Athena Research and Innovation Center In Information Communication & Knowledge Technologies

ABSTRACT Wastewater treatment plants (WWTPs) play a critical role in protecting public health and the environment but are also contributors to carbon emissions. Improving the eco‐efficiency of WWTPs is essential for advancing sustainability in the sector. This study proposes a parametric approach using stochastic frontier analysis to evaluate the eco‐efficiency of 108 WWTPs in Spain and estimate the marginal cost of reducing GHG emissions. The eco‐efficiency index (EcoEI) integrates operational costs, pollutant removal efficiency, and indirect carbon emissions. Results show an average EcoEI of 0.638, suggesting that WWTPs could improve performance by approximately 36%. The estimated marginal abatement cost of carbon emissions ranges from €0.010 to €1.240 per kg CO 2 eq, with an average of €0.309/kg CO 2 eq. Significant differences in marginal costs were found across secondary treatment technologies, though not in eco‐efficiency scores. These findings have important implications for utilities and regulators aiming to enhance sustainability and meet the targets set by EU Directive 2024/3019.

Biodiversity loss, resource stress, and climate impacts interact in ways that demand integrated and systems-based approaches. This study operationalises the “biodiversity nexus” by applying the JUNIPER system dynamics model to quantify interlinkages between Βiodiversity and the Water - Energy - Food - Transport - Health - Climate Nexus in Greece from 2000 -2019. By embedding species-level threats, ecosystem vulnerability, and cumulative pressures within a unified modelling environment, JUNIPER offers an actionable framework consistent with the IPBES Nexus Assessment for understanding how biophysical, socio-economic, and infrastructural drivers shape biodiversity outcomes.The results reveal persistent biodiversity stress, reflected in gradually declining Red List Index and Red List of Ecosystems Index values. While changes are subtle, they indicate the slow but cumulative deterioration characteristic of ecological systems under multiple interacting pressures. Agriculture emerges as a major source of biodiversity risk, particularly livestock intensity, nutrient loading, pesticide use, and irrigation demand, which together link water scarcity, land-use pressures, and agricultural emissions. Agriculture accounts for ~90% of freshwater withdrawals, demonstrating how water - food linkages strongly mediate biodiversity vulnerability.Energy and transport systems further contribute through air pollution, GHG emissions, and spatial infrastructure expansion. Despite progress toward renewables, continued fossil-fuel use and road-transport dependence generate sustained pressures on both biodiversity and public health. Climate change acts as a compounding stressor through rising temperatures and increased extremes. The Health module illustrates how environmental degradation undermines physical and mental wellbeing, reinforcing the interdependence of ecological integrity and human health.By highlighting the slow biodiversity response relative to more dynamic indicators such as GHG emissions or Quality of Life, this study underscores the need for governance approaches that minimise trade-offs, harness synergies, and support systemic transformation. JUNIPER identifies leverage points, including sustainable irrigation, dietary shifts, decarbonisation pathways, and greener mobility, that can deliver co-benefits across systems. As such, it provides a concrete tool for advancing integrated nexus thinking and informing more coherent, equitable, and sustainable responses to interconnected crises.

Recent advances in automated programming have the potential to reduce human involvement in the software engineering process, but this can lead to less trustworthy software. We envision a three-pronged approach to automating the engineering of trustworthy software that involves (1) eliciting requirements from users and automatically generating formal specifications encoding users’ intent, (2) automatically synthesizing source code conforming to those specifications, and (3) automatically synthesizing formal proofs to verify the correctness of the produced software. We describe this vision and the state of the art in each of these three areas, and the research challenges that must be overcome in each area and in their integration.

This paper introduces SmartHouseOperator, a multi-agent intelligent control framework for adaptive and energy-efficient smart-home management. Modern smart homes integrate heterogeneous devices and sensors, yet most existing solutions rely on static rules or manual coordination, limiting their ability to adapt to dynamic environmental conditions and evolving user preferences. SmartHouseOperator addresses these limitations through an agentic architecture that coordinates device-specific agents for air conditioning, lighting, refrigeration, and shutters under a central orchestrator. The system combines contextual inputs (e.g., weather, occupancy, power load), persistent knowledge, reinforcement-learning-based preference modeling, and LLM-powered reasoning to enable coordinated and personalized control decisions. Experimental results show that the framework achieves consistent reasoning performance across multiple agent orchestration engines and reduces air-conditioning power consumption by up to 16% under critical load conditions. These findings demonstrate the potential of multi-agent, learning-enabled control systems to deliver intelligent, energy-aware, and user-centric smart-home operation.

This working paper reports on six pilot projects conducted as part of the UK Reproducibility Network’s Open Research Programme (ORP) to explore the feasibility of developing automated indicators for monitoring open research practices across UK universities. The pilots examined data quality, metadata standards, and automated extraction methods across six areas: FAIR data, open data, data availability statements, downstream effects of data sharing, pre-registration, and the use of the CRediT taxonomy. The findings highlight areas of promise, particularly pre-registration and elements of FAIRness, while also identifying major challenges related to inconsistent metadata, terminology, and workflows. The report outlines key recommendations for improving interoperability, enhancing data standards, and supporting the responsible development of future monitoring infrastructure.

