Search Results (317)

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

Background: Monitoring training load and recovery is essential for performance optimization and injury prevention in youth football. However, predicting subjective recovery in preadolescent athletes remains challenging due to biological variability and the multidimensional nature of training responses. This exploratory study examined whether supervised machine learning (ML) models could predict Total Quality of Recovery (TQR) using integrated external load, internal load, anthropometric and maturational variables collected over one competitive microcycle. Methods: Forty male sub-elite U11 and U13 football players (age 10.3 ± 0.7 years; height 1.43 ± 0.08 m; body mass 38.6 ± 6.2 kg; BMI 18.7 ± 2.1 kg/m²) completed a microcycle comprising four training sessions (MD-4 to MD-1) and one official match (MD). A total of 158 performance-related variables were extracted, including external load (GPS-derived metrics), internal load (RPE and sRPE), heart rate indicators (U13 only), anthropometric and maturational measures, and tactical–cognitive indices (FUT-SAT). After preprocessing and aggregation at the player level, five supervised ML algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB)—were trained using a 70/30 train–test split and 5-fold cross-validation to classify TQR into Low, Moderate, and High categories. Results: Tree-based models (DT, GB) demonstrated the highest predictive performance, whereas linear and distance-based approaches (SVM, KNN) showed lower discriminative ability. Anthropometric and maturational factors emerged as the most influential predictors of TQR, with external and internal load contributing modestly. Predictive accuracy was moderate, reflecting the developmental variability characteristics of this age group. Conclusions: Using combined physiological, mechanical, and maturational data, these ML-based monitoring systems can simulate subjective recovery in young football players, offering potential as decision-support tools in youth sub-elite football and encouraging a more holistic and individualized approach to training and recovery management. Full article

The optimal selection of high-arch dam construction schemes involves multi-dimensional indicators. In addition to clear one-way optimization indicators such as construction period and cost, there are also indicators with ambiguous optimization directions. Such indicators are influenced by factors like actual engineering conditions and experience from similar projects, and they have an optimal interval for their value range, which is referred to as “moderate indicators”. However, in most comprehensive evaluation models, the standardization of such indicators is plagued by overly simplistic processing methods and excessive subjective factors. To address the interval optimality of moderate indicators, this paper proposes the concepts of “optimal interval” and “tolerance interval”. By analyzing the distribution characteristics of indicators and combining them with the characteristics of construction simulation calculations, the quartile method and comprehensive weighting method are adopted to determine the subjective and objective interval ranges. Based on the concept of relative membership degree, these intervals are processed as standardized results and incorporated into the comprehensive evaluation system. Accordingly, a multi-attribute decision-making model for high arch dams considering moderate indicators is proposed. This method was verified in the decision-making process for the construction scheme of the TR Double Curved Arch Dam project. Compared with the traditional Ranking Alternatives by Distance from Average Solution (RADAR) method that adopts the vector normalization method, this model enhances the anti-interference ability against the volatility of moderate indicators, improves the accuracy of scheme optimization, and obtains the priority ranking of each alternative scheme. Full article

Traffic accidents remain a major public safety concern, particularly in regions where rapid motorization and limited infrastructure increase crash risk. This study proposes a machine learning-based framework to classify traffic accident severity and identify high-risk road segments using multidimensional crash data from Şırnak Province, Turkey. The dataset, obtained from the General Directorate of Security (EGM), contains 29 variables describing traffic, geometric, and operational roadway characteristics for crashes reported between 2018 and 2023. Due to the severe imbalance between injury and fatal crashes, the Synthetic Minority Oversampling Technique (SMOTE) was applied to enhance model sensitivity to the minority class. Five classifiers—Logistic Regression (LR), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were trained and evaluated using accuracy, F1-score, ROC-AUC, and alarm metrics. Results from the original dataset showed that several models struggled to detect fatal crashes, while LR demonstrated moderate sensitivity. After SMOTE, performance improved across all models. XGBoost achieved the highest F1-score (0.61) with the lowest False Alarm rate (0.01), followed by RF and MLP, whereas SVM and LR yielded comparatively lower accuracy. Computation time analysis indicated that LR and SVM had the fastest runtimes, while MLP and XGBoost required longer training times. Overall, findings highlight the effectiveness of ensemble models—particularly XGBoost—in capturing critical crash patterns and supporting risk-based decision-making. Future work should incorporate time-series analysis and GIS-based spatial modeling to further enhance predictive capability and inform geographically targeted safety interventions. Full article

