Agricultural non-point source pollution (ANPSP) significantly affects worldwide water quality, soil integrity, and ecosystems. Primary factors are nutrient runoff, pesticide leaching, and inadequate livestock waste management. Nonetheless, a thorough assessment of ANPSP sources and efficient control techniques is still lacking. This research delineates the origins and present state of ANPSP, emphasizing its influence on agricultural practices, livestock, and rural waste management. It assesses current evaluation models, encompassing field- and watershed-scale methodologies, and investigates novel technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) that possess the potential to enhance pollution monitoring and predictive precision. The research examines strategies designed to alleviate ANPSP, such as sustainable agricultural practices, fertilizer reduction, and waste management technology, highlighting the necessity for integrated, real-time monitoring systems. This report presents a comprehensive analysis of current tactics, finds significant gaps, and offers recommendations for enhancing both research and policy initiatives to tackle ANPSP and foster sustainable farming practices.

Foodborne disease presents a substantial challenge to researchers, as foliar water intake greatly influences pathogen internalization via stomata. Comprehending plant–pathogen interactions, especially under fluctuating humidity and temperature circumstances, is crucial for formulating ways to prevent pathogen ingress and diminish foodborne hazards. This study introduces a computational model utilizing neural networks to anticipate pathogen internalization via stomata, contrasting with previous research that emphasized biocontrol techniques. Computational modeling assesses the likelihood and duration of internalization for bacterial pathogens such as Salmonella enterica (S. enterica), considering various environmental factors including humidity and temperature. The estimated likelihood ranges from 0.6200 to 0.8820, while the internalization time varies from 4000 s to 5080 s, assessed at 50% and 100% humidity levels. The difference in internalization time, roughly 1042.73 s shorter at 100% humidity, correlates with a 26.2% increase in the likelihood of internalization, rising from 0.6200 to 0.8820. A neural network model has been developed to quantitatively predict these values, thereby enhancing the understanding of plant–microbe interactions. These methods will aid researchers in understanding plant–pathogen interactions, especially in environments characterized by varying humidity and temperature and are essential for formulating strategies to prevent pathogen ingress and tackle foodborne illnesses within a technologically advanced context.

Upstream–downstream nutrient connectivity strongly regulates water-quality risk in reservoir cascades, yet its quantification remains difficult where discharge, reservoir release, and hydraulic residence-time records are unavailable. This study developed a causal–explainable framework to diagnose total nitrogen (TN) connectivity between the upstream Shimantan Reservoir and downstream Banqiao Reservoir in the Huai River Basin, China. Long-term water-quality records, meteorological variables, land-cover indicators, seasonal descriptors, and lagged upstream predictors were integrated within a leakage-safe analytical workflow combining nonlinear causal inference, time–frequency coupling diagnostics, predictive modeling, SHAP-based attribution, and counterfactual analysis. Convergent Cross Mapping identified statistically significant asymmetric bidirectional nonlinear coupling, with a CCM skill of ρ = 0.746 for Shimantan TN → Banqiao TN and p = 0.730 for the reverse direction (p = 0.002 for both directions). Wavelet coherence showed that upstream–downstream TN coupling was concentrated mainly at short temporal scales, with a cone-of-influence-restricted mean squared coherence of 0.7114 in the 2–6-month intra-seasonal band. The validation-selected Gradient Boosting model provided interpretable test-period predictive skill, on independent source-derived observations from 2021–2023, achieving R² = 0.567, RMSE = 0.391 mg/L, and MAE = 0.312 mg/L. The GAN-generated 2024–2025 segment was excluded from empirical model evaluation and retained only for exploratory future-scenario assessment. SHAP decomposition indicated that upstream-related predictors accounted for 77.04% of the total absolute model attribution during high-TN events, while seasonal conditioned counterfactual upstream neutralization produced mean model-predicted changes of 0.162 mg/L across all test observations and 0.807 mg/L during high-TN events. Together, these results demonstrate that hidden upstream-downstream TN connectivity can be diagnosed through convergent causal, temporal, predictive, and model-attribution evidence in data-limited reservoir cascades. The findings support asymmetric coupled dynamics and downstream inheritance of upstream information but should not be interpreted as proof of exclusively one-way physical nutrient transport. The proposed framework offers a diagnostic decision-support approach for reservoir systems with sufficiently long monitoring records where direct hydraulic observations are unavailable.

Plants have evolved intricate signaling pathways, which operate as networks governed by feedback to deal with stressors. Nevertheless, the sophisticated molecular mechanisms underlying these routes still need to be comprehended, and experimental validation poses significant challenges and expenses. Consequently, computational hypothesis evaluation gains prominence in understanding plant signaling dynamics. Biosensors are genetically modified to emit light when exposed to a particular hormone, such as abscisic acid (ABA), enabling quantification. We developed computational models to simulate the relationship between ABA concentrations and bioluminescent sensors utilizing the Hill equation and ordinary differential equations (ODEs), aiding better hypothesis development regarding plant signaling. Based on simulation results, the luminescence intensity was recorded for a concentration of 47.646 RLUs for 1.5 μmol, given the specified parameters and model assumptions. This method enhances our understanding of plant signaling pathways at the cellular level, offering significant benefits to the scientific community in a cost-effective manner. The alignment of these computational predictions with experimental results emphasizes the robustness of our approach, providing a cost-effective means to validate mathematical models empirically. The research intended to correlate the bioluminescence of biosensors with plant signaling and its mathematical models for quantified detection of specific plant hormone ABA.

Total nitrogen (TN) concentrations in interconnected reservoir systems exhibit complex spatiotemporal patterns influenced by hydrological connectivity, multi-scale temporal variations, spatial nutrient linkages, and nonlinear biogeochemical interactions. Existing predictive models predominantly focus on individual reservoirs and often lack the capacity to adequately represent cross-reservoir nutrient transport dynamics and temporal variability across multiple scales. Consequently, modeling across interconnected reservoirs is essential for understanding nutrient exchange patterns and variability in linked aquatic ecosystems. This study analyzed 2,304 monthly observations from eight interconnected reservoirs in Central China (2000-2023) to develop and evaluate a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-enhanced Deep Hybrid Ensemble Framework (CEEMDAN-DHEF) for TN forecasting. The framework integrates CEEMDAN-based multi-scale decomposition with a heterogeneous neural ensemble comprising Temporal Fusion Transformer (TFT), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Deep Neural Network (DNN), along with a Ridge-optimized meta-learner to enhance predictive accuracy and model interpretability. The proposed CEEMDAN-DHEF achieved robust predictive performance (test R² = 0.8345, NSE = 0.8345), representing a 22.2% improvement over AdaBoost (R² = 0.6827). Furthermore, the framework demonstrated notable cross-reservoir transferability (MAE = 0.327 mg/L, RMSE = 0.435 mg/L) without requiring site-specific recalibration. Scenario-based projections for 2024-2030 under Business-as-Usual, Enhanced Management (−10% load reduction), and Deteriorated Conditions (+10% load increase) provide actionable insights for integrated reservoir governance and sustainable water quality management. Overall, this study contributes to advancing TN forecasting methodologies in interconnected reservoir systems and supports evidence-based watershed management and ecosystem monitoring.