria. Among these, sixty-four (84.2%) were non-randomized studies exploring VR’s effectiveness, while twenty-two (15.8%) were randomized-controlled trials. Of the included studies, 38.2% focused on diagnosing, 21.0% on screening, and 38.2% on treating vision impairments. Glaucoma and amblyopia were the most commonly studied visual impairments. (4) Conclusions: The use of standalone, remotely controlled VR headsets for screening and diagnosing visual diseases represents a promising advancement in ophthalmology. With ongoing technological developments, VR has the potential to revolutionize eye care by improving accessibility, efficiency, and personalization. Continued research and innovation in VR applications for vision care are expected to further enhance patient outcomes. Full article

(This article belongs to the Section Assistive Technologies)

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32 pages, 1885 KiB

Open AccessArticle

Mapping Linear and Configurational Dynamics to Fake News Sharing Behaviors in a Developing Economy

by Claudel Mombeuil, Hugues Séraphin and Hemantha Premakumara Diunugala

Technologies 2025, 13(8), 341; https://doi.org/10.3390/technologies13080341 - 6 Aug 2025

Viewed by 143

Abstract

The proliferation of social media has paradoxically facilitated the widespread dissemination of fake news, impacting individuals, politics, economics, and society as a whole. Despite the increasing scholarly research on this phenomenon, a significant gap exists regarding its dynamics in developing countries, particularly how [...] Read more.

The proliferation of social media has paradoxically facilitated the widespread dissemination of fake news, impacting individuals, politics, economics, and society as a whole. Despite the increasing scholarly research on this phenomenon, a significant gap exists regarding its dynamics in developing countries, particularly how predictors of fake news sharing interact, rather than merely their net effects. To acquire a more nuanced understanding of fake news sharing behavior, we propose identifying the direct and complex interplay among key variables by utilizing a dual analytical framework, leveraging Structural Equation Modeling (SEM) for linear relationships and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) to uncover asymmetric patterns. Specifically, we investigate the influence of news-find-me orientation, social media trust, information-sharing tendencies, and status-seeking motivation on the propensity of fake news sharing behavior. Additionally, we delve into the moderating influence of social media literacy on these observed effects. Based on a cross-sectional survey of 1028 Haitian social media users, the SEM analysis revealed that news-find-me perception had a negative but statistically insignificant influence on fake news sharing behavior. In contrast, information sharing exhibited a significant negative association. Trust in social media was positively and significantly linked to fake news sharing behavior. Meanwhile, status-seeking motivation was positively associated with fake news sharing behavior, although the association did not reach statistical significance. Crucially, social media literacy moderated the effects of trust and information sharing. Interestingly, fsQCA identified three core configurations for fake news sharing: (1) low status seeking, (2) low information-sharing tendencies, and (3) a unique interaction of low “news-find-me” orientation and high social media trust. Furthermore, low social media literacy emerged as a direct core configuration. These findings support the urgent need to prioritize social media literacy as a key intervention in combating the dissemination of fake news. Full article

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

18 pages, 1305 KiB

Open AccessArticle

Curriculum–Vacancy–Course Recommendation Model Based on Knowledge Graphs, Sentence Transformers, and Graph Neural Networks

by Valiya Ramazanova, Madina Sambetbayeva, Sandugash Serikbayeva, Aigerim Yerimbetova, Zhanar Lamasheva, Zhanna Sadirmekova and Gulzhamal Kalman

Technologies 2025, 13(8), 340; https://doi.org/10.3390/technologies13080340 - 5 Aug 2025

Viewed by 374

Abstract

This article addresses the task of building personalized educational recommendations based on a heterogeneous knowledge graph that integrates data from university curricula, job vacancies, and online courses. To solve the problem of course recommendations by their relevance to a user’s competencies, a graph [...] Read more.

