Search Results (11,756)

Optimal coordination of directional overcurrent relays (DOCRs) in medium-to-large power systems constitutes a computationally demanding, mixed-integer, nonlinear optimisation problem whose complexity escalates rapidly with system size, making the simultaneous minimisation of relay operating time and computational cost a critical open challenge. This study presents a two-level hierarchical framework in which Type-2 Fuzzy C-Means (T2FCM) clustering partitions 226 fault scenarios into subproblems at the upper level, while the Hybrid Fractional Entropy Evolution (HFEE) algorithm independently optimises relay settings for each cluster at the lower level. HFEE integrates fractional-order velocity updates—derived from the Grünwald–Letnikov formulation—with a Shannon entropy diversity-control mechanism to prevent premature convergence. T2FCM captures inherent fault-current uncertainty through interval-valued type-2 fuzzy memberships, yielding more robust cluster assignments near protection-zone boundaries than crisp partitioning methods. The framework is validated on the extended IEEE 30-bus system. An ablation study demonstrates that standalone HFEE achieves a 29.19% improvement in $T_{\mathrm{op}}$ over the prior best-reported result; however, a comprehensive parameter sweep over cluster counts $K\in \{2,\dots ,8\}$ and fractional orders $\alpha \in \{0.1,\dots ,0.9\}$ across 50 independent runs per configuration shows that the proposed clustering-decomposed method achieves 3.68–66.67% lower wall-clock computation time while maintaining zero CTI violations across all active relay pairs. The communicationless, entirely offline framework demonstrates scalability for simultaneous sub-transmission and distribution protection coordination and offers a practically deployable strategy for modern power networks. Full article

Underground mine production scheduling under uncertainty is a complex and multi-field coupling system project. In this study, underground mine production scheduling seeks to determine the optimal start time of extraction-related projects, with the objectives of maximizing net present value, minimizing makespan, and maximizing resource utilization rate. The Copula function is adopted to formulate the correlation between uncertain project duration and cost and generate a set of stochastic scenarios. Then, the K-means algorithm classifies the scenarios into multiple scenario families, and the SBR algorithm is adopted to perform scenario reduction. Moreover, a rank choice function-based hyper-heuristic algorithm is extended to solve the multi-objective optimization model, which makes an excellent balance among the three objective functions. For determining the optimal scheduling plan, the cross-efficiency DEA algorithm is used to evaluate the archive set, sort the optimal solution, and guide the next iteration. The computational case verifies the effectiveness and efficiency of the multi-objective underground mine scheduling model, stochastic scenario and technical and hyper-heuristic algorithm. Full article

Background/Objectives: Pes planus and pes cavus are common foot deformities, which may lead to pain, functional limitations, and impairment of foot biomechanics. While calcaneal pitch, talar declination, and Meary angles, commonly used in diagnosis, provide objective information, their lack of a gold standard and the observer’s dependence on manual measurements limit their reliability. Therefore, in this study, these angles obtained from weight-bearing lateral foot radiographs were evaluated according to literature references, and the aim was to determine the model that provides the most accurate prediction in the diagnosis of pes planus using machine learning algorithms. It should be emphasized that, because the diagnostic labels were derived from literature-based thresholds of these same angles, the machine-learning task addressed here is the automated reproduction and standardization of expert, angle-threshold-based classification, rather than an independent clinical diagnosis from raw images. Methods: This retrospective study was conducted using weight-bearing lateral foot radiographs of 697 male patients obtained from the archives of public hospitals in Gaziantep. Calcaneal pitch, Meary angle, and talar declination angles were evaluated in both feet, and the data were labeled as normal, pes planus, and pes cavus. The dataset, consisting of a total of 1394 feet, was divided into training and test groups and analyzed using Random Forest, XGBoost, Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms; the diagnostic performance of the models was compared using measures such as accuracy, F1 score, sensitivity, and specificity. Results: A total of 1394 feet from 697 male patients (mean age 24.8 ± 5.57 years) were analyzed using five machine learning algorithms with calcaneal pitch angle (CPA), Meary angle (MA), and talar declination angle (TDA) as reference labels. Ensemble-based methods showed superior performance, with XGBoost achieving perfect classification (Accuracy = 1.000) under all three labels for the left foot and 0.996–1.000 for the right foot, while Random Forest reached 0.986–1.000 across all experiments. Logistic Regression and SVM yielded moderate accuracies (0.905–0.973), whereas KNN consistently performed the weakest (0.905–0.964), particularly in the pes cavus subgroup. The near-perfect accuracy obtained when the labeling angle was itself included among the predictors reflects, at least in part, the algebraic reconstruction of the threshold rule from a same-source variable rather than genuine diagnostic generalization; results should therefore be interpreted with this in mind. Conclusions: This study demonstrates that machine learning, particularly ensemble methods such as XGBoost and Random Forest, provides high accuracy and consistency in diagnosing foot arch deformities based on radiographic angle measurements. Traditional models, such as Logistic Regression, still hold value in terms of clinical interpretability despite their lower performance. The findings suggest that machine learning-based approaches can offer objective, rapid, and reliable decision support tools for diagnosing pes planus and pes cavus, but external validation studies are necessary for clinical generalizability. Full article

