Axact

Urban sprawl represents a critical issue for island and coastal areas. In addition to the intense tourist development, the Cyclades islands also face issues related to the shortcomings and the way of implementation of spatial planning in Greece. This research endeavour seeks to offer a multitemporal quantitative depiction of the built-up area changes of the Cyclades through the utilization of remote sensing methodologies and geoinformatics tools for analysis. The analysis employs Landsat satellite images, spanning the period from 2000 to 2020, with a five-year interval. The Random Forest (RF) machine learning algorithm was selected for image classification. The results obtained demonstrate the significant changes that have occurred in the built-up area. The exurban area has been the focus of spatial metrics, which highlight the fragmentation, complexity and dispersion of the built-up area, indicating ad-hoc practices and the absence of integrated planning. An integrated, contemporary and consistent spatial planning should be implemented in order to counteract the dynamics of urban sprawl on the islands, which poses a significant threat to the distinctive and fragile landscape of the Cyclades, thereby jeopardizing the sustainable development of the region.

This study presents a comprehensive geospatial framework for assessing coastal vulnerability and ecosystem service distribution along the Greek coastline, one of the longest and most diverse in Europe. The framework integrates two complementary components: a Coastal Erosion Vulnerability Index applied to all identified beach units, and Coastal Flood Risk Indexes focused on low-lying and urbanized coastal segments. Both indices draw on harmonized, open-access European datasets to represent environmental, geomorphological, and socio-economic dimensions of risk. The Coastal Erosion Vulnerability Index is developed through a multi-criteria approach that combines indicators of physical erodibility, such as historical shoreline retreat, projected erosion under climate change, offshore wave power, and the cover of seagrass meadows, with socio-economic exposure metrics, including land use composition, population density, and beach-based recreational values. Inclusive accessibility for wheelchair users is also integrated to highlight equity-relevant aspects of coastal services. The Coastal Flood Risk Indexes identify flood-prone areas by simulating inundation through a novel point-based, computationally efficient geospatial method, which propagates water inland from coastal entry points using Extreme Sea Level (ESL) projections for future scenarios, overcoming the limitations of static 'bathtub' approaches. Together, the indices offer a spatially explicit, scalable framework to inform coastal zone management, climate adaptation planning, and the prioritization of nature-based solutions. By integrating vulnerability mapping with ecosystem service valuation, the framework supports evidence-based decision-making while aligning with key European policy goals for resilience and sustainable coastal development.

The rapid proliferation of agentic artificial intelligence (AI) systems, which are autonomous agents capable of perceiving, reasoning, planning, and executing multi-step tasks with minimal human intervention, presents foundational challenges for the design of effective oversight architectures. Although developers report using AI assistance in approximately 60% of their work, empirical estimates suggest that full delegation remains feasible for only 0–20% of tasks, establishing a persistent and consequential human-AI collaboration boundary that current frameworks struggle to characterize with sufficient precision. This study carried out a systematic review that synthesized peer-reviewed studies published between 2020 and 2026 to map the state of the art in human-in-the-loop (HITL) frameworks, oversight mechanisms, and trust calibration strategies across eight high-stakes sectors, which are healthcare, criminal justice, financial services, autonomous transportation, education, manufacturing, content moderation, and human resources. Following a PRISMA-aligned protocol, the study analyzed sources drawn from the Association for Computing Machinery (ACM), Institute of Electrical and Electronics Engineers (IEEE), NeurIPS, the Association for the Advancement of Artificial Intelligence (AAAI), and major journal databases. The analysis revealed four recurring tensions in the literature, which are the explainability–performance tradeoff, autonomy–accountability gap, over-trust/under-trust duality, and the participation–effectiveness paradox. Building on these tensions and the synthesized evidence, the study introduced the Adaptive Oversight Calibration Model (AOCM), a sector-agnostic framework comprising six formal propositions that relate task criticality, AI competency boundaries, human cognitive capacity, institutional constraints, trust dynamics, and feedback loops to optimal oversight configurations. The AOCM advances prior work by operationalizing meaningful oversight as a continuous, context-sensitive function rather than a binary or static design choice, and by providing testable propositions amenable to empirical validation. Implications for system designers, policymakers, and AI practitioners are discussed, with particular attention to the European Union AI Act (2024) and NIST AI Risk Management Framework (2023) as regulatory anchors.

The use of microalgae in wastewater treatment offers a sustainable approach to environmental management and resource recovery. This study investigated the potential of Chlorella sorokiniana cultivated in different types of agro-industrial wastewater, including effluents from dairy processing, citrus fresh juice production, and chili sauce manufacturing, for simultaneous pollutants removal and high-value biomass generation. The microalgae were cultivated under continuous and periodic illumination (51.51 and 17.62 μmol photons m−2 s−1, respectively) in 2-L or 5-L sequencing batch reactors, with and without nitrogen supplementation, using both one-stage and two-stage bioreactor systems. Biomass growth rates were comparable under both continuous (0.13 ± 0.9 mg day−1) and periodic illumination (0.12 ± 0.12 mg day−1), with no statistically significant differences observed among the various types of wastewater. The removal of COD and NH4-N in one-stage experiments ranged up to 90 ± 3

Toll-like receptors (TLRs) and NOD-like receptors (NLRs) are crucial pattern recognition receptors that initiate inflammatory responses and immunological activation upon detecting pathogen- or damage-associated molecular patterns (PAMPs/DAMPS) in the female reproductive tract, thereby maintaining homeostasis and supporting pregnancy success. Their signaling pathways play a significant role in reproductive disorders by mediating the immune response to various pathogenic stimuli. Recurrent pregnancy loss (RPL), defined as the natural ending of two or more pregnancies before 24 weeks of gestation, approximately half of these patients remain idiopathic without precise prognostic, diagnostic, and therapeutic plans. Emerging data point that microRNAs are essential for immunological control in the female reproductive tract. MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to mRNA and preventing translation into protein. miRNAs play a role in many biological processes, including the development and differentiation of trophoblasts, the activation and implantation of embryos, immune tolerance, and the receptivity of the endometrium during implantation. Given their capacity to regulate up to 30 % of the human genome, miRNAs offer a promising avenue for understanding the immunopathogenesis of pregnancy complications. Recent research has detected differential expression of specific miRNAs in reproductive system pathologies. This review focuses on microRNAs and their association with idiopathic recurrent miscarriage, a condition characterized by considerable heterogeneity. Future studies identifying the precise mechanisms linking miRNA-mediated immune dysregulation in RPL immunopathogenesis could open the way for novel personalized therapeutic and diagnostic strategies.