Technologies

32 pages, 12347 KiB

Open AccessReview

Recent Advances in Spatially Incoherent Coded Aperture Imaging Technologies

by Vipin Tiwari, Shivasubramanian Gopinath, Tauno Kahro, Francis Gracy Arockiaraj, Agnes Pristy Ignatius Xavier, Narmada Joshi, Kaupo Kukli, Aile Tamm, Saulius Juodkazis, Joseph Rosen and Vijayakumar Anand

Technologies 2025, 13(5), 210; https://doi.org/10.3390/technologies13050210 - 21 May 2025

Abstract

Coded aperture imaging (CAI) is a powerful imaging technology that has rapidly developed during the past decade. CAI technology and its integration with incoherent holography have led to the development of several cutting-edge imaging tools, devices, and techniques with widespread interdisciplinary applications, such [...] Read more.

Coded aperture imaging (CAI) is a powerful imaging technology that has rapidly developed during the past decade. CAI technology and its integration with incoherent holography have led to the development of several cutting-edge imaging tools, devices, and techniques with widespread interdisciplinary applications, such as in astronomy, biomedical sciences, and computational imaging. In this review, we provide a comprehensive overview of the recently developed CAI techniques in the framework of incoherent digital holography. The review starts with an overview of the milestones in modern CAI technology, such as interferenceless coded aperture correlation holography, followed by a detailed survey of recently developed CAI techniques and system designs in subsequent sections. Each section provides a general description, principles, potential applications, and associated challenges. We believe that this review will act as a reference point for further advancements in CAI technologies. Full article

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

25 pages, 2905 KiB

Open AccessArticle

Does the Choice of Topic Modeling Technique Impact the Interpretation of Aviation Incident Reports? A Methodological Assessment

by Aziida Nanyonga, Keith Joiner, Ugur Turhan and Graham Wild

Technologies 2025, 13(5), 209; https://doi.org/10.3390/technologies13050209 - 19 May 2025

Viewed by 189

Abstract

This study presents a comparative analysis of four topic modeling techniques —Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), Probabilistic Latent Semantic Analysis (pLSA), and Non-negative Matrix Factorization (NMF)—applied to aviation safety reports from the ATSB dataset spanning 2013–2023. The evaluation [...] Read more.

This study presents a comparative analysis of four topic modeling techniques —Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), Probabilistic Latent Semantic Analysis (pLSA), and Non-negative Matrix Factorization (NMF)—applied to aviation safety reports from the ATSB dataset spanning 2013–2023. The evaluation focuses on coherence, interpretability, generalization, computational efficiency, and scalability. The results indicate that NMF achieves the highest coherence score (0.7987), demonstrating its effectiveness in extracting well-defined topics from structured narratives. pLSA performs competitively (coherence: 0.7634) but lacks the scalability of NMF. LDA and BERTopic, while effective in generalization (perplexity: −6.471 and −4.638, respectively), struggle with coherence due to their probabilistic nature and reliance on contextual embeddings. A preliminary expert review by two aviation safety specialists found that topics generated by the NMF model were interpretable and aligned well with domain knowledge, reinforcing its potential suitability for such aviation safety analysis. Future research should explore new hybrid modeling approaches and real-time applications to enhance aviation safety analysis further. The study contributes to advancing automated safety monitoring in the aviation industry by refining the most appropriate topic modeling techniques. Full article

(This article belongs to the Special Issue Aviation Science and Technology Applications)

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5 pages, 160 KiB

Open AccessEditorial

Artificial Intelligence in Biomedical Technology: Advances and Challenges

by Marcos Aviles, Saul Tovar-Arriaga, Gerardo Israel Pérez-Soto, Karla A. Camarillo-Gómez and Juvenal Rodríguez-Reséndiz

Technologies 2025, 13(5), 208; https://doi.org/10.3390/technologies13050208 - 17 May 2025

Viewed by 207

Abstract

Artificial intelligence (AI) has had an increasingly widespread presence in biomedical technology in recent years [...] Full article

(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)

18 pages, 6166 KiB

Open AccessArticle

Design of an Integrated Near-Field Communication and Wireless Power Transfer Coupler for Mobile Device Applications

by Hongguk Bae and Sangwook Park

Technologies 2025, 13(5), 207; https://doi.org/10.3390/technologies13050207 - 17 May 2025

Viewed by 190

Abstract

In this study, we propose a model that integrates a near-field communication (NFC) coupler and a wireless power transfer (WPT) coupler for mobile device applications. The NFC and WPT couplers were independently designed and then combined into a four-port NFC–WPT coupler. The proposed [...] Read more.

In this study, we propose a model that integrates a near-field communication (NFC) coupler and a wireless power transfer (WPT) coupler for mobile device applications. The NFC and WPT couplers were independently designed and then combined into a four-port NFC–WPT coupler. The proposed practical equivalent circuit (PEC) introduces a novel multi-port network representation, where inductive and capacitive coupling structures are modeled using T-model and Pi-model configurations, respectively. Based on this circuit model, we present a detailed theoretical approach for deriving a 4 × 4 S-parameter matrix by converting the transmission matrices of the partitioned circuit networks into S-parameters. The comparison between the theoretical analysis and the simulation results shows an error of less than 2.4%, which demonstrates the high accuracy of the proposed method. Full article

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15 pages, 3259 KiB

Open AccessArticle

Inkjet-Printed Flexible Piezoelectric Sensor for Large Deformation Applications

by Giulia Mecca, Roberto Bernasconi, Valentina Zega, Raffaella Suriano, Marco Menegazzo, Gianlorenzo Bussetti, Alberto Corigliano and Luca Magagnin

Technologies 2025, 13(5), 206; https://doi.org/10.3390/technologies13050206 - 17 May 2025

Viewed by 200

Abstract

Next-generation flexible, soft, and stretchable sensors and electronic devices are conquering the technological scene due to their extremely innovative applications. Especially when produced via innovative technologies like additive manufacturing (AM) and/or inkjet printing (IJP), they represent an undeniable strategic asset for Industry 5.0. [...] Read more.

Next-generation flexible, soft, and stretchable sensors and electronic devices are conquering the technological scene due to their extremely innovative applications. Especially when produced via innovative technologies like additive manufacturing (AM) and/or inkjet printing (IJP), they represent an undeniable strategic asset for Industry 5.0. Within the growing sensor market, they offer advantages in terms of sensitivity and maximum sensing range. In addition, the use of AM/IJP reduces material waste, enhances scalability, and lowers cost production. In the present work, the design and fabrication of a highly flexible inkjet-printed piezoelectric sensor on top of a thin highly flexible polyimide substrate are presented. The silver top and bottom electrodes were inkjet-printed together with a P(VDF-TrFE) active layer with a nominal thickness of 3 μm which is located between them. The experimental results demonstrate that, even in extreme bending conditions and at different bending speeds, the fabricated sensors are able to maintain their performance without mechanical delamination, giving a stable and repeatable output peak-to-peak signal of 850 mV under cyclic bending. The material combination and the IJP-based fabrication technique employed for the first time in this work to produce highly flexible sensors represent a promising novelty in terms of both sensor performance and customization possibilities. Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies: From Material Jetting to 3D Printing)

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25 pages, 1622 KiB

Open AccessReview

ChatGPT as a Digital Tool in the Transformation of Digital Teaching Competence: A Systematic Review

by José Fernández Cerero, Marta Montenegro Rueda, Pedro Román Graván and José María Fernández Batanero

Technologies 2025, 13(5), 205; https://doi.org/10.3390/technologies13050205 - 16 May 2025

Viewed by 182

Abstract

In recent years, the use of tools based on artificial intelligence, such as ChatGPT, has begun to play a relevant role in education, particularly in the development of teachers’ digital competence. However, its impact and the implications of its integration in the educational [...] Read more.