This article addresses the task of building personalized educational recommendations based on a heterogeneous knowledge graph that integrates data from university curricula, job vacancies, and online courses. To solve the problem of course recommendations by their relevance to a user’s competencies, a graph neural network (GNN)-based approach is proposed, specifically utilizing and comparing the Heterogeneous Graph Transformer (HGT) architecture, Graph Sample and Aggregate network (GraphSAGE), and Heterogeneous Graph Attention Network (HAN). Experiments were conducted on a heterogeneous graph comprising various node and relation types. The models were evaluated using regression and ranking metrics. The results demonstrated the superiority of the HGT-based recommendation model as a link regression task, especially in terms of ranking metrics, confirming its suitability for generating accurate and interpretable recommendations in educational systems. The proposed approach can be useful for developing adaptive learning recommendations aligned with users’ career goals. Full article

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

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31 pages, 5644 KiB

Open AccessArticle

Mitigation Technique Using a Hybrid Energy Storage and Time-of-Use (TOU) Approach in Photovoltaic Grid Connection

by Mohammad Reza Maghami, Jagadeesh Pasupuleti, Arthur G. O. Mutambara and Janaka Ekanayake

Technologies 2025, 13(8), 339; https://doi.org/10.3390/technologies13080339 - 5 Aug 2025

Viewed by 294

Abstract

This study investigates the impact of Time-of-Use (TOU) scheduling and battery energy storage systems (BESS) on voltage stability in a typical Malaysian medium-voltage distribution network with high photovoltaic (PV) system penetration. The analyzed network comprises 110 nodes connected via eight feeders to a [...] Read more.

This study investigates the impact of Time-of-Use (TOU) scheduling and battery energy storage systems (BESS) on voltage stability in a typical Malaysian medium-voltage distribution network with high photovoltaic (PV) system penetration. The analyzed network comprises 110 nodes connected via eight feeders to a pair of 132/11 kV, 15 MVA transformers, supplying a total load of 20.006 MVA. Each node is integrated with a 100 kW PV system, enabling up to 100% PV penetration scenarios. A hybrid mitigation strategy combining TOU-based load shifting and BESS was implemented to address voltage violations occurring, particularly during low-load night hours. Dynamic simulations using DIgSILENT PowerFactory were conducted under worst-case (no load and peak load) conditions. The novelty of this research is the use of real rural network data to validate a hybrid BESS–TOU strategy, supported by detailed sensitivity analysis across PV penetration levels. This provides practical voltage stabilization insights not shown in earlier studies. Results show that at 100% PV penetration, TOU or BESS alone are insufficient to fully mitigate voltage drops. However, a hybrid application of 0.4 MWh BESS with 20% TOU load shifting eliminates voltage violations across all nodes, raising the minimum voltage from 0.924 p.u. to 0.951 p.u. while reducing active power losses and grid dependency. A sensitivity analysis further reveals that a 60% PV penetration can be supported reliably using only 0.4 MWh of BESS and 10% TOU. Beyond this, hybrid mitigation becomes essential to maintain stability. The proposed solution demonstrates a scalable approach to enable large-scale PV integration in dense rural grids and addresses the specific operational characteristics of Malaysian networks, which differ from commonly studied IEEE test systems. This work fills a critical research gap by using real local data to propose and validate practical voltage mitigation strategies. Full article

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18 pages, 4182 KiB

Open AccessArticle

Structural Design of a Multi-Stage Variable Stiffness Manipulator Based on Low-Melting-Point Alloys

by Moufa Ye, Lin Guo, An Wang, Wei Dong, Yongzhuo Gao and Hui Dong

Technologies 2025, 13(8), 338; https://doi.org/10.3390/technologies13080338 - 5 Aug 2025

Viewed by 275

Abstract

Soft manipulators have garnered significant research attention in recent years due to their flexibility and adaptability. However, the inherent flexibility of these manipulators imposes limitations on their load-bearing capacity and stability. To address this, this study compares various variable stiffness technologies and proposes [...] Read more.

