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19 pages, 1327 KB  
Article
An IoT Architecture for Sustainable Urban Mobility: Towards Energy-Aware and Low-Emission Smart Cities
by Manuel J. C. S. Reis, Frederico Branco, Nishu Gupta and Carlos Serôdio
Future Internet 2025, 17(10), 457; https://doi.org/10.3390/fi17100457 (registering DOI) - 4 Oct 2025
Abstract
The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents [...] Read more.
The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO2 intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO2 intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article
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23 pages, 2251 KB  
Article
Enhancing FDM Rapid Prototyping for Industry 4.0 Applications Through Simulation and Optimization Techniques
by Mihalache Ghinea, Alex Cosmin Niculescu and Bogdan Dragos Rosca
Materials 2025, 18(19), 4555; https://doi.org/10.3390/ma18194555 - 30 Sep 2025
Abstract
Modern manufacturing is increasingly shaped by the paradigm of Industry 4.0 (Smart Manufacturing). As one of its nine pillars, additive manufacturing plays a crucial role, enabling high-quality final products with improved profitability in minimal time. Advances in this field have facilitated the emergence [...] Read more.
Modern manufacturing is increasingly shaped by the paradigm of Industry 4.0 (Smart Manufacturing). As one of its nine pillars, additive manufacturing plays a crucial role, enabling high-quality final products with improved profitability in minimal time. Advances in this field have facilitated the emergence of diverse technologies—such as Fused Deposition Modelling (FDM), Stereolithography (SLA), and Selective Laser Sintering (SLS)—allowing the use of metallic, polymeric, and composite materials. Within this context, Klipper v.0.12, an open-source firmware for 3D printers, addresses the performance limitations of conventional consumer-grade systems. By offloading computationally intensive tasks to an external single-board computer (e.g., Raspberry Pi), Klipper enhances speed, precision, and flexibility while reducing prototyping time. The purpose of this study is twofold: first, to identify and analyze bottlenecks in low-cost 3D printers and second, to evaluate how these shortcomings can be mitigated through the integration of supplementary hardware and software (Klipper firmware, Raspberry Pi, additional sensors, and the Mainsail interface). The scientific contribution of this study lies in demonstrating that a consumer-grade FDM 3D printer can be significantly upgraded through this integration and systematic calibration, achieving up to a 50% reduction in printing time while maintaining dimensional accuracy and improving surface quality. Full article
(This article belongs to the Section Manufacturing Processes and Systems)
24 pages, 1641 KB  
Article
Intellectual Property Protection Through Blockchain: Introducing the Novel SmartRegistry-IP for Secure Digital Ownership
by Abeer S. Al-Humaimeedy
Future Internet 2025, 17(10), 444; https://doi.org/10.3390/fi17100444 - 29 Sep 2025
Abstract
The rise of digital content has made the need for reliable and practical intellectual property (IP) management systems more critical than ever. Most traditional IP systems are prone to issues such as delays, inefficiency, and data security breaches. This paper introduces SmartRegistry-IP, a [...] Read more.