In recent years, the use of tools based on artificial intelligence, such as ChatGPT, has begun to play a relevant role in education, particularly in the development of teachers’ digital competence. However, its impact and the implications of its integration in the educational environment still need to be rigorously analysed. This study aims to examine the role of ChatGPT as a digital tool in the transformation and strengthening of teachers’ digital competence, identifying its advantages and limitations in pedagogical practices. To this end, a systematic literature review was carried out in four academic databases: Web of Science, Scopus, ERIC and Google Scholar. Eighteen relevant articles addressing the relationship between the use of ChatGPT and professional teacher development were selected. Among the main findings, it was identified that this technology can contribute to the continuous updating of teachers, facilitate the understanding of complex content, optimise teaching planning, and reduce the burden of repetitive tasks. However, challenges related to technology dependency, the need for specific training, and the ethics of its educational application were also noted. The results of this study suggest that the use of ChatGPT in education should be approached from a critical and informed perspective, considering both its benefits and limitations. Empirical studies are recommended to evaluate its real impact in different educational contexts and the implementation of teacher training strategies that favour its responsible and effective use in the classroom. Full article

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34 pages, 3106 KiB

Open AccessSystematic Review

Advances in Mounting Structures for Photovoltaic Systems: Sustainable Materials and Efficient Design

by Luis Angel Iturralde Carrera, Leonel Díaz-Tato, Carlos D. Constantino-Robles, Margarita G. Garcia-Barajas, Araceli Zapatero-Gutiérrez, José M. Álvarez-Alvarado and Juvenal Rodríguez-Reséndiz

Technologies 2025, 13(5), 204; https://doi.org/10.3390/technologies13050204 - 16 May 2025

Viewed by 104

Abstract

This article addresses the technical, aesthetic, and strategic problem of the limited attention paid to design and selection of materials in photovoltaic system (PSS) support structures despite their direct impact on the efficiency, durability and economic viability of these systems. As the costs [...] Read more.

This article addresses the technical, aesthetic, and strategic problem of the limited attention paid to design and selection of materials in photovoltaic system (PSS) support structures despite their direct impact on the efficiency, durability and economic viability of these systems. As the costs of modules and electronic components continues to decrease, the structural elements acquire greater weight in the total cost and long-term performance. Our research comprehensively analyzes the mechanical, environmental, and regulatory factors influencing material selection and structural design in PV mounting systems. The PRISMA methodology was used to perform a systematic review of 122 articles published between 2018 and 2025, which were classified along two axes: materials (mild steel, galvanized steel, aluminum, polymers, and composites) and structural design (angle, orientation, loads, support typology, and adaptation to the environment). The results show that an adequate match between design and climatic conditions improves system stability, efficiency, and service life. With the support of digital modeling and advanced simulations, we identify trends towards modular, lightweight, and adaptive solutions, particularly in architectural applications (BIPV). This work provides a robust and contextualized technical framework that facilitates informed decision-making in solar energy projects, with direct implications for the sustainability, structural resilience, and competitiveness of the PSS sector in different geographical regions. Full article

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23 pages, 12241 KiB

Open AccessArticle

Biodiesel Isomerization Using Sulfated Tin(IV) Oxide as a Superacid Catalyst to Improve Cold Flow Properties

by Yano Surya Pradana, I Gusti Bagus Ngurah Makertihartha, Tirto Prakoso, Tatang Hernas Soerawidjaja and Antonius Indarto

Technologies 2025, 13(5), 203; https://doi.org/10.3390/technologies13050203 - 16 May 2025

Viewed by 40

Abstract

The development of alternative energies has become a concern for all countries to ensure domestic energy supply and provide environmental friendliness. One of the providential alternative energies is biodiesel. Biodiesel, commonly stated as fatty acid alkyl ester (FAAE), is a liquid fuel intended [...] Read more.

The development of alternative energies has become a concern for all countries to ensure domestic energy supply and provide environmental friendliness. One of the providential alternative energies is biodiesel. Biodiesel, commonly stated as fatty acid alkyl ester (FAAE), is a liquid fuel intended to substitute petroleum diesel. Nevertheless, implementation of pure biodiesel is not recommended for conventional diesel engines. It holds poor values of cold flow properties, as the effect of high saturated FAAE content contributes to this constraint. Several processes have been proposed to enhance cold flow properties of biodiesel, but this work focuses on the skeletal isomerization process. This process rearranges the skeletal carbon chain of straight-chain FAAE into branched isomeric products to lower the melting point, related to the good cold flow behavior. This method specifically requires an acid catalyst to elevate the isomerization reaction rate. And then, sulfated tin(IV) oxide emerged as a solid superacid catalyst due to its superiority in acidity. The results of biodiesel isomerization over this catalyst and its modification with iron had not satisfied the expectation of high isomerization yield and significant CFP improvement. However, they emphasized that the skeletal isomers demonstrated minimum impact on biodiesel oxidation stability. They also affirmed the role of an acid catalyst in the reaction mechanism in terms of protonation, isomerization, and deprotonation. Furthermore, the metal promotion was theoretically necessary to boost the catalytic activity of this material. It initiated the dehydrogenation of linear hydrocarbon before protonation and terminated the isomerization by hydrogenating the branched carbon chain after deprotonation. Finally, the overall findings indicated promising prospects for further enhancement of catalyst performance and reusability. Full article

(This article belongs to the Topic Advances in Green Energy and Energy Derivatives)

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53 pages, 3704 KiB

Open AccessReview

A Comprehensive Review of Adversarial Attacks and Defense Strategies in Deep Neural Networks

by Abdulruhman Abomakhelb, Kamarularifin Abd Jalil, Alya Geogiana Buja, Abdulraqeb Alhammadi and Abdulmajeed M. Alenezi

Technologies 2025, 13(5), 202; https://doi.org/10.3390/technologies13050202 - 15 May 2025

Viewed by 400

Abstract

Artificial Intelligence (AI) security research is promising and highly valuable in the current decade. In particular, deep neural network (DNN) security is receiving increased attention. Although DNNs have recently emerged as a prominent tool for addressing complex challenges across various machine learning (ML) [...] Read more.

