ABSTRACT
The recent COVID-19 pandemic showed that supply chain resilience is essential for continuity of many businesses, especially retail chains. However, there are still some challenges that have received little attention in the resilient supply chain network design (RSCND) literature. While numerous resilience strategies have been proposed to make supply chain networks resilient against disruptions, very few papers have discussed why and how those resilience strategies are selected out of many potential candidates given various sources of disruption, i.e., natural, man-made, and pandemic-oriented disruptions. The aim of this paper is to propose a multi-methodological approach, based on resource dependence theory and two-stage stochastic programming, for choosing the right resilience strategies in a RSCND problem considering their positive and negative synergistic effects under resource constraints. These interactions among resilience strategies can be referred to as supply chain dynamics. We then present a novel approach for determining the most suitable combination of candidate strategies with respect to these synergistic effects. The criticality of nodes and the susceptibility of the network in different echelons are also examined via simulating the disruptive risks in hidden and unexpected places. We provide a case study from the retail industry that illustrates the potentially significant impacts of network disruptions. Via extensive stress-testing, we show the benefits of applying multiple resilience capabilities simultaneously. Our findings demonstrate the importance of considering synergistic effects among resilience strategies under budget limitations for supply chain resilience. © 2022 Elsevier Ltd
ABSTRACT
The recent Covid-19 pandemic showed that supply chain resilience is essential for continuity of many businesses, especially retail chains. However, there are still some challenges that have received little attention in the resilient supply chain network design (RSCND) literature. While numerous resilience strategies have been proposed to make supply chain networks resilient against disruptions, very few papers have discussed why and how those resilience strategies are selected out of many potential candidates given various sources of disruption, i.e., natural, man-made, and pandemic-oriented disruptions. The aim of this paper is to propose a multi-methodological approach, based on resource dependence theory and two-stage stochastic programming, for choosing the right resilience strategies in a RSCND problem considering their positive and negative synergistic effects under resource constraints. These interactions among resilience strategies can be referred to as supply chain dynamics. We then present a novel approach for determining the most suitable combination of candidate strategies with respect to these synergistic effects. The criticality of nodes and the susceptibility of the network in different echelons are also examined via simulating the disruptive risks in hidden and unexpected places. We provide a case study from the retail industry that illustrates the potentially significant impacts of network disruptions. Via extensive stress-testing, we show the benefits of applying multiple resilience capabilities simultaneously. Our findings demonstrate the importance of considering synergistic effects among resilience strategies under budget limitations for supply chain resilience.
ABSTRACT
Over the course of the COVID-19 pandemic, wastewater surveillance has become a useful tool for describing SARS-CoV-2 prevalence in populations of varying size, from individual facilities (e.g., university residence halls, nursing homes, prisons) to entire municipalities. Wastewater analysis for SARS-CoV-2 RNA requires specialized equipment, expensive consumables, and expert staff, limiting its feasibility and scalability. Further, the extremely labile nature of viral RNA complicates sample transportation, especially in regions with limited access to reliable cold chains. Here, we present a new method for wastewater analysis, termed exclusion-based sample preparation (ESP), that substantially simplifies workflow (at least 70% decrease in time;40% decrease in consumable usage compared with traditional techniques) by targeting the labor-intensive processing steps of RNA purification and concentration. To optimize and validate this method, we analyzed wastewater samples from residence halls at the University of Kentucky, of which 34% (44/129) contained detectible SARS-CoV-2 RNA. Although concurrent clinical testing was not comprehensive, student infections were identified in the 7 days following a positive wastewater detection in 68% of samples. This pilot study among university residence halls validated the performance and utility of the ESP method, laying the foundation for future studies in regions of the world where wastewater testing is not currently feasible.
