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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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39 pages, 13403 KB  
Review
Additive Manufacturing in Space: Process Physics, Qualification, and Future Directions
by Oana Dumitrescu, Emilia Georgiana Prisăcariu, Raluca Andreea Roșu and Enrico Cozzoni
Technologies 2026, 14(2), 121; https://doi.org/10.3390/technologies14020121 - 14 Feb 2026
Cited by 3 | Viewed by 2966
Abstract
Additive manufacturing has emerged as a key enabling technology for in-space manufacturing, offering the potential to reduce logistics mass, enhance mission autonomy, and support long-duration exploration. The suppression of gravity-driven phenomena fundamentally alters melt pool dynamics, heat transfer, surface-tension-dominated flow, and defect formation, [...] Read more.
Additive manufacturing has emerged as a key enabling technology for in-space manufacturing, offering the potential to reduce logistics mass, enhance mission autonomy, and support long-duration exploration. The suppression of gravity-driven phenomena fundamentally alters melt pool dynamics, heat transfer, surface-tension-dominated flow, and defect formation, limiting the direct transferability of terrestrial AM process knowledge to space applications. This paper reviews the current understanding of metallic additive manufacturing process physics under reduced gravity, with emphasis on melt pool behavior, dimensional stability, and in situ monitoring constraints. Approaches for qualification and certification are critically examined, including the applicability of existing AM standards, the role of digital twins and model-based verification, and emerging strategies for space-based validation. Enabling technologies such as autonomous and AI-assisted fabrication, compact hardware architectures, and alternative energy sources are discussed in the context of reliable in-space operation. By synthesizing current developments and identifying key limitations and open challenges, the review provides a roadmap for advancing additive manufacturing toward operational readiness, supporting sustainable exploration, in-space infrastructure development, and long-duration human presence beyond low Earth orbit. Full article
(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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27 pages, 1586 KB  
Review
A Review on Risk-Averse Bidding Strategies for Virtual Power Plants with Uncertainties: Resources, Technologies, and Future Pathways
by Dongliang Xiao
Technologies 2025, 13(11), 488; https://doi.org/10.3390/technologies13110488 - 28 Oct 2025
Cited by 16 | Viewed by 3124
Abstract
The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from [...] Read more.
The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from price volatility, renewable generation intermittency, and unpredictable prosumer behavior, which necessitate sophisticated, risk-averse bidding strategies to ensure financial viability. This review provides a comprehensive analysis of the state-of-the-art in risk-averse bidding for VPPs. It first establishes a resource-centric taxonomy, categorizing VPPs into four primary archetypes: DER-driven, demand response-oriented, electric vehicle-integrated, and multi-energy systems. The paper then delivers a comparative assessment of different optimization techniques—from stochastic programming with conditional value-at-risk and robust optimization to emerging paradigms such as distributionally robust optimization, game theory, and artificial intelligence. It critically evaluates their application contexts and effectiveness in mitigating specific risks across diverse market types. Finally, the review synthesizes these insights to identify persistent challenges—including computational bottlenecks, data privacy, and a lack of standardization—and outlines a forward-looking research agenda. This agenda emphasizes the development of hybrid AI–physical models, interoperability standards, multi-domain risk modeling, and collaborative VPP ecosystems to advance the field towards a resilient and decarbonized energy future. Full article
(This article belongs to the Section Environmental Technology)
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26 pages, 3749 KB  
Article
Synthesis of Pectin Hydrogels from Grapefruit Peel for the Adsorption of Heavy Metals from Water
by Vinusiya Vigneswararajah, Nirusha Thavarajah and Xavier Fernando
Technologies 2025, 13(9), 403; https://doi.org/10.3390/technologies13090403 - 5 Sep 2025
Cited by 5 | Viewed by 3148
Abstract
The increasing presence of heavy metals in aquatic environments, driven by the production of industrial waste and consumer products, poses serious environmental and health risks due to their toxicity and persistence. Copper (Cu(II)) and nickel (Ni(II)) are particularly harmful, with high concentrations linked [...] Read more.
The increasing presence of heavy metals in aquatic environments, driven by the production of industrial waste and consumer products, poses serious environmental and health risks due to their toxicity and persistence. Copper (Cu(II)) and nickel (Ni(II)) are particularly harmful, with high concentrations linked to neurological, dermatological and carcinogenic effects. This proof-of-concept study explores the synthesis of sustainable hydrogels derived from grapefruit peel (biosorbents) for the adsorption of Cu(II) and Ni(II) from aqueous solutions. Pectin was extracted from the peels and was used to synthesize pectin-based hydrogels (PH) and pectin hydrogel metal–organic frameworks (PHM composites). The hydrogels were characterized using FT-IR, SEM, diameter size and water absorption capacity. Lyophilized hydrogels were significantly smaller than their wet counterparts, and adsorption performance was analyzed using FAAS. PHs demonstrated high Cu(II) removal efficiency, achieving 95.11% adsorption and 97.75 mg/g capacity at pH 5. PHM composites showed comparable Cu(II) adsorption with a maximum capacity of 67.53 mg/g. Notably, PHs also exhibited rapid Ni(II) adsorption, reaching 92.62% efficiency and 28.189 mg/g capacity within one minute. These findings highlight the potential of pectin-based hydrogels as an effective, low-cost and environmentally friendly method for heavy metal remediation in water. Full article
(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)
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24 pages, 1153 KB  
Review
Cryogenic Technologies for Biogas Upgrading: A Critical Review of Processes, Performance, and Prospects
by Dolores Hidalgo and Jesús M. Martín-Marroquín
Technologies 2025, 13(8), 364; https://doi.org/10.3390/technologies13080364 - 16 Aug 2025
Cited by 7 | Viewed by 6545
Abstract
Cryogenic upgrading represents a promising route for the production of high-purity biomethane, aligning with current decarbonization goals and the increasing demand for renewable gases. This review provides a critical assessment of cryogenic technologies applied to biogas purification, focusing on process fundamentals, technological configurations, [...] Read more.
Cryogenic upgrading represents a promising route for the production of high-purity biomethane, aligning with current decarbonization goals and the increasing demand for renewable gases. This review provides a critical assessment of cryogenic technologies applied to biogas purification, focusing on process fundamentals, technological configurations, energy and separation performance, and their industrial integration potential. The analysis covers standalone cryogenic systems as well as hybrid configurations combining cryogenic separation with membrane or chemical pretreatment to enhance efficiency and reduce operating costs. A comparative evaluation of key performance indicators—including methane recovery, specific energy demand, product purity, and technology readiness level—is presented, along with a discussion of representative industrial applications. In addition, recent techno-economic studies are examined to contextualize cryogenic upgrading within the broader landscape of CO2 separation technologies. Environmental trade-offs, investment thresholds, and sensitivity to gas prices and CO2 taxation are also discussed. The review identifies existing technical and economic barriers, outlines research and innovation priorities, and highlights the relevance of process integration with natural gas networks. Overall, cryogenic upgrading is confirmed as a technically viable and environmentally competitive solution for biomethane production, particularly in contexts requiring liquefied biomethane or CO2 recovery. Strategic deployment and regulatory support will be key to accelerating its industrial adoption. The objectives of this review have been met by consolidating the current state of knowledge and identifying specific gaps that warrant further investigation. Future work is expected to address these gaps through targeted experimental studies and technology demonstrations. Full article
(This article belongs to the Section Environmental Technology)
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24 pages, 2199 KB  
Review
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
Cited by 7 | Viewed by 8319
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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31 pages, 9769 KB  
Review
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
Cited by 11 | Viewed by 3167
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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23 pages, 2455 KB  
Review
Agent-Based Modeling of Epidemics: Approaches, Applications, and Future Directions
by Xiangyu Zhang, Jiaojiao Wang, Chunmiao Yu, Jiaqiang Fei, Tianyi Luo and Zhidong Cao
Technologies 2025, 13(7), 272; https://doi.org/10.3390/technologies13070272 - 26 Jun 2025
Cited by 13 | Viewed by 10101
Abstract
The spread of infectious diseases is inherently linked to human social behavior, characterized by complexity, diversity, and openness. Intelligent agents in computer science provide a powerful framework for capturing such dynamics, enabling complex epidemic patterns to emerge from simple local rules. These agents [...] Read more.
The spread of infectious diseases is inherently linked to human social behavior, characterized by complexity, diversity, and openness. Intelligent agents in computer science provide a powerful framework for capturing such dynamics, enabling complex epidemic patterns to emerge from simple local rules. These agents exhibit self-organization, adaptability, and self-optimization, making them well suited for individual-level modeling. Agent-based models (ABMs) have shown promising results in epidemic simulation and policy evaluation. However, current implementations often suffer from simplistic behavioral assumptions and rigid interaction mechanisms, limiting their realism and flexibility. This paper first reviews the current landscape of epidemic modeling approaches. It then analyzes the underlying mechanisms of advanced intelligent agents, highlighting their modeling capabilities. The study focuses on four key advantages of intelligent agent-based modeling and elaborates on three critical roles these agents play in evaluating and optimizing intervention strategies. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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27 pages, 5926 KB  
Article
Evaluation of Machine Learning Models for Enhancing Sustainability in Additive Manufacturing
by Waqar Shehbaz and Qingjin Peng
Technologies 2025, 13(6), 228; https://doi.org/10.3390/technologies13060228 - 3 Jun 2025
Cited by 8 | Viewed by 2755
Abstract
Additive manufacturing (AM) presents significant opportunities for advancing sustainability through optimized process control and material utilization. This research investigates the application of machine learning (ML) models to directly associate AM process parameters with sustainability metrics, which is often a challenge by experimental methods [...] Read more.
