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COVID-19, caused by a member of the coronavirus family of viruses, has spread to most countries around the world since it was first recorded in humans in China in late 2019. Closing universities and cancelling all face-to-face activities have become a COVID-19 inevitable reality in many parts of the world. Its impact on university programs, particularly to maintain academic standards and quality assurance procedures, has become significantly more challenging and complex. New ways of working digitally, to minimize disruption to daily operations, have also led to enormous anxiety and uncertainty within the student population, and meeting students’ expectations has also become significantly more difficult. This paper reviews actions taken by universities to safeguard high academic standards and quality assurance procedures during this time and appraise the challenges and impacts on students’ academic performance.

Recently, robots have been widely adopted in the hospitality and tourism industry. Efficient robots can help hoteliers and tourism suppliers with their repetitive or manual labor. Due to the coronavirus disease (COVID-19) pandemic, there is an increasing number of publications on robotic applications in hospitality and tourism. However, a comprehensive literature review of this realm remains lacking. Therefore, to provide a holistic view of the existing literature on robotic applications in hospitality and tourism, this study reviewed 86 extant robotic application-related articles by conducting descriptive analysis and content analysis. The findings of this study showed that most of the existing relevant studies were conducted from the perspective of consumers in the hospitality context. Potential future research directions for academics are identified herein. Practical implications on robotic adoption are also provided for industry practitioners.

Few studies on population-specific health effects of extreme temperature on cardiovascular diseases (CVDs) deaths have been conducted in the subtropical and tropical climates of China. We examined the association between extreme temperature and CVD across four cities in China. We performed a two-stage analysis; we generated city-specific estimates using a distributed lag non-linear model (DLNM) and estimated the overall effects by conducting a meta-analysis. Heat thresholds of 29 °C, 29 °C, 29 °C, and 30 °C and cold thresholds of 6 °C, 10 °C, 14 °C, and 15 °C were observed in Hefei, Changsha, Nanning, and Haikou, respectively. The lag periods for heat-related CVD mortality were observed only for 0–2 days, while those of cold-related CVD mortality were observed for 10–15 days. The meta-analysis showed that a 1 °C increase above the city-specific heat threshold was associated with average overall CVD mortality increases of 4.6% (3.0%–6.2%), 6.4% (3.4%–9.4%), and 0.2% (−4.8%–5.2%) for all ages, ≥65 years, and <65 years over a lag period of 0–2 days, respectively. Similarly, a 1 °C decrease below the city-specific cold threshold was associated with average overall CVD mortality increases of 4.2% (3.0%–5.4%), 4.9% (3.5%–6.3%), and 3.1% (1.7%–4.5%), for all ages, ≥65 years, and <65 years over a lag period of 0–15 days, respectively. This work will help to take appropriate measures to reduce temperature-mortality risk in different populations in the subtropical and tropical climates of China.

Water resource management in the UK is multifaceted, with a complexity of issues arising from acute and chronic stressors. Below average rainfall in spring 2020 coincided with large-scale changes to domestic water consumption patterns, arising from the first UK-wide COVID-19 lockdown, resulting in increased pressure on nationwide resources. A sector wide survey, semi-structured interviews with sector executives, meteorological data, water resource management plans and market information were used to evaluate the impact of acute and chronic threats on water demand in the UK, and how resilience to both can be increased. The COVID-19 pandemic was a particularly acute threat: water demand increased across the country, it was unpredictable and hard to forecast, and compounding this, below average rainfall resulted in some areas having to tanker in water to ‘top up’ the network. This occurred in regions of the UK that are ‘water stressed’ as well as those that are not. We therefore propose a need to look beyond ‘design droughts’ and ‘dry weather average demand’ to characterise the management and resilience of future water resources. As a sector, we can learn from this acute threat and administer a more integrated approach, combining action on the social value of water, the implementation of water trading and the development of nationwide multi-sectoral resilience plans to better respond to short and long-term disruptors.

