Ferdowsi University of Mashhad

• An explainable Spatiotemporal CNN (ST-CNN) model was developed to predict maize yield, identifying key biofertilizer-driven features (AMF root colonization, leaf chlorophyll content, and plant N status) via SHAP and counterfactual analysis. • The ST-CNN framework decoded complex, nonlinear interactions between microbial inoculants and plant traits, providing a transparent AI tool for optimizing biofertilizer use in sustainable maize production systems. • Counterfactual simulations revealed actionable insights for precision agronomy, showing how incremental changes in root colonization and canopy temperature can maximize yield under climate variability. • The model outperformed traditional methods (Random Forest, Linear Regression), offering a robust, data-efficient pipeline for yield prediction and management recommendation even with limited datasets. • This study bridges the gap between microbial ecology and digital agriculture, delivering a smart, interpretable AI framework to enhance crop resilience and support decision-making for farmers and agronomists. The global demand for maize ( Zea mays L.) necessitates innovative approaches to enhance yield under increasingly adverse environmental conditions. This study quantifies the contributions of arbuscular mycorrhizal fungi (AMF) and plant growth-promoting rhizobacteria (PGPR) to maize grain yield using a multidisciplinary approach that integrates plant ecophysiology, soil ecology, and machine learning. Field experiments were conducted over two growing seasons under varying cropping systems and biofertilizer treatments. A spatiotemporal convolutional neural network (ST-CNN) was developed to predict maize yield from a comprehensive dataset encompassing 31 plant and soil features. Model interpretability was enhanced through SHapley Additive exPlanations (SHAP) analysis, which identified key traits such as leaf chlorophyll content (SPAD indices), plant nitrogen concentration, and root traits as primary determinants of yield. Counterfactual analysis further evaluated the effects of incremental changes in features, revealing nuanced nonlinear relationships, particularly highlighting the complex role of root colonization percentage by AMF. Although the ST-CNN model showed moderate predictive performance (R² = 0.459), it provided valuable insights into the ecophysiological mechanisms governing maize productivity and the synergistic effects of microbial inoculants. Comparative analyses with linear regression and Random Forest models underscored the challenges of modeling with limited datasets. The findings inform practical strategies for optimizing AMF and PGPR applications in maize production systems, advancing sustainable agriculture under climate change pressures. The integrated XAI framework unveils nonlinear and conditional mechanisms (e.g., threshold-dependent AMF effects) that complement traditional analyses, offering actionable insights for biofertilizer optimization.

Background Despite growing scientific evidence and health guidelines, the global health paradox persists, with rising lifestyle-related diseases and escalating healthcare costs exposing the inadequacy of current efforts. Objective Three multidisciplinary congresses were held to generate evidence-based conclusions aimed at addressing the global health paradox. Methods A total of 58 experts from 62 entities participated in the international research and knowledge-exchange panels. Experts reviewed the latest findings to develop practical strategies and identify key research and policy priorities, focusing on the Healthy Eating & Active Living (HEAL) approach. Results, conclusion, and relevance The expert consortium endorsed a 33 evidence-based consensual-statement policy roadmap for addressing global health challenges, emphasizing that the HEAL approach can significantly contribute to the “Prevention First” appeal and broad ethical, social, ecological, and economic advantages, and ultimately supporting policy change.

Adhesive joints are widely used in various industries such as automotive, marine, aerospace, and construction. Owing to their high strength-to-weight ratio, uniform stress distribution with minimized stress concentrations, and excellent corrosion resistance, these mechanical connections demonstrate superior structural efficiency while reducing production and maintenance costs. Accurate prediction of interfacial stress distribution is a prerequisite for the optimal design of adhesive joints. In this study, the multi-term extended Kantorovich method is used to determine the interlayer shear and peel stresses in adhesively bonded sided strap joints with composite adherends under uniaxial tension. The governing equilibrium equations are derived in the framework of three-dimensional elasticity theory and solved analytically using the state-space approach. Unlike previous analytical methods that mainly rely on simplifying assumptions such as equivalent single-layer theories and often result in reduced accuracy in predicting the stress distribution, the present study adopts a highly accurate three-dimensional formulation that enables detailed modeling of wide adhesive joints with composite adherends of general layup configurations. The results, when compared with the full layerwise theory and finite element simulations, demonstrate that the present approach can accurately predict interfacial shear and peel stresses, especially near the joint edges where failure is more likely due to edge effects.

While many studies examine ecological footprint drivers, the impact of economic and political uncertainty is underexplored. This study addresses this gap by analysing the effects of the World Uncertainty Index (WUI) and renewable energy consumption on ecological footprints across 126 countries from 2001 to 2021, using a dynamic spatial Durbin model. Results show that WUI significantly reduces ecological footprints in both the short and long run. Spatial spillovers reveal that uncertainty’s effects extend beyond national borders, amplifying regional environmental impacts. Comparative analysis indicates that the negative effects of uncertainty outweigh the benefits of renewable energy consumption. These findings highlight the critical importance of socioeconomic and political stability, suggesting that effective environmental governance requires both domestic policy coherence and global coordination to mitigate uncertainty-driven spillovers and promote long-term ecological resilience.

In energy storage and conversion systems, the working condition can have a significant influence on their performance. In this article, a phase change material (PCM) block in an enclosure containing a participating medium is studied under the combined impact of nanofluid natural convection and volumetric radiation. The object is to determine the optimal condition that enhances both energy storage and heat transfer performance. For this purpose, the lattice Boltzmann method (LBM) was implemented to solve the governing equations for flow, energy, phase change, and radiative transfer. The simulation results showed that the interaction effects of radiation and buoyancy influence the flow and temperature distribution within the system. Then, an artificial neural network (ANN) model was developed using the obtained data from the numerical simulation. This model was able to provide results within seconds, compared to the time-consuming numerical simulation. Genetic algorithm and particle swarm optimization were both used to find optimal conditions using the developed ANN model. Both optimization methods found similar optimal values, which were validated through numerical simulation. Using the obtained optimal solution can enhance the energy transfer performance by 3.34% and energy storage by 7.86%. Optimization based on LBM-ANNs provided a reliable and computationally efficient tool for the intelligent design of PCM thermal storage units operating under combined heat transfer.