A Model for Assessing the Qquality of a Distance Education Programme in an Online Environment: China's Experience

The COVID-19 pandemic has significantly altered the course of the educational process around the world. The digitalisation of learning through distance learning has become the key to ensuring the quality of education. The study aims to develop a model for continuous assessment of the quality of teaching distance education programmes in an online environment. The main components of the model, two variables, 14 dimensions, and 75 indicators are identified. The validity was assessed by 22 experts from the USA, China, Chile, Portugal, the Netherlands, Poland, and Spain. The research is based on a bibliographic analysis of standards, models and guidelines which formulate principles and methods created by academic researchers and governments in the USA, Latin America and European countries for evaluating distance learning programmes. Experts rated the proposed measurements as clear, important and appropriate for evaluating distance education programmes. Indicators and measurement indicators were assessed by experts as relevant for evaluating distance learning programmes. The main study result is the developed quality assessment model for distance learning programmes for universities. The final model included two main variables, 14 measures, and 75 indicators. The model received content in the form of measurements and corresponding indicators. Among the main features of the proposed model is the possibility of a complete assessment of the quality of teaching within the curriculum in order to subsequently take steps to improve it. The research findings may be of interest to educational researchers, educators, university administrators, distance course coordinators and training programmes. © 2023, FACTEACHEREDUCATION. All rights reserved.

Postpandemic international tourism restart: effect of border control and vaccination

PurposeAs the current Coronavirus 2019 pandemic eases, international tourism, which was greatly affected by the outbreak, is gradually recovering. The attraction of countries to overseas tourists is related to their overall performance in the pandemic. This research integrates the data of vaccination of different countries, border control policy and holidays to explore their differential impacts on the overseas tourists' intention during the pandemic. This is crucial for destinations to built their tourism resilience. It will also help countries and industry organizations to promote their own destinations to foreign tourism enterprises. Design/methodology/approachThis study proposes an analysis based on panel data for ten countries over 1,388 days. The coefficient of variation is used to measure monthly differences of Chinese tourists' intention to visit overseas country destinations. FindingsResults show that, for tourist intention of going abroad: border control of the destination country has a significant negative impact;daily new cases in the destination country have a significant negative impact;domestic daily new cases have a significant positive impact;holidays have significant negative impact;daily vaccination of the destination countries has significant positive impact;and domestic daily vaccination have negative significant impact. Research limitations/implicationsFirst, there is a large uncertainty in studying consumers' willingness to travel abroad in this particular period because of unnecessary travel abroad caused by the control of the epidemic. Second, there are limitations in studying only Chinese tourists, and future research should be geared toward a broader range of research pairs. Practical implicationsFirst, from the government perspective, a humane response can earn the respect and trust of tourists. Second, for tourism industry, to encourage the public take vaccine would be beneficial for both the tourism destination and foreign tourism companies. The same effect can be achieved by helping tourists who are troubled by border control. Social implicationsFirst, this research provides suggestions for the government and the tourism industry to deal with such a crisis in the future. Second, this study found that vaccination has a direct impact on tourism. This provides a basis for improving people's willingness to vaccinate. Thirdly, this study proves suggestion for the destinations to build tourism resilience. Originality/valueThis study analyzes the unique control measures and vaccination in different countries during the pandemic, then provides suggestions for the tourism industry to prepare for the upcoming postpandemic tourism recovery. This study is valuable for improving the economic resilience of tourism destinations. Additionally, it helps to analyze the advantages and disadvantages of different restrain policies around the world.

Racism, Risk, and Supply: U.S. Policies Toward COVID-19 and Asia, and Current and Future Impacts on Asian Americans

COVID-19 illuminates the contradictions of U.S. relations with Asia economically, culturally, and socially in relation to Asian immigrant labor, goods and manufacturing, and with Asian Americans. We explore the importance of Asia as a supplier of labor and goods to the U.S. health system in order to analyze how the U.S. navigates its interdependence with Asia while demonizing Asians/Americans and attempting to protect its borders metaphorically and materially. We analyze how Asian American nurses are fighting the battle against the pandemic on the frontlines while also fighting the stereotypes and stigma that some Americans may have against them because they associate Asian Americans with the spread of COVID-19. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Vulnerability, Infection, and Anti-discrimination Among Asian Americans and COVID-19: Explorations into Race and Health Intersections in California

