Centre for Research and Technology Hellas

Closed and abandoned coal mines are increasing globally, leading to a growing emphasis on their transition into sustainable post-mining areas. This focus marks the era of just transition, highlighting the importance of equitable reclamation efforts. The complexity of mining landscapes makes them susceptible to multiple hazards, which can occur simultaneously or successively, a concept known as multi-hazard. This study emphasizes the need for a comprehensive approach to multi-hazard analysis, which is an essential part of the sustainable reclamation and safety of post-mining areas, directly impacting the safety and well-being of communities. The research compares four widely employed semi-quantitative multi-hazard methodologies: the Analytic Hierarchy Process (AHP), the Entropy Weight Method (EWM), the partial multiplication factor method, and the spatiotemporal method. Each method's strengths and limitations are evaluated by applying it to a specific post-mining area, incorporating an innovative multi-hazard scenario assessment. The implementation and comparison of the methods revealed that each one has particular advantages: AHP is widely used with supporting background, EMW is straightforward to implement, the partial multiplication factor method demonstrates more precisely multi-hazard principles, and the spatiotemporal method is the only one that considers temporal frequency. However, none of the methods accounts for the sequence of hazards within scenarios. While AHP and EWM provide consistent rankings, the partial multiplication factor and spatiotemporal methods show greater variability in multi-hazard index (MHI) values, highlighting the influence of both hazard intensity and interactions.

Cyber Threat Intelligence (CTI) plays a vital role in enhancing cybersecurity by enabling organizations to leverage insights from the analysis of past incidents to better manage future threats. Evaluating the actionability of CTI products (CTIPs), namely CTI in a structured format, is essential for prioritizing intelligence and implementing effective security measures. However, existing methodologies often fall short in evaluating the actionability of CTI by focusing on isolated criteria without considering the full context of the CTI sharing lifecycle, which includes production, dissemination, and consumption stages. Additionally, these methodologies suffer from variability issues, referring to the inconsistent selection and application of actionability criteria by different organizations, as well as subjectivity issues, which arise from a lack of standardized assessment approaches. This paper introduces a novel methodology designed to comprehensively evaluate the actionability of CTIPs across all stages of a proposed CTI sharing lifecycle; the proposed methodology is referred to as Evaluating the Actionability of Cyber Threat Intelligence (EVACTI). EVACTI employs the standardized set of actionability criteria of the European Union Agency for Cybersecurity (ENISA) and considers the CTI sharing lifecycle to ensure consistency and mitigate the variability and subjectivity issues prevalent in existing approaches. By considering the operational context of both producers and consumers, EVACTI offers a more accurate and practical evaluation of CTIP actionability. EVACTI also enhances the effectiveness of cybersecurity efforts by impelling producers to refine CTIPs before sharing them and enabling consumers to make decisions about the use and prioritization of CTIPs. Lastly, EVACTI integrates the actionability into the CTI sharing lifecycle through a custom CTI object, further supporting transparent dissemination of actionability values.

Despite the well-established adverse impact of del(11q) in chronic lymphocytic leukemia (CLL), the prognostic significance of somatic ATM mutations remains uncertain. We evaluated the effects of ATM aberrations (del(11q) and/or ATM mutations) on time-to-first-treatment (TTFT) in 3631 untreated patients with CLL, in the context of IGHV gene mutational status and mutations in nine CLL-related genes. ATM mutations were present in 246 cases (6.8%), frequently co-occurring with del(11q) (112/246 cases, 45.5%). ATM-mutated patients displayed a different spectrum of genetic abnormalities when comparing IGHV-mutated (M-CLL) and unmutated (U-CLL) cases: M-CLL was enriched for SF3B1 and NFKBIE mutations, whereas U-CLL showed mutual exclusivity with trisomy 12 and TP53 mutations. Isolated ATM mutations were rare, affecting 1.2% of Binet A patients and <1% of M-CLL cases. While univariable analysis revealed shorter TTFT for Binet A patients with any ATM aberration compared to ATM-wildtype, multivariable analysis identified only del(11q), trisomy 12, SF3B1, and EGR2 mutations as independent prognosticators of shorter TTFT among Binet A patients and within M-CLL and U-CLL subgroups. These findings highlight del(11q), and not ATM mutations, as a key biomarker of increased risk of early progression and need for therapy, particularly in otherwise indolent M-CLL, providing insights into risk-stratification and therapeutic decision-making.

