Technical University of Crete

Achieving efficient and stable hydrogen evolution reaction (HER) with earth-abundant electrocatalysts is pivotal for boosting the efficiency of alkaline water electrolysis. Here, we report an oxygen vacancy (O v )-rich Cu₂O/NiO heterostructure, which triggers a peculiar interfacial charge transfer from NiO to Cu₂O, creating catalytically active Ni³⁺–Cu 0/+ dual sites to synergistically promote water dissociation and hydrogen adsorption. The Cu₂O/NiO heterostructure exhibits a low overpotential of 19.7 mV at 10 mA cm⁻² with Tafel slope of 36 mV dec⁻¹, outperforming most of the reported catalysts. The anion exchange membrane (AEM) electrolyzer equipped Cu₂O/NiO electrode enables 1.78 V to reach 1 A cm⁻² for more than 200 hours of continuous operation. A thorough characterization study involving HRTEM/XPS/EPR/XANES and DFT calculation exemplifies the interfacial charge transfer dynamics in Cu₂O/NiO heterostructure and its HER catalytic mechanism, offering a new paradigm for rational design of noble metal-free HER electrocatalysts for practical AEM water electrolysis. • Interfacial charge transfer from NiO to Cu₂O in oxygen vacancy-rich Cu₂O/NiO • Catalytically active Ni³⁺–Cu 0/+ dual sites promote HER • 19.7 mV overpotential at 10 mA cm⁻² with 36 mVdec⁻¹ Tafel slopes were obtained • HRTEM/XPS/EPR/XANES and DFT exemplified interfacial charge transfer in Cu₂O/NiO

The Jordan Valley, a critical agro-ecosystem in Jordan, faces escalating challenges from chronic water scarcity compounded by environmental and socio-economic pressures, necessitating a systems perspective to understand cross-sector interactions beyond isolated sectoral issues. This study interprets socio-ecological interactions influencing sustainability outcomes in the region and identifies key feedback loops and adaptive responses under water scarcity through an integrated Socio-Ecological Systems (SES) and Water–Energy–Food–Ecosystems (WEFE) framework. Employing a qualitative document analysis (QDA) design, a purposive collection of peer-reviewed studies and institutional publications (n = 50) published between 2002 and 2025 was assembled and systematically coded using a structured deductive–inductive strategy grounded in SES components and WEFE domain interactions. Results reveal seven interconnected themes: water scarcity as a structural constraint, agricultural intensification and resource pressures, climate change as a stress multiplier, ecosystem degradation and service loss, pollution and environmental quality challenges, socio-economic vulnerability and livelihood constraints, and fragmented governance with coordination gaps. These themes highlight reinforcing loops where scarcity promotes groundwater reliance and non-conventional water use, intensification heightens salinity and contamination risks, climate variability escalates irrigation demands, and ecological degradation diminishes buffering capacity, while socio-economic limitations hinder adaptation and governance fragmentation impairs integrated planning and enforcement. While prior studies have examined water scarcity, agricultural intensification, or climate impacts in isolation, this study advances the literature by synthesizing these dynamics through an integrated SES–WEFE analytical lens, revealing reinforcing system feedbacks and governance constraints that are not visible within single-sector or descriptive syntheses.

The complexity of an extrusion die profile is determined by its geometry. Various metrics such as the complexity index, shape factor, and form factor are used to quantify how geometric intricacy affects production costs, die life, energy consumption, product quality, and overall manufacturability. Bearing geometry plays a critical role in controlling metal flow and tool life in aluminum extrusion. In this study, a simulation-based investigation is performed to investigate the influence of bearing geometry on extrusion behavior using the finite element method. Two extrusion dies are examined: A single-cavity die with uniform bearing geometry and a dual-cavity die with controlled bearing geometry modification in one cavity. The results show that the bearing modification in the dual-cavity die causes severe flow imbalance, with exit velocity deviations. This imbalance leads to localized pressure amplification, increased thermal gradients, and stress concentration in critical die regions. In contrast, the single-cavity die, due to its simple geometry, exhibits uniform flow, stable pressure evolution, and low tool stress. These findings demonstrate the high sensitivity of multi-cavity extrusion dies to bearing geometry and highlight the importance of simulation-driven die design for achieving balanced flow and improved tool performance.

