Gaseous and Particulate Pollution in the Wu-Chang-Shi Urban Agglomeration on the Northern Slope of Tianshan Mountains from 2017 to 2021

Rapid social development has led to serious air pollution problems in cities, and air pollutants, including gaseous pollutants and particulate matter, have an important impact on climate, the environment, and human health. This study analyzed the characteristics, potential sources, and causes of air pollution in the Wu-Chang-Shi urban cluster. The results showed that NO2, CO, SO2, PM10, and PM2.5 had a tendency to decrease, while O-3 showed an increasing trend. The concentrations of SO2, NO2, CO, PM2.5, and PM10 showed the highest values in winter and the lowest values in summer, with similar seasonal variations. However, the concentration of O-3 was highest in the summer and lowest in the winter. Compared with the pollutant concentrations in other Chinese cities, PM2.5, PM10, and NO2 are more polluted in the Wu-Chang-Shi urban. Meteorological factors have a greater impact on pollutant concentrations, with higher concentrations of major pollutants observed when wind speeds are low and specific wind directions are observed, and higher secondary pollutant O-3 concentrations observed when wind speeds are low and specific wind directions are observed. The backward trajectory and concentration weighting analysis show that the particulate pollutants in the Wu-Chang-Shi urban in winter mainly come from Central Asia and surrounding cities. O-3 showed an increasing trend before and after the novel coronavirus outbreak, which may be related to changes in NOX, volatile organic compounds, and solar radiation intensity, and the concentrations of SO2, NO2, CO, PM10, and PM2.5 showed an overall decreasing trend after the outbreak and was smaller than before the outbreak, which is related to the reduction of industrial and anthropogenic source emissions during the outbreak.

Identify Students at Risk Based on Behavioural Patterns in Continuous Assessment

Students' success is the ultimate goal of any institution around the world. Early detection of at-risk students can facilitate the instructor or tutor to provide timely support to those at risk of failing the course. In a traditional face-to-face classroom, students can monitor learning patterns in routine interactions. However, teachers in the online classroom have limited information, compared with the face-to-face classroom, to detect students in trouble due to the lack of instance interactions between teachers and students. Particularly, such a problem has become worse than ever since 2020, as online teaching and learning are ubiquitous in the Post-COVID19 Era. In this work, we aim to predict if the student obtains a low course grade based on their behavioral patterns in continuous assessments, which are easy-to-retrieve attributes and available in most e-learning systems. We leverage the ratio of assessment grade to the time spent on the assessment as a useful feature in the machine-learning prediction framework. Experiments on real-world datasets indicate that such a ratio can improve the accuracy of detecting at-risk students. © 2022 IEEE.

Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling

The global rampancy of COVID-19 has caused profound changes in education sectors. Perhaps the most salient change is the shift of the instructional paradigm from face-to-face instruction to fully online learning. To address the challenges facing the education sector, researchers and educational practitioners have extensively investigated the transition in teaching mode under COVID-19, with a growing contribution to a range of topics in relation to online learning. Against this backdrop, it is necessary to gain a comprehensive understanding of the major hotspots and issues of online learning so as to develop appropriate and effective policies on strategic (re-)allocation of resources to more critical initiatives. This study aims to adopt bibliometrics and topic modeling to identify prominent research topics on online learning under COVID-19 from the large-scale, unstructured text of research publications. Specifically, structural topic modeling will be used to identify predominant topics concerned by scholars working in the field of online learning research. The non-parametrical Mann-Kendell trend test will also be applied to uncover the developmental tendency of each identified topic. In addition, the correlations among the key topics will be revealed and visualized by hierarchical clustering analysis. Based on the analytical results, suggestions will be made to facilitate educational policy formulation to promote the development and effective implementation of technological, scientific, and pedagogical activities of online learning. © 2022 IEEE.

An immobilization-based, loop-mediated isothermal amplification device for nucleic acid detection of SARS-CoV-2 N gene

Confronting the global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), simple, fast and specific non-laboratory SARS-CoV-2 diagnostic tests are urgently required. However, the current nucleic acid assays generally rely on the diagnostic laboratory, trained staff and specialized equipment for execution and analysis, presenting clear limitations in the field detection. Here, we describe a portable and reliable immobilization-based loop-mediated isothermal amplification (LAMP) device which is mobile, without the requirement of any complicated instrument and appropriate for high-throughput testing. This device was constructed by utilizing the interaction between a carboxyl-tagged primer and an amino-tagged substrate, and capable of catching the target sequence in SARS-CoV-2 produced via the immobilization-based LAMP. In this study, the immobilization conditions and immobilized primer structure were explored and optimized. With this proposed device, the analysis result can be obtained rapidly in 30 min with excellent specificity, even if the template is extracted from a complex sample containing pharyngeal swab or human blood. In addition, the device can be applied to detect the nucleic acid of SARS-CoV-2 and various other pathogens, showing attractive potential for rapid and high-throughput detection at airports, railway stations, cold-chain transportations, community hospitals and so on. Therefore, we believe that the immobilization-based LAMP device is an advanced approach to developing a portable, specific, low-cost and high-throughput diagnostic platform.

An Assessment Framework for Online Active Learning Performance

Under the influence of COVID-19, online learning has become the primary way for students to continue their education. At all stages of online learning, active learning is a useful strategy promoting optimal understanding. However, there is a lack of relevant research on how to evaluate students’ active learning performance. This paper presents an online active learning assessment framework based on the learning pyramid and learning dimension theory. After the division of course modules according to the learning pyramid theory, the active learning assessment is performed from five dimensions: (1) positive attitudes and perceptions about learning;(2) acquiring and integrating knowledge;(3) extending and refining knowledge;(4) using knowledge meaningfully, and (5) productive habits of mind. By identifying patterns from each online course module’s weblog data, instructors can assess students’ active learning conveniently from the beginning to the end of the online course. This study helps instructors understand learners’ learning situations and adopt corresponding strategies to adjust teaching activities to ensure high-quality teaching activities. Simultaneously, learners can also actively change their learning status according to active learning assessment to improve the learning effect. © 2021, Springer Nature Switzerland AG.