ABSTRACT
Coronavirus consists of single-stranded, enveloped and RNA virus, largest genome among all RNA viruses and has 4 proteins i.e. envelope, spike, nucleocapsid and membrane. Coronaviruses are classified into 4 genera: Alphacoronavirus, Betacoronavirus, Gammacoronavirus and Deltacoronavirus. Betacoronavirus most probably originated from bats and the virus may have jumped to avian species and evolved as Deltacoronavirus group. The avian coronaviruses jumped among other avian species, giving rise to Gammacoronavirus from Deltacoronavirus, while Betacoronavirus may have given rise to Alphacoronavirus. It is known that SARS-CoV-2 belongs to Betacoronavirus. This most similar virus is verified in bat and Malayan Pangolin. Analysis showed that SARS-CoV-2 most probably originated by recombination of both bat and pangolin viruses. Viral protein seroconversion and viral specific nucleotide positive documented in all COVID-19 patients tested provides confirmation of a link between the presence of this virus and the disease. Copyright 2022 by the authors.
ABSTRACT
The world has a corona virus epidemic and Malaysia does not have an immune system. The Covid-19 epidemic causes considerable global disruption in an effort to stop this spread, leading to lockups in various countries, including Malaysia. The Malay Government proposed the Movement Control Order on 18 March 2020 in its lock-out option (MCO). Malaysian companies were therefore heavily affected by the MCO in this particular construction industry. There is a strong influence on the economy of the country. The epidemic of COVID-19 affected the global financial market, Malaysia, and others. The aim of this study is to analyze the construction industry, project budget, contractual arrangements, construction occupational health and safety, and other relevant factors in the construction industry, with the objective of examining the impacts and challenges facing the Malaysian construction industry. The results of this study could help stakeholders understand the impact of COVID-19 on Malaysia's various industries. The results indicate that the construction industry has faced numerous challenges including time periods, interrupted revenues, deferred licenses, travel bans, significant health and safety concerns and labor and equipment deficiencies, among other issues, all of which have affected the timely delivery of projects in the area of infrastructure. This also illustrates that Malaysian governments and construction agencies have taken steps to protect the construction industry against negative effects of the pandemic, such as financial support programmes, the digitalization of processes, service charges and fees, medical centers, and other contract mediation services. © 2021 IEEE.
ABSTRACT
The usage of new technology has drastically altered the way, work is handled and performed out in recent times. Building Information Modelling (BIM) is increasingly being used in infrastructure projects to boost the effectiveness of a variety of tasks. The purpose of this study is to extract previous knowledge regarding BIM and occupational safety in infrastructure projects. Papers published in the period going from 2008 to 2019 were selected a total number of 50 studies were selected. All studies were targeting BIM, safety adoption, and accidents related to construction infrastructure projects. This paper helps to identify the critical success factors for safety efficiency application in the infrastructure project. New type of policies related to Covid-19 are also incorporated in the construction industry which also set new standards and operating procedures to work safely in the pandemic conditions. Two main group factors were identified safety training, and safety management a networking analysis between all the factors identified from previous studies was run. This networking shows a link of importance between each other and how each cluster overlaps the sub-factors of other clusters. Finally using word cloud analysis, it was possible to identify the top five keywords from the extracted factors. © 2021 IEEE.
ABSTRACT
TOPIC: Critical Care TYPE: Original Investigations PURPOSE: Circulatory shock often requires large volume fluid resuscitation as a part of treatment. While crystalloid fluids are more commonly used, many patients in intensive care are unable to receive crystalloid fluids due to conditions such as congestive heart failure and end stage renal disease. Utilizing traditional fluids in such patients often complicates hospital courses. There are many studies regarding the benefits, effects and potential harms of using IV albumin for fluid resuscitation. The 2004 SAFE study suggested some benefit of using IV albumin in sepsis but equivocal to conventional management in morbidity and mortality. The 2014 ALBIOS Study of IV albumin use resulted in 90 day mortality benefit and decreased vasopressor need in severely septic patients. Despite these studies, the use of IV albumin in the critically ill remains controversial with consensus or guidelines being established regarding its use. METHODS: We conducted a retrospective study of 70 critically ill patients admitted to MICU from 7/2019 to 5/2020 to assess the effects of IV albumin. Patients diagnosed with shock who received 5% IV albumin were included. Patients who received 25% IV albumin were excluded. We analyzed cases by type of shock, conditions limiting crystalloid bolus, serum albumin level before IV albumin, MAP pre- and post- albumin infusion, pressor requirement (type/duration), development of ARDS, HD/CRRT requirement, COVID status and mortality. RESULTS: Septic shock patients received IV albumin most, followed by cardiogenic shock. Those unable to receive fluid boluses were due to primarily cardiac etiology, followed by renal & hepatic. Increased MAP was observed after IV albumin infusion in many cases. Serum albumin <2.7-2.8 on ICU admission was associated with frequent vasopressor use, higher vasopressor requirement and multi-agent use. Serum albumin >2.8 on ICU admission responded better to IV albumin and had lower vasopressor requirements.IV albumin was not related with renal dysfunction and those receiving IV Albumin did not need HD/CRRT. 69.6% did not develop ARDS, 5.8% developed ARDS, 24.6% tested positive for COVID and they were excluded from the ARDS incidence rate. Survival rate was 65.7%, mortality rate 24.3% and 10% went to hospice CONCLUSIONS: The use of 5% IV albumin for volume resuscitation in patients with different forms of shock in ICU showed favorable results, especially in those with serum albumin >2.7-2.8. Our retrospective study showed low incidence of adverse events such as cardiogenic pulmonary edema, ARDS or a need for HD/CRRT, lower vasopressor requirement, in both duration and agents used. Further research should be done in this regard to solidify these findings and to establish clear guidelines/protocols regarding the use of albumin as a resuscitative fluid in the ICU setting. CLINICAL IMPLICATIONS: When treating patients with shock and specially patients with hypoalbuminemia, 5% IV albumin can potentially be used as a means to decrease vasopressor requirements and duration without increasing the risk of pulmonary edema, ARDS and renal dysfunction. Further research should be done in this regard to solidify these findings and to establish clear guidelines/protocols regarding the use of albumin as a resuscitative fluid in the ICU setting. DISCLOSURES: No relevant relationships by Andrew Ajemian, source=Web Response No relevant relationships by Muhammad Umer Altaf, source=Web Response No relevant relationships by Christopher Bowman, source=Web Response No relevant relationships by Gabriel Diaz, source=Web Response No relevant relationships by Jessica Obeahon, source=Web Response No relevant relationships by Travis Yamanaka, source=Web Response
ABSTRACT
In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.