A two-stages mathematical model for a Covid-19 vaccination fuzzy scheme and resource allocation in hub locations considering transmission risk and environment factor

Tackling with Covid-19 dilemma of vaccine distribution needed a stack of analysis and examination. This paper develops a generalizable framework for designing a hub vaccination dispensing network to achieve expand the Covid-19 vaccination coverage for public. Designing a hub location routing network for vaccine distribution is the main concern for this research. The proposed model hinges on maximum coverage and patients' safety by considering high-priority population alongside the cost reduction in an uncertain environment. The hub location model enhances the accessibility of the vaccines to various communities and helps to overcome the challenges. The results of this model were examined through both numerical and case studies in the north of Tehran to demonstrate its application. Furthermore, in order to reduce the costs of vaccine imports, vaccine entry routing can be developed from border and air points to the country in order to be able to perform vaccination in the fastest time and lowest cost in the future. The results concede that increasing the number of outreach dispensing locations per hub dispensing location will not necessarily result in increased coverage.

Multivariate count time series segmentation with "sums and shares” and Poisson lognormal mixture models: a comparative study using pedestrian flows within a multimodal transport hub

This paper deals with a clustering approach based on mixture models to analyze multidimensional mobility count time-series data within a multimodal transport hub. These time series are very likely to evolve depending on various periods characterized by strikes, maintenance works, or health measures against the Covid19 pandemic. In addition, exogenous one-off factors, such as concerts and transport disruptions, can also impact mobility. Our approach flexibly detects time segments within which the very noisy count data is synthesized into regular spatio-temporal mobility profiles. At the upper level of the modeling, evolving mixing weights are designed to detect segments properly. At the lower level, segment-specific count regression models take into account correlations between series and overdispersion as well as the impact of exogenous factors. For this purpose, we set up and compare two promising strategies that can address this issue, namely the "sums and shares” and "Poisson log-normal” models. The proposed methodologies are applied to actual data collected within a multimodal transport hub in the Paris region. Ticketing logs and pedestrian counts provided by stereo cameras are considered here. Experiments are carried out to show the ability of the statistical models to highlight mobility patterns within the transport hub. One model is chosen based on its ability to detect the most continuous segments possible while fitting the count time series well. An in-depth analysis of the time segmentation, mobility patterns, and impact of exogenous factors obtained with the chosen model is finally performed. © 2023, Springer-Verlag GmbH Germany, part of Springer Nature.

Leading by example: Leveraging academic innovation centers in times of crisis

Universities worldwide are increasingly investing in academic innovation centers that are designed to encourage their students to pursue careers focused on innovation and technology. This chapter explores the educational opportunities of these academic innovation centers during crisis situations by documenting how an academic innovation center at Florida State University - the Innovation Hub - was able to encourage university students to engage in creative problem solving through design thinking, emerging technologies, and experiential learning during the COVID-19 pandemic. The results of these efforts demonstrate that academic innovation centers, during times of global crisis, have a unique opportunity to lead by example, enhancing their educational impact by connecting students directly with real-world challenges as creative problem solvers with the power to improve their communities. © Springer Nature Switzerland AG 2021. All rights reserved.

Comprehensive DNA methylation profiling of COVID-19 and hepatocellular carcinoma to identify common pathogenesis and potential therapeutic targets.

BACKGROUND & AIMS: The effects of SARS-CoV-2 infection can be more complex and severe in patients with hepatocellular carcinoma (HCC) as compared to other cancers. This is due to several factors, including pre-existing conditions such as viral hepatitis and cirrhosis, which are commonly associated with HCC. METHODS: We conducted an analysis of epigenomics in SARS-CoV-2 infection and HCC patients, and identified common pathogenic mechanisms using weighted gene co-expression network analysis (WGCNA) and other analyses. Hub genes were identified and analyzed using LASSO regression. Additionally, drug candidates and their binding modes to key macromolecular targets of COVID-19 were identified using molecular docking. RESULTS: The epigenomic analysis of the relationship between SARS-CoV-2 infection and HCC patients revealed that the co-pathogenesis was closely linked to immune response, particularly T cell differentiation, regulation of T cell activation and monocyte differentiation. Further analysis indicated that CD4+ T cells and monocytes play essential roles in the immunoreaction triggered by both conditions. The expression levels of hub genes MYLK2, FAM83D, STC2, CCDC112, EPHX4 and MMP1 were strongly correlated with SARS-CoV-2 infection and the prognosis of HCC patients. In our study, mefloquine and thioridazine were identified as potential therapeutic agents for COVID-19 in combined with HCC. CONCLUSIONS: In this research, we conducted an epigenomics analysis to identify common pathogenetic processes between SARS-CoV-2 infection and HCC patients, providing new insights into the pathogenesis and treatment of HCC patients infected with SARS-CoV-2.

