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In order to use Hermetia illucens to efficiently treat food waste and improve the resource utilization rate, the response surface method(RSM) was used to optimize six parameters, including ambient temperature, moisture content of treated material, daily materials added, living space, initial larva age and permeability of materials, based on the Plackett-Burman(PB) test and the Box-Behnken(BB) test, with the larval yield of Hermetia illucens as the response value. The results of PB test showed that the significant factors in response to the larval yield were moisture content of treated material, ambient temperature, daily materials added, living space and initial larva age. The results of BB test showed that the optimal conditions for larval yield were ambient temperature 28 °C, moisture content of treatment material 75%, daily materials added 26 g⋅1 000 larva-1, living space 1 443 cm³⋅1 000-1 larva, and initial larva age 6 days; under this condition, the maximum larval yield of 243.78 g could be obtained, with a relatively small error compared to the predicted value of 243.82 g by the model, which indicated that response surface methodology could accurately optimize process parameters. Above results provided theoretical basis for the production practice of kitchen waste treatment by Hermetia illucens. [ABSTRACT FROM AUTHOR]

The role of computational tools in drug discovery and development is becoming increasingly important due to the rapid development of computing power and advancements in computational chemistry and biology, improving research efficiency and reducing the costs and potential risks of preclinical and clinical trials. Machine learning, especially deep learning, a subfield of artificial intelligence (AI), has demonstrated significant advantages in drug discovery and development, including high-throughput and virtual screening, ab initio design of drug molecules, and solving difficult organic syntheses. This review summarizes AI technologies used in drug discovery and development, including their roles in drug screening, design, and solving the challenges of clinical trials. Finally, it discusses the challenges of drug discovery and development based on AI technologies, as well as potential future directions. [ABSTRACT FROM AUTHOR]

Point cloud registration plays a great role in many application scenarios; however, the registration of large-scale point clouds for actual different moments suffers from the problems of low efficiency, low accuracy, and a lack of stability. In this paper, we propose a registration framework for large-scale point clouds at different moments, which firstly downsamples large-scale point clouds using a random sampling method, then performs a random expansion strategy to make up for the loss of information caused by the random sampling, then completes the first registration by a deep learning network based on the extraction of keypoints and feature descriptors in combination with RANSAC, and finally completes the registration using the point-to-point ICP method. We conducted validation experiments and application experiments on large-scale point clouds of key train components, and the experimental results are much higher in accuracy or efficiency than other methods, which proves the effectiveness of our framework, which can be applied to actual large-scale point clouds. [ABSTRACT FROM AUTHOR]