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Construction project delays are costly to stakeholders. Therefore, extensive research has been carried out in various countries covering different types of construction projects to identify delay factors and mitigation strategies. However, the literature has not documented the impact of a once-in-a-century event such as the COVID-19 pandemic on construction projects' schedules and costs. The present study uses a case study approach to examine the effect of the COVID-19 pandemic on three lift irrigation projects' construction programs and the financial implications for the contractor in the Indian construction industry. The findings reveal that the main factors responsible for delays due to the COVID-19 pandemic were fear of an outbreak, imposition of a nationwide lockdown, late return of workers, resource shortages, and changes in operating procedures. Furthermore, the study found that the contract conditions and government directives allowed for time extensions in construction projects by recognizing the pandemic as a force majeure. However, such provisions did not protect contractors' financial interests. This study evaluates the financial implications of the COVID-19 pandemic for the contractor in terms of worker compensation during the lockdown, idling of resources, overhead costs, cost escalation, and liquidity. The public health crisis from the COVID-19 pandemic led to a series of preventive measures from the government and organizations worldwide that caused severe disruptions in construction projects and disturbed the construction supply chain. This study recommends rethinking the scope and coverage of force majeure and excusable delay clauses in standard construction contracts beyond provisions for an extension of time to protect the financial interests of different stakeholders. The vagueness in assessing and compensating for the losses from unprecedented events such as the COVID-19 pandemic could expose construction organizations to significant financial risks and threaten their existence in a highly competitive industry. Therefore, construction organizations should carefully review the contract provisions of future projects for proper risk allocations to accommodate unprecedented events and pandemics to help them recover successfully from various disruptions and associated financial losses. Moreover, proper communication and negotiation, a supportive and collaborative project culture, and early government interventions are crucial in dealing with such events and avoiding disputes among project stakeholders. [ABSTRACT FROM AUTHOR]

The outbreak of coronavirus COVID-19 not only brings great disaster to the people of the world, but also brings heavy burden to the medical and health network system. Massive network data traffic and resource optimization requests make traditional network architectures unable to calmly deal with the impact of COVID-19. Artificial intelligence (AI) can effectively raise the upper limit of the medical and health network, as evidenced by the ever-increasing restorative clinical data. In addition, the development of next-generation network (NGN) technologies based on machine learning (ML) has created unlimited possibilities for the emergence of emerging medical methods. In order to reflect the effective results of the current application of AI technologies in the fight against the COVID-19 epidemic and provide a reliable guarantee for subsequent diagnosis and treatment of COVID-19 epidemics, a series of AI technologies which can be used in the diagnosis and treatment of COVID-19 are systematically summarized and analyzed. Based on various AI technologies and methods, we try to propose an AI-based medical network architecture. The architecture uses AI technologies to quickly and effectively realize the monitoring, diagnosis and treatment of patients. Finally, we rationally analyzed the technical challenges and practical problems that may be faced in implementing the architecture. The purpose of this article is to inspire scholars and medical researchers to carry out the latest research in response to the COVID-19 epidemic and make breakthrough medical technology progress. [ABSTRACT FROM AUTHOR]

Unmanned Air Vehicles (UAVs) are becoming popular in real-world scenarios due to current advances in sensor technology and hardware platform development. The applications of UAVs in the medical field are broad and may be shared worldwide. With the recent outbreak of COVID-19, fast diagnostic testing has become one of the challenges due to the lack of test kits. UAVs can help in tackling the COVID-19 by delivering medication to the hospital on time. In this paper, to detect the number of COVID-19 cases in a hospital, we propose a deep convolution neural architecture using transfer learning, classifying the patient into three categories as COVID-19 (positive) and normal (negative), and pneumonia based on given X-ray images. The proposed deep-learning architecture is compared with state-of-the-art models. The results show that the proposed model provides an accuracy of 94.92%. Further to offer time-bounded services to COVID-19 patients, we have proposed a scheme for delivering emergency kits to the hospitals in need using an optimal path planning approach for UAVs in the network. [ABSTRACT FROM AUTHOR]

Background: The second wave of COVID‐19 in India was followed by large number of mucormycosis cases. Indiscriminate use of immunosuppressive drugs, underlying diseases such as diabetes, cancers, or autoimmune diseases was thought to be the cause. However, the mortality was not as high as that seen in non‐COVID mucormycosis. Objective: To study the detailed characteristics of T‐cells for evaluating the underlying differences in the T‐cell immune dysfunction in post‐COVID and non‐COVID mucor patients. Material and method: The study included histopathologically confirmed cases of mucor (13 post‐COVID, 13 non‐COVID) and 15 healthy individuals (HI). Expression of T‐cell activation (CD44, HLADR, CD69, CD38) and exhaustion (CTLA, PD‐1, LAG‐3 and TIM‐3) markers was evaluated by flow cytometry. Results: All cases showed significant depletion of T‐cells compared to HI. Both post‐COVID and non‐COVID groups showed increased activation and exhaustion as compared to HI. Non‐COVID mucor group showed significant activation of CD4+ T cells for HLADR and CD38 (p =.025, p =.054) and marked T‐cell exhaustion in form of expression of LAG‐3 on both CD4+ T and CD8+ T cells in comparison with post‐COVID patients (p =.011, p =.036). Additionally, co‐expression of PD‐1 & LAG‐3 and LAG‐3 & TIM‐3 on CD8+ T cells was statistically significant in non‐COVID mucor patients (p =.016, p =.027). Conclusion: Immunosuppression in non‐COVID mucor showed pronounced exhaustion of T‐cells in comparison to post‐COVID mucor cases implicating T‐cell immune dysfunction is much more severe in non‐COVID mucor which are in a state of continuous activation followed by extreme exhaustion leading to poorer outcome. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19. [ABSTRACT FROM AUTHOR]

[Display omitted] • MIPs-based electrochemical sensors for COVID-19 diagnostic are cost-effective, exhibit high selectivity and specificity. • MIPs nano-sensors are stable at extreme conditions. • MIPs nano-sensors can be easily fabricated and integrated with IoT. The importance of early diagnosis of infectious disease has been revealed well by the COVID-19 pandemic. The current methods for testing SARS-CoV-2 mainly utilize biorecognition elements. The process of production of these biorecognition elements is not only tedious, time-consuming but also costly. The molecularly imprinted polymers recently have gained considerable attention as they are stable and also offer high selectivity and specificity than conventional labels. The present review discussed the MIPs-based electrochemical nano-sensors diagnostic of SARS-CoV-2. [ABSTRACT FROM AUTHOR]