Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 37
Filter
Add filters

Year range
1.
Communication medicale ; 2:113, 2022.
Article in English | MEDLINE | ID: covidwho-2028734

ABSTRACT

Background: The COVID-19 pandemic exit strategies depend on widespread acceptance of COVID-19 vaccines. We aim to estimate the global acceptance and uptake of COVID-19 vaccination, and their variations across populations, countries, time, and sociodemographic subgroups. Methods: We searched four peer-reviewed databases (PubMed, EMBASE, Web of Science, and EBSCO) for papers published in English from December 1, 2019 to February 27, 2022. This review included original survey studies which investigated acceptance or uptake of COVID-19 vaccination, and study quality was assessed using the Appraisal tool for Cross-Sectional Studies. We reported the pooled acceptance or uptake rates and 95% confidence interval (CI) using meta-analysis with a random-effects model. Results: Among 15690 identified studies, 519 articles with 7,990,117 participants are eligible for meta-analysis. The global acceptance and uptake rate of COVID-19 vaccination are 67.8% (95% CI: 67.1-68.6) and 42.3% (95% CI: 38.2-46.5), respectively. Among all population groups, pregnant/breastfeeding women have the lowest acceptance (54.0%, 46.3-61.7) and uptake rates (7.3%, 1.7-12.8). The acceptance rate varies across countries, ranging from 35.9% (34.3-37.5) to 86.9% (81.4-92.5) for adults, and the lowest acceptance is found in Russia, Ghana, Jordan, Lebanon, and Syria (below 50%). The acceptance rate declines globally in 2020, then recovers from December 2020 to June 2021, and further drops in late 2021. Females, those aged < 60 years old, Black individuals, those with lower education or income have the lower acceptance than their counterparts. There are large gaps (around 20%) between acceptance and uptake rates for populations with low education or income. Conclusion: COVID-19 vaccine acceptance needs to be improved globally. Continuous vaccine acceptance monitoring is necessary to inform public health decision making.

2.
4th International Conference on Communications, Information System and Computer Engineering, CISCE 2022 ; : 156-159, 2022.
Article in English | Scopus | ID: covidwho-2018630

ABSTRACT

Agile development has been a common methodology in software development. In response to the covid-19, most software development teams choose to work remotely. As a result of the different network environments, the company cloud center network load cannot meet the requirements of remote development and fault tolerance requirements of the agile development process. We designed a mixed-method called the Edge Development approach for improving Agile software development during the decision-making process. The extensive literature review provided us with three categories of challenges as well as solutions to support Edge Development's decision-support process. In the light of the survey, Five main software development decision-making challenges were identified in this study. In addition, we made a series of recommendations to improve the decision-making process of Edge Development from a variety of perspectives. © 2022 IEEE.

3.
7th International Workshop on New Trends in Medical and Service Robotics, MESROB 2021 ; 106 MMS:139-146, 2022.
Article in English | Scopus | ID: covidwho-1971345

ABSTRACT

Responding to these global COVID-19 changes for daily healthcare services clinic, while maintaining safe social distancing, the paper reports the human-centred iterative design with real-fields feasibility inquiries to investigate the first robotic nurse and her partners in Wales. The research adapted the ancient Eastern human nature of seven emotions and six biological wills for the selection criteria and novel design principles for the care robots. We report the preliminary work for integrating, customising, implementing and evaluating three novel robotic nurses: Robot Nightingale, Robot Almeida and Robot Eureka in a care home and a hospital. Bionic Scenarios Definition with 5 merging principles are extracted from the Feasibility Inquiries 1–3. Limitations are discussed from the stakeholders’ experiences. Our research has no intension to replace human nurses, but a thoughtful feasibility and interdisciplinary study for bionic robotic nurses for conventional engineers’ and practitioners’ references. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