To address the insufficient accuracy of short-term load forecasting caused by the strong randomness of distributed energy output, variable electricity consumption patterns, and complex meteorological factors, this study proposes a load forecasting method that integrates K-means scene clustering and a Transformer–BiGRU–Attention (CTBA) hybrid deep learning architecture. Different from conventional Transformer–BiGRU hybrid forecasters that train a single global predictor across all operating conditions, the proposed CTBA framework first partitions daily load curves into representative scenes and then routes each sample to a scene-specific Transformer–BiGRU–Attention predictor, thereby reducing distributional heterogeneity before temporal modeling. First, the K-means algorithm is used to perform scene clustering on historical daily load curves, and the optimal number of clusters is selected according to the silhouette coefficient and downstream prediction performance. Subsequently, the CTBA model is trained separately for each clustering subset. The Transformer encoder captures the long-range global dependencies of load sequences through the self-attention mechanism, the BiGRU module extracts local bidirectional temporal fluctuation features, and the Attention mechanism further focuses on key time nodes such as morning and evening peaks while fusing multi-source data including historical load, day-ahead electricity price, and multi-dimensional meteorological factors. Experimental results based on the German ENTSO-E power dataset show that the coefficient of determination R² of the proposed model reaches 0.9893, with MAE, RMSE, and MAPE as low as 0.0141, 0.0187, and 3.92%, respectively, which are significantly improved compared to benchmark models such as SVR, LSTM, CNN, and TCN-BiGRU. Ablation experiments further demonstrate that removing the clustering, Transformer, BiGRU, or attention layer will degrade performance, thus verifying the effectiveness and superiority of the method in short-term load forecasting and providing an accurate solution for the short-term load forecasting of power systems. Full article

To evaluate the effects of swimming training on growth, swimming performance, and osmoregulatory-related indices in juvenile coho salmon, freshwater-reared fish were subjected to current of 1 body length per second (BL·s⁻¹) from December 2024 to April 2025. Fork length, body weight, condition factor, insulin-like growth factor-1 (IGF-1), and gill and intestinal Na⁺/K⁺-ATPase (NKA) protein abundance were measured monthly, and critical swimming speed (U_crit) was evaluated after one month of training. Trained fish showed greater fork length in March and higher body weight in March and April than controls. The condition factor was higher in trained fish in February and March, but declined during spring smolt development. Swimming capacity was enhanced by training, as indicated by significantly higher U_crit. Mean IGF-1 levels did not differ between groups, but IGF-1 correlated positively with body size only in trained fish. No significant training effect was detected for either gill or intestinal NKA protein abundance, although gill NKA increased significantly in April, likely reflecting seasonal smoltification. In addition, IGF-1 was significantly correlated with gill NKA in trained fish in March. Collectively, these results indicate that sustained swimming training improves growth and swimming performance and may enhance associations among measured physiological variables during smoltification in juvenile coho salmon. Full article

