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1.
Neural Comput Appl ; : 1-25, 2022 Sep 02.
Article in English | MEDLINE | ID: covidwho-2007150

ABSTRACT

Indoor occupancy detection is essential for energy efficiency control and Coronavirus Disease 2019 traceability. The number and location of people can be accurately identified and determined through classroom surveillance video analysis. This information is used to manage environmental equipment such as HVAC and lighting systems to reduce energy use. However, the mainstream one-stage YOLO algorithm still uses an anchor-based mechanism and couples detection heads to predict. This results in slow model convergence and poor detection performance for densely occluded targets. Therefore, this paper proposed a novel decoupled anchor-free VariFocal loss convolutional network algorithm DFV-YOLOv5 for occupancy detection to tackle these problems. The proposed method uses the YOLOv5 algorithm as a baseline. It uses the anchor-free mechanism to reduce the number of design parameters needing heuristic tuning. Afterwards, to reduce the coupling of the model, speed up the model's convergence ability, and improve the model detection performance, the detection head is decoupled based on the YOLOv5 model. It can resolve the conflict between classification and regression tasks. In addition, we use the VariFocal loss to assign more weights to difficult data points to optimize the class imbalance problem and use the training target q to measure positive samples, treating positive and negative samples asymmetrically. The total loss function is redesigned, the L 1 loss is increased, and the ablation experiment verifies the effect of the improved loss. By applying a hybrid activation function of the sigmoid linear unit and rectified linear unit, we improved the model's nonlinear representation and reduced the model's inference time. Finally, a classroom dataset was constructed to validate the occupancy detection performance of the model. The proposed model was compared with mainstream target detection models regarding average mean precision, memory allocation, execution time, and the number of parameters on the VOC2012, CrowdHuman and self-built datasets. The experimental results show that the method significantly improves the detection accuracy and robustness, shortens the inference time, and proves the practicality of the algorithm in occupancy detection compared with the mainstream target detection model and related variants of the model.

2.
Tourism Economics ; : 13548166211035569, 2021.
Article in English | Sage | ID: covidwho-1360615

ABSTRACT

The COVID-19 pandemic has hindered international travel considerably, greatly affecting the hotel industry. Hong Kong, as a well-known international tourist destination, has also been hit hard by the crisis. Recovery forecasts for hotel room demand are critical to managing this ongoing crisis. This study employs the autoregressive distributed lag error correction model to generate baseline forecasts of hotel room demand for Hong Kong followed by compound scenario analysis to optimize forecasts considering the pandemic?s impacts. The COVID-19 Travelable Index is designed to group source markets by their pandemic situations, vaccinations, policy responses, and health resilience. To capture pandemic-related uncertainty, this study presents three scenarios describing recovery patterns based on trough duration, the quarter for lifting travel restrictions, and the quarter for returning to baseline forecasts. Hotel demand forecasts geared toward each source market are analyzed, revealing strategies to help hotel businesses manage this crisis.

3.
Dalton Trans ; 50(35): 12226-12233, 2021 Sep 14.
Article in English | MEDLINE | ID: covidwho-1358359

ABSTRACT

Numerous organic molecules are known to inhibit the main protease of SARS-CoV-2, (SC2Mpro), a key component in viral replication of the 2019 novel coronavirus. We explore the hypothesis that zinc ions, long used as a medicinal supplement and known to support immune function, bind to the SC2Mpro enzyme in combination with lipophilic tropolone and thiotropolone ligands, L, block substrate docking, and inhibit function. This study combines synthetic inorganic chemistry, in vitro protease activity assays, and computational modeling. While the ligands themselves have half maximal inhibition concentrations, IC50, for SC2Mpro in the 8-34 µM range, the IC50 values are ca. 100 nM for Zn(NO3)2 which are further enhanced in Zn-L combinations (59-97 nM). Isolation of the Zn(L)2 binary complexes and characterization of their ability to undergo ligand displacement is the basis for computational modeling of the chemical features of the enzyme inhibition. Blind docking onto the SC2Mpro enzyme surface using a modified Autodock4 protocol found preferential binding into the active site pocket. Such Zn-L combinations orient so as to permit dative bonding of Zn(L)+ to basic active site residues.


Subject(s)
COVID-19/drug therapy , Coronavirus 3C Proteases/antagonists & inhibitors , Protease Inhibitors/pharmacology , SARS-CoV-2/drug effects , Tropolone/pharmacology , Zinc/pharmacology , Antiviral Agents/chemistry , Antiviral Agents/pharmacology , COVID-19/virology , Catalytic Domain/drug effects , Coronavirus 3C Proteases/chemistry , Coronavirus 3C Proteases/metabolism , Humans , Ligands , Models, Molecular , Molecular Docking Simulation , Protease Inhibitors/chemistry , SARS-CoV-2/enzymology , Tropolone/analogs & derivatives , Zinc/chemistry
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