Journal Description
Technologies
Technologies
is an international, peer-reviewed, open access journal singularly focusing on emerging scientific and technological trends and is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within ESCI (Web of Science), Scopus, Inspec, Ei Compendex, INSPIRE, and other databases.
- Journal Rank: JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q1 (Computer Science (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21.8 days after submission; acceptance to publication is undertaken in 3.9 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Journal Cluster of Mechanical Manufacturing and Automation Control: Aerospace, Automation, Drones, Journal of Manufacturing and Materials Processing, Machines, Robotics and Technologies.
Impact Factor:
3.6 (2024);
5-Year Impact Factor:
4.2 (2024)
Latest Articles
Integrating Artificial Intelligence into Smart Infrastructure Management for Sustainable Urban Planning
Technologies 2025, 13(11), 481; https://doi.org/10.3390/technologies13110481 (registering DOI) - 23 Oct 2025
Abstract
This paper systematically reviewed studies on the integration of Artificial Intelligence (AI) into infrastructure management to support sustainable urban planning across three primary domains: predictive maintenance and energy optimization, traffic and mobility systems, and public participation with ethical considerations. Findings from thirty peer-reviewed
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This paper systematically reviewed studies on the integration of Artificial Intelligence (AI) into infrastructure management to support sustainable urban planning across three primary domains: predictive maintenance and energy optimization, traffic and mobility systems, and public participation with ethical considerations. Findings from thirty peer-reviewed studies underscore how AI-driven models enhance operational efficiency, sustainability, and governance in smart cities. Effective management of AI-driven smart infrastructure can transform urban planning by optimizing resources efficiency and predictive maintenance, including 15% energy savings, 25-30% cost reductions, 25% congestion reduction, and 18% decrease in travel times. Similarly, participatory digital twins and citizen-centric approaches are found to enhance public participation and help address ethical issues. The findings further reveal that AI-based predictive maintenance frameworks improve system reliability, while deep learning and hybrid models achieve up to 92% accuracy in traffic forecasting. Nonetheless, obstacles to equitable implementation, including the digital divide, privacy infringements, and algorithmic bias, persist. Establishing ethical and participatory frameworks, anchored in responsible AI governance, is therefore vital to promote transparency, accountability, and inclusivity. This study demonstrates that AI-enabled smart infrastructure management strengthens urban planning by enhancing efficiency, sustainability, and social responsiveness. It concludes that achieving sustainable and socially accepted smart cities depends on striking a balance between technological innovation, ethical responsibility, and inclusive governance.
Full article
(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)
Open AccessArticle
Evaluating and Forecasting Undergraduate Dropouts Using Machine Learning for Domestic and International Students
by
Songbo Wang and Jiayi He
Technologies 2025, 13(11), 480; https://doi.org/10.3390/technologies13110480 - 23 Oct 2025
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Undergraduate dropout is a multidimensional phenomenon with implications for higher education, economic development, and social and cultural transformation, posing complex challenges for society as a whole. To address this, universities require effective dropout risk assessments for both domestic and international students, enabling the
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Undergraduate dropout is a multidimensional phenomenon with implications for higher education, economic development, and social and cultural transformation, posing complex challenges for society as a whole. To address this, universities require effective dropout risk assessments for both domestic and international students, enabling the implementation of tailored strategies and support. This study sourced a dataset from multiple faculties, comprising 3544 records for domestic students (Portuguese) and 86 for international students, considering 23 features. To balance the data, Conditional Tabular Generative Adversarial Networks were utilized to generate 487 synthetic samples with comparable statistical characteristics for training (85%) while retaining the original 86 real samples for testing (15%), thus maintaining an identical train–test split for evaluating domestic students. An Automated Machine Learning framework, employing ensemble learning algorithms, achieved outstanding performance, with the Light Gradient Boosting Machine proving the most effective for domestic students and Categorical Boosting for international students, both achieving test accuracies exceeding 0.90. The analysis revealed that improving academic performance during the first and second semesters was key to reducing dropout risks. Once a satisfactory level was reached, further improvements had minimal impact. Therefore, the focus should be on achieving satisfactory grades. Other objective identity factors, such as age and gender, were less influential than academic performance. A web-based application incorporating the developed models was created, offering an open-access tool for forecasting dropout risks, with all code made publicly available for further research into undergraduate performance, which could be extended to other nations.