The rise of digital content has made the need for reliable and practical intellectual property (IP) management systems more critical than ever. Most traditional IP systems are prone to issues such as delays, inefficiency, and data security breaches. This paper introduces SmartRegistry-IP, a system developed to simplify the registration, licensing, and transfer of intellectual property assets in a secure and scalable decentralized environment. By utilizing the InterPlanetary File System (IPFS) for decentralized storage, SmartRegistry-IP achieves a low storage latency of 300 milliseconds, outperforming both cloud storage (500 ms) and local storage (700 ms). The system also supports a high transaction throughput of 120 transactions per second. Through the use of smart contracts, licensing agreements are automatically and securely enforced, reducing the need for intermediaries and lowering operational costs. Additionally, the proof-of-work process verifies all transactions, ensuring higher security and maintaining data consistency. The platform integrates an intuitive graphical user interface that enables seamless asset uploads, license management, and analytics visualization in real time. SmartRegistry-IP demonstrates superior efficiency compared to traditional systems, achieving a blockchain delay of 300 ms, which is half the latency of standard systems, averaging 600 ms. According to this study, adopting SmartRegistry-IP provides IP organizations with enhanced security and transparent management, ensuring they can overcome operational challenges regardless of their size. As a result, the use of blockchain for intellectual property management is expected to increase, helping maintain precise records and reducing time spent on online copyright registration. Full article
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21 pages, 6518 KB  
Article
Topological Rainbow Trapping in One-Dimensional Magnetoelastic Phononic Crystal Slabs
by Wen Xiao, Fuhao Sui, Jiujiu Chen, Hongbo Huang and Tao Luo
Magnetochemistry 2025, 11(10), 83; https://doi.org/10.3390/magnetochemistry11100083 - 25 Sep 2025
Abstract
We design a one-dimensional magnetoelastic phononic crystal slab composed of the smart magnetostrictive material Terfenol-D and pure tungsten. Band inversion and topological phase transitions are achieved by modifying the geometric parameters of the non-magnetic medium within the unit cell. The emergence of topological [...] Read more.
We design a one-dimensional magnetoelastic phononic crystal slab composed of the smart magnetostrictive material Terfenol-D and pure tungsten. Band inversion and topological phase transitions are achieved by modifying the geometric parameters of the non-magnetic medium within the unit cell. The emergence of topological interface states within overlapping bandgaps, exhibiting distinct topological properties, along with their robustness against interfacial structural defects, is confirmed. The coupling effects between adjacent topological interface states in a sandwich-like supercell configuration are investigated, and their tunability under external magnetic fields is demonstrated. A Su-Schrieffer-Heeger (SSH) phononic crystal slab system under gradient magnetic fields is proposed. Critically, and in stark contrast to previous static or structurally graded designs, we achieve reconfigurable rainbow trapping of topological interface states solely by reprogramming the gradient magnetic field, leaving the physical structure entirely unchanged. This highly localized, compact, and broadband-tunable topological rainbow trapping system design holds significant promise for applications in elastic energy harvesting, wave filtering, and multi-frequency signal processing. Full article
(This article belongs to the Special Issue Advances in Low-Dimensional Magnetic Materials)
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13 pages, 3006 KB  
Article
A Novel Controller for Fuel Cell Generators Based on CAN Bus
by Ching-Hsu Chan, Fuh-Liang Wen, Chu-Po Wen and Kevin Karindra Putra Pradana
Appl. Syst. Innov. 2025, 8(5), 138; https://doi.org/10.3390/asi8050138 - 24 Sep 2025
Viewed by 71
Abstract
The novel design and modular implementation of a distributed control system for a fuel cell generator, aimed at monitoring and actuation, are presented. Two ESP32 NodeMCU microcontrollers and MCP2515 modules are used for the controller area network (CAN) bus communication protocol. To compare [...] Read more.
The novel design and modular implementation of a distributed control system for a fuel cell generator, aimed at monitoring and actuation, are presented. Two ESP32 NodeMCU microcontrollers and MCP2515 modules are used for the controller area network (CAN) bus communication protocol. To compare this setup with a traditional battery management system (BMS), small rated-power fuel cell generators were connected individually via the CAN bus to form a larger stacked output. An RFID interface was introduced into the CAN bus system to enhance its applicability in stacked fuel cells, without interfering with original message frames, arbitration mechanisms, or CRC efficiency across various sectors. Additionally, to provide a clearer understanding of the system’s features and functions, a PC-based logic analyzer was employed as an analytical tool to monitor and analyze data transmitted over the CAN bus. Comprehensive insights into the system’s performance are supported by logic analysis of its complex applications in series-connected fuel cells. The advantages of the RFID-based CAN bus are further enhanced by modern communication protocols, offering greater scalability and flexibility, with potential applications in industrial automation, autonomous vehicles, and smart green power grids. Full article
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38 pages, 4322 KB  
Article
ENACT: Energy-Aware, Actionable Twin Utilizing Prescriptive Techniques in Home Appliances
by Myrto Stogia, Asimina Dimara, Christoforos Papaioannou, Orfeas Eleftheriou, Alexios Papaioannou, Stelios Krinidis and Christos-Nikolaos Anagnostopoulos
Smart Cities 2025, 8(5), 155; https://doi.org/10.3390/smartcities8050155 - 22 Sep 2025
Viewed by 128
Abstract
A significant portion of home energy consumption is due to concealed faults and the inefficient usage of home appliances, usually because of user ignorance and a lack of proactive maintenance strategies. In this paper, ENACT, a digital-twin-based system, is proposed as the solution [...] Read more.
