Digital Supply Chain Capabilities: Mitigating Disruptions and Leveraging Competitive Advantage Under COVID-19

Researchers and practitioners have highlighted the importance of supply chain analytic capabilities in managing risk while maintaining a competitive advantage (COA). However, the importance of digital supply chain capabilities (DSCCs) in improving resilience, agility, and robustness practices to foster the implementation of sustainable supply chain practices and any resulting COA remains unclear. Based on the dynamic capabilities view, we propose a research model for achieving a COA in contexts of uncertainty, such as the COVID-19 pandemic. A survey of Indian small and medium-sized enterprises in the original equipment manufacturing industry, comprising 310 respondents, was administered. Using structural equation modeling, we examine the proposed model. The findings show a significant positive effect of DSCCs on supply chain resilience and agile practices. The findings also indicate that supply chain resilience, robustness, and agile practices positively affect sustainable supply chain practices. Moreover, sustainable supply chain practices positively influence COA. Furthermore, the study reveals that the effect of DSCCs on sustainable supply chain practices is mediated by supply chain resilience, robustness, and agile practices. Managers concerned with investment in sustainable supply chain practices can obtain a COA through the successful implementation of supply chain resilience, robustness, and agile practices.

Satisfaction of Patients Other Than COVID-19 During the Pandemic at a Multidisciplinary Centre with COVID-19 Services

Background: The services rendered by hospitals during the pandemic may not be efficient. This might impact the satisfaction of patients seeking healthcare. The aim of this study is to assess the satisfaction level of patients other than those with COVID-19 during the pandemic with different services provided by the hospital. Method: A quantitative, analytical and cross-sectional study was carried out in a multidisciplinary hospital. Valid questionnaire, derived from PSQ III and PSQ 18, was used for data collection from 250 outpatients. Ethical approval was obtained. Systematic random sampling was done to enrol patients into the study after taking their consent. Descriptive analysis was performed using frequency, proportion, median and inter-quartile range. Mann–Whitney U test and Kruskal–Wallis test were carried out to find the association between overall satisfaction and different socio-demographic and other variables. Statistical significance was set at p-value < 0.05. Result: Almost two-thirds of the respondents visiting the hospital during the pandemic were female (male: 35.6% and female: 64.4%). More than half (50.4%) of the patients reported that access to the hospital was feasible. Of the patients reporting dissatisfaction, most of them (86.4%) considered the establishment of separate COVID-19 hospitals as the best option. The median satisfaction score for the overall satisfaction of patients towards different service domains was 54.0 (45–60). Almost all respondents (95.6%) found that services were easily available. Patient satisfaction score was significantly associated with expenditure (p < 0.001). Satisfaction score was also significantly associated with the time spent in the hospital by the patients (p < 0.001). Conclusion: Majority of the patients reporting to the multidisciplinary hospital were satisfied with the provisioning of treatment and different services during the COVID-19 pandemic. Relatively lesser satisfaction was reported for the provision of maintenance of social distance, availability of hand washing/sanitisation, overall hospital cleanliness and cost of treatment. Moreover, satisfaction among patients was associated with their perceived fear of the pandemic. © 2023 Indian Institute of Health Management Research.

Intelligent Information System for Detection of COVID-19 Based on AI

The pandemic is changing the clinical needs and potential for AI-driven computer-assisted diagnoses (CDS). Since the beginning, rapid identification of COVID-19 patients has been a significant difficulty, especially in areas with limited diagnostic testing capacity. Intelligent Information System (IIS) represents the knowledge progression of available data. It has been directed by recent technological integration, data processing, and distribution in multiple computational environments. Intelligent Information Systems are aimed to work like an advanced human brain, where, as per the requirement of changing circumstances, the optimal decision can be evolved. IIS tools are expected to be adaptive, which may vary according to their processing data. As a result, the goal of this study was to provide a complete analysis of various technologies for combating COVID-19, with a focus on their features, problems, and domiciliation nation. Our findings demonstrate the performance of developing technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Developing Supply Chain Capabilities Through Digitalization and Viability for Controlling the Ripple Effect