Additive manufacturing (AM) presents significant opportunities for advancing sustainability through optimized process control and material utilization. This research investigates the application of machine learning (ML) models to directly associate AM process parameters with sustainability metrics, which is often a challenge by experimental methods alone. Initially, experimental data are generated by systematically varying key AM parameters, layer height, infill density, infill pattern, build orientation, and number of shells. Subsequently, four ML models, Linear Regression, Decision Trees, Random Forest, and Gradient Boosting, are trained and evaluated. Hyperparameter tuning is conducted using the Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Box constraints (L-BFGS-B) algorithm, which demonstrates the superior computational efficiency compared to traditional approaches such as grid and random search. Among the models, Random Forest yields the highest predictive accuracy and lowest mean squared error across all target sustainability indicators: energy consumption, part weight, scrap weight, and production time. The results confirm the efficacy of ML in predicting sustainability outcomes when supported by robust experimental data. This research offers a scalable and computationally efficient approach to enhancing sustainability in AM processes and contributes to data-driven decision-making in sustainable manufacturing. Full article
(This article belongs to the Special Issue Advanced Manufacturing Technologies: From Material Jetting to 3D Printing)
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20 pages, 1984 KB  
Article
The Use of Perlite and Rhyolite in Concrete Mix Design: Influence on Physical-Mechanical and Environmental Performance
by Giovanna Concu, Marco Zucca, Flavio Stochino, Monica Valdes and Francesca Maltinti
Technologies 2025, 13(6), 224; https://doi.org/10.3390/technologies13060224 - 29 May 2025
Cited by 6 | Viewed by 3030
Abstract
During the last decades, the ever-growing evolution of the construction industry has led to a significant increase in demand for increasingly high-performing construction materials both in terms of mechanical characteristics and sustainability. Focusing on concrete, several researchers have designed different mixes to improve [...] Read more.
During the last decades, the ever-growing evolution of the construction industry has led to a significant increase in demand for increasingly high-performing construction materials both in terms of mechanical characteristics and sustainability. Focusing on concrete, several researchers have designed different mixes to improve mechanical properties such as compressive strength, workability and durability, and in many of the proposed mixes, the use of industrial waste stands out both for their ability to improve the mechanical properties of concrete and for the importance of their reuse from a sustainability point of view. In this paper, the use of two waste materials, perlite and rhyolite, in concrete mix design was studied in detail, considering their influence on the compressive strength at 7 and 28 days of curing. The waste materials were introduced in the mix design as substitutes for cement in percentages of 15% and 30% in weight. In addition, perlite was micronized to two different particle sizes, 20 μm and 63 μm, respectively, according to what is already used in concrete within perlite in the mix design. The behavior of the structural concrete containing perlite and rhyolite was compared in terms of compressive strength, Young modulus and produced equivalent CO2 with that of a standard C25/30 reference concrete, and with that of a mix design created using other waste materials, namely fly ash, metakaolin and silica fume, considering cement replacements that are always at 15% and 30% by weight. Moreover, ultrasonic testing and rebound hammer tests were run to evaluate a possible relationship between the physical-mechanical properties of the design mixes and their volumetric and surface characteristics. Full article
(This article belongs to the Section Construction Technologies)
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25 pages, 2905 KB  
Article
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
Cited by 9 | Viewed by 3399
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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15 pages, 3259 KB  
Article
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
Cited by 2 | Viewed by 2969
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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18 pages, 6692 KB  
Article
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
Cited by 2 | Viewed by 4189
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 Science and Materials Processing)
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35 pages, 10924 KB  
Article
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
Cited by 13 | Viewed by 2957
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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27 pages, 658 KB  
Systematic 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
Cited by 12 | Viewed by 6830
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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28 pages, 5163 KB  
Article
Design of High-Pass and Low-Pass Active Inverse Filters to Compensate for Distortions in RC-Filtered Electrocardiograms
by Dobromir Dobrev, Tatyana Neycheva, Vessela Krasteva and Irena Jekova
Technologies 2025, 13(4), 159; https://doi.org/10.3390/technologies13040159 - 15 Apr 2025
Cited by 5 | Viewed by 8221
Abstract
Distortions of electrocardiograms (ECGs) caused by mandatory high-pass and low-pass analog RC filters in ECG devices are always present. The fidelity of the ECG waveform requires limiting the RC cutoff frequencies of the diagnostic (0.05–150 Hz) and monitoring systems (0.5–40 Hz). However, the [...] Read more.
Distortions of electrocardiograms (ECGs) caused by mandatory high-pass and low-pass analog RC filters in ECG devices are always present. The fidelity of the ECG waveform requires limiting the RC cutoff frequencies of the diagnostic (0.05–150 Hz) and monitoring systems (0.5–40 Hz). However, the use of fixed frequency bands is a compromise between enhanced noise immunity and ECG distortions. This study aims to propose active inverse high-pass and low-pass filters which are able to compensate for distortions in digital recordings of RC-filtered ECGs, thereby overcoming the limitations imposed by analog filtering. A new straightforward design of an inverse high-pass filter (IHPF) uses an integrator as the forward-path gain block, with a feedback loop containing an active digital filter equivalent to the analog RC high-pass filter. In contrast, the inverse low-pass filter (ILPF) employs a constant-gain block in the forward path to ensure stability and prevent phase delay, while its feedback path features an active digital counterpart of the RC low-pass filter. Second-order inverse filters are created by cascading two first-order stages. The proposed filters were validated according to essential performance requirements for electrocardiographs. The low-frequency (impulse) responses of IHPFs with cutoff frequencies of 0.05–5 Hz exhibit no overshoot and undershoot by magnitudes of 0.1–25 µV, well within the ±100 µV compliance limit defined for a test rectangular pulse (3 mV, 100 ms). The high-frequency responses of ILPFs with cutoff frequencies of 10–150 Hz present a relative amplitude drop of only 0.2–2.5%, far below the 10% limit for peak amplitude reduction of a triangular pulse (1.5 mV) with 20 ms vs. 200 ms widths. For any of the eight ECG leads (I, II, and V1–V6) available in the standard signal (ANE20000), the IHPF (0.05–5 Hz) presents ST-segment deviations <5 μV (within the ±25 μV limit) and R- and S-peak deviations <±3.5% (within the ±5% limit). The ILPF (10–150 Hz) preserves R- and S-peak amplitudes with deviations less than −1%. Diagnostic-level recovery of ECG waveforms distorted by first- and second-order analog RC filters in ECG devices is possible with the innovative and comprehensive inverse filter design presented in this study. This approach offers a significant advancement in ECG signal processing, effectively restoring essential waveform components even after aggressive, noise-robust analog filtering in ECG acquisition circuits. Although validated for ECG signals, the proposed inverse filters are also applicable to other biosignal front-end circuits employing RC coupling. Full article
(This article belongs to the Special Issue Digital Data Processing Technologies: Trends and Innovations)
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32 pages, 12463 KB  
Article
Neuro-Visual Adaptive Control for Precision in Robot-Assisted Surgery
by Claudio Urrea, Yainet Garcia-Garcia, John Kern and Reinier Rodriguez-Guillen
Technologies 2025, 13(4), 135; https://doi.org/10.3390/technologies13040135 - 1 Apr 2025
Cited by 6 | Viewed by 3267
Abstract
This study introduces a Neuro-Visual Adaptive Control (NVAC) architecture designed to enhance precision and safety in robot-assisted surgery. The proposed system enables semi-autonomous guidance of the laparoscope based on image input. To achieve this, the architecture integrates the following: (1) a computer vision [...] Read more.
This study introduces a Neuro-Visual Adaptive Control (NVAC) architecture designed to enhance precision and safety in robot-assisted surgery. The proposed system enables semi-autonomous guidance of the laparoscope based on image input. To achieve this, the architecture integrates the following: (1) a computer vision system based on the YOLO11n model, which detects surgical instruments in real time; (2) a Model Reference Adaptive Control with Proportional–Derivative terms (MRAC-PD), which adjusts the robot’s behavior in response to environmental changes; and (3) Closed-Form Continuous-Time Neural Networks (CfC-mmRNNs), which efficiently model the system’s dynamics. These networks address common deep learning challenges, such as the vanishing gradient problem, and facilitate the generation of smooth control signals that minimize wear on the robot’s actuators. Performance evaluations were conducted in CoppeliaSim, utilizing real cholecystectomy images featuring surgical tools. Experimental results demonstrate that the NVAC achieves maximum tracking errors of 1.80 × 10−3 m, 1.08 × 10−4 m, and 1.90 × 10−3 m along the x, y, and z axes, respectively, under highly significant dynamic disturbances. This hybrid approach provides a scalable framework for advancing autonomy in robotic surgery. Full article
(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives)
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32 pages, 1004 KB  
Article
Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
by Mfonobong Uko, Sunday Ekpo, Sunday Enahoro, Fanuel Elias, Rahul Unnikrishnan and Yasir Al-Yasir
Technologies 2025, 13(4), 124; https://doi.org/10.3390/technologies13040124 - 22 Mar 2025
Cited by 6 | Viewed by 3483
Abstract
The deployment of fifth-generation (5G) and beyond-5G wireless communication systems necessitates advanced transceiver architectures to support high data rates, spectrum efficiency, and energy-efficient designs. This paper presents a highly adaptive reconfigurable receiver front-end (HARRF) designed for 5G and satellite applications, integrating a switchable [...] Read more.
