مطالب

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.

INTRODUCTION: The COVID-19 pandemic has revealed major shortcomings in healthcare data systems worldwide, particularly the need for accessible and transparent data sharing. In Iran, these shortcomings were particularly visible due to the lack of a structured open data network in the healthcare sector. Hence, this study addresses the barriers to open data networks in healthcare. METHODS: This study used Interpretive Structural Modeling (ISM) supported by MICMAC analysis to examine and prioritize barriers to the establishment and use of open data platforms in the Iranian healthcare system. Data were collected through expert consultations with eight experts in the field of health information and policy. FINDINGS: The analysis revealed significant barriers to implementation, including lack of government coordination, high startup costs, and inadequate technology infrastructure. For use, the most prominent barriers included the lack of data standards, poor data management, and uncontrolled growth of unstructured data. Many of these barriers were interrelated, and some acted as root causes that hindered systemic progress. CONCLUSION: Addressing these challenges requires coordinated strategic efforts focused on increasing ICT competencies, upgrading infrastructure, and strengthening institutional support. Establishing a functional open data network is essential to improve public health outcomes and enable faster responses to future health crises in Iran.

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.

The obligatory prevalence of online education during the COVID-19 pandemic has drawn researchers’ attention to the challenges involved in foreign language pedagogy in such virtual educational contexts. Against this backdrop, this study investigated the impact of online self-regulated learning, use of communication strategies, and test anxiety on Iranian English as a Foreign Language (EFL) learners’ online speaking test performance. For this purpose, 132 EFL learners were given the e-Oxford Quick Placement Test and the speaking part of a sample A2 Key and B1 Preliminary test. Next, translated versions of the given measures were administered to the pre- and intermediate EFL learners and the obtained data were subjected to Structural Equation Modeling analyses that verified strong links between online self-regulated learning and the use of communication strategies, test anxiety and online self-regulated learning, and test anxiety and EFL learners’ use of communication strategies. Furthermore, the direct impacts of online self-regulated learning and use of communication strategies on learners’ online speaking test performance were verified; however, test anxiety was found to indirectly impact the learners’ online speaking test performance through its negative effect on EFL learners’ online self-regulated learning and use of communication strategies. In addition, online self-regulated learning turned out to be the strongest predictor of the learners’ online speaking test performance. As for the implications of the findings, it appeared that the attested model lends support to Bachman and Palmer’s (1996) language use framework illustrating test performance as a vulnerable construct affected by test takers' attributes and features of the test tasks and the impact of construct-irrelevant factors like test takers’ personal characteristics on their test performance.

As a driver of national development, the construction industry has in recent decades gained substantial environmental importance due to its extent and effects on life, business, and the environment. Due to its economic, social, and environmental impacts, the construction industry has been discussed extensively in the sustainable development literature, contributing to development of Green Building Rating Systems (GBRSs) worldwide. This study introduces some of the most popular GBRSs and employs a Multi- Criteria Decision Making (MCDM) approach to investigate the evaluation indices of GBRSs. GBRS indices were obtained through a comprehensive literature review. To validate the identified indices, 400 questionnaires were handed out to respondents, with 260 completed questionnaires being returned. The responses were statistically analyzed, identifying twenty important indices, which were modeled using Interpretative Structural Modeling (ISM) and categorized using MICMAC analysis. The ISM model showed that the barriers arising from unique characteristics of a region were the main GBRS factor in Iran.

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.