Search Results (1,022)

Conductive core–shell superabsorbent polymers (SAPs) are emerging as multifunctional additives for cementitious materials, combining moisture management with electrical functionality. In cement-based systems, a swellable polymeric core enables internal curing and crack-sealing through controlled water uptake and release, while a conductive shell introduces ionic and/or electronic charge transport, addressing key limitations of conventional non-conductive SAPs. This dual functionality provides a pathway toward smart cementitious composites with enhanced durability, self-sensing capability, and moisture-responsive behavior. This review focuses on the physical chemistry mechanisms governing conductive core–shell SAPs in cementitious environments, with emphasis on swelling thermodynamics, water transport kinetics, interfacial phenomena, and charge transport mechanisms. The roles of osmotic pressure, elastic network constraints, ionic effects, and pore solution chemistry are critically discussed, together with their impact on conductivity, hydration processes, microstructure development, and long-term performance. The relative contributions of ionic and electronic conduction are examined in relation to hydration state, shell morphology, and percolation of conductive networks. In addition, the relevance of core–shell SAP architectures to sustainable packaging is briefly discussed as a secondary application, illustrating how similar physicochemical principles—such as moisture buffering and functional coatings—apply beyond construction materials. Finally, key knowledge gaps are identified, including long-term stability in highly alkaline environments, trade-offs between swelling capacity and conductivity, environmental impacts of conductive phases, and the need for integrated experimental and modeling approaches. Addressing these challenges is essential for the rational design and practical implementation of conductive core–shell SAPs in next-generation cementitious materials. Full article

To mitigate environmental impact, specifically the CO₂ emissions associated with conventional thermal and nuclear facilities, renewable energy sources are increasingly being adopted as primary alternatives. However, integrating these renewable sources into the utility grid poses a significant challenge, primarily due to the stochastic and nonlinear nature of weather. Consequently, it is imperative that power systems operate under an intelligent control model to ensure energy output meets strict power quality standards. In this context, accurate forecasting is a cornerstone of smart power management, particularly in off-grid architectures, where predicting Power Quality Parameters (PQPs) is fundamental for system optimization and error correction. This study conducts a comprehensive comparative evaluation of nine different predictive architectures for estimating PQPs. The algorithms analyzed include $LSTM$, $GRU$, $DNN$, $CNN1D\text{-}LSTM$, $BiLSTM$, attention mechanisms, $DT$, $SVM$, and $XGBoost$. The central objective is to develop a reliable basis for the automated regulation and enhancement of electrical quality in isolated systems. The specific parameters investigated are power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), and short-term flicker severity ($P_{st}$). Data for this investigation were acquired from an experimental off-grid setup at VSB-Technical University of Ostrava (VSB-TUO), Czech Republic. To assess model performance, we utilized root mean square error ($RMSE$) as the primary accuracy metric, while simultaneously evaluating computational efficiency in terms of processing speed and memory consumption during testing. Full article

Flexible and wearable electronics require soft sensing materials that balance mechanical compliance, stable signal transduction, and durability for human–machine interfaces (HMIs). To address the limitations of single-filler systems, we propose a poly(vinyl alcohol) (PVA)/aramid nanofiber (ANF)/MXene organogel (PAM) as a multifunctional soft platform. This design integrates a PVA physically crosslinked network with ANF for mechanical reinforcement and MXene for electrical functionality. The optimized PAM composite exhibits outstanding mechanical properties, including a fracture stress of 2931 kPa, a fracture strain of 676%, and a fracture toughness of 9.04 MJ m⁻³. Importantly, PAM serves as a single material platform configurable into three sensing modalities. The resistive strain sensor achieves a gauge factor of 3.1 over 10–100% strain and enables the reliable recognition of human joint movements and gestures. The capacitive pressure sensor delivers a sensitivity of 0.298 kPa⁻¹, rapid response/recovery times of 30/10 ms, and is integrated with a wireless module to control a smart car. Furthermore, the PAM-based triboelectric nanogenerator (TENG) delivers excellent electrical outputs (V_oc = 123 V, I_sc = 0.52 μA, Q_sc = 58 nC) and functions as a self-powered smart handwriting pad, achieving a machine-learning-based recognition accuracy of 97.6%. This work demonstrates the immense potential of the PAM organogel for advanced, self-powered HMIs. Full article

