Search Results (787)

The increasing complexity of energy systems amid the global push for decarbonization raises important questions about how transitions in the energy matrix affect financial risk in electricity markets. This study investigates the relationship between structural changes in national energy matrices and the systemic risk associated with electricity prices in Europe from 2015 to 2022. Using daily electricity price data, we calculate log returns and estimate the Value at Risk (VaR) at the 1% level as a measure of extreme financial loss. We incorporate energy market variables, including the volatility of Brent oil and coal prices, and an entropy-based indicator derived from the Shannon index, which captures the degree of technological dispersion in the energy mix over time. A fixed-effects panel regression model is applied across 21 European countries to identify the drivers of energy-related financial risk. Results show that higher volatility in Brent and coal prices significantly increases the VaR, and that greater entropy reflecting a more complex and dynamic energy transition also correlates with higher systemic risk. These findings suggest that while energy diversification is a goal of sustainability, it may entail short-term instability. The study contributes to the understanding of how structural transformations in energy systems interact with financial vulnerabilities in liberalized electricity markets. Full article

Escalating Human–Wildlife Conflict (HWC), particularly involving protected large carnivores such as the wolf, poses a significant challenge in Europe. This problem, exacerbated by ecological pressure, necessitates the development of innovative, non-lethal, and effective prevention methods that overcome the limitations of current passive solutions, such as habituation. This article presents the design and implementation of a prototype for an autonomous, multi-sensory preventive system. Its three-layer architecture is based on a decentralized network of sensory-deterrent nodes that utilize Edge AI for real-time species detection and adaptive selection of deterrent stimuli. During field validation, the prototype’s biological efficacy as a proof-of-concept was confirmed in a crop protection scenario against the European roe deer (Capreolus capreolus). The system’s deployment led to a near-total elimination of damages. The paper also presents key technical performance metrics (e.g., response time, energy consumption) and the accuracy of the implemented AI detection model, verified using both field and historical data. The positive test results demonstrate that the developed platform provides an effective and flexible foundation for preventive systems. Its successful validation on a common herbivore species represents a crucial, measurable step toward the target implementation and further research on the system’s effectiveness in providing protection against large carnivores. Full article

Dirofilariosis, a parasitic disease caused by nematodes of the genus Dirofilaria, primarily affects dogs but can also infect other carnivores and, more rarely, humans. In Europe, the most commonly involved species are D. immitis and D. repens, transmitted through the bites of mosquito vectors. This study, conducted in Tulcea County between April and October 2024, aimed to determine the prevalence of D. immitis and D. repens in mosquitoes. A total of 1507 mosquitoes were collected and grouped into 76 pools, and subsequently molecular analysis was carried out using qPCR. The estimated infection rate (EIR) was calculated using statistical methods available in the ‘binGroup’ package in R, which allow the determination of the point estimate and confidence interval (CI) for a single binomial proportion in group testing. The study revealed a high infection rate with D. immitis (48%), while D. repens was identified in only two pools. The species with the highest vector potential was Anopheles maculipennis (PTP = 75%, EIR = 0.1168 with both Dirofilaria species), followed by Aedes vexans. Notably, Aedes albopictus was identified for the first time in Tulcea, and all individuals were positive for D. immitis. Simulations of local thermal conditions using the proposed model show that the favorable time window for mosquitoes will increase until 2100. Our results indicate an established and active transmission cycle of D. immitis in the region, a situation projected to intensify with climate change requiring urgent monitoring. Full article

Cities face mounting pressures to deliver reliable, low-carbon services amid rapid urbanization and budget constraints. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) are widely promoted to automate operations and strengthen decision-support across the built environment; however, it remains unclear whether these interventions are both effective and systemically integrated across domains. We conducted a Preferred Reporting Items for Systematic Reviews (PRISMA) aligned systematic review and meta-analysis (January 2015–July 2025) of empirical AI/ML/DL/IoT interventions in urban infrastructure. Searches across five open-access indices Multidisciplinary Digital Publishing Institute (MDPI), Directory of Open Access Journals (DOAJ), Connecting Repositories (CORE), Bielefeld Academic Search Engine (BASE), and Open Access Infrastructure for Research in Europe (OpenAIRE)returned 7432 records; after screening, 71 studies met the inclusion criteria for quantitative synthesis. A random-effects model shows a large, pooled effect (Hedges’ g = 0.92; 95% CI: 0.78–1.06; p < 0.001) for within-domain performance/sustainability outcomes. Yet 91.5% of implementations operate at integration Levels 0–1 (isolated or minimal data sharing), and only 1.4% achieve real-time multi-domain integration (Level 3). Publication bias is likely (Egger’s test p = 0.03); a conservative bias-adjusted estimate suggests a still-positive effect of g ≈ 0.68–0.70. Findings indicate a dual reality: high efficacy in silos but pervasive fragmentation that prevents cross-domain synergies. We outline actions, mandating open standards and APIs, establishing city-level data governance, funding Level-2/3 integration pilots, and adopting cross-domain evaluation metrics to translate local gains into system-wide value. Overall certainty of evidence is rated Moderate based on Grading of Recommendations Assessment, Development, and Evaluation (GRADE) due to heterogeneity and small-study effects, offset by the magnitude and consistency of benefits. Full article