Artificial Intelligence (AI) security research is promising and highly valuable in the current decade. In particular, deep neural network (DNN) security is receiving increased attention. Although DNNs have recently emerged as a prominent tool for addressing complex challenges across various machine learning (ML) tasks and DNNs stand out as the most widely employed, as well as holding a significant share in both research and industry, DNNs exhibit vulnerabilities to adversarial attacks where slight but intentional perturbations can deceive DNNs models. Consequently, several studies have proposed that DNNs are exposed to new attacks. Given the increasing prevalence of these attacks, researchers need to explore countermeasures that mitigate the associated risks and enhance the reliability of adapting DNNs to various critical applications. As a result, DNNs have been protected against adversarial attacks using a variety of defense mechanisms. Our primary focus is DNN as a foundational technology across all ML tasks. In this work, we comprehensively survey and present the latest research on DNN security based on various ML tasks, highlighting the adversarial attacks that cause DNNs to fail and the defense strategies that protect the DNNs. We review, explore, and elucidate the operational mechanisms of prevailing adversarial attacks and defense mechanisms applicable to all ML tasks utilizing DNN. Our review presents a detailed taxonomy for attacker and defender problems, providing a comprehensive and robust review of most state-of-the-art attacks and defenses in recent years. Additionally, we thoroughly examine the most recent systematic review concerning the measures used to evaluate the success of attack or defense methods. Finally, we address current challenges and open issues in this field and future research directions. Full article

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

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32 pages, 6855 KiB

Open AccessArticle

Advancing CVD Risk Prediction with Transformer Architectures and Statistical Risk Factor Filtering

by Parul Dubey, Pushkar Dubey and Pitshou N. Bokoro

Technologies 2025, 13(5), 201; https://doi.org/10.3390/technologies13050201 - 14 May 2025

Viewed by 203

Abstract

Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, demanding accurate and timely prediction methods. Recent advancements in artificial intelligence have shown promise in enhancing clinical decision-making for CVD diagnosis. However, many existing models fail to distinguish between statistically significant [...] Read more.

Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, demanding accurate and timely prediction methods. Recent advancements in artificial intelligence have shown promise in enhancing clinical decision-making for CVD diagnosis. However, many existing models fail to distinguish between statistically significant and redundant risk factors, resulting in reduced interpretability and potential overfitting. This research addresses the need for a clinically meaningful and computationally efficient prediction model. The study utilizes three real-world datasets comprising demographic, clinical, and lifestyle-based risk factors relevant to CVD. A novel methodology is proposed, integrating the HEART framework for statistical feature optimization with a Transformer-based deep learning model for classification. The HEART framework employs correlation-based filtering, Akaike information criterion (AIC), and statistical significance testing to refine feature subsets. The novelty lies in combining statistical risk factor filtration with attention-driven learning, enhancing both model performance and interpretability. The proposed model is evaluated using key metrics, including accuracy, precision, recall, F1-score, AUC, and Jaccard index. Experimental results show that the Transformer model significantly outperforms baseline models, achieving 93.1% accuracy and 0.957 AUC, confirming its potential for reliable CVD prediction. Full article

(This article belongs to the Section Assistive Technologies)

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35 pages, 10924 KiB

Open AccessFeature PaperArticle

Winding Fault Detection in Power Transformers Based on Support Vector Machine and Discrete Wavelet Transform Approach

by Bonginkosi A. Thango

Technologies 2025, 13(5), 200; https://doi.org/10.3390/technologies13050200 - 14 May 2025

Viewed by 170

Abstract

Transformer winding faults (TWFs) can lead to insulation breakdown, internal short circuits, and catastrophic transformer failure. Due to their low current magnitude—particularly at early stages such as inter-turn short circuits, axial or radial displacement, or winding looseness—TWFs often induce minimal impedance changes and [...] Read more.

Transformer winding faults (TWFs) can lead to insulation breakdown, internal short circuits, and catastrophic transformer failure. Due to their low current magnitude—particularly at early stages such as inter-turn short circuits, axial or radial displacement, or winding looseness—TWFs often induce minimal impedance changes and generate fault currents that remain within normal operating thresholds. As a result, conventional protection schemes like overcurrent relays, which are tuned for high-magnitude faults, fail to detect such internal anomalies. Moreover, frequency response deviations caused by TWFs often resemble those introduced by routine phenomena such as tap changer operations, load variation, or core saturation, making accurate diagnosis difficult using traditional FRA interpretation techniques. This paper presents a novel diagnostic framework combining Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) classification to improve the detection of TWFs. The proposed system employs region-based statistical deviation labeling to enhance interpretability across five well-defined frequency bands. It is validated on five real FRA datasets obtained from operating transformers in Gauteng Province, South Africa, covering a range of MVA ratings and configurations, thereby confirming model transferability. The system supports post-processing but is lightweight enough for near real-time diagnostic use, with average execution time under 12 s per case on standard hardware. A custom graphical user interface (GUI), developed in MATLAB R2022a, automates the diagnostic workflow—including region identification, wavelet-based decomposition visualization, and PDF report generation. The complete framework is released as an open-access toolbox for transformer condition monitoring and predictive maintenance. Full article

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18 pages, 6692 KiB

Open AccessArticle

Ballistic Testing of an Aerogel/Starch Composite Designed for Use in Wearable Protective Equipment

by John LaRocco, Taeyoon Eom, Tanush Duggisani, Ian Zalcberg, Jinyi Xue, Ekansh Seth, Nicolas Zapata, Dheeraj Anksapuram, Nathaniel Muzumdar and Eric Zachariah

Technologies 2025, 13(5), 199; https://doi.org/10.3390/technologies13050199 - 14 May 2025

Viewed by 284

Abstract

Concussion is a costly healthcare issue affecting sports, industry, and the defense sector. The financial impacts, however, extend beyond acute medical expenses, affecting an individual’s physical and cognitive abilities, as well as increasing the burden on coworkers, family members, and caregivers. More effective [...] Read more.

Concussion is a costly healthcare issue affecting sports, industry, and the defense sector. The financial impacts, however, extend beyond acute medical expenses, affecting an individual’s physical and cognitive abilities, as well as increasing the burden on coworkers, family members, and caregivers. More effective personal protective equipment may greatly reduce the risk of concussion and injury. Notably, aerogels are light, but traditionally fragile, non-Newtonian fluids, such as shear-thickening fluids, which generate more resistance when compressive force is applied. Herein, a composite material was developed by baking a shear-thickening fluid (i.e., starch) and combining it with a commercially available aerogel foam, thus maintaining a low cost. The samples were tested through the use of a ballistic pendulum system, using a spring-powered launcher and a gas-powered cannon, followed by ballistic penetration testing, using two electromagnetic accelerators and two different projectiles. During the cannon tests without a hardhat, the baked composite only registered 31 ± 2% of the deflection height observed for the pristine aerogel. The baked composite successfully protected the hygroelectric devices from coilgun projectiles, whereas the projectiles punctured the pristine aerogel. Leveraging the low-cost design of this new composite, personal protective equipment can be improved for various sporting, industrial, and defense applications. Full article

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

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29 pages, 1306 KiB

Open AccessReview

Artificial Vision Systems for Mobility Impairment Detection: Integrating Synthetic Data, Ethical Considerations, and Real-World Applications

by Santiago Felipe Luna-Romero, Mauren Abreu de Souza and Luis Serpa Andrade

Technologies 2025, 13(5), 198; https://doi.org/10.3390/technologies13050198 - 13 May 2025

Viewed by 338

Abstract

Global estimates suggest that over a billion people worldwide—more than 15% of the global population—live with some form of mobility disability, underscoring the pressing need for innovative technological solutions. Recent advancements in artificial vision systems, driven by deep learning and image processing techniques, [...] Read more.