A significant portion of home energy consumption is due to concealed faults and the inefficient usage of home appliances, usually because of user ignorance and a lack of proactive maintenance strategies. In this paper, ENACT, a digital-twin-based system, is proposed as the solution that facilitates better user understanding, encourages sustainable maintenance practices for appliances, and provides prescriptive maintenance recommendations. With the integration of smart plugs, behavioral analysis, and a 3D spatial interface, ENACT offers real-time device monitoring while providing context-aware suggestions. The system was installed in 20 households over a 12-month period, with users engaging with both 2D and 3D models of their surroundings. The quantitative results, including an average System Usability Scale score of 80.5, and qualitative feedback demonstrated intense user engagement, with strong evidence of mindset shifts towards proactive maintenance behavior. The findings confirm that digital twin technologies, when combined with targeted guidance, can significantly improve appliance lifespans, energy efficiency, and user empowerment within homes. Full article
(This article belongs to the Section Applied Science and Humanities for Smart Cities)
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36 pages, 2691 KB  
Review
Advanced Electrochemical Sensors for Rapid and Sensitive Monitoring of Tryptophan and Tryptamine in Clinical Diagnostics
by Janani Sridev, Arif R. Deen, Md Younus Ali, Wei-Ting Ting, M. Jamal Deen and Matiar M. R. Howlader
Biosensors 2025, 15(9), 626; https://doi.org/10.3390/bios15090626 - 19 Sep 2025
Viewed by 575
Abstract
Tryptophan (Trp) and tryptamine (Tryp), critical biomarkers in mood regulation, immune function, and metabolic homeostasis, are increasingly recognized for their roles in both oral and systemic pathologies, including neurodegenerative disorders, cancers, and inflammatory conditions. Their rapid, sensitive detection in biofluids such as saliva—a [...] Read more.
Tryptophan (Trp) and tryptamine (Tryp), critical biomarkers in mood regulation, immune function, and metabolic homeostasis, are increasingly recognized for their roles in both oral and systemic pathologies, including neurodegenerative disorders, cancers, and inflammatory conditions. Their rapid, sensitive detection in biofluids such as saliva—a non-invasive, real-time diagnostic medium—offers transformative potential for early disease identification and personalized health monitoring. This review synthesizes advancements in electrochemical sensor technologies tailored for Trp and Tryp quantification, emphasizing their clinical relevance in diagnosing conditions like oral squamous cell carcinoma (OSCC), Alzheimer’s disease (AD), and breast cancer, where dysregulated Trp metabolism reflects immune dysfunction or tumor progression. Electrochemical platforms have overcome the limitations of conventional techniques (e.g., enzyme-linked immunosorbent assays (ELISA) and mass spectrometry) by integrating innovative nanomaterials and smart engineering strategies. Carbon-based architectures, such as graphene (Gr) and carbon nanotubes (CNTs) functionalized with metal nanoparticles (Ni and Co) or nitrogen dopants, amplify electron transfer kinetics and catalytic activity, achieving sub-nanomolar detection limits. Synergies between doping and advanced functionalization—via aptamers (Apt), molecularly imprinted polymers (MIPs), or metal-oxide hybrids—impart exceptional selectivity, enabling the precise discrimination of Trp and Tryp in complex matrices like saliva. Mechanistically, redox reactions at the indole ring are optimized through tailored electrode interfaces, which enhance reaction kinetics and stability over repeated cycles. Translational strides include 3D-printed microfluidics and wearable sensors for continuous intraoral health surveillance, demonstrating clinical utility in detecting elevated Trp levels in OSCC and breast cancer. These platforms align with point-of-care (POC) needs through rapid response