Research on impacts of COVID-19 on students' clinical social work career determination in Taiwan

A quantitative method is adopted to survey 197 students at the department of social work at a university in Taiwan from April to May in 2020. The study aims to explore the impact of the new coronavirus on social work students' career determination. The result presents the participants with higher social loneliness have lower "Career Determination of Clinical Medical Social Work (CDCMSW)", and the mental burden feeling, and family relationship are predictive of the CDCMSW. © 2023 Author(s).

Knowledge mapping of resilience and human rights in supply chains: A roadmapping taxonomy for twin green and digital transition design

Supply chain sustainability (SCS) has gone beyond the sustainability-performance approach, towards the increasing adoption of the sustainability-practice approach. The use of digital technologies in this approach can enhance resilience and human rights, particularly in the context of the green and digital twin transition post-COVID-19 pandemic. To enrich the sustainability-practice approach, this paper aims to produce a roadmapping taxonomy, based on knowledge mapping of a dataset collected in late December 2022 from the Web of Science Core Collection. As the knowledge map reveals the dimensions of resilience, human rights, and digital technologies, the proposed taxonomy highlights the importance of dynamic capabilities in facing supply chain disruptions, especially their ripple effects, along with the corresponding digital technologies to enhance human social dynamics in facing such disruptions. The proposed taxonomy provides a knowledge-based framework for professionals and researchers to enhance their understanding of supply chain resilience in designing and implementing digital solutions. The proposed roadmapping taxonomy features a people- and community-centric perspective and several managerial insights, contributing to the wider discussions on the green and digital transformation of the supply chain, by shaping actions and interactions in networked, digitized, and datafied forms to enhance supply chain sustainability. Copyright © 2023 Pan, Liao and Zhang.

A large-scale group decision making method based on spatial information aggregation and empathetic relationships of experts

Large-scale group decision making (LSGDM) involving a large number of experts has attracted more and more scholars' attention. Many LSGDM methods assumed that experts were independent to make evaluations, but the development of social media promotes the communication among experts, which makes experts no longer independent. In addition, existing LSGDM methods mainly adopted aggregation strategies such as the weighted average operator and arithmetic average operator to integrate the opinion of experts in a cluster, which makes the aggregation results cannot reflect the real opinion of the expert group. To address these issues, considering the empathetic network of experts, this study proposes an LSGDM method based on a new aggregation method for expert space information. Firstly, we determine objective weights of experts according to the objective empathetic relationships among experts. Then, the Steiner-Weber point problem is used as a prototype to establish an aggregation method called the spatial optimal aggregation (SOA) method to fuse the spatial information of experts. The model is solved by the genetic algorithm. Finally, an illustrative example about the selection of the most urgent risk in the transportation of COVID-19 vaccines is presented to show the validity and practicability of the proposed model. © 2023 Elsevier Inc.

SNIF-DFA: A Signal Processing and Information Fusion Method for Smart Gua Sha Device

Due to the COVID-19 global pandemic, there are more needs for remote patient care especially in rehabilitation requiring direct contact. However, traditional Chinese rehabilitation technologies, such as gua sha, often need to be implemented by well-trained professionals. To automate and professionalize gua sha, it is necessary to record the nursing and rehabilitation process and reproduce the process in developing smart gua sha equipment. This paper proposes a new signal processing and sensor fusion method for developing a piece of smart gua sha equipment. A novel stabilized numerical integration method based on information fusion and detrended fluctuation analysis (SNIF-DFA) is performed to obtain the velocity and displacement information during gua sha operation. The experimental results show that the proposed method outperforms the traditional numerical integration method with respect to information accuracy and realizes accurate position calculations. This is of great significance in developing robots or automated machines that reproduce the nursing and rehabilitation operations of medical professionals. IEEE

A Dashboard for Mitigating the COVID-19 Misinfodemic

This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of, and effective interventions against the COVID-19 misinfodemic on Twitter. Specifically, it introduces a public dashboard which, in addition to displaying case counts in an interactive map and a navigational panel, also provides some unique features not found in other places. Particularly, the dashboard uses a curated catalog of COVID-19 related facts and debunks of misinformation, and it displays the most prevalent information from the catalog among Twitter users in user-selected U.S. geographic regions. The paper explains how to use BERT-based models to match tweets with the facts and misinformation and to detect their stance towards such information. The paper also discusses the results of preliminary experiments on analyzing the spatiotemporal spread of misinformation.