This study examines how climatic factors influence the predictive power of LEED credits in determining certification outcomes for hotel buildings across the United States. Using data from 259 LEED-NC v2009 certified hotels, project-level information was integrated with 30-year climate normals from the PRISM database and Building America climate zones. A three-step hierarchical linear regression was conducted to identify the LEED credits that most strongly predict total certification points while controlling for project size, certification year, and baseline climatic conditions, and to test whether climatic factors moderate these relationships. Regularized Linear Regression (LASSO) was then applied to address multicollinearity and assess model stability, followed by Support Vector Regression (SVR) to capture potential nonlinear relationships. This integrated methodological framework, combining hierarchical regression for interpretability, LASSO for coefficient stability, and Support Vector Regression for nonlinear verification, provides a novel, multi-dimensional assessment of climate-sensitive credit behavior at the individual credit level. Results show that energy- and site-related credits, particularly Optimize Energy Performance (EA1), On-Site Renewable Energy (EA2), Green Power (EA6), and Alternative Transportation (SS4), consistently dominate LEED performance across all climate zones. In contrast, indoor environmental quality credits exhibit modest but significant climate sensitivity: higher mean temperatures reduce the contribution of Increased Ventilation (EQ2) while slightly enhancing Outdoor Air Delivery Monitoring (EQ1). Cross-model consistency confirms the robustness of these findings. The findings highlight the need for climate-responsive benchmarking of indoor environmental quality credits to improve regional equity and advance the next generation of climate-adaptive LEED standards. Full article

The incorporation of carbon nanotubes (CNTs) enhances the mechanical properties of cement-based materials by inhibiting micro-crack propagation. Machine learning provides an efficient approach for predicting the compressive strength of CNT-reinforced concrete, yet existing studies often lack important features and rely on less adaptive models. To address these issues, a multi-dimensional database (429 experimental data points) covering 11 factors (including cement mix ratio, CNT morphology, and dispersion process) was constructed. A hierarchical model verification and optimization was conducted: traditional regression models (Multiple Linear Regression, Multiple Polynomial Regression (MPR), Multivariate Adaptive Regression Splines), mainstream model (Support Vector Regression (SVR)), and ensemble learning models (Random Forest, eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine optimized by Particle Swarm Optimization (PSO)/Bayesian Optimization (BO)) are trained, compared, and evaluated. MPR performs best (test set R² = 0.856) among traditional regression models, while SVR (test set R² = 0.824) is less accurate. The highest accuracy in ensemble models is achieved by the PSO-optimized XGB model, with R² = 0.910 (test set). PSO outperforms BO in optimization precision, while BO is much more efficient. Water–cement ratio, age, and sand–cement ratio are the primary influencing factors for strength. Among CNT parameters, the inner diameter has greater impact than the length and outer diameter. Optimal CNT parameters are CNT–cement mass ratio 0.1–0.3%, inner diameter ≥ 7.132 nm, and length 1–15 μm. Surfactant polycarboxylate can increase strength, while OH⁻ functional groups can decrease it. These findings, integrated into the high-precision PSO-XGB model, provide a powerful tool for optimizing the mix design of CNT-reinforced concrete, accelerating its development and application in the industry. Full article

Background/Objectives: Radiological expertise draws on semantic knowledge and perceptual–cognitive mechanisms that support diagnostic reasoning. Early radiological training is a formative period when key cognitive processes begin to integrate. Nevertheless, how the brain pattern of early radiological expertise reorganizes during the first weeks of clinical exposure remains unknown, as prior work has relied mainly on cross-sectional designs comparing mature experts to beginners. Methods: We therefore conducted a longitudinal resting-state fMRI study in radiology interns (n = 43; 41 valid) scanned before and after short-term training. Behavioral performance improved significantly after training (p < 0.01). Regional homogeneity (ReHo) was computed for 246 Brainnetome ROIs for each subject. Results: Using a Support Vector Machine (SVM)-based recursive feature elimination (RFE) pipeline, 14 of these 246 features were identified as most discriminative, spanning regions involved in visual, semantic, memory, attentional, and decision-making processes. An SVM trained on these features effectively differentiated pre- and post-training brain states (training set: 86.67% accuracy, AUC = 0.97; validation set: 81.82% accuracy, AUC = 0.72). Conclusions: The observed neuroplastic changes provide direct evidence that multidimensional cognitive functions reorganize early in radiological expertise development and offer neural targets to inform evidence-based curriculum design, personalized training, and brain-targeted interventions (e.g., neuromodulation or neurofeedback) in radiology education. Full article