Global estimates suggest that over a billion people worldwide—more than 15% of the global population—live with some form of mobility disability, underscoring the pressing need for innovative technological solutions. Recent advancements in artificial vision systems, driven by deep learning and image processing techniques, offer promising avenues for detecting mobility aids and monitoring gait or posture anomalies. This paper presents a systematic review conducted in accordance with ProKnow-C guidelines, examining key methodologies, datasets, and ethical considerations in mobility impairment detection from 2015 to 2025. Our analysis reveals that convolutional neural network (CNN) approaches, such as YOLO and Faster R-CNN, frequently outperform traditional computer vision methods in accuracy and real-time efficiency, though their success depends on the availability of large, high-quality datasets that capture real-world variability. While synthetic data generation helps mitigate dataset limitations, models trained predominantly on simulated images often exhibit reduced performance in uncontrolled environments due to the domain gap. Moreover, ethical and privacy concerns related to the handling of sensitive visual data remain insufficiently addressed, highlighting the need for robust privacy safeguards, transparent data governance, and effective bias mitigation protocols. Overall, this review emphasizes the potential of artificial vision systems to transform assistive technologies for mobility impairments and calls for multidisciplinary efforts to ensure these systems are technically robust, ethically sound, and widely adoptable. Full article

(This article belongs to the Topic Digital Twins and Artificial Intelligence for Advancing Smart Green Building and City Resilience)

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25 pages, 10677 KiB

Open AccessArticle

Synthesis of Sm-Doped CuO–SnO₂:FSprayed Thin Film: An Eco-Friendly Dual-Function Solution for the Buffer Layer and an Effective Photocatalyst for Ampicillin Degradation

by Ghofrane Charrada, Bechir Yahmadi, Badriyah Alhalaili, Moez Hajji, Sarra Gam Derouich, Ruxandra Vidu and Najoua Turki Kamoun

Technologies 2025, 13(5), 197; https://doi.org/10.3390/technologies13050197 - 13 May 2025

Viewed by 215

Abstract

Synthesis and characterization of undoped and samarium-doped CuO–SnO₂:F thin films using the spray pyrolysis technique are presented. The effect of the samarium doping level on the physical properties of these films was thoroughly analyzed. X-ray diffraction patterns proved the successful synthesis [...] Read more.

Synthesis and characterization of undoped and samarium-doped CuO–SnO₂:F thin films using the spray pyrolysis technique are presented. The effect of the samarium doping level on the physical properties of these films was thoroughly analyzed. X-ray diffraction patterns proved the successful synthesis of pure CuO–SnO₂:F thin films, free from detectable impurities. The smallest crystallite size was observed in 6% Sm-doped CuO–SnO₂:F thin films. The 6% Sm-doped CuO–SnO₂films demonstrated an increasedsurface area of 40.6 m²/g, highlighting improved textural properties, which was further validated by XPS analysis.The bandgap energy was found to increase from 1.90 eV for undoped CuO–SnO₂:F to 2.52 eV for 4% Sm-doped CuO–SnO₂:F, before decreasing to 2.03 eV for 6% Sm-doped CuO–SnO2:F thin films. Photoluminescence spectra revealed various emission peaks, suggesting a quenching effect. A numerical simulation of a new solar cell based on FTO/ZnO/Sm–CuO–SnO₂:F/X/Mo was carried out using Silvaco Atlas software, where X represented the absorber layer CIGS, CdTe, and CZTS. The results showed that the solar cell with CIGS as the absorber layer achieved the highest efficiency of 15.98. Additionally, the thin films demonstrated strong photocatalytic performance, with 6% Sm-doped CuO–SnO₂:F showing 86% degradation of ampicillin after two hours. This comprehensive investigation provided valuable insights into the synthesis, properties, and potential applications of Sm-doped CuO–SnO₂ thin films, particularly for solar energy and pharmaceutical applications. Full article

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

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20 pages, 1298 KiB

Open AccessArticle

NCC—An Efficient Deep Learning Architecture for Non-Coding RNA Classification

by Konstantinos Vasilas, Evangelos Makris, Christos Pavlatos and Ilias Maglogiannis

Technologies 2025, 13(5), 196; https://doi.org/10.3390/technologies13050196 - 12 May 2025

Viewed by 247

Abstract

In this paper, an efficient deep-learning architecture is proposed, aiming to classify a significant category of RNA, the non-coding RNAs (ncRNAs). These RNAs participate in various biological processes and play an important role in gene regulation as well. Because of their diverse nature, [...] Read more.

In this paper, an efficient deep-learning architecture is proposed, aiming to classify a significant category of RNA, the non-coding RNAs (ncRNAs). These RNAs participate in various biological processes and play an important role in gene regulation as well. Because of their diverse nature, the task of classifying them is a hard one in the bioinformatics domain. Existing classification methods often rely on secondary or tertiary RNA structures, which are computationally expensive to predict and prone to errors, especially for complex or novel ncRNA sequences. To address these limitations, a deep neural network classifier called NCC is proposed, which focuses solely on primary RNA sequence information. This deep neural network is appropriately trained to identify patterns in ncRNAs, leveraging well-known datasets, which are publicly available. Additionally, a ten times larger dataset than the available ones is created for better training and testing. In terms of performance, the suggested model showcases a 6% enhancement in precision compared to prior state-of-the-art systems, with an accuracy level of 92.69%, in the existing dataset. In the larger one, its accuracy rate exceeded 98%, outperforming all related tools, pointing to high prediction capability, which can act as a base for further findings in ncRNA analysis and the genomics field in general. Full article

(This article belongs to the Special Issue Breakthroughs in Bioinformatics and Biomedical Engineering)

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13 pages, 8546 KiB

Open AccessArticle

AiWatch: A Distributed Video Surveillance System Using Artificial Intelligence and Digital Twins Technologies

by Alessio Ferone, Antonio Maratea, Francesco Camastra, Angelo Ciaramella, Antonino Staiano, Marco Lettiero, Angelo Polizio, Francesco Lombardi and Antonio Junior Spoleto

Technologies 2025, 13(5), 195; https://doi.org/10.3390/technologies13050195 - 10 May 2025

Viewed by 318

Abstract

The primary purpose of video surveillance is to monitor public indoor areas or the boundaries of secure facilities to safeguard them against theft, unauthorized access, fire, and various other potential threats. Security cameras, equipped with integrated video surveillance systems, are strategically placed throughout [...] Read more.