Mountain photovoltaic (PV) stations are increasingly deployed in complex terrain, where generation is jointly controlled by solar-resource variability, near-surface meteorology, and local topography. However, the quantitative contribution of topographic factors to regional-scale PV generation remains insufficiently evaluated, and many prediction studies rely on single-station or short-term records. In this study, monthly measured generation from 118 standardized village-level mountain PV stations in Badong County, western Hubei Province, China (2019–2021), was integrated with Solargis Global Horizontal Irradiance (GHI)-related solar-resource data, high-resolution gridded meteorological data, a 25 m digital elevation model, seasonal-cycle variables, and historical-generation features. After seasonally grouped median-absolute-deviation (MAD) outlier screening, GIS-based spatial matching, terrain extraction, and viewshed-derived shading analysis, regression models and climatology baselines were compared under both chronological validation and station-exclusion spatial cross-validation. Under the strict chronological validation, CatBoost achieved the best temporal performance among the tested models (R² = 0.3119, MAE = 2719.7 kWh, RMSE = 3245.6 kWh), slightly outperforming the monthly climatology baseline. In the station-exclusion spatial cross-validation, XGBoost achieved the highest mean R² (0.8659), indicating good spatial transferability to unseen stations. Correlation and partial-correlation analyses showed that the temperature-related variable group and monthly radiation were the dominant meteorological controls, whereas elevation, slope, and terrain shading showed weak direct correlations with monthly generation for already-sited stations. Annual 90% prediction intervals were further estimated using residual bootstrapping, with an empirical coverage of 94.9%. The proposed framework provides a practical basis for monthly generation forecasting and operational assessment of already-built distributed PV stations in mountainous regions, while its application to greenfield site selection requires additional site engineering and near-field obstruction information. Full article

Operational landslide susceptibility mapping (LSM) remains challenging in regions with pronounced geo-environmental heterogeneity, where single global models often overlook spatially variable landslide-environment relationships. Northern Guangdong, China, is a typical humid mountainous region where steep terrain, diverse lithology, and highly variable rainfall produce non-stationary landslide controls. To address this challenge, we develop a cluster-informed LSM framework that integrates unsupervised consensus K-means sub-zoning with localized Random Forest (RF) models and SHapley Additive exPlanations (SHAP). We use a harmonized inventory of 1510 landslides (2011–2022), together with twelve 30 m conditioning factors, for model training and validation. Compared with logistic regression, Support Vector Machines (SVM), and Light Gradient Boosting Machine (LightGBM), RF consistently achieves higher accuracy across clusters, and the cluster-wise RF ensemble attains pooled ACC = 0.8212, F1 = 0.8176, and AUC = 0.8956. SHAP highlights both regionally consistent predictors (e.g., NDVI, distance to road) and distinct cluster-specific controls linked to geomorphic and hydrologic settings. The proposed framework enhances predictive accuracy, produces finer susceptibility gradients, and yields better-calibrated probability estimates than a single global model. These results demonstrate that explicitly accounting for geo-environmental heterogeneity can generate interpretable, spatially adaptive susceptibility outputs. By identifying high-risk zones for priority monitoring, land-use regulation, infrastructure protection, and mitigation planning, the proposed framework provides a practical decision-support tool for sustainable mountain development and disaster risk reduction in heterogeneous mountainous regions. Full article

Reducing carbon emissions from daily travel has become an important goal of 15-minute living-circle planning, yet it remains unclear which facility configurations are most supportive of green travel. Using 634 living circles and 20 million mobile-phone travel records and point-of-interest (POI) data, this study examines how facility layout within a 15-minute cycling circle influences residents’ walking and cycling travel behavior. Extreme Gradient Boosting (XGBoost) models and Shapley Additive Explanations (SHAP) suggest that low accessibility is generally associated with lower green travel shares, while moderate facility density promotes green travel, yet for some facility types, high density may show diminishing marginal benefits. Vegetable markets and primary schools emerge as key facilities, with education facilities driven mainly by accessibility, entertainment facilities by density, and commercial and healthcare facilities by both. K-means clustering identifies three types of low-green-travel-performing living circles—characterized by low density and poor accessibility—concentrated in peripheral and newly developed areas. The methodology is transferable, and the derived numerical ranges and living-circle typologies offer context-specific implications for Tangshan, and identified differences in facility importance and diminishing marginal benefits enrich 15-minute city theory. Full article