Agricultural management in Europe faces increasing pressure to reduce its environmental footprint. Implementing precision agriculture for weed management could offer a solution and minimize the use of chemical products. High spatial resolution imagery from real time kinematic (RTK) unmanned aerial vehicles (UAV) in combination with supervised convolutional neural network (CNNs) models have proven successful in making location specific treatments. This site-specific advice limits the amount of herbicide applied to the field to areas that require action, thereby reducing the environmental impact and inputs for the farmer. To develop performant CNN models, there is a need for sufficient high-quality labelled data. To reduce the labelling effort and time, a new labelling method is proposed whereby image subsection pairs are labelled based on their relative differences in weed pressure to train a CNN ordinal regression model. The model is evaluated on detecting weed pressure in potato (Solanum tuberosum L.). Model performance was evaluated on different levels: pairwise accuracy, linearity (Pearson correlation coefficient), rank consistency (Spearman’s (r_s) and Kendal (τ) rank correlations coefficients) and binary accuracy. After hyperparameter tuning, a pairwise accuracy of 85.2%, significant linearity (r_s = 0.81) and significant rank consistency (r_s = 0.87 and τ = 0.69) were found. This suggests that the model is capable of correctly detecting the gradient in weed pressure for the dataset. A maximum binary accuracy and F1-score of 92% and 88% were found for the dataset after thresholding the predicted weed scores into weed versus non-weed images. The model architecture allows us to visualize the intermediate features of the last convolutional block. This allows data analysts to better evaluate if the model “sees” the features of interest (in this case weeds). The results indicate the potential of ordinal regression with relative labels as a fast, lightweight model that predicts weed pressure gradients. Experts have the freedom to decide which threshold value(s) can be used on predicted weed scores depending on the weed, crop and treatment that they want to use for flexible weed control management. Full article

The China Railway Express (China-Europe container railway freight transport) is pivotal to Eurasian freight, yet its transcontinental railway faces escalating cascading risks. We develop a coupled map lattice (CML) model representing the physical infrastructure layer and the operational traffic layer concurrently to quantify and mitigate cascading failures. Twenty critical stations are identified by integrating TOPSIS entropy weighting with grey relational analysis in dual-layer networks. The enhanced CML embeds node-degree, edge-betweenness, and freight-flow coupling coefficients, and introduces two adaptive cargo-redistribution rules—distance-based and load-based for real-time rerouting. Extensive simulations reveal that network resilience peaks when the coupling coefficient equals 0.4. Under targeted attacks, cascading failures propagate within three to four iterations and reduce network efficiency by more than 50%, indicating the vital function of higher importance nodes. Distance-based redistribution outperforms load-based redistribution after node failures, whereas the opposite occurs after edge failures. These findings attract our attention that redundant border corridors and intelligent monitoring should be deployed, while redistribution rules and multi-tier emergency response systems should be employed according to different scenarios. The proposed methodology provides a dual-layer analytical framework for addressing cascading risks of transcontinental networks, offering actionable guidance for intelligent transportation management of international intermodal freight networks. Full article

