Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level. © 2023 ACM.

Understanding Hospitality and Tourism Students' Emotional Intelligence Performance in the E-learning Environment: A Delphi Approach

The COVID 19 pandemic has forced educators and students to embrace e-learning. It has become urgent that educators expedite their efforts in establishing criteria to assess the overall effectiveness of e-learning, in which student emotional intelligence (EI) cultivation and development play an increasingly centric role. However, a survey of the current literature shows that EI in e-learning appears to have received little attention. This study was thus designed to help fill this research void. Specifically, it set out to understand typical hospitality and tourism students' EI behaviors in the e-learning environment. To achieve this goal, this study applied a two-round Delphi approach. The findings show that in the e-learning environment, students commonly exhibit high self-awareness, low self-management, low social management, and low relationship-building competence. Prior EI studies mainly focus on employee performance and behavior but this study extends the effect of EI in education and offers significant implications for hospitality and tourism educators and researchers (word count: 155). © 2022 ICHRIE.

A Survey on Data-driven COVID-19 and Future Pandemic Management

The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens' lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus's rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed.

Multivariate time series analysis on variables that influence pandemic expansion

The ongoing COVID-19 pandemic has wreaked havoc on social and economic systems worldwide. The variance in the rapidly increasing number of illnesses and deaths in each country is primarily due to national policies and actions. As a result, governments and institutions need to get insights into the critical factors influencing COVID-19 future case counts to properly manage the adverse effects of pandemics and promptly prepare appropriate measures. Thus, in this paper, we conduct extensive experiments on the real-world covid-19 datasets to examine the important factors influencing in the pandemic growth. In particular, we perform an exploratory data analysis to get the statistic and characteristics of multivariate time-series data on pandemic dynamic. Also, we utilize a statistical measure such as Pearson correlation to compute the relations of the past on the future daily new cases. The experimental results demonstrate that some restrictions have a positive effect on daily new confirmed cases at the early stage of the local pandemic transmission. Also, the results show that the early trend of COVID-19 can be explained well by human mobility in various categories. Thus, our proposed framework can be served as a guideline for future pandemic prevention and control decision-making. © 2022 IEEE.

Understanding Hospitality and Tourism Students’ Emotional Intelligence Performance in the E-learning Environment: A Delphi Approach

The COVID 19 pandemic has forced educators and students to embrace e-learning. It has become urgent that educators expedite their efforts in establishing criteria to assess the overall effectiveness of e-learning, in which student emotional intelligence (EI) cultivation and development play an increasingly centric role. However, a survey of the current literature shows that EI in e-learning appears to have received little attention. This study was thus designed to help fill this research void. Specifically, it set out to understand typical hospitality and tourism students’ EI behaviors in the e-learning environment. To achieve this goal, this study applied a two-round Delphi approach. The findings show that in the e-learning environment, students commonly exhibit high self-awareness, low self-management, low social management, and low relationship-building competence. Prior EI studies mainly focus on employee performance and behavior but this study extends the effect of EI in education and offers significant implications for hospitality and tourism educators and researchers (word count: 155). © 2022 ICHRIE.

A Cloud-Based Recognition Service for Agriculture During the COVID-19 Period in Taiwan

The great popularity of cloud services, together with the increasingly important aim of providing internet context-aware services, has spurred interest in developing diverse agriculture applications. This paper presents a cloud-based service built by incrementally integrating state-of-the-art models of deep learning, photography, object recognition, and the multi-functionalities of cloud services. This study consists of an object detection phase with a convolutional neural network (CNN) model, which involves enabling simultaneous image data gathered from drones. The experimental results show 97% accurate watermelon recognition. The results also include a two-model comparison in the cloud-based service, with the main findings demonstrating the feasibility of developing accurate object recognition using a CNN model without the need for additional hardware. Finally, this study adopted a confusion matrix to validate the result with RetinaNet for recognizing images taken on the watermelon farm with an average precision in recognizing watermelon quantity of up to 98.8%.

