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1.
Acm Transactions on Sensor Networks ; 19(2), 2023.
Artigo em Inglês | Web of Science | ID: covidwho-20245407

RESUMO

To control the rapid spread of COVID-19, we consider deploying a set of Unmanned Aerial Vehicles (UAVs) to form a quarantine barrier such that anyone crossing the barrier can be detected. We use a charging pile to recharge UAVs. The problem is scheduling UAVs to cover the barrier, and, for any scheduling strategy, estimating theminimum number of UAVs needed to cover the barrier forever. We propose breaking the barrier into subsegments so that each subsegment can be monitored by a single UAV. We then analyze two scheduling strategies, where the first one is simple to implement and the second one requires fewer UAVs. The first strategy divides UAVs into groups with each group covering a subsegment. For this strategy, we derive a closed-form formula for the minimum number of UAVs. In the case of insufficient UAVs, we give a recursive function to compute the exact coverage time and give a dynamic-programming algorithm to allocate UAVs to subsegments to maximize the overall coverage time. The second strategy schedules all UAVs dynamically. We prove a lower and an upper bound on the minimum number of UAVs. We implement a prototype system to verify the proposed coverage model and perform simulations to investigate the performance.

2.
Zhonghua Yu Fang Yi Xue Za Zhi ; 57(2): 268-272, 2023 Feb 06.
Artigo em Chinês | MEDLINE | ID: covidwho-2289052

RESUMO

Objective: To establish a rapid and specific quantitative real-time PCR (qPCR) method for the detection of SARS-CoV-2 subgenomic nucleocapsid RNA (SgN) in patients with COVID-19 or environmental samples. Methods: The qPCR assay was established by designing specific primers and TaqMan probe based on the SARS-CoV-2 genomic sequence in Global Initiative of Sharing All Influenza Data (GISAID) database. The reaction conditions were optimized by using different annealing temperature, different primers and probe concentrations and the standard curve was established. Further, the specificity, sensitivity and repeatability were also assessed. The established SgN and genomic RNA (gRNA) qPCR assays were both applied to detect 21 environmental samples and 351 clinical samples containing 48 recovered patients. In the specimens with both positive gRNA and positive SgN, 25 specimens were inoculated on cells. Results: The primers and probes of SgN had good specificity for SARS-CoV-2. The minimum detection limit of the preliminarily established qPCR detection method for SgN was 1.5×102 copies/ml, with a coefficient of variation less than 1%. The positive rate of gRNA in 372 samples was 97.04% (361/372). The positive rates of SgN in positive environmental samples and positive clinical samples were 36.84% (7/19) and 49.42% (169/342), respectively. The positive rate and copy number of SgN in Wild strain were lower than those of SARS-CoV-2 Delta strain. Among the 25 SgN positive samples, 12 samples within 5 days of sampling time were all isolated with virus; 13 samples sampled for more than 12 days had no cytopathic effect. Conclusion: A qPCR method for the detection of SARS-CoV-2 SgN has been successfully established. The sensitivity, specificity and repeatability of this method are good.


Assuntos
COVID-19 , SARS-CoV-2 , Humanos , SARS-CoV-2/genética , COVID-19/diagnóstico , RNA Subgenômico , Reação em Cadeia da Polimerase em Tempo Real/métodos , RNA Viral/genética , Sensibilidade e Especificidade , Nucleocapsídeo/química , Teste para COVID-19
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