The primary purpose of video surveillance is to monitor public indoor areas or the boundaries of secure facilities to safeguard them against theft, unauthorized access, fire, and various other potential threats. Security cameras, equipped with integrated video surveillance systems, are strategically placed throughout critical locations on the premises, allowing security personnel to observe all areas for specific behaviors that may signal an emergency or a situation requiring intervention. A significant challenge arises from the fact that individuals cannot maintain focus on multiple screens simultaneously, which can result in the oversight of crucial incidents. In this regard, artificial intelligence (AI) video analytics has become increasingly prominent, driven by numerous practical applications that include object identification, detection of unusual behavior patterns, facial recognition, and traffic management. Recent advancements in this technology have led to enhanced functionality, remarkable accuracy, and reduced costs for consumers. There is a noticeable trend towards upgrading security frameworks by incorporating AI into pre-existing video surveillance systems, thus leading to modern video surveillance that leverages video analytics, enabling the detection and reporting of anomalies within mere seconds, thereby transforming it into a proactive security solution. In this context, the AiWatch system introduces digital twin (DT) technology in a modern video surveillance architecture to facilitate advanced analytics through the aggregation of data from various sources. By exploiting AI and DT to analyze the different sources, it is possible to derive deeper insights applicable at higher decision levels. This approach allows for the evaluation of the effects and outcomes of actions by examining different scenarios, hence yielding more robust decisions. Full article

(This article belongs to the Special Issue Artificial Intelligence and Smart Information Systems: Trends and Innovations)

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26 pages, 9549 KiB

Open AccessArticle

The Electromechanical Modeling and Parametric Analysis of a Piezoelectric Vibration Energy Harvester for Induction Motors

by Moisés Vázquez-Toledo, Arxel de León, Francisco López-Huerta, Pedro J. García-Ramírez, Ernesto A. Elvira-Hernández and Agustín L. Herrera-May

Technologies 2025, 13(5), 194; https://doi.org/10.3390/technologies13050194 - 10 May 2025

Viewed by 184

Abstract

Industrial motors generate vibration energy that can be converted into electrical energy using piezoelectric vibration energy harvesters (pVEHs). These energy harvesters can power devices or function as self-powered sensors. However, optimal electromechanical designs of pVEHs are required to improve their output performance under [...] Read more.

Industrial motors generate vibration energy that can be converted into electrical energy using piezoelectric vibration energy harvesters (pVEHs). These energy harvesters can power devices or function as self-powered sensors. However, optimal electromechanical designs of pVEHs are required to improve their output performance under different vibration frequency and amplitude conditions. To address this challenge, we performed the electromechanical modeling of a multilayer pVEH that harvests vibration energy from induction electric motors at frequencies close to 30 Hz. In addition, a parametric analysis of the geometry of the multilayer piezoelectric device was conducted to optimize its deflection and output voltage, considering the substrate length, piezoelectric patch position, and dimensions of the central hole. Our analytical model predicted the deflection and first bending resonant frequency of the piezoelectric device, with good agreement with predictions from finite element method (FEM) models. The proposed piezoelectric device achieved an output voltage of 143.2 V and an output power of 3.2 mW with an optimal resistance of 6309.5 kΩ. Also, the principal stresses of the pVEH were assessed using linear trend analysis, finding a safe operating range up to an acceleration of 0.7 g. The electromechanical design of the pVEH allowed for effective synchronization with the vibration frequency of an induction electric motor. This energy harvester has a potential application in industrial electric motors to transform their vibration energy into electrical energy to power sensors. Full article

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

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19 pages, 897 KiB

Open AccessArticle

Stable Multipoint Flux Approximation (MPFA) Saturation Solution for Two-Phase Flow on Non-K-Orthogonal Anisotropic Porous Media

by Pijus Makauskas and Mayur Pal

Technologies 2025, 13(5), 193; https://doi.org/10.3390/technologies13050193 - 9 May 2025

Viewed by 227

Abstract

This paper extends the multipoint flux approximation (MPFA-O) method to model coupled pressure and saturation dynamics in subsurface reservoirs with heterogeneous anisotropic permeability and non-K-orthogonal grids. The MPFA method is widely used for reservoir simulation to address the limitations of the two-point flux [...] Read more.

This paper extends the multipoint flux approximation (MPFA-O) method to model coupled pressure and saturation dynamics in subsurface reservoirs with heterogeneous anisotropic permeability and non-K-orthogonal grids. The MPFA method is widely used for reservoir simulation to address the limitations of the two-point flux approximation (TPFA), particularly in scenarios involving full-tensor permeability and strong anisotropy. However, the MPFA-O method is known to suffer from spurious oscillations and numerical instability, especially in high-anisotropy scenarios. Existing stability-enhancing techniques, such as optimal quadrature schemes and flux-splitting methods, mitigate these issues but are computationally expensive and do not always ensure monotonicity or oscillation-free solutions. Building upon prior advancements in the MPFA-O method for pressure equations, this work incorporates the saturation equation to enable the simulation of a coupled multiphase flow in porous media. A unified framework is developed to address stability challenges associated with the tight coupling of pressure and saturation fields while ensuring local conservation and accuracy in the presence of full-tensor permeability. The proposed method introduces stability-enhancing modifications, including a local rotation transformation, to mitigate spurious oscillations and preserve physical principles such as monotonicity and the maximum principle. Numerical experiments on heterogeneous, anisotropic domains with non-K-orthogonal grids validate the robustness and accuracy of the extended MPFA-O method. The results demonstrate improved stability and performance in capturing the complex interactions between pressure and saturation fields, offering a significant advancement in subsurface reservoir modeling. This work provides a reliable and efficient tool for simulating coupled flow and transport processes, with applications in CO₂ storage, hydrogen storage, geothermal energy, and hydrocarbon recovery. Full article

(This article belongs to the Section Construction Technologies)

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19 pages, 7039 KiB

Open AccessArticle

A New Contact Force Estimation Method for Heavy Robots Without Force Sensors by Combining CNN-GRU and Force Transformation

by Peizhang Wu, Hui Dong, Pengfei Li, Yifei Bao, Wei Dong and Lining Sun

Technologies 2025, 13(5), 192; https://doi.org/10.3390/technologies13050192 - 9 May 2025

Viewed by 290

Abstract

In response to the safety control requirements of heavy robot operations, to address the problems of cumbersome, time-consuming, poor accuracy and low real-time performance in robot end contact force estimation without force sensors by using traditional manual modeling and identification methods, this paper [...] Read more.

