Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Urban Studies and Planning
Title: Evolution of Enterprise Architecture for Digital Transformation
Abstract: The digital transformation of our life changes the way we work, learn, communicate, and collaborate. Enterprises are presently transforming their strategy, culture, processes, and their information systems to become digital. The digital transformation deeply disrupts existing enterprises and economies. Digitization fosters the development of IT systems with many rather small and distributed structures, like Internet of Things, Microservices and mobile services. Since years a lot of new business opportunities appear using the potential of services computing, Internet of Things, mobile systems, big data with analytics, cloud computing, collaboration networks, and decision support. Biological metaphors of living and adaptable ecosystems provide the logical foundation for self-optimizing and resilient run-time environments for intelligent business services and adaptable distributed information systems with service-oriented enterprise architectures. This has a strong impact for architecting digital services and products following both a value-oriented and a service perspective. The change from a closed-world modeling world to a more flexible open-world composition and evolution of enterprise architectures defines the moving context for adaptable and high distributed systems, which are essential to enable the digital transformation. The present research paper investigates the evolution of Enterprise Architecture considering new defined value-oriented mappings between digital strategies, digital business models and an improved digital enterprise architecture.
Extracted statement — Components : Service perspective
Research field: Urban Studies and Planning
Title: Smart City Ontologies: Improving the effectiveness of smart city applications
Abstract: <jats:p>This paper addresses the problem of low impact of smart city applications observed in the fields of energy and transport, which constitute high-priority domains for the development of smart cities. However, these are not the only fields where the impact of smart cities has been limited. The paper provides an explanation for the low impact of various individual applications of smart cities and discusses ways of improving their effectiveness. We argue that the impact of applications depends primarily on their ontology, and secondarily on smart technology and programming features. Consequently, we start by creating an overall ontology for the smart city, defining the building blocks of this ontology with respect to the most cited definitions of smart cities, and structuring this ontology with the Protégé 5.0 editor, defining entities, class hierarchy, object properties, and data type properties. We then analyze how the ontologies of a sample of smart city applications fit into the overall Smart City Ontology, the consistency between digital spaces, knowledge processes, city domains targeted by the applications, and the types of innovation that determine their impact. In conclusion, we underline the relationships between innovation and ontology, and discuss how we can improve the effectiveness of smart city applications, combining expert and user-driven ontology design with the integration and or-chestration of applications over platforms and larger city entities such as neighborhoods, districts, clusters, and sectors of city activities.</jats:p>
Extracted statement — Linked Ontology: Smart City Ontology
Research field: Urban Studies and Planning
Title: Skunkworks finder: unlocking the diversity advantage of urban innovation ecosystems
Abstract: Entrepreneurs and start-up founders using innovation spaces and hubs often find themselves inside a filter bubble or echo chamber, where like-minded people tend to come up with similar ideas and recommend similar approaches to innovation. This trend towards homophily and a polarisation of like-mindedness is aggravated by algorithmic filtering and recommender systems embedded in mobile technology and social media platforms. Yet, genuine innovation thrives on social inclusion fostering a diversity of ideas. To escape these echo chambers, we designed and tested the Skunkworks Finder - an exploratory tool that employs social network analysis to help users discover spaces of difference and otherness in their local urban innovation ecosystem.
Extracted statement — has Data Source: social media
Research field: Urban Studies and Planning
Title: Encouraging civic participation through local news aggregation
Abstract: Traditional sources of information for small and rural communities have been disappearing over the past decade. A lot of the information and discussion related to such local geographic areas is now scattered across websites of numerous local organizations, individual blogs, social media and other user-generated media (YouTube, Flickr). It is important to capture this information and make it easily accessible to local citizens to facilitate citizen engagement and social interaction. Furthermore, a system that has location-based support can provide local citizens with an engaging way to interact with this information and identify the local issues most relevant to them. A location-based interface for a local geographic area enables people to identify and discuss local issues related to specific locations such as a particular street or a road construction site. We created an information aggregator, called the Virtual Town Square (VTS), to support and facilitate local discussion and interaction. We created a location-based interface for users to access the information collected by VTS. In this paper, we discuss focus group interviews with local citizens that motivated our design of a local news and information aggregator to facilitate civic participation. We then discuss the unique design challenges in creating such a local news aggregator and our design approach to create a local information ecosystem. We describe VTS and the initial evaluation and feedback we received from local users and through weekly meetings with community partners.
