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Design probes, an essential research tool during the COVID-19 pandemic, are ancillary "personal"data gathering tools that enable researchers to enter the private world of research participants. This paper compares two case studies of design probes used during the pandemic for radical placemaking in hybrid digital-physical environments: Digital Art Summer School in Northrock, Ireland, with eleven participants, and Chatty Bench Project in Brisbane, Australia, with sixteen participants. The paper further expands on the design methodology of the probes and their deployment during the online radical placemaking projects. From the participant responses to the probes' activities and interviews, both studies demonstrated that the probes fostered placemaking in digital environments during the pandemic. The paper concludes with three lessons on the potential of probes as a critical research instrument to enable creativity, build social capital and create bonds between people and places during uncertain and turbulent times. © 2022 Owner/Author.
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Organizations experiment with how smart technology can be used to manage employees since before COVID-19 and the possibilities seem almost limitless. However, the question of how this can be achieved without impairing the so-needed trust inside organizations is yet to answer. Hence, in this study, we employ a crisp-set QCA to investigate what trust-enabling datafication control configurations look like. Drawing on unique survey data from Switzerland, we show that datafication control can go hand in hand with trust if organizations make efforts for employee-centricity. Further, we can reveal four distinct ways of how organizations can implement employee-centricity to mitigate possible trust-impairing signals that stem from augmented data-gathering and analysis capabilities. Our results contribute to the still heated debate on the duality of control and trust. They also help leaders to navigate through the unmanageable multitude of possible and even trust-toxic combinations. © 2022 IEEE Computer Society. All rights reserved.
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With the emergence of coronavirus and the rapidly increasing number of cases, we observed that people were suffering from a lack of information about where to source critical requirements such as oxygen cylinders, beds, ambulance service, and ventilators. In this research paper and project, the authors have designed and developed an interactive information dashboard to access the availability of these resources and requirements at different locations across India from myriad sources. The information dashboard will show information for all the states across India. The dashboard visualizes the number of corona cases, hospital beds, blood bank, etc. The end-users and the COVID-19 support team can imagine all the requirements in the dashboard that is factual and credible within their vicinity from multiple information sources. The user need not search various information sites to search for different conditions. The project collates the data of other requirements from primary sources of information like Twitter and government websites and lists them on the dashboard. The authors used open-source programming and database technologies to display the data per user requirements. The dashboard was helpful to the end-users during COVID-19 times in terms of convenient accessibility and efficiency. © 2022 IEEE.
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BACKGROUND: The burden on healthcare systems is mounting continuously owing to population growth and aging, overuse of medical services, and the recent COVID-19 pandemic. This overload is also causing reduced healthcare quality and outcomes. One solution gaining momentum is the integration of intelligent self-assessment tools, known as symptom-checkers, into healthcare-providers' systems. To the best of our knowledge, no study so far has investigated the data-gathering capabilities of these tools, which represent a crucial resource for simulating doctors' skills in medical-interviews. OBJECTIVES: The goal of this study was to evaluate the data-gathering function of currently available chatbot symptom-checkers. METHODS: We evaluated 8 symptom-checkers using 28 clinical vignettes from the repository of MSD-Manual case studies. The mean number of predefined pertinent findings for each case was 31.8 ± 6.8. The vignettes were entered into the platforms by 3 medical students who simulated the role of the patient. For each conversation, we obtained the number of pertinent findings retrieved and the number of questions asked. We then calculated the recall-rates (pertinent-findings retrieved out of all predefined pertinent-findings), and efficiency-rates (pertinent-findings retrieved out of the number of questions asked) of data-gathering, and compared them between the platforms. RESULTS: The overall recall rate for all symptom-checkers was 0.32(2,280/7,112;95 %CI 0.31-0.33) for all pertinent findings, 0.37(1,110/2,992;95 %CI 0.35-0.39) for present findings, and 0.28(1140/4120;95 %CI 0.26-0.29) for absent findings. Among the symptom-checkers, Kahun platform had the highest recall rate with 0.51(450/889;95 %CI 0.47-0.54). Out of 4,877 questions asked overall, 2,280 findings were gathered, yielding an efficiency rate of 0.46(95 %CI 0.45-0.48) across all platforms. Kahun was the most efficient tool 0.74 (95 %CI 0.70-0.77) without a statistically significant difference from Your.MD 0.69(95 %CI 0.65-0.73). CONCLUSION: The data-gathering performance of currently available symptom checkers is questionable. From among the tools available, Kahun demonstrated the best overall performance.
Subject(s)
COVID-19 , Humans , COVID-19/diagnosis , COVID-19/epidemiology , Pandemics , Quality of Health Care , SoftwareABSTRACT
COVID-19 has been spread to many countries all over the world in a relatively short period, largely overwhelmed hospitals have been a direct consequence of the explosive increase of coronavirus cases. In this dire situation, the demand for the development of clinical decision support systems based on predictive algorithms has increased, since these predictive technologies may help to alleviate unmanageable stress on healthcare systems. We contribute to this effort by a comprehensive study over a real dataset of covid-19 patients from a local hospital. The collected dataset is representative of the local policies on data gathering implemented during the pandemic, showing high imabalance and large number of missing values. In this paper, we report a descriptive analysis of the data that points out the large disparity of data in terms of severity and age. Furthermore, we report the results of the principal component analysis (PCA) and Logistic Regression (LR) techniques to find out which variables are the most relevant and their respective weight. The results show that there are two very relevant variables for the detection of the most severe cases, yielding promissing results. One of our paper conclussions is a strong recommendation to the local authorities to improve the data gathering protocols. © 2022, Springer Nature Switzerland AG.