Acute Respiratory Infection Hubs: A Service Model with Potential to Optimise Infection Management.

Patients with acute respiratory infections (ARI)-including those with upper and lower respiratory infections from both bacterial and viral pathogens-are one of the most common reasons for acute deterioration, with large numbers of potentially avoidable hospital admissions. The acute respiratory infection hubs model was developed to improve healthcare access and quality of care for these patients. This article outlines the implementation of this model and its potential impacts in a number of areas. Firstly, by improving healthcare access for patients with respiratory infections by increasing the capacity for assessment in community and non-emergency department settings and also by providing flexible response to surges in demand and reducing primary and secondary care demand. Secondly, by optimising infection management (including the use of point-of-care diagnostics and standardised best practise guidance to improve appropriate antimicrobial usage) and reducing nosocomial transmission by cohorting those with suspected ARI away from those with non-infective presentations. Thirdly, by addressing healthcare inequalities; in areas of greatest deprivation, acute respiratory infection is strongly linked with increased emergency department attendance. Fourthly, by reducing the National Health Service's (NHS) carbon footprint. Finally, by providing a wonderful opportunity to gather community infection management data to enable large-scale evaluation and research.

Candidate SNP Markers Significantly Altering the Affinity of TATA-Binding Protein for the Promoters of Human Hub Genes for Atherogenesis, Atherosclerosis and Atheroprotection.

Atherosclerosis is a systemic disease in which focal lesions in arteries promote the build-up of lipoproteins and cholesterol they are transporting. The development of atheroma (atherogenesis) narrows blood vessels, reduces the blood supply and leads to cardiovascular diseases. According to the World Health Organization (WHO), cardiovascular diseases are the leading cause of death, which has been especially boosted since the COVID-19 pandemic. There is a variety of contributors to atherosclerosis, including lifestyle factors and genetic predisposition. Antioxidant diets and recreational exercises act as atheroprotectors and can retard atherogenesis. The search for molecular markers of atherogenesis and atheroprotection for predictive, preventive and personalized medicine appears to be the most promising direction for the study of atherosclerosis. In this work, we have analyzed 1068 human genes associated with atherogenesis, atherosclerosis and atheroprotection. The hub genes regulating these processes have been found to be the most ancient. In silico analysis of all 5112 SNPs in their promoters has revealed 330 candidate SNP markers, which statistically significantly change the affinity of the TATA-binding protein (TBP) for these promoters. These molecular markers have made us confident that natural selection acts against underexpression of the hub genes for atherogenesis, atherosclerosis and atheroprotection. At the same time, upregulation of the one for atheroprotection promotes human health.

Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning.

Zoonotic virus spillover in human hosts including outbreaks of Hantavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposes a serious impact on the quality of life of patients. Recent studies provide a shred of evidence that patients with Hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) are at risk of contracting SARS-CoV-2. Both RNA viruses shared a higher degree of clinical features similarity including dry cough, high fever, shortness of breath, and certain reported cases with multiple organ failure. However, there is currently no validated treatment option to tackle this global concern. This study is attributed to the identification of common genes and perturbed pathways by combining differential expression analysis with bioinformatics and machine learning approaches. Initially, the transcriptomic data of hantavirus-infected peripheral blood mononuclear cells (PBMCs) and SARS-CoV-2 infected PBMCs were analyzed through differential gene expression analysis for identification of common differentially expressed genes (DEGs). The functional annotation by enrichment analysis of common genes demonstrated immune and inflammatory response biological processes enriched by DEGs. The protein-protein interaction (PPI) network of DEGs was then constructed and six genes named RAD51, ALDH1A1, UBA52, CUL3, GADD45B, and CDKN1A were identified as the commonly dysregulated hub genes among HFRS and COVID-19. Later, the classification performance of these hub genes were evaluated using Random Forest (RF), Poisson Linear Discriminant Analysis (PLDA), Voom-based Nearest Shrunken Centroids (voomNSC), and Support Vector Machine (SVM) classifiers which demonstrated accuracy >70%, suggesting the biomarker potential of the hub genes. To our knowledge, this is the first study that unveiled biological processes and pathways commonly dysregulated in HFRS and COVID-19, which could be in the next future used for the design of personalized treatment to prevent the linked attacks of COVID-19 and HFRS.

