Search Results (436)

Understanding the structural characteristics of Pinus taiwanensis plantations in climatically transitional regions is essential for developing science-based management strategies under global change. This study investigated 23 plots in Huangbai Mountain Forest Farm, Henan Province, China, classified into low-, medium-, and high-density stands (n = 9, 9, and 5, respectively). Diameter distributions were fitted using six probability functions, and four spatial structure parameters—mixing degree (Mc), size ratio (U), uniform angle index (W), and forest layer index (S)—were quantified. In addition, five comprehensive spatial structure indices—average superiority coefficient index (SPV), spatial structure comprehensive index (Q), stand spatial structure distance index (FSI), Comprehensive Distance Evaluation (CDEV), and Comprehensive Assessment of Proximity Vector (CAPV)—were constructed using a combined analytic hierarchy process and entropy weight method. Given the unbalanced sample sizes, non-parametric Kruskal–Wallis tests were employed for comparisons, and bootstrap resampling (1000 iterations) was performed to assess the reliability of mean estimates. The results showed that both the Gamma and Weibull distributions were equally suitable for describing diameter distribution under different stand densities, as their AIC differences were below 2 for all density classes. Correlation analysis indicated that the relative importance of spatial parameters followed the order S > U > Mc > W. Medium-density stands exhibited the most optimal spatial structure, whereas low-density stands showed the poorest performance. These findings suggest that both overly dense and sparse stands negatively affect spatial organization. Appropriate management practices, such as thinning or enrichment planting, are recommended to optimize stand structure and enhance ecological resilience. Full article

To address the challenges of quantifying expert experience, handling missing data, and managing class imbalance in evaluating the autonomous controllability of power equipment, this paper proposes a quantitative evaluation method that integrates expert prior rules with machine learning. First, building upon a five-dimensional evaluation indicator system, expert decision logic—including dimension-average threshold judgments, multi-dimensional weakness-based cumulative downgrading mechanisms, and key sub-item interaction rules—is formalized into a 15-dimensional rule prior feature vector, which is concatenated with the original 21-dimensional raw indicators to construct a RAW + RULE augmented feature space. Second, a KNN algorithm is employed for missing value imputation, while cost-sensitive learning combined with the SMOTE is adopted in a dual-path parallel scheme to address class imbalance. Six machine learning models are evaluated and compared via 30 repeated stratified cross-validations on a real-world dataset of 97 high-voltage bushing suppliers. Experimental results show that, on complete datasets, the RAW + RULE configuration with the Random Forest model achieves a mean test accuracy of 0.936 and a Kappa of 0.938, substantially outperforming the pure raw-feature model (accuracy 0.769, Kappa 0.766). Under weighted random missingness ranging from 10% to 50%, the RAW + RULE configuration demonstrates superior robustness, with ensemble tree models maintaining mean accuracies of 0.614–0.636 even at a 50% missing rate. This study provides a practically deployable technical solution and methodological reference for the quantitative assessment of autonomous controllability levels and early security warning in the power equipment supply chain. Full article

Accurate, non-invasive body weight estimation is essential for management and performance monitoring in beef cattle systems, yet conventional scales and manual measurements require animal handling, infrastructure, and labor. This study presents an integrated pipeline that segments cattle from overhead depth images and predicts body weight from extracted image features. The approach uses a Light Attention Encoder–Decoder (LAED) segmentation model combining depthwise separable convolutions, Gaussian Context Transformer (GCT) attention, a multi-scale dilated bottleneck, and dual heads for region and boundary prediction. Depth videos were collected using an overhead Intel RealSense D435 RGB-D camera from 60 beef heifers. To reduce animal-level leakage, leave-one-animal-out cross-validation was used for segmentation. LAED + GCT achieved 96.91% Dice (95% confidence interval (CI): 96.56–97.21%) and 94.22% IoU (95% CI: 93.58–94.77%), while operating at 33.08 frames per second. For weight prediction, biometric traits and deep features were evaluated using random forest, support vector regression, and fully connected neural networks. The best primary-metric body-weight model used biometric traits with support vector regression, achieving MAPE = 6.75%, pooled $R^2$ = 0.68, MAE = 23.92 kg, and RMSE = 31.79 kg. Among FCNN models trained independently within each cattle-level fold, the best result used ResNet50 features and achieved MAPE = 7.76%, a pooled $R^2$ = 0.56, an MAE = 27.60 kg, and an RMSE = 37.07 kg. The mean signed prediction bias for the biometric-SVR model was −1.04 kg, using predicted minus observed body weight, with a bootstrap 95% confidence interval of −9.63 to 7.41 kg. These results support the promise of overhead depth imaging for non-invasive cattle body segmentation and weight estimation, while larger external validation remains necessary. Full article