In this paper, we apply game theory to study the price competition between drugstores and hospitals in China’s pharmaceutical supply chain. Motivated by drug shortages and price disparity problems, we build a simplified model with one supplier, one hospital, and one drugstore in which the sellers sell one kind of drug and compete on price. The hospital receives a discount from the government when ordering the drug and both sellers face a price-sensitive and uncertain demand. The existence and uniqueness of a Nash equilibrium are proved and closed-form solutions are found for linear demand cases. We characterize the pricing and ordering decisions of the hospital and drugstore. The analysis shows that high ex-factory price, high price sensitivity, and a small discount are three factors contributing to drug shortages. We consider two special kinds of linear demand to obtain insights into the drug price disparity problem.

Transit-Oriented Development (TOD) is advocated for achieving sustainable transportation through development around transit stations. TOD’s global implementation revealed varied outcomes, with many cities failing to achieve the intended objectives. TOD implementation in the Jakarta Metropolitan area still in its infancy. Through a geospatial information system and a survey of 400 commuters who live inside the 1 km radius of planned TOD, this longitudinal study aimed to examine an eight-year lapse between 2013 and 2020 of changes in two aspects, specifically land-use and spatial distribution as well as commuters’ travel behavior and preferences in TOD implementation and travel changes due to the COVID-19 pandemic. Key findings are as follows: increased diversity in the residential function around planned TOD areas in the Jakarta capital and a decrease in the suburbs, reflecting the commuters’ improved readiness to reside in planned TOD areas. Furthermore, kinship relations were the commuters’ main reason when selecting house locations, with no capacity to change their workplaces. A significant increase in public facilities at the expense of green open space (GOS) indicates that TOD implementation was conducted by the government with the sole authority to manage GOS, lacking private sector involvement. The cost factor was the most dominant reason for the commuter’s use of public transportation, instead of new transport modes such as MRT and LRT. Moreover, the commuter’s travel behavior in all studied transit stations, whilst it showed evidence of changes in time and frequency, was not greatly influenced by the COVID-19 related restrictions.

Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

In this paper, we have examined the effects of COVID-19 on the socio-economic condition of the three-wheeled electric vehicle drivers in some selected areas of Bangladesh from the cross-sectional data (September–November 2020). The results of linear regression indicate that under COVID-19 conditions, age (p = 0.022) and hardship (p = 0.000) positively, and education (p = 0.036), driving duration (p = 0.023), COVID consciousness (p = 0.086) and easy bike vehicle (p = 0.000) negatively affects income of the respondents. The deaths of COVID in the district (p = 0.003), income (p = 0.000), age (p = 0.037), easy bike vehicle (p = 0.018), debt (p = 0.059) and sufferings of diseases (p = 0.044) positively, and property holdings (p = 0.028), residence in urban areas (p = 0.004) and COVID consciousness (p = 0.082) negatively affect the family expenditure. The results from binary logistics regressions show that diseases sufferings (adjusted p = <0.001; unadjusted p = <0.001), corona fear (unadjusted p = 0.005; adjusted p = <0.001) have positive, and income (unadjusted p = <0.001; adjusted p = <0.001), cooking fuel (unadjusted p = 0.003; adjusted p = 0.091) and easy bike vehicle (unadjusted p = <0.001; adjusted p = 0.288) have negative association with hardship or misery due to COVID-19; death of COVID-19 in the district (unadjusted p = 0.008; adjusted p = 0.037), hardship or misery (adjusted p = 0.005; adjusted p = 0.001), and urban dwelling area (unadjusted p = 0.002; adjusted p = 0.004) have positive, and access to pure drinking water (unadjusted p = 0.005; adjusted p = 0.011) has negative link with corona fear; and, family savings (unadjusted p = 0.001; adjusted p = 0.013), satisfaction in the current job (unadjusted p = <0.001; adjusted p = <0.001), and government medical service (unadjusted p = 0.065; adjusted p = 0.012) have positive affiliation, and household size (unadjusted p = 0.007; adjusted p = 0.020) has negative affiliation with the continuation desire of the current job of respondents. All the obtained results are consistent and have significant policy implications.