ABSTRACT
Introduction: We analyzed patterns of case completion following COVID-19-related surgical deferments to better delineate the trends and persistent need for surgical intervention in the pediatric populations. Method: We conducted a single-institution retrospective study analyzing surgical cancellations in the division of pediatric otolaryngology during the 62-day period from March 16, 2020, to May 17, 2020 (COVID-19 cancellation period). Parents of the patients were contacted to complete a survey about their experience during this interval. A retrospective chart review was also completed. Chi-square and Kruskal- Wallis multivariate analyses were applied to determine whether patient or surgical factors influenced surgical completion or time to completion, respectively. Results: There were 86 surgical cancellations in the division over the 2-month period of COVID-19-related hospital changes. Over the subsequent 2 months following resumption of elective surgery, only 111 cases were completed, compared with 128 cases over the corresponding dates the year prior. Of 29 survey respondents, 75.9% of parents still felt that their child needed surgery, but 58.6% were uncomfortable returning to the hospital environment. By the time of analysis, only 51.4% of canceled cases had been completed, and 76% of these were completed within 1 month following resumption of elective surgery. There were no significant associations between patient factors or type of surgery and case completion. However, a surgical indication of sleep-disordered breathing and adenotonsillar hypertrophy trended toward higher rates of completion, while acute otitis media trended toward lower rates of completion. Conclusion: There were no significant associations between surgical or patient factors and case completion following COVID-19-related deferments. Nonetheless, the persistent need for surgical intervention for sleep-disordered breathing and adenotonsillar hypertrophy trended toward significance, and perhaps these cases should be prioritized should future events threaten normal operations.
ABSTRACT
Hypertension, insulin resistance, hypertriglyceridemia, decreased high-density lipoprotein (HDL) level, and visceral obesity are the main indexes of metabolic syndrome. Metabolic syndrome is a cluster of conditions that occur together and increase the risk of cardiovascular diseases and mortality. Nowadays, metabolic syndrome has become a global epidemic and approximately 20-25 percent of the world's adult population possess the potential risk factors for developing this disorder. Several studies have shown that patients with various metabolic disorders such as obesity, diabetes, hypertension, and cardiovascular diseases are more susceptible to coronavirus disease 2019 (COVID-19). Furthermore, patients with underlying conditions associated with metabolic syndrome are at higher risk for complications and mortality of COVID-19. Therefore, understanding the mechanisms underlying the crosstalk between COVID-19 and metabolic syndrome are necessary for better management of the affected patients. The aim of the current review was to review any correlation between metabolic syndrome and the COVID-19 infection in terms of the severity in clinical complications and mortality.
ABSTRACT
In this paper, we address the problem of designing/redesigning a resilient retail supply chain network under uncertainty. Considering pre- and post-disruption scenarios, this study proposes a two-stage stochastic optimization framework with multiple resilience strategies for designing a resilient retail supply chain. We consider facility fortification, inventory prepositioning, direct-to-store delivery, reserved capacity, inventory sharing and multiple set covering as our resilience strategies, covering all the three dimensions of resilience capabilities, namely, proactive, reactive, and network design quality. Taking into account the prominent role of retail outlets during Covid-19 epidemic outbreaks, through a real case study, we evaluate the impact of random and targeted disruptions on the performance of the retail supply chain network and provide managerial insights regarding the right balance between cost efficiency and resilience. Furthermore, we evaluate the effectiveness of these resilience capabilities in a number of simulated scenarios to show their applicability and importance under different disruptive situations. We show that when implemented concurrently, these resilience capabilities have a synergistic effect where the whole becomes larger than the sum of its parts. Subject to extensive stress testing, our results show that using any mixture of these resilience capabilities increases the retail network’s resilience while considerably decreasing post-disruption costs.
ABSTRACT
The pandemic threat of COVID-19 causes serious concern for people and world organizations. The effect of Coronavirus disease on the lifestyle and economic status of humans is undeniable, and all of the researchers (biologists, pharmacists, physicians, and chemists) can help decrease its destructive effects. The molecular docking approach can provide a fast prediction of the positive influence the targets on the COVID-19 outbreak. In this work, we choose resveratrol (RV) derivatives (22 cases) and two newly released coordinate structures for COVID-19 as receptors [Papain-like Protease of SARS CoV-2 (PBD ID: 6W9C) and 2019-nCoV RNA-dependent RNA Polymerase (PBD ID: 6M71)]. The results show that conformational isomerism is significant and useful parameter for docking results. A wide spectrum of interactions such as Van der Waals, conventional hydrogen bond, Pi-donor hydrogen bond, Pi-Cation, Pi-sigma, Pi-Pi stacked, Amide-Pi stacked and Pi-Alkyl is detected via docking of RV derivatives and COVID-19 receptors. The potential inhibition effect of RV-13 (-184.99 kj/mol), and RV-12 (-173.76 kj/mol) is achieved at maximum value for 6W9C and 6M71, respectively.