Historical newspapers are invaluable resources that capture the cultural, social, economic, and political dimensions of their time, providing unique insights into past events and daily life. However, the potential of these archives remains underutilised due to the limitations of traditional research methods, which rely on laborious manual examination of individual pages or articles. Digital archives have begun to address this issue by enhancing accessibility, but the lack of effective tools for parsing and analysing the contents still restricts their utility for large-scale historical research. Optical Character Recognition (OCR) technology plays a foundational role in converting scanned images into searchable text. However, OCR alone cannot adequately handle the complexities of historical newspapers, which often feature degraded image quality, inconsistent typography, and intricate layouts. In parallel, the effective segmentation and ordering of newspaper contents is essential for generating well-structured and logically ordered data from specific newspaper sections. This paper focuses on the development of a pipeline that addresses these challenges, using the daily newspaper Le Sémaphore de Marseille as a case study (35,703 issues covering the period 1827 to 1944). The proposed system integrates layout analysis, OCR, and Generative AI-driven information extraction techniques to extract specific data elements from scanned images, namely ship arrivals data, and to store them in a machine-readable format for further analysis. This well-structured output enables historians to analyse historical trade patterns, economic trends, and regional interactions more efficiently and at a much larger scale. An evaluation of the key pipeline components demonstrates the effectiveness of the system, achieving an F1-score of 96% in paragraph segmentation and information extraction, while also highlighting the need for further improvement in the critical OCR component.

Over the past five years, artificial intelligence (AI) has introduced new models and methods for addressing the challenges associated with the broader adoption of AI models and systems in medicine. This paper reviews recent advances in AI for medical image and video analysis, outlines emerging paradigms, highlights pathways for successful clinical translation, and provides recommendations for future work. Hybrid Convolutional Neural Network (CNN) Transformer architectures now deliver state-of-the-art results in segmentation, classification, reconstruction, synthesis, and registration. Foundation and generative AI models enable the use of transfer learning to smaller datasets with limited ground truth. Federated learning supports privacy-preserving collaboration across institutions. Explainable and trustworthy AI approaches have become essential to foster clinician trust, ensure regulatory compliance, and facilitate ethical deployment. Together, these developments pave the way for integrating AI into radiology, pathology, and wider healthcare workflows.

Abstract A key need in different disciplines is to perform analytics over fast-paced data streams, similar in nature to the traditional OLAP analytics in relational databases - i.e., with aggregates and selection predicates. Storing unbounded streams, however, is not a realistic, or desired approach due to the high storage requirements, and the delays introduced when storing massive data. Accordingly, many synopses/sketches have been proposed that can summarize the stream in small memory (usually sufficiently small to be stored in RAM), such that aggregate queries can be efficiently approximated, without storing the full stream. However, past synopses predominantly focus on summarizing single-attribute streams, and cannot handle selection predicates and constraints on arbitrary subsets of multiple attributes efficiently. In this work, we propose OmniSketch, the first sketch that scales to fast-paced and complex data streams (with many attributes), and supports count aggregates with predicates on multiple attributes, dynamically chosen at query time. OmniSketch supports streams containing both inserts and deletes, under the bounded deletes streaming model. OmniSketch offers probabilistic guarantees, a favorable space-accuracy tradeoff, and a worst-case logarithmic complexity for updating and for query execution. We demonstrate experimentally with both real and synthetic data that OmniSketch outperforms the state-of-the-art and can approximate complex ad-hoc queries within the configured accuracy guarantees, with small memory requirements.