Soft manipulators have garnered significant research attention in recent years due to their flexibility and adaptability. However, the inherent flexibility of these manipulators imposes limitations on their load-bearing capacity and stability. To address this, this study compares various variable stiffness technologies and proposes a novel design concept: leveraging the phase-change characteristics of low-melting-point alloys (LMPAs) with distinct melting points to fulfill the variable stiffness requirements of soft manipulators. The pneumatic structure of the manipulator is fabricated via 3D-printed molds and silicone casting. The manipulator integrates a pneumatic working chamber, variable stiffness chambers, heating devices, sensors, and a central channel, achieving multi-stage variable stiffness through controlled heating of the LMPAs. A steady-state temperature field distribution model is established based on the integral form of Fourier’s law, complemented by finite element analysis (FEA). Subsequently, the operational temperatures at which the variable stiffness mechanism activates, and the bending performance are experimentally validated. Finally, stiffness characterization and kinematic performance experiments are conducted to evaluate the manipulator’s variable stiffness capabilities and flexibility. This design enables the manipulator to switch among low, medium, and high stiffness levels, balancing flexibility and stability, and provides a new paradigm for the design of soft manipulators. Full article

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21 pages, 2228 KiB

Open AccessArticle

Multi-Objective Optimization of Abrasive Cutting Process Conditions to Increase Economic Efficiency

by Irina Aleksandrova

Technologies 2025, 13(8), 337; https://doi.org/10.3390/technologies13080337 - 3 Aug 2025

Viewed by 248

Abstract

Existing studies on the abrasive cutting process have primarily focused on the influence of cutting conditions on key parameters such as temperature, cut-off wheel wear, and machined surface quality. However, the choice of working conditions is often made based on the experience of [...] Read more.

Existing studies on the abrasive cutting process have primarily focused on the influence of cutting conditions on key parameters such as temperature, cut-off wheel wear, and machined surface quality. However, the choice of working conditions is often made based on the experience of qualified personnel or using data from reference sources. The literature also provides optimal values for the cutting mode elements, but these are only valid for specific methods and cutting conditions. This article proposes a new multi-objective optimization approach for determining the conditions for the implementation of the abrasive cutting process that leads to Pareto-optimal solutions for improving economic efficiency, evaluated by production rate and manufacturing net cost parameters. To demonstrate this approach, the elastic abrasive cutting process of structural steels C45 and 42Cr4 has been selected. Theoretical–experimental models for production rate and manufacturing net cost have been developed, reflecting the complex influence of the conditions of the elastic abrasive cutting process (compression force of the cut-off wheel on the workpiece and rotational frequency of the workpiece). Multi-objective compromise optimization based on a genetic algorithm has been conducted by applying two methods—the determination of a compromise optimal area for the conditions of the elastic abrasive cutting process and the generalized utility function method. Optimal conditions for the implementation of the elastic abrasive cutting process have been determined, ensuring the best combination of high production rate and low manufacturing net cost. Full article

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

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31 pages, 9769 KiB

Open AccessReview

Recent Advances of Hybrid Nanogenerators for Sustainable Ocean Energy Harvesting: Performance, Applications, and Challenges

by Enrique Delgado-Alvarado, Enrique A. Morales-Gonzalez, José Amir Gonzalez-Calderon, Ma. Cristina Irma Peréz-Peréz, Jesús Delgado-Maciel, Mariana G. Peña-Juarez, José Hernandez-Hernandez, Ernesto A. Elvira-Hernandez, Maximo A. Figueroa-Navarro and Agustin L. Herrera-May

Technologies 2025, 13(8), 336; https://doi.org/10.3390/technologies13080336 - 2 Aug 2025

Viewed by 501

Abstract

Ocean energy is an abundant, eco-friendly, and renewable energy resource that is useful for powering sensor networks connected to the maritime Internet of Things (MIoT). These sensor networks can be used to measure different marine environmental parameters that affect ocean infrastructure integrity and [...] Read more.

