A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model

Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general route optimizations, patients' suffering is taken into account in the model, allowing patients in more urgent situations to receive relief operations first. A new metaheuristic algorithm, the hybrid brain storm optimization (HBSO) algorithm, is proposed to deal with the model. The hybrid algorithm adds the ideas of the simulated annealing (SA) algorithm and large neighborhood search (LNS) algorithm into the BSO algorithm, improving its ability to escape from the local optimum trap and speeding up the convergence. In simulation experiments, the BSO algorithm, BSO+LNS algorithm (combining the BSO with the LNS), and HBSO algorithm (combining the BSO with the LNS and SA) are compared. The results of simulation experiments show the following: (1) The HBSO algorithm outperforms its rivals, obtaining a smaller total cost and providing a more stable ability to discover the best solution for the ERRP;(2) the ERRP model can greatly reduce the level of patient suffering and can prioritize patients in more urgent situations.

Clinical characteristics of 71 death cases of COVID‐19

Objective: To provide a basis for the early identification and treatment of severe and critical coronavirus disease 2019 (COVID - 19) by analyzing the clinical characteristics of the death cases. Methods: We retrospectively analyzed the clinical characteristics of 71 COVID - 19 cases which died during hospitalization. The clinical data included general data, underlying disease, clinical manifestation, biochemical laboratory examination, imaging examination, complications, and treatment, then the influencing factors of in - hospital survival were analyzed. Results: Most of the 71 patients were ≥60 years old (78. 9%) and had underlying diseases (74. 6%), in which hypertension ranked first, and fever was the most common first symptom. Biochemical laboratory tests showed that D-Dimer and C-reactive protein maintained at high levels during hospitalization, and lymphocyte count declined. Leukocyte/neutrophil counts, neutrophil to lymphocyte ratio, procalcitonin, creatine kinase, and lactate dehydrogenase increased. The main imaging features of the dead cases were the multifocal ground glass changes and consolidation of the lungs. The most common complications were acute respiratory distress syndrome (89. 9%), shock (34. 3%), and acute myocardial injury (30. 4%). 90% of the patients received auxiliary ventilation, and the decrease of blood oxygen saturation and the increase of procalcitonin may be the risk factors for shorter in-hospital survival. Conclusion: Severe and critical COVID-19 patients show different characteristics in clinical manifestations, biochemical laboratory examination, imaging examination, complications, and treatment reactions, which need early identification and treatment, and bewaring of acute respiratory distress syndrome and multiple organs failure. © 2022 Editorial Board of Medical Journal of Wuhan University. All rights reserved.

dbSCI: A manually curated database of SARS-CoV-2 inhibitors forCOVID-19 br

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen of the ongoing coronavirusdisease 2019 (COVID-19) global pandemic. Here, by centralizing published cell-based experiments, clinical trials, andvirtual drug screening data from the NCBI PubMed database, we developed a database of SARS-CoV-2 inhibitors forCOVID-19, dbSCI, which includes 234 SARS-CoV-2 inhibitors collected from publications based on cell-basedexperiments, 81 drugs of COVID-19 in clinical trials and 1305 potential SARS-CoV-2 inhibitors from bioinformaticsanalyses. dbSCI provides four major functions: (1) search the drug target or its inhibitor for SARS-CoV-2, (2) browsetarget/inhibitor information collected from cell experiments, clinical trials, and virtual drug screenings, (3) download,and (4) submit data. Each entry in dbSCI contains 18 types of information, including inhibitor/drug name, targetingprotein, mechanism of inhibition, experimental technique, experimental sample type, and reference information. Insummary, dbSCI provides a relatively comprehensive, credible repository for inhibitors/drugs against SARS-CoV-2and their potential targeting mechanisms and it will be valuable for further studies to control COVID-19

FedUSC: Collaborative Unsupervised Representation Learning from Decentralized Data for Internet of Things

Federated Learning (FL) lately has shown much promise in improving the shared model and preserving data privacy. However, these existing methods are only of limited utility in the Internet of Things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized conditions, which typically cannot be found in practical applications. In this paper, we propose a novel federated unsupervised learning method for image classification without the use of any ground truth annotations. In IoT scenarios, a big challenge is that decentralized data among multiple clients is normally non-IID, leading to performance degradation. To address this issue, we further propose a dynamic update mechanism that can decide how to update the local model based on weights divergence. Extensive experiments show that our method outperforms all baseline methods by large margins, including +6.67% on CIFAR-10, +5.15% on STL-10, and +8.44% on SVHN in terms of classification accuracy. In particular, we obtain promising results on Mini-ImageNet and COVID-19 datasets and outperform several federated unsupervised learning methods under non-IID settings. IEEE