4.
Acs Es&T Water ; : 9, 2022.
Article in English | Web of Science | ID: covidwho-1927052

ABSTRACT

The emerging variants of concern (VOCs) of SARS-CoV-2, e.g., Alpha, Beta, Gamma, Delta, and Omicron, have constrained the global response to the COVID-19 pandemic. They challenge our current capability to identify and distinguish variants from wastewater, due to the high likelihood of viral RNA degradation and the prior knowledge required for primer design. This study focused on the detection of multiple VOCs of SARS-CoV-2 using a high-throughput, multiplexed, amplicon-based sequencing technology, namely, ATOPlex. We first demonstrated that this method can discern multiple variants from artificial samples consisting of four synthetic strains of SARS-CoV-2. The ability of ATOPlex to identify VOCs was further validated using real wastewater samples collected from both an international passenger flight and local wastewater treatment plants. On the basis of phylogenetic analysis and the identification of single-nucleotide polymorphism on the genomes, the ATOPlex method was shown to be effective in detecting three VOCs, including two Beta variants and one Delta variant from either local wastewater or flight sewage samples, which were phylogenetically close to the variants that originated from France, Philippines, and the United States. We found this method is mutation-independent, rendering it a tool for proactive detection of SARS-CoV-2 VOCs in wastewater for the application of wastewater-based epidemiology.

5.
Vaccine ; 2022 Jun 27.
Article in English | MEDLINE | ID: covidwho-1907857

ABSTRACT

INTRODUCTION: Employer vaccination requirements have been used to increase vaccination uptake among healthcare personnel (HCP). In summer 2021, HCP were the group most likely to have employer requirements for COVID-19 vaccinations as healthcare facilities led the implementation of such requirements. This study examined the association between employer requirements and HCP's COVID-19 vaccination status and attitudes about the vaccine. METHODS: Participants were a national representative sample of United States (US) adults who completed the National Immunization Survey Adult COVID Module (NIS-ACM) during August-September 2021. Respondents were asked about COVID-19 vaccination and intent, requirements for vaccination, place of work, attitudes surrounding vaccinations, and sociodemographic variables. This analysis focused on HCP respondents. We first calculated the weighted proportion reporting COVID-19 vaccination for HCP by sociodemographic variables. Then we computed unadjusted and adjusted prevalence ratios for vaccination coverage and key indicators on vaccine attitudes, comparing HCP based on individual self-report of vaccination requirements. RESULTS: Of 12,875 HCP respondents, 41.5% reported COVID-19 vaccination employer requirements. Among HCP with vaccination requirements, 90.5% had been vaccinated against COVID-19, as compared to 73.3% of HCP without vaccination requirements-a pattern consistent across sociodemographic groups. Notably, the greatest differences in uptake between HCP with and without employee requirements were seen in sociodemographic subgroups with the lowest vaccination uptake, e.g., HCP aged 18-29 years, HCP with high school or less education, HCP living below poverty, and uninsured HCP. In every sociodemographic subgroup examined, vaccine uptake was more equitable among HCP with vaccination requirements than in HCP without. Finally, HCP with vaccination requirements were also more likely to express confidence in the vaccine's safety (68.3% vs. 60.1%) and importance (89.6% vs 79.6%). CONCLUSION: In a large national US sample, employer requirements were associated with higher and more equitable HCP vaccination uptake across all sociodemographic groups examined. Our findings suggest that employer requirements can contribute to improving COVID-19 vaccination coverage, similar to patterns seen for other vaccines.

6.
Environmental Science: Atmospheres ; 1(5):208-213, 2021.
Article in English | Scopus | ID: covidwho-1900673

ABSTRACT

The immense reduction in aerosol levels during the COVID-19 pandemic provides an opportunity to reveal how atmospheric chemistry is regulating our climate, among which the effect of aerosols on climate is a phenomenon of great interest but still in hot debate. The Intergovernmental Panel on Climate Change (IPCC) has continually identified the effect of aerosols on climate to have the largest uncertainty among the factors contributing to global climate change. Several studies indicate an inverse relationship between aerosol presence in the atmosphere and the diurnal surface air temperature range (DTR). Herein, we test this relationship by analyzing the DTR values from in situ weather station records for periods before and during the COVID-19 epidemic in Chinawhere aerosol levels have substantially reduced, compared with the climatological mean levels for a 19 year period.Our analyses find that DTRs fromFebruary to June during the COVID-19 pandemic are greater than 3 standard deviations above the climatological mean DTR. This anomaly has never occurred before in the 21st century and is at least in part associated with the observed reduction in aerosols. © 2021 The Author(s).