times, minimal fouling, and compatibility with scalable fabrication. However, challenges persist in standardizing saliva collection, mitigating matrix interference, and validating biomarkers across diverse populations. Emerging solutions, such as AI-driven analytics and antifouling coatings, coupled with interdisciplinary efforts to refine device integration and manufacturing, are critical to bridging these gaps. By harmonizing material innovation with clinical insights, electrochemical sensors promise to revolutionize precision medicine, offering cost-effective, real-time diagnostics for both localized oral pathologies and systemic diseases. As the field advances, addressing stability and scalability barriers will unlock the full potential of these technologies, transforming them into indispensable tools for early intervention and tailored therapeutic monitoring in global healthcare. Full article
(This article belongs to the Special Issue Nanomaterial-Based Biosensors for Point-of-Care Testing)
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31 pages, 5071 KB  
Article
Feasibility of an AI-Enabled Smart Mirror Integrating MA-rPPG, Facial Affect, and Conversational Guidance in Realtime
by Mohammad Afif Kasno and Jin-Woo Jung
Sensors 2025, 25(18), 5831; https://doi.org/10.3390/s25185831 - 18 Sep 2025
Viewed by 311
Abstract
This paper presents a real-time smart mirror system combining multiple AI modules for multimodal health monitoring. The proposed platform integrates three core components: facial expression analysis, remote photoplethysmography (rPPG), and conversational AI. A key innovation lies in transforming the Moving Average rPPG (MA-rPPG) [...] Read more.
This paper presents a real-time smart mirror system combining multiple AI modules for multimodal health monitoring. The proposed platform integrates three core components: facial expression analysis, remote photoplethysmography (rPPG), and conversational AI. A key innovation lies in transforming the Moving Average rPPG (MA-rPPG) model—originally developed for offline batch processing—into a real-time, continuously streaming setup, enabling seamless heart rate and peripheral oxygen saturation (SpO2) monitoring using standard webcams. The system also incorporates the DeepFace facial analysis library for live emotion, age detection, and a Generative Pre-trained Transformer 4o (GPT-4o)-based mental health chatbot with bilingual (English/Korean) support and voice synthesis. Embedded into a touchscreen mirror with Graphical User Interface (GUI), this solution delivers ambient, low-interruption interaction and real-time user feedback. By unifying these AI modules within an interactive smart mirror, our findings demonstrate the feasibility of integrating multimodal sensing (rPPG, affect detection) and conversational AI into a real-time smart mirror platform. This system is presented as a feasibility-stage prototype to promote real-time health awareness and empathetic feedback. The physiological validation was limited to a single subject, and the user evaluation constituted only a small formative assessment; therefore, results should be interpreted strictly as preliminary feasibility evidence. The system is not intended to provide clinical diagnosis or generalizable accuracy at this stage. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Social Robots)
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46 pages, 4316 KB  
Review
3D Printing Assisted Wearable and Implantable Biosensors
by Somnath Maji, Myounggyu Kwak, Reetesh Kumar and Hyungseok Lee
Biosensors 2025, 15(9), 619; https://doi.org/10.3390/bios15090619 - 17 Sep 2025
Viewed by 490
Abstract
Biosensors have undergone transformative advancements, evolving into sophisticated wearable and implantable devices capable of real-time health monitoring. Traditional manufacturing methods, however, face limitations in scalability, cost, and design complexity, particularly for miniaturized, multifunctional biosensors. The integration of 3D printing technology addresses these challenges [...] Read more.
