Search Results (90)

The increasing interconnection of inverter-based resources (IBRs) with low short-circuit current has weakened grid strength, making phase-locked loops (PLLs) susceptible to instability due to accumulated phase-angle error under current limiting. This study defines such instability as IBR instability induced by reduced grid robustness and proposes a root-mean-square (RMS) model-based screening method. After fault clearance, the residual q-axis voltage observed by the PLL is treated as a disturbance signal and, using the PLL synchronization equations, is analyzed with a standard second-order formulation. The maximum phase angle at which synchronization fails is defined as ${\theta }_{peak}$, and the corresponding q-axis voltage is defined as $V_{q,crit}$. This value is then mapped to a screening metric $P_{peak}$ suitable for RMS-domain assessment. The proposed methodology is applied to the IEEE 39-bus test system: the stability boundary and $P_{peak}$ are obtained in Power System Simulator for Engineering (PSSE), and the results are validated through electromagnetic transient (EMT) simulations in PSCAD. The findings demonstrate that the RMS-based screening can effectively identify operating conditions that are prone to PLL instability in weak grids, providing a practical tool for planning and operation with high IBR penetration. This screening method supports power system planning for high-penetration inverter-based resources by identifying weak-grid locations that require EMT studies to ensure secure operation after grid faults. Full article

Benchmarking has been central to performance evaluation for more than four decades. Reinhold P. Weicker’s 1990 survey in IEEE Computer offered an early, rigorous critique of standard benchmarks, warning about pitfalls that continue to surface in contemporary practice. This review synthesizes the evolution from classical synthetic benchmarks (Whetstone, Dhrystone) and application kernels (LINPACK) to modern suites (SPEC CPU2017), domain-specific metrics (TPC), data-intensive and graph workloads (Graph500), and Artificial Intelligence/Machine Learning (AI/ML) benchmarks (MLPerf, TPCx-AI). We emphasize energy and sustainability (Green500, SPECpower, MLPerf Power), reproducibility (artifacts, environments, rules), and domain-specific representativeness, especially in biomedical and bioinformatics contexts. Building upon Weicker’s methodological cautions, we formulate a concise checklist for fair, multidimensional, reproducible benchmarking and identify open challenges and future directions. Full article

The displacement of synchronous machines by inverter-based resources raises critical concerns regarding the stability of future low-inertia power systems. Grid-forming (GFM) inverters offer a pathway to address these challenges by autonomously establishing voltage and frequency while emulating inertia and damping. This paper investigates the feasibility of operating a transmission-scale network with 100% GFM penetration by fully replacing all synchronous generators in the IEEE 39-bus system with a heterogeneous mix of droop, virtual synchronous machine (VSM), and synchronverter controls. System stability is assessed under a severe fault-initiated separation, focusing on frequency and voltage metrics defined through center-of-inertia formulations and standard acceptance envelopes. A systematic parameter sweep of virtual inertia (H) and damping ($D_p$) reveals their distinct and complementary roles: inertia primarily shapes the Rate of Change in Frequency and excursion depth, while damping governs convergence speed and steady-state accuracy. All tested parameter combinations remain within established stability limitations, confirming the robust operability of a fully inverter-dominated grid. These findings demonstrate that properly tuned GFM inverters can enable secure and reliable operation of future power systems without reliance on synchronous machines. Full article