Ocean energy is an abundant, eco-friendly, and renewable energy resource that is useful for powering sensor networks connected to the maritime Internet of Things (MIoT). These sensor networks can be used to measure different marine environmental parameters that affect ocean infrastructure integrity and harm marine ecosystems. This ocean energy can be harnessed through hybrid nanogenerators that combine triboelectric nanogenerators, electromagnetic generators, piezoelectric nanogenerators, and pyroelectric generators. These nanogenerators have advantages such as high-power density, robust design, easy operating principle, and cost-effective fabrication. However, the performance of these nanogenerators can be affected by the wear of their main components, reduction of wave frequency and amplitude, extreme corrosion, and sea storms. To address these challenges, future research on hybrid nanogenerators must improve their mechanical strength, including materials and packages with anti-corrosion coatings. Herein, we present recent advances in the performance of different hybrid nanogenerators to harvest ocean energy, including various transduction mechanisms. Furthermore, this review reports potential applications of hybrid nanogenerators to power devices in marine infrastructure or serve as self-powered MIoT monitoring sensor networks. This review discusses key challenges that must be addressed to achieve the commercial success of these nanogenerators, regarding design strategies with advanced simulation models or digital twins. Also, these strategies must incorporate new materials that improve the performance, reliability, and integration of future nanogenerator array systems. Thus, optimized hybrid nanogenerators can represent a promising technology for ocean energy harvesting with application in the maritime industry. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)

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19 pages, 1217 KiB

Open AccessArticle

Temporal Multi-Query Subgraph Matching in Cybersecurity

by Min Lu, Qianzhen Zhang and Xianqiang Zhu

Technologies 2025, 13(8), 335; https://doi.org/10.3390/technologies13080335 - 1 Aug 2025

Viewed by 136

Abstract

Regarding attack scenarios as query graphs and conducting subgraph matching on the data system is an important approach to identify and detect cyber threats. However, existing subgraph matching methods are not suitable for detecting time-evolving attacks since they either focus on single-query graphs [...] Read more.

Regarding attack scenarios as query graphs and conducting subgraph matching on the data system is an important approach to identify and detect cyber threats. However, existing subgraph matching methods are not suitable for detecting time-evolving attacks since they either focus on single-query graphs or ignore the temporal constraints between multiple queries. In this paper, we model the time-evolving attack detection as a novel temporal multi-query subgraph matching problem and propose an efficient algorithm to address this problem. We first give a compact representation of the temporal query graph by merging all queries into one. Based on the temporal query graph, we propose a concise auxiliary data structure to maintain partial solutions. In addition, we employ a query matching tree to generate an efficient matching order and enumerate matchings based on the order. Extensive experiments over real-world datasets confirm the effectiveness and efficiency of our approach. Full article

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

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25 pages, 2661 KiB

Open AccessArticle

Fuzzy Logic-Based Energy Management Strategy for Hybrid Renewable System with Dual Storage Dedicated to Railway Application

by Ismail Hacini, Sofia Lalouni Belaid, Kassa Idjdarene, Hammoudi Abderazek and Kahina Berabez

Technologies 2025, 13(8), 334; https://doi.org/10.3390/technologies13080334 - 1 Aug 2025

Viewed by 304

Abstract

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents [...] Read more.

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents a promising avenue to improve the sustainability, reliability, and efficiency of urban transport networks. A storage system is needed to both ensure a continuous power supply and meet train demand at the station. Batteries (BTs) offer high energy density, while supercapacitors (SCs) offer both a large number of charge and discharge cycles, and high-power density. This paper proposes a hybrid RES (photovoltaic and wind), combined with batteries and supercapacitors constituting the hybrid energy storage system (HESS). One major drawback of trains is the long charging time required in stations, so they have been fitted with SCs to allow them to charge up quickly. A new fuzzy energy management strategy (F-EMS) is proposed. This supervision strategy optimizes the power flow between renewable energy sources, HESS, and trains. DC bus voltage regulation is involved, maintaining BT and SC charging levels within acceptable ranges. The simulation results, carried out using MATLAB/Simulink, demonstrate the effectiveness of the suggested fuzzy energy management strategy for various production conditions and train demand. Full article

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27 pages, 4070 KiB

Open AccessArticle

Quantum Transport in GFETs Combining Landauer–Büttiker Formalism with Self-Consistent Schrödinger–Poisson Solutions

by Modesto Herrera-González, Jaime Martínez-Castillo, Pedro J. García-Ramírez, Enrique Delgado-Alvarado, Pedro Mabil-Espinosa, Jairo C. Nolasco-Montaño and Agustín L. Herrera-May

Technologies 2025, 13(8), 333; https://doi.org/10.3390/technologies13080333 - 1 Aug 2025

Viewed by 348

Abstract

The unique properties of graphene have allowed for the development of graphene-based field-effect transistors (GFETs) for applications in biosensors and chemical devices. However, the modeling and optimization of GFET performance exhibit great challenges. Herein, we propose a quantum transport simulation model for graphene-based [...] Read more.