Extracted statement — has Data Source: social media
Research field: Urban Studies and Planning
Title: Developing E-Government Coursework through the NASPAA Competencies Framework
Abstract: <jats:p> Information technology (IT) is often less emphasized in coursework related to public administration education, despite the growing need for technological capabilities in those joining the public sector workforce. This coupled with a lesser emphasis on e-government/IT skills by accreditation standards adds to the widening gap between theory and practice in the field. This study examines the emphasis placed on e-government/IT concepts in Master of Public Administration (MPA) and Master of Public Policy (MPP) programs, either through complete course offerings or through related courses such as public management, strategic planning, performance measurement and organization theory. Based on a content analysis of their syllabi, the paper analyzes the extent to which the IT/e-government courses in MPA/Master of Public Policy programs address the Network of Schools of Public Policy, Affairs, and Administration competency standards, and further discuss the orientation of the courses with two of the competencies: management and policy. Specifically, are e-government/IT courses more management-oriented or policy-oriented? Do public management, strategic planning, performance measurement, and organization theory courses address IT concerns? </jats:p>
Extracted statement — has business competence: strategic planning
Research field: Urban Studies and Planning
Title: Tools of quality economics: sustainable development of a ‘smart city’ under conditions of digital transformation of the economy
Abstract: The article covers the issues of ensuring sustainable city development based on the achievements of digitalization. Attention is also paid to the use of quality economy tools in managing 'smart' cities under conditions of the digital transformation of the national economy. The current state of 'smart' cities and the main factors contributing to their sustainable development, including the digitalization requirements is analyzed. Based on the analysis of statistical material, the main prospects to form the 'smart city' concept, the possibility to assess such parameters as 'life quality', 'comfort', 'rational organization', 'opportunities', 'sustainable development', 'city environment accessibility', 'use of communication technologies'. The role of tools for quality economics is revealed in ensuring the big city life under conditions of digital economy. The concept of 'life quality' is considered, which currently is becoming one of the fundamental vectors of the human civilization development, a criterion that is increasingly used to compare countries and territories. Special attention is paid to such tools and methods of quality economics as standardization, metrology and quality management. It is proposed to consider these tools as a mechanism for solving the most important problems in the national economy development under conditions of digital transformation.
Extracted statement — Components : Sustainable development
Research field: Virology
Title: COVID-19 outbreak in Algeria: A mathematical model to predict the incidence
Abstract: Abstract Introduction Since December 29, 2019 a pandemic of new novel coronavirus-infected pneumonia named COVID-19 has started from Wuhan, China, has led to 254 996 confirmed cases until midday March 20, 2020. Sporadic cases have been imported worldwide, in Algeria, the first case reported on February 25, 2020 was imported from Italy, and then the epidemic has spread to other parts of the country very quickly with 139 confirmed cases until March 21, 2020. Methods It is crucial to estimate the cases number growth in the early stages of the outbreak, to this end, we have implemented the Alg-COVID-19 Model which allows to predict the incidence and the reproduction number R0 in the coming months in order to help decision makers. The Alg-COVIS-19 Model initial equation 1, estimates the cumulative cases at t prediction time using two parameters: the reproduction number R0 and the serial interval SI. Results We found R0=2.55 based on actual incidence at the first 25 days, using the serial interval SI= 4,4 and the prediction time t=26. The herd immunity HI estimated is HI=61%. Also, The Covid-19 incidence predicted with the Alg-COVID-19 Model fits closely the actual incidence during the first 26 days of the epidemic in Algeria Fig. 1.A. which allows us to use it. According to Alg-COVID-19 Model, the number of cases will exceed 5000 on the 42 th day (April 7 th ) and it will double to 10000 on 46th day of the epidemic (April 11 th ), thus, exponential phase will begin (Table 1; Fig.1.B) and increases continuously until reaching à herd immunity of 61% unless serious preventive measures are considered. Discussion This model is valid only when the majority of the population is vulnerable to COVID-19 infection, however, it can be updated to fit the new parameters values.
Extracted statement — Methods: Alg-COVID-19 Model
Research field: Virology
Title: Identification of antiviral drug candidates against SARS-CoV-2 from FDA-approved drugs
Abstract: Drug repositioning is the only feasible option to immediately address the COVID-19 global challenge. We screened a panel of 48 FDA-approved drugs against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which were preselected by an assay of SARS-CoV. We identified 24 potential antiviral drug candidates against SARS-CoV-2 infection. Some drug candidates showed very low 50% inhibitory concentrations (IC 50 s), and in particular, two FDA-approved drugs—niclosamide and ciclesonide—were notable in some respects.