The purpose of this chapter is to report and analyze the double victimization among Asians/Asian Americans during COVID-19, including their vulnerability to infection and anti-Asian racism. We first test the validity of the CDC's SVI (Social Vulnerability Index) in analyzing COVID-19 infections, then construct an Asian-specific Social Vulnerability Index (ASVI) to compare with the CDC SVI, mapping them out nationally to visualize the differential geographical patterns. We then conduct an empirical study of the state of California with correlation analysis, analysis of variance, and GIS mapping to explore the association of ASVI with Asian COVID-19 infection incidence rate, and anti-Asian discrimination incidents. We conclude that the method of constructing ASVI may be applied to other vulnerable groups. The findings contribute to our knowledge of the unequal social outcomes of pandemics across people and place. The chapter ends with summarizing findings and contributions, revealing data limitations, providing policy suggestions and suggesting future research directions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Analysing responses of Year-12 students to a hands-on IT workshop: Implications for increasing participation in tertiary IT education in regional Australia

Two hand-on workshops on social media apps were conducted for the Year-12 students from two schools, one from a regional city and the other from a remote community, in a computer laboratory on the Rockhampton campus at Central Queensland University before the COVID-19 pandemic. The school in the regional city offered a specialist Digital Technologies Curriculum (DTC) to students in Years 11 & 12 whereas the remote school did not offer a similar DTC to students in Years 11 & 12. Statistical analyses of the students‟ responses to two casual questions during the workshop indicated that firstly the hands-on activities improved all students‟ general IT knowledge, and secondly the Year-12 students from the regional city were more determined to undertake tertiary IT education than the students from the remote school. Therefore, it is recommended that a mandatory specialist DTC for students in Years 11 &12 in ALL schools should be included in the national curriculum in the future. Implications of these findings on improving the participation rate of post-secondary education in Australian regional communities are also discussed in this article. In particular, regional universities can play a unique role in producing "IT allrounders” to meet the needs of the regional communities through collaborations with governments, secondary schools, regional industries and businesses. © 2023 The Author(s).

COVID-19 diagnosis prediction using classical-to-quantum ensemble model with transfer learning for CT scan images

At present, there are problems of low detection efficiency and accuracy in chest CT images of COVID-19 as well as limited computational power of deep learning model training. Developing a classical-to-quantum (CQ) ensemble model with transfer learning to efficiently detect patients with COVID-19 using chest CT images.: Attributes were extracted from chest CT scans using pre-trained networks ResNet50, VGG16 and AlexNet, while dressed quantum circuits were used as classifiers. The overall accuracy of the CQ method based on three aforementioned networks on the chest CT dataset is 83.2%, 86.2% and 85.0%, respectively. The proposed ensemble model has a precision of 89.0% for pneumonia samples, an overall accuracy of 88.6% and a pneumonia class recall rate of 83.0%. In addition, to further verify the robustness of the ensemble model, breast ultrasound and brain tumour images were used in it. The suggested ensemble approach is effective for classifying and detecting medical pictures with complicated features, particularly for detecting COVID-19 patients using chest CT images.

Price Volatility Modeling for the Lumber Futures Market: A Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling Approach

Lumber is one of the most essential forest products in the United States. During the first year of the COVID-19 pandemic, lumber prices almost quadrupled, and fluctuations reached record levels. Although market experts have pointed to various drivers of such high price volatility, no firm conclusions have been drawn yet. Using the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) framework, this study assesses the potential drivers of lumber price volatility, with predictors including the Google Trends Web Search Index, housing starts, US lumber production quantity, and VIX index, representing public attention, housing demand, lumber supply, and macroeconomic concerns, respectively. We have found that housing demand is the key driver of lumber price volatility, followed by public attention. It is worth noting that US lumber supply and macroeconomic concerns have played a modest role in explaining lumber price volatility. Also, forecasting lumber price by using the housing demand variable substantially outperforms others. Market participants, including lumber mills, wholesalers, and home builders can get valuable information from the housing market to manage lumber price risk.Study Implications: The findings of this study can be used to improve hedging strategies, design option pricing formulas, and setting margin requirements. Critical information for price risk management on the lumber market can be gained by lumber market participants from the housing market. For forest management decisions by landowners, giving close attention to housing market would provide valuable information on the appropriate time for timber harvesting, because changes in the housing market affect lumber price that will indirectly affect the demand for timber, which is the most important factor of production for lumber mills.