Biosensors have undergone transformative advancements, evolving into sophisticated wearable and implantable devices capable of real-time health monitoring. Traditional manufacturing methods, however, face limitations in scalability, cost, and design complexity, particularly for miniaturized, multifunctional biosensors. The integration of 3D printing technology addresses these challenges by enabling rapid prototyping, customization, and the production of intricate geometries with high precision. This review explores how additive manufacturing techniques facilitate the fabrication of flexible, stretchable, and biocompatible biosensors. By incorporating advanced materials like conductive polymers, nanocomposites, and hydrogels, 3D-printed biosensors achieve enhanced sensitivity, durability, and seamless integration with biological systems. Innovations such as biodegradable substrates and multi-material printing further expand applications in continuous glucose monitoring, neural interfaces, and point-of-care diagnostics. Despite challenges in material optimization and regulatory standardization, the convergence of 3D printing with nanotechnology and smart diagnostics heralds a new era of personalized, proactive healthcare, offering scalable solutions for both clinical and remote settings. This synthesis underscores the pivotal role of additive manufacturing in advancing wearable and implantable biosensor technology, paving the way for next-generation devices that prioritize patient-specific care and real-time health management. Full article
(This article belongs to the Special Issue Biological Sensors Based on 3D Printing Technologies)
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26 pages, 1080 KB  
Systematic Review
Digital Twin and Computer Vision Combination for Manufacturing and Operations: A Systematic Literature Review
by Haji Ahmed Faqeer and Siavash H. Khajavi
Appl. Sci. 2025, 15(18), 10157; https://doi.org/10.3390/app151810157 - 17 Sep 2025
Cited by 1 | Viewed by 374
Abstract
This paper examines the transformative role of the Digital Twin-Computer Vision combination (DT-CV combo) in industrial operations, focusing on its applications, challenges, and future directions. It aims to synthesize the existing literature and explore the practical use cases in operations management (OM). A [...] Read more.
This paper examines the transformative role of the Digital Twin-Computer Vision combination (DT-CV combo) in industrial operations, focusing on its applications, challenges, and future directions. It aims to synthesize the existing literature and explore the practical use cases in operations management (OM). A comprehensive systematic literature review is conducted using PRISMA guidelines to analyze the DT-CV combo across the classification of industrial OM. However, given the breadth and importance of manufacturing and the OM field, the study excludes the literature on the DT-CV combo applied to other domains such as healthcare, smart buildings and cities, and transportation. We found that the DT-CV combo in OM is a relatively young but growing field of research. To date, only 29 articles have examined DT-CV combo solutions from various OM perspectives. Case studies are rare, with most studies relying on experimentation and laboratory testing to investigate DT-CV applications in the OM context. According to the cases and methods reviewed in the literature, the DT-CV combo has applications in different OM areas such as design, prototyping, simulation, real-time production monitoring, defect detection, process optimization, hazard detection and mitigation, safety training, emergency response simulation, optimal resource allocation, condition monitoring, inventory management, and scheduling maintenance. We also identified several benefits of DT-CV combo solutions in OM, including reducing human error, ensuring compliance with quality standards, lowering maintenance costs, mitigating production downtime, eliminating operational bottlenecks, and decreasing workplace accidents, while simultaneously improving the effectiveness of training. In this paper, we classify current applications of the DT-CV combo in OM, highlight gaps in the existing literature, and propose research questions to guide future studies in this domain. By considering the rapid phase of AI technology development and combining it with the current state of the art applications of the DT-CV combo in OM, we suggest novel concepts and future directions. The digital twin-vision language model (DT-VLM) combo as a future direction, emphasizing its potential to bridge physical–digital interfaces in industrial workflows, is one of the future development directions. Full article
(This article belongs to the Special Issue Digital Twins in the Industry 4.0)
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15 pages, 6691 KB  
Proceeding Paper
Smart Customizable Spinning System
by Wei-Chuan Lin, Yu-Wen Hsu and Wan-Lin Yu
Eng. Proc. 2025, 108(1), 46; https://doi.org/10.3390/engproc2025108046 - 12 Sep 2025
Viewed by 133
Abstract
As global obesity rates rise, cardiovascular diseases increase, and stress-related issues become more severe. This increases the public awareness of health and exercise. However, existing spinning fitness equipment lacks personalized customization for individual needs. To address this, we developed a smart customizable spinning [...] Read more.