Background: Evidence on how everyday walking and sleep relate to mood in health profession students from Central–Eastern Europe remains limited. Methods: We conducted a cross-sectional study among 277 Romanian medical students. Data were collected using validated instruments for physical activity (IPAQ-SF), sleep quality (PSQI), and depressive/anxiety symptoms (HADS). Associations were examined using bivariate and multivariable regression models, including sex-stratified analyses. Results: In bivariate analysis, total physical activity was inversely correlated with depressive symptoms (ρ = −0.19, p < 0.001). However, in the multivariable model, this effect was not statistically significant after controlling for other factors. Poor sleep quality emerged as the dominant independent predictor of both depression (β = 0.37, p < 0.001) and anxiety (β = 0.40, p < 0.001). Walking time and frequency were specifically protective against depressive symptoms. Sex-stratified analyses revealed distinct patterns: female students benefited more from walking, whereas male students showed stronger associations between overall physical activity and lower depressive symptoms. Conclusions: Within the constraints of a cross-sectional design, this study provides novel evidence from Eastern Europe that sleep quality and physical activity are central to student mental health. Psychological benefits of walking appear sex-specific, and the null mediation finding suggests benefits operate via direct or unmodelled pathways. Sleep is a critical independent target for tailored, lifestyle-based strategies. Full article

Cyprus is located between Europe, Asia and Africa, and its location is vulnerable to dust transport from the Sahara Desert, wildfire smoke particles from surrounding regions, and other anthropogenic emissions caused by several factors, mostly due to business activities on harbor areas. Moreover, the country suffers from heavy traffic conditions caused by the limited public transportation system in Cyprus. Therefore, taking into consideration the country’s geographic location, heavy commercial activities, and lack of good public transportation system, Cyprus is exposed to dust episodes and high anthropogenic emissions associated with multiple health and environmental issues. Therefore, continuous and qualitative air quality monitoring is essential. The Department of Labor Inspection of Cyprus (DLI) has established an air quality monitoring network that consists of 11 stations at strategic geographic locations covering rural, residential, traffic and industrial zones. This network measures the following pollutants: nitrogen oxide, nitrogen dioxide, sulfur dioxide, ozone, carbon monoxide, particulate matter 2.5, and particulate matter 10. This case study compares and evaluates the agreement between Copernicus Atmosphere Monitoring Service (CAMS) air quality products and ground-truth data from the DLI air quality network. The study period spans from January to December 2024. This study focuses on the following three pollutants: particulate matter 2.5, particulate matter 10, and ozone, using Ensemble Median, EMEP, and CHIMERE near-real-time model data provided by CAMS. A data analysis was performed to identify the agreement and the error rate between those two datasets (i.e., ground-truth air quality data and CAMS air quality data). In addition, this study assesses the reliability of assimilated datasets from CAMS across rural, residential, traffic and industrial zones. The results showcase how CAMS near-real-time analysis data can supplement air quality monitoring in locations without the availability of ground-truth data. Full article

The temperature-based indicators heating and cooling degree days, are frequently utilized to quantitatively link indoor energy demand and outdoor thermal conditions, especially in the context of climate change. We present a comprehensive study of the heating and cooling degree-days and the degree-days categories for the near past (1976–2005), and the AR5 RCP4.5 and RCP8.5 scenario-driven future (2066–2095) over Southeast Europe based on an elaborated methodology and performed using a 19 combinations of driving global and regional climate models from EURO-CORDEX with horizontal resolution of 0.11°. Alongside the explicit focus of the degree-days categories and the finer grid resolution, the study benefits substantially from the consideration of the monthly, rather than annual, time scale, which allows the assessment of the intra-annual variations of all analyzed parameters. We provide evidences that the EURO-CORDEX ensemble is capable of simulating the spatiotemporal patterns of the degree-days and degree-day categories for the near past period. Generally, we demonstrate also a steady growth in cooling and a decrease in heating degree-days, where the change of the former is larger in relative terms. Additionally, we show an overall shift toward warmer degree-day categories as well as prolongation of the cooling season and shortening of the heating season. As a whole, the magnitude of the projected long-term changes is significantly stronger for the ’pessimistic’ scenario RCP8.5 than the ’realistic’ scenario RCP4.5. These outcomes are consistent with the well-documented general temperature trend in the gradually warming climate of Southeast Europe. The patterns of the projected long-term changes, however, exhibit essential heterogeneity, both in time and space, as well as among the analyzed parameters. This finding is manifested, in particular, in the coexistence of opposite tendencies for some degree-day categories over neighboring parts of the domain and non-negligible month-to-month variations. Most importantly, the present study unequivocally affirms the significance of the anticipated long-term changes of the considered parameters over Southeast Europe in the RCP scenario-driven future with all subsequent and far-reaching effects on the heating, cooling, and ventilation industry. Full article