Immunogenicity of the mRNA-1273 and ChAdOx1 nCoV-19 vaccines in Asian patients with autoimmune rheumatic diseases under biologic and/or conventional immunosuppressant treatments.

OBJECTIVE: Nucleic acid-based vaccines against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection are effective in the general population. However, it is unknown whether this is true in Asian patients with autoimmune rheumatic diseases (ARDs) who have received various combinations of disease-modifying anti-rheumatic drugs (DMARDs). METHOD: We designed a large prospective observational study recruiting 228 patients with ARDs in a tertiary rheumatology centre in Taiwan. Altogether, 142 received biological or targeted synthetic DMARDs and 86 received only conventional synthetic (cs) DMARDs. Serum levels of immunoglobulin G antibody against SARS-CoV-2 spike proteins were measured 2-6 weeks after COVID-19 vaccination with mRNA-1273 (Moderna®) or ChAdOx1 nCoV-19 (Oxford/AstraZeneca®). The immunomodulatory therapies were not modified before or after vaccination. RESULTS: Overall, 194 patients (85.09%) exhibited antibodies (758.33 ± 808.43 ng/mL) but 34 patients did not (103.24 ± 41.08 ng/mL). Patients with systemic lupus erythematosus or rheumatoid arthritis had significantly lower humoral responses to COVID-19 vaccination than those with other ARDs (p < 0.05). There was no significant difference in immunogenicity among patients on different csDMARD treatments. Compared to patients treated with only csDMARDs, those on rituximab or abatacept therapy had significantly lower immune response to the vaccination (p = 0.008 and p = 0.035, respectively). Patients who were treated with anti-tumour necrosis factor-α or interleukin-6 inhibitor exhibited higher titres of vaccination antibodies than those treated with direct lymphocyte inhibitors. CONCLUSIONS: mRNA-1273 and ChAdOx1 nCoV-19 vaccines were immunogenic in the majority of ARD patients. Rituximab and abatacept were associated with significantly diminished COVID-19 vaccination immunogenicity.

The roles of short-lived climate forcers in a changing climate

Atmospheric components that can influence climate change can be classified as long-lived greenhouse gases and short-lived climate forcers (SLCFs), according to their lifetimes in the atmosphere. Considering the important roles of SLCFs in climate change and air quality, IPCC AR6 has for the first time the dedicated chapter for the assessment of SLCFs. This work summarizes the major conclusions on SLCFs, especially those since AR5, including the definition of SLCFs, changes in emissions and abundances of SLCFs, the effective radiative forcings of SLCFs and climate responses, projected future changes in climate and air quality under Shared Socioeconomic Pathways (SSPs), and the impact of COVID-19 lockdown on climate. We also discuss the uncertainties associated with the AR6 conclusions as well as the implications for climate and air quality in China. © 2021 by the authors.