Clinical observation on safety of chloroquine phosphate treatment in patients with novel coronavirus pneumonia.

Objective: To explore the safety of chloroquine phosphate treatment in patients with novel coronavirus pneumonia (COVID-19) and provide references for clinical safety medication. Method(s): Active monitoring for adverse events (AE) was carried out in the Third People's Hospital of Shenzhen from February to March 2020 during the treatment with chloroquine phosphate in patients with COVID-19. The causal relationship between AE and chloroquine phosphate was evaluated. Result(s): A total of 33 patients were entered in the study, including 16 males and 17 females, aged (43+/-13) years. The clinical types of COVID-19 in 26 patients (78.8%) were mild, in 7 patients (21.2%) were common. There were 7 patients (21.2%) with basic diseases, including 6 with hypertension and 1 with hypothyroidism. The treatment course of chloroquine phosphate was (8+/-3) days. During the treatment, a total of 28 cases of AE in 24 (72.7%) of the 33 patients which were probably or possibly related to chloroquine phosphate were detected. The clinical manifestations of AE included abnormal liver function (8/33, 24.2%), gastrointestinal reactions (8/33, 24.2%), neuropsychiatric system reactions (8/33, 24.2%), cardiovascular system reactions (5/33, 15.2%), eye and vision abnormality (2/33, 6.1%), and skin injury (1/33, 3.0%). The severity of AE was grade 1 or grade 2. After drug withdrawal or symptomatic treatments, all the patients' symptoms were improved and the laboratory tests results returned to normal. Conclusion(s): The adverse effects of chloroquine phosphate in the treatment of patients with COVID-19 are mild, but it is still necessary to strengthen the monitoring.Copyright © 2020 by the Chinese Medical Association.

Updated efficacy and safety of taletrectinib in patients (pts) with ROS1+ non-small cell lung cancer (NSCLC)

