ABSTRACT
As the COVID-19 pandemic swept the globe, social media plat-forms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the pro-posed aspect2class annotation algorithm, we labeled bulk unlabeled tweets according to their contained aspect terms. However, we also recognized the challenges of reducing data's high dimensionality and sparsity to improve performance and annotation on unlabeled datasets. To address this issue, we proposed the Volatile Stopwords Filtering (VSF) technique to reduce sparsity and enhance classifier performance. The resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21% when using the random forest as a meta-classifier. Through testing on three benchmark datasets, we found that the SABAC ensemble framework performed exceptionally well. Our findings showed that international students during the pandemic faced various issues, including stress, uncertainty, health concerns, financial stress, and difficulties with online classes and returning to school. By analyzing and summarizing these annotated tweets, decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic.
ABSTRACT
The COVID-19 pandemic required efficient allocation of public resources and transforming existing ways of societal functions. To manage any crisis, governments and public health researchers ex-ploit the information available to them in order to make informed decisions, also defined as situational awareness. Gathering situational awareness using so-cial media, has been functional to manage epidemics. Previous research focused on using discussions during periods of epidemic crises on social media platforms like Twitter, Reddit, or Facebook and developing NLP techniques to filter out important/relevant discussions from a huge corpus of messages and posts. Social media usage varies with internet penetration and other socio-economic factors, which might induce disparity in an-alyzing discussions across different geographies. How-ever, print media is a ubiquitous information source, irrespective of geography. Further, topics discussed in news articles are already 'newsworthy', while on social media 'newsworthiness' is a product of techno-social processes. Developing this fundamental difference, we study Twitter data during the second wave in India focused on six high-population cities with varied macro-economic factors. Through a mixture of qualitative and quantitative methods, we further analyze two Indian newspapers during the same period and compare topics from both Twitter and the newspapers to evaluate sit-uational awareness around the second phase of COVID on each of these platforms. We conclude that factors like internet penetration and GDP in a specific city influence the discourse surrounding situational updates on social media. Thus, augmenting information from newspapers to information extracted from social media would provide a more comprehensive perspective in resource-deficit cities © 2022 IEEE.
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BACKGROUND: Live simulation-based activities are effective tools in teaching situational awareness to improve patient safety training in healthcare settings. The coronavirus disease 2019 (COVID-19) pandemic forced the discontinuation of these in-person sessions. We describe our solution to this challenge: an online interactive activity titled the "Virtual Room of Errors." The aim of this activity is to create an accessible and feasible method of educating healthcare providers about situational awareness in the hospital. Materials and Methods: We applied existing three-dimensional virtual tour technology used in the real estate sector to a hospital patient room with a standardized patient and 46 intentionally placed hazards. Healthcare providers and students from our institution accessed the room online through a link where they independently navigate, and document observed safety hazards. Results: In 2021 and 2022, a total of 510 learners completed the virtual Room of Errors (ROE). The virtual ROE increased annual participation in the activity, as compared to the in-person Room, and demonstrated learner satisfaction. Conclusions: The virtual ROE is an accessible, feasible, and cost-effective method of educating healthcare workers on situational awareness of preventable hazards. Furthermore, the activity is a sustainable way to reach a larger number of learners from multiple disciplines, even as in-person activities resume.
ABSTRACT
The corona virus disease (COVID-19) is a pandemic that facilitate a confrontation space for scientific and social existence of human frontiers. The rapid spread and mortality rate of COVID-19 and the preventive measures including social distancing and its impact on economy, developed an unprecedented consciousness around the globe. It has created an effect on the mental health of individuals employed across various sectors and is outlined in this study. There is currently an inadequate theoretical model that focuses on the comprehensive understanding of the psychology of preventive behaviour during the outbreak of pandemics. In this study, a transnational model is delineated for assessing the adoption of preventive behavioural practices associated with COVID-19 pandemic. It uses the components derived from the theories of situational awareness and health belief model and literatures related to impact of containment strategies on various sectors. The contribution includes policy recommendations that can be helpful for the healthcare professionals and government to control the disease spread.Copyright © 2022 Inderscience Enterprises Ltd.