Extracted statement — Has participant: antiviral drug
Research field: Virology
Title: Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures
Abstract: By 27 February 2020, the outbreak of coronavirus disease 2019 (COVID‐19) caused 82 623 confirmed cases and 2858 deaths globally, more than severe acute respiratory syndrome (SARS) (8273 cases, 775 deaths) and Middle East respiratory syndrome (MERS) (1139 cases, 431 deaths) caused in 2003 and 2013, respectively. COVID‐19 has spread to 46 countries internationally. Total fatality rate of COVID‐19 is estimated at 3.46% by far based on published data from the Chinese Center for Disease Control and Prevention (China CDC). Average incubation period of COVID‐19 is around 6.4 days, ranges from 0 to 24 days. The basic reproductive number (R0) of COVID‐19 ranges from 2 to 3.5 at the early phase regardless of different prediction models, which is higher than SARS and MERS. A study from China CDC showed majority of patients (80.9%) were considered asymptomatic or mild pneumonia but released large amounts of viruses at the early phase of infection, which posed enormous challenges for containing the spread of COVID‐19. Nosocomial transmission was another severe problem. A total of 3019 health workers were infected by 12 February 2020, which accounted for 3.83% of total number of infections, and extremely burdened the health system, especially in Wuhan. Limited epidemiological and clinical data suggest that the disease spectrum of COVID‐19 may differ from SARS or MERS. We summarize latest literatures on genetic, epidemiological, and clinical features of COVID‐19 in comparison to SARS and MERS and emphasize special measures on diagnosis and potential interventions. This review will improve our understanding of the unique features of COVID‐19 and enhance our control measures in the future.
Extracted statement — data source: China CDC
Research field: Virology
Title: Broad anti-coronaviral activity of FDA approved drugs against SARS-CoV-2 in vitro and SARS-CoV in vivo
Abstract: Abstract SARS-CoV-2 emerged in China at the end of 2019 and has rapidly become a pandemic with roughly 2.7 million recorded COVID-19 cases and greater than 189,000 recorded deaths by April 23rd, 2020 (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://www.WHO.org">www.WHO.org</jats:ext-link>). There are no FDA approved antivirals or vaccines for any coronavirus, including SARS-CoV-2. Current treatments for COVID-19 are limited to supportive therapies and off-label use of FDA approved drugs. Rapid development and human testing of potential antivirals is greatly needed. A quick way to test compounds with potential antiviral activity is through drug repurposing. Numerous drugs are already approved for human use and subsequently there is a good understanding of their safety profiles and potential side effects, making them easier to fast-track to clinical studies in COVID-19 patients. Here, we present data on the antiviral activity of 20 FDA approved drugs against SARS-CoV-2 that also inhibit SARS-CoV and MERS-CoV. We found that 17 of these inhibit SARS-CoV-2 at a range of IC50 values at non-cytotoxic concentrations. We directly follow up with seven of these to demonstrate all are capable of inhibiting infectious SARS-CoV-2 production. Moreover, we have evaluated two of these, chloroquine and chlorpromazine, in vivo using a mouse-adapted SARS-CoV model and found both drugs protect mice from clinical disease.
Extracted statement — Has participant: chloroquine and chlorpromazine
Research field: Virology
Title: The impact of social distancing and epicenter lockdown on the COVID-19 epidemic in mainland China: A data-driven SEIQR model study
Abstract: The outbreak of coronavirus disease 2019 (COVID-19) which originated in Wuhan, China, constitutes a public health emergency of international concern with a very high risk of spread and impact at the global level. We developed data-driven susceptible-exposed-infectious-quarantine-recovered (SEIQR) models to simulate the epidemic with the interventions of social distancing and epicenter lockdown. Population migration data combined with officially reported data were used to estimate model parameters, and then calculated the daily exported infected individuals by estimating the daily infected ratio and daily susceptible population size. As of Jan 01, 2020, the estimated initial number of latently infected individuals was 380.1 (95%-CI: 379.8~381.0). With 30 days of substantial social distancing, the reproductive number in Wuhan and Hubei was reduced from 2.2 (95%-CI: 1.4~3.9) to 1.58 (95%-CI: 1.34~2.07), and in other provinces from 2.56 (95%-CI: 2.43~2.63) to 1.65 (95%-CI: 1.56~1.76). We found that earlier intervention of social distancing could significantly limit the epidemic in mainland China. The number of infections could be reduced up to 98.9%, and the number of deaths could be reduced by up to 99.3% as of Feb 23, 2020. However, earlier epicenter lockdown would partially neutralize this favorable effect. Because it would cause in situ deteriorating, which overwhelms the improvement out of the epicenter. To minimize the epidemic size and death, stepwise implementation of social distancing in the epicenter city first, then in the province, and later the whole nation without the epicenter lockdown would be practical and cost-effective.