A case of newly diagnosed type 2 diabetes complicated by SARS-CoV-2 infection

Introduction: Covid-19, a novel Coronavirus SARS-COV-2, has caused major morbidity and mortality worldwide most especially in the high-risk population. SARS-COV-2 has caused more unfavorable outcomes and increased insulin resistance in patients with diabetes mellitus. It has been observed that many of these patients require very high doses of insulin to manage hyperglycemia. This will discuss a case of a young male with newly diagnosed type 2 diabetes complicated with Covid-19 infection. Case Description: 38-year-old Hispanic male with no past medical history presented to the emergency department with shortness of breath, cough, and chest congestion. His only medication was azithromycin. He had no family history of diabetes mellitus. There was no acanthosis nigricans on examination and the patient's BMI was 26.7 kg/m 2 . The patient was admitted for severe acute respiratory syndrome and diabetic ketoacidosis. His hba1c level was 13.7%, c-peptide was inappropriately low with a value of 0.31 ng/mL and glucose of 153 mg/dL and GAD-65 and islet cell antibodies were negative. Endocrinology was consulted for diabetic management. The patient was started on basal insulin 5 units at bedtime;however, the dose was increased to 7 to 9 to 12 and then 20 units at bedtime due to uncontrolled sugar levels. The patient was started on short-acting insulin before meals because his glucose ranged from 156 mg/dL to 381 mg/dL. The patient clinically improved and was discharged on hospital day 12. He got discharged on insulin detemir 20 units at bedtime and insulin lispro 8 units before meals. On a visit to the clinic, the patient was weaned off of insulin due to better glycemic control. His hba1c level significantly dropped to 7.2% and his c-peptide level improved to 3.21 ng/mL. He is now been controlled only on metformin 1000mg twice a day. Discussion: There is no definite explanation for why SARS-COV- 2 infection causes new-onset diabetes and worsening insulin resistance. However, there have been some theories attributed to the effects of the SARS-COV-2 coronavirus on angiotensin-converting enzyme 2 (ACE2). ACE2 is present in metabolic organs and tissues including pancreatic beta cells. As a result, an infection with the SARS-COV-2 virus could affect the pathophysiology of glucose metabolism causing increase insulin resistance. Another theory explains that coronavirus could cause ketosis-prone diabetes causing diabetic ketoacidosis in patients with no known history of hyperglycemia. Therefore, Covid-19 has some association with diabetes mellitus management outcomes.

The Seesaw Pattern of PM2.5 Interannual Anomalies Between Beijing-Tianjin-Hebei and Yangtze River Delta Across Eastern China in Winter

Since 2013, the winter mean fine particulate matter (PM2.5) had been decreased significantly due to stringent emission controls in most of China. Nevertheless, we found a seesaw pattern of PM2.5 interannual anomalies between Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD). Using the multiple linear regression method, meteorology-driven PM2.5 interannual anomalies show that the low (high) PM2.5 relative difference between BTH and YRD (RDB&Y) was associated with the strong (weak) East Asian winter monsoon (EAWM). The strong EAWM transported more air pollutants from BTH to YRD. During the COVID-19 lockdown period, due to the weak EAWM, air pollution still occurred in northern BTH, while the PM2.5 was relatively low in YRD, causing high RDB&Y values. Our results imply that the activity of EAWM and characteristics of regional transport have obvious interannual variations, which is indispensable in evaluating the achievements of PM2.5 quality management between up and downstream regions.

Pleural line and B-lines based image analysis for severity evaluation of COVID-19 pneumonia

This paper proposes a quantitative analysis method for lung ultrasound (LUS) images to evaluate the severity of COVID-19 pneumonia. Specifically, biomarkers related to the pleural line, including the thickness of pleural line (TPL) and the roughness of pleural line (RPL), and biomarkers related to the B-lines, including the accumulated width of B-lines (AWBL) and the acoustic coefficient of B-lines (ACBL), are extracted from LUS images to characterize the image patterns associated with the disease severity. 27 patients of COVID-19 pneumonia are enrolled in this study, including 13 moderate cases, 7 severe cases, and 7 critical cases. Patients of moderate cases are regarded as non-severe patients, and patients of severe and critical cases are regarded as non-severe patients. Biomarkers among different cases are compared, and the performances in the binary diagnosis of severe and non-severe patients are assessed using a support vector machine (SVM) classifier with all the biomarkers as the input. The classification performance is optimal using the SVM classifier (area under the receiver operating characteristics curve = 0.93, sensitivity = 0.93, specificity = 0.85). The proposed method may be a promising tool for the automatic grading and follow-up of patients with COVID-19 pneumonia. © 2021 IEEE.

A NOVEL DECISION-MAKING METHOD FOR RESILIENT SUPPLIER SELECTION DURING COVID-19 PANDEMIC OUTBREAK BASED ON HESITANT FUZZY LINGUISTIC PREFERENCE RELATIONS