Diabrotica virgifera virgifera Le Conte, 1868 (Coleoptera: Chrysomelidae), known as the western corn rootworm, is one of the most important alien insect pests affecting maize crops globally. It causes significant economic losses by feeding on the roots, which affects plant stability and nutrient absorption, as well as by attacking essential aerial organs (leaves, silk, pollen). Since its accidental introduction into Europe, the species has expanded its range across maize-growing regions, raising concerns about future distribution under climate change. This study aimed to estimate the risk of pest establishment across Europe over three future time frames (2034, 2054, 2074) based on geographic coordinates, climate data, and maize distribution. Spatial simulations were performed in QGIS using national centroid datasets, risk classification criteria, and temperature anomaly maps derived from Copernicus and ECA&D databases for 1992–2024. The results indicate consistently high risk in southern and southeastern regions, with projected expansion toward central and western areas by 2074. Risk zones showed clear spatial aggregation and directional spread correlated with warming trends and maize availability. The pest’s high reproductive potential, thermal tolerance, and capacity for human-assisted dispersal further support these predictions. The model emphasizes the need for expanded surveillance in at-risk zones and targeted policies in areas where D. v. virgifera has not yet established. Future work should refine spatial predictions using field validation, genetic monitoring, and dispersal modeling. The results contribute to anticipatory pest management planning and can support sustainable maize production across changing agroclimatic zones in Europe. Full article

Background and Objectives: Laryngeal cancer imposes a disproportionate burden on speech, airway protection and long-term quality of life. Contemporary population-based data for Central and Eastern Europe remain scarce, and the post-pandemic trajectory is uncertain. Materials and Methods: We performed a nationwide, retrospective ecological time-series study using Romanian mortality registers and hospital-discharge files for 2017–2023. Crude and age-standardised mortality rates (ASMRs) were calculated, county-level indirect standardisation and spatial autocorrelation assessed and joinpoint regression quantified temporal trends. Forecasts to 2040 combined Holt–Winters/ARIMA models with Elliott-wave heuristics anchored to Fibonacci retracements. Results: In 2023, 798 laryngeal cancer deaths yielded a crude mortality of 3.65/100,000 (95% CI 3.41–3.91). Male mortality (7.07/100,000) exceeded female mortality 18-fold. Rural residents experienced a higher rate than urban counterparts (4.26 vs. 3.04/100,000), a difference unchanged after indirect age standardisation. National ASMR fell by 3.7% annually (p < 0.01), yet five counties formed a high-risk corridor (standardised mortality ratios 1.59–1.82); Moran’s I = 0.27 (p < 0.01) indicated significant spatial clustering. Pandemic-era surgical throughput collapsed by 48%, generating a backlog projected to persist beyond 2030. Ensemble forecasting anticipates a doubling of discharges and mortality between 2034 and 2037 unless smoking prevalence falls by ≥30% and radon exposure is curtailed. Conclusions: Although overall laryngeal cancer mortality in Romania is declining, the pace lags behind Western Europe and is threatened by geographic inequities and pandemic-related care delays. Aggressive tobacco control, radon-remediation policies and expansion of surgical and radiotherapeutic capacity are required to avert a forecasted surge in the next decade. Full article

Background: Problematic internet use among adolescents is linked to poorer mental health, academic performance, and social functioning, yet evidence from Eastern Europe remains limited. Methods: We conducted a school-based cross-sectional study at a Romanian high school (Arad County) including 308 students aged 15–18 years (154 males, 154 females). Students completed a demographic/behavioral questionnaire and the 20-item Internet Addiction Test (IAT), a widely used measure of problematic internet use. The prespecified primary analysis was a multivariable linear regression of IAT score on sex, age group, residence, daily screen time, prior attempts to reduce use, and main internet purpose; supporting analyses included t-tests, ANOVA, and Pearson correlation (α = 0.05). Results: In bivariable comparisons, males, older adolescents (17–18 years), and urban residents reported higher IAT scores; screen time correlated with IAT (r = 0.460, p < 0.001), and prior reduction attempts were associated with higher scores (Cohen’s d = 0.80). In the adjusted model, male sex (β = 4.97), older age (β = 5.36), greater daily screen time (β = 1.67 per hour), prior attempts to reduce use (β = 4.13), and primarily using the internet for gaming (β = 5.71) remained significant predictors (all p ≤ 0.045); urban residence was not retained (p = 0.218). The model explained 43% of IAT variance (R² = 0.43). Conclusions: Demographic and behavioral factors independently predict adolescent problematic internet use, highlighting high-risk profiles (older males, heavy screen time, gaming focus, prior reduction attempts). These findings support school-based screening and targeted digital-health interventions in underrepresented contexts. Full article