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Open AccessArticle
TOXOS: Spinning Up Nonlinearity in On-Vehicle Inference with a RISC-V CORDIC Coprocessor
by
Luigi Giuffrida, Guido Masera and Maurizio Martina
Technologies 2025, 13(10), 479; https://doi.org/10.3390/technologies13100479 - 21 Oct 2025
Abstract
The rapid advancement of artificial intelligence in automotive applications, particularly in Advanced Driver-Assistance Systems (ADAS) and smart battery management on electric vehicles, increases the demand for efficient near-sensor processing. While the problem of linear algebra in machine learning is well-addressed by existing accelerators,
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The rapid advancement of artificial intelligence in automotive applications, particularly in Advanced Driver-Assistance Systems (ADAS) and smart battery management on electric vehicles, increases the demand for efficient near-sensor processing. While the problem of linear algebra in machine learning is well-addressed by existing accelerators, the computation of nonlinear activation functions is usually delegated to the host CPU, resulting in energy inefficiency and high computational costs. This paper introduces TOXOS, a RISC-V-compliant coprocessor designed to address this challenge. TOXOS implements the COordinateRotation DIgital Computer (CORDIC) algorithm to efficiently compute nonlinear functions. Taking advantage of RISC-V modularity and extendability, TOXOS seamlessly integrates with existing computing architectures. The coprocessor’s configurability enables fine-tuning of the area-performance tradeoff by adjusting the internal parallelism, the CORDIC iteration count, and the overall latency. Our implementation on a 65nm technology demonstrates a significant improvement over CPU-based solutions, showcasing a considerable speedup compared to the glibc implementation of nonlinear functions. To validate TOXOS’s real-world impact, we integrated TOXOS in an actual RISC-V microcontroller targeting the on-vehicle execution of machine learning models. This work addresses a critical gap in transcendental function computation for AI, enabling real-time decision-making for autonomous driving systems, maintaining the power efficiency crucial for electric vehicles.
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(This article belongs to the Section Manufacturing Technology)
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Open AccessArticle
Fibonacci Tessellation for Optimizing Planar Phased Arrays in Satellite Communications
by
Juan L. Valle, Marco A. Panduro, Carlos A. Brizuela, Roberto Conte, Carlos del Río Bocio and David H. Covarrubias
Technologies 2025, 13(10), 478; https://doi.org/10.3390/technologies13100478 - 21 Oct 2025
Abstract
This article presents a novel strategy for the design of planar phased arrays using Fibonacci-based partitioning combined with a random multi-objective search. This approach intends to minimize the number of phase shifters used by the system while maintaining the radiation characteristics required for
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This article presents a novel strategy for the design of planar phased arrays using Fibonacci-based partitioning combined with a random multi-objective search. This approach intends to minimize the number of phase shifters used by the system while maintaining the radiation characteristics required for Ku-band user terminals in Low Earth Orbit (LEO) satellite communications. This methodology efficiently tessellates a antenna array, reducing the solution search space size and improving algorithmic computational time. From a total of 409,600 possible configurations, an optimal candidate solution was obtained in 2 h. This configuration achieves a balanced trade-off between radiation performance metrics, including side lobe level (SLL), first null beamwidth (FNBW), and the number of phase shifters. This optimal design maintains a value of SLL below dB across all the azimuth scanning angles, with a beam steering capability of and . These results demonstrate the suitability of this novel approach regarding Ku-band satellite communications, providing efficient and practical solutions for high-demand internet services via LEO satellite systems.
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(This article belongs to the Special Issue Technologies Based on Antenna Arrays and Applications)
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Open AccessArticle
Sustainable Swarm Intelligence: Assessing Carbon-Aware Optimization in High-Performance AI Systems
by
Vasileios Alevizos, Nikitas Gerolimos, Eleni Aikaterini Leligkou, Giorgos Hompis, Georgios Priniotakis and George A. Papakostas
Technologies 2025, 13(10), 477; https://doi.org/10.3390/technologies13100477 - 21 Oct 2025
Abstract
Carbon-aware AI demands clear links between algorithmic choices and verified emission outcomes. This study measures and steers the carbon footprint of swarm-based optimization in HPC by coupling a job-level Emission Impact Metric with sub-minute power and grid-intensity telemetry. Across 480 runs covering 41
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Carbon-aware AI demands clear links between algorithmic choices and verified emission outcomes. This study measures and steers the carbon footprint of swarm-based optimization in HPC by coupling a job-level Emission Impact Metric with sub-minute power and grid-intensity telemetry. Across 480 runs covering 41 algorithms, we report grams CO2 per successful optimisation and an efficiency index (objective gain per kg CO2). Results show faster swarms achieve lower integral energy: Particle Swarm emits 24.9 g CO2 per optimum versus 61.3 g for GridSearch on identical hardware; Whale and Cuckoo approach the best frontier, while L-SHADE exhibits front-loaded power spikes. Conservative scale factor schedules and moderate populations reduce emissions without degrading fitness; idle-node suppression further cuts leakage. Agreement between CodeCarbon, MLCO2, and vendor telemetry is within 1.8%, supporting reproducibility. The framework offers auditable, runtime controls (throttle/hold/release) that embed carbon objectives without violating solution quality budgets.