Disasters such as the COVID-19 pandemic outbreak can bring challenges to trades all over the world. How to enhance the supply chain resilience and strengthen the competitiveness of enterprises in emergency situations has become a significant topic. To improve the emergency response capability of supply chains, this study aims at establishing a novel decision-making method to deal with the supplier selection problem during the COVID-19 pandemic outbreak based on a novel preference information represent tool, the hesitant fuzzy linguistic preference relation (HFLPR). To achieve this goal, we first introduce the cut of hesitant fuzzy linguistic elements and transform each hesitant fuzzy linguistic element in an HFLPR into the corresponding semantic considering the degree of confidence of the decision-makers regarding the evaluation values. Then, the utility matrix of an HFLPR is proposed. Afterwards, we construct two dual linear programming models to obtain interval weights from an HFLPR based on the concept of the least upper and the greatest lower bounds. An approach to ranking the obtained interval weights is introduced based on the concept of possibility degree between intervals. The procedure is summarised for the facility of implementation and we apply it to a case study about the resilient supplier selection during the COVID-19 pandemic outbreak. Some comparative analyses are presented to demonstrate the advantages of our method.

Chest CT imaging characteristics and their evolution of 48 patients with COVID-19 in Hengyang, China

The novel coronavirus 2019 (2019 nCoV), appeared in Wuhan in December 2019, can cause a novel coronavirus pneumonia (Corona Virus Disease 2019, COVID-19). COVID-19 is highly infectious and easy to infect people. The epidemic has gradually spread to all parts of the country. In order to provide a basis for clinical diagnosis, this study retrospectively analyzed the imaging characteristics, evolution and related imaging manifestations of COVID-19 patients in different stages of the disease. The results suggest that the imaging findings of 48 COVID-19 patients from Hengyang, Hunan Province are comparable in different stages of the disease. Chest CT showed no pneumonia in one mild patient. Chest CT findings of moderate type (n=38) and severe type (n=9) had comparable characteristics. The main manifestations were ground-glass opacity (GGO) (18/38, 47.37%;1/9, 11.11%), and GGO with consolidation (16/38, 42.11%;5/9, 55.56%), which respectively presented in bilateral lungs (34/38, 89.47%;9/9, 100.00%), and multi-lobe distribution (involving 5 lobes) (17/38, 44.74%;8/9, 88.89%). After treatment, 28 patients were isolated for 14 days and returned to the hospital for re-examination;among them, the pulmonary lesion was completely absorbed in 15 moderate patients, while 13 patients mainly manifested as GGO. The CT imaging findings of patients with COVID-19 can detect the lesions early, observe the scope of the lesions, evaluate the severity of the lesions, and assist the clinician in completing rapid isolation, diagnosis and treatment. At the same time, it can help to understand the performance of COVID-19 in different stages and dynamically detect changes in the patient's condition.

Population Mobility Driven COVID-19 Analysis in Shenzhen

In the beginning of 2020, the COVID-19 epidemic broke out and spread all over the world in just a few months. COVID-19 spread analysis has attracted considerable research efforts in many areas including the impact of population mobility on the epidemic development. However, most studies do not use real data on population mobility, or choose an overly wide range of objects. This paper studies the COVID-19 epidemic in Shenzhen from January 26 to February 16, focusing on the impact of population mobility on the epidemic development. Combined with the population mobility data, we propose the Source-SEIR model. We estimated that the basic reproduction number of SARS-CoV-2 is 2.61. The experiment results show that the combination of population mobility data is helpful to the evaluation of the epidemic development, and the restrictions on population mobility in Shenzhen have played a role in curbing the deterioration of COVID-19 epidemic. Without restrictions on population mobility, there will be more than 600 confirmed cases of COVID-19 in Shenzhen. © 2021, Springer Nature Singapore Pte Ltd.

A Systematic Review of Social Media for Intelligent Human-Computer Interaction Research: Why Smart Social Media is Not Enough

As social media shapes human behavior and social interactions, especially with the help of Big Data and artificial intelligence, it becomes an important site for policy and design interventions. Since no systematic review on social media research for intelligent HCI has been conducted, the article presents exploratory findings on a scientometric analysis of the literature at the intersections of social media and AI. By identifying and discussing the main and emerging disciplines and the related keywords from 2,443 articles along with more than 18,000 citations, the findings show that while Twitter and Facebook have been the main platforms for study, Chinese social media platforms emerge as new sites of research with the COVID-19. Also, sentiment analysis appears to be the most prominent research practices, with implications on the issues of privacy, misinformation, depression, and mental health). Four key dimensions of social media are summarized as foundations for the proposed research agenda for intelligent HCI that is not only smart, but also fair and inclusive. © 2021, Springer Nature Switzerland AG.

