Search Results (3,158)

Under the “dual carbon” goals, enhancing the energy supply for communication base stations is crucial for energy conservation and emission reduction. An individual base station with wind/photovoltaic (PV)/storage system exhibits limited scalability, resulting in poor economy and reliability. To address this, a collaborative power supply scheme for communication base station group is proposed. This paper establishes a capacity optimization configuration model for such integrated system and introduces a hybrid solution methodology combining random scenario analysis, Nondominated Sorting Genetic Algorithm II (NSGA-II), and Generalized Power Mean (GPM). Typical scenarios are solved using NSGA-II to generate a candidate solution set, which is then refined under operational constraints. The GPM method is applied to determine the final configuration by accounting for attribute correlations. A case study on a Chinese base station group, considering uncertainties in renewable generation, demonstrates the feasibility and effectiveness of the proposed approach. Full article

(1) Background: The Sustainable Development Goals highlight the importance of ensuring healthy lives and promoting well-being for all. Within this framework, it is essential to improve health outcomes for people with disabilities and to continue generating scientific evidence in this field. This study aimed to (I) analyze differences in anthropometric characteristics and cardiorespiratory fitness among adults with disabilities, and (II) analyze the association between anthropometric characteristics and cardiorespiratory fitness in the total sample and by sex. (2) Methods: Fifty-three adults over 50 years of age with disabilities participated in this study. Anthropometric measurements and the 6 min walk test (6MWT) were conducted, recording physical-physiological and mechanical variables (using heart rate monitors and Stryd devices). (3) Results: Significant differences were observed between men and women with disabilities in height (p < 0.001, ES = −1.10), hip-to-chest ratio (p < 0.05, ES = 0.75), mean heart rate (p < 0.05, ES = 0.67), and absolute minimum power (p < 0.05, ES = 0.64) achieved during the 6MWT. A significant correlation was found between anthropometric characteristics and 6MWT performance across the entire sample (r = −0.29 to −0.67, p < 0.05). Among women, these characteristics were associated with distance covered (r = −0.35 to −0.42, p < 0.05), whereas in men, they were associated with relative power (r = −0.60 to −0.83, p < 0.05). (4) Conclusions: The findings suggest that, in this sample, anthropometric characteristics are associated with specific 6MWT performance variables, with patterns differing by sex. Full article

This paper presents the reconstruction of signal paths acquired from a power electronics system for energy conversion and management. This reconstruction is performed using the Kalman filter (KF) for monitoring photovoltaic (PV) systems enabled for Internet of Things (IoT) systems. This proposal is motivated by the fact that the global energy transition towards renewable sources makes PV systems a crucial alternative. To guarantee the efficiency and stability of these systems, monitoring critical electrical parameters using IoT technology is essential. However, the measurements acquired are frequently corrupted by stochastic noise, which obscures the true behavior of the system and limits its accurate characterization. Based on this problem, the main objective of this work is explicitly defined as evaluating the effectiveness of the KF as a power-path reconstruction method capable of recovering accurate electrical trajectories from noisy measurements in IoT-monitored photovoltaic networks. To achieve this goal, the system is modeled as a discrete-time stochastic process and the KF is implemented as a real-time estimator of power flow behavior. The experiment was conducted using real-world generation and consumption data from a proprietary two-layer IoT platform: an Edge Layer (acquisition with ESP8266 and PZEM-004T-100A sensors) and a Cloud Layer (visualization on Things-Board). To validate the results, quantitative metrics including the mean squared error (MSE), statistical moments, and probability distributions were computed. The MSE values were found to be nearly zero across all reconstructed power-paths. The statistical moments exhibited near-perfect agreement with those of the actual power signals, approaching 100% correspondence. Additionally, the probability distributions were compared visually and assessed statistically using the Kolmogorov–Smirnov (KS) test. The resulting KS values were very low, confirming the high accuracy of the reconstruction for all power-paths. The proposed research concluded that the KF successfully reconstructed the power trajectories, demonstrating high agreement with the measured steady-state behavior. This study thus confirms that integrating Kalman filtering with IoT monitoring delivers a practically viable and statistically accurate method for power trajectory reconstruction, which is fundamental for enhancing the observability and reliability of photovoltaic energy systems. Full article