The unique properties of graphene have allowed for the development of graphene-based field-effect transistors (GFETs) for applications in biosensors and chemical devices. However, the modeling and optimization of GFET performance exhibit great challenges. Herein, we propose a quantum transport simulation model for graphene-based field-effect transistors (GFETs) implemented in the open-source Octave programming language. The proposed simulation model (named SimQ) combines the Landauer–Büttiker formalism with self-consistent Schrödinger–Poisson solutions, enabling reliable simulations of transport phenomena. Our approach agrees well with established models, achieving Landauer–Büttiker transmission and tunneling transmission of 0.28 and 0.92, respectively, which are validated against experimental data. The model can predict key GFET characteristics, including carrier mobilities (500–4000 cm²/V·s), quantum capacitance effects, and high-frequency operation (80–100 GHz). SimQ offers detailed insights into charge distribution and wave function evolution, achieving an enhanced computational efficiency through optimized algorithms. Our work contributes to the modeling of graphene-based field-effect transistors, providing a flexible and accessible simulation platform for designing and optimizing GFETs with potential applications in the next generation of electronic devices. Full article

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)

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24 pages, 2751 KiB

Open AccessArticle

Double Wishbone Suspension: A Computational Framework for Parametric 3D Kinematic Modeling and Simulation Using Mathematica

by Muhammad Waqas Arshad, Stefano Lodi and David Q. Liu

Technologies 2025, 13(8), 332; https://doi.org/10.3390/technologies13080332 - 1 Aug 2025

Viewed by 226

Abstract

The double wishbone suspension (DWS) system is widely used in automotive engineering because of its favorable kinematic properties, which affect vehicle dynamics, handling, and ride comfort; hence, it is important to have an accurate 3D model, simulation, and analysis of the system in [...] Read more.

The double wishbone suspension (DWS) system is widely used in automotive engineering because of its favorable kinematic properties, which affect vehicle dynamics, handling, and ride comfort; hence, it is important to have an accurate 3D model, simulation, and analysis of the system in order to optimize its design. This requires efficient computational tools for parametric study. The development of effective computational tools that support parametric exploration stands as an essential requirement. Our research demonstrates a complete Wolfram Mathematica system that creates parametric 3D kinematic models and conducts simulations, performs analyses, and generates interactive visualizations of DWS systems. The system uses homogeneous transformation matrices to establish the spatial relationships between components relative to a global coordinate system. The symbolic geometric parameters allow designers to perform flexible design exploration and the kinematic constraints create an algebraic equation system. The numerical solution function NSolve computes linkage positions from input data, which enables fast evaluation of different design parameters. The integrated 3D visualization module based on Mathematica’s manipulate function enables users to see immediate results of geometric configurations and parameter effects while calculating exact 3D coordinates. The resulting robust, systematic, and flexible computational environment integrates parametric 3D design, kinematic simulation, analysis, and dynamic visualization for DWS, serving as a valuable and efficient tool for engineers during the design, development, assessment, and optimization phases of these complex automotive systems. Full article

(This article belongs to the Section Manufacturing Technology)

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16 pages, 1134 KiB

Open AccessArticle

Neural Correlates of Loudness Coding in Two Types of Cochlear Implants—A Model Study

by Ilja M. Venema, Savine S. M. Martens, Randy K. Kalkman, Jeroen J. Briaire and Johan H. M. Frijns

Technologies 2025, 13(8), 331; https://doi.org/10.3390/technologies13080331 - 1 Aug 2025

Viewed by 334

Abstract

Many speech coding strategies have been developed over the years, but comparing them has been convoluted due to the difficulty in disentangling brand-specific and patient-specific factors from strategy-specific factors that contribute to speech understanding. Here, we present a comparison with a ‘virtual’ patient, [...] Read more.