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Demand for non-face-to-face services is increasing in all industries due to the impact of the global pandemic caused by COVID-19 over the past three years. This phenomenon also occurs in the travel industry without exception. Travel consumption patterns are changing in the form of FIT (Foreign Independent Tour) rather than package tours provided by existing travel agencies. Now, we are entering the era of travel tech using smartphones. To this end, technological efforts are being made to simultaneously address the two characteristics of individual free travel and non-face-to-face situations. In this paper, we propose an AI-based platform architecture with situation awareness for travel plans. It provides a personalized service using the ontology-based mobile service so that tourists can make travel plans according to their own circumstances, after considering the situation of individual tourists through sensor collection data while analyzing the congestion situation of tourist destinations through public data. The mobile platform consists of a total of four layers (Infrastructure, Cross-Cutting Infrastructure, Domain, Application), and has been implemented in an environment of Android 8.0 or higher and iOS 11.0 or higher through the AWS website. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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PurposeThe purpose of this paper is to investigate the potential synergy between companies' sustainable supply chain management (SSCM) activities and their supply chain resilience (SCRES). The authors propose hypotheses about the impact of buying companies SSCM activities on the inflicted damage by unexpected supply chain disruptions and the recovery time afterwards and test these empirically using data from companies during the global COVID-19 pandemic.Design/methodology/approachThe authors investigate a sample of 231 of the largest publicly traded companies in the European Union with 4.158 firm-year observations. For the analysis, the authors generate variables capturing the companies' intensity and years of experience of their SSCM activities targeted at the supply chain and run regression analyses on the inflicted damage due to the COVID-19 pandemic and the recovery time after the disruption.FindingsBuying companies' SSCM activities have a positive effect on their SCRES. The damage inflicted by unexpected supply chain disruptions is lower when companies have higher levels of SSCM and longer experience with it. The recovery time afterwards is significantly reduced by longer experience with SSCM efforts.Research limitations/implicationsThe authors suggest SCRES is reinforced by transparency, situational awareness, social capital and collaboration resulting from companies SSCM activities translate into increased SCRES.Practical implicationsThe authors show that companies with superior SSCM are more resilient in a crisis and conclude that, therefore, companies should invest in SSCM to prevent future supply disruptions.Originality/valueTo the best of the authors' knowledge, this is the first empirical study analyzing a data set of multi-industry companies, linking their SSCM activities to SCRES during the pandemic.
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Biosurveillance defines the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision making for action at all levels. Animal health surveillance is an important component within biosurveillance systems comprising a continuum of activities from detecting biological threats, to analyzing relevant data, to managing identified threats, and embracing a One Health concept. The animal health community can strengthen biosurveillance by adopting various developments such as increasing the alignment, engagement, and participation of stakeholders in surveillance systems, exploring new data streams, improving integration and analysis of data streams for decision-making, enhancing research and application of social sciences and behavioral methods in animal health surveillance, and performing timely evaluation of surveillance systems. The aim of this paper is to explore components of a biosurveillance system from an animal health perspective and identify opportunities for the animal health surveillance community to enhance biosurveillance. Structural and operational diagrams are presented to demonstrate the required components and relevant data of animal health surveillance as an effective part within a biosurveillance system.
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Purpose: Global supply chains are increasingly becoming more prone to high-impact disruptions, which has been fairly evident with the coronavirus disease 2019 (COVID-19) pandemic. The capacity to address disruptions is essential for the survival of any organization. Coping with increasing complexity and uncertainties requires a systemic view of supply chains. Furthermore, a comprehensive understanding of the governance structure and timely decision-making are critical in times of disruptions. Although several aspects of supply chain resilience (SCRES) are broadly studied in the literature, the relationship between supply chain orientation (SCO), decentralization in decision-making and SCRES is an understudied area. Design/methodology/approach: This study takes a longitudinal approach to address this research gap with a comprehensive meta-analytic review to explore the relationships between the constructs of interest through the lenses of contingency and dynamic capability theories. Furthermore, 2 surveys with sample sizes of 250 and 200 were conducted with supply chain professionals to test the research framework before and after the pandemic to compare the findings. Findings: Structural Equation Modeling (SEM) analysis indicates a positive relationship between SCO and decentralization and between decentralization and SCRES. The post-COVID-19 analysis further validates the influence of agility, collaboration and situational awareness related to decentralization and resilience. More importantly, capabilities have a higher impact on resilience during the pandemic than before. Practical implications: The results entreat organizations to attain decentralized decision-making vis-à-vis dedicated functional teams charged with reacting timely to disruptions. The teams should be empowered to leverage their knowledge and experiences regardless of their position in the hierarchy. Originality/value: Although SCRES is an active research stream, the structural aspects of SCRES and its relationship with SCO are understudied. Therefore, this research puts forth a research framework and empirically tests hypotheses that frame the relationship between SCO, decentralization and resilience outcomes in pre- and post-COVID environments. © 2023, Emerald Publishing Limited.