Real-time prediction of the instantaneous fuel consumption rate (FCR) of any vehicle is the key to improving energy efficiency and reducing emissions. The conventional prediction methods, which include an on-board diagnostic (OBD) system, require the specific vehicle parameters and environmental conditions such as air density. We propose a data-driven Bayesian optimization and Monte Carlo (MC) Dropout methods-based long short-term memory (BMC-LSTM) network FCR prediction model using only the vehicle’s throttle position, velocity, and acceleration data. The cost-effective LSTM network-based solution enhances the high-resolution prediction accuracy within a deep learning framework. The network is integrated with the Bayesian optimization and MC-Dropout methods to ensure a probabilistically optimal hyperparameter set and robust networks. The proposed method presents an FCR model that provides calibrated predictions and reliability against distribution drift by probabilistically tuning hyperparameters with Bayesian optimization and quantifying epistemic uncertainty with the MC-Dropout. Our approach requires only vehicle speed, longitudinal acceleration, and throttle position at inference time. Note, however, that the reference FCR used to train and validate the models was obtained from OBD during data acquisition. The performance of the proposed method is compared with a conventional LSTM and Bidirectional LSTM-based multidimensional models, XGBoost and support vector regression-based models, and first- and fourth-order polynomials, which are derived using the least-squares method. The prediction performance of the method is evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute, and R-squared statistical metrics. The proposed method achieves a superior $R^2$ score and substantially reduces the conventional error metrics. Full article

Background: The entomopathogenic fungus Metarhizium brunneum (M. brunneum) is an effective biocontrol agent against various vector arthropods such as ticks, mosquitoes, and flies. However, its virulence mechanisms remain poorly understood, which hinders its broader application. This study aims to establish an integrative framework for prioritizing virulence-related pathways in M. brunneum to aid in the development of more effective biocontrol strategies. Methods: A multidimensional virulence pathway scoring framework was developed using publicly available protein annotation data of M. brunneum. This approach integrates protein pathway enrichment, Gene Ontology (GO) functional analysis, PHI-base virulence factor mapping, and literature-derived evidence. A total of 20 pathways were evaluated, and a scoring system was applied based on protein coverage, Gene Ontology Biological Process (GO-BP) support, PHI-base hits, and literature support. Results: Among the 20 pathways evaluated, five pathways, including MAPK signaling, apoptosis, endocytosis, carbon metabolism, and biosynthesis of secondary metabolites received the highest priority scores. These pathways were identified as key virulence-related candidates, supported by both functional annotation and existing biological evidence. Conclusions: The proposed framework provides a reliable and scalable strategy for prioritizing virulence pathways in entomopathogenic fungi. It offers a solid foundation for subsequent transcriptomic validation, target screening, and functional characterization. This framework can also be applied to other fungi, contributing to the development of optimized biocontrol formulations. Full article

Accurate State of Health (SOH) estimation is critical for the reliable and safe operation of lithium-ion batteries; this paper proposes an ORIME–Transformer–BILSTM model integrating multiple health factors and achieves high-precision SOH prediction. Traditional single-dimensional health factors (HFs) struggle to predict battery SOH accurately and stably. Therefore, this study employs Spearman and Kendall correlation coefficients to analyze multi-dimensional HFs and determine the key characteristics for quantifying SOH. The self-attention mechanism of the Transformer encoder extracts and fuses the key features of long-term sequences. A BILSTM network receives these input vectors, whose primary function is to uncover the temporal evolution of the SOH. Finally, the optimal random-weight-initialization meta-heuristic estimation (ORIME) algorithm adaptively adjusts the hyperparameters to optimize the model efficiently. Cycle data from four batteries (B5, B6, B7 and B18) provided by NASA are used for testing. The mean absolute error (MAE), mean absolute percentage error (MAPE) and root-mean-square error (RMSE) of the proposed method are 0.2634%, 0.4337% and 0.3106% Compared to recent state-of-the-art methods, this approach significantly reduces prediction errors by 33% to 67%, unequivocally confirming its superiority and robustness. This work provides a highly accurate and generalized solution for SOH estimation in real-world battery management systems. Full article

