Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
This paper introduces the Knowledge-Guided Genetic Algorithm (KGGA), a hybrid metaheuristic that reimagines crossover as a form of genetic engineering rather than random recombination. By embedding knowledge-guided exploitation principles directly into the crossover operator, KGGA selectively amplifies high-quality genetic material, intensifying the search around promising regions of the solution space. Experimental results on a large scale DRC-FJSSP benchmark show that KGGA outperforms state-ofthe- art alternatives—including the Classic Genetic Algorithm (GA), Knowledge-Guided Fruit Fly Optimization Algorithm (KGFOA), and Hybrid Artificial Bee Colony Algorithm (HABCA)—consistently achieving superior solution quality....
Purpose: To develop and validate a two-stage system for automated quality assessment of shoulder true-AP radiographs by combining joint localization with quality classification. Materials and Methods: From the MURA “SHOULDER” subset, 2956 anteroposterior images were identified; 59 images with negative–positive inversion, excessive metallic implants, extreme exposure, or presumed fluoroscopy were excluded, yielding a classbalanced set of 2800 images (1400 OK/1400 NG). A YOLOX-based detector localized the glenohumeral joint, and classifiers operated on both whole images and detector-centered crops. To enhance interpretability, we integrated Grad-CAM into both whole-image and local classifiers and assessed attention patterns against radiographic criteria. Results: The detector achieved AP@0.5 = 1.00 and a mean Dice similarity coefficient of 0.967. The classifier attained AUC = 0.977 (F1 = 0.943) on a held-out test set. Heat map analyses indicated anatomically focused attention consistent with expert-defined regions, and coverage metrics favored local over whole-image models. Conclusions: The two-stage, XAI-integrated approach provides accurate and interpretable assessment of shoulder true-AP image quality, aligning model attention with radiographic criteria....
Over the past two decades, agricultural nitrous oxide (N2O) emissions have increased significantly, further intensifying their impact on global warming. Accurate emission estimates are essential for developing effective N2O-mitigation strategies. However, the high-resolution, dynamic simulations of emissions and comprehensive analysis of their driving mechanisms in China remain unclear. In this study, we constructed a city-level agricultural N2O emission inventory covering 336 cities in China from 2000 to 2022 based on multi-source data and machine learning algorithms. Results demonstrate that China’s cropland N2O emissions averaged 390 Gg year−1 during 2000 and 2022, exhibiting sustained growth until 2016, followed by a 13% reduction driven by the nationwide Fertilizer Reduction Policy implementation. Maize, wheat, and rice are identified as the main sources of cropland N2O emissions. Spatially, higher N2O emission intensities were concentrated in eastern China, and hotspots were identified in the Huang-Huai-Hai Plain (5.23 kg ha−1) and the Middle-Lower Yangtze River Plain (2.95 kg ha−1). These emission patterns are primarily influenced by soil organic carbon, crop type, and fertilizer-management practices. This study provides robust data support and methodological basis for formulating agricultural mitigation policies....
The quality of mangoes is a crucial factor in both domestic and commercial markets that directly influences consumer satisfaction and economic value. Traditional methods of checking mango quality often involve destructive techniques, which lead to the loss of the fruit in the testing process. This study presents an advanced approach that could predict the quality of mangoes using advance non-destructive methods leveraging machine learning algorithms to predict quality parameters such as ripeness, sweetness and overall freshness without damaging the fruit. In this research, a dataset consisting of various mango samples was collected, with attributes including color, texture, size, weight and acidity levels. Sensors, such as pH sensors (for acidity) and e-nose sensors (for aroma and sweetness detection), were used to gather data, while a combination of machine learning models such as Decision Tree, K-Nearest Neighbors (KNN), and Automated Machine Learning (AutoMLP), Naive Bayes were applied to predict the mangoes’ quality. The accuracy of each model was measured based on its ability to classify mangoes as fresh, ripe, or rotten. The results determine that the AutoMLP model performs the best out of the traditional models, achieving an accuracy of 98.46%, making it the most suitable model for mango quality prediction. The research explains the significance of feature extraction methods, model optimization, and sensor data pretreatment in reaching a high prediction accuracy....
This study aimed to evaluate short-term clinical outcomes in COVID-19 pneumonia patients using parameters derived from a commercial deep learning-based automatic detection algorithm (DLAD) applied to serial chest radiographs (CXRs). We analyzed 391 patients with COVID-19 who underwent serial CXRs during isolation at a residential treatment center (median interval: 3.57 days; range: 1.73–5.56 days). Patients were categorized into two groups: the improved group (n = 309), who completed the standard 7-day quarantine, and the deteriorated group (n = 82), who showed worsening symptoms, vital signs, or CXR findings. Using DLAD’s consolidation probability scores and gradient-weighted class activation mapping (Grad-CAM)-based localization maps, we quantified the consolidation area through heatmap segmentation. The weighted area was calculated as the sum of the consolidation regions’ areas, with each area weighted by its corresponding probability score. Change rates (Δ) were defined as per-day differences between consecutive measurements. Prediction models were developed using Cox proportional hazards regression and evaluated daily from day 1 to day 7 after the subsequent CXR acquisition. Among the imaging factors, baseline probability and ΔProbability, ΔArea, and ΔWeighted area were identified as prognostic indicators. The multivariate Cox model incorporating baseline probability and ΔWeighted area demonstrated optimal performance (C-index: 0.75, 95% Confidence Interval: 0.68–0.81; integrated calibration index: 0.03), with time-dependent AUROC (Area Under Receiver Operating Curve) values ranging from 0.74 to 0.78 across daily predictions. These findings suggest that the Δparameters of DLAD can aid in predicting short-term clinical outcomes in patients with COVID-19....
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