Background and Objectives: Dynamic nutrient profiling represents a paradigm shift in personalized nutrition, integrating real-time nutritional assessment with individualized dietary recommendations through advanced algorithmic approaches, biomarker integration, and artificial intelligence. This comprehensive systematic review and meta-analysis examines the current state of dynamic nutrient profiling methodologies for personalized diet planning, evaluating their effectiveness, methodological quality, and clinical outcomes. Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive search of electronic databases (PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar) from inception to December 2024. The protocol was prospectively registered in PROSPERO (Registration: CRD42024512893). Studies were systematically screened using predefined inclusion criteria, quality was assessed using validated tools (RoB 2, ROBINS-I, Newcastle–Ottawa Scale), and data were extracted using standardized forms. Random-effects meta-analyses were performed where appropriate, with heterogeneity assessed using I² statistics. Publication bias was evaluated using funnel plots and Egger’s test. Results: From 2847 initially identified records plus 156 from additional sources, 117 studies met the inclusion criteria after removing 391 duplicates and systematic screening, representing 45,672 participants across 28 countries. Studies employed various methodological approaches: algorithmic-based profiling systems (76 studies), biomarker-integrated approaches (45 studies), and AI-enhanced personalized nutrition platforms (23 studies), with some studies utilizing multiple methodologies. Meta-analysis revealed significant improvements in dietary quality measures (standardized mean difference: 1.24, 95% CI: 0.89–1.59, p < 0.001), dietary adherence (risk ratio: 1.34, 95% CI: 1.18–1.52, p < 0.001), and clinical outcomes including weight reduction (mean difference: −2.8 kg, 95% CI: −4.2 to −1.4, p < 0.001) and improved cardiovascular risk markers. Substantial heterogeneity was observed across studies (I² = 78–92%), attributed to methodological diversity and population characteristics. AI-enhanced systems demonstrated superior effectiveness (SMD = 1.67) compared to traditional algorithmic approaches (SMD = 1.08). However, current evidence is constrained by practical limitations, including the technological accessibility of dynamic profiling systems and equity concerns in vulnerable populations. Additionally, the evidence base shows geographical concentration, with most studies conducted in high-income countries, underscoring the need for research in diverse global settings. These findings have significant implications for shaping public health policies and clinical guidelines aimed at integrating personalized nutrition into healthcare systems and addressing dietary disparities at the population level. Conclusions: Dynamic nutrient profiling demonstrates significant promise for advancing personalized nutrition interventions, with robust evidence supporting improved nutritional and clinical outcomes. However, methodological standardization, long-term validation studies exceeding six months, and comprehensive cost-effectiveness analyses remain critical research priorities. The integration of artificial intelligence and multi-omics data represents the future direction of this rapidly evolving field. Full article

The increasing penetration of distributed energy resources (DERs) in unbalanced distribution networks presents significant challenges for optimal operation, particularly concerning power loss minimization and voltage regulation. This paper proposes a comprehensive Optimal Power Flow (OPF) model that coordinates various assets, including on-load tap changers (OLTCs), reactive power compensators, and controllable electric vehicles (EVs). To solve this complex and non-convex optimization problem, we developed the Shrinking Net Algorithm (SNA), a novel metaheuristic with mathematically proven convergence. The proposed framework was validated using the standard IEEE 123-bus test system. The results demonstrate significant operational improvements: total active power loss was reduced by 32.1%, from 96.103 kW to 65.208 kW. Furthermore, all node voltage violations were eliminated, with the minimum system voltage improving from 0.937 p.u. to a compliant 0.973 p.u. The findings confirm that the proposed SNA is an effective and robust tool for this application, highlighting the substantial economic and technical benefits of coordinated asset control for modern distribution system operators. Full article

Optimally positioning renewable-based distributed generation (DG) units is vital for mitigating technical challenges in active distribution networks (ADNs). With the goal of achieving technical goals such as reduced losses and mitigated unstable voltage, two available optimization methods have been combined for positioning wind-energy DGs: grey wolf optimization (GWO) and opposition-based learning (OBL), which tries out opposite possibilities for each assessed population, thus addressing GWO’s susceptibility to becoming stuck in local optima. This new fusion technique enhances the algorithm’s scrutiny of each area under consideration and reduces the likelihood of premature convergence. Results show that, compared with standard GWO, the proposed OBL-GWO reduced active power losses by up to 95.16%, improved total voltage deviation (TVD) by 99.7%, and increased the minimum bus voltage from 0.907 p.u. to 0.994 p.u. In addition, the voltage stability index (VSI) was also enhanced by nearly 30%. The proposed methodology outperformed both standard GWO on the IEEE 33-bus test system and comparable techniques reported in the literature consistently. By accounting for the uncertainty in wind generation, load demand, and future growth, this framework offers a more reliable and practical planning approach that better reflects real operating conditions. Full article

