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Introduction: The primary aim of this article is to explicate the key role of universities in cultivating moral virtues and promoting a moral culture within society. Nixon believes that universities ought to become environments for nurturing human virtues such as social justice, civic responsibility, honesty, authenticity, and magnanimity. Material and Methods: This study adopts a qualitative approach to the educational analysis of moral virtues in higher education from John Nixon’s perspective. To elucidate moral virtues from Nixon’s viewpoint, the “conceptual analysis method” is employed. The textual corpus of the present study is Nixon’s book Towards a Virtuous University. In addition, sources related to the ethical conditions of higher education systems - such as scholarly articles and research reports - are examined. Data collection tools include library resources, academic documents, and Nixon’s works. Conclusion: The findings emphasize the importance of promoting these virtues within higher education and creating ethically oriented learning environments. This can lead to the cultivation of committed and responsible individuals capable of active participation in building a more ethical society. Overall, the study underscores the significance of fostering moral virtues in universities and academic environments, as well as the role of these institutions in enhancing the moral culture of society.

Driver drowsiness detection plays a critical role in improving road safety, as drowsiness substantially increases the likelihood of traffic accidents. In this study, we propose a novel multi-modal framework within Advanced Driver Assistance Systems (ADAS) that leverages an Attention-Guided Siamese Network coupled with temporal modeling to accurately capture both spatial and temporal patterns of driver fatigue. The Siamese network processes paired facial images, enabling the extraction of discriminative features that highlight subtle changes in driver state. The attention mechanism is explicitly applied to the spatial feature maps within each branch of the Siamese network, allowing the model to focus selectively on key facial regions—such as eyes and mouth—that are most indicative of drowsiness, while also weighting complementary sensor modalities dynamically. Temporal modeling is incorporated through a sequential module (e.g., LSTM or temporal convolution) that analyzes the extracted features over time, capturing gradual and evolving signs of drowsiness that static frame-based methods often overlook. Extensive evaluations on benchmark datasets (YawDD, NTHUDDD) and a novel real-world driving dataset demonstrate superior accuracy exceeding 98.8%, along with strong cross-subject generalization. Ablation studies confirm the critical contributions of the attention mechanism in improving feature discrimination, and the temporal modeling module in enhancing sensitivity to progressive drowsiness. The proposed method surpasses traditional approaches in temporal awareness, data efficiency, and resilience to inter-subject and environmental variations, offering a robust and interpretable solution for real-time driver drowsiness monitoring in intelligent vehicles.

Social media offers a timely lens into public perceptions of emerging technologies. To assess public opinion on the Internet of Things (IoT), we analyzed a corpus of 824,845 IoT-related posts collected from X between 2013 and 2022. Using Latent Dirichlet Allocation (LDA), we identified seven primary themes of discussion: Smart Home, Business Intelligence, Artificial Intelligence, Smart City, IoT Usage, Emerging Technologies, and Blockchain. We then applied an unsupervised machine-learning technique to evaluate sentiment toward each theme. Overall, public discourse was positive: 46.78% of tweets expressed positive sentiment, 43.41% were neutral, and 9.81% were negative. Although predictable, short-term shifts in tone occurred around specific events, interest in these themes remained consistent throughout the study period. These findings suggest that the Internet of Things is generally perceived favorably and demonstrate how large-scale social media analytics can capture authentic, real-time attitudes toward complex technologies. By linking public opinion to specific topics of discussion, our results provide valuable insights for researchers, policymakers, and product teams seeking to align IoT development with societal expectations.

Introduction: The increasing proliferation of artificial intelligence systems and algorithmic decision-making in domains such as criminal justice, employment, resource allocation, and public policymaking has brought about wide-ranging ethical and social implications. Although these systems are often deployed with the aim of enhancing efficiency and neutrality, scientific research shows that algorithms can reproduce or intensify structural discrimination and historical inequalities. This review article adopts an interdisciplinary approach to survey the scholarly literature on the ethics of algorithmic decision-making and to analyze the role of algorithms in justice, discrimination, transparency, responsibility, and accountability. The article focuses on explicating ethical theoretical frameworks, examining social and political consequences, and identifying gaps and contradictions in prior research. Material and Methods: It is a narrative review article that focuses on the existing scholarly literature to analyze the ethical dimensions of algorithmic ethical decision-making. Previous studies were analyzed and then the subject was explained. Conclusion: The findings of this review indicate that addressing the ethical challenges of artificial intelligence requires moving beyond purely technical solutions and paying serious attention to social, institutional, and political contexts. Lack of transparency, diffusion of responsibility, and weak accountability mechanisms can lead to the legitimization of unjust algorithmic decisions. The article concludes that the development and deployment of fair artificial intelligence necessitate ethics-centered design, stakeholder participation, effective institutional oversight, and interdisciplinary, context-sensitive research, so that this technology can serve to reduce discrimination and promote social justice.