7.
3rd International Conference on Electronics and Communication|Network and Computer Technology, ECNCT 2021 ; 12167, 2022.
Article in English | Scopus | ID: covidwho-1874485

ABSTRACT

In December 2019, a new virus called COVID-19 broke out, and in 2020, it rapidly spread all over the world. The fast rate of the spread of the virus and high mortality have brought severe harm to the health of people and the economy of almost all countries around the world. Therefore, the virus has become the object of much researches. As the study moving on, treatment and vaccine have become the leading research directions at present. For treatment, measures should be taken to protect the most severe patients to reduce the death rate, and thus we are supposed to find patients with more serious illnesses. The decision tree and Xgboost are used to get the mathematical model about protease (an essential index in judging the severity of the disease) and realize the visualization of protease data. For vaccine, we solve the problem of predicting COVID-19 Vaccination Progress in the world in 2021 using the ARIMA model, which is obtained through the mean of time-series. Eventually, we got 10-day and 3-month vaccination forecasts. © 2022 SPIE

8.
Plos One ; 16(11):22, 2021.
Article in English | Web of Science | ID: covidwho-1705641

ABSTRACT

Racial/ethnic disparities are among the top-selective underlying determinants associated with the disproportional impact of the COVID-19 pandemic on human mobility and health outcomes. This study jointly examined county-level racial/ethnic differences in compliance with stay-at-home orders and COVID-19 health outcomes during 2020, leveraging two-year geo-tracking data of mobile devices across similar to 4.4 million point-of-interests (POls) in the contiguous United States. Through a set of structural equation modeling, this study quantified how racial/ethnic differences in following stay-at-home orders could mediate COVID-19 health outcomes, controlling for state effects, socioeconomics, demographics, occupation, and partisanship. Results showed that counties with higher Asian populations decreased most in their travel, both in terms of reducing their overall POls' visiting and increasing their staying home percentage. Moreover, counties with higher White populations experienced the lowest infection rate, while counties with higher African American populations presented the highest case-fatality ratio. Additionally, control variables, particularly partisanship, median household income, percentage of elders, and urbanization, significantly accounted for the county differences in human mobility and COVID-19 health outcomes. Mediation analyses further revealed that human mobility only statistically influenced infection rate but not case-fatality ratio, and such mediation effects varied substantially among racial/ethnic compositions. Last, robustness check of racial gradient at census block group level documented consistent associations but greater magnitude. Taken together, these findings suggest that US residents' responses to COVID-19 are subject to an entrenched and consequential racial/ethnic divide.

9.
10th International Conference on Control, Automation and Information Sciences, ICCAIS 2021 ; : 926-930, 2021.
Article in English | Scopus | ID: covidwho-1685068

ABSTRACT

In view of the problem that it is difficult to extract and analyze the medical image feature information, this paper investigates a feature extraction and visualization method of medical image based on principal component analysis. Firstly, the medical image feature extraction and space conversion mechanism is analyzed, and the feature information from the original spatial dimensions of medical images is extracted by using PCA technology. Then the extracted feature image is effectively reconstructed and visualized. The method is verification through COVID-19 CT images, results show that when the cumulative contribution rate of the principal component reaches 85%, the principal component analysis method can restore the information of the original image more clearly, and realize the feature extraction and visual reconstruction of medical images. © 2021 IEEE.

