Search Results (3,534)

Power flow analysis of low-voltage network (LVN) is one of the most crucial methods for achieving refined management of such networks. To accurately calculate the three-phase (TP) probabilistic power flow (PPF) distribution in LVN, this paper first draws on the injection-type Newton method; by leveraging TP power measurements relative to the neutral point obtained from smart meters, the injected power is expressed in terms of injected current equations, thereby establishing TP power flow models for various components within the low-voltage distribution transformer area grid. Subsequently, addressing the stochastic fluctuation models of load power and photovoltaic output, this paper employs conventional numerical methods and an improved Latin hypercube sampling technique. Utilizing linearized power flow equations and based on the improved semi-invariant method (SIM) and Gram–Charlier (GC) series fitting, a calculation method for three-phase PPF in low-voltage distribution transformer area grids using the improved semi-invariant is proposed. Finally, simulations of the proposed three-phase PPF method are conducted using the IEEE-13 node distribution system. The simulation results demonstrate that the proposed method can effectively perform three-phase PPF calculations for the distribution transformer area grid and accurately obtain probabilistic distribution information of the TP power flow within the grid. Full article

Credit card fraud remains a major cause of financial loss around the world. Traditional fraud detection methods that rely on supervised learning often struggle because fraudulent transactions are rare compared to legitimate ones, leading to imbalanced datasets. Additionally, the models must be retrained frequently, as fraud patterns change over time and require new labeled data for retraining. To address these challenges, this paper proposes an ensemble unsupervised learning approach for credit card fraud detection that combines Autoencoders (AEs), Self-Organizing Maps (SOMs), and Restricted Boltzmann Machines (RBMs), integrated with an Adaptive Reconstruction Threshold (ART) mechanism. The ART dynamically adjusts anomaly detection thresholds by leveraging the clustering properties of SOMs, effectively overcoming the limitations of static threshold approaches in machine learning and deep learning models. The proposed models, AE-ASOMs (Autoencoder—Adaptive Self-Organizing Maps) and RBM-ASOMs (Restricted Boltzmann Machines—Adaptive Self-Organizing Maps), were evaluated on the Kaggle Credit Card Fraud Detection and IEEE-CIS datasets. Our AE-ASOM model achieved an accuracy of 0.980 and an F1-score of 0.967, while the RBM-ASOM model achieved an accuracy of 0.975 and an F1-score of 0.955. Compared to models such as One-Class SVM and Isolation Forest, our approach demonstrates higher detection accuracy and significantly reduces false positive rates. In addition to its performance, the model offers considerable computational efficiency with a training time of 200.52 s and memory usage of 3.02 megabytes. Full article

Cardiovascular disease remains the world’s leading cause of mortality, yet everyday care still relies on episodic, symptom-driven interventions that detect ischemia, arrhythmias, and remodeling only after tissue damage has begun, limiting the effectiveness of therapy. A narrative review synthesized 183 studies published between 2016 and 2025 that were located through PubMed, MDPI, Scopus, IEEE Xplore, and Web of Science. This review examines CVD diagnostics using innovative technologies such as digital cardiovascular twins, which involve the collection of data from wearable IoT devices (electrocardiography (ECG), photoplethysmography (PPG), and mechanocardiography), clinical records, laboratory biomarkers, and genetic markers, as well as their integration with artificial intelligence (AI), including machine learning and deep learning, graph and transformer networks for interpreting multi-dimensional data streams and creating prognostic models, as well as generative AI, medical large language models (LLMs), and autonomous agents for decision support, personalized alerts, and treatment scenario modeling, and with cloud and edge computing for data processing. This multi-layered architecture enables the detection of silent pathologies long before clinical manifestations, transforming continuous observations into actionable recommendations and shifting cardiology from reactive treatment to predictive and preventive care. Evidence converges on four layers: sensors streaming multimodal clinical and environmental data; hybrid analytics that integrate hemodynamic models with deep-, graph- and transformer learning while Bayesian and Kalman filters manage uncertainty; decision support delivered by domain-tuned medical LLMs and autonomous agents; and prospective simulations that trial pacing or pharmacotherapy before bedside use, closing the prediction-intervention loop. This stack flags silent pathology weeks in advance and steers proactive personalized prevention. It also lays the groundwork for software-as-a-medical-device ecosystems and new regulatory guidance for trustworthy AI-enabled cardiovascular care. Full article

