Mediterranean University

Euthanasia has been a research subject among scientists for several decades. The aim of the present study was to examine the attitudes of health professionals toward the bioethical issue of euthanasia. The sample consisted of 465 health and social care professionals working in Health Services in Greece. Participants completed an online sociodemographic questionnaire as well as a set of short-form open-ended questions about euthanasia. Although the majority of health professionals (61.9%) agreed with the legalization of euthanasia in Greece, a lower percentage of participants reported that they have desired or asked for euthanasia (28.8%) or they have experienced a euthanasia case (9.0%). Similarly, fewer participants stated they had wished for the possibility of euthanasia for one of their patients (47.5%), while the majority (71.4%) reported that they had never attended a specific course or received training about euthanasia at any educational level. Incurable, irreversible diseases and cancer end-stage patients were the two main categories for which health professionals would suggest the performing of euthanasia. The most consistent sociodemographic variables associated with the majority of the study questionnaire items were age, gender and religiosity, while the most consistent working characteristics were hospitalization of end-stage patients and profession. The findings of this study point out the shortcomings of the educational system as well as the impact of religious beliefs on health professionals' attitudes toward euthanasia.

This study aims to understand the physical connection between the Lithospheric-Atmospheric-Ionospheric Coupling (LAIC) concept, defined by pre-earthquake anomalies, and a macroscopic one, using an AI-driven, multi-scope approach.Currently, earthquake precursor information is divided into two main categories: microscopic (detectable by ground- and satellite-based instruments) and macroscopic (detectable by human senses or direct observation). LAIC relies solely on microscopic observations for analysis. In the broader context, macroscopic precursors are physical, biological, or atmospheric phenomena that can be observed by the human senses or basic instruments without requiring complex laboratory analysis. These "anomalies" often occur during the earthquake preparation stage, when tectonic stress has reached a critical level. Very often, the macroscopic anomalies have been met with scientific skepticism, even though these reports are often rooted in historical accounts. In this study, macroscopic information has been used only as a marker of the physical phenomenon, without quantification.We have analyzed information on the top events in Europe, particularly in the Mediterranean and Alpine regions, which provide some of the most scientifically rigorous documentation of the "Precursor Chain” in Europe. For example, (1) L’Aquila, Italy (2009) – has shown biogeochemical & geodetic synergy, and this event is the modern benchmark for linking radon outgassing with biological responses and satellite-based ground monitoring. (2) Friuli, Italy (1976) – has revealed electromagnetic & luminous synergy, because the Alpine environment of Northern Italy provided a unique case of "rock-to-atmosphere" electrical coupling. (3) Izmit, Turkey (1999) – has revealed geochemical & biological synergy, because the North Anatolian Fault (NAF), though transcontinental, exhibits many European seismic characteristics, including high-density geochemical shifts. (4) Messina, Italy (1908) – has revealed hydrological & atmospheric synergy, and, as one of the most destructive events in European history, this quake was preceded by classic "Dilatancy-Diffusion" indicators. (5) Athens, Greece (1999) – has shown ionospheric & satellite thermal synergy, as Greece sits atop a complex subduction and transform system where atmospheric coupling is frequently observed. (6) Umbria-Marche, Italy (1997) – has shown foreshock & hydrological synergy, along with the Colfiorito sequence, which provided deep insights into how fluids move through the Apennine limestone.In many cases, our out-of-place findings are entirely unexpected, showing that modern instruments, human and animal observations, and some direct measurements were collected within close timeframes, with re-occurrences of anomalies with similar patterns and time-lags across the same and different seismo-tectonic settings. That might indicate that common multi-parameter coupling mechanisms, similar to LAIC, are in place, and validating them is the next step in this exploration to deepen our understanding of the nature and complexity of pre-earthquake phenomena.

Thermal imaging of the Milos volcano (Cyclades, Greece) is used to monitor its active hydrothermal system, specifically focusing on the Eastern part of Milos. Satellite data is essential for tracking Land Surface Temperature (LST) anomalies and radiative heat flux in this dormant but active volcanic field. Milos hosts a well-known shallow geothermal field mainly developed beneath the eastern part of the island. In this work we aim to establish the background surface temperature level for this volcano and observe possible fluctuations related to seasonal effects or changes in the shallow hydrothermal activity.Thermal sensors Landsat 8 and Landsat 9 (8-day sampling interval) at 100-m resolution during the year 2025 were used for this study. The final dataset contained 65 satellite images, each with cloud and shadow coverage below 40%. Initially, a pixel-based geostatistical analysis was done, where 12 monthly mean LST maps and 12 monthly standard deviation maps were produced to investigate the surface thermal conditions of the island. To mitigate climate change's influence, a further investigation was followed by producing 3 more maps, to detect and locate the accurate annual spatial distribution of valid clear-sky Landsat LST observations, derived by each pixel’s counts over 40oC and its relevance to the normalized annual frequency. The analysis was done completely on Google Earth Engine.The results showed that the monthly analysis of land surface temperature imagery consistently detects temperatures inside the Zephyria depression (eastern Milos) that are 5–25 °C warmer than the surrounding terrain, which can reach up to 58oC. Additionally, the final analysis succeeded in mitigating the external weather conditions and revealed that 34 observations (out of 65) present a land surface temperature over 40oC inside the Zephyria depression, with a clear spatial correlation to the shallow geothermal field of the island. Another important outcome was that despite the limitations due to atmospheric interference, the limited land-coverage of the island and the small scale of fumaroles onshore Milos, Landsat 8 and Landsat 9 are both able to detect the thermal anomalous pixels by using one year as a referenced period.