10.
Lecture Notes on Data Engineering and Communications Technologies ; 89:874-882, 2022.
Article in English | Scopus | ID: covidwho-1620218

ABSTRACT

Accurate detection of COVID-19 has become one of the major challenges since the outbreak of the pandemic. An effective and rapid detection will prevent the further spread of the deadly disease and enable doctors to treat infected patients appropriately. Therefore, this paper proposes an architecture to detect COVID-19 lesion areas in lung X-ray images based on Cascade-CNN and is named as COVID-Cascade RCNN. This algorithm integrates multi-scale dilated convolution and gender characteristic data from embedding patients. Firstly, to tackle the problem posed by the different size distribution of lesions in lung X-ray images, the basic Resnet101 network is adopted to perform preliminary feature extraction on the detection images. In addition, a multi-dilation convolutional neural network is added to generate more effective multi-scale featured information. The parallel dilated convolution with different dilation rates can extract more local feature information lesion areas of various sizes, improving the model's adaptability and accuracy. Secondly, gender characteristics that generate distinct effects on COVID-19 infection were innovatively embedded into the network model to improve detection accuracy further. Finally, experimental results on public dataset BIMCV-COVID19 show that compared with the previous model, the reformed one significantly improved detection precision with an average precision mAP of 42.197%, which is 5.368% higher than the original model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

11.
PUBMED; 2020.
Preprint in English | PUBMED | ID: ppcovidwho-292827

ABSTRACT

The recent outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has led to a worldwide pandemic. One week after initial symptoms develop, a subset of patients progresses to severe disease, with high mortality and limited treatment options. To design novel interventions aimed at preventing spread of the virus and reducing progression to severe disease, detailed knowledge of the cell types and regulating factors driving cellular entry is urgently needed. Here we assess the expression patterns in genes required for COVID-19 entry into cells and replication, and their regulation by genetic, epigenetic and environmental factors, throughout the respiratory tract using samples collected from the upper (nasal) and lower airways (bronchi). Matched samples from the upper and lower airways show a clear increased expression of these genes in the nose compared to the bronchi and parenchyma. Cellular deconvolution indicates a clear association of these genes with the proportion of secretory epithelial cells. Smoking status was found to increase the majority of COVID-19 related genes including ACE2 and TMPRSS2 but only in the lower airways, which was associated with a significant increase in the predicted proportion of goblet cells in bronchial samples of current smokers. Both acute and second hand smoke were found to increase ACE2 expression in the bronchus. Inhaled corticosteroids decrease ACE2 expression in the lower airways. No significant effect of genetics on ACE2 expression was observed, but a strong association of DNA- methylation with ACE2 and TMPRSS2- mRNA expression was identified in the bronchus.

12.
Journal of Crohns & Colitis ; 15:S436-S436, 2021.
Article in English | Web of Science | ID: covidwho-1510933
13.
Frontiers in Education ; 6, 2021.
Article in English | Scopus | ID: covidwho-1504909

ABSTRACT

Lockdowns and “stay-at-home” orders, starting in March 2020, shuttered bench and field dependent research across the world as a consequence of the global COVID-19 pandemic. The pandemic continues to have an impact on research progress and career development, especially for graduate students and early career researchers, as strict social distance limitations stifle ongoing research and impede in-person educational programs. The goal of the Bioinformatics Virtual Coordination Network (BVCN) was to reduce some of these impacts by helping research biologists learn new skills and initiate computational projects as alternative ways to carry out their research. The BVCN was founded in April 2020, at the peak of initial shutdowns, by an international group of early-career microbiology researchers with expertise in bioinformatics and computational biology. The BVCN instructors identified several foundational bioinformatic topics and organized hands-on tutorials through cloud-based platforms that had minimal hardware requirements (in order to maximize accessibility) such as RStudio Cloud and MyBinder. The major topics included the Unix terminal interface, R and Python programming languages, amplicon analysis, metagenomics, functional protein annotation, transcriptome analysis, network science, and population genetics and comparative genomics. The BVCN was structured as an open-access resource with a central hub providing access to all lesson content and hands-on tutorials (https://biovcnet.github.io/). As laboratories reopened and participants returned to previous commitments, the BVCN evolved: while the platform continues to enable “a la carte” lessons for learning computational skills, new and ongoing collaborative projects were initiated among instructors and participants, including a virtual, open-access bioinformatics conference in June 2021. In this manuscript we discuss the history, successes, and challenges of the BVCN initiative, highlighting how the lessons learned and strategies implemented may be applicable to the development and planning of future courses, workshops, and training programs. © Copyright © 2021 Tully, Buongiorno, Cohen, Cram, Garber, Hu, Krinos, Leftwich, Marshall, Sieradzki, Speth, Suter, Trivedi, Valentin-Alvarado and Weissman.