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(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies)
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Open AccessSystematic Review
Intelligent Eco-Technologies for End-of-Life Photovoltaic Modules: A Systematic Review
by
Valentina-Daniela Băjenaru, Roxana-Mariana Nechita and Simona-Elena Istrițeanu
Technologies 2025, 13(10), 476; https://doi.org/10.3390/technologies13100476 - 20 Oct 2025
Abstract
This paper explores the evolution of first-generation solar cells by analysing the selection and engineering of materials that led to innovations. It also addresses the potential of using materials other than silicon and issues related to innovative recycling technologies. The paper presents the
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This paper explores the evolution of first-generation solar cells by analysing the selection and engineering of materials that led to innovations. It also addresses the potential of using materials other than silicon and issues related to innovative recycling technologies. The paper presents the evolution of the Romanian photovoltaic sector and assesses the life cycle of photovoltaic panels, focusing on the recovery of high-quality raw materials and their reintroduction into the production process to improve the circular economy in this field. As the number of installed panels grows exponentially, so does the need to manage waste efficiently at the end of their life cycle. Photovoltaic panel recycling is slowly but surely becoming a rapidly developing field that is essential for the sustainability of the solar industry. With the growth of production in the Romanian photovoltaic sector, it has been identified that the need for recycled raw materials will increase from 900 prosumers in 2019 to over 100,000 in 2024. In the future, it will be imperative to develop strategies for recovering, recycling and reintroducing materials, which will bring major benefits. This paper’s specific contributions include a bibliometric mapping of EoL-PV research trends, a technology-recycling matrix for modern cell architectures, and a perspective on the Romanian market contextualised within EU policies.
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(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies)
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Open AccessArticle
A Study of Performance and Emission Characteristics of Diesel-Palm Oil Mill Effluent Gas on Dual-Fuel Diesel Engines Based on Energy Ratio
by
Yanuandri Putrasari, Hafiziani Eka Putri, Achmad Praptijanto, Arifin Nur, Mulia Pratama, Ahmad Dimyani, Suherman, Bambang Wahono, Muhammad Khristamto Aditya Wardana, Ocktaeck Lim, Manida Tongroon and Sakda Thongchai
Technologies 2025, 13(10), 475; https://doi.org/10.3390/technologies13100475 - 20 Oct 2025
Abstract
Biogas from palm oil mill effluent (POME) is a promising fuel that has many advantages as an alternative fuel. The methane content in biogas derived from POME is up to 75% and can be used as an alternative fuel in an internal combustion
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Biogas from palm oil mill effluent (POME) is a promising fuel that has many advantages as an alternative fuel. The methane content in biogas derived from POME is up to 75% and can be used as an alternative fuel in an internal combustion engine. One of the technologies for utilizing biogas in compression ignition engines is the Diesel Dual-Fuel (DDF) technique due to the different characteristics of fuel and the impact on the environment due to significantly reducing emissions. This study aims to find the effect of biogas POME composition and energy ratio on the DDF engine’s performance and emissions. The simulations using AVL BOOST software were confirmed by experimental engine parameters. The modeling was conducted on the biogas energy ratio (20%, 40%, 60%, and 75% POME) and biogas POME composition (55% and 75% methane). The results showed that the fuel consumption of diesel fuel was reduced by up to 69%, and NOx and soot emissions were reduced by up to 92% and 80%, respectively, with dual-fuel mode operation. Meanwhile, the value of brake mean effective pressure (BMEP) and efficiency was reduced by up to 18%, volumetric efficiency decreased by up to 4%, the increase in brake specific energy consumption (BSEC) was up to 23%, and brake specific fuel consumption (BSFC) was up to 155%. The optimum of the engine’s performance and emission was 40% biogas ratio with 75% methane content.
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(This article belongs to the Section Environmental Technology)
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Open AccessArticle
Lightweight Neural Network for Holographic Reconstruction of Pseudorandom Binary Data
by
Mikhail K. Drozdov, Dmitry A. Rymov, Andrey S. Svistunov, Pavel A. Cheremkhin, Anna V. Shifrina, Semen A. Kiriy, Evgenii Yu. Zlokazov, Elizaveta K. Petrova, Vsevolod A. Nebavskiy, Nikolay N. Evtikhiev and Rostislav S. Starikov
Technologies 2025, 13(10), 474; https://doi.org/10.3390/technologies13100474 - 19 Oct 2025
Abstract
Neural networks are a state-of-the-art technology for fast and accurate holographic image reconstruction. However, at present, neural network-based reconstruction methods are predominantly applied to objects with simple, homogeneous spatial structures: blood cells, bacteria, microparticles in solutions, etc. However, in the case of objects
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Neural networks are a state-of-the-art technology for fast and accurate holographic image reconstruction. However, at present, neural network-based reconstruction methods are predominantly applied to objects with simple, homogeneous spatial structures: blood cells, bacteria, microparticles in solutions, etc. However, in the case of objects with high contrast details, the reconstruction needs to be as precise as possible to successfully extract details and parameters. In this paper we investigate the use of neural networks in holographic reconstruction of spatially inhomogeneous binary data containers (QR codes). Two modified lightweight convolutional neural networks (which we named HoloLightNet and HoloLightNet-Mini) with an encoder–decoder architecture have been used for image reconstruction. These neural networks enable high-quality reconstruction, guaranteeing the successful decoding of QR codes (both in demonstrated numerical and optical experiments). In addition, they perform reconstruction two orders of magnitude faster than more traditional architectures. In optical experiments with a liquid crystal spatial light modulator, the obtained bit error rate was equal to only 1.2%. These methods can be used for practical applications such as high-density data transmission in coherent systems, development of reliable digital information storage and memory techniques, secure optical information encryption and retrieval, and real-time precise reconstruction of complex objects.