Peri-urban ecosystems represent underexplored habitats rich in entomopathogenic fungi (EPF) that can serve as valuable resources for managing insect pests. This study characterized the EPF communities in two peri-urban sites near Patras, Greece (Dasyllio and Elos), during 2018–2019. Soil samples were collected seasonally, and fungi were isolated using insect baiting with Tribolium confusum Jacquelin du Val and Sitophilus zeamais Motsch., a selective method favoring generalist, fast-acting entomopathogens. A total of 814 isolates were recovered. Of a randomly selected subset (n = 177) subjected to molecular identification, 46.9% were characterized as known EPF, while 53.1% were classified as putative EPF based on taxonomic affiliation (ITS sequence similarity ≥ 99%), pending confirmation of pathogenicity. The Dasyllio site yielded more isolates (63.4%) than Elos (36.6%). Seasonal trends strongly influenced EPF occurrence, with infective fungi peaking in spring and summer (p < 0.001), while community diversity remained stable throughout the year, with the highest evenness (Evenness Index = 0.93) observed in autumn. These results highlight peri-urban green spaces as reservoirs of diverse and ecologically stable EPF, suggesting their potential as sources of biocontrol agents for future development and seasonal integration into pest management strategies.

Objectives: This study aimed to explore the impact of integrating artificial intelligence (AI) tools on project performance in the Western Sector of the Saudi Electricity Company. It is achieved by analyzing the effect of AI on key project performance dimensions, including project timing, cost, and quality, to enhance project management efficiency and overall performance. Methodology: A descriptive-analytical approach was employed, appropriate for the study’s objectives. The study population consisted of experienced technical and administrative staff at the Western Sector of the Saudi Electricity Company. Data were collected via an online questionnaire distributed to a sample of 78 project managers, engineers, and specialists. Linear regression analysis was used to test the study hypotheses and measure the impact of AI integration on project performance dimensions. Results: The findings indicated a statistically significant positive effect of AI integration on overall project performance. Specifically, AI tools were found to significantly improve project timelines, reduce costs, and enhance the quality of project deliverables. Conclusion: The study underscores the importance of adopting AI technologies and applications in project management at the Western Sector of the Saudi Electricity Company. It also highlights the need for specialized training programs and investment in technological infrastructure to enhance performance efficiency and achieve excellence in project management.

Rapid and accurate disease detection is essential for effective plant health management, which significantly increases crop yields and mitigates losses exacerbated by climate change. While machine-based imaging can detect symptoms invisible to the naked eye, standard models often overlook the influence of climatic and soil factors on disease occurrence. Objectives: To develop and assess the performance of deep learning models employing multimodal fusion strategies for the precise classification of crop diseases. Methodology: Proposed multimodal deep learning approach to combine visual features using MobileNetV2 with environmental and soil, including temperature, humidity, pH, rainfall, and nutrient levels (N, P, K) by the Multilayer Perceptron (MLP). Results: Experimental results demonstrate that MobileNetV2 excels among image-only models, proving to be an efficient lightweight architecture for agricultural tasks. Notably, the multimodal MobileNetV2 + MLP model achieved nearly 100% precision, significantly outperforming unimodal models. This highlights that incorporating environmental variables substantially enhances classification accuracy. Conclusion: Disease detection may become more precise if the multimodal correlations are strengthened by extending the dataset and upgrading the quality of environmental features, advanced fusion and alignment techniques usage that can further facilitate the interaction between the image and structured data, and lightweight CNNs, along with real-world testing that can help in the creation of reliable field-ready plant disease detection systems.

ABSTRACT This article investigates the role of fintech in shaping the development stages of Central Bank Digital Currency (CBDC) projects across 64 countries. Using a panel random-effects ordered logit model (2017–2020), we document strong empirical evidence that fintech-led digital capital raising is positively associated with progress in CBDC initiatives. Our results also highlight the importance of network readiness, financial literacy, and government digital capacity. These findings offer strategic implications for policymakers aiming to balance innovation and governance in digital currency development.