Postmodern tourism is gradually shifting away from mass tourism toward more personalized, experience-based, and interest-driven forms of travel. In response to this trend, many destinations are increasingly emphasizing local cuisine and developing culinary tourism as one of the most dynamic and creative sectors of the tourism industry. However, the successful development of culinary tourism is not possible without a clear understanding of culinary tourists’ behavior. Accordingly, the aim of this study is to explain the behavior of product-oriented culinary tourists, the most specialized segment of culinary tourism, by developing a behavioral model using a mixed-method approach. In the qualitative phase, a meta-synthesis method was applied, while the quantitative phase employed structural equation modeling (SEM) using SmartPLS 4 to identify key dimensions, components, and indicators, as well as to examine the relationships among them. The sample size was determined using the Cochran formula and consisted of 285 participants, who were randomly selected from saffron culinary tourists visiting the Qaenat region. The findings indicate that tourists’ motivation, destination image, and perceived service quality have direct effects on satisfaction and indirect effects on behavioral intentions. Notably, perceived service quality also shows a direct and significant influence on behavioral intentions. Furthermore, the results highlight several key destination characteristics that strongly influence the quality of culinary tourism tours. These include cultural and culinary involvement, accommodation quality, farm-based experiences, food and beverage quality, reliable service delivery, a sense of safety and tranquillity, effective tour organization, and the quality of tour guide services .

Objective : In the competitive global retail industry, achieving sustainable competitive advantage is a key factor for long-term success. This advantage arises when companies effectively utilize their unique resources and capabilities to outperform competitors. Operational efficiency and financial performance are critical for evaluating competitiveness and investment attractiveness. Data Envelopment Analysis (DEA) is a standard method for measuring efficiency, but classical DEA cannot fully rank efficient units. Integrating DEA with multi-criteria decision-making (MCDM) methods addresses this limitation, considering investor-relevant financial ratios. This study proposes a two-stage approach to evaluate and rank retail companies comprehensively.  Methodology: In the first stage, an input-oriented CCR model of DEA is applied, with assets, operating expenses, and the number of employees as inputs, and total revenue and net profit as outputs, to assess relative efficiency. In the second stage, financial indicators—asset turnover, dividend yield, return on equity (ROE), return on assets (ROA), and return on investment (ROI)—alongside DEA efficiency scores are evaluated using the PROMETHEE II method to generate a complete preference-based ranking of retailers. Results : DEA in the first stage provides relative efficiency insights but cannot rank efficient units. Employing PROMETHEE II in the second stage, and considering financial ratios, overcomes this limitation and produces a comprehensive ranking. Validation against DEA, hybrid DEA–PROMETHEE II, and hybrid DEA–AHP rankings demonstrates a strong alignment of the results with the actual market positions of retailers. Conclusion : The proposed method enables investors to identify high-performing companies and provides retailers with a strategic tool to monitor competitiveness, identify strengths and weaknesses, optimize resource allocation, and achieve a sustainable competitive advantage.

Objective : This paper develops a single-objective and a bi-objective mixed-integer linear programming model to optimize the post-earthquake relief logistics network involving transfer points, hospitals, and relief centers in Tehran, Iran. The primary aim is to minimize the total time required to transfer injured individuals through the system, while the bi-objective model additionally minimizes penalties for failing to transfer casualties due to capacity shortages.  Methodology: The methodology involves formulating location-allocation models in which demand points, transfer points, hospitals, and relief centers are represented by specific capacity and travel-time parameters. The models are applied to two earthquake scenarios in south-central Tehran: a magnitude-6 event with lower casualties and selective facility activation, and a magnitude-7 event requiring full capacity utilization and a 30% assumed increase in hospital capacity. Results : The model’s effectiveness in optimizing the relief network is demonstrated. For the magnitude-6 scenario, the model selects 10 transfer points, 15 hospitals, and 25 relief centers to minimize total transfer time. For the magnitude-7 scenario, utilizing all available facilities, the model optimally allocates casualties despite severe capacity constraints. Conclusion : The proposed models offer a practical decision-support tool for designing efficient humanitarian supply chains in earthquake-prone urban areas. They underscore the necessity of pre-disaster planning, including establishing transfer points with triage and outpatient capabilities, increasing hospital surge capacity, and ensuring public awareness to direct casualties to designated transfer points.