The deployment of fifth-generation (5G) and beyond-5G wireless communication systems necessitates advanced transceiver architectures to support high data rates, spectrum efficiency, and energy-efficient designs. This paper presents a highly adaptive reconfigurable receiver front-end (HARRF) designed for 5G and satellite applications, integrating a switchable low noise amplifier (LNA) and a single pole double throw (SPDT) switch. The HARRF architecture supports both X-band (8–12 GHz) and K/Ka-band (23–28 GHz) operations, enabling seamless adaptation between radar, satellite communication, and millimeter-wave (mmWave) 5G applications. The proposed receiver front-end employs a 0.15 μm pseudomorphic high electron mobility transistor (pHEMT) process, optimised through a three-stage cascaded LNA topology. A switched-tuned matching network is utilised to achieve reconfigurability between X-band and K/Ka-band. Performance evaluations indicate that the X-band LNA achieves a gain of 23–27 dB with a noise figure below 7 dB, whereas the K/Ka-band LNA provides 23–27 dB gain with a noise figure ranging from 2.3–2.6 dB. The SPDT switch exhibits low insertion loss and high isolation, ensuring minimal signal degradation across operational bands. Network analysis and scattering parameter extractions were conducted using advanced design system (ADS) simulations, demonstrating superior return loss, power efficiency, and impedance matching. Comparative analysis with state-of-the-art designs shows that the proposed HARRF outperforms existing solutions in terms of reconfigurability, stability, and wideband operation. The results validate the feasibility of the proposed reconfigurable RF front-end in enabling efficient spectrum utilisation and energy-efficient transceiver systems for next-generation communication networks. Full article
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18 pages, 5092 KB  
Article
Predicting the Temperature of a Permanent Magnet Synchronous Motor: A Comparative Study of Artificial Neural Network Algorithms
by Nabil El Bazi, Nasr Guennouni, Mohcin Mekhfioui, Adil Goudzi, Ahmed Chebak and Mustapha Mabrouki
Technologies 2025, 13(3), 120; https://doi.org/10.3390/technologies13030120 - 17 Mar 2025
Cited by 13 | Viewed by 3290
Abstract
The accurate prediction of temperature in Permanent Magnet Synchronous Motors (PMSMs) has always been essential for monitoring performance and enabling predictive maintenance in the industrial sector. This study examines the efficiency of a set of artificial neural network (ANN) models, namely Multilayer Perceptron [...] Read more.
The accurate prediction of temperature in Permanent Magnet Synchronous Motors (PMSMs) has always been essential for monitoring performance and enabling predictive maintenance in the industrial sector. This study examines the efficiency of a set of artificial neural network (ANN) models, namely Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), in predicting the Permanent Magnet Temperature. A comparative evaluation study is conducted using common performance indicators, including root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), to assess the predictive accuracy of each model. The intent is to identify the most favorable model that balances high accuracy with low computational cost. Full article
(This article belongs to the Special Issue Next-Generation Distribution System Planning, Operation, and Control)
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26 pages, 5752 KB  
Review
Towards a Holistic Approach for UAV-Based Large-Scale Photovoltaic Inspection: A Review on Deep Learning and Image Processing Techniques
by Zoubir Barraz, Imane Sebari, Kenza Ait El Kadi and Ibtihal Ait Abdelmoula
Technologies 2025, 13(3), 117; https://doi.org/10.3390/technologies13030117 - 14 Mar 2025
Cited by 12 | Viewed by 4817
Abstract
This paper provides an in-depth literature review on image processing techniques, focusing on deep learning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based PV inspection, including data acquisition protocols, panel segmentation and geolocation, anomaly classification, and optimizations [...] Read more.
This paper provides an in-depth literature review on image processing techniques, focusing on deep learning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based PV inspection, including data acquisition protocols, panel segmentation and geolocation, anomaly classification, and optimizations for model generalization. Furthermore, challenges related to domain adaptation, dataset limitations, and multimodal fusion of RGB and thermal data are also discussed. Finally, research gaps and opportunities are analyzed to create a holistic, scalable, and real-time inspection workflow for large-scale installation. This review serves as a reference for researchers and industry professionals to advance UAV-based PV inspection. Full article
(This article belongs to the Section Environmental Technology)
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29 pages, 4181 KB  
Review
Using Serious Games and Digital Games to Improve Students’ Computational Thinking and Programming Skills in K-12 Education: A Systematic Literature Review
by Sindre Wennevold Gundersen and Georgios Lampropoulos
Technologies 2025, 13(3), 113; https://doi.org/10.3390/technologies13030113 - 11 Mar 2025
Cited by 18 | Viewed by 13419
Abstract
Computational thinking and problem-solving skills have become vital for students to develop. Digital games and serious games are increasingly being used in educational settings and present great potential to aid students’ learning. This study aims to explore the role and impact of serious [...] Read more.
Computational thinking and problem-solving skills have become vital for students to develop. Digital games and serious games are increasingly being used in educational settings and present great potential to aid students’ learning. This study aims to explore the role and impact of serious games and digital games on students’ computational thinking and programming skills in primary, secondary, and K-12 education through a systematic review of the existing literature. Four research questions were set to be examined. Following the PRISMA framework, 78 studies deriving from IEEE, Scopus, and Web of Science over the period of 2011–2024 are examined. The studies are categorized into Theoretical and Review studies, Proposal and Showcase studies, and Experimental and Case studies. Based on the results, serious games and digital games arose as meaningful educational tools that are positively viewed by education stakeholders and that can effectively support and improve K-12 education students’ computational thinking and programming skills. Among the benefits identified, it was revealed that serious games offer enjoyable and interactive learning experiences that can improve students’ learning performance, engagement, and motivation, enhance students’ confidence and focus, and promote self-regulated learning and personalized learning. Additionally, serious games emerged as an educational means that can effectively support social learning and provide real-time feedback. The challenges identified were related to the selection of games and the game-related design elements, decisions, and approaches. Hence, the study highlights the significance of the design of serious games and the need to cultivate students’ computational thinking, problem-solving, and social skills from a young age. Finally, the study reveals key design principles and aspects to consider when developing serious games and digital games and highlights the need to involve education stakeholders throughout the design and development process. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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40 pages, 3792 KB  
Review
Recent Development of Corrosion Inhibitors: Types, Mechanisms, Electrochemical Behavior, Efficiency, and Environmental Impact
by Denisa-Ioana (Gheorghe) Răuță, Ecaterina Matei and Sorin-Marius Avramescu
Technologies 2025, 13(3), 103; https://doi.org/10.3390/technologies13030103 - 5 Mar 2025
Cited by 97 | Viewed by 29513
Abstract
This review examines recent advances in corrosion inhibitor technologies, with a focus on sustainable and environmentally friendly solutions that address both industrial efficiency and environmental safety. Corrosion is a ubiquitous problem, contributing to massive economic losses globally, with costs estimated between 1 and [...] Read more.
This review examines recent advances in corrosion inhibitor technologies, with a focus on sustainable and environmentally friendly solutions that address both industrial efficiency and environmental safety. Corrosion is a ubiquitous problem, contributing to massive economic losses globally, with costs estimated between 1 and 5% of GDP in different countries. Traditional inorganic corrosion inhibitors, while effective, are often based on toxic compounds, necessitating the development of more environmentally friendly and non-toxic alternatives. The present work highlights innovative eco-friendly corrosion inhibitors derived from natural sources, including plant extracts and oils, biopolymers, etc., being biodegradable substances that provide effective corrosion resistance with minimal environmental impact. In addition, this review explores organic–inorganic hybrid inhibitors and nanotechnology-enhanced coatings that demonstrate improved efficiency, durability, and adaptability across industries. Key considerations, such as application techniques, mechanisms of action, and the impact of environmental factors on inhibitor performance, are discussed. This comprehensive presentation aims to contribute to updating the data on the development of advanced corrosion inhibitors capable of meeting the requirements of modern industries while promoting sustainable and safe practices in corrosion management. Full article
(This article belongs to the Section Environmental Technology)
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20 pages, 2678 KB  
Article
Low-Temperature Slow Pyrolysis: Exploring Biomass-Specific Biochar Characteristics and Potential for Soil Applications
by Matheus Antonio da Silva, Adibe Luiz Abdalla Filho, Ruan Carnier, Juliana de Oliveira Santos Marcatto, Marcelo Saldanha, Aline Renee Coscione, Thaís Alves de Carvalho, Gabriel Rodrigo Merlotto and Cristiano Alberto de Andrade
Technologies 2025, 13(3), 100; https://doi.org/10.3390/technologies13030100 - 3 Mar 2025
Cited by 9 | Viewed by 5512
Abstract
The pyrolysis process of residues has emerged as a sustainable method for managing organic waste, producing biochars that offer significant benefits for agriculture and the environment. These benefits depend on the properties of the raw biomass and the pyrolysis conditions, such as washing [...] Read more.