14.
Eur Rev Med Pharmacol Sci ; 25(20): 6378-6385, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1503069

ABSTRACT

OBJECTIVE: The outbreak of SARS-CoV-2 in 2020 has become the world's largest public health event, causing global attention and concern. Despite national efforts to control this emerging infectious disease, it still cannot be contained. China, which reported the disease early, was able to control the outbreak quickly, but there is the problem of imported infections abroad. This review aims to summarize SARS-CoV-2 detected on the outer packaging of imported cold chain food and lead to the transmission of novel coronavirus. MATERIALS AND METHODS: We reviewed information on SARS-COV-2 detected on the outer packaging of imported cold chain food and relevant literature.  We searched the following databases: PubMed, Web of Science, EMBASE and CNKI. search terms were "2019 nCoV", "SARS-CoV-2", "COVID-19", "cold-chain", "item surface", "spread", "people". RESULTS: We found that SARS-CoV-2 survives on the surface of cold-chain food for a long period of time and these active viruses can be transmitted to humans. CONCLUSIONS: We believe that while strictly preventing and controlling the importation of infected patients, we should strengthen the management of imported cold-chain food and its workers to prevent the transmission of SARS-CoV-2 to humans on the surface of cold-chain food objects.


Subject(s)
Food Packaging , Food Preservation , Refrigeration , SARS-CoV-2/isolation & purification , China , Humans , Surface Properties
15.
16th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2021 ; 12937 LNCS:288-300, 2021.
Article in English | Scopus | ID: covidwho-1442051

ABSTRACT

With the rapid development of mobile phones and the Internet of Things, instant delivery services (e.g., UberEats and MeiTuan) have become a popular choice for people to order foods, fruits, and other groceries online, especially after the impact of COVID-19. In instant delivery services, it is important to dispatch massive orders to limited couriers, especially in rush hours. To meet this need, an efficient courier displacement mechanism not only can balance the demand (picking up orders) and supply (couriers’ capacity) but also improve the efficiency of order delivery by reducing idle displacing time. Existing studies on fleet management of rider-sharing or bike rebalancing cannot apply to courier displacement problems in instant delivery due to unique practical factors of instant delivery including region difference and strict delivery time constraints. In this work, we propose an efficient cross-region courier displacement method Courier Displacement Reinforcement Learning (short for CDRL), based on multi-agent actor-critic, considering the dynamic demand and supply at the region level and strict time constraints. Specifically, the multi-agent actor-critic reinforcement learning-based courier displacement framework utilizes a policy network to generate displacement decisions considering multiple practical factors and designs a value network to evaluate decisions of the policy network. One month of real-world order records data-set of Shanghai collecting from Eleme (i.e., one of the biggest instant delivery services in China) are utilized in the evaluation and the results show that our method offering up to 36% increase in courier displacement performance and reduce idle ride time by 17%. © 2021, Springer Nature Switzerland AG.

