In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT1A selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies.
A method based on low rank and sparse decomposition is proposed for moving object detection by the fusion of visual and infrared video. The visual and infrared image sequences are decomposed into the joint low rank background term, the uncorrelated sparse moving nonobject term, and the common sparse moving object term via a joint minimization cost of nuclear norm, F norm, and l(1) norm. This method provides a flexible framework that can easily fuse information from visual and infrared video. The prior fusion strategies are not required. The complementary information on visual and infrared images can be naturally fused in the procedure of object detection. The experimental results show that the proposed algorithm is effective.
Waste masks pose a serious threat to the environment, including marine plastic pollution and soil pollution risks caused by landfills since the outbreak of COVID-19. Currently, numerous effective methods regarding disposal and resource utilization of waste masks have been reported, containing physical, thermochemical, and solvent-based technologies. As for physical technologies, the mechanical properties of the mask-based materials could be enhanced and the conductivity or antibacterial activity was endowed by adding natural fibers or inorganic nanoparticles. Regarding thermochemical technologies, catalytic pyrolysis could yield considerable hydrogen, which is an eco-friendly resource, and would mitigate the energy crisis. Noticeably, the solvent-based technology, as a more convenient and efficient method, was also considered in this paper. In this way, soaking the mask directly in a specific chemical reagent changes the original structure of polypropylene and obtains multi-functional materials. The solvent-based technology is promising in the future with the researches of sustainable and universally applicable reagents. This review could provide guidance for utilizing resources of waste masks and address the issues of plastic pollution.
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