Using system biology and bioinformatics to identify the influences of COVID-19 co-infection with influenza virus on COPD.

Coronavirus disease 2019 (COVID-19) has speedily increased mortality globally. Although they are risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), less is known about the common molecular mechanisms behind COVID-19, influenza virus A (IAV), and chronic obstructive pulmonary disease (COPD). This research used bioinformatics and systems biology to find possible medications for treating COVID-19, IAV, and COPD via identifying differentially expressed genes (DEGs) from gene expression datasets (GSE171110, GSE76925, GSE106986, and GSE185576). A total of 78 DEGs were subjected to functional enrichment, pathway analysis, protein-protein interaction (PPI) network construct, hub gene extraction, and other potentially relevant disorders. Then, DEGs were discovered in networks including transcription factor (TF)-gene connections, protein-drug interactions, and DEG-microRNA (miRNA) coregulatory networks by using NetworkAnalyst. The top 12 hub genes were MPO, MMP9, CD8A, HP, ELANE, CD5, CR2, PLA2G7, PIK3R1, SLAMF1, PEX3, and TNFRSF17. We found that 44 TFs-genes, as well as 118 miRNAs, are directly linked to hub genes. Additionally, we searched the Drug Signatures Database (DSigDB) and identified 10 drugs that could potentially treat COVID-19, IAV, and COPD. Therefore, we evaluated the top 12 hub genes that could be promising DEGs for targeted therapy for SARS-CoV-2 and identified several prospective medications that may benefit COPD patients with COVID-19 and IAV co-infection.

Bioinformatics Approach to Identify the Influences of COVID-19 on Ischemic Stroke.

As severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is becoming more infectious and less virulent, symptoms beyond the lungs of the Coronavirus Disease 2019 (COVID-19) patients are a growing concern. Studies have found that the severity of COVID-19 patients is associated with an increased risk of ischemic stroke (IS); however, the underlying pathogenic mechanisms remain unknown. In this study, bioinformatics approaches were utilized to explore potential pathogenic mechanisms and predict potential drugs that may be useful in the treatment of COVID-19 and IS. The GSE152418 and GSE122709 datasets were downloaded from the GEO website to obtain the common differentially expressed genes (DEGs) of the two datasets for further functional enrichment, pathway analysis, and drug candidate prediction. A total of 80 common DEGs were identified in COVID-19 and IS datasets for GO and KEGG analysis. Next, the protein-protein interaction (PPI) network was constructed and hub genes were identified. Further, transcription factor-gene interactions and DEGs-miRNAs coregulatory network were investigated to explore their regulatory roles in disease. Finally, protein-drug interactions with common DEGs were analyzed to predict potential drugs. We successfully identified the top 10 hub genes that could serve as novel targeted therapies for COVID-19 and screened out some potential drugs for the treatment of COVID-19 and IS.

A model of continuity of care for not residents through medical-nursing teleconsultation in a Hub & Spoke network

Introduction. The Seasonal Continuity of Care (CAS) is a service of the Bergamo Health Protection Agency that provides medical and healthcare services, gua-ranteeing outpatient or home care to Italian and foreign tourists and seasonal workers during the months of July and August. The Covid-19 pandemic and the shortage of doctors made it impossible to provide the service in 2021 as in previous summer seasons. Aims. To activate a CAS service with the involvement of nurses. Methods. A "Hub -Spoke" network model was activated;nurses in the Spoke sites, with the patient in attendance, through teleconsul-tation by video call, made remote contact with a doctor in the Hub. Results. In the 3 Spoke CASs, from 2 to 22 August 2021, 274 services (of which 14.3% were telecon-sultations between the nurse at the Spoke CAS site and the doctor at the Hub site) and 162 repeat prescription re-quests were made. Teleconsultation was mainly performed for patients with acute pathology (71.8%), mainly for arth-ralgia and fever. In the majority of cases, it was sufficient to answer to the needs of the patient (87.2%);a small num-ber of cases were referred to a doctor's appointment (10.3%) or to Emergency Department (2.6%). Conclusions. Nurse triage reduced the time of medical visits, allowing more patients to be taken care of. The need for digital in-frastructure, training and integration with district servi-ces emerged.

