A novel coronavirus (COVID-19) has taken the world by storm. The disease has spread very swiftly worldwide. A timely clue which includes the estimation of the incubation period among COVID-19 patients can allow governments and healthcare authorities to act accordingly. to undertake a review and critical appraisal of all published/preprint reports that offer an estimation of incubation periods for COVID-19. This research looked for all relevant published articles between the dates of December 1, 2019, and April 25, 2020, i.e. those that were related to the COVID-19 incubation period. Papers were included if they were written in English, and involved human participants. Papers were excluded if they were not original (e.g. reviews, editorials, letters, commentaries, or duplications). COVID-19 Open Research Dataset supplied by Georgetown’s Centre for Security and Emerging Technology as well as PubMed and Embase via Arxiv, medRxiv, and bioRxiv. A data-charting form was jointly developed by the two reviewers (NZ and EA), to determine which variables to extract. The two reviewers independently charted the data, discussed the results, and updated the data-charting form. Screening was undertaken 44,000 articles with a final selection of 25 studies referring to 18 different experimental projects related to the estimation of the incubation period of COVID-19. The majority of extant published estimates offer empirical evidence showing that the incubation period for the virus is a mean of 7.8 days, with a median of 5.01 days, which falls into the ranges proposed by the WHO (0–14 days) and the ECDC (2–12 days). Nevertheless, a number of authors proposed that quarantine time should be a minimum of 14 days and that for estimates of mortality risks a median time delay of 13 days between illness and mortality should be under consideration. It is unclear as to whether any correlation exists between the age of patients and the length of time they incubate the virus.

The recent COVID-19 pandemic has forced educational institutions worldwide to adopt e-learning. UAE higher education institutions have implemented e-learning systems and programs to cope with this unprecedented situation. This paper measured the strength of association between key aspects of e-learning systems and programs and students’ motivation to learn in Ajman University (AU). Cronbach’s coefficient alpha was used to test the internal consistency reliability of key aspects of e-learning (EL-8) and students’ motivation to learn (SML-16). Exploratory factor analysis was used to test the validity of, and coherence of patterns in, the data. Parametric and non-parametric methods were used to investigate the strength of association between key aspects of e-learning and students’ motivation to learn in AU. The results indicated that motivation variables were more strongly correlated with both e-teaching materials and e-assessments key aspects relative to others such as e-discussion, and e-grade checking and feedback.

The UN Office for Disaster Risk Reduction listed 10 reasons businesses should reduce their disaster exposure, including risk factoring, which cannot be achieved without historical data about hazards, their locations, magnitudes, and frequencies. Substantial hazard data are reported by newspapers, which could add value to disaster management decision making. In this study, a text-mining program extracted keywords related to floods’ geographic location, date, and damages from newspaper analyses of flash floods in Fujairah, UAE, from 2000–2018. The paper describes extracting such information as well as geocoding and validating flood-prone areas generated through geographic information system (GIS) modeling. The generation of flood-prone areas was based on elevation, slope, land use, soil, and geology coupled with topographic wetness index, topographic position index, and curve number. Analytical Hierarchy Process (AHP) produced relative weight for each factor, and GIS map algebra generated flood-prone areas. AHP inclusion helped minimize weight subjectivity among various experts. Of all areas, 85% are considered medium and low flood-prone zones, mainly mountainous areas. However, the 15% that are high/very high are dominated by urban areas in low coastal plains, predisposing them to flash floods. Eighty-four percent of flood events reported by newspapers were in areas rated as high/very high flood-prone zones. In the absence of flood records, newspapers reports can be used as a reference. Policymakers should assess whether flood-prone area models offer accurate analyses. These findings are useful for organizations related to disaster management, urban planning, insurance, archiving, and documentation.

Banking risk measurement and management remain one of many challenges for managers and policymakers. This study contributes to the banking literature and practice in two ways by (a) proposing a risk ranking index based on the Mahalanobis Distance (MD) between a multidimensional point representing a bank’s risk measures and the corresponding critical ratios set by the banking authorities and (b) determining the relative importance of a bank’s risk ratios in affecting its financial standing using an Adaptive Neuro-Fuzzy Inference System. In this study, ten financial ratios representing five risk areas were considered, namely: Capital Adequacy, Credit, Liquidity, Earning Quality, and Operational risk. Data from 45 Gulf banks for the period 2016–2020 was used to develop the model. Our findings indicate that a bank is in a sound risk position at the 99%, 95%, and 90% confidence level if its Mahalanobis distance exceeds 4.82, 4.28, and 4.0, respectively. The maximum distance computed for the banks in this study was 9.31; only five out of the forty-five banks were below the 4.82 and one below the 4.28 and 4.0 thresholds at 3.96. Sensitivity analysis of the risks indicated that the Net Interest Margin is the most significant factor in explaining variations in a bank’s risk position, followed by Capital Adequacy Ratio, Common Equity Tier1, and Tier1 Equity in order. The remaining financial ratios: Non-Performing Loans, Equity Leverage, Cost Income Ratio, Loans to Total Assets, and Loans to Deposits have the least influence in the order given; the Provisional Loans Ratio appears to have no influence.

Protein complexes are groups of two or more polypeptide chains that bind to form noncovalent networks of protein interactions. Over the past decade, researchers have created a number of means of computing the ways in which protein complexes and their members can be identified through these interaction networks. Although most of the existing methods identify protein functional complexes from the protein-protein interaction networks (PPIs) at a fairly decent level, the applicability of advanced graph network methods has not yet been adequately investigated. This paper proposes various graph convolutional network (GCN) methods to improve the detection of protein complexes. We first formulate the protein complex detection problem as a node classification problem. Then, we developed a Neural Overlapping Community Detection (NOCD) model to cluster the nodes (proteins) using a complex affiliation matrix. A representation learning approach, that combines a multi-class GCN feature extractor (to obtain the nodes’ features) and a mean shift clustering algorithm (to perform the clustering), is also utilized. We convert the dense-dense matrix operations into dense-sparse or sparse-sparse matrix operations to improve the efficiency of the multi-class GCN network by reducing space and time complexities. The proposed solution significantly improves the scalability of the existing GCN. Finally, we apply clustering aggregation to find the best protein complexes. A grid search is then performed on various detected complexes obtained via three well-known protein detection methods, namely ClusterONE, CMC, and PEWCC, with the help of the Meta-Clustering Algorithm (MCLA) and the Hybrid Bipartite Graph Formulation (HBGF). We test the proposed GCN-based methods on various publicly available datasets and find that they perform significantly better than previous state-of-the-art methods. The code/data are available for free download from https://github.com/Analystharsh/GCN_complex_detection.

Predicting students’ academic performance and the factors that significantly influence it can improve students’ completion and graduation rates, as well as reduce attrition rates. In this study, we examine the factors influencing student academic achievement. A fuzzy-neural approach is adopted to build a model that predicts and explains variations in course grades among students, based on course category, student course attendance rate, gender, high-school grade, school type, grade point average (GPA), and course delivery mode as input predictors. The neuro-fuzzy system was used because of its ability to implicitly capture the functional form between the dependent variable and input predictors. Our results indicate that the most significant predictors of course grades are student GPA, followed by course category. Using sensitivity analysis, student attendance was determined to be the most significant factor explaining the variations in course grades, followed by GPA, with course delivery mode ranked third. Our findings also indicate that a hybrid course delivery mode has positively impacted course grades as opposed to online or face-to-face course delivery alone.