伊斯兰自由大学

Multimodal sentiment analysis (MSA) aims to extract affective knowledge from various types of input media, including image, audio, and text. Given the significant impact of leveraging the vast amount of multimodal data from social networks and of fusing and integrating information extracted from different modalities on enhancing the validity and accuracy of the sentiment analysis process, MSA has become an important, fascinating, and widespread topic. The MSA methods presented in the literature exploit the latest advances in deep learning and transformers for multimodal feature extraction, fusion, classification, and polarity identification to enhance efficiency and performance. Despite various research having been conducted and recent articles presented with diverse approaches for MSA, the lack of a comprehensive study that examines several aspects of the problem addressed in these studies, including approaches, paradigms, methods, tools, and techniques, and evaluates them by considering related issues such as the datasets and benchmarks used, criteria and parameters for measuring results, and the extent to which intermediate and final objectives are addressed and achieved, is noticeable. In this paper, MSA and its various processes and phases are addressed within a comprehensive study framework. In this regard, techniques, tools, and datasets for feature extraction, multimodal fusion, and sentiment classification are thoroughly reviewed, categorized, summarized, compared, and evaluated, and for better comparison, some evaluation factors are proposed at each stage. Furthermore, a comprehensive overview of existing challenges, open research topics, and future research directions is provided along with a set of guidelines for conducting future studies.

Background: The modern retail landscape is undergoing a structural transformation, one defined not merely by the adoption of new technologies but by the fundamental reconstitution of the service encounter itself. Across sectors ranging from consumer electronics to luxury fashion, retailers are deploying interactive kiosks, augmented reality displays, AI-driven recommendation engines, and sensor-embedded product zones in a deliberate effort to dissolve the boundary between the physical store and the digital platform.Aims: This study investigates the experiential and neurophysiological dynamics of technology-based service failures in physical retail environments to develop a theoretical model for effective human-led service recovery. As retailers increasingly automate the customer journey, technology failures represent a critical threat to customer experience, creating a paradoxical need for skilled, human intervention.Methodology: This research employs a neurophenomenological approach, integrating first-person data from Interpretative Phenomenological Analysis (IPA) with third-person, objective data from electroencephalography (EEG). A video-cued recall protocol was designed to capture rich, in-the-moment experiential accounts from eight participants in a flagship phygital electronics store, while ensuring high-fidelity, artifact-free neural data. Data saturation was confirmed after the eighth participant, justifying the sample size within IPA norms.Findings: The analysis revealed that technology failures induce a distinct "neural signature of frustration", characterized by negative emotional valence (low Frontal Alpha Asymmetry), high cognitive load, and profound disengagement. Conversely, effective human intervention acts as a regulatory mechanism, resolving technical issues and transforming this negative state into a more positive and engaged emotional state. This "Human Anchor" role, dependent on employee competence and situational awareness, is particularly critical in low-trust market contexts where the physical store functions as a validation point for the online brand.Conclusion: Theoretically, this study extends service recovery theory into the phygital domain by proposing a model of human intervention as a cognitive-emotional regulation mechanism. Methodologically, it validates a neurophenomenological protocol for consumer research, bridging the gap between subjective lived experience and objective neurophysiology.

MXene quantum dots (MQDs) provide a chemically versatile platform for metal-ion sensing because their surface coordination environments, defect-associated states, and size-dependent electronic structure can couple molecular recognition to measurable optical responses. This review develops a mechanism-centered framework for understanding that coupling rather than treating sensing performance as a collection of isolated analytical metrics. It first examines how surface terminations, coordination-site accessibility, electronic-state organization, solvation, and interfacial binding thermodynamics define the recognition landscape of MQDs. It then analyzes how recognition events are translated into fluorescence or electrochemiluminescence responses through charge transfer, energy transfer, static or dynamic quenching, inner-filter effects, and related excited-state perturbations. Particular emphasis is placed on structure–property–signal relationships, mechanistic distinguishability, and trade-offs among sensitivity, selectivity, reversibility, and robustness. Evidence from reported metal-ion sensing systems is comparatively assessed to identify recurring design trends and limitations in mechanistic validation, cross-platform benchmarking, and real-matrix translation. The analysis indicates that future progress depends less on maximizing signal magnitude than on establishing reproducible coordination environments, experimentally resolved transduction pathways, and standardized performance criteria. This perspective positions MQD sensing as a recognition-to-signal engineering problem governed by coupled surface chemistry and excited-state dynamics.