Evaluation of Simplified HCV Diagnostics in HIV/HCV Co-Infected Patients in Myanmar.

To evaluate a decentralised testing model and simplified treatment protocol of hepatitis C virus (HCV) infection to facilitate treatment scale-up in Myanmar, this prospective, observational study recruited HIV-HCV co-infected outpatients receiving sofosbuvir/daclatasvir in Yangon, Myanmar. The study examined the outcomes and factors associated with a sustained virological response (SVR). A decentralised "hub-and-spoke" testing model was evaluated where fingerstick capillary specimens were transported by taxi and processed centrally. The performance of the Xpert HCV VL Fingerstick Assay in detecting HCV RNA was compared to the local standard of care ( plasma HCV RNA collected by venepuncture). Between January 2019 and February 2020, 162 HCV RNA-positive individuals were identified; 154/162 (95%) initiated treatment, and 128/154 (84%) returned for their SVR12 visit. A SVR was achieved in 119/154 (77%) participants in the intent-to-treat population and 119/128 (93%) participants in the modified-intent-to-treat population. Individuals receiving an antiretroviral therapy were more likely to achieve a SVR (with an odds ratio (OR) of 7.16, 95% CI 1.03-49.50), while those with cirrhosis were less likely (OR: 0.26, 95% CI 0.07-0.88). The sensitivity of the Xpert HCV VL Fingerstick Assay was 99.4% (95% CI 96.7-100.0), and the specificity was 99.2% (95% CI 95.9-99.9). A simplified treatment protocol using a hub-and-spoke testing model of fingerstick capillary specimens can achieve an SVR rate in LMIC comparable to well-resourced high-income settings.

Identification of biomarkers, pathways, and potential therapeutic targets for heart failure using next-generation sequencing data and bioinformatics analysis.

BACKGROUND: Heart failure (HF) is the most common cardiovascular diseases and the leading cause of cardiovascular diseases related deaths. Increasing molecular targets have been discovered for HF prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might aid the diagnosis and treatment of HF. METHODS: We searched next-generation sequencing (NGS) dataset (GSE161472) and identified differentially expressed genes (DEGs) by comparing 47 HF samples and 37 normal control samples using limma in R package. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g: Profiler database. The protein-protein interaction (PPI) network was plotted with Human Integrated Protein-Protein Interaction rEference (HiPPIE) and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. Then, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. RESULTS: A total of 930 DEGs, 464 upregulated genes and 466 downregulated genes, were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections, and the citric acid tricarboxylic acid (TCA) cycle and respiratory electron transport. After combining the results of the PPI network miRNA-hub gene regulatory network and TF-hub gene regulatory network, 10 hub genes were selected, including heat shock protein 90 alpha family class A member 1 (HSP90AA1), arrestin beta 2 (ARRB2), myosin heavy chain 9 (MYH9), heat shock protein 90 alpha family class B member 1 (HSP90AB1), filamin A (FLNA), epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), cullin 4A (CUL4A), YEATS domain containing 4 (YEATS4), and lysine acetyltransferase 2B (KAT2B). CONCLUSIONS: This discovery-driven study might be useful to provide a novel insight into the diagnosis and treatment of HF. However, more experiments are needed in the future to investigate the functional roles of these genes in HF.

Impact of the COVID-19 pandemic on an online cancer screening training programme for healthcare providers in the public sector in India: learnings from a hub and spoke model perspective.