Objective : This study addresses the critical gap in understanding how Industry 4.0 technologies—Artificial Intelligence (AI), Internet of Things (IoT), blockchain, and Extended Reality (XR)—synergistically transform customer-centric supply chains in creative industries (e.g., music, fashion, film). It moves beyond fragmented analysis to develop a unified framework for customer-integrated value delivery. Methodology: A systematic meta-synthesis was conducted following established seven-stage protocols. A targeted search across Scopus and Web of Science (2016–2025) identified relevant literature. After stringent screening and quality appraisal (the Critical Appraisal Skills Programme (CASP) checklist), 59 high-quality publications were analyzed through iterative coding and thematic analysis. Results : The analysis produced a novel framework of five interdependent components: (1) Customer-Integrated Value Creation (e.g., AI co-design), (2) Omnichannel & Immersive Fulfillment (e.g., XR commerce), (3) Dynamic Value Capture Models (e.g., fan-driven financing), (4) Algorithmic & Gamified Community Engagement , and (5) Networked Co-Creation Ecosystems . The framework demonstrates how AI, IoT, blockchain, and XR interconnect to transform linear supply chains into agile, experiential, community-integrated value networks. Conclusion : This study presents the first synthesized framework for Industry 4.0-enabled, customer-centric supply chains in creative industries. It bridges technology, culture, and operations management, offering researchers a structured model for future inquiry and providing practitioners with a strategic roadmap for building responsive, community-driven operations. The research fills a significant literature gap and offers a blueprint for competitive advantage in the digital creative economy.

Russia’s invasion of Ukraine involved a combination of hard and soft power measures, with geopolitical representations strategically employed to justify violations of international law. Despite their significance for understanding Russia’s regional actions, academic literature has mainly underexplored geopolitical representations. This paper aims to address this gap and answer to the following questions: (1) how did Russia use geopolitical narrations to justify the war against Ukraine, and (2) why did Russian geopolitical representations fail to achieve the aims? The research hypothesis is that 'the purpose of geopolitical narratives of perceived threats from Ukraine was to receive internal support and legitimize the war form the point of view of International Laws'. The research findings suggest that Russia’s initial military intervention was primarily based on a geopolitical narrative portraying Ukraine as an immediate threat through the alleged presence of Nazis, which served as a pretext to justify military action. In reality, this narrative was also used to retain Ukraine as a buffer zone against NATO expansion. This paper examines how geopolitical representations have influenced Russia’s foreign policy and its implications for regional security, and shows how they have been used to justify and sustain Russia’s military actions against Ukraine.

L’interprétation, en tant que processus cognitif, vise à transférer le vouloir-dire de l’orateur en reformulant le discours dans la langue cible. Ce processus, qu’il soit en consécutive ou en simultanée, exige une gestion efficace des ressources cognitives pour accomplir plusieurs tâches simultanément. Une charge cognitive excessive peut compromettre la performance de l’interprète, d’où la nécessité de développer des compétences cognitives adaptées. Cette étude explore l’impact de la mise en œuvre de doubles tâches sur le développement des capacités cognitives des étudiants en interprétation. Un groupe expérimental a suivi des cours spécifiques intégrant des doubles tâches, tandis qu’un groupe de contrôle n’a reçu aucune formation supplémentaire. Les résultats de cette recherche indiquent une amélioration significative de l’empan mnésique chez le groupe expérimental, mais l’effet sur la performance en interprétation reste incertain. Ces résultats soulignent l’importance de poursuivre les recherches pour mieux comprendre le lien entre les compétences cognitives et la performance en interprétation.

In Hegel’s Philosophy of History, one encounters the idea of the “cunning of reason” [List der Vernunft], which describes the unintended (universal) consequences of (particular) individual actions. However, the Philosophy of History is one of the most (if not the most) maligned of Hegel’s works, attacked by non-specialists and anti-Hegelians who use it to easily stereotype and dismiss Hegel, for instance, as a teleological anti-individualist, while most serious Hegel interpreters avoid this work at all costs. To redress the lack of serious attention to Hegel’s Philosophy of History, this paper aims to offer the strongest possible reading of Hegel’s weakest “text,” reading it alongside his strongest, the Science of Logic, thereby bringing together two seemingly contradictory instances of the cunning of reason in Hegel’s corpus. In the Logic, the cunning of reason shows how the universal emerges through the means which individuals use toward their particular ends. However, in the Philosophy of History, the cunning of reason describes how the universal acts through individuals, as it were, behind their backs and, problematically, Hegel goes on to claim that the universal (spirit [Geist]) ultimately sacrifices individuals on the “slaughter bench” of history to advance its own purpose(s). This paper’s two-part thesis is: (1) the cunning of reason in the Philosophy of History is an internal illusion of the structure of cunning of reason in the Logic, and (2) this illusion is absolutely necessary. In particular, this paper builds upon the Hegel interpretations of Todd McGowan and Slavoj Žižek.