Introduction Political participation is widely recognized as one of the fundamental pillars of political development, democratic governance, and legitimacy of political systems. Citizens' participation in political affairs not only reflects the realization of the right to self-determination, but also contributes to social cohesion, political stability, and institutional accountability. Over the past decade, political participation has become one of the most extensively studied topics in Iranian political science, sociology, public administration, and communication studies. Existing research has investigated numerous determinants of political participation, including social capital, political trust, political culture, economic conditions, social media, citizenship rights, gender, ethnic diversity, and electoral behavior. Despite the considerable growth of empirical and theoretical studies, the overall intellectual structure of this body of knowledge has remained largely unexplored. Most previous studies have focused on individual variables or have employed meta-analysis to synthesize empirical findings, while no comprehensive bibliometric investigation has mapped the evolution of scientific production in this field. Therefore, this study aimed to analyze the scientific landscape of political participation research in Iran through bibliometric techniques and science mapping. Specifically, the study sought to identify publication trends, prolific authors and institutions, core journals, thematic clusters, and the conceptual structure of political participation research conducted in Iran between 2015 and 2025. Materials & Methods This study was an applied quantitative bibliometric research based on science mapping techniques. The statistical population consisted of all Persian scientific publications related to political participation produced by Iranian researchers and indexed in Iranian academic journal databases during the period from 2015 to 2025. A systematic search strategy was employed by using the keyword "Political Participation" in article titles and keywords. The search covered journals in political science, sociology, social sciences, economics, and management. After applying inclusion and exclusion criteria and removing irrelevant records, a final dataset of 591 scientific articles was selected for analysis. The extracted metadata included article titles, authors, institutional affiliations, journal titles, publication years, and keywords. Because domestic databases did not provide standardized bibliometric outputs, all records were manually extracted, cleaned, and standardized. Variations in author names, keyword spellings, and synonymous concepts were harmonized to improve analytical accuracy. Data analysis was conducted by using Microsoft Excel and VOSviewer software. Excel was employed to generate descriptive statistics and identify publication trends, productive authors, institutions, and core journals. VOSviewer was used to perform keyword co-occurrence analysis and visualize the conceptual structure of the field. Network maps, overlay visualization, and density visualization were generated to identify thematic clusters, conceptual relationships, emerging research topics, and underexplored areas. Discussion of Results & Conclusion The keyword co-occurrence analysis identified 85 highly connected keywords, which were organized into 11 thematic clusters representing the conceptual foundations of political participation research in Iran. These clusters included governance, political identity, political behavior, political security, civil society, public sphere, elections, political culture, political communication, constitutional rights, and political development. The network visualization demonstrated that "Political Participation" occupied the central position within the conceptual network and exhibited the strongest relationships with social capital, elections, political culture, and political development. These findings indicated that Iranian scholars had predominantly conceptualized political participation within sociological and institutional frameworks, rather than through behavioral or technological perspectives alone. 5 major research streams emerged from the network analysis. The first focused on social capital, public trust, institutional trust, civil society, and good governance as the principal foundations of political participation. The second emphasized political culture, political socialization, political awareness, and political efficacy, particularly among younger generations. The third investigated electoral participation, voting behavior, democratic institutions, and parliamentary elections. The fourth examined political participation from the perspectives of gender, ethnic identity, citizenship rights, constitutional rights, and minority participation. Finally, the fifth stream represented the growing influence of social media, digital communication, public sphere, and globalization on contemporary patterns of political participation. Overlay visualization revealed a clear temporal transformation in research priorities. Earlier studies mainly concentrated on traditional concepts, such as political trust, civil society, voting behavior, and public institutions, whereas more recent publications had increasingly emphasized social media, digital communication, universities, students, political parties, and globalization. This finding suggested that Iranian political participation research was gradually shifting from classical institutional approaches toward digital and network-based perspectives. Density visualization further demonstrated that political participation, social capital, and elections constituted the densest and most frequently studied concepts within the literature. Conversely, topics like women's rights, minorities, constitutional rights, human rights, sustainable development, and political jurisprudence remained relatively peripheral despite their theoretical significance. These findings revealed important research gaps and indicated opportunities for future interdisciplinary investigations. Overall, the bibliometric evidence suggested that political participation research in Iran had experienced substantial quantitative growth while simultaneously becoming increasingly interdisciplinary. Nevertheless, the intellectual structure of the field continued to be dominated by election-centered and social capital-oriented approaches. Compared with international research, Iranian scholarship showed considerable convergence regarding the importance of political trust, social capital, and digital media; however, greater attention should be devoted to digital activism, online political participation, marginalized social groups, citizenship rights, and emerging forms of networked political engagement. The findings provide a comprehensive scientific map of political participation studies in Iran and offer valuable guidance for researchers, policymakers, and academic institutions in identifying dominant research trends, conceptual gaps, and future research priorities. Keywords: Political Participation, Social Capital, Political Culture, Elections, Social Media. Acknowledgments The authors thank everyone who contributed to this article. Conflicts of Interest There is no conflict of interest. Funding The authors received no financial support for the research, authorship, and/or publication of this article. Statement on the Use of AI No artificial intelligence tools were used in the writing, editing, or preparation of this manuscript.