Digital substations (DSs) play a critical role in modern Energy and Power Electrical Systems (EPESs), enabling intelligent control, monitoring, and automation. With increased reliance on communication and sensing technologies, DSs are vulnerable to cyberattacks such as False Data Injection (FDI). An adversary may falsify transformer temperature readings, misleading protection mechanisms and resulting in incorrect disconnection actions. These false disconnections may disrupt power delivery, cause economic losses, and reduce equipment lifespan. To address these challenges, we propose a reinforcement learning-based approach for cyber protection of smart grids against false temperature data injection attacks. Specifically, this work designs a Long Short-Term Memory Deep Deterministic Policy Gradient (LSTM-DDPG) deep reinforcement learning algorithm that learns to detect normal patterns and responds to suspicious thermal patterns by dynamically adjusting disconnection decisions. The agents process sequential state features to differentiate between legitimate overload conditions and sudden anomalies caused by FDI attacks. We implement the proposed approach on the IEEE 30-bus distribution network using the Pandapower simulator. The experimental results indicate that the LSTM-DDPG controller outperforms conventional DDPG and DQN baselines, achieving a recall of 0.897, F1 of 0.945, precision of 1.00 and accuracy of 0.981 with a confidence interval of 95%. In addition, grid stability reaches up to 0.9815, 1.0, 1.0, 0.9926 with respect to the voltage stability score, transformer stability value, disconnection stability, and stability index, respectively. The proposed method led to fewer false disconnections, providing improved robustness against sensor manipulations. Full article

Vehicle-to-grid (V2G) technology has emerged as a key enabler for coupling large-scale electric vehicle (EV) deployment with the operation of smart energy systems. By allowing bidirectional power and information exchange between EVs and the grid, V2G transforms EVs from passive loads into distributed energy resources capable of supporting grid flexibility, reliability, and renewable energy integration. However, the practical realization of V2G remains challenged by technical complexity, system coordination, user participation, and regulatory constraints. This paper presents a comprehensive review of V2G integration from a system-level perspective. Rather than focusing solely on individual technologies, the review examines how V2G is embedded within smart energy systems, emphasizing the interactions among EVs, aggregators, grid operators, energy markets, and end users. Key enabling technologies, including bidirectional charging, aggregation mechanisms, communication frameworks, and data-driven control strategies, are discussed in relation to their system-level roles and limitations. The impacts of V2G on grid operation, energy management, and market participation are analyzed, with particular attention to reliability, battery lifetime, and user trust. Furthermore, this review identifies critical open issues that hinder large-scale deployment, spanning infrastructure readiness, standardization, economic incentives, and cybersecurity. Emerging application scenarios, such as building-integrated V2G, fleet-based services, and artificial intelligence (AI) supported coordination, are also discussed to illustrate potential evolution pathways. By synthesizing technological developments with system-level impacts and unresolved challenges, this paper aims to provide a structured reference for researchers, system planners, and policymakers seeking to advance the integration of V2G into future smart energy systems. Full article

Due to the high energy demand of buildings, especially educational buildings, it is crucial to improve total building energy consumption. The proposed methodology is the integration of a photovoltaic (PV) system with a smart control plan for educational buildings. The main aim is to improve energy consumption in an educational building (Electrical Engineering Department, Assiut University, Egypt) using photovoltaic integration and a smart control plan to regulate energy and boost indoor comfort without requiring a significant change in the building architecture. This study was conducted in two main phases: field measurements for annual energy consumption in Assiut University over a five-year period from 2009 to 2014, and an analysis of energy consumption for the Electrical Engineering Department. Then, integration of PV panels on the roof to generate electricity was considered, with the calculation of the shading factor and tilt angle to ensure a realistic estimation of energy yield and to improve energy efficiency using smart control plans. The findings indicate that the average annual peak consumption reached about 30 GWh in Assiut University during the academic years 2009 to 2014. The maximum energy consumption for a typical occupied day in the educational building is 47 kWh. An improvement in building energy consumption was achieved using PV, producing 33–35 MWh annually with an effective smart control plan and without installing sensor-based systems. The results of this study will help improve energy consumption for educational buildings in hot arid climates without building modifications. This study highlights that unoccupied periods—when human activity is absent in classrooms and other rooms—account for up to 40% of the scheduled energy consumption. Using PV panels will result in a shading factor of 0.562 from the total roof area. Full article