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(This article belongs to the Section Information and Communication Technologies)
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Open AccessReview
Hailstorm Impact on Photovoltaic Modules: Damage Mechanisms, Testing Standards, and Diagnostic Techniques
by
Marko Katinić and Mladen Bošnjaković
Technologies 2025, 13(10), 473; https://doi.org/10.3390/technologies13100473 - 18 Oct 2025
Abstract
This study examines the effects of hailstorms on photovoltaic (PV) modules, focussing on damage mechanisms, testing standards, numerical simulations, damage detection techniques, and mitigation strategies. A comprehensive review of the recent literature (2017–2025), experimental results, and case studies is complemented by advanced simulation
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This study examines the effects of hailstorms on photovoltaic (PV) modules, focussing on damage mechanisms, testing standards, numerical simulations, damage detection techniques, and mitigation strategies. A comprehensive review of the recent literature (2017–2025), experimental results, and case studies is complemented by advanced simulation methods such as finite element analysis (FEA) and smoothed particle hydrodynamics (SPH). The research emphasises the crucial role of protective glass thickness, cell type, number of busbars, and quality of lamination in improving hail resistance. While international standards such as IEC 61215 specify test protocols, actual hail events often exceed these conditions, leading to glass breakage, micro-cracks, and electrical faults. Numerical simulations confirm that thicker glass and optimised module designs significantly reduce damage and power loss. Detection methods, including visual inspection, thermal imaging, electroluminescence, and AI-driven imaging, enable rapid identification of both visible and hidden damage. The study also addresses the financial risks associated with hail damage and emphasises the importance of insurance and preventative measures. Recommendations include the use of certified, robust modules, protective covers, optimised installation angles, and regular inspections to mitigate the effects of hail. Future research should develop lightweight, impact-resistant materials, improve simulation modelling to better reflect real-world hail conditions, and improve AI-based damage detection in conjunction with drone inspections. This integrated approach aims to improve the durability and reliability of PV modules in hail-prone regions and support the sustainable use of solar energy amidst increasing climatic challenges.
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(This article belongs to the Special Issue Innovative Power System Technologies)
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Open AccessArticle
Carbon Emission Reduction Capability Analysis of Electricity–Hydrogen Integrated Energy Storage Systems
by
Rankai Zhu, Yuxi Li, Xu Huang, Yaoxuan Xia, Yunjin Tu, Bowen Zheng, Jing Qiu and Xiaoshun Zhang
Technologies 2025, 13(10), 472; https://doi.org/10.3390/technologies13100472 - 18 Oct 2025
Abstract
Against the dual backdrop of intensifying carbon emission constraints and the large-scale integration of renewable energy, integrated electricity–hydrogen energy systems (EH-ESs) have emerged as a crucial technological pathway for decarbonising energy systems, owing to their multi-energy complementarity and cross-scale regulation capabilities. This paper
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Against the dual backdrop of intensifying carbon emission constraints and the large-scale integration of renewable energy, integrated electricity–hydrogen energy systems (EH-ESs) have emerged as a crucial technological pathway for decarbonising energy systems, owing to their multi-energy complementarity and cross-scale regulation capabilities. This paper proposes an operational optimisation and carbon reduction capability assessment framework for EH-ESs, focusing on revealing their operational response mechanisms and emission reduction potential under multi-disturbance conditions. A comprehensive model encompassing an electrolyser (EL), a fuel cell (FC), hydrogen storage tanks, and battery energy storage was constructed. Three optimisation objectives—cost minimisation, carbon emission minimisation, and energy loss minimisation—were introduced to systematically characterise the trade-offs between economic viability, environmental performance, and energy efficiency. Case study validation demonstrates the proposed model’s strong adaptability and robustness across varying output and load conditions. EL and FC efficiencies and costs emerge as critical bottlenecks influencing system carbon emissions and overall expenditure. Further analysis reveals that direct hydrogen utilisation outperforms the ‘electricity–hydrogen–electricity’ cycle in carbon reduction, providing data support and methodological foundations for low-carbon optimisation and widespread adoption of electricity–hydrogen systems.