In response to the safety control requirements of heavy robot operations, to address the problems of cumbersome, time-consuming, poor accuracy and low real-time performance in robot end contact force estimation without force sensors by using traditional manual modeling and identification methods, this paper proposes a new contact force estimation method for heavy robots without force sensors by combining CNN-GRU and force transformation. Firstly, the CNN-GRU machine learning method is utilized to construct the robot Joint Motor Current-Joint External Force Model; then, the Joint External Force-End Contact Force Model is constructed through the Kalman filter and Jacobian force transformation method, and the robot end contact force is estimated by finally uniting them. This method can achieve robot end contact force estimation without a force sensor, avoiding the cumbersome manual modeling and identification process. Compared with traditional manual modeling and identification methods, experiments show that the proposed method in this paper can approximately double the estimation accuracy of the contact force of heavy robots and reduce the time consumption by approximately half, with advantages such as convenience, efficiency, strong real-time performance, and high accuracy. Full article

(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)

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14 pages, 7632 KiB

Open AccessCommunication

A Dynamic Mechanical Analysis Device for In Vivo Material Characterization of Plantar Soft Tissue

by Longyan Wu, Ran Huang, Jun Zhu and Xin Ma

Technologies 2025, 13(5), 191; https://doi.org/10.3390/technologies13050191 - 9 May 2025

Viewed by 204

Abstract

Understanding the viscoelastic properties of plantar soft tissue under dynamic conditions is crucial for assessing foot health and preventing injuries. In this work, we document an in vivo device, employing the principles of dynamic mechanical analysis (DMA), which, for the first time, enables [...] Read more.

Understanding the viscoelastic properties of plantar soft tissue under dynamic conditions is crucial for assessing foot health and preventing injuries. In this work, we document an in vivo device, employing the principles of dynamic mechanical analysis (DMA), which, for the first time, enables in situ, real-time multidimensional mechanical characterization of plantar soft tissues. This device overcomes the limitations of conventional ex vivo and single-DOF testing methods by integrating three sinusoidal mechanism-based multi-DOF dynamic testing modules, providing measurements of tensile, compressive, shear, and torsional properties in a physiological setting. The innovative modular design integrates advanced sensors for precise force and displacement detection, allowing for comprehensive assessment under cyclic loading conditions. Validation tests on volunteers demonstrate the device’s reliability and highlight the significant viscoelastic characteristics of the plantar soft tissue. The example dataset was analyzed to calculate the storage modulus, loss modulus, loss factor, and energy dissipation. All design files, CAD models, and assembly instructions are made available as open-source resources, facilitating replication and further research. This work paves the way for enhanced diagnostics and personalized treatments in orthopedic and rehabilitative medicine. Full article

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

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31 pages, 8036 KiB

Open AccessArticle

The Tuning of a CFD Model for External Ballistics, Followed by Analyses of the Principal Influences on the Drag Coefficient of the .223 Rem Caliber

by Jiří Maxa, Pavla Šabacká, Robert Bayer, Tomáš Binar, Petr Bača, Jana Švecová, Jaroslav Talár, Martin Vlkovský and Lenka Dobšáková

Technologies 2025, 13(5), 190; https://doi.org/10.3390/technologies13050190 - 8 May 2025

Viewed by 252

Abstract

This paper presents the subject of external ballistics. The presented research employs a contemporary methodological approach, integrating theoretical analysis, CFD simulations, and experimental measurements. External ballistics is characterized by a wide spectrum of physical phenomena that influence projectile trajectory. This contribution focuses on [...] Read more.

This paper presents the subject of external ballistics. The presented research employs a contemporary methodological approach, integrating theoretical analysis, CFD simulations, and experimental measurements. External ballistics is characterized by a wide spectrum of physical phenomena that influence projectile trajectory. This contribution focuses on the analysis of drag force acting on a .223 rem caliber projectile in both subsonic and supersonic regimes. Based on experimental findings, a CFD model was refined and subsequently used to evaluate the drag force and drag coefficient, with a comparative analysis performed against G1 and G7 ballistic coefficient functions. Furthermore, the effect of the barrel length on the resultant outcome was assessed. The validated CFD model was employed to analyze the characteristics of shock waves generated at the projectile’s nose and their impact on the drag force, along with the influence of ambient temperature, particularly within the supersonic domain. Full article

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13 pages, 1567 KiB

Open AccessArticle

The Impact of Real-World Interaction on the Perception of a Humanoid Social Robot in Care for Institutionalised Older Adults

by Slawomir Tobis, Joanna Piasek-Skupna, Aleksandra Suwalska and Katarzyna Wieczorowska-Tobis

Technologies 2025, 13(5), 189; https://doi.org/10.3390/technologies13050189 - 7 May 2025

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Abstract

(1) Background: For the residents of long-term care (LTC) units, a humanoid social robot (HSR) may be not only a caregiver but also a companion. The aim of our study was to analyse changes in its perception following a real-world interaction; (2) Methods: [...] Read more.

(1) Background: For the residents of long-term care (LTC) units, a humanoid social robot (HSR) may be not only a caregiver but also a companion. The aim of our study was to analyse changes in its perception following a real-world interaction; (2) Methods: One hundred LTC residents were assessed twice with the Godspeed Questionnaire Series (GQS): after viewing a photograph of HSR TIAGo only and after interacting with it in a practical manner. The perception parameters were evaluated on a scale of 1–5 in five series: I-Anthropomorphism, II-Animation, III-Likeability, IV-Perceived intelligence, and V-Perceived safety. (3) Results: In the post-interaction assessment of the TIAGo robot, no lower scores were observed relative to the first (photo-based) scoring. Positive changes were observed in III (p < 0.001), I (p < 0.01), II (p < 0.05), and IV (p < 0.05). In multivariable analysis, high levels of loneliness constituted a correlate for improvement after interaction in I (p < 0.05); computer skills—in III (p < 0.01), and GDS score corresponding to depression—in IV (p < 0.01). (4) Conclusions: Our study reveals a positive change in older people’s perception of an HSR after interacting with it. Interaction is thus an indispensable element in the development process. Developers and implementers should pay particular attention to the robot’s smart functions, movements, and responsiveness. Full article

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

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18 pages, 7147 KiB

Open AccessArticle

A Novel Sustainable and Cost-Effective Triboelectric Nanogenerator Connected to the Internet of Things for Communication with Deaf–Mute People

by Enrique Delgado-Alvarado, Muhammad Waseem Ashraf, Shahzadi Tayyaba, José Amir González-Calderon, Ricardo López-Esparza, Ma. Cristina Irma Pérez-Pérez, Victor Champac, José Hernandéz-Hernández, Maximo Alejandro Figueroa-Navarro and Agustín Leobardo Herrera-May

Technologies 2025, 13(5), 188; https://doi.org/10.3390/technologies13050188 - 7 May 2025

Viewed by 182

Abstract

Low-cost and sustainable technological systems are required to improve communication between deaf–mute and non-deaf–mute people. Herein, we report a novel low-cost and eco-friendly triboelectric nanogenerator (TENG) composed of recycled and waste components. This TENG can be connected to a smartphone using the internet [...] Read more.

