Desiccation and cracking behaviour of clay layer from slurry state under wetting-drying cycles

International audienceLaboratory tests were conducted to investigate the effect of wetting-drying (W-D) cycles on the initiation and evolution of cracks in clay layer. Four identical slurry specimens were prepared and subjected to five subsequent W-D cycles. The water evaporation, surface cracks evolution and structure evolution during the W-D cycles were monitored. The effect of W-D cycles on the geometric characteristics of crack patterns was analyzed by image processing. The results show that the desiccation and cracking behaviour was significantly affected by the applied W-D cycles: the measured cracking water content c, surface crack ratio Rsc and final thickness hf of the specimen increased significantly in the first three W-D cycles and then tended to reach equilibrium; the formed crack patterns after the second W-D cycle were more irregular than that after the first W-D cycle; the increase of surface cracks was accompanied by the decrease of pore volume shrinkage during drying. In addition, it was found that the applied W-D cycles resulted in significant rearrangement of specimen structure: the initially homogeneous and non-aggregated structure was converted to a clear aggregated-structure with obvious inter-aggregate pores after the second W-D cycle; the specimen volume generally increased with increasing cycles due to the aggregation and increased porosity. The image analysis results show that the geometric characteristics of crack pattern were significantly influenced by the W-D cycles, but this influence was reduced after the third cycle. This is consistent with the observations over the experiment, and indicates that the image processing can be used for quantitatively analyzing the W-D cycle dependence of clay desiccation cracking behaviour

LT4REC:A Lottery Ticket Hypothesis Based Multi-task Practice for Video Recommendation System

Click-through rate prediction (CTR) and post-click conversion rate prediction (CVR) play key roles across all industrial ranking systems, such as recommendation systems, online advertising, and search engines. Different from the extensive research on CTR, there is much less research on CVR estimation, whose main challenge is extreme data sparsity with one or two orders of magnitude reduction in the number of samples than CTR. People try to solve this problem with the paradigm of multi-task learning with the sufficient samples of CTR, but the typical hard sharing method can't effectively solve this problem, because it is difficult to analyze which parts of network components can be shared and which parts are in conflict, i.e., there is a large inaccuracy with artificially designed neurons sharing. In this paper, we model CVR in a brand-new method by adopting the lottery-ticket-hypothesis-based sparse sharing multi-task learning, which can automatically and flexibly learn which neuron weights to be shared without artificial experience. Experiments on the dataset gathered from traffic logs of Tencent video's recommendation system demonstrate that sparse sharing in the CVR model significantly outperforms competitive methods. Due to the nature of weight sparsity in sparse sharing, it can also significantly reduce computational complexity and memory usage which are very important in the industrial recommendation system.Comment: 6 pages,4 figure

Immune-related potential biomarkers and therapeutic targets in coronary artery disease

BackgroundCoronary artery disease (CAD) is a complex illness with unknown pathophysiology. Peripheral biomarkers are a non-invasive method required to track the onset and progression of CAD and have unbeatable benefits in terms of early identification, prognostic assessment, and categorization of the diagnosis. This study aimed to identify and validate the diagnostic and therapeutic potential of differentially expressed immune-related genes (DE-IRGs) in CAD, which will aid in improving our knowledge on the etiology of CAD and in forming genetic predictions.MethodsFirst, we searched coronary heart disease in the Gene Expression Omnibus (GEO) database and identified GSE20680 (CAD = 87, Normal = 52) as the trial set and GSE20681 (CAD = 99, Normal = 99) as the validation set. Functional enrichment analysis using protein-protein interactions (PPIs), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out on the identified differentially expressed genes. Optimal feature genes (OFGs) were generated using the support vector machine recursive feature elimination algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm. Furthermore, immune infiltration in CAD patients and healthy controls was compared using CIBERSORT, and the relationship between immune cells and OFGs was examined. In addition, we constructed potential targeted drugs for this model through the Drug-Gene Interaction database (DGIdb) database. Finally, we verify the expression of S100A8-dominated OFGs in the GSE20681 dataset to confirm the universality of our study.ResultsWe identified the ten best OFGs for CAD from the DE-IRGs. Functional enrichment analysis showed that these marker genes are crucial for receptor-ligand activity, signaling receptor activator activity, and positive control of the response to stimuli from the outside world. Additionally, CIBERSORT revealed that S100A8 could be connected to alterations in the immune microenvironment in CAD patients. Furthermore, with the help of DGIdb and Cytoscape, a total of 64 medicines that target five marker genes were subsequently discovered. Finally, we verified the expression of the OFGs genes in the GSE20681 dataset between CAD patients and normal patients and found that there was also a significant difference in the expression of S100A8.ConclusionWe created a 10-gene immune-related prognostic model for CAD and confirmed its validity. The model can identify potential biomarkers for CAD prediction and more accurately gauge the progression of the disease

New observation of perceptive mechanism behind the long-lasting change of people's community mobility: evidence from COVID-19 in China

Abstract COVID-19 pandemic provides an opportunity to investigate how a new and long-lasting threat affects public risk perception and social distancing behavior, which is important for pandemic risk management and recovery of the tertiary industry. We have found that the mechanism that perception decides behavior changes over time. At the beginning of the pandemic, risk directly shapes people’s willingness of going out. But under a persistent threat, perception no longer plays the direct role of shape people’s willingness. Instead, perception indirectly influences the willingness by shaping people’s judgment about the necessity of traveling. Switching from direct to indirect influence, perception’s effect is enlarged, which partially prevents people from returning to normal life even if the governmental ban is removed in a zero-COVID community