Bismuth–telluride-based alloys are excellent thermoelectric materials for Peltier modules and thermoelectric generators (TEGs). Owing to the emergence of the Internet of Things (IoT), the demand for sensors has increased considerably and self-power supplies to sensors using TEGs are garnering attention. To apply TEGs to IoT sensors, the thermoelectric materials used must be sufficiently small and thin while exhibiting high thermoelectric performance. In this study, Bi_0.5Sb_1.5Te₃ thin films were prepared using a pressure-gradient sputtering system. The obtained films exhibit a nanocrystalline structure with a significantly smooth surface and no preferred crystal orientation. Because the Bi_0.5Sb_1.5Te₃ thin films exhibit a high Seebeck coefficient and low thermal conductivity, the in-plane dimensionless figure of merit is 0.98, which is one of the highest values reported for thermoelectric materials measured near 300 K. Furthermore, the phonon mean-free path is 0.19 nm, as estimated using the 3ω method and nanoindentation. This value is significantly smaller than the average crystallite size of the thin film, thus indicating that phonon scattering occurs more frequently via ternary-alloy scattering inside the crystallites than via boundary scattering at the crystallite boundaries. The results of this study can advance thin-film TEGs as a source of self-sustaining power for IoT systems. Full article

Background: Accurate recognition of promoter sequences in Escherichia coli is fundamental for understanding gene regulation and engineering synthetic biological systems. However, existing computational methods struggle to simultaneously model long-range genomic dependencies and fine-grained local motifs, particularly the degenerate −10 and −35 elements of $\sigma 70$ promoters. To address this gap, we propose DNABERT2-CAMP, a novel hybrid deep learning framework designed to integrate global contextual understanding with high-resolution local motif detection for robust promoter identification. Methods: We constructed a balanced dataset of 8720 experimentally validated and negative 81-bp sequences from RegulonDB, literature, and the E. coli K-12 genome. Our model combines a pre-trained DNABERT-2 Transformer for global sequence encoding with a custom CAMP module (CNN-Attention-Mean Pooling) for local feature refinement. We evaluated performance using 5-fold cross-validation and an independent external test set, reporting standard metrics including accuracy, ROC AUC, and Matthews correlation coefficient (MCC). Results: DNABERT2-CAMP achieved 93.10% accuracy and 97.28% ROC AUC in cross-validation, outperforming existing methods including DNABERT. On an independent test set, it maintained strong generalization (89.83% accuracy, 92.79% ROC AUC). Interpretability analyses confirmed biologically plausible attention over canonical promoter regions and CNN-identified AT-rich/-35-like motifs. Conclusions: DNABERT2-CAMP demonstrates that synergistically combining pre-trained Transformers with convolutional motif detection significantly improves promoter recognition accuracy and interpretability. This framework offers a powerful, generalizable tool for genomic annotation and synthetic biology applications. Full article

Wind power forecasting is critical to renewable energy generation, as accurate predictions are essential for the efficient and reliable operation of power systems. However, wind power output is inherently unstable and is strongly affected by meteorological factors such as wind speed, wind direction, and atmospheric pressure. Weather conditions and wind power data are recorded by sensors installed in wind turbines, which may be damaged or malfunction during extreme or sudden weather events. Such failures can lead to inaccurate, incomplete, or missing data, thereby degrading data quality and, consequently, forecasting performance. To address these challenges, we propose a method that integrates a pre-trained large-scale language model (LLM) with the spatiotemporal characteristics of wind power networks, aiming to capture both meteorological variability and the complexity of wind farm terrain. Specifically, we design a spatiotemporal graph neural network based on multi-view maps as an encoder. The resulting embedded spatiotemporal map sequences are aligned with textual representations, concatenated with prompt embeddings, and then fed into a frozen LLM to predict future wind turbine power generation sequences. In addition, to mitigate anomalies and missing values caused by sensor malfunctions, we introduce a novel frequency-domain learning-based interpolation method that enhances data correlations and effectively reconstructs missing observations. Experiments conducted on real-world wind power datasets demonstrate that the proposed approach outperforms state-of-the-art methods, achieving root mean square errors of 17.776 kW and 50.029 kW for 24-h and 48-h forecasts, respectively. These results indicate substantial improvements in both accuracy and robustness, highlighting the strong practical potential of the proposed method for wind power forecasting in the renewable energy industry. Full article

