HighlightsCase presentation of a patient treated with subclavian artery stenting.Case presentation of a patient treated with transposition of the left subclavian artery onto the left common carotid artery.Case presentation of a patient treated with carotid- subclavian artery bypass with a PTFE graft.Case presentation of a patient treated with carotid to axillary bypass.Discussion and literature review of methods and indications of treatment of subclavian artery occlusive disease.
With the aim of appraising the impact of Emergency Remote Teaching (ERT) amidst the COVID-19 pandemic on college students, an online survey was conducted in December 2020 on a total of 588 undergraduate students at the American University of Sharjah in the United Arab Emirates. The purpose of the study was to probe into the perceptions of college students regarding their learning process and its influence on their mental health with the abrupt transition from face-to-face classes to ERT in the Spring 2020 semester. A comprehensive analysis was performed using structural equation modeling and other statistical techniques to reveal crucial results associated with the factors that have an effect on the students’ psychological distress, such as quality of courses, academic performance, and readiness for future work or studies. Findings suggest that the students’ perceived quality of courses under ERT has a significant impact on their academic performance and readiness for future work or studies. Moreover, they indicate that these factors serve as a vital mediating role in provoking psychological distress among the students. The study also shows that gender, previous history of anxiety/distress, education being at risk due to financial issues caused by COVID-19, and year of study significantly affect the students’ distress levels. In order to ensure and prioritize the well-being of college students during these turbulent times, new strategies are urgently needed to develop and enhance resilient ERT environments in higher education. The study concludes with limitations and suggestions for further research.
In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a common concern in feature selection is that different approaches can give very different results when applied to similar datasets. Aggregating the results of different selection methods helps to resolve this concern and control the diversity of selected feature subsets. In this work, we implemented a general framework for the ensemble of multiple feature selection methods. Based on diversified datasets generated from the original set of observations, we aggregated the importance scores generated by multiple feature selection techniques using two methods: the Within Aggregation Method (WAM), which refers to aggregating importance scores within a single feature selection; and the Between Aggregation Method (BAM), which refers to aggregating importance scores between multiple feature selection methods. We applied the proposed framework on 13 real datasets with diverse performances and characteristics. The experimental evaluation showed that WAM provides an effective tool for determining the best feature selection method for a given dataset. WAM has also shown greater stability than BAM in terms of identifying important features. The computational demands of the two methods appeared to be comparable. The results of this work suggest that by applying both WAM and BAM, practitioners can gain a deeper understanding of the feature selection process.
To mitigate the curse of dimensionality in high-dimensional datasets, feature selection has become a crucial step in most data mining applications. However, no feature selection method consistently delivers the best performance across different domains. For this reason and in order to improve the stability of the feature selection process, ensemble feature selection frameworks have become increasingly popular. While many have examined the construction of ensemble techniques under various considerations, little work has been done to shed light on the influence of the aggregation process on the stability of the ensemble feature selection. In contribution to this field, this work aims to explore the impact of some selected aggregation strategies on the ensemble’s stability and accuracy. Using twelve classification real datasets from various domains, the stability and accuracy of five different aggregation techniques were examined under four standard filter feature selection methods. The experimental analysis revealed significant differences in both the stability and accuracy behavior of the ensemble under different aggregations, especially between score-based and rank-based aggregation strategies. Moreover, it was observed that the simpler score-based strategies based on the Arithmetic Mean or L2-norm aggregation appear to be efficient and compelling in most cases. Given the data structure or associated application domain, this work’s findings can guide the construction of feature selection ensembles using the most efficient and suitable aggregation rules.
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