The pyrolysis process of residues has emerged as a sustainable method for managing organic waste, producing biochars that offer significant benefits for agriculture and the environment. These benefits depend on the properties of the raw biomass and the pyrolysis conditions, such as washing and drying. This study investigated biochar production through slow pyrolysis at 300 °C, using eight biomass types, four being plant residues (PBR)—sugarcane bagasse, filter cake, sawdust, and stranded algae—and four non-plant-based residues (NPBR)—poultry litter, sheep manure, layer chicken manure, and sewage sludge. The physicochemical properties assessed included yield, carbon (C) and nitrogen (N) content, electrical conductivity, pH, macro- and micronutrients, and potentially toxic metals. Pyrolysis generally increased pH and concentrated C, N, phosphorus (P), and other nutrients while reducing electrical conductivity, C/N ratio, potassium (K), and sulfur (S) contents. The increases in the pH of the biochars in relation to the respective biomasses were between 0.3 and 1.9, with the greatest differences observed for the NPBR biochars. Biochars from sugarcane bagasse and sawdust exhibited high C content (74.57–77.67%), highlighting their potential use for C sequestration. Filter cake biochar excelled in P (14.28 g kg⁻1) and micronutrients, while algae biochar showed elevated N, calcium (Ca), and boron (B) levels. NPBR biochars were rich in N (2.28–3.67%) and P (20.7–43.4 g kg⁻1), making them ideal fertilizers. Although sewage sludge biochar contained higher levels of potentially toxic metals, these remained within regulatory limits. This research highlights variations in the composition of biochars depending on the characteristics of the original biomass and the pyrolysis process, to contribute to the production of customized biochars for the purposes of their application in the soil. Biochars derived from exclusively plant biomasses showed important aspects related to the recovery of carbon from biomass and can be preferred as biochar used to sequester carbon in the soil. On the other hand, biochars obtained from residues with some animal contributions are more enriched in nutrients and should be directed to the management of soil fertility. Full article
(This article belongs to the Special Issue Recent Advances in Applied Activated Carbon Research)
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26 pages, 3719 KB  
Article
Design of Multi-Sourced MIMO Multiband Hybrid Wireless RF-Perovskite Photovoltaic Energy Harvesting Subsystems for IoTs Applications in Smart Cities
by Fanuel Elias, Sunday Ekpo, Stephen Alabi, Mfonobong Uko, Sunday Enahoro, Muhammad Ijaz, Helen Ji, Rahul Unnikrishnan and Nurudeen Olasunkanmi
Technologies 2025, 13(3), 92; https://doi.org/10.3390/technologies13030092 - 1 Mar 2025
Cited by 17 | Viewed by 3910
Abstract
Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband [...] Read more.
Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband hybrid wireless RF-perovskite photovoltaic energy harvesting subsystems for IoT application. The research findings evaluate the efficiency and power output of different RF configurations (1 to 16 antennas) within MIMO RF subsystems. A Delon quadruple rectifier in the RF energy harvesting system demonstrates a system-level power conversion efficiency of 51%. The research also explores the I-V and P-V characteristics of the adopted perovskite tandem cell. This results in an impressive array capable of producing 6.4 V and generating a maximum power of 650 mW. For the first time, the combined mathematical modelling of the system architecture is presented. The achieved efficiency of the combined system is 90% (for 8 MIMO) and 98% (for 16 MIMO) at 0 dBm input RF power. This novel study holds great promise for next-generation 5G/6G smart IoT passive electronics. Additionally, it establishes the hybrid RF-perovskite energy harvester as a promising, compact, and eco-friendly solution for efficiently powering IoT devices in smart cities. This work contributes to the development of sustainable, scalable, and smart energy solutions for IoT integration into smart city infrastructures. Full article
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18 pages, 9690 KB  
Article
Reducing Energy Consumption in Embedded Systems Applications
by Ioannis Sofianidis, Vasileios Konstantakos and Spyridon Nikolaidis
Technologies 2025, 13(2), 82; https://doi.org/10.3390/technologies13020082 - 16 Feb 2025
Cited by 11 | Viewed by 5721
Abstract
One of the most important challenges in modern digital systems, especially regarding autonomous embedded systems, is energy efficiency. This work studies an energy consumption optimization approach on a microcontroller that implements IoT-like applications, featuring Dynamic Voltage and Frequency Scaling (DVFS) capabilities, by dynamically [...] Read more.
One of the most important challenges in modern digital systems, especially regarding autonomous embedded systems, is energy efficiency. This work studies an energy consumption optimization approach on a microcontroller that implements IoT-like applications, featuring Dynamic Voltage and Frequency Scaling (DVFS) capabilities, by dynamically changing the supply voltage and clock frequency. The proposed approach categorizes tasks according to their demands on timing requirements and analyzes speed–energy efficiency trade-offs. Results strongly indicate that energy performance is improved due to the proper adjustment of configurations towards required tasks. The findings are verified within a set of scenarios that highlight the potential balance between energy economy and operational demands for specialized IoT contexts. Full article
(This article belongs to the Collection Modern Circuits, System Technologies, and Selected Papers from MOCAST)
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28 pages, 12512 KB  
Article
The Design, Simulation, and Construction of an O2, C3H8, and CO2 Gas Detection System Based on the Electrical Response of MgSb2O6 Oxide
by José Trinidad Guillen Bonilla, Maricela Jiménez Rodríguez, Héctor Guillen Bonilla, Alex Guillen Bonilla, Emilio Huízar Padilla, María Eugenia Sánchez Morales, Ariadna Berenice Flores Jiménez and Juan Carlos Estrada Gutiérrez
Technologies 2025, 13(2), 79; https://doi.org/10.3390/technologies13020079 - 13 Feb 2025
Cited by 5 | Viewed by 2615
Abstract
In this paper, the prototype of a gas detector based on the electrical response of MgSb2O6 oxide at 400 °C and with a concentration of 560 ppm was designed, simulated, and fabricated. This design considers a PIC18F4550 microcontroller and a [...] Read more.
In this paper, the prototype of a gas detector based on the electrical response of MgSb2O6 oxide at 400 °C and with a concentration of 560 ppm was designed, simulated, and fabricated. This design considers a PIC18F4550 microcontroller and a response time of 3 s for the sensor. It is worth noting that the response system can be reduced in concordance with the mathematical model of the sensor’s electrical response. The proposed device is capable of detecting one to three gases: O2, C3H8, and CO2. The configuration is achieved through three switches. In programming the prototype, factors such as the gas sensor signals, device configuration, corrective gas signals, and indicator signals were carefully considered. The characteristic of the gas detector is an operational temperature of 400 °C, which is ideal for industrial processing. This can be configured to detect a single gas or all three of them O2,C3H8,and CO2. Each gas type has its corresponding corrective signal and an indicator-led diode. The operation concentration is 560 ppm, the device is scalable, and its programming can be extended to cover industrial networks. Full article
(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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22 pages, 14692 KB  
Review
A Systematic Review of Locomotion Assistance Exoskeletons: Prototype Development and Technical Challenges
by Weiqi Lin, Hui Dong, Yongzhuo Gao, Wenda Wang, Yi Long, Long He, Xiwang Mao, Dongmei Wu and Wei Dong
Technologies 2025, 13(2), 69; https://doi.org/10.3390/technologies13020069 - 5 Feb 2025
Cited by 22 | Viewed by 12635
Abstract
Exoskeletons can track the wearer’s movements in real time, thereby enhancing physical performance or restoring mobility for individuals with gait impairments. These wearable assistive devices have demonstrated significant potential in both rehabilitation and industrial applications. This review focuses on the major advancements in [...] Read more.
Exoskeletons can track the wearer’s movements in real time, thereby enhancing physical performance or restoring mobility for individuals with gait impairments. These wearable assistive devices have demonstrated significant potential in both rehabilitation and industrial applications. This review focuses on the major advancements in exoskeleton technology published since 2020, with particular emphasis on the development of structural designs for lower-limb exoskeletons employed in locomotion assistance. We employed a systematic literature review methodology, categorizing the included studies into three main types: rigid exoskeleton, soft exoskeleton, and tethered platform. The current development status of robotic exoskeletons is analyzed based on publication year, system weight, target assistive joints, and main effects. Furthermore, we examine the factors driving these advancements and their implications for the field. The key challenges and opportunities that may influence the future development of exoskeleton technologies are also highlighted in this review. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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51 pages, 26899 KB  
Review
Robotic Systems for Hand Rehabilitation—Past, Present and Future
by Bogdan Gherman, Ionut Zima, Calin Vaida, Paul Tucan, Adrian Pisla, Iosif Birlescu, Jose Machado and Doina Pisla
Technologies 2025, 13(1), 37; https://doi.org/10.3390/technologies13010037 - 16 Jan 2025
Cited by 27 | Viewed by 17872
Abstract
Background: Cerebrovascular accident, commonly known as stroke, Parkinson’s disease, and multiple sclerosis represent significant neurological conditions affecting millions globally. Stroke remains the third leading cause of death worldwide and significantly impacts patients’ hand functionality, making hand rehabilitation crucial for improving quality of life. [...] Read more.
Background: Cerebrovascular accident, commonly known as stroke, Parkinson’s disease, and multiple sclerosis represent significant neurological conditions affecting millions globally. Stroke remains the third leading cause of death worldwide and significantly impacts patients’ hand functionality, making hand rehabilitation crucial for improving quality of life. Methods: A comprehensive literature review was conducted analyzing over 300 papers, and categorizing them based on mechanical design, mobility, and actuation systems. To evaluate each device, a database with 45 distinct criteria was developed to systematically assess their characteristics. Results: The analysis revealed three main categories of devices: rigid exoskeletons, soft exoskeletons, and hybrid devices. Electric actuation represents the most common source of power. The dorsal placement of the mechanism is predominant, followed by glove-based, lateral, and palmar configurations. A correlation between mass and functionality was observed during the analysis; an increase in the number of actuated fingers or in functionality automatically increases the mass of the device. The research shows significant technological evolution with considerable variation in design complexity, with 29.4% of devices using five or more actuators while 24.8% employ one or two actuators. Conclusions: While substantial progress has been made in recent years, several challenges persist, including missing information or incomplete data from source papers and a limited number of clinical studies to evaluate device effectiveness. Significant opportunities remain to improve device functionality, usability, and therapeutic effectiveness, as well as to implement advanced power systems for portable devices. Full article
(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives)
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22 pages, 6268 KB  
Article
Real-Time Deployment of Ultrasound Image Interpretation AI Models for Emergency Medicine Triage Using a Swine Model
by Sofia I. Hernandez Torres, Lawrence Holland, Theodore Winter, Ryan Ortiz, Krysta-Lynn Amezcua, Austin Ruiz, Catherine R. Thorpe and Eric J. Snider
Technologies 2025, 13(1), 29; https://doi.org/10.3390/technologies13010029 - 11 Jan 2025
Cited by 10 | Viewed by 6527
Abstract
Ultrasound imaging is commonly used for medical triage in both civilian and military emergency medicine sectors. One specific application is the eFAST, or the extended focused assessment with sonography in trauma exam, where pneumothorax, hemothorax, or abdominal hemorrhage injuries are identified. However, the [...] Read more.
