مطالب

Contrairement à l’idée répandue qui réduit le silence à une absence ou à un simple vide, il importe d’envisager celui-ci comme unélément constitutif du langage et du discours. Dans le Masnavi de Jalâl od-Din Rûmî, figure majeure du soufisme et de la littérature persane, le silence apparaît non pas comme une négation de la parole, mais comme une composante essentielle de la production du sens. L’objectif de cette recherche est d’analyser les fonctions sémiotiques du silence dans le Masnavi et de montrer son rôle fondamental dans la dynamique discursive et spirituelle de l’œuvre. La présente étude relève de la recherche théorique et descriptive, fondée sur une analyse textuelle et sémiotique. Elle mobilise la grille conceptuelle de la sémiotique peircienne, articulée avec la sémiotique discursive, pour examiner le fonctionnement du silence dans le discours mystique de Rûmî. L’approche repose principalement sur l’étude des passages du Masnavi où le silence est explicitement évoqué ou se manifeste implicitement à travers la rétention de l’énonciation, les interruptions narratives ou les injonctions au mutisme. Les résultats montrent que le silence, loin d’être neutre, constitue un signe pluriel et dynamique. Il assume différentes valeurs sémiotiques (icône, indice, symbole) et s’articule aux autres signes matériels et discursifs pour former une chaîne interprétative continue. Sur le plan plus spécifique, il apparaît comme une présence signifiante, à la fois linguistique et spirituelle.

Moral identity refers to those characteristics and attributes that play an important role in the formation of an individual’s identity. Indeed, the role performed by moral attributes in the evolution of a person’s identity is such that quite a few of today’s scholars maintain the oneness of the “self/psyche” and “moral character”. Adopting this approach, researchers have set out to produce a wide range of research works in the field of moral philosophy. The present study is an attempt to measure the effect of three components of ‘exercise’, ‘nutrition’, and ‘sleep’ on shaping moral character from Avicenna’s viewpoint. The findings of the present research illuminate the fact that the latter three elements have a direct influence on an individual’s temperament, whereby suitable grounds are created in which moral character develops. Avicenna holds that this statement may not challenge man’s will and authority. The reason is that every individual–by identifying his own physical coordinates and applying the most apt instructions–can approximate his temperament to moderation. As a result, the necessary ground for developing the most desirable moral character is created.

This study aims to apply automatic machine-learning approaches using genetic algorithms to enhance heart disease prediction. Heart disease has remained the major cause of mortality in the world, necessitating an effective and timely diagnosis. Most current diagnostic and assessment processes are lengthy and expensive, relying heavily on clinical expert knowledge. To help address these issues, machine learning approaches, which derive their utility from examining substantial datasets for the recognition of patterns, have emerged as a potential solution, providing solutions beyond those achievable by human recognition alone. Genetic algorithms are also suited to addressing these issues as they mimic natural evolution to perfect high-caliber machine-learning models, feature selection, and parameter selection in machine-learning applications. This study examines the utilization of genetic algorithms working alongside AutoML frameworks to improve accuracy in heart disease predictions. Reducing to the best combination of attributes and the optimum parameters for each attribute is a time-consuming task, so automating this aspect of the process allows for more accurate and prompt predictions, consequently reducing the manual work. The AutoML approach followed in this research is TPOT, which uses genetic algorithms to ascertain optimally designed machine-learning pipelines. The application of AutoML, together with genetic algorithms, is the most prominent finding that yields a significant improvement in the quality of the predictions for heart disease compared to the traditional assessment approaches, with an accuracy of 93.8%. This approach will enhance diagnostic accuracy and enable early diagnosis, thereby reducing the likelihood of misdiagnoses or ineffective treatments and ultimately lowering associated costs.

Background: The effects of wearing common masks during maximal exercise activities on individuals remain unclear.Aim: This study aimed to examine the effect of wearing surgical and N-95 masks on physiological, perceptual, and performance responses in healthy men performing a maximal shuttle run test.Materials and Methods: Fifteen healthy men (age 23.78 ± 2.0) participated in three sessions: without a mask, with a surgical mask, and with an N-95 mask, with a one-week interval between each session. After each test, physiological variables (heart rate, blood lactate concentration, and oxygen saturation), perceptual variables (comfort/discomfort and perceived exertion), and performance variables (maximum oxygen consumption and shuttle run test duration) were measured. Data were analyzed using repeated measures ANOVA to compare differences between the conditions.Results: Significant differences were observed between the conditions with and without a mask in terms of oxygen saturation (P= 0.043), blood lactate concentration (P= 0.026), and perceived exertion, maximum oxygen consumption, and shuttle run test duration (P= 0.001), with the no-mask condition demonstrating superior outcomes in all variables. The type of mask (surgical or N-95) did not significantly affect the measured variables. Wearing both masks led to an increased sensation of moisture, heat, shortness of breath, and fatigue.Conclusion: Wearing masks, especially during intense physical activities, may impair physiological and performance metrics, recommending mask-free outdoor settings when feasible.

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.