Retrieval-augmented generation (RAG) has emerged as an effective approach for analyzing massive and diverse data. It offers promising avenues for energy management and intelligent decision support amid the accelerating digital transformation of the power industry. However, when applied to this specialized domain, traditional RAG systems face two key challenges: (1) poor comprehension of domain-specific terminology, leading to irrelevant retrieval, and (2) limited precision in re-ranking the retrieved results. To address these limitations, this paper presents an innovative integrated optimization framework. The framework enhances RAG performance in the electric power domain through two key strategies. First, we adapt a base embedding model to the domain using contrastive learning and iteratively refine hard negative samples to improve retrieval quality. Second, we employ a large language model (LLM) as a teacher to distill re-ranking knowledge into a lightweight bidirectional encoder representations from transformers (BERT) model, using a hybrid loss function that combines mean squared error (MSE) loss and margin ranking loss. The framework aims to simultaneously improve the model’s understanding of domain-specific terminology and the re-ranking accuracy of critical information. Experimental results on both a private power-domain dataset and the public DuReader_robust benchmark demonstrate that the proposed framework achieves significant performance gains. Comprehensive ablation studies confirm the necessity of each component and reveal their synergistic effects within the framework. Furthermore, sensitivity analyses of key hyperparameters confirm the effectiveness of our hybrid loss and identify optimal configurations that enhance both retrieval and generation performance. This work not only introduces an effective optimization framework tailored for domain-specific RAG applications but also advances industrial intelligence by enhancing the accuracy and reliability of information services. Full article

This paper presents an Artificial Neural Network (ANN) approach for estimating total real and reactive power losses in electrical power systems. Three network architectures were explored: the Multilayer Perceptron (MLP), the Radial Basis Function (RBF) network, and the Generalized Regression Neural Network (GRNN). The main advantage of the proposed methodology lies in its ability to rapidly compute power loss values throughout the system. ANN models are especially effective due to their capacity to capture the nonlinear characteristics of power systems, thus eliminating the need for iterative procedures. The applicability and effectiveness of the approach were evaluated using the IEEE 14-bus test system and compared with the continuation power flow method, which estimates losses using conventional numerical techniques. The results indicate that the ANN-based models performed well, achieving mean squared error (MSE) values below the predefined threshold during both training and validation (0.001). Notably, the networks accurately estimated the total power losses within the expected range, with residuals on the order of 10⁻⁴. Among the models tested, the RBF network showed slightly superior performance in terms of error metrics, requiring fewer centers to meet the established criteria compared to the MLP and GRNN models (11 centers). However, the GRNN achieved the shortest processing time; even so, all three networks produced satisfactory and consistent results, particularly in identifying the critical points of electrical power systems, which is of fundamental importance for ensuring system stability and operational reliability. Full article

Traditional single-prediction models often exhibit limitations in meeting wind power prediction requirements in complex operational scenarios. Furthermore, the inherent “black-box” nature of deep learning models leads to limited interpretability of predictions, hindering effective support for grid dispatch planning. To address these issues, this study proposes a novel day-ahead wind power prediction method, referred to as SHapley Additive exPlanations (SHAP)–Mixture of Experts (MoE), which integrates SHAP into an MoE framework. Here, SHAP is employed for interpretability purposes. This study innovatively transforms SHAP analysis into prior knowledge to guide the decision-making of the MoE gating network and proposes a two-layer dynamic interpretation mechanism based on the collaborative analysis of gating weights and SHAP values. This approach clarifies key meteorological factors and the model’s advantageous scenarios, while quantifying the uncertainty among multiple expert decisions. Firstly, each expert model was pre-trained, and its parameters were frozen to construct a candidate expert pool. Secondly, the SHAP vectors for each pre-trained expert were computed over all sample features to characterize their decision-making logic under varying scenarios. Thirdly, an augmented feature set was constructed by fusing the original meteorological features with SHAP attribution matrices from all experts; this set was used to train the gating network within the MoE framework. Finally, for new input samples, each frozen expert model generates a prediction along with its corresponding SHAP vector, and the gating network aggregates these predictions to produce the final forecast. The proposed method was validated using operational data from an offshore wind farm located in southeastern China. Compared with the best individual expert model and traditional ensemble forecasting models, the proposed method reduces the Root Mean Square Error (RMSE) by 0.23% to 4.92%. Furthermore, the method elucidates the influence of key features on each expert’s decisions, offering insights into how the gating network adaptively selects experts based on the input features and expert-specific characteristics across different scenarios. Full article