Ultrasound imaging is commonly used for medical triage in both civilian and military emergency medicine sectors. One specific application is the eFAST, or the extended focused assessment with sonography in trauma exam, where pneumothorax, hemothorax, or abdominal hemorrhage injuries are identified. However, the diagnostic accuracy of an eFAST exam depends on obtaining proper scans and making quick interpretation decisions to evacuate casualties or administer necessary interventions. To improve ultrasound interpretation, we developed AI models to identify key anatomical structures at eFAST scan sites, simplifying image acquisition by assisting with proper probe placement. These models plus image interpretation diagnostic models were paired with two real-time eFAST implementations. The first implementation was a manual AI-driven ultrasound eFAST tool that used guidance models to select correct frames prior to making any diagnostic predictions. The second implementation was a robotic imaging platform capable of providing semi-autonomous image acquisition combined with diagnostic image interpretation. We highlight the use of both real-time approaches in a swine injury model and compare their performance of this emergency medicine application. In conclusion, AI can be deployed in real time to provide rapid triage decisions, lowering the skill threshold for ultrasound imaging at or near the point of injury. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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45 pages, 7034 KB  
Review
A Review of Fused Filament Fabrication of Metal Parts (Metal FFF): Current Developments and Future Challenges
by Johnson Jacob, Dejana Pejak Simunec, Ahmad E. Z. Kandjani, Adrian Trinchi and Antonella Sola
Technologies 2024, 12(12), 267; https://doi.org/10.3390/technologies12120267 - 19 Dec 2024
Cited by 59 | Viewed by 14116
Abstract
Fused filament fabrication (FFF) is the most widespread and versatile material extrusion (MEX) technique. Although powder-based systems have dominated the metal 3D printing landscape in the past, FFF’s popularity for producing metal parts (“metal FFF”) is growing. Metal FFF starts from a polymer–metal [...] Read more.
Fused filament fabrication (FFF) is the most widespread and versatile material extrusion (MEX) technique. Although powder-based systems have dominated the metal 3D printing landscape in the past, FFF’s popularity for producing metal parts (“metal FFF”) is growing. Metal FFF starts from a polymer–metal composite feedstock and proceeds through three primary stages, namely shaping (i.e., printing), debinding, and sintering. As critically discussed in the present review, the final quality of metal FFF parts is influenced by the characteristics of the composite feedstock, such as the metal loading, polymer backbone, and presence of additives, as well as by the processing conditions. The literature shows that a diverse array of metals, including steel, copper, titanium, aluminium, nickel, and their alloys, can be successfully used in metal FFF. However, the formulation of appropriate polymer binders represents a hurdle to the adoption of new material systems. Meanwhile, intricate geometries are difficult to fabricate due to FFF-related surface roughness and sintering-induced shrinkage. Nonetheless, the comparison of metal FFF with other common metal AM techniques conducted herein suggests that metal FFF represents a convenient option, especially for prototyping and small-scale production. Whilst providing insights into the functioning mechanisms of metal FFF, the present review offers valuable recommendations, facilitating the broader uptake of metal FFF across various industries. Full article
(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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29 pages, 6302 KB  
Review
Impact of 3D Digitising Technologies and Their Implementation
by Paula Triviño-Tarradas, Diego Francisco García-Molina and José Ignacio Rojas-Sola
Technologies 2024, 12(12), 260; https://doi.org/10.3390/technologies12120260 - 14 Dec 2024
Cited by 9 | Viewed by 4928
Abstract
In recent years, 3D digitalisation has experienced significant growth, revolutionising the way we capture, process and use geometric data. Initially conceived for industrial applications, these technologies have expanded to multiple fields, offering unprecedented accuracy and versatility. Depending on the accuracy and efficiency to [...] Read more.
In recent years, 3D digitalisation has experienced significant growth, revolutionising the way we capture, process and use geometric data. Initially conceived for industrial applications, these technologies have expanded to multiple fields, offering unprecedented accuracy and versatility. Depending on the accuracy and efficiency to be achieved in a specific field of application, and on the analytical capacity, a specific 3D digitalisation technique or another will be used. This review aims to delve into the application of 3D scanning techniques, according to the implementation sector. The optimal geometry capturing and processing 3D data techniques for a specific case are studied as well as their limitations. Full article
(This article belongs to the Section Assistive Technologies)
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22 pages, 13321 KB  
Article
Particle Movement in DEM Models and Artificial Neural Network for Validation by Using Contrast Points
by Barbora Černilová, Jiří Kuře, Rostislav Chotěborský and Miloslav Linda
Technologies 2024, 12(12), 257; https://doi.org/10.3390/technologies12120257 - 12 Dec 2024
Cited by 5 | Viewed by 3137
Abstract
The calibration and validation of input parameters in the Discrete Element Method (DEM) are crucial for accurately simulating physical processes, typically achieved through experimental particle behavior analysis. Enhancing the accuracy of DEM models allows for more reliable predictions of material behavior, which is [...] Read more.
The calibration and validation of input parameters in the Discrete Element Method (DEM) are crucial for accurately simulating physical processes, typically achieved through experimental particle behavior analysis. Enhancing the accuracy of DEM models allows for more reliable predictions of material behavior, which is essential for optimizing engineering applications that involve particulate materials. In this study, we present a methodology for analyzing the movement properties of particulate materials, employing a combination of Caliscope software to obtain the real-world co-ordinates based on pixel values from both cameras and artificial neural networks for regression as straightforward and efficient tools. This approach enables the validation and calibration of digital twins of particulate matter systems with respect to motion characteristics. The method of contrast points was utilized to acquire spatial co-ordinates of particulate material movement from experimental measurements, facilitating precise trajectory determination and the subsequent verification of simulation predictions. The neural network analysis demonstrated high accuracy, achieving R2 values of 0.9988, 0.9972, and 0.9982 for the X–, Y–, and Z–axes, respectively. The standard deviation between the predicted and actual co-ordinates was found to be 1.8 mm. A comparative analysis of particle trajectories from both the model and experimental data indicated strong agreement, underscoring the soundness and reliability of this approach. Full article
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21 pages, 1503 KB  
Article
Deep Neural Networks for Estimating Regularization Parameter in Sparse Time–Frequency Reconstruction
by Vedran Jurdana
Technologies 2024, 12(12), 251; https://doi.org/10.3390/technologies12120251 - 1 Dec 2024
Cited by 4 | Viewed by 4477
Abstract
Time–frequency distributions (TFDs) are crucial for analyzing non-stationary signals. Compressive sensing (CS) in the ambiguity domain offers an approach for TFD reconstruction with high performance, but selecting the optimal regularization parameter for various signals remains challenging. Traditional methods for parameter selection, including manual [...] Read more.
Time–frequency distributions (TFDs) are crucial for analyzing non-stationary signals. Compressive sensing (CS) in the ambiguity domain offers an approach for TFD reconstruction with high performance, but selecting the optimal regularization parameter for various signals remains challenging. Traditional methods for parameter selection, including manual and experimental approaches, as well as existing optimization procedures, can be imprecise and time-consuming. This study introduces a novel approach using deep neural networks (DNNs) to predict regularization parameters based on Wigner–Ville distributions (WVDs). The proposed DNN is trained on a comprehensive dataset of synthetic signals featuring multiple linear and quadratic frequency-modulated components, with variations in component amplitudes and random positions, ensuring wide applicability and robustness. By utilizing DNNs, end-users need only provide the signal’s WVD, eliminating the need for manual parameter selection and lengthy optimization procedures. Comparisons between the reconstructed TFDs using the proposed DNN-based approach and existing optimization methods highlight significant improvements in both reconstruction performance and execution time. The effectiveness of this methodology is validated on noisy synthetic and real-world signals, emphasizing the potential of DNNs to automate regularization parameter determination for CS-based TFD reconstruction in diverse signal environments. Full article
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12 pages, 4513 KB  
Article
Malaria Cell Image Classification Using Compact Deep Learning Architectures on Jetson TX2
by Adán-Antonio Alonso-Ramírez, Alejandro-Israel Barranco-Gutiérrez, Iris-Iddaly Méndez-Gurrola, Marcos Gutiérrez-López, Juan Prado-Olivarez, Francisco-Javier Pérez-Pinal, J. Jesús Villegas-Saucillo, Jorge-Alberto García-Muñoz and Carlos-Hugo García-Capulín
Technologies 2024, 12(12), 247; https://doi.org/10.3390/technologies12120247 - 27 Nov 2024
Cited by 6 | Viewed by 4858
Abstract
Malaria is a significant global health issue, especially in tropical regions. Accurate and rapid diagnosis is critical for effective treatment and reducing mortality rates. Traditional diagnostic methods, like blood smear microscopy, are time-intensive and prone to error. This study introduces a deep learning [...] Read more.