Many speech coding strategies have been developed over the years, but comparing them has been convoluted due to the difficulty in disentangling brand-specific and patient-specific factors from strategy-specific factors that contribute to speech understanding. Here, we present a comparison with a ‘virtual’ patient, by comparing two strategies from two different manufacturers, Advanced Combination Encoder (ACE) versus HiResolution Fidelity 120 (F120), running on two different implant systems in a computational model with the same anatomy and neural properties. We fitted both strategies to an expected T-level and C- or M-level based on the spike rate for each electrode contact’s allocated frequency (center electrode frequency) of the respective array. This paper highlights neural and electrical differences due to brand-specific characteristics such as pulse rate/channel, recruitment of adjacent electrodes, and presence of subthreshold pulses or interphase gaps. These differences lead to considerably different recruitment patterns of nerve fibers, while achieving the same total spike rates, i.e., loudness percepts. Also, loudness growth curves differ significantly between brands. The model is able to demonstrate considerable electrical and neural differences in the way loudness growth is achieved in CIs from different manufacturers. Full article

(This article belongs to the Special Issue The Challenges and Prospects in Cochlear Implantation)

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24 pages, 3553 KiB

Open AccessArticle

A Hybrid Artificial Intelligence Framework for Melanoma Diagnosis Using Histopathological Images

by Alberto Nogales, María C. Garrido, Alfredo Guitian, Jose-Luis Rodriguez-Peralto, Carlos Prados Villanueva, Delia Díaz-Prieto and Álvaro J. García-Tejedor

Technologies 2025, 13(8), 330; https://doi.org/10.3390/technologies13080330 - 1 Aug 2025

Viewed by 295

Abstract

Cancer remains one of the most significant global health challenges due to its high mortality rates and the limited understanding of its progression. Early diagnosis is critical to improving patient outcomes, especially in skin cancer, where timely detection can significantly enhance recovery rates. [...] Read more.

Cancer remains one of the most significant global health challenges due to its high mortality rates and the limited understanding of its progression. Early diagnosis is critical to improving patient outcomes, especially in skin cancer, where timely detection can significantly enhance recovery rates. Histopathological analysis is a widely used diagnostic method, but it is a time-consuming process that heavily depends on the expertise of highly trained specialists. Recent advances in Artificial Intelligence have shown promising results in image classification, highlighting its potential as a supportive tool for medical diagnosis. In this study, we explore the application of hybrid Artificial Intelligence models for melanoma diagnosis using histopathological images. The dataset used consisted of 506 histopathological images, from which 313 curated images were selected after quality control and preprocessing. We propose a two-step framework that employs an Autoencoder for dimensionality reduction and feature extraction of the images, followed by a classification algorithm to distinguish between melanoma and nevus, trained on the extracted feature vectors from the bottleneck of the Autoencoder. We evaluated Support Vector Machines, Random Forest, Multilayer Perceptron, and K-Nearest Neighbours as classifiers. Among these, the combinations of Autoencoder with K-Nearest Neighbours achieved the best performance and inference time, reaching an average accuracy of approximately 97.95% on the test set and requiring 3.44 min per diagnosis. The baseline comparison results were consistent, demonstrating strong generalisation and outperforming the other models by 2 to 13 percentage points. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)

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33 pages, 1512 KiB

Open AccessReview

Advances and Challenges in Deep Learning for Acoustic Pathology Detection: A Review

by Florin Bogdan and Mihaela-Ruxandra Lascu

Technologies 2025, 13(8), 329; https://doi.org/10.3390/technologies13080329 - 1 Aug 2025

Viewed by 266

Abstract

Recent advancements in data collection technologies, data science, and speech processing have fueled significant interest in the computational analysis of biological sounds. This enhanced analytical capability shows promise for improved understanding and detection of various pathological conditions, extending beyond traditional speech analysis to [...] Read more.

