Oxidative hair dyes consist of two components (I and II) that are mixed before use. Aromatic amines in component I and their reaction with hydrogen peroxide after mixing them with component II have been of primary concern. In addition, two in vitro genotoxicity assays are still required for the approval of the final products of oxidative hair dyes in China, and the substance in the oxidative hair dye that causes the high rate of positive results in genotoxicity tests, especially the Ames test, has not been fully elucidated. In this study, we analyzed the formulation of 55 different oxidative hair dyes from 7 color series and performed Ames tests in the strain TA98 with the S9 mix (oxidative hair dyes No. 1–30) and in strain TA97a without the S9 mix (oxidative hair dyes No. 31–55). We found that toluene-2,5-diamine sulfate (2,5-diaminotoluene sulfate, DATS) in component I may be the cause of mutagenicity in TA98, and hydrogen peroxide in component II may be the cause of mutagenicity in TA97a, and their positive concentrations were consistent with those that we calculated from Ames tests. The results suggest that the positive results for the oxidative hair dye in the Ames test were inevitable because of the existence of DATS in component I and of hydrogen peroxide in component II. Therefore, we should carry out safety assessments on each raw material and carry out risk assessments on the final products of oxidative hair dyes instead of genotoxicity tests in China.
The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator.
Heavy-duty trucks in open-pit mines are of huge size and blind areas, so it is difficult for drivers to see other vehicles around the blind areas. In addition, due to the dullness of transportation in mines, drivers are prone to distraction and other phenomena, so collisions between vehicles occur from time to time. In existing technologies, such as radar and infrared ranging, it is difficult to detect vehicles on the other side of the bend at the bend, and it is vulnerable to dust and weather, resulting in false alarm. Aiming at the above problems, a collision prevention and warning scheme for heavy truck in open pit mine based on RBF network and WIFI is proposed. That is to say, a WIFI ranging module is installed in the middle of each mining truck. When the distance between the two trucks is less than the defined range, an early warning signal will be sent, indicating that there are other vehicles near the driver and paying attention to driving safety. The measurement error of WIFI ranging is easy to fluctuate in a long distance, but WIFI ranging has the advantages of long measurement distance, not easily affected by weather and dust, low cost and so on. For the system modeling, the author went to Deerni Copper Mine to collect data, such as the main test parameters: ranging, signal intensity and so on. The model between WIFI signal intensity and distance is constructed. The results show that the system has better measurement accuracy, and thus realizes the early warning function. It realizes the automatic collection and identification of wireless information when vehicles approach, which is of great significance for reducing and eliminating the collision accidents of open-pit transport vehicles and improving the level of safety management.
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