COVID-19 and labour market resilience: evidence from large-scale recruitment behaviour

We construct a theoretical model to interpret the structural shock from the COVID-19 pandemic and the response of local labour market and industry specialization. The empirical study takes the large-scale online labour market of China to analyse firms' hiring demand for 20 industries across 380 cities with monthly recruitment data from May 2017 to September 2020. Post-event quantitative analysis on job postings and employer demand highlighted that the pandemic resulted in an unemployment shock and industry- and city-level redistribution of the worker. China's local job market resilience also revealed a regional imbalance, correlated with pandemic risk, city scale and industry structure. © 2023 Regional Studies Association.

An Empirical Statistical Research on Digital Reading of College Students Based on Marxism Epistemology

Digital reading by college students has become a basic trend with the development of digitalization and network technology, and its role has become more and more prominent under the influence of COVID-19. From the perspective of Marxism epistemology, this paper conducts a large-scale sample survey and SPSS statistical research. By means of independent sample t-test, variance analysis, multi-corresponding variable analysis and cross-tabulation and other statistical commands, this paper analyzes the pattern, preference and attitude of digital reading among college students. It proposes corresponding countermeasures from three levels of digital content, university library and college students as the subject. © 2022 IEEE.

Futures hedging in crude oil markets: A trade-off between risk and return

Risk and return are two fundamentals that have an impact on an investor's or hedger's investing choices. Based on the proposed synchronous movement intensity index, this paper aims to improve the hedging performance by adjusting the model-driven hedge ratio and realize the trade-off between return and risk in futures hedging. First, without loss of generality, we forecast crude oil spot and futures volatility using 10 GARCH-type models, including three linear models and seven nonlinear models, to obtain the ex-ante hedging ratio under the minimum variance framework. Then, we develop a novel and tractable method to identify the market state based on the index of consistency intensity, in which the index portrays the synchronous degree of stock price movements in the energy sector. Last but not least, we propose the hedge ratio adjustment criteria based on the identified state, and adjust the ratio driven by GARCH-type models of futures in accordance with the market state. Empirical results of crude oil futures markets indicate that the proposed state-dependent hedging model is superior to the commonly used models in terms of three criteria including mean of returns, variance, and ratio of mean to variance of returns for measuring hedging effect. We apply the DM test to make a statistical inference and discover that while the mean and the ratio of mean to variance of returns are increasing, the variance and hedging effectiveness of the hedged portfolio based on the modified methods are not significantly affected. Furthermore, the superiority of the proposed method is robust to different market conditions, including significant rising or falling trends, large basis, and COVID-19 pandemic. We also test the robustness of the proposed method with respect to the baseline model, quantile, and evaluation window. Overall, this paper provides a more realistic approach for crude oil risk managers to hedge crude oil price risk, some corresponding implications are also concluded. © 2022 Elsevier Ltd

Different associations of parental involvement with children's learning of Chinese, English, and math: a three-wave longitudinal study

Due to the impact of COVID-19, children and their parents are spending more time at home, which increases parent–child interactions. The goals of the present study were to examine the mediating effects of children's learning engagement on the relationships of parental involvement in Chinese, English, and math performance and to investigate whether parent-perceived parental involvement and child-perceived parental involvement consistently affected children's academic performance. Data were collected from 253 Chinese primary school students (117 boys, Mage = 10.53) during the COVID-19 pandemic. We included parental involvement perceived by the parents and by the children to comprehensively describe parental involvement (in wave 2);we collected children's learning engagement (wave 2);and we compared children's Chinese, English and math academic performances before (wave 1) and after (wave 3) China's first wave of COVID-19 in 2020. The results showed that after controlling for gender, age, and SES, the parental involvement perceived by parents could be directly and positively related to children's learning engagement, and it also indirectly influenced children's learning engagement through the children's perceived parental involvement. Learning engagement was a mediator of the relationship between parental involvement and children's academic performance. Parental involvement significantly predicted children's Chinese and English performances through their learning engagement, while parental involvement failed to predict children's mathematics performances during the COVID-19 pandemic. The current research provides insights into the underlying mechanisms of how parental involvement affects children's academic performances during school closures and hopes to guide parents and schools to consider how to cooperate and continue to use rapidly developing digital education resources amid the long-term impact of COVID-19 to provide children using more effective and suitable guidance in the future. © 2022, Instituto Universitário de Ciências Psicológicas, Sociais e da Vida.

Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches

We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the "demand-driven” properties of the SP and DRO solutions. Our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage. © 2022 Elsevier Ltd

Episcan: A Synthetic Biology Platform for Targeted Immunopeptidomics

Background Identification of CD8+ T-cell epitopes is critical for the development of immunotherapeutics. Existing methods for empirical determination of peptide binding are time-intensive, expensive and highly specialized. Mass spectrometry, the predominant high-throughput approach for MHC-I ligand discovery, is unable to easily interrogate defined subsets of proteins. Thus, we sought a high-throughput, accessible method for the identification of MHC-I ligands derived from user chosen, synthetically encoded sources of peptides. Here we present EpiScan, a programmable genetic strategy to identify MHC-I ligands amongst predetermined starting pools comprising >100,000 peptides. Methods To accomplish this, we used CRISPR-Cas9 to create 'EpiScan cells' that lack both endogenous MHC-I and short peptides in ER. Then, separate lentiviral introduction of an MHC-I allele and a single exogenous short peptide into the ER restores cell surface MHC-I levels according to the affinity of the peptide to the chosen MHC-I allele. We exploited the programmability of EpiScan to screen 12 different MHC-I alleles with large peptide libraries including the entire SARSCoV- 2 proteome. Results These screens uncovered an unappreciated role for cysteine that increases the number of predicted ligands by 12- 21%, revealed affinity hierarchies by analysis of biased-anchor peptide libraries, and identified conserved, high-affinity, T-cell reactive SARS-CoV-2 epitopes. Using these data, we generated and iteratively refined peptide binding predictions to create EpiScan Predictor, or ESP. ESP performed comparably to other state-of-the-art MHC-I peptide binding prediction algorithms while not suffering from underrepresentation of cysteine-containing peptides. Overall, the new specificities identified by EpiScan and ESP increase the number of peptides predicted to bind MHCs by over 15% on average. This significantly expands the potential human epitope landscape, facilitating epitope discovery efforts and the design of immunotherapeutics. Conclusions Our work significantly expands the potential human epitope landscape, facilitating epitope discovery efforts and the design of immunotherapeutics. .

Shocks, cycles and adjustments: The case of China's Hog Market under external shocks

China's hog market has faced the challenge of several external shocks, which arise from the ongoing COVID-19 pandemic, African Swine Fever (ASF) and related global trade uncertainties. This article develops a shocks, cycles and adjustments (SCA) model to evaluate the dynamic impact of different shock scenarios. The SCA model contributes to the existing toolbox for impact evaluation in commodity markets and provides insights into the timing of impact dynamics at refined time intervals. The SCA model is applied to evaluate five sets of shock scenarios, which include a demand shock, a corn price increase, pork import restrictions, a second wave of ASF, and a combination of these shocks. Simulation results demonstrate the reaction of the hog cycle to different shocks with quantitive outcomes. Based on the simulation results, we find that production and economic adjustment lags generate constant and predictable hog cycles, while the external shocks lead hog cycles to be irregular with varying phase and amplitude. [EconLit Citations: Q10, Q11]. © 2022 Wiley Periodicals LLC.

Construction Strategy of China's Agricultural Risk Management System Under the Background of COVID-19

With the acceleration of economic globalization and the intensification of global climate change, the risks faced by agriculture are also increasing, which has seriously affected the improvement of farmers' living standards and the development of the national economy. In particular, in recent years, the global COVID-19 has had a certain impact on various industries, among which the impact on the agricultural economy presents a short-term diversified trend. How to reduce the impact of agricultural risks on agricultural economy is an urgent problem to be solved in China's agricultural development. Establishing a complete set of perfect agricultural risk management system plays a vital role in reducing agricultural risks, improving economic benefits and the production efficiency. At the same time, it is conducive to improve China's agricultural risk environment, narrowing the gap between urban and rural areas and improving China's agricultural competitiveness. Therefore, this paper discusses the current situation of China's agricultural risks, the causes of agricultural risks and the necessity of establishing an agricultural risk management system. The agricultural information collection system based on RFID technology is proposed, which can effectively improve the ability of farmers to resist risks. Finally, from the formulation and implementation of agricultural protection policies, the improvement of agricultural risk early warning and prevention management system, the establishment of a complete agricultural risk guarantee system, the construction of a futures market in line with China's rural reality improve the dissemination channels of agricultural information and improve the construction of agricultural infrastructure, and put forward suggestions on the construction of China's agricultural risk management system. © 2022 SPIE.