Recent advancements in data collection technologies, data science, and speech processing have fueled significant interest in the computational analysis of biological sounds. This enhanced analytical capability shows promise for improved understanding and detection of various pathological conditions, extending beyond traditional speech analysis to encompass other forms of acoustic data. A particularly promising and rapidly evolving area is the application of deep learning techniques for the detection and analysis of diverse pathologies, including respiratory, cardiac, and neurological disorders, through sound processing. This paper provides a comprehensive review of the current state-of-the-art in using deep learning for pathology detection via analysis of biological sounds. It highlights key successes achieved in the field, identifies existing challenges and limitations, and discusses potential future research directions. This review aims to serve as a valuable resource for researchers and clinicians working in this interdisciplinary domain. Full article

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20 pages, 3380 KiB

Open AccessArticle

The Effect of Airfoil Geometry Variation on the Efficiency of a Small Wind Turbine

by José Rafael Dorrego Portela, Orlando Lastres Danguillecurt, Víctor Iván Moreno Oliva, Eduardo Torres Moreno, Cristofer Aguilar Jimenez, Liliana Hechavarría Difur, Quetzalcoatl Hernandez-Escobedo and Jesus Alejandro Franco

Technologies 2025, 13(8), 328; https://doi.org/10.3390/technologies13080328 - 1 Aug 2025

Viewed by 225

Abstract

This study analyzes the impact of geometric variations induced by the manufacturing process on the aerodynamic efficiency of an airfoil used in the design of a 3 kW wind turbine blade. For this purpose, a computational fluid dynamics (CFD) analysis was implemented, and [...] Read more.

This study analyzes the impact of geometric variations induced by the manufacturing process on the aerodynamic efficiency of an airfoil used in the design of a 3 kW wind turbine blade. For this purpose, a computational fluid dynamics (CFD) analysis was implemented, and the results were compared with those obtained using QBlade software. After blade fabrication, experimental evaluation was performed using the laser triangulation technique, enabling the reconstruction of the deformed airfoils and their comparison with the original geometry. Additional CFD simulations were carried out on the manufactured airfoil to quantify the loss of aerodynamic efficiency due to geometrical deformations. The results show that the geometric deviations significantly affect the aerodynamic coefficients, generating a decrease in the lift coefficient and an increase in the drag coefficient, which negatively impacts the airfoil aerodynamic efficiency. A 14.9% reduction in the rotor power coefficient was observed with the deformed airfoils compared to the original design. This study emphasizes the importance of quality control in wind turbine blade manufacturing processes and its impact on turbine power performance. In addition, the findings can contribute to the development of design compensation strategies to mitigate the adverse effects of geometric imperfections on the aerodynamic performance of wind turbines. Full article

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24 pages, 9448 KiB

Open AccessArticle

Distributed Online Voltage Control with Feedback Delays Under Coupled Constraints for Distribution Networks

by Jinxuan Liu, Yanjian Peng, Xiren Zhang, Zhihao Ning and Dingzhong Fan

Technologies 2025, 13(8), 327; https://doi.org/10.3390/technologies13080327 - 31 Jul 2025

Viewed by 139

Abstract

High penetration of photovoltaic (PV) generation presents new challenges for voltage regulation in distribution networks (DNs), primarily due to output intermittency and constrained reactive power capabilities. This paper introduces a distributed voltage control method leveraging reactive power compensation from PV inverters. Instead of [...] Read more.

High penetration of photovoltaic (PV) generation presents new challenges for voltage regulation in distribution networks (DNs), primarily due to output intermittency and constrained reactive power capabilities. This paper introduces a distributed voltage control method leveraging reactive power compensation from PV inverters. Instead of relying on centralized computation, the proposed method allows each inverter to make local decisions using real-time voltage measurements and delayed communication with neighboring PV nodes. To account for practical asynchronous communication and feedback delay, a Distributed Online Primal–Dual Push–Sum (DOPP) algorithm that integrates a fixed-step delay model into the push–sum coordination framework is developed. Through extensive case studies on a modified IEEE 123-bus system, it has been demonstrated that the proposed method maintains robust performance under both static and dynamic scenarios, even in the presence of fixed feedback delays. Specifically, in static scenarios, the proposed strategy rapidly eliminates voltage violations within 50–100 iterations, effectively regulating all nodal voltages into the acceptable range of [0.95, 1.05] p.u. even under feedback delays with a delay step of 10. In dynamic scenarios, the proposed strategy ensures 100% voltage compliance across all nodes, demonstrating superior voltage regulation and reactive power coordination performance over conventional droop and incremental control approaches. Full article