As global obesity rates rise, cardiovascular diseases increase, and stress-related issues become more severe. This increases the public awareness of health and exercise. However, existing spinning fitness equipment lacks personalized customization for individual needs. To address this, we developed a smart customizable spinning system that enables health monitoring, central computation, flywheel, voice interaction, notification, and query subsystems. Users can set fitness goals based on their personal needs, monitor workout data via sensors, and utilize voice interaction and control to track their exercise status in real time. The system notifies users of workout progress through a buzzer and message queuing telemetry transport, while the Web interface provides access to past workouts and health records. Additionally, the system supports bilingual functionality (Chinese and English), allowing users to operate it in their preferred language, enhancing global usability. Full article
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25 pages, 8078 KB  
Article
Robust Sensorless Predictive Power Control of PWM Converters Using Adaptive Neural Network-Based Virtual Flux Estimation
by Noumidia Amoura, Adel Rahoui, Boussad Boukais, Koussaila Mesbah, Abdelhakim Saim and Azeddine Houari
Electronics 2025, 14(18), 3620; https://doi.org/10.3390/electronics14183620 - 12 Sep 2025
Viewed by 334
Abstract
The rapid evolution of modern power systems, driven by the large-scale integration of renewable energy sources and the emergence of smart grids, presents new challenges in maintaining grid stability, power quality, and control reliability. As critical interfacing elements, three-phase pulse width modulation (PWM) [...] Read more.
The rapid evolution of modern power systems, driven by the large-scale integration of renewable energy sources and the emergence of smart grids, presents new challenges in maintaining grid stability, power quality, and control reliability. As critical interfacing elements, three-phase pulse width modulation (PWM) converters must now ensure resilient and efficient operation under increasingly adverse and dynamic grid conditions. This paper proposes an adaptive neural network-based virtual flux (VF) estimator for sensorless predictive direct power control (PDPC) of PWM converters under nonideal grid voltage conditions. The proposed estimator is realized using an adaptive linear neuron (ADALINE) configured as a quadrature signal generator, offering robustness against grid voltage disturbances such as voltage unbalance, DC offset and harmonic distortion. In parallel, a PDPC scheme based on the extended pq theory is developed to reject active-power oscillations and to maintain near-sinusoidal grid currents under unbalanced conditions. The resulting VF-based PDPC (VF-PDPC) strategy is validated via real-time simulations on the OPAL-RT platform. Comparative analysis confirms that the ADALINE-based estimator surpasses conventional VF estimation techniques. Moreover, the VF-PDPC achieves superior performance over conventional PDPC and extended pq theory-based PDPC strategies, both of which rely on physical voltage sensors, confirming its robustness and effectiveness under non-ideal grid conditions. Full article
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22 pages, 3865 KB  
Article
AI-Based Prediction-Driven Control Framework for Hydrogen–Natural Gas Blends in Natural Gas Networks
by George Calianu, Ștefan-Ionuț Spiridon, Andrei-Catalin Militaru, Antoaneta Roman, Marius Constantinescu, Felicia Bucura, Roxana Elena Ionete and Eusebiu Ilarian Ionete
Energies 2025, 18(18), 4799; https://doi.org/10.3390/en18184799 - 9 Sep 2025
Viewed by 446
Abstract
This study presents the development and implementation of an AI-driven control system for dynamic regulation of hydrogen blending in natural gas networks. Leveraging supervised machine learning techniques, a Random Forest Classifier was trained to accurately identify the origin of gas blends based on [...] Read more.