Low-cost and sustainable technological systems are required to improve communication between deaf–mute and non-deaf–mute people. Herein, we report a novel low-cost and eco-friendly triboelectric nanogenerator (TENG) composed of recycled and waste components. This TENG can be connected to a smartphone using the internet of things (IoT), which allows the transmission of information from deaf–mute to non-deaf–mute people. The proposed TENG can harness kinetic energy to convert it into electrical energy with advantages such as a compact portable design, a light weight, cost-effective fabrication, good voltage stability, and easy signal processing. In addition, this nanogenerator uses recycled and waste materials composed of radish leaf, polyimide tape, and a polyethylene terephthalate (PET) sheet. This TENG reaches an output power density of 340.3 µWm⁻² using a load resistance of 20.5 MΩ at 23 Hz, respectively. This nanogenerator achieves a stable performance even after 41,400 working cycles. Also, this device can power a digital calculator and chronometer, as well as light 116 ultra-bright blue commercial LEDs. This TENG can convert the movements of the fingers of a deaf–mute person into electrical signals that are transmitted as text messages to a smartphone. Thus, the proposed TENG can be used as a low-cost wireless communication device for deaf–mute people, contributing to an inclusive society. Full article

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

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27 pages, 658 KiB

Open AccessSystematic Review

Advances in the Automated Identification of Individual Tree Species: A Systematic Review of Drone- and AI-Based Methods in Forest Environments

by Ricardo Abreu-Dias, Juan M. Santos-Gago, Fernando Martín-Rodríguez and Luis M. Álvarez-Sabucedo

Technologies 2025, 13(5), 187; https://doi.org/10.3390/technologies13050187 - 6 May 2025

Viewed by 260

Abstract

The classification and identification of individual tree species in forest environments are critical for biodiversity conservation, sustainable forestry management, and ecological monitoring. Recent advances in drone technology and artificial intelligence have enabled new methodologies for detecting and classifying trees at an individual level. [...] Read more.

The classification and identification of individual tree species in forest environments are critical for biodiversity conservation, sustainable forestry management, and ecological monitoring. Recent advances in drone technology and artificial intelligence have enabled new methodologies for detecting and classifying trees at an individual level. However, significant challenges persist, particularly in heterogeneous forest environments with high species diversity and complex canopy structures. This systematic review explores the latest research on drone-based data collection and AI-driven classification techniques, focusing on studies that classify specific tree species rather than generic tree detection. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, peer review studies from the last decade were analyzed to identify trends in data acquisition instruments (e.g., RGB, multispectral, hyperspectral, LiDAR), preprocessing techniques, segmentation approaches, and machine learning (ML) algorithms used for classification. Findings of this study reveal that deep learning (DL) models, particularly convolutional neural networks (CNN), are increasingly replacing traditional ML methods such as random forest (RF) or support vector machines (SVMs) because there is no need for a feature extraction phase, as this is implicit in the DL models. The integration of LiDAR with hyperspectral imaging further enhances classification accuracy but remains limited due to cost constraints. Additionally, we discuss the challenges of model generalization across different forest ecosystems and propose future research directions, including the development of standardized datasets and improved model architectures for robust tree species classification. This review provides a comprehensive synthesis of existing methodologies, highlighting both advancements and persistent gaps in AI-driven forest monitoring. Full article

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

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23 pages, 700 KiB

Open AccessSystematic Review

Remote Rehabilitation and Virtual Reality Interventions Using Motion Sensors for Chronic Low Back Pain: A Systematic Review of Biomechanical, Pain, Quality of Life, and Adherence Outcomes

by Marina Garofano, Rosaria Del Sorbo, Mariaconsiglia Calabrese, Massimo Giordano, Maria Pia Di Palo, Marianna Bartolomeo, Chiara Maria Ragusa, Gaetano Ungaro, Gianluca Fimiani, Federica Di Spirito, Massimo Amato, Michele Ciccarelli, Claudio Pascarelli, Giuseppe Scanniello, Placido Bramanti and Alessia Bramanti

Technologies 2025, 13(5), 186; https://doi.org/10.3390/technologies13050186 - 6 May 2025

Viewed by 264

Abstract

Background: Chronic low back pain (CLBP) is a leading cause of disability, impacting quality of life (QoL), function, and work productivity. Traditional rehabilitation faces challenges in accessibility and adherence. Remote rehabilitation and virtual reality (VR) interventions using motion sensors offer real-time movement tracking, [...] Read more.

Background: Chronic low back pain (CLBP) is a leading cause of disability, impacting quality of life (QoL), function, and work productivity. Traditional rehabilitation faces challenges in accessibility and adherence. Remote rehabilitation and virtual reality (VR) interventions using motion sensors offer real-time movement tracking, biofeedback, and personalized exercises. This systematic review evaluates their effectiveness in pain reduction, functional improvement, adherence, and QoL. Methods: A systematic search was performed across PubMed, Scopus, Web of Science, and PEDro (2015–2025), including randomized controlled trials, observational, and feasibility studies on adults with CLBP undergoing sensor-based digital rehabilitation. The primary outcomes included pain, functional mobility, and movement biomechanics; secondary outcomes included adherence, QoL, and cost-effectiveness. Eight studies involving 7166 participants were included. Overall, sensor-based remote rehabilitation and VR interventions demonstrated positive effects on pain, function, and adherence. Pain reductions ranged from modest short-term decreases to over 60% in long-term programs (e.g., −68.5% in VAS). Functional improvements included lumbar ROM gains up to +9.9° and better movement control. Adherence was consistently high, with some programs reporting completion rates between 73% and 90%, particularly those incorporating gamification or real-time feedback. Selected studies also showed QoL improvements (e.g., +9.10 points on SF-36) and reductions in work impairment by over 60%. A few trials reported significant decreases in inflammatory markers (e.g., CRP −1.16 mg/L, TNF-α −8.9 pg/mL). Conclusions: Motion sensor-based remote rehabilitation and VR interventions show promising results in pain management, mobility, and adherence for individuals with CLBP. Gamification and biofeedback features enhance engagement, addressing a key challenge of conventional rehabilitation. However, more long-term RCTs and economic evaluations are needed to confirm their effectiveness and cost-efficiency. Full article

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

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25 pages, 5491 KiB

Open AccessArticle

Exploring the Economic Hypothetical for Downhill Belt Conveyors Equipped with Three-Phase Active Front-End Load Converters

by Daniel Chelopo and Kapil Gupta

Technologies 2025, 13(5), 185; https://doi.org/10.3390/technologies13050185 - 5 May 2025

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Abstract

This paper integrates empirical assessments of energy recovery in downhill belt conveyor systems with rigorous theoretical modeling and economic analysis. An alternative approach for capturing and transforming the potential energy of a descending conveyor into electrical energy is proposed using an active front-end [...] Read more.