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25 pages, 1765 KiB

Open AccessArticle

Trigger-Based Systems as a Promising Foundation for the Development of Computing Architectures Based on Neuromorphic Materials

by Dina Shaltykova, Kaisarali Kadyrzhan, Jelena Caiko, Yelizaveta Vitulyova and Ibragim Suleimenov

Technologies 2025, 13(8), 326; https://doi.org/10.3390/technologies13080326 - 31 Jul 2025

Viewed by 158

Abstract

It is demonstrated that neuromorphic materials designed for computational tasks can be effectively implemented by drawing an analogy with trigger-based systems built upon classical binary elements. Among the most promising approaches in this context are systems that perform computations based on the Residue [...] Read more.

It is demonstrated that neuromorphic materials designed for computational tasks can be effectively implemented by drawing an analogy with trigger-based systems built upon classical binary elements. Among the most promising approaches in this context are systems that perform computations based on the Residue Number System (RNS). A specific implementation of a trigger-based adder employing the proposed methodology is presented and tested through simulation modeling. This adder utilizes the representation of natural numbers as elements of a subtraction ring modulo P, where P is the product of Mersenne prime numbers. This configuration enables component-wise, independent execution of arithmetic operations. It is further shown that analogous trigger-based systems can be realized using recurrent neural network analogs, particularly those implemented with neuromorphic materials. The study emphasizes that it is possible to construct a neural network, especially one based on neuromorphic substrates, that can perform logical operations equivalent to those executed by conventional binary circuitry. A key challenge in the proposed approach lies in implementing an operation analogous to the carry mechanism employed in classical binary adders. An algorithm addressing this issue is proposed, based on the transition from computations modulo P to computations modulo 2P. Full article

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

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29 pages, 1520 KiB

Open AccessReview

Methodologies for Technology Selection in an Industry 4.0 Environment: A Methodological Analysis Using ProKnow-C

by Luis Quezada, Isaias Hermosilla, Guillermo Fuertes, Astrid Oddershede, Pedro Palominos and Manuel Vargas

Technologies 2025, 13(8), 325; https://doi.org/10.3390/technologies13080325 - 31 Jul 2025

Viewed by 451

Abstract

In an ever-evolving digital environment, organizations must adopt advanced technologies for real-time big data processing to maintain their competitiveness and growth. However, selecting appropriate technologies is a challenge, particularly for small and medium-sized enterprises (SMEs). This study develops a literature review to analyze [...] Read more.

In an ever-evolving digital environment, organizations must adopt advanced technologies for real-time big data processing to maintain their competitiveness and growth. However, selecting appropriate technologies is a challenge, particularly for small and medium-sized enterprises (SMEs). This study develops a literature review to analyze the methodologies used in the selection of technologies, with a special focus on those associated with the Industry 4.0. Knowledge Development Process-Constructivist (ProKnow-C) method, which was used to build a bibliographic portfolio, examining approximately 3400 articles published between 2005 and 2024, from which 80 were selected for a detailed analysis. The main methodological contributions come from research articles, the ScienceDirect database, the Expert Systems with Applications Journal, studies conducted in Turkey, and publications from the year 2023. The results highlight the predominant use of multi-criteria techniques, emphasizing hybrid approaches that combine various decision-making methodologies. In particular, the analytic hierarchy process (AHP) and TOPSIS methods were employed in 51.25% of the analyzed cases, either individually or in combination. It is concluded that technology selection should be based on flexible and adaptive approaches tailored to the organizational context, aligning long-term strategic objectives to ensure business sustainability and success. Full article

(This article belongs to the Collection Review Papers Collection for Advanced Technologies)

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