Capturing attitudinal characteristics of decision-makers in group decision making: application to select policy recommendations to enhance supply chain resilience under COVID-19 outbreak

The impact of COVID-19 on the global outbreak of supply chain is enormous. It is crucial for governments to take policy recommendations to enhance the supply chain resilience to mitigate the negative impact of COVID-19. For such a major issue, it is a common occurrence that a large number of decision-makers (DMs) are invited to participate in the decision-making process so as to ensure the comprehensiveness and reliability of decision results. Since the attitudinal characteristics of DMs are important factors affecting decision results, this study focuses on capturing the attitudinal characteristics of DMs in the large-scale group decision making process. The capturing process combines the ordinal k-means clustering algorithm, gained and lost dominance score method and personalized quantifiers. To enable DMs to express their cognitions in depth, we use the probabilistic linguistic term set to express the evaluation information of DMs. A case study on selecting the optimal policy recommendation for improving the integration capability of supply chain is given to illustrate the applicability of the proposed process. The superiority of the proposed algorithm is highlighted through sensitive analysis and comparative analysis. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Investigation on the Difficulties and Challenges of Teachers Online Teaching in Primary and Middle Schools of Guangxi Middle School

Affected by the situation of COVID-19, the new semester has been delayed in many places of the country. In response to the request of the Ministry of Education on 'suspended class, ongoing learning' during the epidemic period, schools at all levels throughout the country have carried out online teaching. Through the questionnaire survey, this paper would like to find out the difficulties and challenges facing teachers' online teaching during the epidemic situation, and puts forward some suggestions for online teaching, which may hope to help the promotion of the online teaching. © 2020 IEEE.

Driving Forces of Changes in Air Quality during the COVID-19 Lockdown Period in the Yangtze River Delta Region, China

During the COVID-19 lockdown period (from January 23 to February 29, 2020), ambient PM2.5 concentrations in the Yangtze River Delta (YRD) region were observed to be much lower, while the maximum daily 8 h average (MDA8) O-3 concentrations became much higher compared to those before the lockdown (from January 1 to 22, 2020). Here, we show that emission reduction is the major driving force for the PM2.5 change, contributing to a PM2.5 decrease by 37% to 55% in the four YRD major cities (i.e., Shanghai, Hangzhou, Nanjing, and Hefei), but the MDA8 O-3 increase is driven by both emission reduction (29%-52%) and variation in meteorological conditions (17%-49%). Among all pollutants, reduction in emissions mainly of primary PM contributes to a PM2.5 decrease by 28% to 46%, and NOx emission reduction contributes 7% to 10%. Although NOx emission reduction dominates the MDA8 O-3 increase (38%-59%), volatile organic compounds (VOCs) emission reduction lead to a 5% to 9% MDA8 O-3 decrease. Increased O-3 promotes secondary aerosol formation and partially offsets the decrease of PM2.5 caused by the primary PM emission reductions. The results demonstrate that more coordinated air pollution control strategies are needed in YRD.

An overlap graph model for large-scale group decision making with social trust information considering the multiple roles of experts

Social network analysis is an efficient tool to investigate the relationships of decision-makers in large-scale group decision making (LSGDM). Existing social network-based LSGDM studies generally assumed that each decision-maker has a single role or belongs to only one subgroup. The assumption that a decision-maker has multiple roles or belongs to multiple subgroups is rarely taken into consideration. In this regard, this study proposes an overlap graph model (OGM) in which decision-makers can participate in the decision-making process in multiple roles to solve LSGDM problems with social trust information. In the OGM, decision-makers are firstly divided into two types: multiple-role decision-makers and single-role decision-makers. Since it is unpractical for a decision-maker to evaluate all others in a LSGDM problem, we then investigate how to construct a complete social trust network based on an Agent mechanism. A two-stage consensus reaching process is proposed to reduce the discrepancies among decision-makers: The first stage is for single-role decision-makers within a subgroup while the second stage is for Agents and multiple-role decision-makers. Finally, an illustrative example regarding selecting treatment plans for critical patients in COVID-19 is provided to test the applicability and rationality of the proposed model. © 2020 John Wiley & Sons, Ltd