The beef production chain plays a strategic role in Brazilian and global agri-food systems and faces growing demands for sustainability, transparency, and traceability. Building on official Animal Transit Guide (GTA) records from Mato Grosso do Sul, Brazil, this study examines whether a parsimonious agent-based model (ABM) can generate the main structural signatures of an observed cattle-trade network. The empirical benchmark is a directed and weighted network with 20,827 nodes and 258,120 weighted edges. The ABM represents producers and slaughterhouses as spatial agents connected by trade decisions based on three mechanisms: destination attractiveness, defined as the accumulated pull of a slaughterhouse based on previous simulated throughput; geographic distance, representing spatial friction; and relational memory, representing the tendency to repeat previous commercial ties. Producer choice is formalized through a local utility function that combines attractiveness, distance penalty, and relational memory under capacity, sourcing-radius, and saturation constraints. In the simulated scenarios, the top-five slaughterhouses accounted for 38.49 ± 2.56% of throughput at reduced scale and 14.40 ± 0.65% at intermediate scale, while weighted mean distances were 11.94 ± 0.56 and 9.07 ± 0.39 model units, respectively. The model reproduced, in structural and mechanistic terms, the emergence of dominant hubs, the concentration of flows, and the bounded increase in transaction distance with connectivity around the empirical threshold of kw ≈ 256. Sensitivity analyses indicated that attractiveness increases concentration, distance localizes transactions, and relational memory can stabilize repeated ties when recurrent activation is represented. Rather than reconstructing individual transactions, estimating policy impacts, or identifying a unique parameter vector, the model provides a generative explanation of how local trade rules can produce macro-level network patterns consistent with the observed cattle-trade regime. These findings support future prospective analyses of cattle governance, traceability, and sustainability within the broader context of Livestock 4.0. Full article

Real-time cooperative control of quadrotor swarms in cluttered environments requires balancing formation maintenance, obstacle avoidance, inter-UAV safety, and per-step computational cost. This paper proposes a multilayer perceptron (MLP) surrogate for high-level objective-weight selection in a modified multi-objective pigeon-inspired optimization (modified MPIO) distributed controller. The proposed MLP surrogate learns the state-to-weight mapping of the online search and directly predicts the two-dimensional objective-weight vector, while the original flocking, gap-based obstacle-avoidance, and command generation rules are retained unchanged. The surrogate is trained from teacher-generated weight labels using randomized scenes, DAgger-based state aggregation, and risk-weighted supervision. On a fixed closed-loop benchmark, the proposed controller increases the true collision free rate from 48.00% to 86.89% and the safe success rate from 38.67% to 74.22% relative to modified MPIO, while reducing the mean per-step decision latency for the whole swarm from 8494.70 ms to 0.92 ms. The improvement is most pronounced in safety-related and runtime metrics, while the formation-related gain is comparatively modest. Ablation results show that the final benchmark performance is not explained by DAgger or risk weighting alone, and that the medium-sized surrogate provides the best safety-latency tradeoff among the tested network architectures. A qualitative AirSim case study further indicates that the same high-level surrogate controller can be executed in a higher-fidelity asynchronous multirotor simulator. Full article