Ghost nets are the result of fishing nets ending up at sea by fishing vessels during operations, repairs, accidental loss, and from aquaculture activities. This is a major threat to the marine environment due to the entrapment of marine species, which often leads to the mortality of important species, the alteration of the marine benthic habitat, and the release of microplastics. In the current study, the authors conducted underwater clean-up activities in the marine protected area of Sounio in Greece (NATURA2000) to identify, evaluate whether they can be removed, and remove ghost nets. A total of 1200 Kg of ghost nets was removed within one year, with 68 different species reported to have colonized the nets. The reported groups were Mollusca, Porifera, Chordata, Arthropoda, Echinodermata, Bryozoa, Ochrophyta, Tracheophyta, Rhodophyta, Cnidaria, Chlorophyta, and Annelida. The species were not listed as threatened by the IUCN conservation status, while 86% were native, and 14% were invasive in the Mediterranean Sea. The current work presents the need to expand research efforts in the field of underwater plastic pollution, implement monitoring campaigns to a greater extent in the study area, and perform an assessment before the removal of ghost nets.

Adolescence is a critical period for brain development, during which dietary patterns may influence neuropsychological functioning. To examine cross-sectional associations and determine if baseline adherence to the Mediterranean diet (MD) and ultra-processed food (UPF) consumption is associated with changes in adolescent neuropsychological outcomes over 6months. This study represents a secondary analysis of the WALNUTs Smart-Snack Trial. We evaluated 653 adolescents (aged 12-16years) from Barcelona at baseline and at 6-month follow-up. All dietary data used for these analyses were collected at the baseline timepoint only. MD adherence was measured with the KIDMED index and UPF consumption was assessed using a food frequency questionnaire and classified using the NOVA system. Cognitive domains were assessed at both time points using standardised computer-based tasks: attention (alerting, orienting, executive control) with the Attention Network Test; working memory (4-back) with the N-back task; fluid intelligence with the Primary Mental Abilities-Revised; decision-making with the Roulettes Task; and emotion recognition with the Emotion Recognition Task. Behavioural outcomes were also evaluated at both time points using the self-reported Strengths and Difficulties Questionnaire and teacher-reported Attention-Deficit/Hyperactivity Disorder-DSM-IV scales. Associations were examined using multivariable generalised linear models. To further validate UPF consumption estimations, polyphenol biomarkers were measured in urine in a subsample of 257 participants. Greater adherence to the MD was cross-sectionally linked to fewer behavioural problems and higher scores of executive functioning, while higher UPF consumption was associated with poorer emotion recognition, reduced alerting attention, less advantageous decision-making, more behavioural problems and internalising symptoms. Longitudinally, higher UPF consumption was associated with more internalising symptoms and less advantageous decision-making, whereas MD adherence showed no associations. Greater MD adherence appears associated with more favourable behavioural and cognitive profiles. In contrast, higher UPF consumption seems to be associated with less favourable profiles across multiple neuropsychological domains in adolescents. However, the limited longitudinal evidence points to complex relationships that warrant further investigation. Overall, these findings highlight the importance of promoting healthier dietary habits during adolescence.

This study evaluates the agri-environmental performance of the EU-27 Member States using the PROMETHEE multiple-criteria decision analysis method, based on three Eurostat indicators linked to the sustainability pillars: Harmonized Risk Indicator 1 (HRI1, social pillar), pesticide sales intensity (kg/ha UAA, environmental pillar), and environmental protection investments (% GDP, economic pillar). The analysis uses the most recent available Eurostat data (primarily from 2023) and examines three weighting scenarios: (i) equal weights, (ii) higher emphasis on the economic pillar, and (iii) higher emphasis on the environmental and social pillars. Across all scenarios, Slovenia ranked first (net flow, φ = 0.4173 to 0.4734), followed by Czechia (φ = 0.2796 to 0.3260) and France (φ = 0.1886 to 0.2240), while Malta (φ = −0.3356 to −0.3691), Cyprus (φ = −0.2916 to −0.3027), and Estonia (φ = −0.2641 to −0.2903) consistently occupied the lowest positions. The stability of rankings across alternative weighting schemes indicates robust performance patterns, with minimal shifts for most Member States. Overall, the results highlight persistent cross-country differences in agri-environmental performance despite common EU regulatory frameworks, underlining the relevance of national implementation capacity and investment strategies. The proposed PROMETHEE-based ranking provides a transparent and policy-aligned benchmarking tool that can support monitoring and prioritization of interventions related to pesticide risk reduction and environmental investment across EU Member States.