This paper introduces a new optimization problem-solving method based on how the stingless bee Tetragonula carbonaria builds and regulates temperature in the hive. The Tetragonula carbonaria Optimization Algorithm (TGCOA) models three different behaviors: strengthening the structure’s hive when it is cold, building combs in a spiral pattern at medium temperatures, and stabilizing the hive when it is hot. These temperature-dependent strategies dynamically balance global exploitation and local exploration within the solution space, enabling a more efficient search. To validate the efficiency and effectiveness of the proposed method, the TGCOA algorithm was tested using ten unimodal and ten multimodal benchmark functions, twenty-eight constrained problems with dimensions set to 10, 30, 50, and 100 taken from the IEEE CEC 2017, and seven real-world engineering design challenges. Furthermore, it was compared with ten algorithms from the literature. Wilcoxon signed-rank and Friedman statistical tests were performed to assess the outcomes. The results on the benchmark problems showed that the approach outperformed 80% of the algorithms at a 5% significance level in the Wilcoxon signed-rank test and ranked first overall according to the Friedman test. Additionally, in multidimensional problems, the TGCOA was ranked first in dimensions 30, 50, and 100. Moreover, in engineering problems, the approach demonstrated a high capacity to solve constraint problems, obtaining better results than the algorithms that were compared. Full article

This paper presents an exhaustive search approach to determine the optimal placement of a Unified Power Quality Conditioner (UPQC) in a distribution system that integrates a distributed generation (DG) unit based on photovoltaic (PV) panels. The main objective is to enhance voltage profiles and reduce total harmonic distortion (THD) in the presence of nonlinear loads. A multi-objective optimization model is formulated, combining THD minimization and voltage deviation reduction through a weighted cost function. Two case studies are conducted using the IEEE 33-bus test system modeled in MATLAB Simulink, considering different scenarios: one with nonlinear loads and another with additional DG integration. The UPQC is tested at critical nodes to assess its impact on power quality indicators. Results show that placing the UPQC at node 14 yields the lowest cost function value in both cases, with THD reductions exceeding 90% at the installation node and notable improvements across the system. These findings confirm that brute-force optimization is a reliable and effective strategy for UPQC siting, especially in distribution networks subjected to nonlinear disturbances and renewable-based DG. The proposed methodology provides a practical framework for power quality enhancement and supports decision-making in modern smart grid environments. Full article

The Internet of Things (IoT) has emerged as a transformative technology in managing Alzheimer’s Disease (AD), offering novel solutions for early diagnosis, continuous patient monitoring, and assistive care. This review presents a comprehensive analysis of IoT-enabled systems tailored to AD care, focusing on wearable biosensors, cognitive monitoring tools, smart home automation, and Artificial Intelligence (AI)-driven analytics. A systematic literature survey was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify, screen, and synthesize 236 relevant studies primarily published between 2020 and 2025 across IEEE Xplore, PubMed, Scopus and Web of Science. The inclusion criteria targeted peer-reviewed articles that proposed or evaluated IoT-based solutions for AD detection, progression monitoring, or patient assistance. Key findings highlight the effectiveness of the IoT in detecting behavioral and cognitive changes, enhancing safety through real-time alerts, and improving patient autonomy. The review also explores integration challenges such as data privacy, system interoperability, and clinical adoption. The study reveals critical gaps in real-world deployment, clinical validation, and ethical integration of IoT-based systems for Alzheimer’s care. This study aims to serve as a definitive reference for researchers, clinicians, and developers working at the intersection of the IoT and neurodegenerative healthcare. Full article

This paper presents the design, development, and evaluation of the REDI Monitoring System, a web-based platform aimed at enhancing the management and monitoring of funded research projects at Romblon State University (RSU). The system provides streamlined functionalities for proposal creation, submission, collaborator management, and administrative oversight, tailored to the needs of both students and faculty members. The development process adhered to established software engineering standards to ensure robustness and usability. A comprehensive testing phase was conducted with 50 participants, including students and faculty, following the ISO/IEC/IEEE 29119 software testing framework. Results demonstrated high user satisfaction, with over 90% of participants finding the system user-friendly and reliable. Minor areas for improvement were identified in notification delivery and interface responsiveness for faculty users. The REDI Monitoring System presents an effective and efficient solution that supports RSU’s research administration processes, fostering greater collaboration and transparency in funded research activities. Full article