Data-Driven In-Crisis Community Identification for Disaster Response and Management

Since 2019, the world has been seriously impacted by the global pandemic, COVID-19, with millions of people adversely affected. This is coupled with a trend in which the intensity and frequency of natural disasters such as hurricanes, wildfires, and earthquakes have increased over the past decades. Larger and more diverse communities have been negatively influenced by these disasters and they might encounter crises socially and/or economically, further exacerbated when the natural disasters and pandemics co-occurred. However, conventional disaster response and management rely on human surveys and case studies to identify these in-crisis communities and their problems, which might not be effective and efficient due to the scale of the impacted population. In this paper, we propose to utilize the data-driven techniques and recent advances in artificial intelligence to automate the in-crisis community identification and improve its scalability and efficiency. Thus, immediate assistance to the in-crisis communities can be provided by society and timely disaster response and management can be achieved. A novel framework of the in-crisis community identification has been presented, which can be divided into three subtasks: (1) community detection, (2) in-crisis status detection, and (3) community demand and problem identification. Furthermore, the open issues and challenges toward automated in-crisis community identification are discussed to motivate future research and innovations in the area.

Community-Connect: COVID-19 Small Business Marketplace with Automated Regulation Matching and Company Lead Retrieval

Periods of unique economic distress such as the COVID-19 pandemic can be quite difficult for small businesses. Challenges acquiring the supplies necessary to adhere to safety regulations created in the wake of such events can introduce stress on these businesses. This is further exacerbated when supply chains have slowed down, leading to global shortages from most large suppliers. This paper proposes a platform to aid such businesses in procuring COVID-19 related supplies such as Personal Protective Equipment (PPE) from one another, leveraging advanced data acquisition, integration, and Natural Language Processing (NLP) methods. With the pandemic end in sight, the platform described in this paper can be reused for other emergencies such as hurricanes, floods, among others. The proposed platform supports business transactions within a Buyer's Club (BC), keyword-based sourcing of new businesses to join the platform, and matching products to relevant regulations using greater-than-word length encoding, helping businesses comply with the ever-changing regulatry landscape. © 2021 IEEE.

A novel automated guided vehicle for guidance applications

In recent years, as a result of the significant development of information and communications technology, people have been paying much attention to automated guided vehicles (AGVs). In the ongoing global coronavirus disease 2019 (COVID-19) pandemic, hospital services have been seriously impacted. In the severe medical situation in hospitals, there is a serious shortage of human resources. This paper presents a novel automated guided vehicle (AGV) for guidance and service. The AGV is comprised of a microcontroller unit (MCU), a power unit, a human sensing unit, a collision warning unit, and a path sensing unit. The motion speed of the AGV and the distance between the AGV and the person being guided are determined by the MCU, the power unit, sensors, and an algorithm. The collision warning unit comprises three ultrasonic sensors and an infrared sensor in the front of the AGV in order to avoid obstacles. The trajectory results of the planned and actual paths are in good agreement. The AGV provides safe and effective guidance for people moving towards their destination. The AGV achieves excellent guidance results and shows great potential for guidance applications in hospitals. © 2021 Institute of Physics Publishing. All rights reserved.

Multimedia Research for Response and Management of COVID-19 and Future Pandemics

Coronavirus Disease 2019 (COVID-19) has been affecting most of the countries and impacting almost every aspect of people's lives. More than one hundred million confirmed cases and two million deaths have been reported due to COVID-19 as of February 2021. While our society suffers an unanticipated epidemic, researchers and engineers have developed various technologies to manage this global emergency. Specifically, multimedia tools, techniques, and applications have been developed and played essential roles in facilitating the recovery, resilience, and management of COVID-19, including pandemic status monitoring and impact prediction, enhancing public awareness and telehealth, etc. However, there are many challenges that require further investigation and research to better manage COVID-19 and prepare for future pandemics.

