درخت حوزه‌های تخصصی

The supply chain of perishable products has high complexity due to the short lifespan of the products. For this reason, it is necessary to evaluate various issues in the supply chain such as location, inventory and logistics in an integrated manner by using flexible tools. The purpose of this research is to present an optimization approach based on event-factor-based discrete hybrid simulation for the integrated optimization of facility location, inventory control, fleet composition and routing in the supply chain of perishable products.The optimization process is performed using meta-heuristic algorithms. In order to conduct a case study, the supply chain of one of the largest producers of dairy products in the country has been considered, and due to the high demand and production of ice cream, this product has been considered as a perishable product of the supply chain. The results show that the integrated optimization of the research topic leads to a significant improvement in product waste, reducing the time of processing and delivering orders, and reducing the number of fleets.

Timurid coins serve as a mirror reflecting the significant economic and political events of the era, and their study can provide deeper insights into the economic and social dynamics of the Timurid period. This research examines the Timurid coins housed in the Museum of Bu-Ali Sina Mausoleum using X-ray fluorescence spectroscopy. A specialized review of the initial results obtained from XRF spectroscopy, particularly in numismatic studies, holds significant importance and can be a fundamental step toward reducing errors and enhancing precision in interdisciplinary analyses. XRF technology, as a non-destructive method, enables precise examination of the chemical composition and identification of elements present in coins. However, without meticulous monitoring and comprehensive reevaluation of the obtained results, misinterpretations and uncertainty in historical analyses may arise. The research questions focus on the value of reinterpreting and analyzing XRF spectroscopy data and their role in identifying counterfeit coins and understanding economic and cultural characteristics in numismatic studies. The central hypothesis of the study posits that raw XRF spectroscopy data do not provide accurate results, and in-depth analysis by experts is required for a precise examination of coins. This research also highlights the importance of detailed spectrum analysis, technical challenges in data interpretation, and the use of interdisciplinary approaches to enhance the accuracy of results. The findings reveal that a more profound reinterpretation of XRF spectroscopy data significantly contributes to better understanding the chemical composition of coins and preserving cultural heritage treasures. It also provides deeper insights into the economic policies and activities of the Timurid period.

This paper investigates the role of Zen philosophy, as derived from haiku poetry, in the architectural design process. It explores the nature of Zen, its unique perspective on art and literature, and how this perspective is reflected in architectural design processes. The paper also examines how the creation of architectural spaces can be integrated with the poetry and (haiku) influenced by this philosophy. This research seeks to answer the question of how Zen thought can be applied in architectural design through the utilization of haiku poetry. A descriptive-analytical approach, combining inductive and deductive reasoning, is employed. Initially, Zen theories based on haiku are deductively applied to case studies. Subsequently, Zen architectural theories based on haiku are extracted from these case studies. Finally, the results are obtained by combining inductive and deductive methods. This paper aims to introduce haiku, elucidate the role of Zen philosophy in haiku poetry, and explore the influence of Japanese Buddhism on the architectural design process. The result of this research reveal that haiku is the product of immediate, intuitive experience and cannot be evaluated based on conventional poetic criteria. Haiku is a form of enlightenment or illumination, in which the poet perceives the vitality of objects and grasps the meaning of the ordinary and commonplace. Haiku poetry presents viewers with perspectives on nature, humanity, and life. By integrating literary concepts into the architectural design process, haiku can serve as a powerful cultural and artistic medium, fostering audience engagement with literature and cultivating a unique, philosophical understanding of phenomena.

The concept of management ability is one of the most important factors in determining and determining the audit fee. Management ability is a dimension of human capital in companies that is classified as intangible assets. Financial distress is also an important factor that may affect audit fees. The findings of this study contribute to the emerging literature on management ability and audit costs by showing whether auditing services pricing is a result of higher management ability in the ongoing financial crisis. In light of the above, this study seeks to examine the impact of managerial skills and auditing fees on the condition of financial distress. This research has been reviewed during a 10-year study from 2009 to 2018. The results of this study show that there is a negative and significant relationship between managerial ability and audit fees despite financial distress. There is also a significant inverse relationship between financial distress and management ability.

This study explores the impact of emotions on financial markets. Recent research highlights the role of psychological factors in financial crises. Investors, not always rational, base asset risk decisions on emotions and beliefs. Optimism, pessimism, and self-confidence influence decision making processes over time. Irrational investors can cause market prices to deviate from fundamental values. This emotional price anomaly can be seen as a persistent sentiment risk factor significantly affecting stock returns. The research investigates the effect of emotional shocks (positive and negative) on order flow imbalances and their impact on price movements. A three-stage clustering approach is used. The sample includes 172 companies listed on the Tehran Stock Exchange from 2021, with daily data extracted from Rahvard Navin software. Information on independent and dependent variables is analysed using a three-step clustering method to identify different time scales. Finally, regression analysis in MATLAB software is used to examine the relationship between variables within specified time intervals. The results reveal a positive relationship between positive shocks caused by order imbalances and company returns. This relationship is reversed for negative shocks. Three-stage clustering separates companies based on co-movement, revealing distinct behaviour and relationships between variables within each cluster. These findings demonstrate the effectiveness of the three-step clustering method for analysing company data.