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Coronavirus disease 2019 (COVID-19) is caused by a new virus called SARS-CoV-2. Its impact on public health creates adverse effects. Because it is a brand-new virus, scientists are getting to know more every day. Although the majority who've COVID-19 have slight symptoms, COVID- 19 can also cause intense infection or even demise. a few corporations, together with older adults and those who've certain underlying scientific conditions, are at accelerated risk of extreme contamination So monitoring and Visualization of COVID-19 cases and simply representing the information for a higher understanding of the COVID-19 instances around the world helps humans recognize the present-day state of affairs and to try this a cellular application is a fine way. In this paper, We summarize and illustrate with examples the way to amass and Visualize the statistics of the COVID-19 cases in a cellular application using Flutter. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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On March 23, 2020, the Government of India (GoI) announced one of the strictest nationwide lockdowns in the world to curb the spread of novel SARS-CoV-2, otherwise known as CoVID-19. The country came to a standstill overnight and the service industry, including small businesses and restaurants, took a massive financial hit. The unknown nature of the virus and its spread deepened anxiety among the general public, quickly turning to distrust towards any "outside"contact with goods and people. In the hopes of (re)building consumer trust, food delivery platforms Zomato and Swiggy began providing digital solutions to exhibit care towards their customers, including: (1) sharing delivery workers' live temperatures alongside the workers' profile inside the app;(2) mandating the use of the controversial contact tracing app Aarogya Setu for the workers;(3) monitoring workers' usage of masks through random selfie requests;and (4) sharing specific worker vaccination details on the app for customers to view, including vaccination date and the vaccine's serial number. Such invasive data gathering infrastructures to address public health threats have long focused on the surveillance of laborers, migrants, and the bodies of other marginalized communities. Framed as public health management, such biometric and health data gathering is treated as a necessary feature of caring for the well-being of the general public. However, such datafication practices - ones which primarily focus on the extraction of data from one specific community in order to mollify the concerns of another - normalizes the false perception that disease is transmitted unidirectionally: from worker to the consumer. By centering food delivery workers' experiences during the pandemic and examining the normalization of such surveillance in the name of care and recovery, this paper aims to examine how new regimes of care are manufactured and legitimized using harmful and unethical datafication practices. © 2022 ACM.
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Data integration is a classical problem in databases, typically decomposed into schema matching, entity matching and record merging. To solve the latter, it is mostly assumed that ground truth can be determined, either as master data or from user feedback. However, in many cases, this is not the case because firstly the merging processes cannot be accurate enough, and also the data gathering processes in the different sources are simply imperfect and cannot provide high quality data. Instead of enforcing consistency, we propose to evaluate how concordant or discordant sources are as a measure of trustworthiness (the more discordant are the sources, the less we can trust their data). Thus, we define the discord measurement problem in which given a set of uncertain raw observations or aggregate results (such as case/hospitalization/death data relevant to COVID-19) and information on the alignment of different data (for example, cases and deaths), we wish to assess whether the different sources are concordant, or if not, measure how discordant they are. Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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This paper summarises a ONE development success story of reviving a mature brownfield in South of Oman, Field β, just within ONE year through collaboration between different disciplines, comprehensive data analysis, optimising and recompletion of existing wells. Field β, comprised of multi-stacked clastic reservoirs, was put on stream in 1980s and peaked in early 1990s. Pilot water injection started in 1993 and full field water flooding continued in 1997. After more than 35 years since start of production, one can say the field was already in the tail end of its life. It had been stabilizing at low rate after 25 years and starting to decline further and at some point was one of the potential candidates to be decommissioned. A new FDP (FDP18) for part of the field was delivered in 2018 with the first well drilled at the end of that year. In 2019, despite drilling further wells on the FDP18, production was declining and was at 2018 rate towards the year end. Intensive data analysis and integrated reservoir reviews per reservoir layers were actively performed and new opportunities and data gathering were identified. FDP18 wells from 2019 onwards were then deepened to also acquire log data over deeper than the target reservoirs. Further synergy between asset and exploration teams also instigated in new discoveries including oil in shallower carbonate reservoirs, which were logged and sampled when drilling the FDP18 wells. Declining production, low oil price and COVID-19 crisis that hit 2020 challenged the team to be more resilient and with ONE development mindset between development and WRFM team, also between asset and exploration team, existing long-term closed in and very low productivity wells were utilised to tap these new opportunities. As a result, the field production has been increased by more than double, highest since 10 years ago, with a potential of triple its production rate, all achieved through optimizing and recompletion of existing wells within 1 year, at a very attractive low UTC. © Copyright 2021, Society of Petroleum Engineers
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Creating a public understanding of the dynamics of a pandemic, such as COVID-19, is vital for introducing restrictive regulations. Gathering diverse data responsibly and sharing it with experts and citizens in a timely manner is challenging. This article reviews methodologies of COVID-19 dashboard design and discusses both technical and non-technical challenges associated. Advice and lessons learned from building a citizen-focused, automated county-precision dashboard for Germany are shared. Within four months, the web-based tool had 5 million unique visitors and 70 million sessions. Three developers set up the basic version in less than one week. Early on, data was screen scraped. An iterative process improved timeliness by adding more fine-grained data sources. A collaborative online table editor enabled near real-time corrections. Alerting was setup for errors, and statistics apply for sanity checking. Static site generation and a content delivery network help to serve large user loads in a timely manner. The flexible design allowed to iteratively integrate more complex statistics based on expert knowledge built on top of the collected data and secondary data sources such as ICU beds and citizen movement. © 2020 Owner/Author.