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BACKGROUND: Safety briefings can help promoting situational awareness, interprofessional communication and improve patient safety. LOCAL PROBLEM: A clinical survey highlighted that 90% of the participants including the medical team and the critical care outreach team nurses perceived the meeting for escalating acutely ill and deteriorating patients during the out-of-hours period (20.00 to 08.00) to have unconstructive and unwelcoming atmosphere with belittling, hostility and unhelpful criticisms. The participants reported that the communication across teams lacked in structure and clear information given; but staff also self-reported lacking confidence in communicating key issues. METHOD: A quality improvement project with Plan-Do-Study-Act was adopted to design and implement a dedicated multidisciplinary safety briefing with a structured format. RESULTS: The multidisciplinary safety briefing was to 90% of clinicians, and it took a median of 10 min to complete. Delayed referrals to the critical care outreach team were reduced by 46%. Positive changes included increased situational awareness and clearer communication across teams. Barriers identified were variable usage and need for face-to-face presence. Considering all the findings and the time constraint during the SARS-CoV-2 pandemic, we changed to a telephonic safety briefing directly to the team leaders. CONCLUSION: A structured multidisciplinary safety briefing can improve patient safety and support management of deteriorating and acutely ill patients on the wards during the out-of-hours period.
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PurposeThe main aim of this study was to know the role of digital information and communication channels in developing citizens' understanding regarding COVID-19 with reference to situation awareness. Furthermore, the impact of gender, age, qualification and area of respondents on citizens' perception and comprehension of COVID-19 was also investigated. Design/methodology/approachThis quantitative study used an online survey and received 377 usable responses. The instrument was developed with the help of literature, and it was based on two constructs (perception of the element and comprehension of the current situation) of the situational awareness (SA) model. Data were collected from Pakistani citizens through a convenient sampling technique. FindingsIt was observed that the majority of citizens used electronic media, Facebook and WhatsApp for accessing COVID-19-related information. World Health Organization website played an important role in developing citizens' understanding. Alert messages through mobile phone and apps played role in developing citizen's understanding regarding COVID-19 situation. They perceived that media was effective in providing social distancing techniques. Moreover, they believed that government campaigns were helpful. Findings revealed that there was no significant impact of gender, age, qualification and area on citizens' perceptions and comprehension of COVID-19 through media. Originality/valueThis study is helpful for authorities in decision-making regarding COVID-19 and also filled the literature gap as, to the best of the authors' knowledge, no comprehensive study has been conducted regarding the SA of COVID-19.
