درخت حوزه‌های تخصصی

Methods and Materials: A quasi-experimental design with a pretest-posttest-follow-up framework was employed, involving two experimental groups (ACT and RT) and one control group. The sample consisted of 54 incompatible married individuals (18 per group) recruited from psychological centers in District 7 of Tehran. Participants completed the Spanier Dyadic Adjustment Scale at pretest, posttest, and follow-up stages. The ACT intervention comprised 8 weekly sessions focusing on mindfulness, acceptance, and value-based actions, while the RT intervention consisted of 8 sessions emphasizing need satisfaction and responsible behavior. Data were analyzed using repeated measures ANOVA to assess changes over time and between groups. Findings: Both ACT and RT significantly improved overall marital adjustment compared to the control group, with significant interaction effects observed for dyadic cohesion (F = 11.161, p = 0.001, η² = 0.247), dyadic consensus (F = 24.787, p = 0.001, η² = 0.422), and affectional expression (F = 22.477, p = 0.001, η² = 0.398). RT demonstrated greater effectiveness in enhancing dyadic cohesion and consensus, while ACT showed stronger outcomes in improving affectional expression. Post-hoc analyses revealed significant mean differences between pretest and posttest for dyadic cohesion in RT (mean difference = -8.167, p < 0.001) and affectional expression in ACT (mean difference = 2.694, p < 0.001). Follow-up assessments indicated sustained improvements, with RT maintaining higher scores in dyadic cohesion (mean = 15.139) and consensus (mean = 25.694), and ACT in affectional expression (mean = 4.78). Conclusion: Both ACT and RT are effective interventions for improving marital adjustment, with RT being more beneficial for addressing behavioral and communication aspects of marital conflict, and ACT more effective in fostering emotional intimacy.

Objective: The objective of this study is to explore the lived experiences of Zoroastrian-Muslim interfaith marriages in Iranian society, focusing on the challenges, outcomes, and strategies adopted by couples. Methods: This qualitative study employed interpretative phenomenological analysis (IPA) to understand the participants' perspectives. Data were collected through in-depth, semi-structured interviews with 13 male participants involved in interfaith marriages between Zoroastrians and Muslims in Iran. Theoretical sampling was used, and interviews were analyzed using open, axial, and selective coding, with data saturation achieved after the 13th interview. The Atlas.ti software was used for coding and analysis to derive themes from the participants' narratives. Findings: The findings reveal both positive and negative outcomes of interfaith marriages. Negative consequences include social exclusion, personal dissatisfaction, and emotional stress due to religious and cultural conflicts. These challenges were exacerbated by legal constraints, such as the requirement for non-Muslims to convert to Islam for formal marriage registration. On the other hand, positive outcomes included personal growth, intellectual development, and enhanced social cohesion through exposure to different religious and cultural perspectives. Acceptance of differences and maturity were key factors in successful marriages. Social exclusion and family opposition were prevalent but varied based on gender and minority status. Conclusion: Interfaith marriages in Iran, particularly between Zoroastrians and Muslims, are fraught with complexities. While many couples experience significant challenges related to social exclusion and cultural conflicts, others find personal growth and mutual understanding through their relationships. These marriages reflect broader societal dynamics, where religious hegemony and legal frameworks play crucial roles in shaping outcomes. The study highlights the need for legal reforms and social support to foster inclusivity in interfaith marriages.

Recent advancements in neurophysiological recording technologies have led to significant complexities in managing large-scale neural data, creating potential bottlenecks in the storage, sharing, and processing within the neuroscience community. To address these challenges, we developed the Big Neuronal Data Framework (BNDF), a distributed high-performance computing (HPC) solution. BNDF leverages open-source big data frameworks, Hadoop and Spark, to offer a flexible and scalable architecture. We tested BNDF on three large-scale electrophysiological datasets from nonhuman primate brains, demonstrating improved runtimes and scalability due to its distributed design. In comparative analyses against MATLAB, a widely used platform, BNDF showcased over five times faster performance in spike sorting, a common task in neuroscience. This significant speed advantage highlights BNDF’s potential to enhance the efficiency of neural data processing and analysis, making it a valuable tool for researchers navigating the complexities of modern neural datasets. Overall, BNDF represents a promising approach to streamline the handling of extensive neural data in the field of neuroscience.