We explore the application of deep learning techniques to accelerate gravitational wave surrogate modeling. We focus on two recent approaches, using artificial neural networks (ANNs) with residual error modeling and autoencoder-driven spiral representation learning. For the ANN method, we demonstrate that adding a second network to learn residual errors significantly improves surrogate model accuracy. The autoencoder approach reveals an inherent spiral structure in the latent space representation of empirical interpolation coefficients. We take advantage of this insight to develop a neural spiral module that can be integrated into network architectures to accelerate training and improve performance. Comprehensive evaluations show that these methods achieve state-of-the-art accuracy while enabling faster waveform generation. The techniques presented have the potential to substantially accelerate gravitational wave data analysis as detector sensitivity improves and event rates increase.

Commercial tomato hybrids exhibit robust performance in modern high-input agricultural systems. However, their suitability for low-input farming remains uncertain. With the goal that by 2030, 25% of European agricultural production must be organic as part of the European Green Deal, this study aims to assess whether existing commercial tomato hybrids can offer a viable solution for low-input farming. Additionally, the impact of beneficial microorganisms such as plant growth-promoting rhizobacteria (PGPR), in relation to the growth and productivity of tomato hybrids under low-input cultivation is assessed. For this purpose, a well-defined microbial consortium, including Azotobacter chroococcum, Clostridium pasteurianum, Lactobacillus plantarum, Bacillus subtilis, and Acetobacter diazotrophicus, was applied as a liquid suspension to enhance root colonization and promote plant growth. Seven commercial tomatoes (Solanum lycopersicum L.) hybrids—the most popular in the Greek market—were evaluated for their performance under high-input (hydroponic) and low-input cultivation systems (with and without the use of PGPR). Several parameters related to yield, fruit quality, nutritional value, descriptive traits, and leaf elemental concentration were evaluated. In addition, a techno-economic analysis was conducted to assess whether hybrids developed under high-input conditions and intended for such cultivation environments suit low-input farming systems. The results indicated that such hybrids are not a viable, efficient, or profitable strategy for low-input cultivation. These findings underscore the importance of breeding tomato varieties, specifically adapted to low-input farming, highlighting the need for targeted breeding strategies to enhance sustainability and resilience in future agricultural systems. Notably, this study is among the first to comprehensively assess the response of commercial tomato hybrids under low-input conditions, addressing a critical gap in the current literature.

Dietary interventions constitute powerful approaches for disease prevention and treatment. However, the molecular mechanisms through which diet affects health remain underexplored in humans. Here, we compare plasma metabolomic and proteomic profiles between dietary states for a unique group of individuals who alternate between omnivory and restriction of animal products for religious reasons. We find that short-term restriction drives reductions in levels of lipid classes and of branched-chain amino acids, not detected in a control group of individuals, and results in metabolic profiles associated with decreased risk for all-cause mortality. We show that 23% of proteins whose levels are affected by dietary restriction are druggable targets and reveal that pro-longevity hormone FGF21 and seven additional proteins (FOLR2, SUMF2, HAVCR1, PLA2G1B, OXT, SPP1, HPGDS) display the greatest magnitude of change. Through Mendelian randomization we demonstrate potentially causal effects of FGF21 and HAVCR1 on risk for type 2 diabetes, of HPGDS on BMI, and of OXT on risk for lacunar stroke. Collectively, we find that restriction-associated reprogramming improves metabolic health and emphasise high-value targets for pharmacological intervention.

The global challenges of sustainability intensify the demand for advanced decision-making systems capable of integrating cognitive capabilities. This survey article comprehensively examines the recent advancements, diverse applications, state-of-the-art techniques, and persistent challenges in cognition and context-aware decision-making systems (CCA-DMS) aimed at fostering sustainability across smart environments. We first present the cognition and context-aware decision-making applications covering topics such as environmental conservation, energy management, urban planning, resource usage, and healthcare. Subsequently, we make a categorization of techniques, methods and cognition-aware models employed. Through a systematic review of the literature, we highlight the significant contributions and breakthroughs in each use case. Furthermore, we identify and analyze the open challenges and research gaps that remain to be addressed, including data gathering, scalability, interpretability, robustness, and ethical considerations. By synthesizing the current state-of-the-art and future directions, this survey serves as a valuable resource for researchers, practitioners, and policymakers interested in harnessing CCA-DMS to address the complex sustainability challenges facing our planet.