Background Taletrectinib is a potent, next-generation, CNS-active, ROS1 tyrosine kinase inhibitor (TKI) with selectivity over TRKB. In previous reports from TRUST-I, taletrectinib showed meaningful clinical efficacy and was well tolerated in pts with ROS1+ NSCLC (n = 109) regardless of crizotinib (CRZ) pretreatment status. We report updated efficacy and safety data with ~1.5 yr follow-up. Methods TRUST-I is a multicenter, open-label, single-arm study with two cohorts: ROS1 TKI-naive and CRZ-pretreated. Pts in both cohorts received taletrectinib 600 mg QD. Key study endpoints included IRC-confirmed ORR (cORR), DoR, disease control rate (DCR), PFS, and safety. A pooled analysis of ORR, PFS, and safety including pts from additional clinical trials was also conducted. Results In the 109 pts from TRUST-I (enrolled prior to Feb 2022) the median follow-up was 18.0 mo in TKI-naive (n = 67) and 16.9 mo in CRZ-pretreated pts (n = 42). cORR was 92.5% in TKI-naive and 52.6% in CRZ-pretreated pts (table). Median DoR (mDoR) and mPFS were not reached. Intracranial-ORR was 91.6%;ORR in pts with G2032R was 80.0%. In a pooled analysis with phase I studies, ORR was 89.5% and 50.0% for TKI-naive and CRZ-pretreated pts, respectively;mPFS was 33.2 mo and 9.8 mo. In 178 pts treated at 600 mg QD, treatment-emergent adverse events (TEAEs) were 92.7%;most (64.0%) were grade 1-2. The most common TEAEs were increased AST (60.7%), increased ALT (55.6%), and diarrhea (55.6%). Neurological TEAEs (dizziness, 18.5%;dysgeusia, 12.4%) and discontinuations due to TEAEs (3.4%) were low. Further updated results will be presented. [Formula presented] Conclusions With additional follow-up, taletrectinib continued to demonstrate meaningful efficacy outcomes including high response rates, prolonged PFS, robust intracranial activity, activity against G2032R, and tolerable safety with low incidence of neurological AEs. Clinical trial identification NCT04395677. Editorial acknowledgement Medical writing and editorial assistance were provided by Arpita Kulshrestha of Peloton Advantage, LLC, an OPEN Health company, and funded by AnHeart Therapeutics, Inc Legal entity responsible for the study AnHeart Therapeutics, Inc. Funding AnHeart Therapeutics, Inc. Disclosure S. He: Financial Interests, Personal, Other, Employment: AnHeart Therapeutics. T. Seto: Financial Interests, Institutional, Research Grant: AbbVie, Chugai Pharmaceutical, Daiichi Sankyo, Eli Lilly Japan, Kissei Pharmaceutical, MSD, Novartis Pharma, Pfizer Japan, Takeda Pharmaceutical;Financial Interests, Personal, Other, Employment: Precision Medicine Asia;Financial Interests, Personal, Speaker's Bureau, Honoraria for lectures: AstraZeneca, Bristol-Myers Squibb, Chugai Pharmaceutical, Covidien Japan, Daiichi Sankyo, Eli Lilly Japan, Kyowa Hakko Kirin, MSD, Mochida Pharmaceutical, Nippon Boehringer Ingelheim, Novartis Pharma, Ono Pharmaceutical, Pfizer Japan, Taiho Pharmaceutical, Takeda Pharmaceutical, Towa Pharmaceutical. C. Zhou: Financial Interests, Personal, Other, Consulting fees: Innovent Biologics Qilu, Hengrui, TopAlliance Biosciences Inc;Financial Interests, Personal, Speaker's Bureau, Payment or honoraria: Eli Lilly China, Sanofi, BI, Roche, MSD, Qilu, Hengrui, Innovent Biologics, C-Stone LUYE Pharma, TopAlliance Biosciences Inc, Amoy Diagnositics, AnHeart. All other authors have declared no conflicts of interest.Copyright © 2023 International Association for the Study of Lung Cancer. Published by Elsevier Inc.

Classroom Assessment and Instructional Modes: An Exploration of School-Level Contextualized Psychometric Challenges

The global COVID-19 health pandemic caused major interruptions to educational assessment systems, partially due to shifts to remote learning environments, entering the post-COVID educational world into one that is more open to heterogeneity in instructional and assessment modes for secondary students. In addition, in 2020, educational inequities were brought to the forefront of social conscience. The purpose of this study is to empirically explore how contextual (i.e., school-level) race and economic factors may relate to and explain measurement challenges that can arise during shifts to remote learning. We fit a series of multilevel models to explore school-level factors in assessment data alongside psychometric problems of differential item functioning and person fit in classroom assessment measurement models. Our results demonstrate ways in which our project's classroom assessments were impacted by shifts to remote learning, emphasizing the importance of researchers and practitioners evaluating such concerns when seeking validity evidence for interpretation of classroom assessment data. © 2023 Taylor & Francis Group, LLC.

DP-A*: For Path Planing of UGV and Contactless Delivery

The unmanned logistics and distribution urgently require a large number of unmanned ground vehicles(UGVs) under the influence of the potential spread of the Coronavirus Disease 2019 (COVID-19). The path planning of UGV relies excessively on SLAM map, and has no self-optimization and learning ability for the space containing a large number of unknown obstacles. In this paper, a new dynamic parameter-A* (DP-A*) algorithm is proposed, which is based on the A* algorithm and enables the UGV to continuously optimize the path while performing the same task repeatedly. First, the original evaluation functions of the A* algorithm are modified by Q-Learning to memory the coordinates of unknown obstacle. Then, Q-table is adopted as an auxiliary guidance for recording the characteristics of environmental changes and generating heuristic factor to overcome the shortcoming of the A* algorithm. At last, the DP-A* algorithm can realize path planning in the instantaneous changing environment, record the actual situation of obstacles encountered, and gradually optimize the path in the task that needs multiple explorations. By several simulations with different characteristics, it is shown that our algorithm outperforms Q-learning, Sarsa and A* according to the evaluation criteria such as convergence speed, memory systems consume, Optimization ability of path planning and dynamic learning ability. IEEE

