The impact of COVID-19 on students’ anxiety and its clarification: a systematic review

IntroductionSince the emergence of COVID-19 in 2019, every country in the world has been affected to varying degrees. Long-term psychological pressure and anxiety will inevitably damage the physical and mental health of students. This study aimed to examine the effects of the COVID-19 pandemic on students who experienced stress and anxiety and to clarify which intervention was more effective.MethodsA comprehensive literature search was conducted between January 2020 and December 2022 using online databases such as PubMed, Web of Science, Scopus, and Google Scholar by using the following keywords in combination: “COVID-19,” “stress,” “anxiety,” “depression,” and “intervention.” The retrieved literature was screened and reviewed.ResultsA total of 2,924 articles were retrieved using subject and keyword searches. After screening through the titles and abstracts, 18 related studies were retained. Their review revealed that: (1) most studies did not use medication to control stress and anxiety; (2) the standard methods used to reduce stress and anxiety were religion, psychological counseling, learning more about COVID-19 through the media, online mindfulness courses, improving sleep quality, and physical exercise; (3) the most effective interventions were physical activity and raising awareness about COVID-19 through the media and online mindfulness programs. However, some studies show that physical activity cannot directly relieve psychological stress and anxiety.ConclusionLimited interventions are effective, but learning more about COVID-19 and using active coping strategies may help reduce stress and anxiety. The implications of COVID-19 are also discussed

LiSum: Open Source Software License Summarization with Multi-Task Learning

Open source software (OSS) licenses regulate the conditions under which users can reuse, modify, and distribute the software legally. However, there exist various OSS licenses in the community, written in a formal language, which are typically long and complicated to understand. In this paper, we conducted a 661-participants online survey to investigate the perspectives and practices of developers towards OSS licenses. The user study revealed an indeed need for an automated tool to facilitate license understanding. Motivated by the user study and the fast growth of licenses in the community, we propose the first study towards automated license summarization. Specifically, we released the first high quality text summarization dataset and designed two tasks, i.e., license text summarization (LTS), aiming at generating a relatively short summary for an arbitrary license, and license term classification (LTC), focusing on the attitude inference towards a predefined set of key license terms (e.g., Distribute). Aiming at the two tasks, we present LiSum, a multi-task learning method to help developers overcome the obstacles of understanding OSS licenses. Comprehensive experiments demonstrated that the proposed jointly training objective boosted the performance on both tasks, surpassing state-of-the-art baselines with gains of at least 5 points w.r.t. F1 scores of four summarization metrics and achieving 95.13% micro average F1 score for classification simultaneously. We released all the datasets, the replication package, and the questionnaires for the community

Pruning Adapters with Lottery Ticket

Massively pre-trained transformer models such as BERT have gained great success in many downstream NLP tasks. However, they are computationally expensive to fine-tune, slow for inference, and have large storage requirements. So, transfer learning with adapter modules has been introduced and has become a remarkable solution for those problems. Nevertheless, recent studies reveal that the parameters in adapters are actually still quite redundant, which could slow down inference speed when fusing multiple adapters for a specific downstream task, and thus, they can be further reduced. To address this issue, we propose three novel ways to prune the adapter modules iteratively based on the prestigious Lottery Ticket Hypothesis. Extensive experiments on the GLUE datasets show that the pruned adapters can achieve state-of-the-art results, with sizes reduced significantly while performance remains unchanged, and some pruned adapters even outperform the ones with the same size that are fine-tuned alone without pruning

Pruning Adapters with Lottery Ticket

Massively pre-trained transformer models such as BERT have gained great success in many downstream NLP tasks. However, they are computationally expensive to fine-tune, slow for inference, and have large storage requirements. So, transfer learning with adapter modules has been introduced and has become a remarkable solution for those problems. Nevertheless, recent studies reveal that the parameters in adapters are actually still quite redundant, which could slow down inference speed when fusing multiple adapters for a specific downstream task, and thus, they can be further reduced. To address this issue, we propose three novel ways to prune the adapter modules iteratively based on the prestigious Lottery Ticket Hypothesis. Extensive experiments on the GLUE datasets show that the pruned adapters can achieve state-of-the-art results, with sizes reduced significantly while performance remains unchanged, and some pruned adapters even outperform the ones with the same size that are fine-tuned alone without pruning

Dynamic Medical Material Distribution Model Based on Epidemic Diffusion Rule and Clustering Approach

Due to the fact that the dynamic medical material distribution is vital to the quick response to urgent demand when the Ebola virus occurs, the optimal distribution approach is explored according to the Ebola virus diffusion rule and different severity of the epidemic. First, we choose the more serious epidemic state of Sierra Leone in West Africa as the research object and the SIQR (susceptible, infected, quarantined, required) epidemic model with pulse vaccination is introduced to describe the Ebola diffusion rule and obtain the demanded vaccine and drug in each pulse. Based on the SIQR model, thirteen areas in Sierra Leone are classified into three emergency levels by clustering analysis. Then a dynamic medical material distribution model is formulated, with goals of both reducing the transportation cost and shortages. The results indicate that the proposed approach can make an outstanding contribution to fight against the Ebola virus

Reversible metal and ligand redox chemistry in two-dimensional iron-organic framework for sustainable lithium-ion batteries

Metal-organic frameworks (MOFs) are emerging as attractive electrode materials for lithium-ion batteries, owing to their fascinating features of sustainable resources, tunable chemical components, flexible molecular skeletons, and renewability. However, they are faced with a limited number of redox-active sites and unstable molecular frameworks during electrochemical processes. Herein, we design a novel two-dimensional (2D) iron(III)-tetraamino-benzoquinone (Fe-TABQ) with dual redox centers of Fe cations and TABQ ligands for high-capacity and stable lithium storage. It is constructed of square-planar Fe-N2O2 linkages and phenylenediamine building blocks, between which the Fe-TABQ chains are connected by multiple hydrogen bonds, and then featured as an extended π-d-conjugated 2D structure. The redox chemistry of both Fe3+ cations and TABQ anions is revealed to render its remarkable specific capacity of 251.1 mAh g-1. Benefiting from the intrinsic robust Fe-N(O) bonds and reinforced Li-N(O) bonds during cycling, Fe-TABQ delivers high capacity retentions over 95% after 200 cycles at various current densities. This work will enlighten more investigations for the molecular designs of advanced MOF-based electrode materials.This work was supported by the National Key R&D Program of China (2022YFB2402200), National Natural Science Foundation of China (52171215), Tianjin Natural Science Foundation (19JCJQJC62400), and Haihe Laboratory of Sustainable Chemical Transformations