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The healthcare system is a complex system which exhibits conditions of uncertainty, ambiguity emergence that incurs incoming patient congestion. Discrete event simulation (FlexSim) is considered as a viable decision support tool in analyzing a system for improvement. Using a data-driven discrete event simulation approach, this paper portrays a comprehensive analysis to maximize the number of patients in an on-campus clinic, located at Mississippi State University. The outcome of the analysis of current system exhibits that deploying a few nurse practitioners results in bottlenecks which decreases the systems’ throughput substantially due to the overall longer patients’ waiting time.  Access to the laboratory is characterized through multi-server queuing network, arrival process is followed discrete distributions, and batch sizes and arrival times are stochastic in nature. In an effort to plummet inpatient congestion at the outpatient clinic, by using empirically calibrated simulation model, we will figure out the best balance between the number of the lab technician and incoming patient during working hour. An analysis of optimal solutions is demonstrated, which is followed by recommendation and avenues for future research.

This study investigates the relationship between the personality type and cognitive-metacognitive strategies utilized by test-takers in reading comprehension tests. One hundred undergraduate Iranian English Foreign Learning (EFL) students participated in a reading comprehension test followed by a questionnaire and the Myers & Briggs Type Inventory. The questionnaire consisted of 30 cognitive-metacognitive items (Phakiti, 2003). These questions inquired about the thought process that occurred while completing the test. The 93-item Myers-Brigs Type Indicator (MBTI) questionnaire is a tool that provides individuals with a personality type. The study employed a quantitative data analysis where the input data was analyzed in two ways. First, descriptive statistics were used to describe the sample characteristics, and then a two-way ANOVA was calculated to obtain a general view of the relationship between the variables. The data analysis resulted in the identification of 14 personality types along with three groups of readers distinguished by their reading comprehension test scores as highly successful, moderately successful, or unsuccessful. However, the results suggested that there were no significant relationships between personality types of test-takers and the cognitive-metacognitive strategies utilized during a reading comprehension test. Using a 90 percent Confidence Interval (CI), there was meaningful interaction between the personality traits (Extroversion/Introversion and Judging/Perceiving) of Iranian EFL test-takers and their use of cognitive-metacognitive strategies.

In a wide range of industries and academic fields, artificial intelligence is becoming increasingly prevalent. AI models are taking on more crucial decision-making tasks as they grow in popularity and performance. Although AI models, particularly machine learning models, are successful in research, they have numerous limitations and drawbacks in practice. Furthermore, due to the lack of transparency behind their behavior, users need more understanding of how these models make specific decisions, especially in complex state-of-the-art machine learning algorithms. Complex machine learning systems utilize less transparent algorithms, thereby exacerbating the problem. This survey analyzes the significance and evolution of explainable AI (XAI) research across various domains and applications. Throughout this study, a rich repository of explainability classifications and summaries has been developed, along with their applications and practical use cases. We believe this study will make it easier for researchers to understand all explainability methods and access their applications simultaneously.

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Previously, Morteza received a bachelor's degree in Mechanical Engineering from the University of Tehran and a master's degree in Business Administration from Mazandaran University of Science and Technology in 2012 and 2014, respectively. Currently, Morteza is working as a graduate research assistant on an NSF funded project in the area of systems thinking. Morteza's works have been published in prestigious journals including,

This paper compares and contrasts the hedge strategies through derivative instruments by Indian and USA corporate houses. The derivative instruments have little predictive power in explaining corporate hedging strategies both in the USA and Indian firms. The purpose of the study is to provide a setting where reconciling conflicting results from the literature may be appropriate and to compare different hedge strategies in a specific period in two different countries (USA and India). The evidence based on multivariate empirical relations between hedging in American firms and firm's characteristics fails to provide any support for any of the tested hypotheses except for profitability represented by dividend yield. We conclude that the relationship between hedging and dividend yield in the proposed model is negative. The same analysis conducted for Indian companies has shown that there is no statistically significant explanatory variable for hedging; therefore, it is not dependent on any of the predicted theories of hedging. On the other hand, we find some significant relationships between firms' characteristics. Large Indian firms use internal hedge strategies rather than market strategies, such as derivatives. The derivative market development then could play a major role in terms of risk management of firms across countries.