مطالب

Background: Players are under more pressure in soccer matches. In those conditions, they must use visual information to make better decisions. However, few studies have investigated such conditions of soccer players.Aim: The aim of the present study investigates the effect of under-pressure conditions on decision-making and visual search behavior of soccer players.Materials and Methods: Eighteen soccer players with a mean age of 14.5 (and SD ±1.5) from Tehran Youth Premier League took part in this study. Decision-making task included 60 images of 3 simulated soccer conditions on a monitor including neutral (no pressure), result pressure, and monitoring pressure. During all of these conditions, eye movements were assessed by an eye tracker device.Results: The results of repeated measures ANOVA test showed the speed and accuracy of decision making, the number of fixations and the mean duration of eye fixation were significantly affected in all three conditions. In addition, the results showed a significant reduction in decision-making speed in two conditions of result and monitoring pressures and a significant increase in decision accuracy and the mean number and duration of eye fixation in the two conditions of result and monitoring pressures compared to neutral pressure.Conclusion: Due to the changes in visual and speed-accuracy mechanism of decision making and the importance of these factors in the successful performance of soccer players, it is recommended that soccer coaches create psychological pressure conditions during training sessions, ask players to see with better concentration and attention, and to decide more quickly and accurately so that they can better overcome the challenges in competition environment.

This article attempts to explore the influence of T. S. Eliot’s modernist poetics on the works of Iranian poets, Ahmad Shamlou and Sohrab Sepehri. It examines how these Iranian poets engaged with the concepts of “tradition,” “language,” and “myth” in their writings, drawing upon Eliot’s critical perspectives. The article begins with an overview of T. S. Eliot’s significance as an Anglo-American figure of twentieth century literature, and how his modernist themes and techniques resonated with Iranian poets navigating their own cultural transformations. It then reviews relevant secondary sources that provide critical frameworks for understanding the relationships between Eliot, Shamlou, Sepehri, and the broader modernist poetic tradition. The core discussion analyzes how Shamlou and Sepehri, inspired by Eliot’s innovative spirit, may have sought to redefine their engagement with Persian classical poetry. They utilized new approaches to language and expression to challenge established forms, especially through using defamiliarization and mythical method, while still drawing upon their rich literary heritage. By tracing these intertextual and cross-cultural connections, the article aims to contribute to a deeper understanding of the transnational circulation of modernist poetic ideals, especially Eliot’s preoccupations, and how they were reinterpreted and reinvigorated within the Iranian literary context. In fact, this comparative analysis sheds light on the complexities of cultural exchange and the evolution of modern poetry across geographic boundaries.

The preservation of invaluable cultural heritage assets, including ancient artifacts and historical sites, remains a critical global concern. Existing perimeter protection methods, such as CCTV systems, infrared sensors, and conventional surveillance technologies, often exhibit significant limitations in terms of spatial coverage, sensitivity to subtle intrusions, and adaptability to varying environmental conditions. This paper presents an innovative and highly effective solution utilizing fiber optic sensor (FOS) technology, which offers numerous advantages, including lightweight construction, immunity to electromagnetic interference, ease of installation, high sensitivity, and reliable performance across a broad range of applications. Specifically, the study introduces novel designs for distributed acoustic FOS systems based on Michelson, Mach-Zehnder, and Sagnac interferometric configurations. Through comprehensive theoretical analysis, simulation modeling, and experimental validation, the research explores their practical utility in protecting cultural heritage. Two detailed case studies—focused on the Louvre Museum in France and the ancient archaeological site of Persepolis in Iran—demonstrate the implementation, functionality, and real-world efficacy of the proposed systems. The findings highlight how these FOS networks, particularly when combined with advanced artificial intelligence and machine learning algorithms, can effectively detect, classify, and localize human-induced acoustic disturbances such as walking or digging, while minimizing false alarms caused by environmental noise. This enhanced discrimination capability not only improves overall security but also significantly reduces the need for extensive human surveillance. Moreover, these systems enable precise, continuous, and real-time 24-hour monitoring over vast indoor and outdoor areas. In conclusion, the integration of FOS technology offers a scalable, intelligent, and transformative approach to addressing the complex and evolving challenges associated with the long-term preservation and protection of cultural heritage sites around the world.

Several research investigations have indicated that asset returns exhibit notable skewness and kurtosis, which have a substantial impact on the utility function of investors. Additionally, it has been observed that Average Value-at-Risk (AVaR) provides a more accurate estimation of risk compared to variance. This study focuses on the computational challenge associated with portfolio optimization in an uncertain context, employing the Mean-AVaR-skewness-kurtosis paradigm.The uncertainty around the total return is con-sidered and analyzed in the context of the challenge of selecting an optimal portfolio. The concepts of Value-at-Risk (VaR), Average Value-at-Risk (AVaR), skewness, and kurtosis are initially introduced to describe uncertain variables. These concepts are then further explored to identify and analyse relevant aspects within specific distributions. The outcomes of this study will convert the existing models into deterministic forms and uncertain mean-AVaR-skewness-kurtosis optimization models for portfolio selection. These models are designed to cater to the demands of investors and mitigate their apprehensions.

Exploring L2 affective variables requires innovative research analytic approaches that can adequately address the complexity and dynamicity of the variables. Not always can the normality of distribution and linearity of relationships be assumed. Neither can the homogeneity of variance be always met. Latent profile analysis (LPA) is suggested to deal with the heterogeneity of data and non-linear relationships. It allows for hypothesis testing and model testing to tackle the ergodicity issue in second language acquisition (SLA) research from a person-centered approach. LPA, which primarily serves to classify a population or sample into several sub-groups, can be effectively employed in SLA research to classify L2 teachers or learners in terms of the different positive and negative emotions (e.g., enjoyment, boredom, anxiety, etc.) they experience while learning a foreign or second language. A classroom-based L2 learning experience occurs interactively with several personal and contextual variables involved. The relationship between any single affective variable can hardly be conceived as linear. Thus, LPA holds promises for dealing with these non-linear relations and provides insightful information about the profiles of learners or teachers concerning a particular affective variable. The purpose of this conceptual analysis is to provide a review of the basic tenets of LPA and to explain how it can contribute to the exploration of emotional variables in SLA. By identifying distinct emotional profiles, the study offers guidance on tailoring instructional strategies to address the specific emotional needs of language learners, thereby enhancing the effectiveness of language acquisition interventions.

 Task sequencing (TS) has been a focus of empirical investigations in task-based language teaching (TBLT). Previous studies on TS failed to represent classroom contexts because they did not provide learners with corrective feedback (CF). This study aimed to address this gap by examining the combined effects of TS and CF on writing complexity, accuracy, and fluency (CAF). To do so, 113 upper-intermediate EFL learners were selected and divided into two groups. Participants took a pretest at the beginning of the study. Each group performed two three-task sets in simple-to-complex(S-C) or complex-to-simple(C-S) order. In each group, the errors in the first set of tasks were given CF by error codes (ECs), while the errors in the second set were only underlined. Participants in both groups were asked to revise their texts based on the provided CF and take a posttest at the end of the study. Multivariate analysis of variance (MANOVA) was run to analyze the pretest and posttest data. The results showed that the group performing tasks in S-C order and receiving ECs outperformed the others. Findings supported the SSARC (Stabilize, Simplify, Automatize, Reconstruct, Complexify) model. The study has implications for material development.