Accurate mapping of winter rapeseed cultivation areas is crucial for food security assessment and agricultural resource management, yet remains a persistent challenge in mountainous regions characterized by complex topography and highly fragmented field parcels. To address these challenges, this study develops a progressive deep learning framework using single growing-season data from the Hanzhong Basin. We conducted a structured comparison of remote sensing indices, machine learning, and deep learning approaches for rapeseed identification in heterogeneous landscapes. First, sensitivity analysis of the Flowering Index for Rapeseed was performed to identify the optimal parameterization, yielding high inter-class separability (ND = 0.959) during peak flowering and a threshold-based overall accuracy (OA) of 94.41%. Second, a multidimensional feature space was constructed by integrating Sentinel-2 spectral bands, image texture metrics, and topographic variables; Random Forest-based feature importance selection subsequently enhanced Support Vector Machine classification performance to an OA of 90.70%. Third, we proposed an innovative three-stage progressive UNet++ architecture: Stage1 focuses on binary rapeseed/non-rapeseed classification to establish spatial priors; Stage2 refines discrimination among spectrally similar vegetation classes (rapeseed and other vegetation); and Stage3 achieves comprehensive seven-class semantic segmentation. A weighted focal loss function combined with a weight inheritance mechanism was employed to mitigate class imbalance and facilitate inter-stage knowledge transfer. The final model attained an OA of 98.65% and a mean intersection over union of 95.29%, while effectively suppressing salt-and-pepper noise artifacts in geometrically fragmented parcels. Our findings demonstrate the substantial advantages of progressive deep learning strategies for crop monitoring in topographically constrained environments. Full article

Particle swarm optimization (PSO) has been widely used in engineering optimization because of its simple structure and easy implementation. However, standard PSO and most of its variants mainly learn from the personal best position and the global best position. Thus, they often fail to preserve and reuse population-level knowledge generated during the search process. This problem becomes more evident when the search state changes or the swarm falls into stagnation, at which point useful search information may be ignored or forgotten. To address this issue, this paper proposes a state-adaptive knowledge recall PSO algorithm, termed SKRPSO. It includes three cooperative components. First, a state-aware adaptive aggregation mechanism adjusts the elite knowledge-pool size according to population dispersion and builds a rank-weighted knowledge vector for stable population-level guidance. Second, a stagnation-driven knowledge recall mechanism stores historical knowledge associated with global improvements in a bounded memory buffer and recalls recently successful knowledge with a time-decay preference when stagnation is detected. Third, a knowledge-fusion position update strategy uses current aggregated knowledge during normal search and recalled knowledge under stagnation, balancing local exploitation and stagnation escape. Experiments on the CEC2017 benchmark suite show that, based on 30 independent runs, SKRPSO achieves the best mean error on 22 of 29 functions and the best overall Friedman average rank of 1.431 among all compared algorithms. Engineering design results further indicate stable performance. Full article

Objective: Chiari Type I Malformation (CM1) is a structural abnormality of the hindbrain that can cause a range of neurological symptoms and often requires radiological confirmation using magnetic resonance imaging (MRI). The aim of this study was to develop and evaluate a deep feature-based machine learning framework for the automated detection of CM1 from sagittal MRI images. Materials and Methods: The cohort comprised 550 adults: 250 patients with CM1 (168 women, 82 men; age range, 18–65 years) and 300 healthy control participants (210 women, 90 men; age range, 18–65 years). A total of 764 T2-weighted sagittal MR images (384 CM1, 380 healthy) acquired from two different 1.5T MRI scanners (Siemens Magnetom Altea and Symphony) between 2020 and 2024 were retrospectively analyzed. Deep features were extracted using ResNet-50 and MobileNetV2 architectures and subsequently classified using Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), XGBoost, and voting-based ensemble models. Model performance was assessed through patient-level 5-fold cross-validation using accuracy, sensitivity, specificity, F1-score, PPV, NPV, and AUC metrics. Code and trained models are available from the corresponding author upon reasonable request; imaging data are not publicly available due to patient privacy and institutional restrictions. Results: Across patient-level five-fold cross-validation, models built on ResNet-50 deep features demonstrated extremely high and stable diagnostic performance. The final soft-voting ensemble classifier based on ResNet-50 achieved perfect mean performance, with accuracy, balanced accuracy, sensitivity, specificity, F1-score, and AUC all equal to 1.000 ± 0.000 across folds. Other ResNet-based classifiers also achieved near-perfect results. MobileNetV2-based models also demonstrated strong performance but showed slightly lower stability compared with ResNet-based models, with mean accuracies ranging from 0.984 to 0.993 and mean AUC values between 0.99947 and 0.99984 across classifiers. Conclusions: The proposed deep feature-based machine learning framework demonstrated excellent performance for the automated detection of Chiari Type I Malformation from sagittal MRI images. In particular, the ResNet-50–based soft-voting ensemble model achieved perfect classification performance in cross-validation testing, suggesting that deep feature representations combined with machine learning classifiers may serve as a promising computer-aided diagnostic tool for supporting radiological evaluation of CM1. Full article