Very-large-scale integration (VLSI) remains the foundation of modern digital systems; however, continued CMOS scaling is increasingly limited by power dissipation, leakage current, device variability, interconnect overhead, and rising design complexity. These challenges have motivated the search for next-generation computing paradigms and new electronic design automation methodologies beyond conventional transistor-based design. In this context, quantum computing and reversible logic provide a promising framework for information-preserving computation, resource-aware synthesis, and emerging post-CMOS design flows. Since arithmetic logic units are fundamental components of digital datapaths, a quantum arithmetic logic unit serves as a meaningful benchmark for studying synthesis, optimization, decomposition, and verification in a quantum-aware VLSI setting. In this work, a quantum arithmetic logic unit is presented in IBM Qiskit, integrating reversible logic optimization, modular arithmetic and logic-unit construction, Clifford + T mapping, representative functional verification, and noise-aware simulation.The proposed arithmetic unit achieves a quantum cost of 21, corresponding to a 4.55% reduction relative to the selected arithmetic comparison baseline. At the complete-architecture level, the proposed QALU uses 12 reversible gates, five constant inputs, 13 garbage outputs, and a quantum cost of 39 while supporting 16 operation modes. Relative to the selected comparison baseline, the proposed design reduces gate count by 25.00% and quantum cost by 11.36%, while increasing the number of supported operations by 14.29%. These improvements are accompanied by trade-offs of 25.00% more constant inputs and 18.18% more garbage outputs.These percentages are specific to the selected baseline because previously reported designs differ in their architectures, supported functions, gate libraries, and cost assumptions; therefore, the comparison should not be interpreted as demonstrating universal superiority. In addition, representative Qiskit Aer simulations assess the response of the selected logic-unit configuration to separately applied bit-flip, depolarizing, amplitude-damping, and phase-damping noise channels. This preliminary synthetic-noise analysis complements the structural and functional evaluations but does not constitute hardware-calibrated validation or general evidence of noise resilience. Collectively, these evaluations provide a software-level, implementation-oriented design workflow relevant to emerging quantum and VLSI design methodologies.