The COVID-19 pandemic affected all industries and presented manufacturing firms with enormous challenges, with considerable changes in consumer demand for goods and services. Supply chain management disruption caused by the COVID-19 outbreak resulted in several socio-economic roadblocks. The slow propagation of disruption risk results in a ripple effect along the entire chain. The lack of resilience and risk management capability is the prime cause, attributed to the unavailability of digital resources, skills, and knowledge. The main objective of this article is to develop supply chain capability for disruption risk management and supply chain resilience for competitive gain in terms of controlling the ripple effect. The resource-based view approach was used to develop the theoretical structure in this article. Supply chain digitalization and viability provide necessary resources to develop the capability for managing risk and resilience to tackle the impact of disruptions due to pandemics, war, recession, and other such massive challenges on the supply chain. Seven hypotheses were proposed and evaluated for relevance using structural equation modeling (SEM). In total, 199 valid responses to a survey on SEM were gathered and examined using the AMOS V-21 software. Our research findings supported all the proposed hypotheses, thereby generating positive theoretical evidence for practitioners to digitalize their supply chain for enhanced supply chain capabilities and effective control of the ripple effect. IEEE

Two years of COVID-19 pandemic: Understanding the role of knowledge-based supply chains towards resilience through bibliometric and network analyses

Coronavirus disease (COVID-19) catastrophically disrupted most of the global supply chains (SC). Knowledge-based SC can cope with the pandemic disruptions by the efficient use of data, information, knowledge, human intelligence and emerging technologies. This article aims to critically analyse the SC research during the two years of COVID-19 pandemic to understand the role of knowledge-based supply chain towards SC resilience. A review of the 281 shortlisted articles is presented, along with bibliometric and network analyses in order to create an intellectual map of the domain and to identify the emerging knowledge themes. Bibliometric analysis reveals that the knowledge focus during this short span has migrated from COVID-19 pandemic to SC risk management and finally to risk mitigation strategies. The network analysis identifies five emerging knowledge themes, namely impact of COVID-19 on SC;SC risk mitigation and resilience;supply chain viability;sustainable SC strategies;and food SC. This review also elucidates the strategies to mitigate COVID-19 disruptions for incorporating resilience in SC. Future research directions for a knowledge-based sustainable-leagile-resilient (S-leagilient) supply chain have also been propounded.

NNPCov19: Artificial Neural Network-Based Propaganda Identification on Social Media in COVID-19 Era

The latest trend of sharing information has evolved many concerns for the current researchers, which are working on computational social sciences. Online social network platforms have become a tool for sharing propagandistic information. This is being used as a lethal weapon in modern days to destabilize democracies and other political or religious events. The COVID-19 affected almost every corner of the world. Various propagandistic tweets were shared on Twitter during the peak time of COVID-19. In this paper, improved artificial neural network algorithm is proposed to classify tweets into propagandistic and nonpropagandistic class. The data are extracted using multiple ambiguous hashtags and are manually annotated into binary class. Hybrid feature engineering is being performed by combining "Term Frequency (TF)/Inverse Document Frequency (IDF)," "Bag of Words," and Tweet Length. The proposed algorithm is compared with logistic regression, support vector machine, and multinomial Naive Bayes. Results showed that improved artificial neural network algorithm outperforms other machine learning algorithms by having 77.15% accuracy, 77% of recall, and 79% precision. In future, deep learning approaches like LSTM may be used for this classification task.

FPGA-Based Deep Learning Models for Analysing Corona Using Chest X-Ray Images

Coronavirus is a large family of viruses that affects humans and damages respiratory functions ranging from cold to more serious diseases such as ARDS and SARS. But the most recently discovered virus causes COVID-19. Isolation at home or hospital depends on one's health history and conditions. The prevailing disease that might get instigated due to the existence of the virus might lead to deterioration in health. Therefore, there is a need for early detection of the virus. Recently, many works are found to be observed with the deployment of techniques for the detection based on chest X-rays. In this work, a solution has been proposed that consists of a sample prototype of an AI-based Flask-driven web application framework that predicts the six different diseases including ARDS, bacteria, COVID-19, SARS, Streptococcus, and virus. Here, each category of X-ray images was placed under scrutiny and conducted training and testing using deep learning algorithms such as CNN, ResNet (with and without dropout), VGG16, and AlexNet to detect the status of X-rays. Recent FPGA design tools are compatible with software models in deep learning methods. FPGAs are suitable for deep learning algorithms to make the design as flexible, innovative, and hardware acceleration perspective. High-performance FPGA hardware is advantageous over GPUs. Looking forward, the device can efficiently integrate with the deep learning modules. FPGAs act as a challenging substitute podium where it bridges the gap between the architectures and power-related designs. FPGA is a better option for the implementation of algorithms. The design attains 121μW power and 89 ms delay. This was implemented in the FPGA environment and observed that it attains a reduced number of gate counts and low power. © 2022 Anupama Namburu et al.

