Search Results (43)

The extension of the European Union Emissions Trading System to buildings and road transport (ETS2) raises important questions about the distribution of carbon pricing burdens across Member States. While existing research has primarily focused on income differences or household typologies, the structural heterogeneity of vulnerability across EU countries remains insufficiently explored. This study develops a dual-channel framework of household vulnerability to ETS2, distinguishing between structural carbon exposure and socio-economic sensitivity. Using a balanced panel of 27 EU Member States over 2010–2024, we construct composite indices based on Eurostat data and combine cluster analysis, sigma-convergence tests, and two-way fixed-effects models with Driscoll–Kraay standard errors. The results suggest that the two vulnerability channels are empirically distinct and geographically differentiated across Member States, with no country group simultaneously characterized by high exposure and high sensitivity. Energy productivity and renewable energy expansion are associated with lower structural exposure but higher socio-economic sensitivity, consistent with a transitional burden mechanism. Over time, composite vulnerability exhibits statistically significant divergence, despite partial and uneven convergence across the underlying vulnerability dimensions. These findings highlight the need for differentiated compensation mechanisms and structural policy interventions that address both structural exposure and socio-economic sensitivity, supporting a socially equitable and sustainable energy transition under ETS2. Full article

This study investigates the convergence of energy intensity in the member states of the European Union (EU) between 1993 and 2021. To assess convergence, sigma and stochastic (beta) convergence analyses are performed using conventional, Fourier transform, and quantile-based unit root tests. Unlike previous studies, this work combines Fourier transform and quantile approaches to capture both gradual structural breaks and distributional differences between EU countries. The results show that convergence was strong between 1993 and 2009 but weakened after 2009, indicating increasing structural divergence between member states. The results also show that the convergence behavior is not uniform, suggesting that linear methods alone provide an incomplete picture. These findings imply that a single EU policy may not be sufficient and that a sustainable energy transition requires coordinated yet flexible strategies that take national differences into account. All empirical analyses were performed using R (version 4.3.2) and Stata (version 18). Full article

Background: Neuropathic pain remains a major unmet clinical challenge. Growing evidence identifies sigma receptors (σRs) as pivotal intracellular modulators of maladaptive stress signaling, positioning them as promising non-opioid targets for chronic pain management. Notably, despite the pleiotropic nature of σRs in regulating diverse cellular pathways—which might theoretically suggest a high risk of off-target effects—current selective antagonists have demonstrated remarkable safety and tolerability profiles. Sigma-1 and sigma-2 receptors (σ1R and σ2R) are molecularly and functionally distinct proteins that regulate neuronal excitability, proteostasis, and neuroimmune communication, all mechanisms that characterize neuronal excitability and cellular stress adaptation. σ1R acts as a ligand-operated molecular chaperone at the mitochondria-associated endoplasmic reticulum membrane. Extensive preclinical data demonstrate that σ1R antagonism attenuates peripheral and central sensitization, suppresses neuroinflammation, and restores opioid analgesic efficacy. These findings are supported by the advanced clinical candidate E-52862, which has shown efficacy and a favorable safety profile in neuropathic pain conditions. σ2R, identified as transmembrane protein 97 (σ2R/TMEM97), functions as a regulator of cholesterol trafficking, lysosomal integrity, and integrated stress response (ISR). σ2R modulation alleviates neuropathic pain by restoring proteostatic balance and reducing ISR-driven neuronal vulnerability rather than directly suppressing excitability. Emerging σ2R ligands such as FEM-1689, UKH-1114, and CM-398 provide compelling proof-of-concept for durable, disease-modifying analgesia. Methods: A structured literature search was conducted using PubMed, Scopus, and Web of Science to identify studies published within the last decade describing σ1R and σ2R/TMEM97 biology, ligand development, and their preclinical or clinical evaluation in neuropathic pain. Reference lists were manually screened to ensure comprehensive coverage. Conclusions: This review synthesizes pharmacology, ligand development, and translational evidence supporting σRs as next-generation targets for neuropathic pain therapy, highlighting convergent roles of σ1R and σ2R in pain chronification and outlining future directions for structure-guided therapeutic strategies. Full article

This paper examines income convergence in Europe by jointly analyzing European Union member states and Western Balkan economies over the period 2004–2023. While classical growth theory predicts that poorer economies should grow faster than richer ones, empirical evidence for Europe remains mixed, particularly when institutional and structural heterogeneity is taken into account. Using panel data techniques, the study tests for absolute and conditional β-convergence and complements this analysis with an assessment of σ-convergence. The results provide strong evidence of absolute income convergence across the sample, indicating that economies with lower initial income levels tend to grow faster. Conditional convergence is also confirmed, although the direct effect of institutional quality weakens once structural factors such as foreign direct investment and human capital are included, suggesting that institutions operate primarily through indirect channels. An interaction analysis shows no systematic evidence that institutional quality alters the speed of convergence. Finally, σ-convergence analysis reveals pronounced regional heterogeneity, with strong convergence among new EU member states, stable but low dispersion within the Western Balkans, and more modest convergence patterns in the EU core. Overall, the findings highlight that European convergence remains uneven and highly conditional on institutional and structural characteristics. Full article