This study presents the development and implementation of an AI-driven control system for dynamic regulation of hydrogen blending in natural gas networks. Leveraging supervised machine learning techniques, a Random Forest Classifier was trained to accurately identify the origin of gas blends based on compositional fingerprints, achieving rapid inference suitable for real-time applications. Concurrently, a Random Forest Regression model was developed to estimate the optimal hydrogen flow rate required to meet a user-defined higher calorific value target, demonstrating exceptional predictive accuracy with a mean absolute error of 0.0091 Nm3 and a coefficient of determination (R2) of 0.9992 on test data. The integrated system, deployed via a Streamlit-based graphical interface, provides continuous real-time adjustments of gas composition, alongside detailed physicochemical property estimation and emission metrics. Validation through comparative analysis of predicted versus actual hydrogen flow rates confirms the robustness and generalizability of the approach under both simulated and operational conditions. The proposed framework enhances operational transparency and economic efficiency by enabling adaptive blending control and automatic source identification, thereby facilitating optimized fuel quality management and compliance with industrial standards. This work contributes to advancing smart combustion technologies and supports the sustainable integration of renewable hydrogen in existing gas infrastructures. Full article
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23 pages, 15956 KB  
Article
A Photovoltaic Light Sensor-Based Self-Powered Real-Time Hover Gesture Recognition System for Smart Home Control
by Nora Almania, Sarah Alhouli and Deepak Sahoo
Electronics 2025, 14(18), 3576; https://doi.org/10.3390/electronics14183576 - 9 Sep 2025
Viewed by 397
Abstract
Many gesture recognition systems with innovative interfaces have emerged for smart home control. However, these systems tend to be energy-intensive, bulky, and expensive. There is also a lack of real-time demonstrations of gesture recognition and subsequent evaluation of the user experience. Photovoltaic light [...] Read more.
Many gesture recognition systems with innovative interfaces have emerged for smart home control. However, these systems tend to be energy-intensive, bulky, and expensive. There is also a lack of real-time demonstrations of gesture recognition and subsequent evaluation of the user experience. Photovoltaic light sensors are self-powered, battery-free, flexible, portable, and easily deployable on various surfaces throughout the home. They enable natural, intuitive, hover-based interaction, which could create a positive user experience. In this paper, we present the development and evaluation of a real-time, hover gesture recognition system that can control multiple smart home devices via a self-powered photovoltaic interface. Five popular supervised machine learning algorithms were evaluated using gesture data from 48 participants. The random forest classifier achieved high accuracies. However, a one-size-fits-all model performed poorly in real-time testing. User-specific random forest models performed well with 10 participants, showing no significant difference in offline and real-time performance and under normal indoor lighting conditions. This paper demonstrates the technical feasibility of using photovoltaic surfaces as self-powered interfaces for gestural interaction systems that are perceived to be useful and easy to use. It establishes a foundation for future work in hover-based interaction and sustainable sensing, enabling human–computer interaction researchers to explore further applications. Full article
(This article belongs to the Special Issue Human-Computer Interaction in Intelligent Systems, 2nd Edition)
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33 pages, 16564 KB  
Article
Design and Implementation of an Off-Grid Smart Street Lighting System Using LoRaWAN and Hybrid Renewable Energy for Energy-Efficient Urban Infrastructure
by Seyfettin Vadi
Sensors 2025, 25(17), 5579; https://doi.org/10.3390/s25175579 - 6 Sep 2025
Viewed by 2322
Abstract
The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid [...] Read more.
The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid smart street lighting system that combines solar photovoltaic generation with battery storage and Internet of Things (IoT)-based control to ensure continuous and efficient operation. The system integrates Long Range Wide Area Network (LoRaWAN) communication technology for remote monitoring and control without internet connectivity and employs the Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm to maximize energy extraction from solar sources. Data transmission from the LoRaWAN gateway to the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, enabling real-time access and management via a graphical user interface. Experimental results demonstrate that the proposed system achieves a maximum MPPT efficiency of 97.96%, supports reliable communication over distances of up to 10 km, and successfully operates four LED streetlights, each spaced 400 m apart, across an open area of approximately 1.2 km—delivering a practical, energy-efficient, and internet-independent solution for smart urban infrastructure. Full article
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