Search

The COVID-19 pandemic has forced higher education to enter into a phase of blended learning that combines in-person and mandatory online learning. This new combination has presented great challenges in promoting academic wellbeing. Anchored by the conservation of resources theory, this study undertakes to explore the contributions of personal resources to academic wellbeing. A total of 1934 undergraduates at a public university in China voluntarily participated in a survey during the 2022 spring semester. They were asked to rate their psychological capital (PsyCap) in general. They were also asked to rate their learning burnout, learning engagement, and academic buoyancy on the course level (namely through a course with English as a foreign language). Partial least squares structural equation modelling was used for the data analysis. The results confirm a direct positive association between PsyCap and students' learning engagement. In particular, the contribution of PsyCap to learning engagement is partially mediated by learning burnout. The moderation estimation reports that the positive effect of PsyCap on learning engagement is stronger for Chinese undergraduates with a higher academic buoyancy and that the negative link of learning burnout with either PsyCap or learning engagement is stronger for Chinese undergraduates with a lower academic buoyancy. This research is the first one to explore the beneficial joint roles of PsyCap and academic buoyancy in promoting academic wellbeing. The findings of this research offer novel and subtle insights into why and how PsyCap and academic buoyancy has contributed to easing learning burnout and boosting learning engagement in blended learning environments during the prolonged COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The healthcare utilization inequality among middle-aged and older adults has been a serious concern shared in the aging process. Successful policy implementation regarding the supply of medical resources and the targeted welfare is an important means to alleviate the healthcare utilization inequalities. However, the effectiveness of the current efforts remains unclear. This study employed self-organizing map and concentration index to explore inequalities in healthcare utilization between age subgroups in similar healthcare settings. The results suggest that utilization inequalities in rich-medical resource regions are not necessarily smaller than those in poor-medical resource regions, which the inpatient and preventive care utilization support the idea. And inequalities of healthcare utilization in rich-medical resource regions show age-lag compared with that in poor-medical resource regions, which indicates a higher level of accessibility to rich medical resources. But rich medical resources still fail to better protect the adults aged 70 and above. The economy, education, and health status, as key contributing factors to the unequal utilization of healthcare, may exacerbate their contribution to inequality in rich-medical resource regions. So a rich supply of healthcare does not necessarily have a one-way positive impact on alleviating inequality in the healthcare utilization. It needs to be combined with other factors to better alleviate inequality. Welfare policy with targeted groups needs to be wary of whether it will lead to greater inequalities. It also called for popularizing free seminar, comprehensive outpatient health management, and serious illness insurance. [ABSTRACT FROM AUTHOR]

The cold and high altitude Tibetan area (hereinafter referred to as Tibetan area) is a special geographical area in western China. Whether the local economic development and EE (ecological environment) are coordinated or not directly affects the sustained and healthy economic and social development of the whole Tibetan area. Firstly, this paper discusses the relationship between economic growth and EE on the basis of existing related theoretical research. Then, 15 indicators of economic system and environmental system were selected, and the coupling degree model and coupling coordination degree model were constructed to quantitatively analyze the dynamic model of regional economic and EE coupling and coordinated development in Tibetan areas from 2010 to 2020. The research found that in the past 11 years, the comprehensive index of EE of four regions in Tibetan areas has changed from 0.5 to 0.6, and the comprehensive index of the economy of four regions in Tibetan areas has changed from 0.51 to 0.74. On the whole, the coordination degree and coordinated development degree of green development in Tibetan areas have changed at the same time compared with the years, and since 2016, both of them have had the same trend and both of them are beginning to develop in a good direction. Since 2017, the economic development has been relatively advanced, while the EE has lagged behind, showing a fluctuating state, while the level of coordinated development has been continuously improved. This study has certain reference value for promoting ecological protection and green development in Tibetan areas. [ABSTRACT FROM AUTHOR]

Macroalgae and macroalgae-associated bacteria together constitute the most efficient metabolic cycling system in the ocean. Their interactions, especially the responses of macroalgae-associated bacteria communities to algae in different geographical locations, are mostly unknown. In this study, metagenomics was used to analyze the microbial diversity and associated algal-polysaccharide-degrading enzymes on the surface of red algae among three remote regions. There were significant differences in the macroalgae-associated bacteria community composition and diversity among the different regions. At the phylum level, Proteobacteria, Bacteroidetes, and Actinobacteria had a significantly high relative abundance among the regions. From the perspective of species diversity, samples from China had the highest macroalgae-associated bacteria diversity, followed by those from Antarctica and Indonesia. In addition, in the functional prediction of the bacterial community, genes associated with amino acid metabolism, carbohydrate metabolism, energy metabolism, metabolism of cofactors and vitamins, and membrane transport had a high relative abundance. Canonical correspondence analysis and redundancy analysis of environmental factors showed that, without considering algae species and composition, pH and temperature were the main environmental factors affecting bacterial community structure. Furthermore, there were significant differences in algal-polysaccharide-degrading enzymes among the regions. Samples from China and Antarctica had high abundances of algal-polysaccharide-degrading enzymes, while those from Indonesia had extremely low abundances. The environmental differences between these three regions may impose a strong geographic differentiation regarding the biodiversity of algal microbiomes and their expressed enzyme genes. This work expands our knowledge of algal microbial ecology, and contributes to an in-depth study of their metabolic characteristics, ecological functions, and applications. [ABSTRACT FROM AUTHOR]

• A hybrid deep learning model was developed for delay propagation patterns. • Train timetables were modeled as images. • A cost-sensitive technique was used to address the data imbalance challenge. • The proposed model shows satisfactory performance on different situations. As a vital component of train operational control, train delay propagation pattern discovery is critically important for both railway controllers and passengers. In this study, we present a carefully designed deep learning model, called FCF-Net, that comprises fully connected neural networks (FCNN) and convolutional neural networks (CNN) for train delay propagation pattern recognition in railway systems. FCF-Net first uses a CNN component that handles train timetables as images to capture interactions of train events and an FCNN component to capture the influence of non-operational features separately; then it uses another FCNN component to combinedly learn the dependencies between operational and non-operational features. In addition, considering the imbalance of train delay data, a cost-sensitive technique that assigns different misclassification costs for different class was used to better deal with the imbalanced data. The main goal of the FCF-Net is to realize efficient and accurate train delay propagation pattern recognition by mining potential knowledge from train operation data. The predictive and computational performance of the model was tested and evaluated on data from two high-speed railway lines with different operational features in China. The results show that FCF-Net, once trained with sufficient data, outperforms conventional deep learning with common loss and other state-of-the-art deep learning models for train delay propagation pattern recognition, indicating its capability in knowledge discovery from train operation data. In addition, the computational results show that FCF-Net exhibits more efficient training process than existing state-of-the-art deep learning models. [ABSTRACT FROM AUTHOR]