Abstract The acid mine water inrush resulting from coal mining activities poses a threat to the regional groundwater, leading to heavy metal pollution that can adversely impact the ecological environment and human health. In this study, several mines in the Guangyuan area were selected as research subjects to determine the concentrations of eight heavy metals (Cr, Fe, Mn, Hg, As, Zn, Pb, Cd) present in the mine water inrush. Ecological risks and human health risks were evaluated using species-sensitive distribution curves and health risk assessment models. The results showed that Fe had the largest excess rate and was the main substance causing ecological risks. The total health risk in the study area was high, mainly derived from the carcinogenic metal elements Cr, Cd and As, and the carcinogenic risk was higher than the maximum risk limit of 10-4, and the total non-carcinogenic risk was less than the maximum acceptable health risk limit 1, indicating that mine water inrush had a lower non-carcinogenic health risk, among which Fe had the largest non-carcinogenic risk and Cr6+ had the largest carcinogenic risk, which should be paid attention to in subsequent treatment and repair.
Plasma chemical reactions on a dielectric surface under discharge pulse conditions have been studied. A new model of a dielectric rectangular cavity is presented in studying this kind of reaction. Reactions are studied based on simultaneous nonstationary chemical kinetic equations and stationary wave equations. Furthermore, by using the Vlasov equation and the plasma dispersion law, the dipole layer effect caused by fluctuations on the dielectric surface has been studied. The results show that the thickness of the dipole layer is determined by the disturbance from the discharge pulse and the cohesion energy of electrons in the plasma. Cohesion energy and the pulse wave will excite various free radicals, resulting in a supernormal catalytic function in the ageing process of the dielectric.
The correction of uneven illumination in microscopic image is a basic task in medical imaging. Most of the existing methods are designed for monochrome images. An effective fully convolutional network (FCN) is proposed to directly process color microscopic image in this paper. The proposed method estimates the distribution of illumination information in input image, and then carry out the correction of the corresponding uneven illumination through a feature encoder module, a feature decoder module, and a detail supplement module. In this process, overlapping residual blocks are designed to better transfer the illumination information, and in particular a well-designed weighted loss function ensures that the network can not only correct the illumination but also preserve image details. The proposed method is compared with some related methods on real pathological cell images qualitatively and quantitatively. Experimental results show that our method achieves the excellent performance. The proposed method is also applied to the preprocessing of whole slide imaging (WSI) tiles, which greatly improves the effect of image mosaicking.
Precise perception of articulated objects is vital for empowering service robots. Recent studies mainly focus on point cloud, a single-modal approach, often neglecting vital texture and lighting details and assuming ideal conditions like optimal viewpoints, unrepresentative of real-world scenarios. To address these limitations, we introduce MARS, a novel framework for articulated object characterization. It features a multi-modal fusion module utilizing multi-scale RGB features to enhance point cloud features, coupled with reinforcement learning-based active sensing for autonomous optimization of observation viewpoints. In experiments conducted with various articulated object instances from the PartNet-Mobility dataset, our method outperformed current state-of-the-art methods in joint parameter estimation accuracy. Additionally, through active sensing, MARS further reduces errors, demonstrating enhanced efficiency in handling suboptimal viewpoints. Furthermore, our method effectively generalizes to real-world articulated objects, enhancing robot interactions. Code is available at https://github.com/robhlzeng/MARS.
The relationship between the period time and argument of simple pendulm is mesured with intergraded switch Hall sensor.The period time is determined precisely using extrapolation method in this paper.So the experiment principle of gravitation acceleration with simple pendulum is complemented effectively.
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