University of the Aegean

This study analyses the current state of deliberation platforms in Greece. The analysis was based on two sets of criteria focusing on technical features and participatory design. The findings reveal limited data availability, regardless of laws promoting government data being open and reusable. Multilingual support is also an issue, with Greek being the main language, while English is present but not at a satisfactory level. Engagement is also an issue with uneven numbers of participation between platforms. While these platforms have allowed processes to take place online, thus allowing easier participation, true deliberation has not yet been achieved, as most currently work as a one-way communication tool for collecting comments and feedback. The Conference on the Future of Europe stands as an example of how these platforms can improve in the future.

This paper presents findings from the European project INFINITE, which explores the integration of Artificial Intelligence (AI) into professional and pedagogical practices in Higher Education (HE). Amid rapid social and technological change, developing AI-related competencies has become a priority for the academic community. The project seeks to empower faculty and students to use AI critically, responsibly, and with strong ethical awareness. The methodology combined desk research with a needs analysis survey conducted in five partner countries (Cyprus, Greece, Ireland, the Netherlands, and Belgium) involving 259 participants, including faculty members and students. Results show AI’s diverse applications in HE: supporting research through data analysis and literature reviews; streamlining administration by automating processes and enabling data-driven decision-making; and enhancing pedagogy through personalised learning, improved assessment, and accessibility support. However, significant challenges emerged. Concerns include data privacy, algorithmic bias, overreliance on AI, tool reliability, and the urgent need for professional development and policy frameworks. Addressing these requires structured training, clear institutional guidelines, and collaboration among academics, students, and policymakers. The INFINITE project contributes to this effort by developing an AI Literacy Toolkit, an AI Digital Hub, capacity-building programmes, and strategic actions.

Abstract Understanding the geographical and environmental drivers of α- and β-diversity is essential in biogeography and ecology. Biogeographical patterns of microorganisms are harder to define and interpret compared to larger organisms, and are mostly apparent on broad regional scales. This study examines biodiversity patterns of benthic diatoms at a smaller scale, i.e. in Greek rivers. We used 233 sites across four biogeographical areas defined by freshwater fish communities. We investigated Species-Area Relationships and α-diversity patterns through taxonomic and functional richness and β-diversity using the Sorensen index on presence-absence data in each biogeographical area. Our findings revealed a positive relationship between species richness and drainage area of river basins. Moreover, α-diversity was higher in northern Greece, whereas β-diversity differentiated communities of western Greece through species turnover, highlighting spatial influences. Environmental variables correlated with taxonomic α-diversity but not with taxonomic or functional β-diversity. This study constitutes the first effort to investigate different biodiversity components of benthic diatoms in a Balkan hotspot by elucidating the role of spatial and environmental factors in shaping biogeographical patterns at a smaller scale. Since the emergence of biogeographical patterns is often scale and data dependent, additional data could further improve our understanding of benthic diatom diversity patterns.

The multifaceted concept of body image (BI) refers to an individual's attitudes and impressions of their body. Negative BI is associated with a number of harmful health consequences, including obesity, eating disorders, and symptoms of sadness. The contemporary digital era, marked by the dominance of platforms, has brought about a considerable transformation in the landscape of BI issues. This study's goal is to compile and assess the connections between social media (SM) use, body weight, and BI in adult populations. This is a narrative review that comprehensively searches across multiple academic databases, such as PubMed, Medline, Scopus, Web of Science, and Google Scholar. Studies that used SM (online blogs, microblogs, content communities, or social networking sites) for engagement (e.g., sharing, commenting, liking) or image-related activities (e.g., viewing, posting, or engaging with images) with healthy adults (aged 18-70 years) of any body mass index (BMI kg/m2) met the inclusion criteria. Included were observational and experimental studies that examined habitual SM use. Only peer-reviewed works published in English between 2015 and 2025 met the search criteria. The currently available findings suggest that obese people are more dissatisfied with their bodies than people of normal weight, and obese women are more dissatisfied with their bodies than their peers of normal weight. Furthermore, experimental studies have demonstrated that immediate BI is adversely affected by acute exposure to idealized social media photographs. Policies should support specialized training that emphasizes a holistic approach to health and puts functionality and health above attractiveness. This training is crucial for dispelling weight-related stigmas and enabling healthcare providers to offer compassionate treatment that supports mental and physical health. Future research must concentrate on internalization and social pressure or reinforcement because these subjects have not gotten as much emphasis in prior studies. Such mechanism research could help better contextualize the role of recently introduced SM items.

We are currently witnessing a mass extinction crisis due to increasingly overwhelming human impacts on the biosphere. Extinctions from the last few hundred years have been well-known for some taxonomic groups, but represent only the tip of the iceberg. While there is accumulating evidence that the number of historic human-induced extinctions is far higher than documented in the ‘gold standard’ for assessing extinction risks - the IUCN Red List - the extent and characteristics of poorly documented or ‘dark extinctions’ remain insufficiently known. Such dark extinctions, which are preceded by undocumented local declines and extinctions, have profound implications for assessing long-term trajectories of biodiversity change of local assemblages.Dark extinctions and undocumented local species declines span a gradient from events that left no direct trace, to documented losses supported by substantial evidence (e.g., subfossil remains, ancient DNA, historic descriptions, specimens in herbaria or museums, observation records) waiting to be formally accepted as actual local decline or extinction by rigorous scientific standards. Here, we synthesize the state of knowledge on the discrepancy between documented and dark extinctions, and assess the implications of undocumented local species losses on biodiversity assessments. We identify six essential dimensions for Red List assessments, and we provide an assessment of their coverage in the IUCN Red List. To this end we review the evidence on how extinction drivers have changed in importance over time, and how well they are actually covered by Red List assessments. We identify ten sources of gaps and biases in documenting extinctions and local species losses and evaluate the role of temporal lags in these processes. We discuss the implications of these findings for studying species richness and the temporal dynamics of local assemblages, and more broadly, for assessing the true scale of the unfolding extinction crisis and assess the implications for conservation policy. Finally, we identify key steps to better account for undocumented species declines from local to global scales.