Society for the Improvement of Psychological Science Global Engagement Task Force Report

The Society for the Improvement of Psychological Science (SIPS) is an organization whose mission focuses on bringing together scholars who want to improve methods and practices in psychological science. The organization reaffirmed in June 2020 that "[we] cannot do good science without diverse voices," and acknowledged that "right now the demographics of SIPS are unrepresentative of the field of psychology, which is in turn unrepresentative of the global population. We have work to do when it comes to better supporting Black scholars and other underrepresented minorities." The purpose of the Global Engagement Task Force, started in January 2020, was to explore suggestions made after the 2019 Annual Conference, held in Rotterdam, the Netherlands, around inclusion and access for scholars from regions outside of the United States, Canada, and Western Europe (described in the report as "geographically diverse" regions), a task complicated by the COVID-19 pandemic and civil unrest in several task force members' countries of residence. This report outlines several suggestions, specifically around building partnerships with geographically diverse open science organizations;increasing SIPS presence at other, more local events;diversifying remote events;considering geographically diverse annual conference locations;improving membership and financial resources;and surveying open science practitioners from geographically diverse regions.

THE EFFECTS OF COVID-19 ON MANUFACTURER OPERATIONS: EVIDENCE FROM CHINA

The COVID-19 outbreak has spread globally at an extremely fast pace and has seriously affected the economic development and stability of the social order in various countries, impacting the normal business operations of manufacturers. In this study, a sample comprising 1329 manufacturers in Hangzhou, China is analysed using logistic regression and path analysis methods to identify the main factors related to COVID-19 that affect manufacturers' operations, as well as the possible causal relationships between them. The empirical results of the logistic regression reveal that COVID-19 primarily affects the operating performance of manufacturers in five regards: business continuity, capital chain gap, supply chain integration, laborforce availability, and stimulating policies. The conclusions derived from the path analysis indicate that the degree of traffic and logistics congestion is a key factor, as it hinders manufacturers' business continuity, which ultimately causes a gap in the capital chain and determines manufacturers' demand for stimulating policies. Based on the research results, we propose recommendations to support manufacturers in their efforts to resume operations and realise economic recovery.

Laboratory diagnosis of severe acute respiratory syndrome coronavirus 2.

The first laboratory confirmed case of Coronavirus disease 2019 (COVID-19) in Australia was in Victoria on 25 January 2020 in a man returning from Wuhan city, Hubei province, the People's Republic of China. This was followed by three cases in New South Wales the following day. The Australian Government activated the Australian Health Sector Emergency Response Plan for Novel Coronavirus on 27 February 2020 in anticipation of a pandemic. Subsequently, the World Health Organization declared COVID-19 to be a Public Health Emergency of International Concern followed by a pandemic on 30 January 2020 and 11 March 2020, respectively. Laboratory testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, is key in identifying infected persons to guide timely public health actions of contact tracing and patient isolation to limit transmission of infection. This article aims to provide a comprehensive overview of current laboratory diagnostic methods for SARS-CoV-2, including nucleic acid testing, serology, rapid antigen detection and antibody tests, virus isolation and whole genome sequencing. The relative advantages and disadvantages of the different diagnostic tests are presented, as well as their value in different clinical, infection control and public health contexts. We also describe the challenges in the provision of SARS-CoV-2 diagnostics in Australia, a country with a relatively low COVID-19 incidence in the first pandemic wave but in which prevalence could rapidly change.

Correlation of population mortality of COVID-19 and testing coverage: a comparison among 36 OECD countries.

Although testing is widely regarded as critical to fighting the COVID-19 pandemic, what measure and level of testing best reflects successful infection control remains unresolved. Our aim was to compare the sensitivity of two testing metrics - population testing number and testing coverage - to population mortality outcomes and identify a benchmark for testing adequacy. We aggregated publicly available data through 12 April on testing and outcomes related to COVID-19 across 36 OECD (Organization for Economic Development) countries and Taiwan. Spearman correlation coefficients were calculated between the aforementioned metrics and following outcome measures: deaths per 1 million people, case fatality rate and case proportion of critical illness. Fractional polynomials were used to generate scatter plots to model the relationship between the testing metrics and outcomes. We found that testing coverage, but not population testing number, was highly correlated with population mortality (rs = -0.79, P = 5.975 × 10-9vs. rs = -0.3, P = 0.05) and case fatality rate (rs = -0.67, P = 9.067 × 10-6vs. rs = -0.21, P = 0.20). A testing coverage threshold of 15-45 signified adequate testing: below 15, testing coverage was associated with exponentially increasing population mortality; above 45, increased testing did not yield significant incremental mortality benefit. Taken together, testing coverage was better than population testing number in explaining country performance and can serve as an early and sensitive indicator of testing adequacy and disease burden.