Structure Of Corona Virus

Background: Coronavirus illness is a new pathological ailment that is sweeping the world. Sars (severe acute respiratory syndrome) the virus that causes it is coronavirus 2. (sars-cov-2). The sars-spike cov-2 protein (s) has two subtypes: s1 and s2.it is vital to the recognition of receptors and the fusion of biological membranes that the (s) component of the sars-spike cov-2 influenza is present. Covalent coupling of the transcription factor domain of the s1 component to the host ligand binding of angiotensin-converting enzyme 2 allows it to function, whereas the s2 subunit's two-helix recurring domain enhances the attachment of the virus to cells. In this appraisal, we high spot topical expansions in the edifice, function, and advance of antiviral medications that target the s protein. There has been an explosion of viruses that have been creating a new beginning in recent years such as coronaviruses (covs). Human and animal hosts have been infected with these pathogens, causing disorders that range from upper respiratory tract infections in humans to encephalitis and demyelination in animals, which are all potentially life-threatening. From across the world, they've killed many people and animals, and caused havoc in the healthcare system. The current threat from coronaviruses (covs) is among the most horrifying illnesses. They've infected a wide range of human and animal hosts, instigating diseases that assortment from upper respiratory infections in humans to encephalitis and demyelination in animals, all of which are potentially lethal. They've killed a lot of people and animals, and they've caused a lot of health problems all around the world. Result:- It's vital to create new tactics to avert or switch coronavirus infections, as well as a better understanding of their biology, replication, and pathogenesis. As a result, we used experimental and computational research to characterise the structure and functions of covs proteins in this review. Conclusion:- This knowledge might principal to a better sympathetic of cov proteins' roles in virus repetition and transcription, as well as the development of novel antivirals. © The Electrochemical Society

The Impact of IoT on the Performance of Vaccine Supply Chain Distribution in the COVID-19 Context

The Government of India has started the distribution of the Covishield and Covaxin vaccines across all the comprising states. In developing states like Bihar, the vaccine supply chain (VSC) is likely to face many challenges due to poor health infrastructure, multidimensional poverty, and poor literacy. Supply chain practitioners are experimenting with available technologies to deal with supply, demand, and behavioral challenges. The present research work seeks to inspect the potential impact of Internet of Things (IoT) on the performance of the VSC. This study draws literature on the impact of IoT on product management, demand management, supply management, social behavior, and government rules and regulations to develop and test the conceptual model in Bihar. Consequently, the study administrated a survey and used structural equation modeling to investigate the proposed hypothesis. The analysis illustrates the positive influence of IoT adoption on the performance of the VSC in distributing the COVID vaccine. The finding also shows the positive relationship between product, supply, demand, and social behavior in IoT adoption. Analysis displays that Indian politicians can substantially impact vaccine distribution because they have influence and awareness of their local districts. This research work has a significant theoretical and managerial contribution for government and practitioners, supporting regulatory officials and policymakers in improving vaccine distribution.

Hygieia: Smart Health and Sanitizing Dispenser

Coronavirus (COVID-19), one of the deadliest pandemic diseases of the century, escalated at such a fast rate that around 25 million people around the world got infected. The impact of the virus made it a compulsion for the people to wear masks and apply sanitizer at regular intervals. Thus, for the safe and hygiene buying process of masks, sanitizer and other pharmaceutical products, an idea of a cashless and contactless dispenser was brought up named Hygieia. Hygieia is the name of the Greek goddess of health and sanitization. The purpose of this project is to make the buying process fast as well as ensure the safety of the customers by using digital payment methods. The customer can place the order with the help of speech instead of touching the keypads which is observed in most of the vending machines. Cashless payment option is also provided which is making this product a smart and viable automated dispenser. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

To determine the critical factors for the adoption of cloud computing in the educational sector in developing countries - a fuzzy DEMATEL approach

