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This paper presents a hybrid model of teaching and learning that proposes new possibilities for exchanging tangible and intangible cross-cultural knowledge in textile craft education. The paper aims to demonstrate how online platforms can be used creatively to disseminate traditional craft knowledge and skills in new ways. The discussion centres on a unique virtual global studio between fashion and textile undergraduate students at the University of Technology Sydney and on an artisanal woodblock print studio, Tharangini, based in Bengaluru (Bangalore), India. The hybrid workshop was an adaptation of the studio in response to travel restrictions caused by the pandemic. The author argues that while the internet cannot replace the immersive cultural experience of studying in another country, digital platforms have a place alongside teaching to offer otherwise impossible opportunities. This paper explores a methodology for disseminating craft knowledge and skills across cultures through a combination of online and in-house practicum. Classes were structured around weekly Zoom sessions with Director Padmini Govind, where sustainable approaches to print production were disseminated through a suite of commissioned films and hand-carved woodblocks to explore on campus. The results show how this unique adaptation allowed students to interact with the artisan craft of woodblock printing in rich and varied ways, and it proposes that this novel hybrid model can be creatively adapted to future craft education in the 2020s. [ABSTRACT FROM AUTHOR]

This paper examines how India's national lockdown (March 25-May 31, 2020), in response to the spread of Covid-19, affected the on-farm family labor supply of 351 farm households in the rural-urban interface of Bangalore. We combine face-to-face survey data collected just before the start of the lockdown with phone survey data collected during the last 2 weeks of the lockdown. We find that 66% of farm households reduced their daily on-farm family labor supply during the lockdown, by on average almost 40% compared with prelockdown levels. Changes in on-farm family labor supply differed by key pre-Covid-19 household characteristics. Farm households that were engaged in crop marketing decreased their on-farm family labor supply by an average of 3-4 h/day. In turn, farm households that relied on off-farm income increased their on-farm family labor supply by on average 3-4 h/day [EconLit Citations: J22, J43, Q12, Q13, Q54]. [ABSTRACT FROM AUTHOR]

Aim/Purpose This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendations for Researchers Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the dataset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India. [ABSTRACT FROM AUTHOR]

The unprecedented socio-economic scenario induced by the COVID-19 pandemic of 2020 adversely hit the lives of millions across the globe. When a lockdown was declared in India on March 24, 2020 as a preventive measure to avoid the spread of the pandemic, the migrant workers whose livelihood depended upon daily wages lost their jobs, and among these workers were women who were engaged in domestic work with total dependence on the income from their employment in working at households. This research paper throws light on the plight of women who struggled to secure a decent income during the pandemic and consequently faced difficulties in psycho-social fronts. The study followed a sequential explanatory design sampling 15 migrant women domestic workers employed in Bangalore city during the lockdown phase of the pandemic. The results of the study throw light on how the population under study struggles on the economic front, which in turn adversely affects their mental health and quality of life. A critical evaluation of the policies and programmes expose how the state measures are inadequate in ensuring the bare minimum necessities of migrant women domestic workers, signaling the need for social action through lobbying for more humanitarian legislations that ensure social security of the underprivileged. [ABSTRACT FROM AUTHOR]

COVID-19 have significant impact on travel behaviour and greenhouse gases (GHG), especially for the most affected city in India, Mumbai metropolitan region (MMR). The present study attempts to explore the risk on different modes of transportation and GHG emissions (based on change in travel behavior) during peak/non-peak hours in a day by an online/offline survey for commuters in Indian metropolitan cities like MMR, Delhi and Bengaluru. In MMR, the probability of infection in car estimated to be 0.88 and 0.29 during peak and non-peak hour, respectively, considering all windows open. The risk of infection in public transportation system such as in bus (0.307), train (0.521), and metro (0.26) observed to be lower than in private vehicles. Furthermore, impact of COVID-19 on GHG emissions have also been explored considering three scenarios. The GHG emissions have been estimated for base (3.83–16.87 tonne), lockdown (0.22–0.48 tonne) and unlocking (2.13–9.30 tonne) scenarios. It has been observed that emissions are highest during base scenario and lowest during lockdown situation. This study will be a breakthrough in understanding the impact of pandemic on environment and transportation. The study shall help transport planners and decision makers to operate public transport during pandemic like situation such that the modal share of public transportation is always highest. It shall also help in regulating the GHG emissions causing climate change. [ABSTRACT FROM AUTHOR]

How has COVID-19 affected the urban poor across cities and over time? This research note serves as a follow-up to an earlier study on the impacts of the first wave of the pandemic on informal settlements in two Indian cities. This note draws on additional interviews with key informants from 20 settlements to compare the economic and health impacts of the first two major waves of the pandemic in Bengaluru and Patna, as well as respondents' attitudes towards the government's response. Informants in both cities reported higher levels of food insecurity during the second wave resulting from a substantial reduction in government aid, cumulative economic impacts from the first and second waves and, in the case of Bengaluru only, a surge in COVID-19 infections and associated healthcare costs. While slum residents in Bengaluru universally describe the health and economic effects of the second wave as more severe than the first, residents in Patna report the opposite. I argue these disparate characterisations reflect different expectations and approval of local government's response to the crisis across cities and waves. [ABSTRACT FROM AUTHOR]

