Wildfires influence the global carbon cycle, and the regularity of wildfires is mostly determined by elements such as meteorological conditions, combustible material states, and human activities. The time series and spatial dispersion of wildfires have been studied by some scholars. Wildfire samples were acquired in a monthly series for the Montesinho Natural Park historical fire site dataset (January 2000 to December 2003), which can be used to assess the possible effects of geographical and temporal variations on forest fires. Based on the above dataset, dynamic wildfire distribution thresholds were examined using a K-means++ clustering technique for each subgroup, and monthly series data were categorized as flammable or non-flammable depending on the thresholds. A five-fold hierarchical cross-validation strategy was used to train four machine learning models: extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and decision tree (DT). Finally, to explore the performance of those we have mentioned, we used accuracy (ACC), F1 score (F1), and the values for the area under the curve (AUC) of the receiver operating characteristics (ROCs). The results depicted that the XGBoost model works best under the evaluation of the three metrics (ACC = 0.8132, F1 = 0.7862, and AUC = 0.8052). The model performance is significantly improved when compared to the approach of classifying wildfires by burned area size (ACC = 72.3%), demonstrating that spatiotemporal heterogeneity has a broad influence on wildfire occurrence. The law of a spatiotemporal distribution connection in wildfires could aid in the prediction and management of wildfires and fire disasters.

Accurately estimating soil moisture (SM) without direct measurements poses significant challenges due to nonlinear interactions in meteorological variables and the lagged response of SM to precipitation. This study evaluates two approaches: the auto-regressive integrated moving average (ARIMA) model for one-day-ahead SM forecasting and a K-means clustering-based multilayer perceptron (K-MLP) for real-time SM estimation at depths of 5 cm, 20 cm, and 50 cm in Changbai Mountain region. Although the K-MLP model outperformed the MLP model, achieving a maximum R² of 0.728, its estimation accuracy remains suboptimal. By contrast, the ARIMA model effectively leveraged SM persistence, achieving high accuracy in one-day-ahead forecasting. Specifically, the ARIMA (0, 1, 6), ARIMA (1, 1, 2), and ARIMA (2, 1, 1) models yield R² values of 0.9677, 0.9853, and 0.9684 and RMSE values of 0.02 m³·m³, 0.015 m³·m³, and 0.006 m³·m³ at depths of 5 cm, 20 cm, and 50 cm, respectively. This study explores ARIMA’s robustness in short-term SM forecasting and its adaptability to dynamic meteorological conditions, offering potential applications in agricultural water management and ecological monitoring.

To construct an improved deep learning model that provides a more accurate and cost-effective solution for diagnosing abnormalities in the cervical physiol

Major depressive disorder (MDD) is a common disabling psychiatric disorder mainly diagnosed through clinical interviews and rating-scale assessments. Magnetic resonance imaging (MRI) provides a promising basis for computer-aided MDD identification, particularly through multimodal models that can integrate structural and functional brain information. However, existing multimodal MRI fusion models often use data-level fusion or post hoc feature concatenation, potentially overlooking differences in spatial resolution, voxel organization, and region of interest (ROI)-level semantic correspondence. To address this issue, this study proposes MUSA, a unified ROI semantic space model for multimodal MRI-based MDD classification. An Automated Anatomical Labeling (AAL) atlas-guided ROI semantic space establishes consistent brain-region correspondence across heterogeneous MRI features. An ROI-level 3D Vision Transformer (3D ViT) encodes ROI voxel information into fixed-dimensional representations, allowing structural, local functional, and connectivity information to be organized and integrated within a consistent brain-region semantic space. Experiments on 2380 participants from REST-meta-MDD validated MUSA for MDD and normal controls (NCs) classification. The feature combination of white matter (WM), functional connectivity (FC), regional homogeneity (ReHo), and degree centrality (DC) achieved 79.20% ± 0.65% accuracy and an AUC of 87.08% ± 0.51%. Brain-region importance analysis identified classifier-derived discriminative patterns across frontoparietal, sensorimotor, visual, temporal, and limbic-related regions.