Would dermoscopic photographs helptriage teledermatology consults in the COVID-19 era?

Background: The coronavirus pandemic led to a rapid transition to telemedicine across medical specialties.Dermatologists can utilize digital images to remotely manage skin diseases, including determining the urgency withwhich patients need to be seen in person. Accurately triaging urgent cases can be impacted by imaging modality.Dermoscopy is the examination of the skin with polarized light, which for some providers can increase theirdiagnostic accuracy and confidence in managing potential skin cancers. It has been increasingly viewed as astandard adjunct for examining skin lesions. Dermoscopic photographs have been shown to improve providers'abilities to correctly diagnose telemedicine consults. However, this has only been studied with a limited subset ofskin neoplasms and is limited to a workflow involving a photographer with a dermatoscopic imaging capability. In thepandemic, the ability of patients to access dermoscopy is exceedingly limited. This study aims to assess the impactof dermoscopy on decision-making by dermatologists, including confidence, diagnostic accuracy, and triagingurgency. Our premise is that if dermoscopy is impactful, innovative means to increase accessibility to dermoscopywould be valuable for cancer management during COVID. Methods: Twenty sets of clinical and dermoscopic photographs were selected as representative of commonly seenskin pathologies in our teledermatology consults. Study participants were first shown a clinical photograph andasked to determine their diagnoses, management decisions, level of urgency, and confidence in their triagedecisions. The responses were scaled ranging from non-neoplastic to malignant, 0-100% confidence, and non-urgent (1) to emergent (3), respectively. They were then asked to answer the same questions after viewing theassociated dermoscopic image. Results: Twenty-six physicians participated in the study: 16 dermatology attendings and 10 residents. The majorityreported using dermoscopy in ≥25% of their clinical practice. Fifty-nine percent rated themselves as “somewhatconfident” in their dermoscopic abilities. Providers correctly diagnosed 45.3% of study cases using clinical imagesalone. This increased to 53.6% after viewing the associated dermoscopic images (p=0.02). The greatest increase was for malignant neoplasms (31% vs. 54%, p=0.0007). Dermoscopy significantly reduced triage urgency scores forboth non-neoplastic (mean 1.6 vs. 1.2, p<0.001) and benign neoplastic (mean 1.43 vs. 1.35, p=0.01) pathologies.Dermoscopy significantly increased urgency scores for malignant neoplasms (mean 1.47 vs. 1.64, p=0.01). There was a 7.6% increase in providers' confidence in their management decisions with dermoscopy (p<0.0001). Conclusions: Dermoscopic photographs improve providers' abilities to correctly diagnose and prioritize skinlesions. In the COVID-19 era, innovative means to make dermoscopy available to patients at risk for skin cancerwould be valuable.

Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling

In January 2020, a novel betacoronavirus (family Coronaviridae), named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the etiological agent of a cluster of pneumonia cases occurring in Wuhan City, Hubei Province, China. The disease arising from SARS-CoV-2 infection, coronavirus disease 2019 (COVID-19), subsequently spread rapidly causing a worldwide pandemic. Here we examine the added value of near real-time genome sequencing of SARS-CoV-2 in a subpopulation of infected patients during the first 10 weeks of COVID-19 containment in Australia and compare findings from genomic surveillance with predictions of a computational agent-based model (ABM). Using the Australian census data, the ABM generates over 24 million software agents representing the population of Australia, each with demographic attributes of an anonymous individual. It then simulates transmission of the disease over time, spreading from specific infection sources, using contact rates of individuals within different social contexts. We report that the prospective sequencing of SARS-CoV-2 clarified the probable source of infection in cases where epidemiological links could not be determined, significantly decreased the proportion of COVID-19 cases with contentious links, documented genomically similar cases associated with concurrent transmission in several institutions and identified previously unsuspected links. Only a quarter of sequenced cases appeared to be locally acquired and were concordant with predictions from the ABM. These high-resolution genomic data are crucial to track cases with locally acquired COVID-19 and for timely recognition of independent importations once border restrictions are lifted and trade and travel resume.