Distribution System Operators (DSOs) increasingly need planning tools that coordinate utility-influenced assets—such as electric-vehicle charging stations (EVCS) and voltage-support resources—with customer-sited distributed generation (DG). We present a Nash-equilibrium-based Iterative Best Response Algorithm (IBRA-NE) for joint planning of DG and EVCS in radial distribution networks. The framework supports two applicability modes: (i) a DSO-plannable mode that co-optimizes EVCS siting/sizing and utility-controlled reactive support (DG operated as VAR resources or functionally equivalent devices), and (ii) a customer-sited mode that treats DG locations as fixed while optimizing DG reactive set-points/sizes and EVCS siting. The objective minimizes network losses and voltage deviation while incorporating deployment costs and EV charging service penalties, subject to standard operating limits. A backward/forward sweep (BFS) load flow with Monte Carlo simulation (MCS) captures load and generation uncertainty; a Bus Voltage Deviation Index (BVDI) helps identify weak buses. On the EEU 114-bus system, the method reduces base-case losses by up to 57.9% and improves minimum bus voltage from 0.757 p.u. to 0.931 p.u.; performance remains robust under a 20% load increase. The framework explicitly accommodates regulatory contexts where DG siting is customer-driven by treating DG locations as fixed in such cases while optimizing EVCS siting and sizing under DSO planning authority. A mixed scenario with 5 DGs and 3 EVCS demonstrates coordinated benefits and convergence properties relative to PSO, GWO, RFO, and ARFO. Additionally, the proposed algorithm is also tested on the IEEE 69-bus system and results in acceptable performance. The results indicate that game-theoretic coordination, applied in a manner consistent with regulatory roles, provides a practical pathway for DSOs to plan EV infrastructure and reactive support in networks with uncertain DER behavior. Full article

This paper presents an adaptive Maximum Power Point Tracking (MPPT) strategy for grid-connected photovoltaic (PV) systems that uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Particle Swarm Optimization (PSO) to enhance energy extraction efficiency under diverse environmental conditions. The proposed ANFIS-PSO-based MPPT controller performs dynamic adjustment Pulse Width Modulation (PWM) switching to minimize Total Harmonic Distortion (THD); this will ensure rapid convergence to the maximum power point (MPP). Unlike conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, which struggle with tracking delays and local maxima in partial shading scenarios, the proposed approach efficiently identifies the Global Maximum Power Point (GMPP), improving energy harvesting capabilities. Simulation results in MATLAB/Simulink R2023a demonstrate that under stable irradiance conditions (1000 W/m², 25 °C), the controller was able to achieve an MPPT efficiency of 99.2%, with THD reduced to 2.1%, ensuring grid compliance with IEEE 519 standards. In dynamic irradiance conditions, where sunlight varies linearly between 200 W/m² and 1000 W/m², the controller maintains an MPPT efficiency of 98.7%, with a response time of less than 200 ms, outperforming traditional MPPT algorithms. In the partial shading case, the proposed method effectively avoids local power maxima and successfully tracks the Global Maximum Power Point (GMPP), resulting in a power output of 138 W. In contrast, conventional techniques such as P&O and INC typically fail to escape local maxima under similar conditions, leading to significantly lower power output, often falling well below the true GMPP. This performance disparity underscores the superior tracking capability of the proposed ANFIS-PSO approach in complex irradiance scenarios, where traditional algorithms exhibit substantial energy loss due to their limited global search behavior. The novelty of this work lies in the integration of ANFIS with PSO optimization, enabling an intelligent self-adaptive MPPT strategy that enhances both tracking speed and accuracy while maintaining low computational complexity. This hybrid approach ensures real-time adaptation to environmental fluctuations, making it an optimal solution for grid-connected PV systems requiring high power quality and stability. The proposed controller significantly improves energy harvesting efficiency, minimizes grid disturbances, and enhances overall system robustness, demonstrating its potential for next-generation smart PV systems. Full article

Background. Needle-related procedures (NRPs) in cancer care are often associated with significant pain and anxiety, contributing to psychological and physiological distress. This study aimed to assess the effectiveness of virtual reality (VR)-based interventions in reducing anxiety, pain, depression, fear, and physiological parameters (pulse rate and respiratory rate) in patients with cancer undergoing NRPs. Methods. A systematic search of 11 databases (CINAHL, Cochrane Library, Embase, IEEE Xplore, Medline, ProQuest, PsycINFO, PubMed, Scopus, Web of Science, and CNKI) was conducted from inception to 15 May 2025. Two independent reviewers selected and extracted studies based on predefined inclusion and exclusion criteria. Meta-analyses were performed using Cochrane RevMan 2024 software. Heterogeneity was assessed using Higgins’ I² statistics and Cochran’s Q test. The GRADE framework was applied to evaluate the quality of evidence. Results. Fourteen randomized controlled trials (RCTs) with 1089 participants were included. VR interventions showed significant benefits compared to controls in reducing anxiety (standard mean difference [SMD] = −1.74, 95% confidence interval [CI]: −2.47 to −1.01, p < 0.001), pain (SMD = −1.30, 95% CI: −1.93 to −0.67, p < 0.001), depression (SMD = −0.73, 95% CI: −0.96 to −0.50, p < 0.001), fear (mean difference [MD] = −1.31, 95% CI: −1.56 to −1.06, p < 0.001), and respiratory rate (MD = −3.85, 95% CI: −6.18 to −1.52, p = 0.001). However, no significant difference was found in pulse rate (MD = 0.25, 95% CI: −14.32 to 14.81, p = 0.97). Conclusions. VR-based interventions are effective in alleviating psychological symptoms (anxiety, depression, fear) and physiological distress (pain, respiratory rate) in patients with cancer undergoing NRPs. However, they do not significantly impact pulse rate. Interpretation of findings should consider limitations such as the small number of studies, limited sample sizes, and high heterogeneity. Further high-quality RCTs with follow-up assessments are warranted. Customizing VR interventions to address demographic and procedural needs may further enhance their effectiveness. Full article

