Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data

Environmental assessments are critical for ensuring the sustainable development of human civilization.The integration of artificial intelligence(AI)in these assessments has shown great promise,yet the"black box"nature of AI models often undermines trust due to the lack of transparency in their decision-making processes,even when these models demonstrate high accuracy.To address this challenge,we evaluated the performance of a transformer model against other AI approaches,utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators.We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments,enabling the identification of individual indicators'contributions to the model's predictions.We find that the transformer model outperforms others,achieving an accuracy of about 98%and an area under the receiver operating characteristic curve(AUC)of 0.891.Regionally,the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas,level IV in the northern region,and level V in the western region.Through explainability analysis,we identify that water hardness,total dissolved solids,and arsenic concentrations are the most influential indicators in the model.Our AI-driven environmental assessment model is accurate and explainable,offering actionable insights for targeted environmental management.Furthermore,this study advances the application of AI in environmental science by presenting a robust,explainable model that bridges the gap between machine learning and environmental governance,enhancing both understanding and trust in AI-assisted environmental assessments.

Is China Threat a Hoax?

The Beifing Olympics has focused unprecedented world attention on China this year.Many people hail the Games as an occasion that showcases China's growing contribution to world development and harmony.But intent on politicizing this global event,a few modern Cassandras still cling to the flawed China Threat theory. In this paper,the author traces the origins of this fallacious theory. He sees it as a product of Western empiricism viewed through an historical and philosophical prism.He argues that the assertion of threat arises from a generalization of historical facts.The assertion links China's growing clout with declining Western dominance in international affairs.Starting with the myth that peace is possible only among democracies,the theory predicts the inevitability of conflict between the West and China,a country with an alleged expansionist tradition and under an authoritarian system.

Advances and challenges in developing a stochastic model for multi-scale fluid dynamic simulation:One-dimensional turbulence

The modeling of turbulence,especially the high-speed compressible turbulence encountered in aerospace engineering,has always being a significant challenge in terms of balancing efficiency and accuracy.Most traditional models typically show limitations in universality,accuracy,and reliance on past experience.The stochastic multi-scale models show great potential in addressing these issues by representing turbulence across all characteristic scales in a reduced-dimensional space,maintaining sufficient accuracy while reducing computational cost.This review systematically summarizes advances in methods related to a widely used and refined stochastic multi-scale model,the One-Dimensional Turbulence(ODT).The advancements in formulations are emphasized for stand-alone incompressible ODT models,stand-alone compressible ODT models,and coupling methods.Some diagrams are also provided to facilitate more readers to understand the ODT methods.Subsequently,the significant developments and applications of stand-alone ODT models and coupling methods are introduced and critically evaluated.Despite the extensively recognized effectiveness of ODT models in low-speed turbulent flows,it is crucial to emphasize that there is still a research gap in the field of ODT coupling methods that are capable of accurately and efficiently simulating complex,three-dimensional,high-speed compressible turbulent flows up to now.Based on an analysis of the advantages and limitations of existing ODT methods,the recent advancement in the conservative compressible ODT model is considered to have provided a promising approach to tackle the modeling challenges of high-speed compressible turbulence.Therefore,this review outlines several recommended new research subjects and challenging issues to inspire further research in simulating complex,three-dimensional,high-speed compressible turbulent flows using ODT models.

Enhancement of the Seebeck Coefficient of Organic Thermoelectric Materials via Energy Filtering of Charge Carriers

Recently,organic materials have emerged as nextgeneration thermoelectric(TE)materials because of their unique advantages including low cost,high mechanical flexibility,low or no toxicity,and low intrinsic thermal conductivity over inorganic TE materials.However,the Seebeck coefficient of organic materials with high TE properties is remarkably lower than that of its inorganic counterparts.Therefore,it is important to improve their Seebeck coefficient and thus,overall TE properties.