Full article
(This article belongs to the Special Issue AI for Smart Grid Optimization—Technological Advances and Future Perspectives)
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Open AccessArticle
From Continuous Integer-Order to Fractional Discrete-Time: A New Computer Virus Model with Chaotic Dynamics
by
Imane Zouak, Ahmad Alshanty, Adel Ouannas, Antonio Mongelli, Giovanni Ciccarese and Giuseppe Grassi
Technologies 2025, 13(10), 471; https://doi.org/10.3390/technologies13100471 - 17 Oct 2025
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Computer viruses remain a persistent technological challenge in information security. They require mathematical frameworks that realistically capture their propagation in digital networks. Classical continuous-time, integer-order models often overlook two key aspects of cyber environments: their inherently discrete nature and the memory-dependent effects of
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Computer viruses remain a persistent technological challenge in information security. They require mathematical frameworks that realistically capture their propagation in digital networks. Classical continuous-time, integer-order models often overlook two key aspects of cyber environments: their inherently discrete nature and the memory-dependent effects of networked interactions. In this work, we introduce a fractional-order discrete computer virus (FDCV) model, derived from a three-dimensional continuous integer-order formulation and reformulated into a two-dimensional fractional discrete framework. We analyze its rich dynamical behaviors under both commensurate and incommensurate fractional orders. Leveraging a comprehensive toolbox including bifurcation diagrams, Lyapunov spectra, phase portraits, the 0–1 test for chaos, spectral entropy, and complexity measures, we demonstrate that the FDCV system exhibits persistent chaos and high dynamical complexity across broad parameter regimes. Our findings reveal that fractional-order discrete models not only enhance the dynamical richness compared to integer-order counterparts but also provide a more realistic representation of malware propagation. These insights advance the theoretical study of fractional discrete systems, supporting the development of potential technologies for cybersecurity modeling, detection, and prevention strategies.
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Open AccessArticle
Quantum-Driven Chaos-Informed Deep Learning Framework for Efficient Feature Selection and Intrusion Detection in IoT Networks
by
Padmasri Turaka and Saroj Kumar Panigrahy
Technologies 2025, 13(10), 470; https://doi.org/10.3390/technologies13100470 - 17 Oct 2025
Abstract
The rapid development of the Internet of Things (IoT) poses significant problems in securing heterogeneous, massive, and high-volume network traffic against cyber threats. Traditional intrusion detection systems (IDSs) are often found to be poorly scalable, or are ineffective computationally, because of the presence
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The rapid development of the Internet of Things (IoT) poses significant problems in securing heterogeneous, massive, and high-volume network traffic against cyber threats. Traditional intrusion detection systems (IDSs) are often found to be poorly scalable, or are ineffective computationally, because of the presence of redundant or irrelevant features, and they suffer from high false positive rates. Addressing these limitations, this study proposes a hybrid intelligent model that combines quantum computing, chaos theory, and deep learning to achieve efficient feature selection and effective intrusion classification. The proposed system offers four novel modules for feature optimization: chaotic swarm intelligence, quantum diffusion modeling, transformer-guided ranking, and multi-agent reinforcement learning, all of which work with a graph-based classifier enhanced with quantum attention mechanisms. This architecture allows as much as 75% feature reduction, while achieving 4% better classification accuracy and reducing computational overhead by 40% compared to the best-performing models. When evaluated on benchmark datasets (NSL-KDD, CICIDS2017, and UNSW-NB15), it shows superior performance in intrusion detection tasks, thereby marking it as a viable candidate for scalable and real-time IoT security analytics.
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(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)
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Open AccessArticle
Aircraft Propeller Design Technology Based on CST Parameterization, Deep Learning Models, and Genetic Algorithm
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Evgenii I. Kurkin, Jose Gabriel Quijada Pioquinto, Oleg E. Lukyanov, Vladislava O. Chertykovtseva and Artem V. Nikonorov
Technologies 2025, 13(10), 469; https://doi.org/10.3390/technologies13100469 - 16 Oct 2025
Abstract
This article presents aircraft propeller optimal design technology; including an algorithm and OpenVINT 5 code. To achieve greater geometric flexibility, the proposed technique implements Class-Shape Transformation (CST) parameterization combined with Bézier curves, replacing the previous fully Bézier-based system. Performance improvements in the optimization
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This article presents aircraft propeller optimal design technology; including an algorithm and OpenVINT 5 code. To achieve greater geometric flexibility, the proposed technique implements Class-Shape Transformation (CST) parameterization combined with Bézier curves, replacing the previous fully Bézier-based system. Performance improvements in the optimization process are accomplished through deep learning models and a genetic algorithm, which substitute XFOIL and Differential Evolution-based approaches, respectively. The scientific novelty of the article lies in the application of a neural network to predict the aerodynamic characteristics of profiles in the form of contour diagrams, rather than scalar values, which execute the neural network repeatedly per ISM algorithm iteration and speed up the design time of propeller blades by 32 times as much. A propeller for an aircraft-type UAV was designed using the proposed methodology and OpenVINT 5. A comparison was made with the results to solve a similar problem using numerical mathematical models and experimental studies in a wind tunnel.