Cooperative Intelligent Transportation Systems (C-ITSs) are emerging as transformative technologies, paving the way for safe and fully automated driving solutions. As the demand for autonomous vehicles accelerates, the development of advanced Radio Access Technologies capable of delivering reliable, low-latency vehicular communications has become paramount. Standardized approaches for Vehicular-to-Everything (V2X) communication often fall short in addressing the dynamic and diverse requirements of multi-service, multi-priority systems. Conventional vehicular networks employ static parameters such as Access Category (AC) in IEEE 802.11p-based ITS-G5 and Resource Reservation Interval (RRI) in C-V2X PC5 for prioritizing different V2X services. This static parameter assignment performs unsatisfactorily in dynamic and diverse requirements. To bridge this gap, we propose intelligent Multi-Attribute Decision-Making algorithms for adaptive AC selection in ITS-G5 and RRI adjustment in C-V2X PC5, tailored to the varying priorities of active V2X services. These adaptations are integrated with a priority-aware rate-control mechanism to enhance congestion management. Through extensive simulations conducted using NS3, our proposed strategies demonstrate superior performance compared to standardized methods, achieving improvements in one-way end-to-end latency, Packet Reception Ratio (PRR) and overall communication reliability. Full article

This review analyzes the role of artificial intelligence (AI) and automation in optimizing vegetable production within hydroculture systems. Methods: Following the PRISMA methodology, this study examines research on IoT-based monitoring and AI techniques, particularly Deep Neural Networks (DNNs), K-Nearest Neighbors (KNNs), Fuzzy Logic (FL), Convolutional Neural Networks (CNNs), and Decision Trees (DTs). Additionally, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models were analyzed due to their effectiveness in processing temporal data and improving predictive capabilities in nutrient optimization. These models have demonstrated high precision in managing key parameters such as pH, temperature, electrical conductivity, and nutrient dosing to enhance crop growth. The selection criteria focused on peer-reviewed studies from 2020 to 2024, emphasizing automation, efficiency, sustainability, and real-time monitoring. After filtering out duplicates and non-relevant papers, 72 studies from the IEEE, SCOPUS, MDPI, and Google Scholar databases were analyzed, focusing on the applicability of AI in optimizing vegetable production. Results: Among the AI models evaluated, Deep Neural Networks (DNNs) achieved 97.5% accuracy in crop growth predictions, while Fuzzy Logic (FL) demonstrated a 3% error rate in nutrient solution adjustments, ensuring reliable real-time decision-making. CNNs were the most effective for disease and pest detection, reaching a precision rate of 99.02%, contributing to reduced pesticide use and improved plant health. Random Forest (RF) and Support Vector Machines (SVMs) demonstrated up to 97.5% accuracy in optimizing water consumption and irrigation efficiency, promoting sustainable resource management. Additionally, LSTM and RNN models improved long-term predictions for nutrient absorption, optimizing hydroponic system control. Hybrid AI models integrating machine learning and deep learning techniques showed promise for enhancing system automation. Conclusion: AI-driven optimization in hydroculture improves nutrient management, water efficiency, and plant health monitoring, leading to higher yields and sustainability. Despite its benefits, challenges such as data availability, model standardization, and implementation costs persist. Future research should focus on enhancing model accessibility, interoperability, and real-world validation to expand AI adoption in smart agriculture. Furthermore, the integration of LSTM and RNN should be further explored to enhance real-time adaptability and improve the resilience of predictive models in hydroponic environments. Full article