The excess volatility puzzle refers to the observation of returns that cannot be explained only by fundamentals, and this research attempts to explain such volatilities using the concept of endogenous jumps and modelling them based on the generalized Langevin equation. Based on stylized facts, price behaviour in financial markets is not simply a continuous process, but rather jumps are observed in asset prices that may be exogenous or endogenous. It is claimed that the source of exogenous jumps is news, and the source of endogenous jumps is internal interactions between the agents. The goal is to extract these endogenous jumps as a function of the state variable and time. For this purpose, the generalized Langevin equation is introduced and it is shown that the parameters of this model can be extracted based on the Kramers-Moyal coefficients. The results of self-consistency tests to evaluate the accuracy of the Kramers-Moyal method in extracting the generalized Langevin equation show that this method has good accuracy. In a practical application of the aforementioned method, Ethereum cryptocurrency price data was used between October 2017 and February 2024 with a sampling rate of one minute. By simulating the extracted dynamics, the probability distribution of the first time passage of this cryptocurrency from a specific level was calculated, and an examination of the price behavior of this asset shows that the aforementioned distribution was extracted with good accuracy. The potential function, which is calculated from the first KM coefficient, will be a quadratic parabola for the studied process, and as a result, we have a stable equilibrium at the zero point. Also using the extracted dynamics we show that this model has good out-of-sample prediction ability.

This study explored the rhetorical strategies employed by Republican and Democratic nominees during the 2024 U.S. presidential campaigns, focusing on the discursive techniques used to appeal to partisan bases and shape voter perceptions. Grounded in Van Dijk’s Ideological Square—a theoretical framework for analyzing how language constructs ideological divides—the research investigated the role of positive self-presentation, negative other-presentation, and the strategic framing of political ideologies in the construction of party identity. A mixed-methods approach, combining both quantitative analysis of discourse patterns and qualitative examination of rhetorical strategies, uncovered the distinctive ways each party uses language to reinforce ideological commitments and polarize the electorate. The findings reveal significant differences in how Republicans and Democrats position themselves and their opponents, with Republicans often employing fear-based rhetoric, in-group/out-group distinctions, and a focus on "threat" narratives. In contrast, Democrats tend to emphasize unity, inclusivity, and collective responsibility, though occasionally at the risk of alienating moderates or dissenting voices within their own ranks. The study also highlights the growing role of digital media and algorithm-driven communication, demonstrating how technological platforms have amplified the impact of these rhetorical strategies, especially in the form of targeted misinformation and tailored inclusivity messages. The implications of the study extend to the fields of sociolinguistics, political communication, and media studies, emphasizing how language shapes ideological alignment, reinforces social divisions, and influences democratic processes in the digital age.

This study offers a full symbolic interactionist examination of Anne Tyler's novel A Slipping-Down Life, examining how identity is developed, acted out, and challenged by symbolic movements, emotional labor, and social recognition. With a focus on the protagonist Evie Decker, this study takes up seven primary questions of inquiry about the formation of identity in terms of performance, the influence of gender expectations, the utilization of music and silence as communication, and the intersections of trauma, emotion, and resistance in terms of negotiating identity. Basing itself on the classic theoretical models constructed by Mead, Blumer, Goffman, and Cooley—and recent reformulations and explications provided by Charmaz, Burke and Stets, and feminist theorists such as Butler and Gilligan—this article places Evie's transformation within generic sociological and psychological contexts. Through acts of self-scarification, deliberate silence, and remaking maternity roles, Evie operates within a condition of marginality and misrecognition. Her experience demonstrates identity as an active, networked, and emotional process, with a strong impact of gender roles and social norms. The research points out that literature, and especially texts richly textured with sociological context, such as Tyler's, offers a fertile backdrop for comprehending identity formation as an emotional, symbolic, and performative process. By drawing upon knowledge from narrative psychology, feminist theory, and cultural sociology, this study highlights the benefit of applying interdisciplinary approaches to literary analysis and the sociological imagination.

English for Specific Purposes (ESP) education has gained prominence as a specialized field designed to address the distinct linguistic needs of learners. Although AI-enhanced instruction has been investigated within general English language learning, its implementation in ESP contexts particularly concerning vocabulary recall and retention has remained insufficiently explored. This study sought to evaluate the impact of AI-supported instruction on vocabulary recall and retention among Iranian ESP learners. Based on non-random convenience sampling, a total of 48 undergraduate students in an ESP course from a university in a southeastern province in Iran participated in the present quasi-experimental study. The participants were non-randomly assigned into two different groups: an experimental group (n=24) receiving AI-powered vocabulary instruction through Diffit, and a control group (n=24) receiving traditional print-based vocabulary instruction. Data were collected during a twelve-week intervention through AI-powered instruction of ESP vocabulary. To address the research questions, a univariate repeated measures analysis of covariate (ANCOVA) was employed. Findings revealed that AI-enhanced instruction significantly improved vocabulary recall and retention among ESP learners. The results offer robust evidence supporting the efficacy of AI-based instructional approaches in enhancing vocabulary learning outcomes within ESP settings.