Background/Objectives: A review of the current literature does not provide a clear answer regarding the effectiveness of incorporating stretching exercises into warm-ups on performance and improving motor skills. The aim of this study was to compare the effects of a single application of sciatic neuromobilization and static stretching of the hamstring muscles on lower limb explosiveness, expressed by height of countermovement jump (CMJ) test. Methods: The study included 39 physically active men aged 20 to 26 (mean age 21.4 ± 2.2 years). Participants were randomly divided into 3 groups: 1. neuromobilization, 2. static stretching, 3. control group—no intervention. Immediately after the intervention, a CMJ test was performed. Jump height was measured at four timings: 1. before stretching (Pre), 2. immediately after (Post_0), 3. after 5 min (Post_5), 4. and after 10 min (Post_10). Results: Statistical analysis revealed a statistically significant difference in CMJ height between the neuromobilization and static groups and between the neuromobilization and control groups (p < 0.001). No statistically significant differences were observed between the static and control groups (p = 0.073). Post hoc comparisons revealed substantially higher vertical jump height in the neuromobilization group compared with the static group. Hedges’ g indicated a very large magnitude of effect, with values ranging from 3.73 to above 4.10. Conclusions: Neuromobilization induces short-term activation of lower limb muscles, resulting in increased explosive strength, whereas hamstrings static stretching of them does not positively impact short-term power generation. Full article

Side-Channel-based Anomaly Detection (SCAD) offers a powerful and non-intrusive means of detecting unauthorized behavior in IoT and cyber–physical systems. It leverages signals that emerge from physical activity—such as electromagnetic (EM) emissions or power consumption traces—as passive indicators of software execution integrity. This capability is particularly critical in IoT/IIoT environments, where large fleets of deployed devices are at heightened risk of firmware tampering, malicious code injection, and stealthy post-deployment compromise. However, its deployment remains constrained by the costly and time-consuming need to re-fingerprint whenever a program is updated or modified, as fingerprinting involves a precision-intensive manual capturing process for each execution path. To address this challenge, we propose a generative modeling framework that synthesizes realistic EM signals for newly introduced or updated execution paths. Our approach utilizes a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) framework trained on real EM traces that are conditioned on Execution State Descriptors (ESDs) that encode instruction sequences, operands, and register values. Comprehensive evaluations at instruction-level granularity demonstrate that our approach generates synthetic signals that faithfully reproduce the distinctive features of real EM emissions—achieving 85–92% similarity to real emanations. The inclusion of ESD conditioning further improves fidelity, reducing the similarity distance by ∼$13\%$. To gauge SCAD utility, we train a basic semi-supervised detector on the synthetic signals and find ROC-AUC results within ±1% of detectors trained on real EM data across varying noise conditions. Furthermore, the proposed 1DCNNGAN model (a CWGAN-GP variant) achieves faster training and reduced memory requirements compared with the previously leading ResGAN. Full article

In the context of a high proportion of renewable energy integration, active splitting section search—one of the “three defense lines” of a power system—is crucial for the security, stability, and long-term sustainability of islanded grids. Addressing the random fluctuations of high-penetration wind power and the weakened voltage support capability caused by multi-infeed HVDC, this paper proposes a slow-coherency-based active splitting section optimization model that explicitly accounts for wind power uncertainty and multi-infeed DC stability constraints. First, a GMM-K-means method is applied to historical wind data to model, sample, and cluster scenarios, efficiently generating and reducing a representative set of typical wind outputs; this accurately captures wind uncertainty while lowering computational burden. Subsequently, an improved particle swarm optimizer enhanced by genetic operators is used to optimize a multi-dimensional coherency fitness function that incorporates a refined equivalent power index, frequency constraints, and connectivity requirements. Simulations on a modified New England 39-bus system demonstrate that the proposed model markedly enlarges the post-split voltage stability margin and effectively reduces power-flow shocks and power imbalance compared with existing methods. This research contributes to enhancing the sustainability and operational resilience of power systems under energy transition. Full article

