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As a key tool in implementing monetary policy and government financing, government bonds play an essential role in financial markets. By means of Islamic financial innovations in the Islamic capital market, the instrument of Islamic treasury bill is published and tradable in the over-the-counter market. Islamic treasury bills have many risks in terms of execution, including the ease of trading and liquidity in the secondary market. Therefore, this research aims to examine some microstructural elements of government bonds using a vector autoregressive model. In this article, the effect of trading volume on bid-ask spread of orders has been investigated. To survey the impact of these variables, the vector autoregressive (VAR) model has been used on intraday data of 17 symbols of Islamic treasury bills in the over-the-counter market, which had the most trading days from 2021 to September 2023. According to the studies, there is a significant relationship between the trading volume and bid-ask spread only in Islamic treasury bills with long-term maturity. Therefore, the effect of the bid-ask spread of orders in different periods is greater than the trading volume, especially in longer-term Islamic treasury bills. Hence, in this research, by analyzing the impulse response function, if there is a shock on the variables, the effect of the trading volume's shock remains for several periods and affects unremarkably the bid-ask spread of orders in most of the short-term and long-term Islamic treasury bills, while effects of the bid-ask spread shocks during initial periods for long-term and short-term Islamic treasury bills is excellent, but decrease sharply during the following periods. These results help traders pay attention and reduce the risk of trading in the over-the-counter market, specifically the long-term treasury bills. [ABSTRACT FROM AUTHOR]

Objective The information-based model is one of the prevalent frameworks in the market microstructure literature, examining trader behavior in the presence of information asymmetry and its subsequent impact on price formation. Information asymmetry significantly contributes to market inefficiency, manifesting in traders' transactions and orders. Informed traders, possessing knowledge of an asset's intrinsic value through access to comprehensive market information, engage in high-volume transactions and orders to maximize their informational advantages. These value-relevant orders influence the actions of other market participants and consequently affect market prices. Given the critical role of information asymmetry and its effects on traders' order behavior, the study of orders--rather than mere transactions--has gained prominence. Analyzing traders' behavioral patterns through their order prices, volumes, and types provides a clearer understanding of the price effects resulting from market participants' decisions. Considering the price impact of orders and the significance of the secondary market for Islamic treasury bills as a pivotal tool in the central bank's monetary policy and government financing, this study aims to investigate the price effects of order flow on the price fluctuations of Islamic treasury bills in the market. Methods In this article, according to the price effect of orders in the market, the order flow imbalance variable that shows the changes in supply and demand volume in the first line of the order book has been used. Therefore, the vector autoregressive model has been used to investigate the effect of order flow imbalance on treasury bill price changes. Therefore, to check the price effect of orders, 17 symbols of treasury bills in the over-the-counter market from 2021 to September 2023, which had the most trading days compared to other treasury bills, were used. Therefore, in this research by using the vector autoregressive model and analyzing it through the impulse response function, the effect of order flow imbalance on price changes is investigated in three categories of treasury bills with different maturities to investigate the impact of information asymmetry on orders. Results According to the findings obtained in this research, the order flow imbalance variable in long-term maturity treasury bills had a greater effect on price changes. Moreover, based on the analyses conducted using the impulse response function, in the event of a shock, the effect of order flow imbalance in long-term maturity treasury bills will remain for extended periods and affect price changes. However, the magnitude of the shock's effect on price changes is much greater than that of the order flow imbalance variable. Conclusion Therefore, it can be concluded that in long-term maturity treasury bills, the effect of information asymmetry in the order flow is greater than the other bills and can lead to price volatility. Hence, traders should pay more attention to the supply and demand volume of orders and other factors to prevent potential losses. [ABSTRACT FROM AUTHOR]

Background: In medical education, determining the strengths, weaknesses, desirability, and success of training courses from the perspective of its stakeholders is of particular importance because it can be the basis for subsequent decisions. Objectives: This cross-sectional research investigated the virtual summer semester from the perspectives of its many stakeholders. Methods: We conducted a multi-methods cross-sectional study. First, remote qualitative interviews were conducted with teachers and heads of departments to explain the features of the virtual summer semester course from their perspective. Twelve teachers and heads of departments participated in qualitative interviews using the intensity sampling method. A matrix was created in Microsoft Word according to the qualitative interview questions (web and telephone), and the answers were categorized based on thematic review. In the second phase, a mini literature review was conducted to develop the desirability and success indicators of online learning courses using questionnaires and checklists. In the third phase, a questionnaire-based survey was conducted among students (82 people). In the last phase, a checklist-based survey was performed among various stakeholders (100 people). Quantitative and qualitative data about the virtual summer semester course have been collected from 194 individuals. Results: Based on the thematic review of remote qualitative interviews and created matrix, strengths, weaknesses, and suggestions for improving this course were categorized. Based on descriptive statistics, the mean and SD of the age of teachers and heads of departments participating in the interview was (52.5 ± 8.68), and the mean and SD of the age of students participating in the questionnaire-based survey was (22.10 ± 2.78). In addition, the mean and SD of the age of students, teachers, heads of departments, and course executive team participating in the checklist-based survey were respectively (22.5 ± 3.5), (49.1 ± 7.5), (54.0 ± 2.8) and (34.3 ± 4.9). The findings of the questionnaire-based survey revealed that about 91% of the students have a positive attitude towards the course. In this regard, the average score of the students on all items about the desirability of the course was higher than the hypothetical average (P = 0.048). In addition, the quantitative findings indicated that all the stakeholders had evaluated the course as successful in terms of the organizational capacity of Smart University of Medical Sciences, pedagogy and educational effectiveness, and sufficient and capable human resources. In this regard, the average score of the course success factors differs from stakeholder to stakeholder for the organizational capacity (P = 0.004) and for the two components of pedagogy and educational effectiveness (P = 0.035) and sufficient and competent human resources (P = 0.043). Conclusions: According to the results and the conditions of the COVID-19 pandemic and its effect on medical education, these short-term and intensive structured virtual courses can be expanded and developed into regular semesters. The course's sustainability can be maintained by continual improvement. [ABSTRACT FROM AUTHOR]