Metaverse presents significant opportunities for educational advancement by facilitating immersive, personalized, and interactive learning experiences through technologies such as virtual reality (VR), augmented reality (AR), extended reality (XR), and artificial intelligence (AI). However, this potential is compromised if digital environments fail to uphold individuals’ privacy, autonomy, and equity. Despite their widespread adoption, the privacy implications of these environments remain inadequately understood, both in terms of technical vulnerabilities and legislative challenges, particularly regarding user consent management. Contemporary Metaverse systems collect highly sensitive information, including biometric signals, spatial behavior, motion patterns, and interaction data, often surpassing the granularity captured by traditional social networks. The lack of privacy-by-design solutions, coupled with the complexity of underlying technologies such as VR/AR infrastructures, 3D tracking systems, and AI-driven personalization engines, makes these platforms vulnerable to security breaches, data misuse, and opaque processing practices. This study presents a structured literature review and comparative analysis of privacy risks, consent mechanisms, and digital boundaries in metaverse platforms, with particular attention to educational contexts. We argue that privacy-aware design is essential not only for ethical compliance but also for supporting the long-term sustainability goals of digital education. Our findings aim to inform and support the development of secure, inclusive, and ethically grounded immersive learning environments by providing insights into systemic privacy and policy shortcomings.

Numerous geneticvariants have been identified by genome-wide associationstudies as being associated with colorectal cancer (CRC) risk. Metabolome-wideassociation analysis was performed for 187 CRC-associated geneticvariants using genomic data and untargeted 1H nuclear magneticresonance urine metabolomics from 1951 Airwave Health Monitoring Studyparticipants. We identified statistically significant associationsbetween seven CRC single-nucleotide polymorphisms (SNPs) and urinarymetabolites. This included SNPs within or close to RHPN2 with sucrose (P = 1.2 × 10–7), SLC6A18 with amino acids (P =6.9 × 10–5 with tyrosine, P = 9.9 × 10–5 with leucine), and MAP2K5 and BMP2 with gut microbial metabolites (P = 1.6 × 10–4 and P = 4.4 × 10–4). The most significant correlationwas followed by functional experiments in Caco-2 colon cancer cells.CRISPR-mediated knockout of a 48-nt RHPN2 intronicregion containing rs10411210 in colon cancer cells compromised cellgrowth. RNA sequencing was performed in the two sets of clones (3edited and 3 unedited) followed by pathway enrichment, and gene ontologyanalysis depicted extensive deregulation of genes (448 up- and 195downregulated) involved in cell division and several metabolic processes.Overall, these findings demonstrate that integrating genetic and metabolomicdata highlights the importance of the RHPN2 introniclocus in CRC potentially through metabolic processes affecting excretionof dietary and other metabolites.

Despite its high prevalence and major prognostic implications, coronary microvascular disease (CMD) is frequently underdiagnosed owing to the complexity and invasiveness of current diagnostic procedures. This study aimed to introduce and validate the usefulness of a non-invasive index of microcirculatory resistance (IMR) derived from coronary computed tomography angiography (CCTA), called IMRCT, for accurate diagnosis of CMD. This retrospective cohort study comprised consecutive patients referred for invasive coronary functional assessments who underwent CCTA within the 30 days preceding an invasive evaluation between January 2022 and March 2024. IMRCT was calculated by blinded evaluators and compared against invasively determined IMR, with IMR values ≥25 indicating CMD, to assess its diagnostic performance. A total of 176 patients (216 vessels) were included in the analysis. IMRCT showed good correlation with invasively measured IMR, both at the vessel level (r=0.71, 95% confidence interval [CI]: 0.62-0.76; p<0.001) and the patient level (r=0.72, 95% CI: 0.64-0.78; p<0.001). At the vessel level, diagnostic accuracy, sensitivity, specificity, and area under the curve were 81.9%, 80.8%, 82.5%, and 0.82, respectively; corresponding values at the patient level were 80.7%, 81.5%, 80.2%, and 0.81. In patients with non-obstructive coronary artery disease defined by CCTA stenosis <50%, coronary angiogram stenosis <50%, or fractional flow reserve>0.8, IMRCT reduced underdiagnosis rates from 38.8%, 35.3%, and 36.3% to 4.5%, 5.9%, and 5.6%, respectively. IMRCT serves as a valuable complement to current diagnostic approaches, addressing their limitations and offering a promising alternative for the diagnosis of CMD, with the potential to significantly reduce misdiagnosis rates.

This study presents a professional development (PD) program designed within the “SciArt—Promoting 21st-century skills through an inclusive STEAM approach to Cultural Heritage” project, which aims to prepare teachers to implement an inclusive, inquiry-based STEAM approach in their classrooms. The approach, developed collaboratively by academics from the arts, sciences, and cultural sectors across Cyprus, Greece, and Portugal, integrates archaeometric methods with cultural heritage to support identity exploration and inclusive pedagogy. The study explores how participating teachers evaluated the PD program, how it influenced their self-efficacy in applying inclusive, inquiry-based STEAM approaches, and how teacher trainers experienced its implementation across different national contexts. The study utilizes both qualitative and quantitative methods of data collection, comprising questionnaires and a focus group. Results show high teacher satisfaction and increased self-efficacy across four thematic areas: inquiry-based learning, science-heritage integration, cultural identity exploration, and use of multimodality for inclusion. Teacher trainers described the process as demanding but professionally enriching, emphasizing the role of interdisciplinary collaboration. These findings highlight the potential of well-supported, theoretically grounded PD programs to build capacity for inclusive STEAM education, while also revealing structural barriers that must be addressed for wider implementation.