Large Language Models (LLMs) in Intelligent Computer-Assisted Language Learning enable highly personalized learning, yet raise significant challenges related to pedagogical grounding, data privacy, and instructional validity. Although Knowledge Graphs (KGs) and Federated Learning (FL) can mitigate these issues in isolation, evidence on systematic FL–KG–LLM integration for educational language learning remains limited. This scoping review maps the FL–KG–LLM convergence landscape. Following PRISMA-ScR guidelines, we searched six databases and screened 51 papers (2019–2025) using automated extraction. Our findings indicate limited convergence: no papers integrate all three domains, and 58.8% of approaches remain confined to isolated technological silos. Reporting is also uneven across the corpus, with an average “Not Reported” (NR) rate of 84.5%, most notably for privacy mechanisms (92.2%), validation metrics (90.2%), and Common European Framework of Reference for Languages (CEFR) alignment (88.2%). Domain-specific analysis reveals two distinct patterns: inter-domain gaps (disciplinary silos resulting in expected CEFR absence in single-domain papers) and intra-domain gaps (failure to report domain-critical variables, including 100% parameter NR in FL studies, 86.7% validation NR in KG studies, and 100% CEFR NR in convergence papers). Taken together, these gaps suggest that pedagogical grounding is treated as optional rather than structural. We therefore identify two pillars of pedagogical grounding: a Grounding Pillar, which constrains LLM outputs via Knowledge Graph rules, and a Validation Pillar, which concerns how authoritative frameworks (e.g., CEFR) are mapped onto Knowledge Graph schemas and evaluated. The near-universal absence of CEFR alignment and validation reporting suggests that this second pillar is currently missing, which we term the Integrity Gap—a systematic disconnection between technological innovation and pedagogical grounding inin Intelligent Computer-Assisted Language Learning. By reframing the problem as upstream control and validation, this review informs the design of user-facing automated systems where trust, transparency, and human oversight are critical.

Hydroponic systems are gaining prominence in sustainable agriculture, yet their nutrient-rich effluents remain an underexplored source of microbial biodiversity with potential biotechnological interest. In this study, shotgun metagenomic sequencing was employed to profile, with a high taxonomic resolution, the photosynthetic microbial community in hydroponic effluent before and after a natural algal bloom, revealing pronounced shifts in microbial composition. Notably, relative abundance increased sixfold for Chlamydomonas reinhardtii and tenfold for Bigelowiella natans. Four dominant microalgal strains (PR1-PR4) were subsequently isolated and characterized through integrative morphological and molecular taxonomy, with phylogenetic analyses based on four genetic markers (18S rRNA, ITS, rbcL and tufA) confirming that each isolate represents a distinct lineage within Chlorophyceae families, including Chlorella sp., Chlamydomonas sp., and Scenedesmus sp. Growth kinetics under three temperature regimes, typical of Greek environmental conditions from spring to autumn (15 °C, 23 °C, 32 °C), demonstrated broad ecological plasticity and rapid biomass production, highlighting strains with strong adaptive resilience. Biochemical profiling of the isolates revealed significant inter-strain differences in primary and secondary metabolite content, including proteins (up to 43% DW), lipids (up to 31% DW), carbohydrates (up to 44% DW), photosynthetic pigments, phenolics, flavonoids, and antioxidant activity. The observed metabolic diversity of autochthonous microalgal strains from hydroponic environments, combined with their high growth rates, underscores their potential for applications in bioremediation, bioenergy, and the development of value-added products within a circular bioeconomy framework.

The maritime sector operates in an environment characterized by multidimensional uncertainty—technical, operational, and financial. Mechanical failures, volatile fuel prices, and fluctuating freight rates simultaneously influence vessel performance and profitability, making effective decision-making increasingly complex. This study develops an integrated reliability–financial modeling framework that combines Multi-State Systems (MSS) Theory, Markov Chains, Markov Switching Models, and the Universal Generating Function (UGF) methodology to capture both physical system reliability and market-driven volatility. Using the Panamax Index as a case study, the model evaluates vessel availability, fuel price dynamics, and freight rate fluctuations to forecast profitability under uncertain operating conditions. The findings indicate that transient probabilities of vessel availability converge faster than financial variables, suggesting higher predictability of technical systems compared with market indices. The proposed framework provides maritime stakeholders with a strategic decision-support tool capable of forecasting profitability and identifying optimal operating routes within a 26-week horizon. According to our Panamax data, 3/5 routes have positive expected profit, availability stabilizes by ~ week 3, and freight regimes by ~ week 15. By integrating reliability engineering with financial modeling, the study enhances operational planning and risk management in the maritime industry.