Introduction: Teaching-learning is the heart of medical education in the clinical setting. The objective of this research was to develop a conceptual model of effective clinical teaching in undergraduate medical education and conceptualize its operational framework based on the best fit approach. Materials and methods: This research consisted of three sub-studies conducted using a multi-method approach. The first sub-study was conducted using a qualitative meta-synthesis approach. The second sub-study used Clarke's situational analysis approach as a postmodern version of grounded theory. Finally, the third sub-study was designed in two stages. First, it was conducted using the expert panel, in the second step, framework of synthesis based on best fit, and the framework of Ottenhoff- de Jonge et al., which formed the basis of this study. Results: In the first sub-study, qualitative evidence on the factors of effective teaching-learning in clinical education was synthesized into five dimensions. Based on the second sub-study, the clinical teaching-learning situation in undergraduate medical education in Iran was represented in three maps, including situational, social worlds/arenas, and positional. Finally, in the third sub-study, based on model modification and development in the expert panel, the effective teaching-learning dimensions were developed into behavioral, social, pedagogical, technology, contextual, educational leadership, and financial dimensions. In the second step, based on the framework of Ottenhoff- de Jonge et al., a three-dimensional matrix was developed concerning epistemological orientations about teaching and learning. Discussion: Moving from a single teaching-centered and learning-centered orientation to a teaching-learning-centered orientation is required for effective teaching-learning in clinical medical education. [ABSTRACT FROM AUTHOR]

Background: Educational evaluation is one of the main pillars of educational systems, and course evaluation is a survey that students or course members complete at the end of a class or academic course. This study aims to evaluate the 'Artificial Intelligence Fall School Program' at Smart University of Medical Sciences. Methods: This study was conducted by collecting on various aspects of the course, including the course structure, teaching methods, instructors, scientific evaluations, and pre- and post-course tests. The course evaluation was conducted using an online questionnaire. In the initial phase of the study, the sample size was determined to be 96 participants, as calculated using Cochran's formula. The research data were statistically analyzed at two levels: descriptive and inferential. Descriptive analysis was performed using statistical indicators such as frequency, percentage, and mean. The inferential analysis was conducted using the paired t test. Analyses were performed using SPSS 22. Results: From the viewpoint of the participants, all artificial intelligence (AI) schools in the field of medical sciences were deemed satisfactory. A paired t test was used to analyze and compare the pre-test and post-test scores of participants in the Fall AI schools. The results indicated an increase in the post-test scores of participants, following their involvement in the seven-week AI schools, compared to their pre-test scores. Conclusion: This evaluative study offers crucial insights into the effectiveness of the "Fall AI Schools" training program in fostering AI proficiency among medical professionals. The quantitative findings reveal a statistically significant positive response and learning outcomes among the participants across the seven specialized schools. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the critical periods in the economy that are characterized by unusual and large fluctuations in macroeconomic indicators, like those measuring inflation and unemployment. We analyze U.S. data for 70 years from 1948 until 2018. To capture their fluctuation essence, we concentrate on the non-Gaussianity of their distributions. We investigate how the non-Gaussianity of these variables affects the coupling structure of them. We distinguish "regular" from "rare" events, in calculating the correlation coefficient, emphasizing that both cases might lead to a different response of the economy. Through the "multifractal random wall" model, one can see that the non-Gaussianity depends on time scales. The non-Gaussianity of unemployment is noticeable only for periods shorter than one year; for longer periods, the fluctuation distribution tends to a Gaussian behavior. In contrast, the non-Gaussianities of inflation fluctuations persist for all time scales. We observe through the "bivariate multifractal random walk" that despite the inflation features, the non-Gaussianity of the coupled structure is finite for scales less than one year, drops for periods larger than one year, and becomes small for scales greater than two years. This means that the footprint of the monetary policies intentionally influencing the inflation and unemployment couple is observed only for time horizons smaller than two years. Finally, to improve some understanding of the effect of rare events, we calculate high moments of the variables' increments for various q orders and various time scales. The results show that coupling with high moments sharply increases during crises. [ABSTRACT FROM AUTHOR]