This study details the design, fabrication, and experimental validation of a wearable, non-invasive microwave sensor for continuous blood glucose monitoring. It incorporates a crescent-loaded elliptical patch antenna with a complementary split-ring resonator (CSRR) tag unit to greatly improve sensing sensitivity. The sensor operates across multiple resonant frequencies, enabling broadband dielectric characterization of glucose-dependent blood permittivity. Incorporation of the CSRR tag unit leads to a marked improvement in electromagnetic coupling and field confinement, resulting in a substantial increase in sensitivity, achieving 1.14 MHz/mg/dL in resonant frequency shift and 0.015 dB/mg/dL in reflection coefficient sensitivity compared to conventional designs. The sensor was fabricated on an FR-4 substrate and experimentally characterized using a vector network analyzer (VNA), showing strong agreement between simulated and measured S11 responses, with minimal frequency deviations and consistent resonance behavior. Experimental results confirmed improved sensitivity in response to glucose concentration variations over the range of 0–500 mg/dL, validating the sensor’s performance under realistic conditions. Furthermore, a physics-informed deep learning (PI-DL) model was developed to predict glucose concentration directly from measured S11 data. The model achieved enhanced prediction accuracy, with a mean absolute error below 1 mg/dL and a strong generalization across unseen samples, demonstrating the power of combining physical modeling with data-driven approaches. These results confirm that the proposed sensor, enhanced with the CSRR tag unit and supported by a PI-DL framework, offers a promising pathway toward next-generation non-invasive, accurate, and wearable glucose monitoring solutions. Full article

To address the problems of low detection accuracy, slow inference speed, and high computational cost in power-line detection during autonomous operations of agricultural UAVs, this study proposes an improved object detection model based on YOLOv12n. A power-line dataset was constructed using real-field images supplemented with the TTPLA dataset. The lightweight EfficientNetV2 was introduced as the backbone network to replace the original backbone. In the neck, dynamic snake convolution and a multi-scale cross-axis attention mechanism were incorporated, while the region attention partitioning and residual efficient layer aggregation network from the baseline model were retained. In the head, a Mixture of Experts (MoE) layer from ParameterNet was integrated. The improved model achieved 80.07%, 43.07%, and 77.35% of the original model’s parameters, computation, and weight size, respectively. With an IoU threshold greater than 0.5, the mean average precision (mAP0.5) reached 75.5%, representing improvements of 13.53%, 15.09%, 7.5% and 7.54% over YOLOv8n, YOLOv11n, YOLOv5n, and Line-YOLO, respectively. Only inferior to RF-DETR-Nano. On mobile-end testing, the inference speed reached 88.36 FPS and exhibits the highest inference speed across all experimental models. The improved model demonstrates excellent generalization, robustness, detection accuracy, target localization, and processing speed, making it highly suitable for power-line detection in agricultural UAV applications and providing technical support for future autonomous and intelligent agricultural operations. Full article

The high penetration rate of renewable energy poses significant challenges to the planning and operation of power systems in regions with scarce data. In these regions, it is impossible to accurately simulate the complex nonlinear dependencies among hydro–wind–solar energy resources, which leads to huge operational risks and investment uncertainties. To bridge this gap, this study proposes a new data-driven framework that embeds the natural climate cycle (24 solar terms) into a physically consistent scenario generation process, surpassing the traditional linear approach. This framework introduces the Comprehensive Similarity Distance (CSD) indicator to quantify the curve similarity of power amplitude, pattern trend, and fluctuation position, thereby improving the K-means clustering. Compared with the K-means algorithm based on the standard Euclidean distance, the accuracy of the improved clustering pattern extraction is increased by 3.8%. By embedding the natural climate cycle and employing a two-stage dimensionality reduction architecture: time compression via improved clustering and feature fusion via Kernel PCA, the framework effectively captures cross-source dependencies and preserves climatic periodicity. Finally, combined with the simplified Vine Copula model, high-fidelity joint scenarios with a normalized root mean square error (NRMSE) of less than 3% can be generated. This study provides a reliable and computationally feasible tool for stochastic optimization and reliability analysis in the planning and operation of future power systems with high renewable energy grid integration. Full article