Hybrid renewable energy systems (HRES) combining photovoltaic, wind, fuel cell, and energy storage technologies are becoming established as viable options for reliable, environmentally friendly distributed electricity generation. In this review, we examine the key architectures, monitoring and forecast approaches, and control systems that improve the efficiency of HRES and facilitate the just-energy transition to low-carbon power generation systems. The main optimization and decision-aware approaches, particularly the evolutionary generation algorithms and machine learning-based prediction models, are addressed with a focus on improving energy allocation, cost minimization, and increased use of clean renewable energy sources. Technical, economic, and environmental performance indicators, such as the levelized cost of energy (LCOE), net present cost (NPC), renewable fraction (RF), and CO₂ emissions reduction, have been compared to demonstrate the feasibility of various system scenarios. This paper evaluates and summarizes recent case studies from around the world and presents the best practices and the challenges they encounter, including resource availability, governance, and economic drivers. The balance of the paper demonstrates that smart forecasting with advanced energy management approaches is crucial for developing sustainable and resilient hybrid distributed power systems for the future. Full article

This article introduces a new Multi-Source DC-DC converter-based smart energy management system on a common DC bus architecture, utilizing solar PV and wind sources for electric vehicle applications. The common DC bus enables coordinated power flow control among multiple sources while maintaining modularity and flexibility. To promote efficient battery charging and discharging, as well as enhanced protection from faults, an artificial neural network (ANN) approach has been incorporated. The main function of the ANN controller is to detect faults in the EV battery for timely intervention. Compared to existing topologies, its coordinated integration and control can operate effectively under dynamic conditions and improve stability. Additionally, the article presents the operating principle, modes of operation, design analysis, and control strategy. The simulation results of the proposed system are evaluated through MATLAB Simulink software 2024b. Furthermore, a 200 W laboratory prototype was developed to validate the system’s dynamic performance under various operating conditions. Full article

This research paper presents Volvo SmartCell, an AC battery technology that integrates modular multilevel converters and battery cells to form a unified system for electric vehicle propulsion and power supply. The research work addresses the broader challenge of reducing driveline cost and complexity by replacing traditional components such as inverters, onboard chargers, centralized DC/DC converters, vehicle control units and many more. SmartCell uses distributed Cluster Boards comprised of H-bridges which are controlled via wireless communication to generate AC voltage, deliver redundant low voltage power, and support cell level protection mechanisms. The prototype testing demonstrates that the system can supply traction power by engaging clusters according to the required voltage depending on motor speed, achieve AC grid charging by synthesizing sinusoidal voltages without a dedicated charger, and provide autonomous DC/DC operation through cluster level voltage regulation. Simulations further indicate that multilevel voltage generation can reduce switching losses and improve electric machine efficiency compared to conventional systems. Additional benefits include active cell balancing, support for mixed cell chemistries, and high redundancy through multiple independent power branches. Challenges remain in wireless bandwidth limitations and cost optimization of Cluster Boards. Ongoing development aims to enhance communication robustness and validate safety for non-isolated grid charging. Full article

Cold thermal energy storage (CTES) has emerged as a critical clean-energy technology for enhancing postharvest management in rural agricultural supply chains, where losses often exceed 20–40% due to inadequate cooling infrastructure and unreliable electricity. This review synthesizes the recent literature on CTES systems, including ice-, chilled-water-, and phase-change material (PCM)-based storage, with a focus on smart-farm integration, IoT-based monitoring, predictive control, and solar photovoltaic (PV) energy coupling. Trends in village-level cold rooms, micro-dairy milk cooling, and fruit–vegetable storage are critically examined, highlighting efficiency, resilience, and scalability relative to battery-dominant and conventional refrigeration systems. Current research gaps are identified in multi-scale modeling, PCM stability, state-of-charge estimation, techno-economic optimization, and AI-based operational strategies. Addressing these gaps is essential to realizing sustainable, low-carbon, and energy-efficient rural cold chains. Full article