As Industry 4.0 transforms manufacturing operations, maintenance organizations face a group decision-making problem: how to consolidate diverse expert judgments into a defensible, transparent ranking of the competencies that maintenance personnel most need. This paper applies an integrated Delphi–AHP framework—with explicit notation, operators, and diagnostics—to prioritize maintenance competencies in advanced-manufacturing settings. The Delphi stage consolidates expert-generated items under median–interquartile-range consensus and round-to-round stability rules, while the Analytic Hierarchy Process (AHP) transforms validated pairwise comparisons into ratio-scale priority weights through geometric-mean Aggregation of Individual Judgments (AIJ) and eigenvector derivation. Consistency screening (CI/CR), inter-rater agreement (Kendall’s W), and perturbation-based sensitivity analysis accompany the resulting weight vector. A bounded AI-assisted consistency-check step supports terminology harmonization during Delphi statement consolidation, subject to explicit human-validation constraints. A panel of fifteen industry experts participated in the study; five competency dimensions and twenty-nine indicators were retained through three Delphi rounds. AHP weighting identified Basic Knowledge and Skills as the highest-priority dimension, followed by Safety and Regulation Awareness and Problem-Solving Ability. Aggregated pairwise comparison matrices, local and global weights, and sensitivity results are reported to support reproducibility. The study contributes a rigorously specified application of combined Delphi–AHP to a domain—Industry 4.0 maintenance asset management—where multi-criteria decision analysis has seen limited formal application, and closes common specification gaps in published Delphi–AHP implementations. Full article

Despite advances in using multi-criteria decision-making (MCDM) methods and their fuzzy set extensions for human evaluations of large language models (LLMs), several gaps remain in the literature, particularly in task-specific evaluations that offer a more tractable and interpretable approach. Thus, this work develops a generalized intuitionistic fuzzy MCDM approach that bridges methodological gaps by outlining two contributions. First, the integration of SWARA (Stepwise Weight Assessment Ratio Analysis) and WINGS (Weighted Influence Non-linear Gauge System) is demonstrated to compute the priority weights of the evaluation criteria, thereby augmenting the independence limitation in prior relevant studies. Second, we introduce a newly improved IF-EDAS (intuitionistic fuzzy Evaluation based on Distance from Average Solution) that preserves more uncertain information and provides a more natural extension of the canonical EDAS framework, starting with the adoption of the IFWAM (intuitionistic fuzzy weighted arithmetic mean) operator for a more intuitive approach in generating the intuitionistic fuzzy average solution vector. Also, the proposed IF-EDAS variant employs three decision rules and the Hamming distance metric in its novel computational approach. The proposed hybrid approach was deployed in two case studies evaluating five popular LLMs for text summarization across seven interdependent criteria. Results show that SWARA initially prioritizes accuracy, coherence, and consistency, but these were revised when accounting for criteria interdependence, with coherence and language quality emerging as the most preferred criteria. Both case studies suggest that Gemini may perform favorably, while Copilot may consistently rank last. The findings of the case studies share similar insights with those of three other similar IF-EDAS variants, although our claims may have limited external validity, which requires more case studies and experts in future task-specific human evaluations. The proposed approach, along with its deployment in two case studies, demonstrates human evaluations of LLMs with greater computational interpretability, which contribute to the general MCDM literature. Full article