The Huomaxie granite in Ningdu, southern Jiangxi Province, is located in the central part of the Cathaysia Block. Previous studies assigned this pluton to the Huitong batholith as S-type granite, but lacked precise geochronological and petrogenetic constraints. This paper presents systematic petrography, whole-rock geochemistry, zircon U–Pb dating, in situ Hf isotopic analysis, and electron microprobe analysis (EPMA) of muscovite from the muscovite monzogranite of the pluton. The weighted mean ²⁰⁶Pb/²³⁸U age is 420.1 ± 3.1 Ma. The rocks are silicic, high-K calc-alkaline, and peraluminous S-type granites. Zircon ε_Hf(t) values range from −15.0 to −11.8, with two-stage Hf model ages (T_DM2) of 2360-2150 Ma. Geochemical characteristics and muscovite composition data indicate that the magma was derived from high-temperature partial melting of psammitic sedimentary rocks. Tectonic discrimination diagrams suggest that the pluton formed in a post-orogenic extensional setting. It was generated by lower crustal melting induced by asthenospheric upwelling. Full article

Historic blue bricks are fundamental to Beijing’s architectural heritage, yet cross-site compositional data for guiding material-compatible restoration remain scarce. This study applies WD-XRF, XRD, SEM, thermal expansion measurement, and physical property testing to 21 blue brick specimens from four Beijing-area sites spanning the Tang through Qing dynasties, with PCA and K-means clustering used to explore compositional grouping structures. Within this exploratory dataset, a compositional distinction separates the Ming and Qing Great Wall bricks: CaO falls from 7.7 to 1.5 wt.% as anorthite gives way to albite, while Qing specimens are denser (1.79 vs. 1.65 g·cm⁻³) with lower water absorption (15.9% vs. 20.9%). Two Wanping City bricks are strongly sulfate-enriched (SO₃ up to 9.8%), and WP-SE3 additionally carries a heavy chloride load (Cl 2.1%), masking their original clay signatures and illustrating how unrecognized weathering can distort compositional grouping and source-related interpretation from bulk chemistry. K-means clustering yields compositional types that overlap only partially with site boundaries, capturing raw material variation rather than site-specific manufacturing fingerprints. Despite constraints in sample size and physical property coverage, the integrated dataset offers preliminary compositional benchmarks and limited performance data to inform period-specific brick replacement at these heritage sites. Full article

The study analyzed the effect of diffusion path lengths (initial, average, and final half-thicknesses) on the shrinkage, effective moisture diffusivity, activation energy, and pre-exponential factor of thin-layer red delicious apple slices under convective drying conditions (temperature from 40 °C to 80 °C at 10 h drying time). The results show that the shrinkage increased from 31.09% at 40 °C to a maximum of 42.65% at 70 °C, then slightly decreased to 36.77% at 80 °C, indicating that shrinkage did not increase linearly with drying temperature. The diffusion path lengths yielded effective moisture diffusivities ranging from 1.43 × 10⁻¹⁰ to 10.31 × 10⁻¹⁰ m²/s, with the average characteristic length providing the most realistic representation of the effective moisture diffusivity. The high coefficient of determination (R² = 0.965), consistent with the model efficiency value, confirms that the Arrhenius model fits the experimental diffusivity data across the temperature range studied. The mean absolute percentage error of 12% between the experimental and predicted diffusivities confirms the reliability of Fick’s and Arrhenius models. The activation energy ranged from 21.56 to 26.03 kJ/mol across diffusion path lengths, indicating a moderate sensitivity of moisture diffusion to temperature. Full article