Introduction: This article elicits our experiences and strategic approaches to ensure the sustainability of the online capacity-building programmes for healthcare providers (HCPs) in comprehensive cancer screening through the 'Hub and Spoke' model during the coronavirus disease (COVID-19) pandemic. Methods: During the first wave of COVID-19, training for three cohorts of medical officers (MO) (Batch-A) was ongoing (May-December 2020). The Indian health system abruptly shifted focus towards containing the COVID-19 spread, leading to new challenges in conducting training courses. A new five-step strategic approach for cohort MO-14 (Batch-B) was adopted to spread awareness about the importance of cancer screening and the roles and responsibilities of HCPs in the implementation and conduct of practical sessions in their states in collaboration with their respective state governments. We also adopted social media - WhatsApp for official communication. Results: Enrolling Batch-B following the new strategic approach reduced refusals by 25% and dropouts by 36% compared to Batch-A. Course compliance and completion was a significant 96% in Batch-B. Conclusion: The COVID-19 pandemic opened a window of opportunity to understand the need for vital changes to improve the quality of our hybrid cancer screening training. Inclusion of the state government in planning and implementing the changes, awareness among HCPs about the importance of training and responsible acceptance of cancer screening, district-wise approach, use of social media in sharing course materials and conducting in-person training in the respective state have demonstrated significant impact on the quality of the training and in scaling-up of cancer screening. Prolonged mentorship, robust Internet connectivity for providers and training on handling gadgets and online video communication would profoundly benefit remote training programmes.A well-devised backup system is essential for training programmes during unforeseen eventualities such as the COVID pandemic.

Identification of Common Dysregulated Genes in COVID-19 and Hypersensitivity Pneumonitis: A Systems Biology and Machine Learning Approach.

A comprehensive knowledge on systems biology of severe acute respiratory syndrome coronavirus 2 is crucial for differential diagnosis of COVID-19. Interestingly, the radiological and pathological features of COVID-19 mimic that of hypersensitivity pneumonitis (HP), another pulmonary fibrotic phenotype. This motivated us to explore the overlapping pathophysiology of COVID-19 and HP, if any, and using a systems biology approach. Two datasets were obtained from the Gene Expression Omnibus database (GSE147507 and GSE150910) and common differentially expressed genes (DEGs) for both diseases identified. Fourteen common DEGs, significantly altered in both diseases, were found to be implicated in complement activation and growth factor activity. A total of five microRNAs (hsa-miR-1-3p, hsa-miR-20a-5p, hsa-miR-107, hsa-miR-16-5p, and hsa-miR-34b-5p) and five transcription factors (KLF6, ZBTB7A, ELF1, NFIL3, and ZBT33) exhibited highest interaction with these common genes. Next, C3, CFB, MMP-9, and IL1A were identified as common hub genes for both COVID-19 and HP. Finally, these top-ranked genes (hub genes) were evaluated using random forest classifier to discriminate between the disease and control group (coronavirus disease 2019 [COVID-19] vs. controls, and HP vs. controls). This supervised machine learning approach demonstrated 100% and 87.6% accuracy in differentiating COVID-19 from controls, and HP from controls, respectively. These findings provide new molecular leads that inform COVID-19 and HP diagnostics and therapeutics research and innovation.

Identification of common molecular signatures of SARS-CoV-2 infection and its influence on acute kidney injury and chronic kidney disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein-protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)-gene, miRNA-gene, and gene-disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases.

An Analysis of Dynamic Correlations among Oil, Natural Gas and Ethanol Markets: New Evidence from the Pre- and Post-COVID-19 Crisis

After the economic shock caused by COVID-19, with relevant effects on both the supply and demand for energy assets, there was greater interest in understanding the relationships between key energy prices. In order to contribute to a deeper understanding of energy price relationships, this paper analyzes the dynamics between the weekly spot prices of oil, natural gas and benchmark ethanol in the US markets. The analysis period started on 23 June 2006 and ended on 10 June 2022. This study used the DMCA cross-correlation coefficient in a dynamic way, using sliding windows. Among the main results, it was found that: (i) in the post-pandemic period, oil and natural gas were not correlated, in both short- and long-term timescales;and (ii) ethanol was negatively associated with natural gas in the most recent post-pandemic period, especially in short-term scales. The results of the present study are potentially relevant for both market and public agents regarding investment diversification strategies and can aid public policies due to the understanding of the interrelationship between energy prices. © 2023 by the authors.