This manuscript presents an energy management strategy (EMS) for a commercial smart building participating in a tertiary frequency regulation market. The building integrates non-controllable components, such as loads and photovoltaic generation, and controllable resources such as a battery storage system and a set of electric vehicle (EV) chargers that are available for customers of the smart building. The EMS is based on model predictive control due to its innate ability to deal with operational constraints and different optimization criteria, which are critical for the operation of the EMS, and consists of two stages. The first iteratively optimizes energy costs and revenues from tertiary regulation reserves and activations in order to determine the optimal operation of the smart building and the regulation offers in nominal conditions. Then, a second problem determines the operation whenever an activation request is made. Simulation-based analyses are performed to study the performance of the EMS and its financial viability in diverse scenarios relevant to the smart commercial building. The results show that profits are greater if both upward and downward regulation can be provided, for a larger number of EVs and chargers and for longer connection times. Most notably, incomes from regulation almost match operation costs for a large number of chargers and EVs (240), obtaining a deficit of only EUR 39.12 for a day of operations. Full article

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines. Full article

Background/Objectives: Wound management presents a substantial clinical challenge due to the rising incidence of chronic wounds, infections, and the limitations of conventional dressings in creating an ideal healing microenvironment. This review aims to provide a comprehensive overview of advanced smart hydrogel platforms designed to improve wound healing outcomes, focusing on their capacity to respond adaptively to physiological and external stimuli. Methods: This article analyzes the core characteristics of smart hydrogels, specifically examining stimuli-responsive systems (pH, temperature, enzyme, light, and electricity). The review evaluates advanced configurations—including injectable, self-healing, and 3D-printable systems—and functionalized hydrogels integrated with antimicrobials, drugs, and nanocomposites. Additionally, essential characterization methodologies, biological assessments, and regulatory considerations for clinical translation are synthesized. Results: The literature, which is predominantly preclinical in nature, indicates that functionalized hydrogels significantly enhance tissue regeneration, angiogenesis, and infection control compared to traditional methods. Conductive hydrogels utilizing bioelectrical signals show particular promise in accelerating the healing process. While current clinical applications and commercial products demonstrate efficacy, significant barriers remain regarding large-scale manufacturing and regulatory approval. Conclusions: Smart hydrogels represent a transformative approach to precision wound management, offering superior adaptability and therapeutic delivery. To achieve widespread clinical adoption, future research must address manufacturing scalability and focus on emerging trends, such as the integration of biosensors and AI-powered monitoring systems, to create fully intelligent wound care solutions. Full article

The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyber–physical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities. Full article

Non-Intrusive Load Monitoring (NILM) enables appliance-level classification from aggregate electrical measurements and supports efficient energy management in smart buildings. However, the accuracy of existing NILM methods is often limited by the inability of conventional feature extraction techniques to capture nonlinear steady-state behavior. This study proposes a novel feature extraction framework for appliance classification, which integrates phase-space reconstruction (PSR) with 2-D Fourier series to derive geometry-based descriptors of appliance current waveforms. Unlike traditional signal-processing methods, the proposed approach utilizes the nonlinear geometric structure revealed by PSR and encodes it through Fourier descriptors, offering a discriminative, low-dimensional feature space suitable for classification using supervised machine learning algorithms. The method is evaluated on the high-resolution controlled single-appliance recordings from the COOLL dataset using the K-Nearest Neighbor (KNN) classifier. Extension to aggregated multi-appliance NILM scenarios would require additional stages such as event detection and load separation. Sensitivity analysis demonstrates that classification performance depends strongly on the choice of time delay and harmonic order, with optimal settings yielding an accuracy of up to 99.52% using KNN. The results confirm that larger time delays and a small number of harmonics effectively capture appliance-specific signatures. The findings highlight the effectiveness of PSR–Fourier-based geometric features as a robust alternative to conventional NILM feature extraction strategies. Full article