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Populism is deeply embedded in a democratic society, and a thin ideology composed of many different concepts affects people's attitudes toward political elites and their vote choices. Based on voters' populist inclination, this paper analyzes the voting in the recall of the mayor and the by-election of Kaohsiung City in 2020. In the two different voting periods, the voters' populist inclination was affected by their demographic background and political attitudes. This study uses two sets of data to analyze the voting for the recall and the byelection. One is "A Voting Study of Kaohsiung City's Third Mayoral Recall" hosted by Tsai Chia-hung and conducted by the National Chengchi University's Election Study Center, and the other is "Taiwan's Election and Democratization Study, 2020-2024(I): Kaohsiung City Mayor By-Elections: Telephone Interview hosted by Chen Luhui" conducted by the National Chengchi University's Election Study Center. We found that during the recall period, populism was affected by the age and education of the voters. During the by-election period, the populist inclination was affected by voters' party identity, Taiwanese/Chinese identity, education and occupation. In addition, the populist inclination of Kaohsiung voters did not affect their decision to vote for recall. Their satisfaction with Han Guo-yu was the main reason for voters to vote for the recall and in the by-election, voters with lower populist inclination voted for Chen Chi-mai, but it did not influence voters to vote for Li Mei-jhen. Although Han Guo-yu helped Li Mei-jhen in the campaign, it did not affect the voters' choice. [ABSTRACT FROM AUTHOR]

Populism is deeply embedded in a democratic society, and a thin ideology composed of many different concepts affects people's attitudes toward political elites and their vote choices. Based on voters' populist inclination, this paper analyzes the voting in the recall of the mayor and the by-election of Kaohsiung City in 2020. In the two different voting periods, the voters' populist inclination was affected by their demographic background and political attitudes. This study uses two sets of data to analyze the voting for the recall and the byelection. One is "A Voting Study of Kaohsiung City's Third Mayoral Recall" hosted by Tsai Chia-hung and conducted by the National Chengchi University's Election Study Center, and the other is "Taiwan's Election and Democratization Study, 2020-2024(I): Kaohsiung City Mayor By-Elections: Telephone Interview hosted by Chen Luhui" conducted by the National Chengchi University's Election Study Center. We found that during the recall period, populism was affected by the age and education of the voters. During the by-election period, the populist inclination was affected by voters' party identity, Taiwanese/Chinese identity, education and occupation. In addition, the populist inclination of Kaohsiung voters did not affect their decision to vote for recall. Their satisfaction with Han Guo-yu was the main reason for voters to vote for the recall and in the by-election, voters with lower populist inclination voted for Chen Chi-mai, but it did not influence voters to vote for Li Mei-jhen. Although Han Guo-yu helped Li Mei-jhen in the campaign, it did not affect the voters' choice. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ObjectiveTo study the implementation of food recalls managed by U. S. Food and Drug Administration (FDA) and explore experiences for the food recalling in China.MethodsFood recall information was collected from FDA website. The information was analyzed by EXCEL 2013.ResultsInformation including 461 recall events and 1734 types of food were obtained from the weekly enforcement reports in 2019. Sixty percent of the recalls were class Ⅱ. About one third of recalls were class Ⅰ. Milk products were the most frequently recalled food, accounted for 17.88% of the total recalling food. Microbiology contamination was the main recall reason, resultsing in 31.45% of recall events and 37.89% of recall food types. Not notifying allergen was responsible for 35.01% of the recall events, which was the highest proportion among the reasons. The proportion of recall food types caused by not notifying allergen was only 18.40%. Inappropriate proceeding resulted in 3.47% of recall events and 24.63% of recall food types, respectively. More than 99% of the recalls were carried voluntarily by food firms except 4 mandatory recalls by FDA.ConclusionFood recall is implemented efficiently in America. FDA publishes food recall information and makes it easily obtainable to public. Manufacture process monitoring is a high efficiency food safety managing measure. Food firms have strong conscientiousness to recall defective food voluntarily. It’s important to promote the participation of the public, transfer the traditional end-product managing model, and cultivate the conscientiousness of the firms for the implementation of food recall in China.

Introduction: In the environment of sports with highly intensive training and competition as well as full of discipline, the various possible traumatic episodes may result in individual or collective traumatic memory of athletes. However, there is still a lack of existing research on this topic. Based on the attention to the life course of the competitive athlete, this research puts forward the following two research questions: What is the patterns of individual traumatic memory and the collective traumatic memory of athletes? How does athletes' individual traumatic memory further connects collective traumatic memory? Methods: The discourse of "collective memory" which was proposed by French sociologist Maurice Halbwachs was used for interpretive analyze the traumatic memory of athletes. Results: Because of the unbearable nature of trauma, traumatic memory of individuals often present broken appearances. Its contents depend on the traumatic events and traumatic situations people has experienced, and the common influencing factors include the attitude of other people, physical injury, environmental system, group joint, failure experience, sense of existence and achievement, etc. Through different media or memory sites, the traumatic memory of individuals can recall and reveal collective memory under the social framework by connecting with the traumatic memory of other people from selection, restatement, regulation, integration and shaping in the process of interaction. This also makes reconstruction possible. Different individuals are influenced by multiple factors such as self-interpretation and social nature, the different perspectives of traumatic memory they provide are showing that the collective traumatic memory appearance is similar to the individual traumatic memory, but it is more complete, stereoscopic and continuous. The continuous development of co-constructedness also shows the dynamic of adjustment of collective traumatic memory appearance. Conclusions: Through Maurice Halbwachs's social constructive horizon, it can be found that athletes' collective traumatic memory is affected by traumatic events, self-interpretation and collective co-constructedness in a specific social framework. It is also recalled and revealed through athletes' exchanges and communications of their individual traumatic memory. The collective traumatic memory and the personal traumatic memory present coexistence and constantly shape the appearances of each other. [ABSTRACT FROM AUTHOR]

Copyright of China Textile Leader is the property of China Textile Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There exists several issues in traditional collaborative filtering algorithms: a) It lakes the impact of all users’ historical feedback information into account when calculating the similarities between any two items; b)It only utilizes the user’s rating data when calculating the similarities. However, the user group that has similar interests with the target user has a higher reference value than other users. Considering the fact that irrelevant historical information leaded to poor recommendation results, this paper proposed a novel collaborative filtering recommendation algorithm based on K-means clustering. The new algorithm refined the user’s similarity metric with the user’s common rating weight and popular items weight, the item’s similarity metric with time difference weight and user’s rating weight respectively, and clustered all uses into several partitions according to the similarities. Then, it applied recommend algorithm in each of the clusters. Experimental results show that, compared with traditional item-based lop-iV collaborative filtering recommendation algorithm, the proposed algorithm can improve the recall by 2. 1% on average. The proposed algorithm can improve the accuracy and the quality of the recommendation effectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Bohai University (Philosophy & Social Science Edition) / Bohai Daxue Xuebao (Zhexue Shehuikexue Ban) is the property of Bohai Daxue and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The major objecive of recommendation algorithms is to accurately predict users?rating values of the unknown product items. However, it has recognized that an accurate prediction of rating values is not the only way for achieving user satisfaction, and the diversity of recommendation lists has gained importance recently. In this study, we propose a novel method which can adjust the weights of product categories to enhance the diversity levels of their own recommendation lists with little decrease in accuracy. Experimental results show that our method outperforms other methods, which has a very low computational time complexity and is highly scalable. [ABSTRACT FROM AUTHOR]