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(This article belongs to the Special Issue Aviation Science and Technology Applications)
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Open AccessArticle
Multi-Field Functional Electrical Stimulation with Fesia Grasp for Hand Rehabilitation in Multiple Sclerosis: A Randomized, Controlled Trial
by
Olalla Saiz-Vázquez, Montserrat Santamaría-Vázquez, Aitor Martín-Odriozola, Tamara Martín-Pérez and Hilario Ortiz-Huerta
Technologies 2025, 13(10), 468; https://doi.org/10.3390/technologies13100468 - 15 Oct 2025
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This study investigates the use of multi-field electrostimulation with the Fesia Grasp device for hand rehabilitation in patients with Multiple Sclerosis (MS). This research aims to evaluate the effectiveness of this novel approach in improving hand function and dexterity in MS patients. A
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This study investigates the use of multi-field electrostimulation with the Fesia Grasp device for hand rehabilitation in patients with Multiple Sclerosis (MS). This research aims to evaluate the effectiveness of this novel approach in improving hand function and dexterity in MS patients. A cohort of MS patients with varying degrees of hand impairment underwent a structured rehabilitation program using the Fesia Grasp device, which delivers targeted electrical stimulation to specific muscle groups. Outcome measures assessed multiple aspects of hand function, including gross and fine motor skills, strength, and functional independence, at baseline, post-intervention, and 1-month follow-up. The main finding was a sustained between-group improvement in gross manual dexterity, measured by the Box and Block Test, at 1-month follow-up (p = 0.008, η2ₚ = 0.429). Secondary analyses showed task-specific gains in the experimental group, with significant intragroup improvements in Jebsen–Taylor Hand Function Test items related to simulated feeding (p = 0.012) and lifting light objects (p = 0.036), and a trend toward better performance in stacking checkers (p = 0.069) and faster page-turning (p = 0.046) after the intervention. Other outcomes showed non-significant changes favoring the experimental group. This research contributes to the growing body of evidence supporting the use of advanced electrostimulation techniques in neurological rehabilitation and offers promising implications for enhancing the quality of life for individuals with MS-related hand dysfunction.
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Open AccessCommunication
Self-Powered Electrochemical Humidity Sensor Based on Hydroxylated Multi-Walled Carbon Nanotubes-Modified CeO2 Nanoparticles
by
Zhen Yuan, Chong Tan, Zaihua Duan, Yadong Jiang and Huiling Tai
Technologies 2025, 13(10), 467; https://doi.org/10.3390/technologies13100467 - 15 Oct 2025
Abstract
Electrochemical humidity (ECH) sensors with self-generating capability have attracted widespread attention. In this work, a self-powered ECH sensor is developed using hydroxylated multi-walled carbon nanotubes (OH-MWCNTs)-modified CeO2 nanoparticles as the humidity sensing materials. The results show that the OH-MWCNTs are beneficial for
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Electrochemical humidity (ECH) sensors with self-generating capability have attracted widespread attention. In this work, a self-powered ECH sensor is developed using hydroxylated multi-walled carbon nanotubes (OH-MWCNTs)-modified CeO2 nanoparticles as the humidity sensing materials. The results show that the OH-MWCNTs are beneficial for improving the humidity sensing performances of the CeO2 nanoparticles. The optimized OH-MWCNTs/CeO2 ECH sensor exhibits a wide detection range (0–91.5% relative humidity (RH)) and fast response and recovery times (18.6 and 6.9 s), attributed to the synergistic effect of OH-MWCNTs and CeO2 nanoparticles. In addition, a single OH-MWCNTs/CeO2 ECH sensor can output a voltage of 0.711 V and a load power of 0.376 μW at 91.5% RH. When applied for respiratory rate monitoring, the OH-MWCNTs/CeO2 ECH sensor can accurately detect respiratory rate by converting exhaled humidity into voltage signal. This work demonstrates that the OH-MWCNTs-modified oxide material of CeO2 nanoparticles is a good candidate for fabricating self-powered ECH sensor.
