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1,582 results on '"Xu, Yanan"'

6. The Effect of Education Groups on the Quality of Urban and Rural Compulsory Education: An Empirical Analysis Based on CEPS Data

Author

Cheng, Gang, Du, Sihui, Xu, Yanan, and Li, Luoping

Abstract

We retrieved data from China Education Panel Survey (CEPS) and adopted the hierarchical linear model as well as the quantile regression method to examine the effect of education groups on the quality of urban and rural compulsory education. Student cognitive ability was cited as the evaluation index of education quality. According to the research findings, the overall effect of education groups on the quality of compulsory education is significantly positive, but there is urban-rural heterogeneity in this effect: the positive impact occurs only to urban member schools, while the education quality of rural member schools has not been improved by school grouping. In addition, there exists individual difference in this effect; school grouping significantly and negatively affects students from rural member schools and with medium and lower cognitive ability.

Published
2022

7. A Qualitative Study on the Social Isolation Experience of Young and Middle-aged Patients with Schizophrenia in Remission

Author

XU Yanan, LI Wenjuan, ZHAO Shuqin, TANG Yuji, YUAN Fuqiang, ZHAO Kunpeng

Subjects
schizophrenia, social isolation, relief period, middle-aged and young people, semi-structured interviews, qualitative research, Medicine
Abstract

Background Schizophrenia often experience severe social discrimination throughout their entire survival period, resulting in self isolation or social isolation. Limited studies have been conducted on social isolation in young and middle-aged patients with schizophrenia in remission. Objective To describe the social isolation experience of young and middle-aged patients with schizophrenia in remission, aiming to provide reference for developing supportive intervention plans to alleviate the social isolation experience of this population. Methods A purposive sampling method was used to select young and middle-aged schizophrenia patients aged 18-59 who had been hospitalized at the Second Affiliated Hospital of Xinxiang Medical University and were willing to continue follow-up after discharge as the research subjects. From May to December 2022, a semi-structured in-depth interview was conducted face-to-face with the respondents, and the themes were summarized and extracted using content analysis method. Results A total of 18 young and middle-aged patients with schizophrenia in remission were included. By analyzing the interview data, a total of 3 themes and 8 sub themes were extracted, including the performance of social isolation in young and middle-aged patients with schizophrenia in remission (containing 2 subthemes of negative emotional experience and withdrawal between expectation and behavior) ; subjective reasons for social isolation in young and middle-aged patients with schizophrenia in remission (containing 2 subthemes of self isolation and self discrimination) , and the objective reasons for social isolation in young and middle-aged patients with schizophrenia in remission (containing 4 subthemes of social discrimination and passive containing, effects of adverse drug reactions, increased burden of living and care caused by mental illness, and the lack of social support) . Conclusion Young and middle-aged patients with schizophrenia in remission may experience social isolation due to impaired physical health, poor psychological status, and insufficient social support. Healthcare professionals should strengthen symptom management and psychological counseling for this population. The government and all sectors of society should actively guide positive perception of mental illness and provide certain social support; family support should be strengthened to help young and middle-aged patients with schizophrenia in remission reintegrate and adapt to society.

Published
2024
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12. TMEM147 aggravates the progression of HCC by modulating cholesterol homeostasis, suppressing ferroptosis, and promoting the M2 polarization of tumor-associated macrophages

Author

Huang, Jingjing, Pan, Huayang, Sun, Jing, Wu, Jiaming, Xuan, Qiyue, Wang, Jinge, Ke, Shanjia, Lu, Shounan, Li, Zihao, Feng, Zhigang, Hua, Yongliang, Yu, Qingan, Yin, Bing, Qian, Baolin, Zhou, Menghua, Xu, Yanan, Bai, Miaoyu, Zhang, Yingmei, Wu, Yaohua, Ma, Yong, Jiang, Hongchi, and Dai, Wenjie

Published
2023
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35. Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks

Author

Liu, Haobing, Zhu, Yanmin, Zang, Tianzi, Xu, Yanan, Yu, Jiadi, and Tang, Feilong

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints which encode heterogeneous behaviors continuously. In this paper, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of LSTM and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model.

Published
2021
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43. Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-based Recommendation

Author

Zhu, Jing, Xu, Yanan, and Zhu, Yanmin

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

The aim of session-based recommendation is to predict the users' next clicked item, which is a challenging task due to the inherent uncertainty in user behaviors and anonymous implicit feedback information. A powerful session-based recommender can typically explore the users' evolving interests (i.e., a combination of his/her long-term and short-term interests). Recent advances in attention mechanisms have led to state-of-the-art methods for solving this task. However, there are two main drawbacks. First, most of the attention-based methods only simply utilize the last clicked item to represent the user's short-term interest ignoring the temporal information and behavior context, which may fail to capture the recent preference of users comprehensively. Second, current studies typically think long-term and short-term interests as equally important, but the importance of them should be user-specific. Therefore, we propose a novel Parallel Attention Network model (PAN) for Session-based Recommendation. Specifically, we propose a novel time-aware attention mechanism to learn user's short-term interest by taking into account the contextual information and temporal signals simultaneously. Besides, we introduce a gated fusion method that adaptively integrates the user's long-term and short-term preferences to generate the hybrid interest representation. Experiments on the three real-world datasets show that PAN achieves obvious improvements than the state-of-the-art methods.

Published
2020

46. ALCNN: Attention-based Model for Fine-grained Demand Inference of Dock-less Shared Bike in New Cities

Author

Liu, Chang, Xu, Yanan, and Zhu, Yanmin

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

In recent years, dock-less shared bikes have been widely spread across many cities in China and facilitate people's lives. However, at the same time, it also raises many problems about dock-less shared bike management due to the mismatching between demands and real distribution of bikes. Before deploying dock-less shared bikes in a city, companies need to make a plan for dispatching bikes from places having excessive bikes to locations with high demands for providing better services. In this paper, we study the problem of inferring fine-grained bike demands anywhere in a new city before the deployment of bikes. This problem is challenging because new city lacks training data and bike demands vary by both places and time. To solve the problem, we provide various methods to extract discriminative features from multi-source geographic data, such as POI, road networks and nighttime light, for each place. We utilize correlation Principle Component Analysis (coPCA) to deal with extracted features of both old city and new city to realize distribution adaption. Then, we adopt a discrete wavelet transform (DWT) based model to mine daily patterns for each place from fine-grained bike demand. We propose an attention based local CNN model, \textbf{ALCNN}, to infer the daily patterns with latent features from coPCA with multiple CNNs for modeling the influence of neighbor places. In addition, ALCNN merges latent features from multiple CNNs and can select a suitable size of influenced regions. The extensive experiments on real-life datasets show that the proposed approach outperforms competitive methods.

Published
2019

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