Malaria is a significant global health issue, especially in tropical regions. Accurate and rapid diagnosis is critical for effective treatment and reducing mortality rates. Traditional diagnostic methods, like blood smear microscopy, are time-intensive and prone to error. This study introduces a deep learning approach for classifying malaria-infected cells in blood smear images using convolutional neural networks (CNNs); Six CNN models were designed and trained using a large labeled dataset of malaria cell images, both infected and uninfected, and were implemented on the Jetson TX2 board to evaluate them. The model was optimized for feature extraction and classification accuracy, achieving 97.72% accuracy, and evaluated using precision, recall, and F1-score metrics and execution time. Results indicate deep learning significantly improves diagnostic time efficiency on embedded systems. This scalable, automated solution is particularly useful in resource-limited areas without access to expert microscopic analysis. Future work will focus on clinical validation. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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18 pages, 5773 KB  
Article
Isolated High-Gain DC-DC Converter with Nanocrystalline-Core Transformer: Achieving 1:16 Voltage Boost for Renewable Energy Applications
by Tania Sandoval-Valencia, Dante Ruiz-Robles, Jorge Ortíz-Marín, Jesus Alejandro Franco, Quetzalcoatl Hernandez-Escobedo and Edgar Moreno-Goytia
Technologies 2024, 12(12), 246; https://doi.org/10.3390/technologies12120246 - 27 Nov 2024
Cited by 3 | Viewed by 3352
Abstract
This paper presents an isolated DC-DC converter with high voltage gain that features an advanced inter-built nanocrystalline-core medium-frequency transformer (NC-MFT). The isolated DC-DC converter with an NC-MFT is specifically designed for applications such as interconnect photovoltaic (PV) systems, DC microgrids, DC loads, and [...] Read more.
This paper presents an isolated DC-DC converter with high voltage gain that features an advanced inter-built nanocrystalline-core medium-frequency transformer (NC-MFT). The isolated DC-DC converter with an NC-MFT is specifically designed for applications such as interconnect photovoltaic (PV) systems, DC microgrids, DC loads, and DC buses, where voltage gain is one of the essential issues to consider. The NC-MFT inside the DC-DC converter is designed with a new approach that not only provides isolation but also contributes to achieving high efficiency and a higher step-up ratio. The high efficiency of the converters contributes to the integration of PV systems into DC microgrids. The converter yields a high voltage conversion ratio of 16.17. The experimental results obtained at 41.8 V/676 V and 275 W for the prototype revealed high efficiency (95.63% at full load). The experimental results validate the theoretical analysis and simulation, confirming that the converter achieves the main objective of high voltage conversion and high efficiency. These results will contribute to the interest in the use of this type of energy and its impact on the reduction in CO2 emissions. Full article
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29 pages, 2679 KB  
Article
Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques
by Claudio Urrea and Carlos Domínguez
Technologies 2024, 12(11), 225; https://doi.org/10.3390/technologies12110225 - 8 Nov 2024
Cited by 9 | Viewed by 3469
Abstract
This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, [...] Read more.
This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications. Full article
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20 pages, 2669 KB  
Review
Exploring Silica Nanoparticles: A Sustainable Solution for Pest Control in Sri Lankan Rice Farming
by Zeyu Wang, Nirusha Thavarajah and Xavier Fernando
Technologies 2024, 12(11), 210; https://doi.org/10.3390/technologies12110210 - 23 Oct 2024
Cited by 4 | Viewed by 6435
Abstract
Rice cultivation stands as a cornerstone of Sri Lanka’s economy, serving as a vital source of employment for rural communities. However, the constraints of limited land availability have prompted an escalating dependence on agrochemicals, notably for pest management, thereby posing significant threats to [...] Read more.
Rice cultivation stands as a cornerstone of Sri Lanka’s economy, serving as a vital source of employment for rural communities. However, the constraints of limited land availability have prompted an escalating dependence on agrochemicals, notably for pest management, thereby posing significant threats to human health and the environment. This review delves into the exploration of silica nanoparticles as a promising eco-friendly substitute for conventional pesticides in the context of Sri Lankan rice farming. It comprehensively examines various aspects, including the synthesis methods of silica nanoparticles, their encapsulation with synthetic pesticides, and an evaluation of their efficacy in pest control. Furthermore, it sheds light on the innovative utilization of agricultural waste such as rice husk and straw in the production of silica-based nanopesticides. This approach not only demonstrates a shift towards sustainable agricultural practices but also aligns with the principles of green chemistry and circular economy, offering a holistic solution to the challenges faced by the rice farming sector in Sri Lanka. Full article
(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2024)
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20 pages, 4236 KB  
Article
Enhancing Autonomous Visual Perception in Challenging Environments: Bilateral Models with Vision Transformer and Multilayer Perceptron for Traversable Area Detection
by Claudio Urrea and Maximiliano Vélez
Technologies 2024, 12(10), 201; https://doi.org/10.3390/technologies12100201 - 17 Oct 2024
Cited by 6 | Viewed by 4167
Abstract
The development of autonomous vehicles has grown significantly recently due to the promise of improving safety and productivity in cities and industries. The scene perception module has benefited from the latest advances in computer vision and deep learning techniques, allowing the creation of [...] Read more.
The development of autonomous vehicles has grown significantly recently due to the promise of improving safety and productivity in cities and industries. The scene perception module has benefited from the latest advances in computer vision and deep learning techniques, allowing the creation of more accurate and efficient models. This study develops and evaluates semantic segmentation models based on a bilateral architecture to enhance the detection of traversable areas for autonomous vehicles on unstructured routes, particularly in datasets where the distinction between the traversable area and the surrounding ground is minimal. The proposed hybrid models combine Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and Multilayer Perceptron (MLP) techniques, achieving a balance between precision and computational efficiency. The results demonstrate that these models outperform the base architectures in prediction accuracy, capturing distant details more effectively while maintaining real-time operational capabilities. Full article
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15 pages, 5547 KB  
Article
Improvement of Sound-Absorbing Wool Material by Laminating Permeable Nonwoven Fabric Sheet and Nonpermeable Membrane
by Shuichi Sakamoto, Kodai Sato and Gaku Muroi
Technologies 2024, 12(10), 195; https://doi.org/10.3390/technologies12100195 - 12 Oct 2024
Cited by 5 | Viewed by 3683
Abstract
Thin sound-absorbing materials are particularly desired in space-constrained applications, such as in the automotive industry. In this study, we theoretically analyzed the structure of relatively thin glass wool or polyester wool laminated with a nonpermeable polyethylene membrane and a permeable nonwoven fabric sheet. [...] Read more.
Thin sound-absorbing materials are particularly desired in space-constrained applications, such as in the automotive industry. In this study, we theoretically analyzed the structure of relatively thin glass wool or polyester wool laminated with a nonpermeable polyethylene membrane and a permeable nonwoven fabric sheet. We also measured and compared the sound-absorption coefficients of these samples between experimental and theoretical values. The sound-absorption coefficient was derived using the transfer matrix method. The Rayleigh model was applied to describe the acoustic behavior of glass wool and nonwoven sheet, while the Miki model was used for polyester wool. Mathematical formulas were employed to model an air layer without damping and a vibrating membrane. These acoustic components were integrated into a transfer matrix framework to calculate the sound-absorption coefficient. The sound-absorption coefficients of glass wool and polyester wool were progressively enhanced by sequentially adding suitable nonwoven fabric and PE membranes. A sample approximately 10 mm thick, featuring permeable and nonpermeable membranes as outer layers of porous sound-absorbing material, achieved a sound-absorption coefficient equivalent to that of a sample occupying 20 mm thickness (10 mm of porous sound-absorbing material with a 10 mm back air layer). Full article
(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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19 pages, 6834 KB  
Article
Advancing Nanopulsed Plasma Bubbles for the Degradation of Organic Pollutants in Water: From Lab to Pilot Scale
by Stauros Meropoulis and Christos A. Aggelopoulos
Technologies 2024, 12(10), 189; https://doi.org/10.3390/technologies12100189 - 3 Oct 2024
Cited by 18 | Viewed by 5243
Abstract
The transition from lab-scale studies to pilot-scale applications is a critical step in advancing water remediation technologies. While laboratory experiments provide valuable insights into the underlying mechanisms and method effectiveness, pilot-scale studies are essential for evaluating their practical feasibility and scalability. This progression [...] Read more.
The transition from lab-scale studies to pilot-scale applications is a critical step in advancing water remediation technologies. While laboratory experiments provide valuable insights into the underlying mechanisms and method effectiveness, pilot-scale studies are essential for evaluating their practical feasibility and scalability. This progression addresses challenges related to operational conditions, effectiveness and energy requirements in real-world scenarios. In this study, the potential of nanopulsed plasma bubbles, when scaled up from a lab environment, was explored by investigating critical experimental parameters, such as plasma gas, pulse voltage, and pulse repetition rate, while also analyzing plasma-treated water composition. To validate the broad effectiveness of this method, various classes of highly toxic organic pollutants were examined in terms of pollutant degradation efficiency and energy requirements. The pilot-scale plasma bubble reactor generated a high concentration of short-lived reactive species with minimal production of long-lived species. Additionally, successful degradation of all pollutants was achieved in both lab- and pilot-scale setups, with even lower electrical energy-per-order (EEO) values at the pilot scale, 2–3 orders of magnitude lower compared to other advanced oxidation processes. This study aimed to bridge the gap between lab-scale plasma bubbles and upscaled systems, supporting the rapid, effective, and energy-efficient destruction of organic pollutants in water. Full article
(This article belongs to the Section Environmental Technology)
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20 pages, 4837 KB  
Article
Optical Particle Tracking in the Pneumatic Conveying of Metal Powders through a Thin Capillary Pipe
by Lorenzo Pedrolli, Luigi Fraccarollo, Beatriz Achiaga and Alejandro Lopez
Technologies 2024, 12(10), 191; https://doi.org/10.3390/technologies12100191 - 3 Oct 2024
Cited by 2 | Viewed by 5783
Abstract
Directed Energy Deposition (DED) processes necessitate a consistent material flow to the melt pool, typically achieved through pneumatic conveying of metal powder via thin pipes. This study aims to record and analyze the multiphase fluid–solid flow. An experimental setup utilizing a high-speed camera [...] Read more.