Urban tree biodiversity represents a valuable natural resource. However, some fast-growing tree species with limited esthetic value play an important ecological role by colonizing degraded areas, such as closed landfills. Our observations indicate that trees like Betula pendula (Roth), Acer negundo (L.), and Populus tremula (L.) reached the size of adult trees in less than 30 years after the landfill’s closure in the 1990s, forming a nature area similar to a natural forest. A resident survey conducted among the inhabitants of Zgierz confirmed that the lack of space provides opportunities for various forms of recreation. The example analyzed indicates a trend that can be replicated in other cities with minimal human intervention and low financial costs for landfill reclamation. The case study presents an ecological approach to managing degraded sites, where nature determines the quality of the soil environment by eliminating pollutants from the residential surroundings. Furthermore, the research framework provides a basis for developing future models for cleaning up urban landfill sites and promoting placemaking. This pilot study shows a model for old landfills in Europe with well-developed spontaneous vegetation that can be transformed into recreation and sports facilities in the urban areas with industrial past times. Full article

The sustainability of regional development in Europe is deeply influenced by heterogeneous globalization processes, yet the divergent long-term trajectories of these processes remain poorly quantified, hindering the design of targeted policies. This study aims to identify and characterize clusters of European countries with similar patterns of overall globalization development in order to assess implications for sustainable and cohesive growth. A novel clustering algorithm is developed that integrates Dynamic Time Warping with k-means to account for temporal misalignments and capture similarities in development dynamics rather than just static levels. Analysis based on the KOF Globalization Index for 40 countries reveals four distinct clusters: highly globalized and stable Western European economies, converging Central and Eastern European countries, microstates with niche integration models, and a peripheral group of Southeastern European nations facing significant challenges. The results demonstrate a persistent core–periphery divergence in globalization paths across Europe. This divergence presents a major obstacle to achieving territorial cohesion and equitable sustainable development outcomes. Methodologically, this study provides a robust framework for analyzing longitudinal socioeconomic processes. The main conclusion is that a one-size-fits-all EU cohesion policy is insufficient; instead, cluster-specific strategies are necessary in order to mitigate regional inequalities, enhance resilience, and ensure that the benefits of globalization contribute to the goals of sustainable development. The findings offer a quantitative basis for such targeted policy interventions. Full article

Digital twins represent a transformative innovation for battery energy storage systems (BESS), offering real-time virtual replicas of physical batteries that enable accurate monitoring, predictive analytics, and advanced control strategies. These capabilities promise to significantly enhance system efficiency, reliability, and lifespan. Yet, despite the clear technical potential, large-scale deployment of digital twin-enabled battery systems faces critical governance barriers. This study identifies three major challenges: fragmented standards and lack of interoperability, weak or misaligned market incentives, and insufficient cybersecurity safeguards for interconnected systems. The central contribution of this research is the development of a comprehensive governance framework that aligns these three pillars—standards, market and regulatory incentives, and cybersecurity—into an integrated model. Findings indicate that harmonized standards reduce integration costs and build trust across vendors and operators, while supportive regulatory and market mechanisms can explicitly reward the benefits of digital twins, including improved reliability, extended battery life, and enhanced participation in energy markets. For example, simulation-based evidence suggests that digital twin-guided thermal and operational strategies can extend usable battery capacity by up to five percent, providing both technical and economic benefits. At the same time, embedding robust cybersecurity practices ensures that the adoption of digital twins does not introduce vulnerabilities that could threaten grid stability. Beyond identifying governance gaps, this study proposes an actionable implementation roadmap categorized into short-, medium-, and long-term strategies rather than fixed calendar dates, ensuring adaptability across different jurisdictions. Short-term actions include establishing terminology standards and piloting incentive programs. Medium-term measures involve mandating interoperability protocols and embedding digital twin requirements in market rules, and long-term strategies focus on achieving global harmonization and universal plug-and-play interoperability. International examples from Europe, North America, and Asia–Pacific illustrate how coordinated governance can accelerate adoption while safeguarding energy infrastructure. By combining technical analysis with policy and governance insights, this study advances both the scholarly and practical understanding of digital twin deployment in BESSs. The findings provide policymakers, regulators, industry leaders, and system operators with a clear framework to close governance gaps, maximize the value of digital twins, and enable more secure, reliable, and sustainable integration of energy storage into future power systems. Full article