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The metal additive manufacturing process can inevitably lead to a great temperature gradient in the workpiece. Therefore, the thermal stress deformation and defects seriously affect the processing quality. In this paper, an array acoustic probe is designed on the base plate with the consideration of the time reversal technology. Corresponding simulations is implemented, which are designed to verify the ability of detecting and positioning the workpiece stress release acoustic emission signal. The simulation results demonstrate that the proposed method can position and monitor the random acoustic emission.

Selection pressure caused by long-term intensive use of insecticides is the key driving force in resistance development. Additional parameters such as environmental conditions may affect both the mosquito response to insecticides and the selection of resistance mechanisms. In this context, we analyzed the environmental determinants of kdr prevalence in Anopheles sinensis across China. We collected kdr frequency from 48 sites across central and southern China, together with key environmental factors including long-term climatic data, topographic features, main crops, and land cover types. Trend surface analysis found that the distribution of kdr frequency can be partitioned into three regions, namely central China (kdr frequency >80%), western China (kdr frequency varies from 0% to 60%), and southern China (kdr frequency

To solve the complexity of convolutional neural network and the large number of parameters in image recognition task, this paper proposes a lightweight network SepNet. In this structure, the traditional fully-connected layer is replaced by Kronecker product in the classifier module. In order to further optimize network structure, in the feature extraction module, by balancing the depth and width of the network, a separable residual network module using the deep separable convolution and residual network is designed. Finally, a lightweight network architecture which can realize end-to-end training is formed, which is called sep_res18_s3. The experiments are conducted on MNIST, CIFAR-10 and CIFAR-100 datasets respectively. The results show that compared with the VGG10 network, the designed SepNet can reduce the number of parameters and computation by 94.15% without losing its accuracy. At the same time, compared with cov_res18_s3, sep_res18_s3 can still reduce the parameter amount by 58.33% and 81.82% of FLOPs. Experimental results show that replacing the fully-connected layer with Kronecker product can not only maintain the accuracy of training results, but also significantly reduce the number of parameters and calculation costs, and to a certain extent, it can prevent overfitting. On this basis, combining the deep separable convolution and residual structure, it proves the effectiveness of sep_res18_s3.

The relationship among chemical structure, physicochemical property and aggregation behavior of organic functional material is an important research topic. Here, we designed and synthesized three bis(squaraine) dyes BSQ1, BSQ2 and BSQ3 through the combination of two kinds of unsymmetrical azulenyl squaraine monomers. Their physicochemical properties were investigated in both molecular and aggregate states. Generally, BSQ1 displayed different assembly behaviors from BSQ2 and BSQ3. Upon fabrication into nanoparticles, BSQ1 tend to form J‐aggregates while BSQ2 and BSQ3 tend to form H‐aggregates in aqueous medium. When in the form of thin films, three bis(squaraine) dyes all adopted J‐aggregation packing modes while only BSQ1 presented the most significant rearrangement of aggregate structures as well as the improvement in the carrier mobilities upon thermal annealing. Our research highlights the discrepancy of aggregation behaviors originating from the molecular structure and surrounding circumstances, providing guidance for the molecular design and functional applications of squaraines. [ABSTRACT FROM AUTHOR]

The identification of human-herpesvirus protein–protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/. [ABSTRACT FROM AUTHOR]