Purpose The paper focuses on reviewing and theorizing the factors that affect the adoption of cloud computing in the education sector narrowing the focus to developing countries such as India. Design/methodology/approach Through an extensive literature survey, critical factors of cloud computing for education were identified. Further, the fuzzy DEMATEL approach was used to define their interrelationship and its cause and effect. Findings A total of 17 factors were identified for the study based on the literature survey and experts' input. These factors were classified as causes and effects and ranked and interrelated. "Required Learning Skills and Attitude," "Lack of Infrastructure," "Learners' Ability" and "Increased Investment" are found to be the most influential factors. Practical implications The resultant ranking factors can be used as a basis for managing the process of cloud adoption in several institutions. The study could guide academicians, policymakers and government authorities for the effective adoption of cloud computing in education. Originality/value The study investigates interdependency amongst the factors of cloud computing for education in context with developing economy. This is one of first study in higher education institutes of India.

COVID-19 critical success factors in Indian healthcare industry-A DEMATEL approach

The prosed study aims to provide COVID-19 critical success factors (CSF) associated with pandemic circumstances in the Indian healthcare industry (HCI). The CSF was identified via expert team inputs and a detailed literature review. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to determine the causal relationship between identified CSF. The methodology was supported by the case study of the Indian HCI. A total of 15 CSF in the Indian HCI during COVID-19 are identified and prioritized using the DEMATEL method. The findings indicate that the high-quality personal protective equipment (PPEs;LC8) and testing laboratories/facilities, centres, and kits (LC15) are the significant cause, and appropriate healthcare laws (LC13) are the least effect group. The study shows that policy and decision-makers need to emphasize on LC8 and LC15 CSF in the Indian HCI and act accordingly to win the battle against post-COVID-19 circumstance. The policy/decision-makers and healthcare administrations can identify the CSF and focus on that particular CSF. The identified CSF will help policy and decision-makers swiftly build up the HCI to cope with the future pandemic.

Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic

Purpose In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI's influence on supply chain risk mitigation (SCRM). Design/methodology/approach This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses. Findings This study's findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence. Originality/value This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

Are artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective

Purpose: This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context. Design/methodology/approach: 20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of “Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used. Findings: The study's outcome indicates that “lack of central and state regulations and rules” and “lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties. Research limitations/implications: This study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care. Originality/value: This study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP. © 2021, Emerald Publishing Limited.

Clinical profile and outcome of COVID 19 patients at tertiary cardiovascular center of Nepal

Background and Aims: Cardiovascular comorbidities are common in patients with COVID-19 and these patients are at higher risk of morbidity and mortality. It is not known if the presence of cardiovascular co-morbid conditions poses independent risk or whether this is mediated by other factors. Methods: This is a retrospective follow up study done at Shahid Gangalal National Heart Centre (SGNHC). The main objective of this study was to study the clinical profile, baseline comorbidities, and outcome of cardiac patients and health care worker diagnosed with COVID 19. This study retrospectively evaluated case records of all cardiovascular disease (CVD) patients admitted at SGNHC with COVID 19 cases from 1st case diagnosed on July at SGNHC till September 2020. Results: During this study period, 90 patients with COVID 19 with cardiovascular disease were admitted. The mean age of the study population was 52.3±19 years with 65.6% being male. Among the study population 52 (57.8%) had past history of cardiovascular disease, hypertension in 18 (20%) cases, diabetes in 8 (8.9%) cases. Among the patients with cardiovascular diagnosis, acute coronary syndrome was most common cardiovascular diagnosis in 23 (25.6% cases) followed by rheumatic heart disease in 21 (23.4%) cases, dilated cardiomyopathy in 7 (7.8% cases), ischemic cardiomyopathy with reduced ejection fraction (EF) in 7 (7.8%) cases, post coronary artery bypass graft (CABG) in 8 (8.9%), post valve replacement in 5 (5.5%), congenital heart disease in 3.3% cases and complete heart block in 3.3% cases. Most of the cases were symptomatic with moderate illness in 46.7% cases, mild illness in 41.4% cases and severe/critical illness in 11.1% cases. Among COVID patients with cardiovascular disease, the mortality was 11.1%. Conclusion: Patients with cardiovascular disease with COVID 19 have more severe COVID 19 symptoms and has higher COVID 19 related death, so strict vigilance and early intervention is needed to improve its outcome.