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(This article belongs to the Special Issue New Technologies for Sensors)
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Open AccessArticle
Multilayer Plasmonic Nanodisk Arrays for Enhanced Optical Hydrogen Sensing
by
Junyi Jiang, Mingyu Cheng, Xinyi Chen and Bin Ai
Technologies 2025, 13(10), 466; https://doi.org/10.3390/technologies13100466 - 14 Oct 2025
Abstract
Plasmonic metasurfaces that convert hydrogen-induced dielectric changes into optical signals hold promise for next-generation hydrogen sensors. Here, we employ simulations and theoretical analysis to systematically assess single-layer, bilayer, and trilayer nanodisk arrays comprising magnesium, palladium, and noble metals. Although monolithic Mg nanodisks show
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Plasmonic metasurfaces that convert hydrogen-induced dielectric changes into optical signals hold promise for next-generation hydrogen sensors. Here, we employ simulations and theoretical analysis to systematically assess single-layer, bilayer, and trilayer nanodisk arrays comprising magnesium, palladium, and noble metals. Although monolithic Mg nanodisks show strong optical contrast after hydrogenation, the corresponding surface plasmon resonance disappears completely, preventing quantitative spectral tracking. In contrast, bilayer heterostructures, particularly those combining Mg and Au, achieve a resonance red-shift of Δλ = 62 nm, a narrowed full width at half maximum (FWHM) of 207 nm, and a figure of merit (FoM) of 0.30. Notably, the FoM is boosted by up to 15-fold when tuning both material choice and stacking sequence (from Mg-Ag to Au-Mg), underscoring the critical role of interface engineering. Trilayer “sandwich” architectures further amplify performance, achieving a max 10-fold and 13-fold enhancement in Δλ and FoM, respectively, relative to its bilayer counterpart. Particularly, the trilayer Mg-Au-Mg reaches Δλ = 120 nm and FoM = 0.41, outperforming most previous plasmonic hydrogen sensors. These enhancements arise from maximized electric-field overlap with dynamically changing dielectric regions at noble-metal–hydride interfaces, as confirmed by first-order perturbation theory. These results indicate that multilayer designs combining Mg and noble metals can simultaneously maximize hydrogen-induced spectral shifts and signal quality, providing a practical pathway toward high-performance all-optical hydrogen sensors.
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(This article belongs to the Special Issue New Technologies for Sensors)
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Open AccessArticle
Industrial Internet of Things (IIoT)-Based Monitoring of Frictional, Vibration, and Sound Generation in Lubricated Automotive Chains
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Shubrajit Bhaumik, Krishnamoorthy Venkatsubramanian, Sharvani Varadharajan, Suruthi Meenachinathan, Shail Mavani, Vitalie Florea and Viorel Paleu
Technologies 2025, 13(10), 465; https://doi.org/10.3390/technologies13100465 - 14 Oct 2025
Abstract
This work assesses the frictional wear of lubricated transmission chains, correlating the coefficient of friction, root mean square (RMS) acoustic emissions, and vibrations induced by friction, incorporating Industrial Internet of Things (IIoT) components. The work is divided into two phases: understanding the frictional
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This work assesses the frictional wear of lubricated transmission chains, correlating the coefficient of friction, root mean square (RMS) acoustic emissions, and vibrations induced by friction, incorporating Industrial Internet of Things (IIoT) components. The work is divided into two phases: understanding the frictional interactions between the steel pins of commercial transmission chain and high chrome steel plate (mimicking the interaction between the pin and roller of the chain) using a reciprocating tribometer (20 N, 2.5 Hz, 15.1 stroke length) in the presence of three commercial lubricant aerosols (Grade A, Grade B, and Grade C) and analyzing the frictional wear, sound, and vibration signals generated during the tribo-tests. In the second phase, the findings from the laboratory scale are validated using a commercial transmission chain under aerosol lubrication. Results indicated that the coefficient of friction in the case of dry conditions was 41% higher than that of Grade A aerosol and Grade C aerosol and 28% higher than that of Grade B aerosol. However, the average wear scar diameter on the pin with Grade C (0.401 ± 0.129 mm) was higher than that on the pins with Grades A (0.209 ± 0.159 mm) and B (0.204 ± 0.165 mm). Grade A and Grade B aerosols exhibited similar frictional conditions, while the wear-scar diameter in Grade C was the highest among Grades A and B but still less than in dry conditions. Analyzing the sound and vibrations generated during the friction test, it can be seen that the dry condition produced approximately 60% more sound level than the Grade A and Grade B conditions, and 41% more sound than the Grade C condition. The laboratory results were validated with a real-time transmission chain using an in-house chain wear test rig. Results from the chain wear test rig indicated that the elongation of the chain with Grade B is the least amongst the aerosols and dry conditions. The surface characterizations of the steel pins also indicated intense deep grooves and surface damage in dry conditions, with Grade A exhibiting the most severe damage, followed by Grade C, and the least severe in Grade B. Additionally, dark patches were visually observed on the rollers of the lubricated commercial chains, indicating stressed areas on the rollers, while polished wear was observed on the rollers under dry conditions.
