Forest fires are one of the most serious natural disasters that threaten forest resources. The early and accurate identification of forest fires is crucial for reducing losses. Compared with satellites and sensors, unmanned aerial vehicles (UAVs) are widely used in forest fire monitoring tasks due to their flexibility and wide coverage. The key to fire monitoring is to accurately segment the area where the fire is located in the image. However, for early forest fire monitoring, fires captured remotely by UAVs have the characteristics of a small area, irregular contour, and susceptibility to forest cover, making the accurate segmentation of fire areas from images a challenge. This article proposes an FBC-ANet network architecture that integrates boundary enhancement modules and context-aware modules into a lightweight encoder–decoder network. FBC-Anet can extract deep semantic features from images and enhance shallow edge features, thereby achieving an effective segmentation of forest fire areas in the image. The FBC-ANet model uses an Xception network as the backbone of an encoder to extract features of different scales from images. By transforming the extracted deep semantic features through the CIA module, the model’s feature learning ability for fire pixels is enhanced, making feature extraction more robust. FBC-ANet integrates the decoder into the BEM module to enhance the extraction of shallow edge features in images. The experimental results indicate that the FBC-ANet model has a better segmentation performance for small target forest fires compared to the baseline model. The segmentation accuracy on the dataset FLAME is 92.19%, the F1 score is 90.76%, and the IoU reaches 83.08%. This indicates that the FBC-ANet model can indeed extract more valuable features related to fire in the image, thereby better segmenting the fire area from the image.

The prediction and analysis of students’ academic performance are essential tools for educators and learners to improve teaching and learning methods. Effective predictive methods assist learners in targeted studying based on forecast results, while effective analytical methods help educators design appropriate educational content. However, in actual educational environments, factors influencing student performance are multidimensional across both temporal and spatial dimensions. Therefore, a student performance prediction and analysis method incorporating multidimensional spatiotemporal features has been proposed in this study. Due to the complexity and nonlinearity of learning behaviors in the educational process, predicting students’ academic performance effectively is challenging. Nevertheless, machine learning algorithms possess significant advantages in handling data complexity and nonlinearity. Initially, a multidimensional spatiotemporal feature dataset was constructed by combining three categories of features: students’ basic information, performance at various stages of the semester, and educational indicators from their places of origin (considering both temporal aspects, i.e., performance at various stages of the semester, and spatial aspects, i.e., educational indicators from their places of origin). Subsequently, six machine learning models were trained using this dataset to predict student performance, and experimental results confirmed their accuracy. Furthermore, SHAP analysis was utilized to extract factors significantly impacting the experimental outcomes. Subsequently, this study conducted data ablation experiments, the results of which proved the rationality of the feature selection in this study. Finally, this study proposed a feasible solution for guiding teaching strategies by integrating spatiotemporal multi-dimensional features in the analysis of student performance prediction in actual teaching processes.

Over the past few decades, harmful algal blooms (HABs) have occurred frequently worldwide. The application of harmful algal bloom detection when based solely on water quality measurements proves challenging in achieving broad generalization across various regions. Satellite remote sensing, due to its low risk, cost effectiveness, and wide ground-coverage capabilities, has been extensively employed in HAB detection tasks. However, relying solely on remote sensing data poses issues of false positives, false negatives, and the incomplete consideration of contributing factors in HAB detection. This study proposes a model for harmful algal bloom detection by integrating MODIS multifactor data with heterogeneous meteorological data. Initially, a dataset named MODIS_MI_HABs is constructed by gathering information from 192 instances of harmful algal bloom events worldwide. Subsequently, remote sensing data corresponding to specific regions are collected; all were obtained from a moderate resolution imaging spectroradiometer (MODIS) aboard an ocean-color-detecting satellite. This dataset encompasses variables such as chlorophyll-a concentration, the sea surface temperature, photosynthetically active radiation, the relative radiation stability differences, the six seawater-absorption coefficients, and three scattering coefficients. By fusing six meteorological factors, latitude and longitude information, and remote sensing data, a regression dataset for harmful algal bloom detection is established. Finally, employing harmful algal bloom cell concentration as the data label, seven machine learning models are employed to establish correlations between the remote sensing data, heterogeneous meteorological data, and harmful algal bloom cell concentrations. The root mean square error ($RMSE$), mean absolute error ($MAE$), explained variance ($EV$), and coefficient of determination ($R^2$) parameters are used to evaluate the regression performance. The results indicate that the extreme gradient boosting (XGR) model demonstrates the best predictive capability for harmful algal blooms (leave-one-out: $RMSE$/$MAE$ = 0.0714). The XGR model, trained with the entire dataset, yields the optimal predictive performance ($RMSE$ = 0.0236, $MAE$ = 0.0151, $EV$ = 0.9593, $R^2$ = 0.9493). When compared to the predictions based on the fixed-area water quality analysis and single-source remote sensing data usage, the proposed approach in this paper displays wide applicability, offering valuable support for the sustainable development of marine ecology.

Farmland straw burning can produce incipient fire spots that occupy only a few pixels in UAV images and are easily confused with straw reflections, soil highlights, smoke, and illumination changes. This study proposes FireFly-YOLOv11, a real-time detector for tiny fire-spot detection under edge-device constraints. A stride-4 P2 detection head is added to the YOLOv11 prediction hierarchy to preserve fine spatial details for small targets. A Warm-Contrast Cue Attention (WCCA) module enhances fire-related saliency by jointly modeling local contrast variation and learnable warmth-inspired appearance cues. An Adaptive Asymmetric Atrous Feature Fusion (A3F) module adaptively aggregates multi-scale context while retaining low-level details through asymmetric gated residual fusion. Experiments on the StrawBurning UAV dataset show that FireFly-YOLOv11 achieves 88.6% Precision, 92.3% Recall, 91.7% mAP@0.5, and 41.0% mAP@0.5:0.95 at 50 FPS on an NVIDIA Jetson Orin Nano Super Developer Kit. Compared with baseline YOLOv11, it improves the four accuracy metrics by 2.1, 1.8, 2.7, and 2.0 percentage points. Ablation results confirm that P2, WCCA, and A3F provide complementary gains for UAV-based farmland fire monitoring.

Educational records alone may not capture changes in students’ classroom affective states. This study proposes Bi-LSTM-SCPM, a student comprehensive performance model (SCPM) integrating bidirectional long short-term memory (Bi-LSTM) with mixture of experts (MoE) regression. The dataset comprised 14,645 facial images from 71 students across three stages of 12-week computer-assisted translation courses. ResNet50 achieved 93.8% accuracy in positive–negative facial state classification. Bi-LSTM integrated stage scores derived from instructor-designated key periods and Bayesian smoothing into a student-level affective score (ZHCJ). ZHCJ was combined with educational information and prerequisite course performance to predict final grades. Across 30 matched student-level splits, MoE achieved an R² of 0.943 ± 0.002 and an MAE of 1.144 ± 0.128 points, compared with 0.927 ± 0.002 and 1.274 ± 0.149 points for XGBoost. Both differences remained significant after Holm adjustment (p < 0.001). ZHCJ also outperformed summary statistics and direct stage score inputs under both predictors. The setting ϖ = 2 and k = 20 performed best within the nine-setting grid. These findings support the predictive value of integrating temporal affective representations and educational information within the study cohort.