Background/Objectives: Personalized nutrition programs enhanced with artificial intelligence (AI)-based tools hold promising potential for the development of healthy and sustainable diets and for disease prevention. This study aimed to explore the impact of an AI-based personalized nutrition program on the gut microbiome of healthy individuals. Methods: An intervention using an AI-based mobile application for personalized nutrition was applied for six weeks. Fecal and blood samples from 29 healthy participants (females 52%, mean age 35 years) were collected at baseline and at six weeks. Gut microbiome through 16s ribosomal RNA (rRNA) amplicon sequencing, anthropometric and biochemical data were analyzed at both timepoints. Dietary assessment was performed using food frequency questionnaires. Results: A significant increase in richness (Chao1, 220.4 ± 58.5 vs. 241.5 ± 60.2, p = 0.024) and diversity (Faith’s phylogenetic diversity, 15.5 ± 3.3 vs. 17.3 ± 2.8, p = 0.0001) was found from pre- to post-intervention. Following the intervention, the relative abundance of genera associated with the reduction in cholesterol and heart disease risk (e.g., Eubacterium coprostanoligenes group and Oscillobacter) was significantly increased, while the abundance of inflammation-associated genera (e.g., Eubacterium ruminantium group and Gastranaerophilales) was decreased. Alterations in the abundance of several butyrate-producing genera were also found (e.g., increase in Faecalibacterium, decrease in Bifidobacterium). Further, a decrease in carbohydrate (272.2 ± 97.7 vs. 222.9 ± 80.5, p = 0.003) and protein (113.6 ± 38.8 vs. 98.6 ± 32.4, p = 0.011) intake, as well as a reduction in waist circumference (78.4 ± 12.1 vs. 77.2 ± 11.2, p = 0.023), was also seen. Changes in the abundance of Oscillospiraceae_UCG_002 and Lachnospiraceae_UCG_004 were positively associated with changes in olive oil intake (Rho = 0.57, p = 0.001) and levels of triglycerides (Rho = 0.56, p = 0.001). Conclusions: This study highlights the potential for an AI-based personalized nutrition program to influence the gut microbiome. More research is now needed to establish the use of gut microbiome-informed strategies for personalized nutrition.

The integration of renewable energy systems into modern buildings is essential for enhancing energy efficiency, reducing carbon footprints, and advancing intelligent energy management. However, optimizing RES operations within building energy management systems introduces significant complexity, requiring advanced control strategies. One significant branch of modern control algorithms concerns reinforcement learning, a data-driven strategy capable of dynamically managing renewable energy sources and other energy subsystems under uncertainty and real-time constraints. The current review systematically examines RL-based control strategies applied in BEMS frameworks integrating RES technologies between 2015 and 2025, classifying them by algorithmic approach and evaluating the role of multi-agent and hybrid methods in improving real-time adaptability and occupant comfort. Following a thorough explanation of a rigorous selection process—which targeted the most impactful peer-reviewed publications from the last decade, the paper presents the mathematical concepts of RL and multi-agent RL, along with detailed summaries and summary tables of the integrated works to facilitate quick reference to key findings. For evaluation, the paper examines and outlines the different attributes in the field considering the following: methodologies of RL; agent types; value-action networks; reward functions; baseline control approaches; RES types; BEMS types; and building typologies. Grounded on the findings presented in the evaluation section, the paper offers a structured synthesis of emerging research trends and future directions, identifying the strengths and limitations of RL in energy management.

As sleep appears to be closely related to cognitive status, we aimed to explore the association between the percentage of deep sleep, cognitive state, and the cerebrospinal fluid (CSF) biomarker amyloid-beta 42 in non-demented individuals. In this cross-sectional study, 90 non-demented participants from the Aiginition Longitudinal Biomarker Investigation of Neurodegeneration cohort underwent a one-night WatchPAT sleep evaluation. Participants were categorized by cognitive status (patients with mild cognitive impairment [MCI] or cognitively normal [CN] individuals) and CSF Aβ42 status (Aβ42 ≤ 1,030 pg/mL [A+] or Ab42 > 1,030 pg/mL [A−]). After controlling for age, sex, and years of education, a significant inverse association was found between the percentage of deep sleep and the odds of being classified as MCI compared to CN (OR = 0.86, 95% CI [0.76–0.97], p = 0.012). However, a non-significant trend for an inverse association between the percentage of deep sleep and the odds of being classified as A+ was observed (OR = 0.92, 95% CI [0.84–1.01], p = 0.092). This study demonstrates a significant link between deep sleep and MCI. Although more longitudinal studies are needed, deep sleep could potentially serve as a novel biomarker of cognitive decline and an intervention target for dementia prevention.