In Optical Camera Communication (OCC), precise localization of LED arrays under complex tilt conditions is a core challenge for reliable decoding. This paper proposes an OCC reception scheme for RGB-LED arrays that integrates YOLO-OBB rotated object detection with two-stage geometric correction. The system first employs a YOLOv8n-OBB model to extract a quadrilateral region of interest that tightly encloses the LED array boundary. This effectively suppresses background interference caused by superimposed perspective tilt and in-plane rotation. A coarse-to-fine two-stage correction framework is then applied. The first stage rapidly eliminates the dominant perspective distortion based on the detected bounding-box corners. The second stage performs a refined correction using the actual LED center positions. Two homography matrices are cascaded into a combined transformation, achieving two-stage correction accuracy through a single coordinate mapping. In the corrected image, K-Means clustering constructs a 16 × 16 LED topological grid. A locking strategy is adopted so that subsequent frames skip repeated LED detection and clustering. The steady-state per-frame processing time is reduced to approximately 78.9 ms. Experiments covered 16 cross-combinations of vertical tilt from 0° to 45° (0°, 15°, 30°, 45°) and in-plane rotation from 0° to 40° (0°, 15°, 30°, 40°). The uncorrected scheme and the horizontal-box scheme experienced severe bit errors or complete failure under complicated distortion. The proposed scheme maintained error-free transmission under all 16 tested conditions. The ratios of opposite sides of the corrected LED grid remained stable between 0.997 and 1.004. The system simultaneously achieves high reliability and low-latency real-time processing under complex geometric distortions. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

Pre-Cenomanian Nubia sandstone is recognized one of the most prolific reservoirs in the Gulf of Suez, Egypt. Accurately determining its reservoir rock type (RRT) is crucial for reservoir characterization and modeling, especially when the reservoir is extremely heterogeneous. This study addresses the critical challenge of characterization in extremely heterogeneous reservoirs by introducing a novel integrated workflow that bridges the gap between traditional sedimentological geology, traditional x-y approaches, and advanced machine learning methods. To achieve this, this study utilizes sedimentological core description, routine core analysis, and conventional well log data from two wells (well A and well B) located in the southern Gulf of Suez, Egypt. The results demonstrate that the complete Nubia interval in the southern Gulf of Suez can be separated into seven distinct lithofacies (LF1–LF7). The first six lithofacies comprise various types of sandstone, while the seventh is composed of shale. The traditional techniques used to predict the RRTs show that the normalized reservoir quality index (NRQI) was the most effective method for predicting the Nubia rock types. The machine learning K–means clustering and self-organizing map (SOM) techniques utilizing raw log data and principal component analysis (PCA) can properly predict the Nubia reservoir rock types. The reservoir quality ranges from poor to very good; well A is dominated by moderate reservoir quality, while well B exhibits predominantly very good reservoir quality. This discernible difference in reservoir quality between the two wells is probably attributed to post-depositional diagenetic processes and variations in sandstone texture. Full article

Waste-to-energy (WtE) systems are frequently proposed as complementary waste-management strategies; however, their climate performance depends on the interaction between thermodynamic efficiency, material circularity, and electricity-system characteristics. Existing life-cycle assessments generally provide static comparisons between landfill and WtE but rarely identify the operating conditions under which WtE remains environmentally competitive. To address this gap, a parametric life cycle–energy framework was developed by integrating attributional LCA with an analytical energy model capable of evaluating critical efficiency thresholds under varying recovery rates and electricity-grid conditions. Four representative thermoplastics (PET, HDPE, PP, and LDPE) were evaluated using ReCiPe 2016 Midpoint (H) in SimaPro under Mexican electricity conditions ($E{F}_{\mathrm{grid}}=0.444$ kg CO₂eq/kWh). Results indicate that total life-cycle climate impacts are dominated by upstream polymer production, whereas end-of-life management contributes only marginally to overall GWP. Critical-efficiency analysis revealed strong sensitivity to both recovery rate and electricity-grid carbon intensity. For PET, the minimum efficiency required for WtE to outperform landfill increased from 13.1% to 73.5% across the evaluated scenarios, whereas HDPE remained competitive at efficiencies below 1.3%. Monte Carlo simulations (10,000 realizations) further demonstrated that avoided emissions decline systematically with increasing recovery rates, with LDPE exhibiting the highest mean avoided emissions (1735 kg CO₂eq) and PET the lowest (811 kg CO₂eq). These results demonstrate that WtE climate performance is governed primarily by residual waste availability and electricity-system evolution rather than thermodynamic efficiency alone. Consequently, WtE should be interpreted as a transitional residual-waste management strategy whose long-term climate relevance decreases as material circularity and electricity-grid decarbonization advance. Full article