Extracted statement — Method: data-driven susceptible-exposed-infectious-quarantine-recovered (SEIQR) models
Research field: Virology
Title: Transmission potential of COVID-19 in Iran
Abstract: Abstract We estimated the reproduction number of 2020 Iranian COVID-19 epidemic using two different methods: R 0 was estimated at 4.4 (95% CI, 3.9, 4.9) (generalized growth model) and 3.50 (1.28, 8.14) (epidemic doubling time) (February 19 - March 1) while the effective R was estimated at 1.55 (1.06, 2.57) (March 6-19).
Extracted statement — Method: generalized growth model
Research field: Virology
Title: Estimating the generation interval for COVID-19 based on symptom onset data
Abstract: Background: Estimating key infectious disease parameters from the COVID-19 outbreak is quintessential for modelling studies and guiding intervention strategies. Whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for COVID-19 have not been provided. Methods: We used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates we obtained the proportions pre-symptomatic transmission and reproduction numbers. Results: The mean generation interval was 5.20 (95%CI 3.78-6.78) days for Singapore and 3.95 (95%CI 3.01-4.91) days for Tianjin, China when relying on a previously reported incubation period with mean 5.2 and SD 2.8 days. The proportion of pre-symptomatic transmission was 48% (95%CI 32-67%) for Singapore and 62% (95%CI 50-76%) for Tianjin, China. Estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Conclusions: Estimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network. Detailed contact tracing information is essential for correctly estimating these quantities.
Extracted statement — Method: generation interval
Research field: Virology
Title: Estimating the generation interval for COVID-19 based on symptom onset data
Abstract: Background: Estimating key infectious disease parameters from the COVID-19 outbreak is quintessential for modelling studies and guiding intervention strategies. Whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for COVID-19 have not been provided. Methods: We used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates we obtained the proportions pre-symptomatic transmission and reproduction numbers. Results: The mean generation interval was 5.20 (95%CI 3.78-6.78) days for Singapore and 3.95 (95%CI 3.01-4.91) days for Tianjin, China when relying on a previously reported incubation period with mean 5.2 and SD 2.8 days. The proportion of pre-symptomatic transmission was 48% (95%CI 32-67%) for Singapore and 62% (95%CI 50-76%) for Tianjin, China. Estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Conclusions: Estimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network. Detailed contact tracing information is essential for correctly estimating these quantities.
Extracted statement — Methods: generation interval
Research field: Virology
Title: Modelling the Potential Health Impact of the COVID-19 Pandemic on a Hypothetical European Country
Abstract: A SEIR simulation model for the COVID-19 pandemic was developed (http://covidsim.eu) and applied to a hypothetical European country of 10 million population. Our results show which interventions potentially push the epidemic peak into the subsequent year (when vaccinations may be available) or which fail. Different levels of control (via contact reduction) resulted in 22% to 63% of the population sick, 0.2% to 0.6% hospitalised, and 0.07% to 0.28% dead (n=6,450 to 28,228).
Extracted statement — location: Hypothetical European Country
Research field: Virology
Title: Lessons drawn from China and South Korea for managing COVID-19 epidemic: insights from a comparative modeling study
Abstract: We conducted a comparative study of COVID-19 epidemic in three different settings: mainland China, the Guangdong province of China and South Korea, by formulating two disease transmission dynamics models incorporating epidemic characteristics and setting-specific interventions, and fitting the models to multi-source data to identify initial and effective reproduction numbers and evaluate effectiveness of interventions. We estimated the initial basic reproduction number for South Korea, the Guangdong province and mainland China as 2.6 (95% confidence interval (CI): (2.5, 2.7)), 3.0 (95%CI: (2.6, 3.3)) and 3.8 (95%CI: (3.5,4.2)), respectively, given a serial interval with mean of 5 days with standard deviation of 3 days. We found that the effective reproduction number for the Guangdong province and mainland China has fallen below the threshold 1 since February 8th and 18th respectively, while the effective reproduction number for South Korea remains high, suggesting that the interventions implemented need to be enhanced in order to halt further infections. We also project the epidemic trend in South Korea under different scenarios where a portion or the entirety of the integrated package of interventions in China is used. We show that a coherent and integrated approach with stringent public health interventions is the key to the success of containing the epidemic in China and specially its provinces outside its epicenter, and we show that this approach can also be effective to mitigate the burden of the COVID-19 epidemic in South Korea. The experience of outbreak control in mainland China should be a guiding reference for the rest of the world including South Korea.
Extracted statement — location: mainland China