Elon Musk in India - a Success Story in the Making

One of Elon Musk's Twitter posts created much buzz in India and most certainly in the southern State of Karnataka in India. His EV company Tesla is planning on expanding to another East Asian Market after the Shanghai branch dealt with R&D and sold almost 50, 000 units. Moreover, India is one of the most likely targets which Tesla would be aiming for, not only because of the humongous workforce capability that the country could offer but also for the development of rules and regulations for the improvement and the enhancement of EV's inthe country. Since the development of the EV policy, 2017 in the state of Karnataka, which pioneered the same and proved itself to be worthy of the badge of "EV Hub" of India, with many tech start-ups pushing the boundaries in the field of EV. Some of the latest news articles in the papers discussed the probability of Tesla being interested i n forming another enterprise in the country, which is exciting news. This would not only mean the change in the economic spectrum of the country because of the remarkable improvement of the Tesla Stocks in the USA but also a significant chance for employment, leading to the push in the Indian economy, which is most required at this time of distress and dilemma because of the COVID-19 pandemic which struck.Copyright © 2020 European Journal of Molecular and Clinical Medicine. All rights reserved.

An experimental case study on forum-based online teaching to improve student's engagement and motivation in higher education

With the spread of the coronavirus disease 2019 (COVID-19), online education has been increasingly adopted globally. However, whether the online teaching approach is effective for students' learning engagement and motivation is still an open question. To improve students' learning engagement and motivation to minimize students' indulgent in procrastination and plagiarism behavior, an experimental case study on forum-based online teaching was carried out. Results showed that compared to traditional class teaching, the forum-based online teaching effectively improved students' learning engagement and motivation as well as reduced procrastination and plagiarism. Although some students are emotionally resistant to this new forum-based online teaching method, most of the students believed their presentation and other academic skills could be improved through forum-based online teaching. Moreover, students generally accepted moderate levels of peer pressure and competition that were created by the forum-based learning process. Therefore, forum-based online teaching can be considered as a useful complementary approach to traditional class teaching. The implications of this study include that breaking a final "term paper" into multiple small online submissions helps students proactively complete homework assignments and avoids plagiarism. Moreover, educators integrate theories into students' life experiences through online teaching forums, which also improve student learning engagement and motivation. [ FROM AUTHOR] Copyright of Interactive Learning Environments is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Bicycle tourism, from pandemic to sustainability: "Terre di Casole Bike Hub” project

The outbreak of COVID-19 and the subsequent lockdowns have impelled us to question and reconsider our standards, schedules and lifestyle. One of the market sectors that was most affected by the mobility reduction was tourism. Its demand in 2020 showed indeed a significant change: tourists opted for unusual destinations, presumably less crowded, favouring smaller villages and mountain areas. Moreover, tourists have chosen destinations that feature the possibility to do sports, especially trekking and cycling. Considering these trends, this paper focuses on the bicycle tourism in Italy and analyses its development potential. More specifically, it analyses the "Terre di Casole Bike Hub” project as an example of best practices to promote the territory through cycling, aiming to support the territory recovery in the post-pandemic tourism. © 2022 Societe Belge de Geographie. All rights reserved.

IMPACTS OF COVID-19 NON-PHARMACEUTICAL INTERVENTIONS ON TRADE FLOWS: A GLOBAL PANEL VECTOR AUTOREGRESSION ANALYSIS

The COVID-19 outbreak has contributed to a tremendous global decline in international trade flows. The rapid spread of the disease and the control measures implemented by governments to contain the virus have led to serious consequences for the global economy. The pandemic has affected the international movement of people, goods, and services. Currently, the systematic quantitative research investigating the effects of specific non-pharmaceutical intervention policy clusters on country-level international trade flows, remains limited. In this study, the Panel Vector Autoregression (PVAR) method was conducted using country-level panel data collected from various international sources including the United Nations, World Bank, and University of Oxford. The results show that stringent COVID-19 closure, social distancing, and containment measures and health-related measures, had significant negative impacts on trade flows. In contrast, economic support measures showed significant positive effects on trade. In summary, the findings suggest that policymakers should maintain less stringent containment measures related to public closure and movement restrictions and stimulate economic activities through economic support policies in order to minimize losses in trade flows during the pandemic. © 2023,ABAC Journal. All Rights Reserved.