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This paper addresses the phenomenon of overtourism in Budapest from multiple perspectives, starting with an overview that uses information collected from news, media, and academic tourism literature. Further, the phenomenon of overtourism is addressed quantitatively using different indicators, including tourism density and intensity. According to these indicators, the center of Budapest (formed by districts I, V, VI, VII, VIII, and IX) has been strongly affected by the presence of tourists, while districts physically far from the center have been less affected. This fact suggests the heterogeneity of the city in terms of overtourism. The number one catalyst of the negative impacts of foreign visitors’ behavior is party tourism (‘ruin pub’ tourism), which involves an unconventional use of the Hungarian capital. Finally, using an unconventional optimization method called fuzzy linear programming, we attempt to explore the challenging problem of identifying the optimal number of tourists for the city. The results of the study have important theoretical, methodological, and practical implications. On the theoretical side, we offer a comprehensive understanding of the phenomenon of overtourism in Budapest. Methodologically, the integrated approach in terms of data gathering and unconventional analytical methodologies (comprised of a case study analysis, the assessment of effective indicators for measuring the discussed phenomenon, and the demonstration of the sustainable number of visitors) represents a novel perspective about the extent of overtourism in Budapest. On the practical side, our findings provide valuable guidance for policymakers to help mitigate the problem of overtourism in the city. With regard to future research, we suggest extending and updating the results presented in this study to develop more sustainable tourism strategies.
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In this study, a new approach is proposed based on drone-assisted smart data gathering for pandemic situations. Drones can play important roles in highly dynamic and dense disaster areas for the data gathering process. Under these conditions, if big data gathering is necessary, the network traffic can be lightened and balanced with smart techniques. For these reasons, the drones construct the aerial network and scan the frequency bands in their coverage area. Then the collected data on the related drone is processed in terms of importance and priority levels. The drones take on fog computing capabilities for the specific duties. So, the unnecessary data will not be transmitted to the related destinations and the most priority data will be transferred immediately to the related units. The proposed mechanism is developed and examined with various scenarios. The throughput, delay and energy consumption performance metrics are considered for performance evaluation.
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In unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs), a UAV is employed as a mobile sink to gather data from sensor nodes. Incorporating UAV helps prolong the network lifetime and avoid the energy-hole problem faced by sensor networks. In emergency applications, timely data collection from sensor nodes and transferal of the data to the base station (BS) is a prime requisite. The timely and safe path of UAV is one of the fundamental premises for effective UWSN operations. It is essential and challenging to identify a suitable path in an environment comprising various obstacles and to ensure that the path can efficiently reach the target point. This paper proposes a hybrid path planning (HPP) algorithm for efficient data collection by assuring the shortest collision-free path for UAV in emergency environments. In the proposed HPP scheme, the probabilistic roadmap (PRM) algorithm is used to design the shortest trajectory map and the optimized artificial bee colony (ABC) algorithm to improve different path constraints in a three-dimensional environment. Our simulation results show that the proposed HPP outperforms the PRM and conventional ABC schemes significantly in terms of flight time, energy consumption, convergence time, and flight path.
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Since the first reported case in Wuhan in late 2019, COVID-19 has rapidly spread worldwide, dramatically impacting the lives of millions of citizens. To deal with the severe crisis resulting from the pandemic, worldwide institutions have been forced to make decisions that profoundly affect the socio-economic realm. In this sense, researchers from diverse knowledge areas are investigating the behavior of the disease in a rush against time. In both cases, the lack of reliable data has been an obstacle to carry out such tasks with accuracy. To tackle this challenge, COnVIDa (https://convida.inf.um.es) has been designed and developed as a user-friendly tool that easily gathers rigorous multidisciplinary data related to the COVID-19 pandemic from different data sources. In particular, the pandemic expansion is analyzed with variables of health nature, but also social ones, mobility, etc. Besides, COnVIDa permits to smoothly join such data, compare and download them for further analysis. Due to the open-science nature of the project, COnVIDa is easily extensible to any other region of the planet. In this way, COnVIDa becomes a data facilitator for decision-making processes, as well as a catalyst for new scientific researches related to this pandemic.