16.
Sleep ; 44(SUPPL 2):A78, 2021.
Article in English | EMBASE | ID: covidwho-1402573

ABSTRACT

Introduction: Sleep disturbance is a transdiagnostic risk factor that is so prevalent among emerging adults it is considered to be a public health epidemic. For emerging adults, who are already at greater risk for psychopathology, the COVID-19 pandemic has disrupted daily routines, potentially changing sleep patterns and heightening risk factors for the emergence of affective dysregulation, and consequently moodrelated disturbances. This study aimed to determine whether variability in sleep patterns across a 3-month period was associated with next-day positive and negative affect, and affective dynamics, proximal affective predictors of depressive symptoms among young adults during the pandemic. Methods: College student participants (N=20, 65% female, Mage=19.80, SDage=1.0) wore non-invasive wearable devices (the Oura ring https://ouraring.com/) continuously for a period of 3-months, measuring sleep onset latency, sleep efficiency, total sleep, and time spent in different stages of sleep (light, deep and rapid eye movement). Participants reported daily PA and NA using the Positive and Negative Affect Schedule on a 0-100 scale to report on their affective state. Results: Multilevel models specifying a within-subject process of the relation between sleep and affect revealed that participants with higher sleep onset latency (b= -2.98, p<.01) and sleep duration on the prior day (b= -.35, p=.01) had lower PA the next day. Participants with longer light sleep duration had lower PA (b= -.28, p=.02), whereas participants with longer deep sleep duration had higher PA (b= .36, p=.02) the next day. On days with higher total sleep, participants experienced lower NA compared to their own average (b= -.01, p=.04). Follow-up exploratory bivariate correlations revealed significant associations between light sleep duration instability and higher instability in both PA and NA, whereas higher deep sleep duration was linked with lower instability in both PA and NA (all ps< .05). In the full-length paper these analyses will be probed using linear regressions controlling for relevant covariates (main effects of sleep, sex/age/ethnicity). Conclusion: Sleep, an important transdiagnostic health outcome, may contribute to next-day PA and NA. Sleep patterns predict affect dynamics, which may be proximal predictors of mood disturbances. Affect dynamics may be one potential pathway through which sleep has implications for health disparities.

17.
Chinese Automation Congress (CAC) ; : 4572-4577, 2020.
Article in English | Web of Science | ID: covidwho-1398265

ABSTRACT

Since the beginning of 2020, the COVID-19 infection caused by a virus called SARS-CoV-2 has spread rapidly around the world. Recently, researchers and public health officials from different disciplines studied the pathogenesis of SARS CoV 2 and found that the imaging pattern of patients with SARS CoV 2 infection had been observed on computed tomography (CT). This article is to measure whether the traditional deep learning algorithm can rely solely on lung CT images as a basis for the presence of new coronary pneumonia. Using the classic deep learning algorithms of AlexNet, VGG, ResNet, SqueezeNet and DenseNet as the basis, using the lung CT data of patients with new coronary pneumonia published on Kaggle as training and testing, and testing whether the pretraining migration learning method will Make the algorithm get a higher accuracy rate. According to the results, the accuracy rate of all algorithms without the pre-training model is more than 70%, and the accuracy rate of some algorithms reaches 82%. It shows that the deep learning algorithm, driven by a small amount of data, can not be completely used as a means of identification, but the algorithm using deep learning can help doctors identify. Moreover, with the increase of data, a more optimized learning algorithm can also obtain higher accuracy.