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(This article belongs to the Section Manufacturing Technology)
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Open AccessArticle
Patient Experiences of Remote Patient Monitoring: Implications for Health Literacy and Therapeutic Relationships
by
Josephine Stevens, Amir Hossein Ghapanchi, Afrooz Purarjomandlangrudi and Stephanie Bruce
Technologies 2025, 13(10), 464; https://doi.org/10.3390/technologies13100464 - 13 Oct 2025
Abstract
This study explores patients’ experiences participating in a home-based remote patient monitoring program for chronic disease management. Using a mixed-methods approach, data was collected through semi-structured interviews and surveys from participants with Chronic Obstructive Pulmonary Disease (COPD) and diabetes. Two key themes emerged:
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This study explores patients’ experiences participating in a home-based remote patient monitoring program for chronic disease management. Using a mixed-methods approach, data was collected through semi-structured interviews and surveys from participants with Chronic Obstructive Pulmonary Disease (COPD) and diabetes. Two key themes emerged: “knowing” and “relationship.” The “knowing” theme encompassed data-driven awareness and contextualized education that empowered patients in their health management. The “relationship” theme highlighted the importance of interpersonal connections with healthcare providers and the sense of security from clinical oversight. Technology served as a communication platform supporting patient-clinician interactions rather than replacing them. The findings demonstrate that remote monitoring programs enhance chronic disease self-management through two interconnected mechanisms: the development of ‘situated health literacy’ through real-time, personalized data interpretation, and strengthened therapeutic relationships enabled by technology-mediated clinical oversight. Rather than replacing human interaction, technology serves as a platform for meaningful patient-provider communication that supports both immediate health management and long-term self-management capability development. These exploratory findings suggest potential design considerations for patient-centered telehealth services that integrate health literacy enhancement with relationship-centered care.
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(This article belongs to the Special Issue Advanced Technologies for Enhancing Safety, Health, and Well-Being)
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Open AccessArticle
Design and Validation of a Walking Exoskeleton for Gait Rehabilitation Using a Dual Eight-Bar Mechanism
by
Fidel Chávez, Juan A. Cabrera, Alex Bataller and Javier Pérez
Technologies 2025, 13(10), 463; https://doi.org/10.3390/technologies13100463 - 13 Oct 2025
Abstract
Improvements in exoskeletons and robotic systems are gaining increasing attention because of their potential to improve neuromuscular rehabilitation and assist people in their daily activities, significantly improving their quality of life. However, the high cost and complexity of current devices limit their accessibility
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Improvements in exoskeletons and robotic systems are gaining increasing attention because of their potential to improve neuromuscular rehabilitation and assist people in their daily activities, significantly improving their quality of life. However, the high cost and complexity of current devices limit their accessibility to many patients and rehabilitation centers. This work presents the design and development of a low-cost walking exoskeleton, conceived to offer an affordable and simple alternative. The system uses a compact eight-bar mechanism with only one degree of freedom per leg, drastically simplifying motorization and control. The exoskeleton is customized for each patient using a synthesis process based on evolutionary algorithms to replicate a predefined gait. Despite the reduced number of degrees of freedom, the resulting mechanism perfectly matches the desired ankle and knee trajectories. The device is designed to be lightweight and affordable, with components fabricated using 3D printing, standard aluminum bars, and one actuator per leg. A working prototype was fabricated, and its functionality and gait accuracy were confirmed. Although limited to a predefined gait pattern and requiring crutches for balance and steering, this exoskeleton represents a promising solution for rehabilitation centers with limited resources, offering accessible and effective gait assistance to a wider population.
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(This article belongs to the Special Issue Advanced Technologies for Enhancing Safety, Health, and Well-Being)
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Open AccessArticle
A Hierarchical Distributed Control System Design for Lower Limb Rehabilitation Robot
by
Aihui Wang, Jinkang Dong, Rui Teng, Ping Liu, Xuebin Yue and Xiang Zhang
Technologies 2025, 13(10), 462; https://doi.org/10.3390/technologies13100462 - 13 Oct 2025
Abstract
With the acceleration of global aging and the rising incidence of stroke, the demand for lower limb rehabilitation has been steadily increasing. Traditional therapeutic methods can no longer meet the growing needs, which has led to the widespread application of robotic solutions to
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With the acceleration of global aging and the rising incidence of stroke, the demand for lower limb rehabilitation has been steadily increasing. Traditional therapeutic methods can no longer meet the growing needs, which has led to the widespread application of robotic solutions to address labor shortages. In this context, this paper presents a hierarchical and distributed control system based on ROS 2 and Micro-ROS. The distributed architecture decouples functional modules, improving system maintainability and supporting modular upgrades. The control system consists of a three-layer structure, including a high-level controller, Jetson Nano, for gait data processing and advanced command generation; a middle-layer controller, ESP32-S3, for sensor data fusion and inter-layer communication bridging; and a low-level controller, STM32F405, for field-oriented control to drive the motors along a predefined trajectory. Experimental validation in both early and late rehabilitation stages demonstrates the system’s ability to achieve accurate trajectory tracking. In the early rehabilitation stage, the maximum root mean square error of the joint motors is 1.143°; in the later rehabilitation stage, the maximum root mean square error of the joint motors is 1.833°, confirming the robustness of the control system. Additionally, the hierarchical and distributed architecture ensures maintainability and facilitates future upgrades. This paper provides a feasible control scheme for the next generation of lower limb rehabilitation robots.
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(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)
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