Simultaneous Speciation Analysis of Trace Heavy Metals (Cu, Pb, Cd and Zn) in Seawater from Sishili Bay, North Yellow Sea, China.

Different species of trace heavy metals (HMs) in seawater samples were simultaneously analyzed by anodic stripping voltammetric method, an analytical technique that does not require sample pre-concentration or the addition of reagents. The effects of the crucial parameters, deposition potential and time, on the determination of HMs were investigated. Concentrations of the total dissolved, dissolved active, and dissolved inert HMs were obtained through different analysis processes. The three species of Cu, Pb, Cd and Zn in seawater samples collected in different locations across Sishili Bay, North Yellow Sea, China were studied. The relative concentration of the dissolved active Cu, Pb, Cd and Zn in the total dissolved concentrations is 59.0%, 69.6%, 87.3% and 84.1%, respectively. The concentrations of different HMs species in Sishili Bay could be affected by the discharged effluent, sea current, and uptake of marine organism.

Deep reinforcement learning navigation via decision transformer in autonomous driving.

In real-world scenarios, making navigation decisions for autonomous driving involves a sequential set of steps. These judgments are made based on partial observations of the environment, while the underlying model of the environment remains unknown. A prevalent method for resolving such issues is reinforcement learning, in which the agent acquires knowledge through a succession of rewards in addition to fragmentary and noisy observations. This study introduces an algorithm named deep reinforcement learning navigation via decision transformer (DRLNDT) to address the challenge of enhancing the decision-making capabilities of autonomous vehicles operating in partially observable urban environments. The DRLNDT framework is built around the Soft Actor-Critic (SAC) algorithm. DRLNDT utilizes Transformer neural networks to effectively model the temporal dependencies in observations and actions. This approach aids in mitigating judgment errors that may arise due to sensor noise or occlusion within a given state. The process of extracting latent vectors from high-quality images involves the utilization of a variational autoencoder (VAE). This technique effectively reduces the dimensionality of the state space, resulting in enhanced training efficiency. The multimodal state space consists of vector states, including velocity and position, which the vehicle's intrinsic sensors can readily obtain. Additionally, latent vectors derived from high-quality images are incorporated to facilitate the Agent's assessment of the present trajectory. Experiments demonstrate that DRLNDT may achieve a superior optimal policy without prior knowledge of the environment, detailed maps, or routing assistance, surpassing the baseline technique and other policy methods that lack historical data.