Advances in understanding and mitigating Atrazine's environmental and health impact: A comprehensive review.

Atrazine is a widely used herbicide in agriculture, and it has garnered significant attention because of its potential risks to the environment and human health. The extensive utilization of atrazine, alongside its persistence in water and soil, underscores the critical need to develop safe and efficient removal strategies. This comprehensive review aims to spotlight atrazine's potential impact on ecosystems and public health, particularly its enduring presence in soil, water, and plants.

MCMC: Multi-Constrained Model Compression via One-Stage Envelope Reinforcement Learning.

Model compression methods are being developed to bridge the gap between the massive scale of neural networks and the limited hardware resources on edge devices. Since most real-world applications deployed on resource-limited hardware platforms typically have multiple hardware constraints simultaneously, most existing model compression approaches that only consider optimizing one single hardware objective are ineffective. In this article, we propose an automated pruning method called multi-constrained model compression (MCMC) that allows for the optimization of multiple hardware targets, such as latency, floating point operations (FLOPs), and memory usage, while minimizing the impact on accuracy.

Learning Multi-Agent Cooperation via Considering Actions of Teammates.

Recently value-based centralized training with decentralized execution (CTDE) multi-agent reinforcement learning (MARL) methods have achieved excellent performance in cooperative tasks. However, the most representative method among these methods, Q-network MIXing (QMIX), restricts the joint action Q values to be a monotonic mixing of each agent's utilities. Furthermore, current methods cannot generalize to unseen environments or different agent configurations, which is known as ad hoc team play situation.