Coronavirus disease 2019 (COVID-19) pandemic caused infection in a season when influenza is still prevalent. Both viruses have similar transmission characteristics and common clinical manifestations. Influenza has been described to cause respiratory infection with some other respiratory pathogens. However, the information of COVID-19 and influenza coinfection is limited. In this study, we reported our coinfected cases and reviewed the literature. We included all COVID-19 diagnosed patients. All patients with a presumed diagnosis of COVID-19 were routinely screened for influenza. Their thorax radiology was reviewed for COVID-19-influenza differentiation. During the study period, 1103 patients have been diagnosed with COVID-19. Among them, six patients (0.54%) were diagnosed coinfected with influenza. There have been 28 more coinfected patients reported. Laboratory-based

The present study demonstrates that endurance exercise capacity in a hot environment falls progressively as solar radiation increases.

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It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining, simply because characteristics of quantitative data are in general unknown. Besides, it is unrealistic that the most appropriate fuzzy sets can always be provided by domain experts. Motivated by this, in this paper we propose an automated method for mining fuzzy association rules. For this purpose, we first present a genetic algorithm (GA) based clustering method that adjusts centroids of the clusters, which are to be handled later as midpoints of triangular membership functions. Next, we give a different method for generating the membership functions by using Clustering Using Representatives (CURE) clustering algorithm, which is known as one of the most efficient clustering algorithms described in the literature. Finally, we compared the proposed GA-based approach with other approaches from the literature. Experiments conducted on 100K transactions from the US census in the year 2000 show that the proposed method exhibits a good performance in terms of execution time and interesting fuzzy association rules.

Association rules form one of the most widely used techniques to discover correlations among attribute in a database. So far, some efficient methods have been proposed to obtain these rules with respect to an optimal goal, such as: to maximize the number of large itemsets and interesting rules or the values of support and confidence for the discovered rules. This paper first introduces optimized fuzzy association rule mining in terms of three important criteria; strongness, interestingness and comprehensibility. Then, it proposes multi-objective Genetic Algorithm (GA) based approaches for discovering these optimized rules. Optimization technique according to given criterion may be one of two different forms; The first tries to determine the appropriate fuzzy sets of quantitative attributes in a prespecified rule, which is also called as certain rule. The second deals with finding both uncertain rules and their appropriate fuzzy sets. Experimental results conducted on a real data set show the effectiveness and applicability of the proposed approach.