Machine Vision Surveillance System - Artificial Intelligence for Covid-19 Norms

This paper presents the design and implementation of the Machine Vision Surveillance System Artificial Intelligence (MaViSS-AI) for Covid-19 Norms using jetson nano. This system is designed to be cost-effective, accurate, efficient, and secure. The proposed system tracks and counts humans for monitoring social distancing and detects face masks using object detection methods. We used YOLO as an object detection method and neural network to detect a person and count them. And for social distancing monitoring the concept of the centroid is based on calculating the distance between pairs of centroids, and thus checking whether there are any violations of the threshold or not. To detect the face mask, a YOLO V4 deep learning model is used as the mask detection algorithm. The system also raises alerts when any suspicious event occurs. Given this alert, security personnel can take relevant actions. This research aims to provide a holistic approach to overcoming the real-time challenges encountered during the monitoring of Covid-19 norms. © 2022 IEEE.

The Effect of Face Mask and Approach Pattern on Interpersonal Distance in COVID-19 Pandemic Using VR Technology

In the COVID-19 pandemic, control measures including wearing masks, ensuring hand hygiene, and maintaining a physical distance of at least 1 m were recommended to prevent the spread of virus. The purpose of this study was to investigate the influence of face mask, approach pattern and participants’ gender on interpersonal distance in the pandemic environment. Virtual reality (VR) technology was applied to build the experimental environment. This study recruited 31 participants including 17 males and 14 females, who were asked to interact with virtual confederates with and without a face mask. The interpersonal distance was recorded when participants actively walk towards the virtual confederate or approached passively by the confederate. Three-way ANOVA results showed that face mask and approach pattern had significant effects on interpersonal distance. The distance when facing the confederate with a face mask was significantly closer than without a face mask. Moreover, participants preferred a significantly larger distance in the passive pattern than in the active pattern. The participants’ gender showed no significant effect on interpersonal distance and no interaction effects were found. The findings in this study helped to further investigate the nature of interpersonal distance and contributed to a better understanding of the human behaviors in the pandemic environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The University of Michigan Implements a Hub-and-Spoke Design to Accommodate Social Distancing in the Campus Bus System Under COVID-19 Restrictions

The outbreak of coronavirus disease 2019 (COVID-19) has led to significant challenges for schools and communities during the pandemic, requiring policy makers to ensure both safety and operational feasibility. In this paper, we develop mixed-integer programming models and simulation tools to redesign routes and bus schedules for operating a real university campus bus system during the COVID-19 pandemic. We propose a hub-and-spoke design and utilize real data of student activities to identify hub locations and bus stops to be used in the new routes. To reduce disease transmission via expiratory aerosol, we design new bus routes that are shorter than 15 minutes to travel and operate using at most 50% seat capacity and the same number of buses before the pandemic. We sample a variety of scenarios that cover variations of peak demand, social distancing requirements, and bus breakdowns to demonstrate the system resiliency of the new routes and schedules via simulation. The new bus routes were implemented and used during the academic year 2020-2021 to ensure social distancing and short travel time. Our approach can be generalized to redesign public transit systems with a social distancing requirement to reduce passengers' infection risk.

Efficient and Secure Pairing Protocol for Devices with Unbalanced Computational Capabilities

Wearable devices that collect data about human beings are widely used in healthcare applications. Once collected, the health data will be securely transmitted to smartphones in most scenarios. Authenticated Key Exchange (AKE) can protect wireless communications between wearables and smartphones, and a typical solution is the Bluetooth Secure Simple Pairing (SSP) protocol with numeric comparison. However, this protocol requires equivalent computation on both devices, even though their computational capabilities are significantly different. This paper proposes a lightweight numeric comparison protocol for communications in which two parties have unbalanced computational capabilities, e.g., a wearable sensor and a smartphone, named UnBalanced secure Pairing using numeric comparison (UB-Pairing for short). The security of UB-Pairing is analyzed using the modified Bellare-Rogaway model (mBR). The analysis results show that UB-Pairing achieves the security goals. We also carry out a number of experiments to evaluate the performance of UB-Pairing. The results show that UB-Pairing is friendly to wearable devices, and more efficient than standard protocols when the computation capabilities of the two communication parties are highly unbalanced.