Alternative face masks made of common materials for general public: Fractional filtration efficiency and breathability perspective

As COVID-19 pandemic has caused more than 24 million confirmed cases globally (as of August 28th, 2020), it is critical to slow down the spreading of SARS-CoV-2 to protect the healthcare system from overload. Wearing a respirator or a mask has been proven as an effective method to protect both the wearer and others, but commercially available respirators and masks should be reserved for healthcare workers under a currently desperate shortage. The use of alternative materials becomes an option for the general public to make the do-it-yourself (DIY) masks, with their efficacy seldom reported. In this study, we tested commercial respirators and masks, furnace filters, vacuum cleaner filters, and common household materials. We evaluated the materials’ fractional filtration efficiency and breathing resistance, which are primary factors affecting respiratory protection. To compare the efficiency-resistance tradeoff, the figure of merit of each tested common material was also calculated. Filter media with electrostatic charges (electret) is recommended due to its high efficiency with low flow resistance;multiple-layer household fabrics and sterilization wraps are acceptable materials;a coffee filter is inadvisable due to its low efficiency. The outcome of this study can not only offer guidance for the general public under the current pandemic but also suggest the appropriate alternative respiratory protection materials under heavy air pollution episodes. © The Author(s).

Measure of medical workers stress using fuzzy inference system: A case study by 2019-NCOV medical staff

Background: A new type of coronavirus is now around the world. At present, all countries in the world are trying to stop the spreading of this virus. Wuhan pneumonia is the seventh kind of coronavirus, which is called “2019 new coronavirus”. There are currently six known human coronaviruses, four of which are less pathogenic, and the other two are severe acute respiratory syndrome coronavirus (SARS) and Middle East respiratory syndrome coronavirus (MERS). The new coronavirus (2019-nCoV) that caused the outbreak was listed as the seventh species. Because the symptoms of this virus are similar to SARS and MERS, medical staff and experts have been very cautious about this epidemic for the past two months. Due to this unexpected and unprecedented event, everyone was tense. Based on this situation the authors propose an idea to measure the work/stress levels of these front-line workers. Objectives: The primary objective of this study is to measure the working pressure of frontline medical staff. It can be used as a reference for future psychological testing or decompression to improve work efficiency. Method: The concept of “fuzzy logic” was first proposed by Zadeh. The main goals were to study uncertainty and imprecision that can be solved by fuzzy logic. It can simulate human language and thinking to efficiently deal with a question. The author uses four influencing factors as the membership function and rule base of “fuzzy inference” as follows: 1. Professional Attitude: Work performance and attitude;this also includes professional skills and knowledge. 2. Human Relationships: Coexistence between colleagues;has good relationships, will get more help if they are active. 3. Emotional Quotient: Management and control of emotions;this includes crisis process and response capabilities. 4. Work Pressure: Professional burnout or enthusiasm. A low degree of work enthusiasm may be expressed as high professional burnout. If (Professional Attitude = Good) and (Human Relationships = Poor) and (Emotional Quotient = Moderate) then (Non-Work Pressure = Moderate). For example: if a staff member has a perfect work attitude and a poor relationship with a colleague, their emotional quotient is moderate and their professional enthusiasm is moderate. It also means the work pressure is moderate, because their burnout is equal to enthusiasm. Thus, we think the medical worker's pressure is medium. Results and Conclusion: Medical Staff must deal with the major force of public opinion. This implies the management of interpersonal relationships. In this article, the authors use the fuzzy inference system to establish working pressure from a rule base. However, we still cannot analyze the relationship between the working conditions of health care workers, work stress and interpersonal conflicts among colleagues. We hope that this exploration will be included in future work to make up for the lack of this research.