Late deliveries represent one of the most persistent operational disruptions in global supply chains, eroding service reliability, triggering contractual penalties, and undermining the resilience of logistics networks. As supply chains become increasingly digitalized, the integration of advanced predictive analytics into operational decision-making offers a pathway toward proactive rather than reactive disruption management. This study develops and evaluates a digital analytics framework in which eXtreme Gradient Boosting (XGB), a high-performance ensemble learning algorithm, is optimized by three recent population-based metaheuristic algorithms: the weighted mean of vectors algorithm (INFO), Harris Hawks Optimization (HHO), and the Red-Billed Blue Magpie Optimizer (RBMO). Four critical XGB hyperparameters, number of estimators, maximum tree depth, learning rate, and complexity penalty, are tuned on a supply chain dataset. A population-size sensitivity analysis at two swarm configurations reveals that all three optimizers converge to functionally equivalent solutions at sufficient population diversity, providing practical guidance for computational resource allocation. The best-performing configuration, HHO-XGB, achieves a test accuracy of 97.47% and a Matthews correlation coefficient of 0.949, substantially outperforming the baseline XGB and other benchmark classifiers. To ensure transparency and support data-driven decision-making, SHapley Additive exPlanations (SHAP) analysis is applied to the optimized model, revealing that shipping mode, scheduled shipment days, shipping date, order day, order status, and order month are the dominant predictive features, confirming that late-delivery risk is primarily driven by shipment configuration and temporal patterns. The proposed framework demonstrates that integrating metaheuristic intelligence with machine learning delivers better predictive performance. Interpretability is essential to trustworthy, resilient supply chain decision-support systems. Full article

Support vector regression (SVR) is a well-established kernel-based method for nonlinear regression. However, standard SVR formulations minimize absolute-error losses, which are not consistent with the scale-free, relative-accuracy criteria prevalent in forecasting and industrial applications, where uncertainty is typically expressed as a percentage. This study proposes a unified SVR framework that incorporates percentage-error loss functions and symmetry constraints. Four specific variants are introduced: $\epsilon$-SVR with mean absolute percentage error (MAPE), its symmetric kernel extension, least-squares SVR (LS-SVR) with root mean square percentage error (RMSPE), and its symmetric counterpart. Each variant is formulated in primal, Lagrangian, and dual forms using Karush–Kuhn–Tucker analysis. The principal structural finding is that percentage scaling results in sample-dependent box constraints for $\epsilon$-SVR and a target-weighted diagonal regularization matrix for LS-SVR. In contrast, symmetry modifies only the kernel matrix, leaving the optimization structure unchanged. Convexity and the representer theorem are preserved in all cases. Experiments are conducted on three cross-sectional datasets (Boston Housing, Diabetes, and Energy Efficiency) and a time-series dataset on Victorian electricity demand. Evaluation utilizes three metrics (MAPE, MASE, and MAAPE), 95% bootstrap confidence intervals, and paired Wilcoxon tests, and compares performance against percentage-error-native baselines (weighted-MAE, quantile regression, and log-target SVR), classical $\epsilon$-SVR, Random Forest, and XGBoost. An additional reflection-based experiment assesses the symmetric-kernel variants. The results demonstrate that optimizing for percentage error consistently improves the targeted metric without adversely affecting absolute-error metrics. Full article