Background/Objectives: Rapid, objective phenotyping of donor kidneys is needed to support peri-implant decisions. Label-free Fourier-transform infrared (FTIR) spectroscopy of static cold-storage Celsior^® perfusion fluid can discriminate kidneys recovered from donation after circulatory death (DCD) versus donation after brain death (DBD). Methods: Preservation solution from isolated kidney allografts (n = 10; 5 DCD/5 DBD) matched on demographics was analyzed in the Amide I and fingerprint regions. Several spectral preprocessing steps were applied, and feature extraction was based on the Fast Correlation-Based Filter. Support vector machines and Naïve Bayes were evaluated. Unsupervised structure was assessed based on cosine distance, multidimensional scaling, and hierarchical clustering. Two-dimensional correlation spectroscopy (2D-COS) was used to examine band co-variation. Results: Donor cohorts were well balanced, except for higher terminal serum creatinine in DCD. Quality metrics were comparable, indicating no systematic technical bias. In Amide I, derivatives improved classification, but performance remained modest (e.g., second derivative with feature selection yielded an area under the curve (AUC) of 0.88 and an accuracy of 0.90 for support vector machines; Naïve Bayes reached an AUC of 0.92 with an accuracy of 0.70). The fingerprint window was most informative. Naïve Bayes with second derivative plus feature selection identified bands at ~1202, ~1203, ~1342, and ~1413 cm⁻¹ and achieved an AUC of 1.00 and an accuracy of 1.00. Unsupervised analyses showed coherent grouping in the fingerprint region, and 2D correlation maps indicated coordinated multi-band changes. Conclusions: Performance in this 10-sample pilot should be interpreted cautiously, as perfect leave-one-out cross-validation (LOOCV) estimates are vulnerable to overfitting. The findings are preliminary and hypothesis-generating, and they require confirmation in larger, multicenter cohorts with a pre-registered analysis pipeline and external validation. Full article

Three-dimensional assets have evolved into a pivotal element within the domains of electronic entertainment, medicine, and engineering. Unfortunately, 3D models comprise voluminous data, which is not readily amenable to analysis or to the application of simple algorithms. This study thoroughly analyses the usability of the classical Isolation Forest (IF) method as a novel instrument for 3D mesh analysis. Given the atypical nature of 3D model data, it was necessary to obtain the special multidimensional feature vector (FV) associated with each vertex. The FV codes information regarding the local curvature of the surface in the vicinity of the vertex. As demonstrated by experimental studies, the IF analysis is capable of detecting geometrical details, areas of dense, complex geometry, strong bends, and folds in the mesh. This finding indicates a significant steganographic potential, which prompted the authors to employ research findings in the domain of 3D mesh watermarking as a practical illustration. Thanks to IF, a new steganographic method was developed that is characterised by higher transparency, achieved by hiding data in areas of complex geometry. The study proves the high potential of IF for analysing and watermarking 3D models, representing a meaningful step toward broader applications of IF in this field. Full article

This paper proposes a bearing fault diagnosis method based on the Dempster–Shafer evidence fusion of cloud model memberships from multi-channel data, which provides an explicable calculation process and a final result. Firstly, vibration signals from the drive end and fan end of the rolling bearing are used as dual-channel data sources to extract multi-dimensional features from time and frequency domains. Then, cloud models are employed to build models for each feature under different conditions, utilizing three digital characteristic parameters to characterize the distribution and uncertainty of features under different operating conditions. Thus, the membership degree vectors of test samples from two channels can be calculated using reference models. Subsequently, D-S evidence theory is applied to fuse membership degree vectors of the two channels, effectively enhancing the robustness and accuracy of the diagnosis. Experiments are conducted on the rolling bearing fault dataset from Case Western Reserve University. Results demonstrate that the proposed method achieves an accuracy of 96.32% using evidence fusion of the drive-end and fan-end data, which is obviously higher than that seen in preliminary single-channel diagnosis. Meanwhile, the final results can give suggestions of the possibilities of anther, which is benefit for technicists seeking to investigate the actual situation. Full article

Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R² ≈ 0.937474; Richness R² ≈ 0.855305; Evenness R² ≈ 0.631796). Full article

User preference mining uses rating data, item content or comments to learn additional knowledge to support the prediction task. For the use of rating data, the usual approach is to take rating matrix as data source, and collaborative filtering as the algorithm to predict user preferences. Item content and comments are usually used in sentiment analysis or as auxiliary information for other algorithms. However, factors such as data sparsity, category diversity, and numerical processing requirements for aspect sentiment analysis affect model performance. This paper proposes a hybrid method, which uses the deep neural network as the basic structure, considers the complementarity of text and numeric data, and integrates the numeric and text embedding into the model. In the construction of text-based embedding, extracts the text summary of each text-based review, and uses the Doc2vec to convert the text summary into multi-dimensional vector. Experiments on two Amazon product datasets show that the proposed model consistently outperforms other baseline models, achieving an average reduction of 15.72% in RMSE, 24.13% in MAE, and 28.91% in MSE. These results confirm the effectiveness of our proposed method for learning user preferences. Full article