Today we are living in the digital era. In this age of Information Technology, any information can be found through the internet with a single click. However, in reality, the field of 'information technology' is not limited to the Internet. Information Technology (IT) is a major contributor to the radical changes in the way of our life as well as information. IT sectors in India are facing so many challenges. In addition to that, the pandemic situation has created more crisis in the sector. The high speed of community transfer of covid-19 changed all aspects of human life including the IT Sector and jobs in IT sectors. The pandemic situation has created a challenging environment for human resource management (HRM). They have been playing a central role to keep their workforce to cope up with the challenges and get engaged, productive and resilient, through online mode. Most of the employees are still working on online mode or in other words, we can say work from home. There are several platforms such as Google meet, zoom meet, WebEx meet, etc. used by organizations to work in pandemic situations. Current work is an attempt to focus on the impact of Covid-19, on HR practices in IT Sectors with special reference to Bangalore. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) spread across the globe through the human transmission. The World Health Organization suggested social distancing to curb the community spread. After national social lockdown started in India, air quality improved drastically. This further hypothesized to influence the environment and human health, and this study is positively the first to weigh it using multiple indices. The calculated environmental indices are photochemical ozone creation potential (POCP), acidification potential (AP), and eutrophication potential (EP). The cancer risk, chronic health index (CHI), and acute health index (AHI) were considered to calculate the health risk. The spatial trend change in the air pollution reflecting on these indices are calculated for four Indian megacities Delhi, Bangalore, Hyderabad, and Kolkata. Temporal variation was accounted for monthly (2019 vs 2020), one-week and two-weeks period during the social lockdown. The results showed a significant decrease in environmental and health risk during the lockdown due to a corresponding decrement in air pollution. The decrease in the particulate matter was found to play a vital role in altering the air pollution mediated risks of interest. Delhi showed a maximum difference in POCP and Acute HI by recording a dip of 70.79% and 43.53% respectively in 2020 during lockdown. The maximum reduction in health risk indices was 41%, 31%, 17%, 19% for Delhi, Bangalore, Hyderabad, and Kolkata. Bangalore recorded the maximum decline in EP, Cancer risk, Chronic HI by 66.66%, 58.62%, and 58.76% in 2020 compared to 2019. A maximum fall in AP was seen in Kolkata by 57.23% in 2020 among all cities. The connection between these drop-in indices and the cause of air pollutants were well discussed. This present paper gives more in-depth insights into air pollution's effect on environmental and health parameters by connecting and converging various air pollution aspects into a single scale. This study also enlightens the importance of controlling air pollution to have a better environment and healthy life to attain sustainable development. Conceptual illustration of impact of COVID-19 enabled social lockdown on environmental and health risk indices in India. [Display omitted] • Detailed study on COVID-19 lockdown vs environmental and health indices in India. • Included three time periods & four megacities Delhi, Mumbai, Bangalore & Kolkata. • Noticeable reduction in particulate matter and indices during lockdown. • Maximum health risk reduction in Delhi (41%) and Bangalore (31%). • Bangalore recorded maximum reduction in EP, Cancer risk, Chronic HI. [ABSTRACT FROM AUTHOR]

Rural-urban linkages are vital elements in a sustainable food system. As the COVID-19 pandemic unfolded, supply chains were disrupted and fear of infection impacted food shopping decisions, pushing consumers to seek local and safer options for procuring fresh produce. Direct marketing arose as a promising alternative for both consumers and producers. We undertook a study in Bengaluru, India, in order to understand what direct marketing activities have unfolded with the COVID-19 pandemic. Media reports highlighted the plight of farmers struggling to market their harvest during lockdown as well as the farm to fork initiatives and lockdown farmers markets that have been created as a response. We see this moment as an opportunity to develop Bengaluru's food system to be more sustainable, specifically through the City Region Food System framework. This study conducted online and telephone surveys with both consumers and producers in Bengaluru to explore the elements of supply and demand that have fostered and hindered direct marketing schemes. We found that consumers are interested in sourcing fruits and vegetables directly from farmers, but communication and logistics between consumers and producers are major hindrances. Although producers are diversifying their marketing strategies, they need to be implemented at economically viable scales to ensure long-term success. We find that the role of technology, specifically messaging apps, can streamline direct marketing activities and remove the barriers that currently hamper ruralurban linkages. Furthermore, existing community and farmer organizations have the size and scale to make direct marketing schemes a worthy endeavor for both consumers and producers. [ABSTRACT FROM AUTHOR]