Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2025-01-11
Abstract： With the widespread emergence of the "lying flat" phenomenon in society, its impact on individual psychological health and well-being has become an important research topic. This study employs dictionary analysis and panel data modeling methods, utilizing Weibo data from 31 provinces/municipalities/autonomous regions in China between 2010 and 2021. A "lying flat" dictionary is constructed to quantify the degree of lying flat, and the study explores its effects on suicide risk, life satisfaction, and psychological well-being. The results show that the overall level of lying flat in the 31 provinces/municipalities/autonomous regions of China has shown an upward trend from 2010 to 2021. Furthermore, lying flat negatively predicts life satisfaction and psychological well-being, while positively predicting several sub-dimensions of suicide risk. This suggests that lying flat is not a positive coping mechanism; it undermines individual life satisfaction and psychological well-being, and increases suicide risk. This study provides new perspectives and empirical evidence for understanding the "lying flat" phenomenon and its psychological impacts, helping relevant departments and society at large to timely recognize and address the potential social consequences of the "lying flat" phenomenon.
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Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-05-10 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problems of low computational efficiency and low clustering performance of clustering algorithms for big data, a clustering algorithm of big data based on the improved artificial bee colony algorithm and MapReduce is presented. The grey wolf optimizer algorithm and artificial bee colony algorithm are combined, in order to improve the exploration and exploitation of the artificial bee colony algorithm simultaneously, this strategy helps to improve the clustering performance effectively. The chaotic map and backward learning are utilized as the initial strategy of ABC colony to improve the solution quality of search procedure. The clustering algorithm is realized based on MapReduce programming model, and the clustering process for big data is realized by minimizing the quadratic sum of inner class distances. Experimental results demonstrated that the proposed algorithm improves the clustering quality of big data, and it speedups the clustering procedure.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-04-01 Cooperative journals: 《计算机应用研究》
Abstract： To fix the randomness of the cluster centers and the limited representation of the discrete binary codes, this paper presented a method termed Iterative Self-organizing Hashing (ISOH) . This algorithm employed the Iterative Self-organizing Data Analysis to quantify the original space. As a result, the above measurement improves the retrieval accuracy largely. During initializing the clustering centers, this method utilized the farthest average distance to fix the randomness problem. As the fixed binary bits can represent a limited number of the codes, the hash based image ANN retrieval method has poor performance. To this end, this paper established the multi-encoding mechanism. In terms of the training time complexity, this method employed the product space mechanism to obtain longer encoding results at a lower cost. This paper conducted the comparative experiments in SIFT, GIST and CIFAR10 datasets. The experimental results show that ISOH is superior K-means Hashing and Scalable Graph Hashing etc. in achieving image ANN retrieval.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》
Abstract： With the increase in the number of motor vehicles, parking has become a common problem in cities. When many people take a long time to line up for a parking space at the workplaces, there are many parking spaces are available in the residential area that around the workplaces, which cause the use of parking space ineffectively. However, the existing shared parking methods are difficult to implement because of its randomness. In order to reduce its randomness, reduce the difficulty of implementing shared parking, fianl to reduce the waste of parking space resources, and because of the complementarity of travel times between commuter groups in residential areas and office buildings where around the residential areas, we present a data-driven one-to-one pairing shared solution . To solve this problem, we analyze the entry and exit record data of vehicles , then obtain the characteristics of the idle duration of the parking space in the residential area and the duration of use of the office building , finally get the results of matching parking spaces and vehicles according to the matching method of maximizing the duration. In the experiment for selected area, the proportion of fully matched parking spaces was 37.66%, and the average utilization rate of all matching parking spaces increased by 15.24%, and the maximum increase was 57.84%. The result shows that paired shared parking is extremely feasible.
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