This study investigates the impact of wall roughness on the performance of the Non-Line-of-Sight (NLOS) component in Visible Light Communication (VLC) systems designed for underground mining environments, adhering to safety and communication standards such as IEC 60079-28(intrinsic safety in explosive atmospheres) and IEEE 802.15.7 (VLC parameters). Using probabilistic models aligned with the ITU-R P.1238 propagation guidelines, the research evaluates how wall materials (e.g., coal, shale, limestone) and their irregular geometries, characterized by surface roughness profiles compliant with ISO 8503-2,influence reflection coefficients (0.05–0.85 range), incidence angles (0°–90°), and irradiance angles (5°–180°), which are critical for signal propagation. Simulation scenarios, parameterized with material reflectivity data from ASTM E423, explore the effects of statistical distributions (uniform, normal with $\mu$ = 0.3, $\sigma$ = 0.2; exponential $\lambda$ = 2; gamma $\alpha$ = 0.5, $\beta$ = 0.2) on power distribution, channel impulse response, and reflection coefficients. The results indicate variations in maximum received power: a decrease of 80% for uniform distribution, an increase of 150% for exponential distribution, and a 100% increase for gamma distribution in reflection conditions. Under incidence and irradiance conditions, uniform distribution exhibited a 158.62% increase, whereas exponential distribution and gamma distribution experienced reductions of 72.22% and 7.04%, respectively. These variations align with IEC 62973-1 EMI limits and emphasize the role of roughness (Ra = 0.8–12.5 μm per ASME B46.1). Full article

In the contemporary global context, excessive fossil fuel consumption remains a critical issue, particularly within the transportation sector. Electric vehicles offer a promising alternative due to their durability and reduced greenhouse gas emissions. However, their rapid adoption has introduced significant challenges, including increased network power losses, deteriorating voltage profiles, and declining substation power factors. This study proposes an approach that integrates fuzzy objective optimization with African Vulture Optimization (AVO) to determine the optimal sitting and sizing of distributed generations (DG), shunt capacitors (SC), and electric vehicle charging stations (EVCS) within radial distribution systems (RDS). The proposed methodology is evaluated on the standard IEEE-69 bus RDS. A detailed comparative analysis between the proposed simultaneous optimization approach for DGs, SCs, and EVCSs and with the traditional two-staged method is presented. The findings indicate that the proposed strategy not only matches but surpasses the performance of existing strategies for the reduction of power losses and enhancement of bus voltage profiles. Key findings include a significant reduction in active and reactive power line loss, with losses minimized by 85.90% and 82.15%, respectively. In addition, an improvement in the minimum bus voltage to 0.98 p.u. is also achieved. Thereafter, the proposed issue is solved in different loading scenarios to present the effectiveness of the approach under different operating conditions. This research effectively demonstrates the complexities introduced by EVCS integration and addresses the issue with simultaneous optimal sitting and sizing of DGs, SCs, and EVCSs that significantly enhance the sustainability and efficiency of RDS. Full article

This paper presents a method of enhancing voltage and power stability in a distribution system with the photovoltaic benefits of battery energy storage. The objective is to use photovoltaic-distributed generation and a battery energy storage system in order to reduce power loss to a minimum and generate a voltage up to or above 0.95 p.u, which is the voltage standard in Thailand. This paper used MATLAB (Version R2024b-acdemic use) to conduct test experiments, and the system for the case studies is an IEEE 33-bus radial distribution system. There are five study cases in this paper. Case 1 is before the installation of the photovoltaic and battery energy storage system. Case 2 is the installation of the photovoltaic-distributed generator in four buses. Case 3 is the installation of the photovoltaic-distributed generator in only one bus. Case 4 is the installation of the photovoltaic distributed generator in two buses that have the most voltage drop. Case 5 is the installation of the photovoltaic-distributing generator in four buses and the battery energy storage system in two buses. The results show that Case 5 is the best because the voltage drop is never below 0.95 per unit or below the lowest power loss. Full article

The simulation results presented based on the proposed system demonstrated significant improvements in communication reliability, packet loss reduction and signal stability, highlighting its superiority in real urban traffic conditions. Using the IEEE 802.11p standard and a modular dual-antenna architecture, the system maintained a latency below 10 ms over distances of over 3 km, without noticeable signal loss. GNSS synchronization ensured precise vehicle positioning and dynamic signal optimization. There are results and approaches that highlight the limitations of IEEE 802.11p in dense traffic scenarios; the current approach has reduced packet loss to below 5%. Its integration also allows compatibility with future technologies such as 5G and C-V2X, guaranteeing scalability and long-term relevance. The proposed prototype sets a new standard in vehicular communications, combining high performance with a flexible and extensible architecture, making it a viable solution for large-scale deployments in smart cities, supporting the transition to safer and more sustainable transportation infrastructures. Full article