مطالب

Zygmunt Bauman (1925-2017) consistently oscillates between the roles of sociologist and novelist throughout his works, and, unlike many other sociologists, he makes conscious and deliberate use of metaphor. The aim of this article is to examine the metaphorical concepts in Zygmunt Bauman’s imagination by employing the “metaphor identification method” and drawing on the theoretical frameworks of Lakoff and Johnson as well as Paul Ricoeur. The findings of the study reveal that Bauman employs metaphors at both the ontological and methodological levels. For Bauman, metaphor is not merely a descriptive tool, but a research strategy for examining abstract and intangible aspects of human life such as inequality, globalization, ethics, suffering, love, solidarity, and oppression. The overarching and central metaphor in Bauman’s thought is liquidity. His other metaphors are subordinate to the metaphor of liquid modernity and are coherently interconnected within this conceptual constellation. He believes the old order is collapsing in the transition from solid to liquid modernity, but the new has not yet been born. Moreover, many of Bauman’s metaphors exhibit temporal, spatial, and binary characteristics. These metaphors allow Bauman not only to describe the complexities of modern society but also to make them experientially accessible and intellectually comprehensible. Bauman’s notion of a liquid society can be seen as a metaphorical extension of concepts such as Giddens’s “late modernity,” Ulrich Beck’s “risk society,” and Lyotard’s “postmodern.” However, Bauman reconfigures and re-narrates these ideas through a distinct theoretical framework inspired by Paul Ricoeur.

The Neighborhood Rough Set (NRST) method is a valuable approach for selecting a subset of features from a complete dataset, enabling us to preserve the essential information that the entire feature set provides. In financial datasets, which often contain high-dimensional input features, effective feature selection techniques are crucial to identify the features that yield the most predictable results. In this work, we use neighborhood concepts to discover data dependencies and reduce the number of features in a financial dataset based solely on the data itself, without relying on additional information. This process also includes removing extra features. To facilitate a simple algorithm, we use the properties of neighbourhood rough sets to formulate a Binary Integer Linear Programming (BILP) model. Optimal solutions to these problems are obtained using genetic algorithms. Our approach allows for feature reduction from minimum to maximum cardinality. We demonstrate the efficiency of our proposed method compared to other techniques through various tables showing the results on several benchmark datasets characterized by unbalanced class distributions. The financial dataset used in the present study is taken from the UCI Machine Learning Repository.

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.

While one version of belief in inspiration, for which the inspired human passively receives inspired deliverances, precludes human creativity, another, for which inspired compositions reflect human agency and ingenuity, presupposes it. Margaret Boden, however, suggests (in The Creative Mind: Myths and Mechanisms (2004)) that creativity is continuous with generic human powers, and also arises through infringing recognised rules. While the former suggestion (about continuity) is argued to be readily acceptable, problems are raised for the rule-breaking account of creativity. Accounts of creativity need to be supplemented with awareness that creativity commonly involves participation in traditions of skill or craftsmanship, and in a creative community, whether rules are broken or not. Further, the continuity approach is argued to be consistent with at least one particular variant of belief in inspiration, according to which God, as the universal Creator, can communicate through the imagination of receptive minds that reflect his/her creative imagination, as suggested by Austin Farrer in ‘Inspiration: Poetical and Divine’ (1963). Other faculties as well as the imagination are held to be involved, in a manner consistent with the continuity approach: perception, memory, reflectiveness, historical awareness and artistic ingenuity.