The proliferation of deepfake technology poses significant challenges due to its potential for misuse in creating highly convincing manipulated videos. Deep learning (DL) techniques have emerged as powerful tools for analyzing and identifying subtle inconsistencies that distinguish genuine content from deepfakes. This paper introduces a novel approach for video deepfake detection that integrates 3D Morphable Models (3DMMs) with a hybrid CNN-LSTM-Transformer model, aimed at enhancing detection accuracy and efficiency. Our model leverages 3DMMs for detailed facial feature extraction, a CNN for fine-grained spatial analysis, an LSTM for short-term temporal dynamics, and a Transformer for capturing long-term dependencies in sequential data. This architecture effectively addresses critical challenges in current detection systems by handling both local and global temporal information. The proposed model employs an identity verification approach, comparing test videos with reference videos containing genuine footage of the individuals. Trained and validated on the VoxCeleb2 dataset, with further testing on three additional datasets, our model demonstrates superior performance to existing state-of-the-art methods, maintaining robustness across different video qualities, compression levels and manipulation types. Additionally, it operates efficiently in time-sensitive scenarios, significantly outperforming existing methods in inference speed. By relying solely on pristine, unmanipulated data for training, our approach enhances adaptability to new and sophisticated manipulations, setting a new benchmark for video deepfake detection technologies. This study not only advances the framework for detecting deepfakes but also underscores its potential for practical deployment in areas critical for digital forensics and media integrity.

Purpose The present study aims to investigate the experiences of hematologists providing care to patients with hematological malignancies, whose care is pertinent to oncology. Methods Semi-structured interviews with 30 hematologists across Greece were conducted. The interviews took place over the course of 6 months at 2020. Reflexive thematic analysis was employed for data analysis. Results Three key themes (personal impact, organizational framework, and relating to patients) and eight subthemes were generated: (1) Hematologists were greatly affected on a personal level, as they had poor life-work balance and impacted social relationships. They experienced a great emotional toll, sometimes questioning the meaning of their work. They frequently struggled with loss, by witnessing people’s passing. Nevertheless, they reported coping better over time. (2) On an organizational level, hematologists supported each other emotionally, but only rarely had formal support in managerial or administrative recourses. They were also hindered by structural restraints, both in terms of limited psychosocial training and supportive services. (3) Hematologists’ relationship with their patients increased their job satisfaction. However, they strived to keep boundaries while balancing how close they got to their patients. Conclusion High job demands, organizational shortcomings, and emotional challenges negatively impact their well-being and pose the risk of developing compassion fatigue or burnout. At the same time, individual resources, teamwork, and strong personal relationships emerged as crucial coping elements, providing meaning and resilience. Psychosocial training and institutional support should be offered both personally and professionally to enhance hematologists’ well-being and reduce potential turnover.

Splenic marginal zone lymphoma (SMZL) is a distinct clinical and pathological entity among marginal zone lymphomas. Genetic and microenvironmental factors leading to aberrant activation of the NF‐κB pathway have been implicated in SMZL pathogenesis. CYLD is a negative regulator of NF‐κB and other signaling pathways acting as a deubiquitinase of regulatory molecules and has been reported as a tumor suppressor in different types of cancer, including B‐cell malignancies. To assess whether CYLD is implicated in the natural history of SMZL, we profiled primary cells from patients with SMZL and SMZL cell lines for CYLD expression and functionality. We report that CYLD is downregulated in patients with SMZL and that CYLD ablation in vitro leads to NF‐κB pathway hyperactivation, promoting the proliferation of SMZL cells. In addition, we found that CYLD deficiency was associated with increased migration of SMZL cells in vitro, through CCR7 receptor signaling, and with increased dissemination in vivo. CYLD loss was sufficient to induce BcR signaling, conferring increased resistance to ibrutinib treatment in vitro. In summary, our work uncovers a novel role of CYLD as a key regulator in SMZL pathogenesis, dissemination, and resistance to targeted agents. On these grounds, CYLD could be proposed as a novel target for patient stratification and personalized interventions.