Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
The objective of this study is to explore a noble application of the improved homotopy perturbation procedure bases in structural engineering by applying it to the geometrically nonlinear analysis of the space trusses. The improved perturbation algorithm is proposed to refine the classical methods in numerical computing techniques such as the Newton–Raphson method. A linear of sub-problems is generated by transferring the nonlinear problem with perturbation quantities and then approximated by summation of the solutions related to several sub-problems. In this study, a nonlinear load control procedure is generated and implemented for structures. Several numerical examples of known trusses are given to show the applicability of the proposed perturbation procedure without considering the passing limit points. The results reveal that perturbation modeling methodology for investigating the structural performance of various applications has high accuracy and low computational cost of convergence analysis, compared with the Newton–Raphson method.
Semantic interoperability of distributed electronic health record (EHR) systems is a crucial problem for querying EHR and machine learning projects. The main contribution of this paper is to propose and implement a fuzzy ontology-based semantic interoperability framework for distributed EHR systems. First, a separate standard ontology is created for each input source. Second, a unified ontology is created that merges the previously created ontologies. However, this crisp ontology is not able to answer vague or uncertain queries. We thirdly extend the integrated crisp ontology into a fuzzy ontology by using a standard methodology and fuzzy logic to handle this limitation. The used dataset includes identified data of 100 patients. The resulting fuzzy ontology includes 27 class, 58 properties, 43 fuzzy data types, 451 instances, 8376 axioms, 5232 logical axioms, 1216 declarative axioms, 113 annotation axioms, and 3204 data property assertions. The resulting ontology is tested using real data from the MIMIC-III intensive care unit dataset and real archetypes from openEHR. This fuzzy ontology-based system helps physicians accurately query any required data about patients from distributed locations using near-natural language queries. Domain specialists validated the accuracy and correctness of the obtained results.
The paper focused on an experimental study on the microstructural, mechanical, and wear characteristics of 15 wt.% alumina (Al2O3) particulates with an average particle size of 20 µm, reinforced in Al2014 alloy matrix composite as-cast and heat-treated samples. The metal matrix composite (MMC)samples were produced via a novel two-stage stir-casting technique. The fabricated composite samples were subjected to evaluate hardness, tensile strength, fatigue behavior and wear properties for both as cast and T6 heat-treated test samples. The Al2014 alloy and Al2014-15 wt.% Al2O3 MMCs were in solution for 1 h at a temperature of 525 °C, quenched instantly in cold water, and then artificially aged for 10 h at a temperature of 175 °C. SEM and X-ray diffraction analyses were used to investigate the microstructure and dispersion of the reinforced Al2O3 particles in the composite and the base alloy Al2014. The obtained results indicated that the hardness, tensile and fatigue strength and wear resistance increased when an amount of Al2O3 particles was added, compared to the as-cast Al2014 alloy and it was observed that after subjecting the same composite samples to heat treatment, there was further enhancement in the mechanical and wear properties in the Al2014 matrix alloy and Al2014-15 wt.% Al2O3 composite samples.
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