Directed Energy Deposition (DED) processes necessitate a consistent material flow to the melt pool, typically achieved through pneumatic conveying of metal powder via thin pipes. This study aims to record and analyze the multiphase fluid–solid flow. An experimental setup utilizing a high-speed camera and specialized optics was constructed, and the flow through thin transparent pipes was recorded. The resulting information was analyzed and compared with coupled Computational Fluid Dynamics-Discrete Element Modeling (CFD-DEM) simulations, with special attention to the solids flow fluctuations. The proposed methodology shows a significant improvement in accuracy and reliability over existing approaches, particularly in capturing flow rate fluctuations and particle velocity distributions in small-scale systems. Moreover, it allows for accurately analyzing Particle Size Distribution (PSD) in the same setup. This paper details the experimental design, video analysis using particle tracking, and a novel method for deriving volumetric concentrations and flow rate from flat images. The findings confirm the accuracy of the CFD-DEM simulations and provide insights into the dynamics of pneumatic conveying and individual particle movement, with the potential to improve DED efficiency by reducing variability in material deposition rates. Full article
(This article belongs to the Section Manufacturing Technology)
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23 pages, 3516 KB  
Article
Proposed Modbus Extension Protocol and Real-Time Communication Timing Requirements for Distributed Embedded Systems
by Nicoleta Cristina Găitan, Ionel Zagan and Vasile Gheorghiță Găitan
Technologies 2024, 12(10), 187; https://doi.org/10.3390/technologies12100187 - 2 Oct 2024
Cited by 12 | Viewed by 7020
Abstract
The general evolution of fieldbus systems has been variously affected by both computer electrical engineering and science. First, the main contribution undoubtedly originated from network IT systems, when the Open Systems Interconnection model was presented. This reference model with seven layers was and [...] Read more.
The general evolution of fieldbus systems has been variously affected by both computer electrical engineering and science. First, the main contribution undoubtedly originated from network IT systems, when the Open Systems Interconnection model was presented. This reference model with seven layers was and remains the foundation for the development of numerous advanced communication protocols. In this paper, the conducted research resulted in a major contribution; specifically, it describes the mathematical model for the Modbus protocol and defines the acquisition cycle model that corresponds to incompletely defined protocols in order to provide a timestamp and achieve temporal consistency for proposed Modbus Extension. The derived technical contribution of the authors is to exemplify the functionality of a typical industrial protocol that can be decomposed to improve the performance of data acquisition systems. Research results in this area have significant implications for innovations in industrial automation networking because of increasing distributed installations and Industrial Internet of Things (IIoT) applications. Full article
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12 pages, 12365 KB  
Article
Comparing Elastocaloric Cooling and Desiccant Wheel Dehumidifiers for Atmospheric Water Harvesting
by John LaRocco, Qudsia Tahmina, John Simonis and Vidhaath Vedati
Technologies 2024, 12(10), 178; https://doi.org/10.3390/technologies12100178 - 30 Sep 2024
Cited by 1 | Viewed by 9057
Abstract
Approximately two billion people worldwide lack access to clean drinking water, negatively impacting national security, hygiene, and agriculture. Atmospheric water harvesting (AWH) is the conversion of ambient humidity into clean water; however, conventional dehumidification is energy-intensive. Improvement in AWH may be achieved with [...] Read more.
Approximately two billion people worldwide lack access to clean drinking water, negatively impacting national security, hygiene, and agriculture. Atmospheric water harvesting (AWH) is the conversion of ambient humidity into clean water; however, conventional dehumidification is energy-intensive. Improvement in AWH may be achieved with elastocaloric cooling, using temperature-sensitive materials in active thermoregulation. Potential benefits, compared to conventional desiccant wheel designs, include substantial reductions in energy use, size, and complexity. A nickel–titanium (NiTi) elastocaloric water harvester was designed and compared with a desiccant wheel design under controlled conditions of relative humidity, air volume, and power. In a 30 min interval, the NiTi device harvested more water on average at 0.18 ± 0.027 mL/WH, compared to the 0.1567 ± 0.023 mL/WH of the desiccant wheel harvester. Moreover, the NiTi harvester required half the power input and was thermoregulated more efficiently. Future work will focus on mechanical design parameter optimization. Elastocaloric cooling is a promising advancement in dehumidification, making AWH more economical and feasible. Full article
(This article belongs to the Section Environmental Technology)
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38 pages, 17450 KB  
Article
Open-Source Hardware Design of Modular Solar DC Nanogrid
by Md Motakabbir Rahman, Sara Khan and Joshua M. Pearce
Technologies 2024, 12(9), 167; https://doi.org/10.3390/technologies12090167 - 13 Sep 2024
Cited by 4 | Viewed by 5820
Abstract
The technical feasibility of solar photovoltaic (PV) direct current (DC) nanogrids is well established, but the components of nanogrids are primarily commercially focused on alternating current (AC)-based systems. Thus, DC converter-based designs at the system level require personnel with high degree of technical [...] Read more.
The technical feasibility of solar photovoltaic (PV) direct current (DC) nanogrids is well established, but the components of nanogrids are primarily commercially focused on alternating current (AC)-based systems. Thus, DC converter-based designs at the system level require personnel with high degree of technical knowledge, which results in high costs. To enable a democratization of the technology by reducing the costs, this study provides a novel modular plug-and-play open-source DC nanogrid. The system can be customized according to consumer requirements, enabling the supply of various voltage levels to accommodate different device voltage needs. The step-by-step design process of the converter, controller, data logger, and assembly of the complete system is provided. A time-domain simulation and stability analysis of the designed system were conducted in MATLAB/Simulink (version 2024b) as well as experimental validation. The results show that transforming the nanogrid from a distribution network to a device makes it suitable for various user-specific applications, such as remotely supplying power to campsites, emergency vehicles like ambulances, and small houses lacking grid electricity. The modular DC nanogrid includes all the features available in a DC distribution network, as well as data logging, which enhances the user experience and promotes the use of solar-powered DC grid systems. Full article
(This article belongs to the Section Environmental Technology)
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16 pages, 533 KB  
Article
Regularizing Lifetime Drift Prediction in Semiconductor Electrical Parameters with Quantile Random Forest Regression
by Lukas Sommeregger and Jürgen Pilz
Technologies 2024, 12(9), 165; https://doi.org/10.3390/technologies12090165 - 13 Sep 2024
Cited by 4 | Viewed by 3767
Abstract
Semiconductors play a crucial role in a wide range of applications and are integral to essential infrastructures. Manufacturers of these semiconductors must meet specific quality and lifetime targets. To estimate the lifetime of semiconductors, accelerated stress tests are conducted. This paper introduces a [...] Read more.
Semiconductors play a crucial role in a wide range of applications and are integral to essential infrastructures. Manufacturers of these semiconductors must meet specific quality and lifetime targets. To estimate the lifetime of semiconductors, accelerated stress tests are conducted. This paper introduces a novel approach to modeling drift in discrete electrical parameters within stress test devices. It incorporates a machine learning (ML) approach for arbitrary panel data sets of electrical parameters from accelerated stress tests. The proposed model involves an expert-in-the-loop MLOps decision process, allowing experts to choose between an interpretable model and a robust ML algorithm for regularization and fine-tuning. The model addresses the issue of outliers influencing statistical models by employing regularization techniques. This ensures that the model’s accuracy is not compromised by outliers. The model uses interpretable statistically calculated limits for lifetime drift and uncertainty as input data. It then predicts these limits for new lifetime stress test data of electrical parameters from the same technology. The effectiveness of the model is demonstrated using anonymized real data from Infineon technologies. The model’s output can help prioritize parameters by the level of significance for indication of degradation over time, providing valuable insights for the analysis and improvement of electrical devices. The combination of explainable statistical algorithms and ML approaches enables the regularization of quality control limit calculations and the detection of lifetime drift in stress test parameters. This information can be used to enhance production quality by identifying significant parameters that indicate degradation and detecting deviations in production processes. Full article
(This article belongs to the Topic Advances in Microelectronics and Semiconductor Engineering)
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30 pages, 1427 KB  
Review
Wearable Fall Detectors Based on Low Power Transmission Systems: A Systematic Review
by Manny Villa and Eduardo Casilari
Technologies 2024, 12(9), 166; https://doi.org/10.3390/technologies12090166 - 13 Sep 2024
Cited by 11 | Viewed by 6964
Abstract
Early attention to individuals who suffer falls is a critical aspect when determining the consequences of such accidents, which are among the leading causes of mortality and disability in older adults. For this reason and considering the high number of older adults living [...] Read more.
