ABSTRACT
Involvement of multiple stakeholders in healthcare industry, even the simple healthcare problems become complex due to classical approach to treatment. In the Covid-19 era where quick and accurate solutions in healthcare are needed along with quick collaboration of stakeholders such as patients, insurance agents, healthcare providers and medicine supplier etc., a classical computing approach is not enough. Therefore, this study aims to identify the role of quantum computing in disrupting the healthcare sector with the lens of organizational information processing theory (OIPT), creating a more sustainable (less strained) healthcare system. A semi-structured interview approach is adopted to gauge the expectations of professionals from healthcare industry regarding quantum computing. A structured approach of coding, using open, axial and selective approach is adopted to map the themes under quantum computing for healthcare industry. The findings indicate the potential applications of quantum computing for pharmaceutical, hospital, health insurance organizations along with patients to have precise and quick solutions to the problems, where greater accuracy and speed can be achieved. Existing research focuses on the technological background of quantum computing, whereas this study makes an effort to mark the beginning of quantum computing research with respect to organizational management theory. © 2022
ABSTRACT
Building a local supply chain requires separating the regions and creating alliances with local partners and customers, resulting in a new business model. In local supply chains, the factory procures material, parts, and preassembled elements from local suppliers and sells the final products to local customers. Three-dimensional printing (3DP) has the potential to enable a more local, globally connected, and efficient supply chain through reduced inventory and transportation costs transforming the make-to-stock to the make-on-demand production cycle. In this study, we use an integrated Interpretive Structural Model and Decision-making Trial and Evaluation Laboratory technique to explore and assess the challenges faced by the 3DP companies to become enabling partners in the localized supply chains. The scope of the study, which was limited to 3DP of medical parts and components, identified that regulatory compliance, stringent quality standards, and lack of design expertise are significant barriers to developing localized three-dimensional printing ecosystems. Furthermore, the study identified immediate support from the local government, the high collaboration between the stakeholders, and the need for change in business approach as the key drivers for developing 3DP-enabled localized supply chain ecosystems. IEEE
ABSTRACT
As today a disease called COVID-19 is causing health crisis and deaths, it became most essential to wear a mask for protecting ourselves from Corona virus. Even in public areas, where is more rush we should wear mask as no virus can spread from person to person if any one of person from public is Corona positive. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID19. So basically in this project we are going to use Python, Keras, OpenCV alongwith MobileNet for this Face Mask Detection System. This includes some steps like data preprocessing, training and testing the model, run and view the accuracy and applying model in the camera. The inputs has provided here are 1000+ images of people with mask and without mask. First the data get processed and then by checking features of each image it will train all the models and the persons with and without mask get separated to two categories: with mask and without mask. If person is wearing mask with 90 or more percent of accuracy, then he will get added to with mask category and person not wearing mask get added to without mask category, so that we can permit with mask person to public areas. © 2022 IEEE.
ABSTRACT
Grounded on resource-based view and dynamic capability perspectives, this research aims to explore linkages between the firm's big data management activities (BDMA), green manufacturing (GM) practices, and sustainable business performance (SBP). The research model was empirically evaluated using data collected from 248 pharmaceutical manufacturers in India during the COVID-19 pandemic. The analysis was performed using a covariance-based structural equation modeling using AMOS 20. The results indicate that GM activities impact SBP directly. Further results imply the mediating role of GM practices on the relationship between BDMA and SBP. The analysis reveals that senior management's resource commitment in pharmaceutical firms is a moderating mechanism in strengthening the association between BDMA and GM practices. This study is significant as it provides key theoretical and managerial implications for pharmaceutical sectors during emergent situations.
ABSTRACT
This paper study the mental health during COVID-19 pandemic. In the end of 2019,new global disease was found called as coronavirus and it is also named as COVID-19. The main origin of this global virus is began in Wuhan (China) approximately in December 2019,and after sometime this virus is spread globally, specially in nearby countries of china[1]. The first COVID-19 positive patient was found in India 30 January 2020[1], and over the time virus was spread all over the India, and till date total number of cases in India is 10.4 Million and from this number of recovered cases are 10 Million and death cases are 151 Thousand[2] [3]. The way these global virus has badly affected the physical health of the people, the same way it has affected the mental health of people, that is the main reason of this study. In some cases early detection of COVID-19 is possible because there was common symptoms such as fever, cough, sore throat and shortness of breath and in some of the cases symptoms are not visible. COVID-19 is the infectious disease caused by the most recently discovered coronavirus. Over a period the number of COVID-19 cases increased across the world as a corona virus is spreading rapidly it create stress and fear, so because of this uncertain situations are happened globally. Front line COVID-19 warriors and other essential workers face many challenges while they work to keep us safe and maintain services also social distancing and lockdown restrictions have badly affected mental health of the people.[4]The main aim of this research paper is to examine mental health after the COVID-19.It's important to understand the immediate and eternal impact of this pandemic on the people's mental health because normal lifestyle as a before will not come back until we get vaccine for it. Nowadays Mental health is one of the most important thing but no one gives proper attention towards it so it has become a topic of more concern. Also it focuses mental health awareness by demonstrating on areas of diagnosis, treatment and support, research etc so that people can recover through it and understand itsokaynottobeokay. © 2021 IEEE.
ABSTRACT
Purpose: Agriculture value chains (AVCs) have experienced unprecedented disruption during the COVID-19 pandemic, with lockdowns and stringent social distancing restrictions making buying and selling behaviours complex and uncertain. This study aims provide a theoretical framework describing the stakeholder behaviours that arise in severely disrupted value chains, which give rise to inter-organisational initiatives that impact industry sustainability. Design/methodology/approach: A mixed-methods approach is adopted, in which uncertainty theory and relational governance theory and structured interviews with 15 AVC stakeholders underpin the initial conceptual model. The framework is empirically validated via partial least squares structural equation modelling using data from an online survey of 185 AVC stakeholders based in India. Findings: The findings reveal that buyer and supplier uncertainty created by the COVID-19 lockdowns gives rise to behaviours that encourage stakeholders to engage in relational governance initiatives. Progressive farmers and other AVC stakeholders welcome this improved information sharing, which encourages self-reliance that positively impacts agricultural productivity and sustainability. Practical implications: The new framework offers farmers and other stakeholders in developing nations possibilities to sustain their AVCs even in dire circumstances. In India, this also requires an enabling ecosystem to enhance smallholders' marketing power and help them take advantage of recent agricultural reforms. Originality/value: Research is scarce into the impact of buyer and seller behaviour during extreme supply chain disruptions. This study applies relational governance and uncertainty theories, leading to a proposed risk aversion theory. © 2021, Emerald Publishing Limited.
ABSTRACT
The World Health Organization claims (WHO),Corona Viruses the COVID-19 pandemic is causing a nationwide crisis, wearing a mask on a face in public places is an effective protection measure. The COVID-19 pandemic forced governments all over the world to implement quarantine measures in order to deter virus spread. Reports suggest that the risk of transmission is clearly minimized by wearing face masks when at work. An effective and economic approach to the use of AI in a manufacturing setting to build a secure environment. Using a face mask detection dataset, we will use Open CV to perform real-time face detection from a live stream from our webcam. Using Keras, Python, Tensorflow and Open CV, and, it will build a COVID-19 face mask detector with computer vision. Using computer vision and CNN, I aim to decide whether or not the person in the image or video streaming is wear a mask.