ABSTRACT
Identification of fine-grained location mentions in crisis tweets is central in transforming situational awareness information extracted from social media into actionable information. Most prior works have focused on identifying generic locations, without considering their specific types. To facilitate progress on the fine-grained location identification task, we assemble two English tweet crisis datasets and manually annotate them with specific location types. The first dataset contains tweets from a mixed set of crisis events, while the second dataset contains tweets from the global COVID-19 pandemic. We investigate the performance of state-of-the-art deep learning models for sequence tagging on these datasets, in both in-domain and cross-domain settings. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
ABSTRACT
Nowadays, an increasing number of crises worldwide, triggered by climate extremes, natural and human-made hazards, the coronavirus pandemic, and more, pose a high pressure on crisis, emergency, and disaster management. Spatial data and Volunteered Geographic Information (VGI) are key issues in the successful and immediate response to crises. This paper aims to explore the use of VGI in crisis management, including emergency and disaster management, based on a scoping review of existing literature in English for five years (2016-2020). Specifically, the research intends to answer Scoping Review Questions (SRQ) regarding the use of VGI in crisis, emergency, and disaster management, and the verified cases' spatial distribution, the VGI sources utilized (e.g. OpenStreetMap - OSM, Crowdsourcing, Twitter), the types of hazards (e.g. natural and human-made hazards, pandemic), the specific tasks in crisis, emergency or disaster management and VGI use in the management of actual crisis events, e.g. COVID-19 pandemic, Hurricane Katrina, etc. Eligible papers on VGI use in crisis, emergency, and disaster management are geolocated based on first-author affiliation, and as a result, a spatial bibliography is provided. Thus, the term Spatial Scoping Review is introduced. Scoping Review Questions are answered, and the results are analyzed and discussed. Finally, implementing the "VGICED Atlas", a web atlas, permits the publication of the research results to a broad audience and the visualization of the analysis with several interactive maps.
ABSTRACT
The corona virus disease (COVID-19) is a pandemic that facilitate a confrontation space for scientific and social existence of human frontiers. The rapid spread and mortality rate of COVID-19 and the preventive measures including social distancing and its impact on economy, developed an unprecedented consciousness around the globe. It has created an effect on the mental health of individuals employed across various sectors and is outlined in this study. There is currently an inadequate theoretical model that focuses on the comprehensive understanding of the psychology of preventive behaviour during the outbreak of pandemics. In this study, a transnational model is delineated for assessing the adoption of preventive behavioural practices associated with COVID-19 pandemic. It uses the components derived from the theories of situational awareness and health belief model and literatures related to impact of containment strategies on various sectors. The contribution includes policy recommendations that can be helpful for the healthcare professionals and government to control the disease spread.
ABSTRACT
In the context of the emergency of COVID-19, students have experienced moments of strong emotional stress, with the risk of generating a state of frustration and discouraging them from studying. The proposed adaptive e-learning system, based on situational awareness, and remodeled teaching were essential in limiting this phenomenon. The system has been designed and developed according to the design principles of Situation Awareness. The feedback selection process, updated through the years to face the emergency situation, is driven by a Fuzzy Cognitive Map, implemented to identify the learners' situation, defined through their levels of engagement, motivation, and participation. The experimentation was conducted using the SAGAT methodology, involving students participating in classes during the courses held over the academic years 2018/2019, 2019/2020, and 2020/2021. The results show that the system is capable of increasing the level of situation awareness of the students even in a context of emergency. © 2022 IEEE.
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Astrotourism and related citizen science activities are becoming a major trend of a sustainable, high-quality tourism segment, core elements to the protection of Dark skies in many countries. In the Summer of 2020, in the middle of COVID pandemics, we started an initiative to train young students - Cyber-Cosmos - using an Unistellar eVscope, a smart, compact and user-friendly digital telescope that offers unprecedented opportunities for deep-sky observation and citizen science campaigns. Sponsored by the Ciência Viva Summer program, this was probably the first continuous application of this equipment in a pedagogical and citizen-science context, and in a pandemic context. Pampilhosa da Serra, home to a certified Dark Sky destination (Aldeias do Xisto) in central Portugal, was the chosen location for this project, where we expect astrotourism and citizen science to flourish and contribute to space sciences education.
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The Covid-19 crisis has led to widespread impacts on society, not only in terms of high death tolls, but also in terms of cascading effects for essentially all societal sectors;especially due to all measures taken to reduce the spread of the disease. A range of actors have become involved in the response to the pandemic and to ensure appropriate actions being taken, these actors need good situational awareness, which can be facilitated by developing and distributing common operational pictures (COPs). This paper presents a case study of the COPs compiled by the Swedish Counties’ Coordination Office (SCCO). A point of departure for the paper is that for a COP to become useful, they should not only contain information about the present but also contain description about future potential states and uncertainties. Since this is a perspective that is addressed by the risk science, this paper addresses the question of how risk science can add value to the work with COPs in general and the SCCO’s COPs in particular. Our results indicate that SCCO’s COPs fulfil the expectations of the Government Office who are the main target organization. But there is room for improvements, where risk science can add value, if the aim is to improve their usefulness as decision basis. © ESREL 2021. Published by Research Publishing, Singapore.