Early attention to individuals who suffer falls is a critical aspect when determining the consequences of such accidents, which are among the leading causes of mortality and disability in older adults. For this reason and considering the high number of older adults living alone, the development of automatic fall alerting systems has garnered significant research attention over the past decade. A key element for deploying a fall detection system (FDS) based on wearables is the wireless transmission method employed to transmit the medical alarms. In this regard, the vast majority of prototypes in the related literature utilize short-range technologies, such as Bluetooth, which must be complemented by the existence of a gateway device (e.g., a smartphone). In other studies, standards like Wi-Fi or 3G communications are proposed, which offer greater range but come with high power consumption, which can be unsuitable for most wearables, and higher service fees. In addition, they require reliable radio coverage, which is not always guaranteed in all application scenarios. An interesting alternative to these standards is Low Power Wide Area Network (LPWAN) technologies, which minimize both energy consumption and hardware costs while maximizing transmission range. This article provides a comprehensive search and review of that works in the literature that have implemented and evaluated wearable FDSs utilizing LPWAN interfaces to transmit alarms. The review systematically examines these proposals, considering various operational aspects and identifying key areas that have not yet been adequately addressed for the viable implementation of such detectors. Full article
(This article belongs to the Special Issue The Future of Healthcare: Biomedical Technology and Integrated Artificial Intelligence 2nd Edition)
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31 pages, 73552 KB  
Article
Enhancing 3D Rock Localization in Mining Environments Using Bird’s-Eye View Images from the Time-of-Flight Blaze 101 Camera
by John Kern, Reinier Rodriguez-Guillen, Claudio Urrea and Yainet Garcia-Garcia
Technologies 2024, 12(9), 162; https://doi.org/10.3390/technologies12090162 - 12 Sep 2024
Cited by 5 | Viewed by 3839
Abstract
The mining industry faces significant challenges in production costs, environmental protection, and worker safety, necessitating the development of autonomous systems. This study presents the design and implementation of a robust rock centroid localization system for mining robotic applications, particularly rock-breaking hammers. The system [...] Read more.
The mining industry faces significant challenges in production costs, environmental protection, and worker safety, necessitating the development of autonomous systems. This study presents the design and implementation of a robust rock centroid localization system for mining robotic applications, particularly rock-breaking hammers. The system comprises three phases: assembly, data acquisition, and data processing. Environmental sensing was accomplished using a Basler Blaze 101 three-dimensional (3D) Time-of-Flight (ToF) camera. The data processing phase incorporated advanced algorithms, including Bird’s-Eye View (BEV) image conversion and You Only Look Once (YOLO) v8x-Seg instance segmentation. The system’s performance was evaluated using a comprehensive dataset of 627 point clouds, including samples from real mining environments. The system achieved efficient processing times of approximately 5 s. Segmentation accuracy was evaluated using the Intersection over Union (IoU), reaching 95.10%. Localization precision was measured by the Euclidean distance in the XY plane (EDXY), achieving 0.0128 m. The normalized error (enorm) on the X and Y axes did not exceed 2.3%. Additionally, the system demonstrated high reliability with R2 values close to 1 for the X and Y axes, and maintained performance under various lighting conditions and in the presence of suspended particles. The Mean Absolute Error (MAE) in the Z axis was 0.0333 m, addressing challenges in depth estimation. A sensitivity analysis was conducted to assess the model’s robustness, revealing consistent performance across brightness and contrast variations, with an IoU ranging from 92.88% to 96.10%, while showing greater sensitivity to rotations. Full article
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26 pages, 8417 KB  
Article
An Innovative Vision-Guided Feeding System for Robotic Picking of Different-Shaped Industrial Components Randomly Arranged
by Nicola Ivan Giannoccaro, Giuseppe Rausa, Roberta Rizzi, Paolo Visconti and Roberto De Fazio
Technologies 2024, 12(9), 153; https://doi.org/10.3390/technologies12090153 - 5 Sep 2024
Cited by 7 | Viewed by 6332
Abstract
Within an industrial plant, the handling of randomly arranged objects is becoming increasingly popular. The technology industry has introduced ever more powerful devices to the market, but they are often unable to meet the demands of the industry in terms of processing times. [...] Read more.
Within an industrial plant, the handling of randomly arranged objects is becoming increasingly popular. The technology industry has introduced ever more powerful devices to the market, but they are often unable to meet the demands of the industry in terms of processing times. Using a multi-component feeder, which facilitates the automatic picking of objects arranged in bulk, is the ideal element to speed up the identification of objects by the vision system. The innovative designed feeder eliminates the dead time of the vision system since the feeder has two working surfaces, thus making the viewing time hidden in relation to the total handling cycle time. In addition, the step feeder integrated into the feeder structure allows for control over the number of objects that fall onto the work surface, optimizing the material flow. The feeder was designed to palletize aluminum hinge fins but can also handle other products with different shapes and sizes. A two-dimensional (2D) vision system is integrated into the robotic cell to identify the components to be palletized, obtaining a reduced cycle time. The innovative feeder is fully adaptable to industrial applications and allows for easy integration into the robotic cell in which it is installed; by testing its operation with different aluminum fins, male and female, significant results were obtained in terms of cycle times ranging from 1.44 s to 1.68 s per piece, with an average productivity level (PL) of 1175 pcs every 30 min. Full article
(This article belongs to the Section Manufacturing Technology)
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21 pages, 5219 KB  
Article
Ensemble Learning for Nuclear Power Generation Forecasting Based on Deep Neural Networks and Support Vector Regression
by Jorge Gustavo Sandoval Simão and Leandro dos Santos Coelho
Technologies 2024, 12(9), 148; https://doi.org/10.3390/technologies12090148 - 2 Sep 2024
Cited by 2 | Viewed by 3726
Abstract
Forecasting nuclear energy production is essential for market operations such as security, economic efficiency, resource optimization, grid stability, and the integration of renewable energy sources. Forecasting approaches allow nuclear power plants to operate consistently, contributing to the overall reliability and long-term viability of [...] Read more.
Forecasting nuclear energy production is essential for market operations such as security, economic efficiency, resource optimization, grid stability, and the integration of renewable energy sources. Forecasting approaches allow nuclear power plants to operate consistently, contributing to the overall reliability and long-term viability of the energy system. It is noted that energy systems researchers are increasingly interested in machine learning models used to face the challenge of time series forecasting. This study evaluates a hybrid ensemble learning of three time series forecasting models including least-squares support vector regression, gated recurrent unit, and long short-term memory models applied to nuclear power time series forecasting on the dataset of French power plants from 2009 to 2020. Furthermore, this research evaluates forecasting results in which approaches are directed towards the optimized RreliefF (Robust relief Feature) selection algorithm using a hyperparameter optimization based on tree-structured Parzen estimator and following an ensemble learning approach, showing promising results in terms of performance metrics. The suggested ensemble learning model, which combines deep learning and the RreliefF algorithm using a hold-out, outperforms the other nine forecasting models in this study according to performance criteria such as 75% for the coefficient of determination, a root squared error average of 0.108, and an average absolute error of 0.080. Full article
(This article belongs to the Collection Electrical Technologies)
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14 pages, 3453 KB  
Article
MIRA: Multi-Joint Imitation with Recurrent Adaptation for Robot-Assisted Rehabilitation
by Ali Ashary, Ruchik Mishra, Madan M. Rayguru and Dan O. Popa
Technologies 2024, 12(8), 135; https://doi.org/10.3390/technologies12080135 - 16 Aug 2024
Cited by 3 | Viewed by 3364
Abstract
This work proposes a modular learning framework (MIRA) for rehabilitation robots based on a new deep recurrent neural network (RNN) that achieves adaptive multi-joint motion imitation. The RNN is fed with the fundamental frequencies as well as the ranges of the joint trajectories, [...] Read more.
This work proposes a modular learning framework (MIRA) for rehabilitation robots based on a new deep recurrent neural network (RNN) that achieves adaptive multi-joint motion imitation. The RNN is fed with the fundamental frequencies as well as the ranges of the joint trajectories, in order to predict the future joint trajectories of the robot. The proposed framework also uses a Segment Online Dynamic Time Warping (SODTW) algorithm to quantify the closeness between the robot and patient motion. The SODTW cost decides the amount of modification needed in the inputs to our deep RNN network, which in turn adapts the robot movements. By keeping the prediction mechanism (RNN) and adaptation mechanism (SODTW) separate, the framework achieves modularity, flexibility, and scalability. We tried both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) RNN architectures within our proposed framework. Experiments involved a group of 15 human subjects performing a range of motion tasks in conjunction with our social robot, Zeno. Comparative analysis of the results demonstrated the superior performance of the LSTM RNN across multiple task variations, highlighting its enhanced capability for adaptive motion imitation. Full article
(This article belongs to the Collection Selected Papers from the PETRA Conference Series)
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22 pages, 12633 KB  
Article
MediaPipe Frame and Convolutional Neural Networks-Based Fingerspelling Detection in Mexican Sign Language
by Tzeico J. Sánchez-Vicinaiz, Enrique Camacho-Pérez, Alejandro A. Castillo-Atoche, Mayra Cruz-Fernandez, José R. García-Martínez and Juvenal Rodríguez-Reséndiz
Technologies 2024, 12(8), 124; https://doi.org/10.3390/technologies12080124 - 1 Aug 2024
Cited by 14 | Viewed by 5915
Abstract
This research proposes implementing a system to recognize the static signs of the Mexican Sign Language (MSL) dactylological alphabet using the MediaPipe frame and Convolutional Neural Network (CNN) models to correctly interpret the letters that represent the manual signals coming from a camera. [...] Read more.
This research proposes implementing a system to recognize the static signs of the Mexican Sign Language (MSL) dactylological alphabet using the MediaPipe frame and Convolutional Neural Network (CNN) models to correctly interpret the letters that represent the manual signals coming from a camera. The development of these types of studies allows the implementation of technological advances in artificial intelligence and computer vision in teaching Mexican Sign Language (MSL). The best CNN model achieved an accuracy of 83.63% over the sets of 336 test images. In addition, considering samples of each letter, the following results are obtained: an accuracy of 84.57%, a sensitivity of 83.33%, and a specificity of 99.17%. The advantage of this system is that it could be implemented on low-consumption equipment, carrying out the classification in real-time, contributing to the accessibility of its use. Full article
(This article belongs to the Topic Theoretical and Applied Problems in Human-Computer Intelligent Systems)
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