This paper integrates empirical assessments of energy recovery in downhill belt conveyor systems with rigorous theoretical modeling and economic analysis. An alternative approach for capturing and transforming the potential energy of a descending conveyor into electrical energy is proposed using an active front-end (AFE) load energy recovery system. Adjusting the drive configuration from a standard direct-on-line (DOL) system to a regenerative AFE converter, the conveyor’s excess kinetic energy can be fed back into the grid. The investigation shows that operating a 300 kW downhill conveyor at full capacity would consume about 142,800 kWh per month in a conventional setup. However, at 90% of the maximum capacity over 17 h per day (~476 h per month), the conveyor with an AFE system produces a regenerative power of 188 kW (negative demand), yielding a net generation of 89,488 kWh per month. The results indicate that integrating a regenerative AFE control system can achieve energy savings of approximately 37% compared to a non-regenerative system. The key economic indicators, including lifecycle cost, payback period, and net present value, confirm the financial viability of the proposed system over a 20-year span. Full article

(This article belongs to the Section Manufacturing Technology)

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19 pages, 1110 KiB

Open AccessArticle

Vision-Based Activity Recognition for Unobtrusive Monitoring of the Elderly in Care Settings

by Rahmat Ullah, Ikram Asghar, Saeed Akbar, Gareth Evans, Justus Vermaak, Abdulaziz Alblwi and Amna Bamaqa

Technologies 2025, 13(5), 184; https://doi.org/10.3390/technologies13050184 - 4 May 2025

Viewed by 431

Abstract

As the global population ages, robust technological solutions are increasingly necessary to support and enhance elderly autonomy in-home or care settings. This paper presents a novel computer vision-based activity monitoring system that uses cameras and infrared sensors to detect and analyze daily activities [...] Read more.

As the global population ages, robust technological solutions are increasingly necessary to support and enhance elderly autonomy in-home or care settings. This paper presents a novel computer vision-based activity monitoring system that uses cameras and infrared sensors to detect and analyze daily activities of elderly individuals in care environments. The system integrates a frame differencing algorithm with adjustable sensitivity parameters and an anomaly detection model tailored to identify deviations from individual behavior patterns without relying on large volumes of labeled data. The system was validated through real-world deployments across multiple care home rooms, demonstrating significant improvements in emergency response times and ensuring resident privacy through anonymized frame differencing views. Upon detecting anomalies in daily routines, the system promptly alerts caregivers and family members, facilitating immediate intervention. The experimental results confirm the system’s capability for unobtrusive, continuous monitoring, laying a strong foundation for scalable remote elderly care services and enhancing the safety and independence of vulnerable older individuals. Full article

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

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31 pages, 6177 KiB

Open AccessArticle

Transforming Neural Networks into Quantum-Cognitive Models: A Research Tutorial with Novel Applications

by Milan Maksimovic and Ivan S. Maksymov

Technologies 2025, 13(5), 183; https://doi.org/10.3390/technologies13050183 - 4 May 2025

Viewed by 398

Abstract

Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and [...] Read more.

Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and high-tech industries. This paper demonstrates how traditional neural networks can be transformed into neuromorphic quantum models, enabling anyone with a basic understanding of undergraduate-level machine learning to create quantum-inspired models that mimic the functioning of the human brain—all using a standard laptop. We present several examples of these quantum machine learning transformations and explore their potential applications, aiming to make quantum technology more accessible and practical for broader use. The examples discussed in this paper include quantum-inspired analogues of feedforward neural networks, recurrent neural networks, Echo State Network reservoir computing, and Bayesian neural networks, demonstrating that a quantum approach can both optimise the training process and equip the models with certain human-like cognitive characteristics. Full article

(This article belongs to the Topic Quantum Information and Quantum Computing, 2nd Volume)

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31 pages, 18036 KiB

Open AccessArticle

Development and Evaluation of Solar Radiation Sensor Using Cost-Effective Light Sensors and Machine Learning Techniques

by Jesús Antonio Nava-Pintor, Uriel E. Alcalá-Rodríguez, Héctor A. Guerrero-Osuna, Marcela E. Mata-Romero, Emmanuel Lopez-Neri, Fabián García-Vázquez, Luis Octavio Solís-Sánchez, Rocío Carrasco-Navarro and Luis F. Luque-Vega

Technologies 2025, 13(5), 182; https://doi.org/10.3390/technologies13050182 - 3 May 2025

Viewed by 287

Abstract

The accurate measurement of solar radiation is essential for applications in agriculture, renewable energy, and environmental monitoring. Traditional pyranometers provide high-precision readings but are often costly and inaccessible for large-scale deployment. This study explores the feasibility of using low-cost ambient light sensors combined [...] Read more.

The accurate measurement of solar radiation is essential for applications in agriculture, renewable energy, and environmental monitoring. Traditional pyranometers provide high-precision readings but are often costly and inaccessible for large-scale deployment. This study explores the feasibility of using low-cost ambient light sensors combined with statistical and machine learning models based on linear, random forest, and support vector regressions to estimate solar irradiance. To achieve this, an Internet of Things-based system was developed, integrating the light sensors with cloud storage and processing capabilities. A dedicated solar radiation sensor (Davis 6450) served as a reference, and results were validated against meteorological API data. Experimental validation demonstrated a strong correlation between sensor-measured illuminance and solar irradiance using the random forest model, achieving a coefficient of determination (R²) of 0.9922, a root mean squared error (RMSE) of 44.46 W/m², and a mean absolute error (MAE) of 27.12 W/m². These results suggest that low-cost light sensors, when combined with data-driven models, offer a viable and scalable solution for solar radiation monitoring, particularly in resource-limited regions. Full article

(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)

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34 pages, 4246 KiB

Open AccessArticle

Using Prediction Confidence Factors to Enhance Collaborative Filtering Recommendation Quality

by Dionisis Margaris, Dimitris Spiliotopoulos, Kiriakos Sgardelis and Costas Vassilakis

Technologies 2025, 13(5), 181; https://doi.org/10.3390/technologies13050181 - 2 May 2025

Viewed by 323

Abstract

Recommender systems suggest items that users are likely to accept by predicting ratings for items they have not already rated. Collaborative filtering is a widely used method that produces these predictions, based on the ratings of similar users, termed as near neighbors. However, [...] Read more.

Recommender systems suggest items that users are likely to accept by predicting ratings for items they have not already rated. Collaborative filtering is a widely used method that produces these predictions, based on the ratings of similar users, termed as near neighbors. However, in many cases, prediction errors occur and, therefore, the recommender system ends up either recommending unwanted products or missing out on products the user would actually desire. As a result, the quality of the recommendations that are produced is of major importance. In this paper, we introduce an advanced collaborative filtering recommendation algorithm that upgrades the quality of the recommendations that are produced by considering, along with the rating prediction value of the items computed by the plain collaborative filtering procedure, a number of confidence factors that each rating prediction fulfills. The presented algorithm maintains high recommendation coverage, and can be applied to every collaborative filtering dataset, since it is based only on the very basic information. Based on the application of the algorithm on widely used recommender systems datasets, the proposed algorithm significantly upgrades the recommendation quality, surpassing the performance of state-of-the-art research works that also consider confidence factors. Full article

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