Background/Objectives: Lung adenocarcinoma (LUAD) is molecularly heterogeneous and often requires rational drug combinations rather than single-agent therapy. Many computational repurposing methods use global signature matching or network scores, but they often treat dysregulated genes equally and optimize a single scalar objective. This study aimed to develop a contribution-aware computational framework for prioritizing repurposed multi-drug combinations that counteract LUAD driver modules; Methods: Ten LUAD driver scenarios were curated from the LUAD and non-small cell lung cancer literature and encoded as gene-level counteraction vectors across 44 unique genes. Direction-aware drug–gene interactions from the Comparative Toxicogenomics Database were processed into a weighted contribution matrix. A genetic algorithm was then used to search for small combinations of up to six drugs. The fitness function combined mean absolute error with terms for waste, mismatch, entropy, coverage, combination size, and optional cost. Orthogonal computational support was assessed using CLUE/Connectivity Map transcriptomic reversal analysis; Results: After filtering and optimization, 42 drugs and chemicals remained as candidate components across the scenarios. Increasing the combination size from one to three drugs usually reduced the mean absolute error, whereas larger combinations provided more limited gains. Compared with an MAE-only baseline, the full contribution-aware objective improved or preserved MAE in 54 of 60 scenario–drug-count comparisons. Drug and gene clustering identified interchangeable candidate groups and shared mechanisms across LUAD scenarios. CLUE-based analysis provided strong or moderate transcriptomic reversal support for several prioritized compounds; Conclusions: The proposed framework provides a transparent, scenario-based method for prioritizing repurposed drug combinations in LUAD. The results are computational and hypothesis-generating. They should guide future experimental testing, not clinical treatment decisions. Full article

Market indices serve as a benchmark for performance comparison, guide asset allocation decisions, and reflect overall market sentiment and economic conditions, thereby influencing investment strategies by representing a segment of the market. Unquestionably, investor sentiment impacts price movement. In this paper, the objectives were to study the effectiveness of the Money Flow Index (MFI) in enhancing the performance of predictive analysis by capturing market psychology, developing an investment strategy, and analyzing the performance of the method mentioned. This study applies machine learning algorithms with technical indicators and optimizes portfolio allocation based on three notable market indices in Southeast Asia (SEA): SET50 in Thailand, STI in Singapore, and VN30 in Vietnam. Firstly, we combined technical indicators with machine learning—Support Vector Classifier (SVC), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—by comparing datasets with and without MFI over the period from 2013 to 2023. The results showed that XGBoost with MFI delivered the best predictive performance across three indices. These findings indicate that MFI significantly enhances prediction accuracy, even during volatile market conditions (COVID-19). Additionally, the predictions were integrated into the Markowitz Mean-Variance (MV) model to construct an optimal portfolio, which was then benchmarked against an equal-weight portfolio (1/N). Ultimately, the findings demonstrate that incorporating the machine learning predictions into the MV framework efficiently generates wealth. Full article

Accurate short-horizon downlink throughput prediction is essential for automation in high-density 5G deployments (e.g., stadiums and events), where user load, scheduling decisions, and interference conditions change rapidly and produce highly variable user-perceived rates. This paper benchmarks lightweight regression models for per-user throughput prediction from readily available radio access network (RAN) key performance indicators (KPIs) and studies a risk-aware extension that augments point forecasts with calibrated uncertainty and an abstention (deferral) rule. Experiments use a strictly time-ordered train/calibration/test protocol on the Liverpool 5G High-Density Demand (L5GHDD) dataset. The target is strongly zero-inflated (about 62% of samples at 0 Mbps) and heavy-tailed, creating regimes where average-error optimization can mask rare but operationally important bursts. In the point-prediction benchmark, the best model is a tuned two-stage support vector regressor with a mean absolute error (MAE) of $0.452$ Mbps, while the strongest single-stage model attains a weighted mean absolute percentage error (WMAPE) of $56.200\%$. For uncertainty quantification, we compare standard split conformal prediction against two input-adaptive alternatives. Constant-width split conformal attains $88.900\%$ marginal coverage for a nominal 90% target with an average interval width of $2.288$ Mbps, but width-based deferral is degenerate because all intervals have the same size. Variable-length conformal intervals preserve near-nominal coverage ($91.100\%$) while producing informative width variation: normalized conformal reduces the average width to $1.344$ Mbps, and conformalized quantile regression reduces it to $0.641$ Mbps. At a deferral threshold of $1.500$ Mbps, constant-width conformal defers all samples, whereas normalized conformal still acts on $61.200\%$ of samples with selective MAE $0.219$ Mbps. These results show that input-adaptive uncertainty is necessary for meaningful selective prediction in heteroscedastic 5G throughput dynamics. Full article