To address the impact of distributed energy resource volatility on distribution network fault restoration, this paper proposes a strategy that incorporates net load forecasting. A Bayesian-optimized long short-term memory neural network is used to accurately predict the net load within fault-affected areas, achieving an R² of 0.9569 and an RMSE of 12.15 kW. Based on the forecasting results, a fast restoration optimization model is established, with objectives to maximize critical load recovery, minimize switching operations, and reduce network losses. The model is solved using a genetic algorithm enhanced with quantum particle swarm optimization (GA-QPSO), a hybrid metaheuristic known for its superior global exploration and local refinement capabilities. GA-QPSO has been successfully applied in various power system optimization problems, including service restoration, network reconfiguration, and distributed generation planning, owing to its effectiveness in navigating large, complex solution spaces. Simulation results on the IEEE 33-bus system show that the proposed method reduces network losses by 33.2%, extends the power supply duration from 60 to 120 min, and improves load recovery from 72.7% to 75.8%, demonstrating enhanced accuracy and efficiency of the restoration process. Full article

Hybrid offshore wind-wave systems play an important role in renewable energy transition. To maximize energy utilization efficiency, a comprehensive assessment to select optimal locations is urgently needed. The hydraulic power characteristics of these systems cause power fluctuations that reduce grid frequency stability. Thus, a site suitability assessment and a grid-forming battery energy storage system (BESS) configuration method are proposed. Considering energy efficiency, dynamic complementary characteristics, and output stability, a framework integrating three indices of Composite Energy Output Index (CEOI), Time-Shifted Cross-Covariance Index (TS-CCI), and Energy Penetration Balance Index (EPBI) is constructed to assess site suitability. To ensure secure and stable operation of microgrid, the frequency response characteristics of the hybrid system are analyzed, and the corresponding frequency constraint is given. A BESS configuration method considering frequency constraint is developed to minimize life cycle costs and maintain grid stability. Applied to a case study along China’s southeast coast, the assessment method successfully identified the optimal offshore station, confirming its practical applicability. The BESS configuration method is validated on a modified IEEE 30-bus system, with a 6.35% decrease in life cycle cost and complete renewable utilization. This research provides a technical and cost-effective solution for integrating hybrid wind-wave energy into island microgrids. Full article

Energy storage will play an important role in the new power system with a high penetration of renewable energy due to its flexibility. Large-scale energy storage can participate in electricity market clearing, and knowing how to make more profits through bidding strategies in various types of electricity markets is crucial for encouraging its market participation. This paper considers differentiated bidding parameters for energy storage in a two-stage market with wind power integration, and transforms the market clearing process, which is represented by a two-stage bi-level model, into a single-level model using Karush–Kuhn–Tucker conditions. Nonlinear terms are addressed using binary expansion and the big-M method to facilitate the model solution. Numerical verification is conducted on the modified IEEE RTS-24 and 118-bus systems. The results show that compared to bidding as a price-taker and with marginal cost, the proposed mothod can bring a 16.73% and 13.02% increase in total market revenue, respectively. The case studies of systems with different scales verify the effectiveness and scalability of the proposed method. Full article

Singular value decomposition (SVD) plays a critical role in signal processing, image analysis, and particularly in MIMO channel estimation, where it enables spatial multiplexing and interference mitigation. This study presents a configurable parallel architecture for computing SVD on 4 × 4 and 8 × 8 correlation matrices using the Jacobi algorithm with Givens rotations, optimized via CORDIC. Exploiting algorithmic parallelism, the design achieves low-latency performance on a Virtex-5 FPGA, with processing times of 5.29 µs and 24.25 µs, respectively, while maintaining high precision and efficient resource usage. These results confirm the architecture’s suitability for real-time wireless systems with strict latency demands, such as those defined by the IEEE 802.11n standard. Full article