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The unprecedented increase in data availability in many science and technology fields (e.g., genomic data, data from industrial environments, sensory data of smart cities, and social network data) require new methods and solutions for data processing, information extraction, and decision support. ‘Multi-Language Spam/Phishing Classification by Email Body Text: Toward Automated Security Incident Investigation’ by Rastenis et al. The authors proposed a semi-automatic information security model, which can deal with situational awareness data, strategies prevailing information security activities, and protocols monitoring specific types of the network next to the real-time information environment. [...]the paper entitled ‘Simulation of Authentication in Information-Processing Electronic Devices Based on Poisson Pulse Sequence Generators’ by Maksymovych [17] was devoted to modelling authenticators of information-processing electronic devices by creating a bit template simulator based on a Poisson pulse sequence generator.
ABSTRACT
In response to the COVID-19, social media big data has played an important role in epidemic warning, tracking the source of infection, and public opinion monitoring, providing strong technical support for China’s epidemic prevention and control work. The paper used Sina Weibo posts related to COVID-19 hashtags as the data source, and built a BERT-CNN deep learning model to perform fine-grained and high-precision topic classificationon massive social media posts. Taking Shenzhen as a region of interest, we mined the “epidemic data bulletin” and “daily life impact” posts during the epidemic for spatial analysis. The results show that the confirmed communities and designated hospitals in Shenzhen as a whole present the characteristics of “sparse east and dense west”, and there is a strong positive spatial correlation between the number of confirmed cases and social media response. Specifically, Nanshan District, Futian District and Luohu District have more confirmed cases due to large population movements and dense transportation networks, and social media has responded more violently, and people’s lives have been greatly affected. However, Yantian District, Pingshan District and Dapeng New District showed opposite characteristics. The case study results further show that using deep learning methods to mine text information in social media is scientifically feasible for improving situational awareness and decision support during the COVID-19.
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Daily, companies compete for customers in order to increase their revenue. The food industry, and in particular, very large restaurant chains, are no different. Customers are drawn to the opportunity to conveniently retrieve their food with minimum wait times using drive-Thru services. While such services are not new and are used by a large number of restaurants, the fundamental paradigm (i.e., the configuration of employee agents and their interaction with consumer agents) through which drive-Thru services continue to be used is difficult to observe in a meaningful way. Recently, with the onset of the COVID-19 pandemic, drive-Thru services were heavily relied upon to provide much of the limited person-To-person contact service necessary to help reduce the spread of disease. While this presented many opportunities for existing businesses to scale their operations, it also revealed many inefficiencies with drive-Thru services and the way they conduct their business, leading to longer waiting times. This paper addresses this issue by developing a simulation-based tool for identifying inefficiencies in existing drive-Thru services. The tool allows a range of both employee and customer agent scenarios to be tested, providing important situational awareness for restaurant owners. Questions that the tool can help businesses answer include: identifying the most optimized configuration for minimizing customer wait times due to resources constraints (e.g., employee availability), possible impacts to business with switching strategies, and service point bottlenecks. A set of best practices, in line with industry standards and based on a review of the literature, were used in the design phase of this work. The developed tool is open-sourced1 and presents an interactive and easy-To-use interface that businesses can use to improve their service wait times. © 2022 IEEE.
ABSTRACT
In order to address the needs of those caught in a pandemic, one needs situational awareness for tracking the development of the pandemic and the resources needed to help those affected by it. That can enable governments to connect those who need help with healthcare providers who can help them and as well connect healthcare providers with the resources they need for helping the sick. Using a war room as a central node for centralizing responsibility otherwise normally delegated to numerous stakeholders can provide minimal multimodal protocols for effectively capturing data for developing situational awareness of the pandemic. Having situational awareness in real time can enable a war room to use its authority for exercising responsibility for connecting the dots and taking action for addressing the needs of a pandemic. © 2021 IEEE.