18.
IEEE Internet of Things Journal ; 2021.
Article in English | Scopus | ID: covidwho-1367246

ABSTRACT

Covid-19 is primarily spread through contact with the virus which may survive on surfaces with a lifespan of hours or even days if not sanitized. To curb its spread, it is hence of vital importance to detect those who have been in contact with the virus for a sustained period of time, the so-called close contacts. Most of the existing digital approaches for contact tracing focus only on direct face-to-face contacts. There has been little work on detecting indirect environmental contact, which is to detect people coming into a contaminated area with the live virus, i.e., an area last visited by an infected person within the virus lifespan. In this work, we study automatic IoT contact tracing when the virus has a lifespan which may depend on the disinfection frequency at a location. Leveraging the ubiquity of WiFi signals, we propose vContact, a novel, private, pervasive and fully distributed WiFi-based IoT contact tracing approach. Users carrying an IoT device (phone, wearable, dongle, etc.) continuously scan WiFi access points (APs) and store their hashed IDs. Given a confirmed case, the signals are then uploaded to a server for other users to match in their local IoT devices for virus exposure notification. vContact is not based on device pairing, and no information of other users is stored locally. The confirmed case does not need to have the device for it to work properly. As WiFi data are sampled sporadically and asynchronously, vContact uses novel and effective signal processing approaches and a similarity metric to align and match signals at any time. We conduct extensive indoor and outdoor experiments to validate vContact performance. Our results demonstrate that vContact is effective and accurate for contact detection. The precision, recall and F1-score of contact detection are high (up to 90%) for close contact proximity (2m). Its performance is robust against AP numbers, AP changes and phone heterogeneity. Having implemented vContact as an Android SDK and installed it on phones and smart watches, we present a case study to demonstrate the validity and implementability of our design in notifying its users about their exposure to the virus with a specific lifespan. IEEE

19.
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS ; 27(7):2156-2170, 2021.
Article in Chinese | Scopus | ID: covidwho-1355259

ABSTRACT

After the outbreak of epidemic of COVID-19, earthquakes or floods, the suppliers who urgently provide related goods or materials may be dominant in the transaction between buyers and sellers. Even if the quantities of goods fluctuate within a certain range, the customer can accept it and expects transaction. Under the mode of Make-to-order (MTO), for the supply chain consisting of a manufacturer and a supplier with random yields, the optimization models about centralized and decentralized situation were established respectively based on flexible and fixed procurement strategy, and the differences of optimal ordering quantities, input quantities and performances between the two procurement strategies was analyzed. For the flexible procurement strategy, the coordination mechanism of revenues and risks sharing, outputs deficiency punishment was proposed;while for the fixed procurement one, the coordination mechanism was revenues and risks sharing, and wholesale price discount. The research showed that the optimal order quantities of manufacturer were the largest under flexible procurement strategy/centralized situation, then the second largest under fixed procurement strategy/centralized situation, and was the minimum and equal under decentralized situation for different strategies;for the supplier, the input quantities under flexible strategy was closer to the order quantities than the fixed strategy;from the perspective of performances analysis, the flexible procurement strategy was more beneficial to the whole supply chain and the supplier, while the fixed procurement one was more beneficial to the manufacturer. The internal and external conditions for determining purchasing strategies were given, and some measures were proposed to strengthen information sharing, promote strategic mutual trust and prevent moral hazard. © 2021, Editorial Department of CIMS. All right reserved.

20.
Environmental Science and Technology Letters ; 2021.
Article in English | Scopus | ID: covidwho-1340970

ABSTRACT

The application of wastewater-based epidemiology (WBE) to support the global response to the COVID-19 pandemic has shown encouraging outcomes. The accurate, sensitive, and high-throughput detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in municipal wastewater is critical for WBE. Here, we present a novel approach based on multiplexed amplicon-based sequencing, namely the ATOPlex platform, for detecting SARS-CoV-2. The ATOPlex platform is capable of quantifying SARS-CoV-2 RNA at concentrations that are at least 1 order of magnitude lower than the detection limit of reverse transcription quantitative polymerase chain reaction (RT-qPCR). Robust and accurate phylogenetic placement can be done at viral concentrations 4 times lower than the detection limit of RT-qPCR. We further found that the solid fraction in wastewater harbors a considerable amount of viral RNA, highlighting the need to extract viral RNA from the solid and liquid fractions of wastewater. This study delivers a highly sensitive, phylogenetically informative, and high-throughput analytical workflow that facilitates the application of WBE. ©

SELECTION OF CITATIONS
SEARCH DETAIL