Aim: This scoping review examines the application of artificial intelligence (AI) in sports biomechanics, with a focus on enhancing performance and preventing injuries. The review addresses key research questions, including primary AI methods, their effectiveness in improving athletic performance, their potential for injury prediction, sport-specific applications, strategies for translating knowledge, ethical considerations, and remaining research gaps. Following the PRISMA-ScR guidelines, a comprehensive literature search was conducted across five databases (PubMed/MEDLINE, Web of Science, IEEE Xplore, Scopus, and SPORTDiscus), encompassing studies published between January 2015 and December 2024. After screening 3248 articles, 73 studies met the inclusion criteria (Cohen’s kappa = 0.84). Data were collected on AI techniques, biomechanical parameters, performance metrics, and implementation details. Results revealed a shift from traditional statistical models to advanced machine learning methods. Based on moderate-quality evidence from 12 studies, convolutional neural networks reached 94% agreement with international experts in technique assessment. Computer vision demonstrated accuracy within 15 mm compared to marker-based systems (6 studies, moderate quality). AI-driven training plans showed 25% accuracy improvements (4 studies, limited evidence). Random forest models predicted hamstring injuries with 85% accuracy (3 studies, moderate quality). Learning management systems enhanced knowledge transfer, raising coaches’ understanding by 45% and athlete adherence by 3.4 times. Implementing integrated AI systems resulted in a 23% reduction in reinjury rates. However, significant challenges remain, including standardizing data, improving model interpretability, validating models in real-world settings, and integrating them into coaching routines. In summary, incorporating AI into sports biomechanics marks a groundbreaking advancement, providing analytical capabilities that surpass traditional techniques. Future research should focus on creating explainable AI, applying rigorous validation methods, handling data ethically, and ensuring equitable access to promote the widespread and responsible use of AI across all levels of competitive sports. Full article

Recent advancements in synchrophasor measurement technologies have introduced an unprecedented level of visibility in power distribution networks (PDNs), providing a high-quality data foundation for the accurate perception of event source locations. However, the high cost and deployment expense pose a significant challenge in balancing system observability and event source location identification (ESLI) accuracy. In this paper, we propose a staged ESLI scheme based on voltage measurement deviation (VMD), which can achieve high-precision ESLI and event current calculations under extremely low-observability conditions, where the measurement devices are deployed only at the head substation and terminal buses. By setting an unknown event injection current and traversing each bus along the target feeder to derive the terminal bus voltage and its outgoing current, an ESLI model based on virtual event current injection (VCI) is constructed, which not only assists in the ESLI task but also confers the solving capability of the event current. Leveraging the event current calculation ability of the ESLI model, a VMD-based staged ESLI algorithm is developed, achieving an ordered and accurate search for the exact location of the event source in a goal-oriented manner. The effectiveness of the developed ESLI algorithm is evaluated on the IEEE 33-bus test system. Experimental results demonstrate that our VMD achieves high-precision ESLI and event current solving in PDNs under extremely low observability, significantly outperforming the state-of-the-art ESLI methods. Full article

Extreme damage poses a serious challenge to the safe operation of power grids. Optimizing the allocation of defense resources to improve the grid’s disaster resistance capabilities is the main concern of the power system. In this paper, a distributed robust optimal defense resource allocation method based on the defender–attacker–defender model is proposed to improve the disaster resilience of power grids. This method takes into account the uncertainty of restoration time due to different damage intensities and improves the efficiency of restoration resource scheduling in the restoration process. Meanwhile, a set covering-column and constraint generation (SC-C&CG) algorithm is proposed for the case that the mixed integer model does not satisfy the Karush–Kuhn–Tucker (KKT) condition. A case study based on the IEEE 24-bus system is conducted, and the results verify that the proposed method can minimize the system dumping load under the uncertainty of the maintenance time involved. Full article

This systematic literature review examines the integration of Artificial Intelligence (AI) and Extended Reality (XR) technologies in language education, synthesizing findings from 32 empirical studies published between 2017 and 2024. Guided by the PRISMA framework, we searched four databases—ERIC, Web of Science, Scopus, and IEEE Xplore—to identify studies that explicitly integrated both AI and XR to support language learning. The review explores publication trends, educational settings, target languages, language skills, learning outcomes, and theoretical frameworks, and analyzes how AI–XR technologies have been pedagogically integrated, and identifies affordances, challenges, design considerations, and future directions of AI–XR integration. Key integration strategies include coupling AI with XR technologies such as automatic speech recognition, natural language processing, computer vision, and conversational agents to support skills like speaking, vocabulary, writing, and intercultural competence. The reported affordances pertain to technical, pedagogical, and affective dimensions. However, challenges persist in terms of technical limitations, pedagogical constraints, scalability and generalizability, ethical and human-centered concerns, and infrastructure and cost barriers. Design recommendations and future directions emphasize the need for adaptive AI dialogue systems, broader pedagogical applications, longitudinal studies, learner-centered interaction, scalable and accessible design, and evaluation. This review offers a comprehensive synthesis to guide researchers, educators, and developers in designing effective AI–XR language learning experiences. Full article