Tillandsia-Inspired Composite Materials for Atmospheric Water Harvesting

Atmospheric water harvesting (AWH) is a potentially promising small-scale approach to alleviate the water crisis in arid or semiarid regions. Inspired by the asymmetric structure of tillandsia leaves, a plant species native to semiarid regions, we report the development of a bioinspired composite (BiC) to draw moisture for AWH applications. With the advent of the post-COVID era, the nonwoven materials in used masks are discarded, landfilled, or incinerated along with the masks as medical waste, and the negative impact on the environment is inevitable. The nonwoven sheet has porosity, softness, and certain mechanical strength. We innovatively developed BiCs, immobilizing hygroscopic salt with a nonwoven mask for fast vapor liquefaction and using a polymer network to store water. The resulting BiC material manages to achieve a high-water adsorption capacity of 1.24 g g-1 under a low-moderate humidity environment and a high-water release ratio of ca. 90% without the use of photothermal materials, while maintaining high structural integrity in cyclic testing. © 2023 American Chemical Society.

Encaged and Enraged: A Study of How Level of Aggression Relates to Perceived Crowdedness, Risk, and Boredom

Social distancing policies have been practiced in different regions around the world to minimize the number of cases of COVID-19. After an outbreak in mid-July 2020, the Hong Kong government adopted a series of adminis-trative measures and strongly encouraged residents to stay at home. This lockdown period provided an oppor-tunity to study variations in levels of aggression when people spend more time than usual in an overcrowded liv-ing environment. A total of 185 Hong Kong residents were recruited for this study. Their perceptions of the crowdedness of their living space, aggression level (measured using the BPAQ-SF), proneness to boredom (meas-ured by the BFS-SF), and perceptions of risk regarding COVID-19 were collected via online questionnaires. Perceived crowdedness, proneness to boredom, and perceptions of susceptibility to COVID-19 were found to signi-ficantly predict the variance of different types of aggression in a regression model. In a mediation analysis, anger acted as a mediator of the relationship between proneness to boredom and different types of aggression. Parti-cipants' perceptions of their susceptibility to COVID-19 suggested an underlying worry about the contagious-ness of the virus, which was in turn associated with feelings of uncertainty and a rise in aggression level. © 2023, Universitaet Bielefeld. All rights reserved.

User Sentiment Analysis of COVID-19 via Adversarial Training Based on the BERT-FGM-BiGRU Model

With the rapid development of social network platforms, Sina Weibo has become the main carrier for modern netizens to express public views and emotions. How to obtain the tendency of public opinion and analyze the text's emotion more accurately and reasonably has become one of the main challenges for the government to monitor public opinion in the future. Due to the sparseness of Weibo text data and the complex semantics of Chinese, this paper proposes an emotion analysis model based on the Bidirectional Encoder Representation from Transformers pre-training model (BERT), Fast Gradient Method (FGM) and the bidirectional Gated Recurrent Unit (BiGRU), namely BERT-FGM-BiGRU model. Aiming to solve the problem of text polysemy and improve the extraction effect and classification ability of text features, this paper adopts the BERT pre-training model for word vector representation and BiGRU for text feature extraction. In order to improve the generalization ability of the model, this paper uses the FGM adversarial training algorithm to perturb the data. Therefore, a BERT-FGM-BiGRU model is constructed with the goal of sentiment analysis. This paper takes the Chinese text data from the Sina Weibo platform during COVID-19 as the research object. By comparing the BERT-FGM-BiGRU model with the traditional model, and combining the temporal and spatial characteristics, it further studies the changing trend of user sentiment. Finally, the results show that the BERT-FGM-BiGRU model has the best classification effect and the highest accuracy compared with other models, which provides a scientific method for government departments to supervise public opinion. Based on the classification results of this model and combined with the temporal and spatial characteristics, it can be found that public sentiment is spatially closely related to the severity of the pandemic. Due to the imbalance of information sources, the public showed negative emotions of fear and worry in the early and middle stages, while in the later stage, the public sentiment gradually changed from negative to positive and hopeful with the improvement of the epidemic situation. © 2023 by the authors.