The publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics.The spread of COVID-19 has created a fundamental need for coordinated mechanisms responding to outbreaks in different sectors. One of the main sectors relates to information supply and demand in the middle of this pandemic in the digital environment. It could be called an infodemiology. It is known as a promising approach to solving the challenge in the present age. At this level, the purpose of this article is to investigate the COVID-19 related search process by field research. Data were retrieved from Google Trends in Middle Eastern countries alongside scientific research output of Middle Eastern scientists towards COVID-19 in Web of Science, Scopus, and PubMed. Daily COVID-19 cases and deaths were retrieved from the World Health Organization. We searched for descriptive statistical analyses to detect coronavirus-seeking behavior versus coronavirus releases in the Middle East in 2020. Findings show that people in the Middle East use various keyword solutions to search for COVID-19 in Google. There is a significant correlation between coronavirus confirmed cases and scientific productivity (January 2020-December 2020). Also, there is a positive association between the number of deaths and the number of scientific publications (except Jordan). It was a positive and significant association between online coronavirus-seeking behavior on Google (RSVs) and the confirmed cases (except Syria and Yemen). Furthermore, it was a positive relationship between RSVs and scientific productivity in the Middle East (except Bahrain and Qatar). From an infodemiological viewpoint, there is a significant correlation between coronavirus information demand and its information provision.In addition to academic impact, researchers are increasingly concerned with understanding and demonstrating the practical impact of research outside academia. Several frameworks capturing key impact types have been developed based on project experiences, expert opinions, and surveys. This empirical study seeks to contribute to this development by identifying impact types documented in 6,882 case studies submitted to impact evaluation groups in Australia (Engagement and Impact Assessment) and the United Kingdom (Research Excellence Framework). check details The results of text mining indicate three emerging impact types that extend existing frameworks in terms of the recognition of new opportunities, the length of use, and experience improvement, thereby allowing a variety of researchers, not just those who address popular, short-term, and instrumental issues, to understand and demonstrate their practice impact.Network capacity, defined as the largest sum of origin-destination (O-D) flows that can be accommodated by the network based on link performance function and traffic equilibrium assignment, is a critical indicator of network-wide performance assessment in transportation planning and management. The typical modeling rationale of estimating network capacity is to formulate it as a mathematical programming (MP), and there are two main approaches single-level MP formulation and bi-level programming (BLP) formulation. Although single-level MP is readily solvable, it treats the transportation network as a physical network without considering level of service (LOS). Albeit BLP explicitly models the capacity and link LOS, solving BLP in large-scale networks is challenging due to its non-convexity. Moreover, the inconsideration of trip LOS makes the existing models difficult to differentiate network capacity under various traffic states and to capture the impact of emerging trip-oriented technologies. Therefore, this paper proposes the α-max capacity model to estimate the maximum network capacity under trip or O-D LOS requirement α. The proposed model improves the existing models on three aspects (a) it considers trip LOS, which can flexibly estimate the network capacity ranging from zero to the physical capacity including reserve, practical and ultimate capacities; (b) trip LOS can intuitively reflect users’ maximum acceptable O-D travel time or planners’ requirement of O-D travel time; and (c) it is a convex and tractable single-level MP. For practical use, we develop a modified gradient projection solution algorithm with soft constraint technique, and provide methods to obtain discrete trip LOS and network capacity under representative traffic states. Numerical examples are presented to demonstrate the features of the proposed model as well as the solution algorithm.Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression.