This research aims to record hotel owners' perceptions as subjective measures of the degree of integration of local traditional cheese varieties in the hospitality sector. Within the context of cheese tourism, this specific type of alternative tourism is operationalized through B2B co-creation among tourism businesses and cheese factories, serving as a framework for perceived business development. Specifically, this study fills a gap in the literature by exploring the managerial views on the current state of cheese tourism in relation to the entrepreneurship strengthening, the opportunities, and challenges that could favor cooperation between the two sectors. Descriptive and inductive statistics were conducted, collecting primary data from hotels in the Peloponnese, Greece, which has a long tradition of cheese production. Regional tradition and star rating determine the integration of local cheese. While 4-5-star hotels leverage cheese heritage for differentiation and experiential services, lower-end hotels face cost and supply chain barriers, requiring supporting strategies and cross-sector partnerships. The study offers original knowledge for the development of specific strategic proposals for the use of cheese tourism through co-creation for business development of hotels. Future research is recommended to record the views of all stakeholders and correlate them with objective financial performance.
The rise of data-centric artificial intelligence (AI) has exposed a persistent misalignment between modern data-intensive practices and the structure of computing education, where databases, machine learning (ML), and scalable data systems are commonly taught as independent components. Although prior surveys address AI literacy, data science education, or Big Data infrastructures separately, they do not explain how competencies, pedagogy, assessment, and technological infrastructure must operate together within end-to-end data pipelines. This survey aims to address this gap. Based on a structured analysis of peer-reviewed literature published since 2020, this study synthesizes how contemporary curricula can be aligned with the engineering requirements of data-centric AI systems. The analysis adopts an engineering-oriented perspective that treats robustness, reproducibility, scalability, and system integration as foundational properties that shape educational design. Core data-centric competencies are identified and explicitly linked to learning objectives, instructional strategies, assessment models, and supporting technological systems in order to enable coherent pipeline-level reasoning. The survey further analyzes representative institutional deployments from academic and industrial contexts to illustrate how these dimensions are instantiated in practice and to delineate their current limitations. By separating conceptual, pedagogical, and infrastructural concerns while preserving their functional alignment, this study provides a modular basis for curriculum design in data-centric computing education. It clarifies what students are expected to learn, how instruction is structured, which systems support it, and how learning outcomes are evaluated within integrated data-centric curricula.
Professional identity constitutes a fundamental element of social work practice, as it is directly linked to the empowerment of the social worker's role, professional motivation, commitment to the profession and personal satisfaction derived from practising that role. This article explores how social work practice within the framework of local government influences the development and formation of social workers' professional identity. The analysis demonstrates that social work practice is profoundly shaped by the social, political and economic structures that define its context. Within this framework, an empirical study was conducted to document and analyse the perceptions of social workers employed in the social services of local government organisations in the Attica region regarding the factors that affect their professional identity and role in the contemporary work environment.
Assessment of the geographical origin of wine is important in checking the quality of the products and protecting the regulatory provisions. Therefore, the current study suggests a low-cost, multi-block, data-fusion approach that combines the Commission Internationale de l'& Eacute;clairage (CIE) Lab* (CIELAB) color parameters, spectrophotometric color indices and antioxidant/antiradical activities to categorize wines produced in four Greek regions, including Aegean Islands, Crete, Macedonia and Peloponnese. Patterns that were found to be region-specific were revealed through exploratory principal component analysis and were mainly influenced by chromatic and pigment-related variables. A full modeling strategy utilizing linear classifiers (Linear Discriminant Analysis (LDA) and Partial Least Squares Discriminant Analysis (PLS-DA)) and non-linear ensemble approaches (Random Forest, Support Vector Machine with radial basis function kernel (SVM-RBF) and Gradient Boosting) was employed and assessed using out-of-bag bootstrap estimation and fivefold cross-validation. Random Forest consistently achieved the best predictive performance, with 0.900 out-of-bag and 0.865 cross-validated accuracy using the integrated dataset, thereby outperforming any single-block model. Visualization of proximity-maps defined chemically coherent clusters such as a tightly clustered region of the Aegean Islands, and a partial adjacency between continental regions. Analysis of feature importance showed that the most significant classifiers were absorbance at 520 and 420 nm, color density, hue, and other CIELAB parameters. Together, the results demonstrate that multi-block fusion datasets, offers a strong and cost-effective platform on which to perform routine wine-origin characterization.
Pro-vegetarian (PVG) diets may reduce gastric cancer (GC) risk, but evidence remains limited. We aimed to evaluate the association between three predefined PVG patterns—general (gPVG), healthful (hPVG), and unhealthful (uPVG)—and GC risk stratifying by sex in the context of the Stomach Cancer Pooling (StoP) Project Consortium. We analysed data from six case–control studies from the StoP Consortium. The final sample included 1,857 incidents, histologically confirmed GC cases and 5,646 controls. Food intakes were assessed using country-specific food frequency questionnaires, which allowed the estimation of PVG patterns using established scoring methods. Adherence to PVG dietary patterns was classified into quintiles. Logistic mixed models with random intercepts for each study were used to estimate odds ratios (ORs) and 95