This study assesses the innovation performance and convergence dynamics across 237 European regions (NUTS 2 level) from 2016 to 2023, explicitly accounting for the structural and behavioural changes triggered by the COVID-19 pandemic. The article provides a novel regional-level assessment of how an unprecedented external shock reshaped innovation trajectories before and after the pandemic. To this end, the analysis combines Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), sigma-convergence measures, and a Difference-in-Differences (DiD) framework within an integrated multi-method empirical approach to evaluate shifts in regional innovation patterns over time. The results reveal a highly uneven distribution of innovation activities, with increasing polarization in the post-pandemic period. Northern and Western European regions strengthened their competitive advantage through robust digital infrastructure, strong human capital, and substantial R&D investments. In contrast, many Southern and Eastern European regions faced heightened structural barriers, leading to a widening innovation gap. Nevertheless, several regions exhibited notable resilience and achieved significant innovation catch-up, providing new empirical evidence on heterogeneous regional adaptive dynamics supported by targeted regional policies and improved local capabilities. The sigma-convergence analysis indicates a general increase in overall disparities, as reflected by rising dispersion in the Regional Innovation Index (RII) during 2020–2023. However, according to the DiD estimation, regions most severely affected by COVID-19 experienced a statistically significant relative increase (approximately 2.17%) in innovation performance, highlighting the pandemic’s role as a catalyst for accelerated digital transformation and innovation adjustment at the regional level. Full article

Objectives: To examine age-specific growth patterns derived from Japanese cross-sectional data according to infants’ feeding and determine whether differences persist through preschool age. Methods: We analyzed secondary data from the 2023 National Growth Survey on Preschool Children in Japan, a single-wave nationwide cross-sectional survey. The participants were 8028 singleton, term-born, appropriate-for-gestational-age children aged 0–60 months without major health conditions. The feeding history up to 24 months was reported by parents and categorized as breastfed, formula-fed, or mixed-fed. Anthropometric measurements were obtained at a 1-month postnatal checkup or at checkups arranged for the survey, converted to standard deviation scores using Japanese references, and modeled with growth curves using the Lambda–Mu–Sigma method to summarize cross-sectional distributions by age. The feeding groups were compared at selected ages. Results: Breastfed infants were smaller in length/height and weight than formula-fed peers during the first 2 years, with the largest differences in infancy. The mean stature in the feeding groups became similar at older ages; by 60 months, standard deviation scores for stature and weight were comparable across the feeding groups. Head circumference patterns up to 36 months were not different by the feeding category. Conclusions: In Japan, early size differences by the feeding group show age-related convergence of cross-sectional group means by preschool, and head circumference patterns are similar across the groups. These findings support breastfeeding as sufficient for long-term growth without unnecessary formula supplementation. Full article

Mismatch repair (MMR) system alterations can trigger transient hypermutation, promoting adaptive mutations under stress, such as antibiotic exposure. While most organisms use MutS and MutL protein families for MMR, many archaea and actinobacteria, including the major human pathogen Mycobacterium tuberculosis, lack these components and instead rely on NucS, a structurally distinct enzyme driving a non-canonical MMR pathway. Given the role of MMR in mutation control, understanding how nucS expression is regulated could be essential for uncovering the molecular basis of antibiotic resistance development in mycobacteria. In this study, we characterized the nucS promoter and transcription start site in Mycobacterium smegmatis. We found that nucS expression declines during the stationary phase in both M. smegmatis and M. tuberculosis, paralleling replication activity and canonical MMR downregulation. Our data suggest that the alternative sigma factor σ^B may negatively regulate nucS expression during this phase. Additionally, we identified candidate compounds that may modulate nucS expression, underscoring its responsiveness to environmental cues. These findings enhance our understanding of mycobacterial stress responses and lay the groundwork for exploring antibiotic resistance mechanisms. Strikingly, our work reveals a case of double convergent evolution: both canonical (MutS/MutL) and non-canonical (NucS) pathways have independently evolved not only the same DNA repair function, but also similar regulatory frameworks for genome integrity preservation under stress conditions. Full article

Background: Logistics plays a key role in economic performance, yet its contribution to global growth and convergence remains underexplored. This study examines how different logistics dimensions have influenced GDP per worker across countries over the period 2007–2022. Methods: Using econometric panel data techniques and convergence models (β and σ), data from 86 countries are analysed by incorporating logistics performance indicators—such as infrastructure quality, customs efficiency, and shipment traceability—into an endogenous growth framework. Results: The analysis confirms the existence of both β- and σ-convergence, suggesting that lower-income countries are catching up with higher-income ones. Improvements in logistics competence and tracking systems positively affect economic growth, while inefficiencies in shipping services and delivery timeliness negatively impact convergence. Conclusions: These findings highlight the dual role logistics can play in fostering or hindering growth. Enhancing logistics infrastructure and services through targeted policies is essential to promote sustained economic development and reduce global income disparities. Full article

This research integrates stochastic optimization techniques with robust modeling and probabilistic modeling approaches to enhance photovoltaic cell manufacturing processes and product reliability. The study employed an adapted genetic algorithm to tackle uncertainties in the manufacturing process, resulting in improved operational efficiency. It consistently achieved optimal fitness, with values remaining at 1.0 over 100 generations. The model displayed a dynamic convergence rate, demonstrating its ability to adjust performance in response to process fluctuations. The system preserved resource efficiency by utilizing approximately 2600 units per generation, while minimizing machine downtime to 0.03%. Product reliability reached an average level of 0.98, with a maximum value of 1.02, indicating enhanced consistency. The manufacturing process achieved better optimization through a significant reduction in defect rates, which fell to 0.04. The objective function value fluctuated between 0.86 and 0.96, illustrating how the model effectively managed conflicting variables. Sensitivity analysis revealed that changes in sigma material and lambda failure had a minimal effect on average reliability, which stayed above 0.99, while average defect rates remained below 0.05. This research exemplifies how stochastic, robust, and probabilistic optimization methods can collaborate to enhance manufacturing system quality assurance and product reliability under uncertain conditions. Full article

Orbit determination for non-cooperative low Earth orbit (LEO) objects undergoing continuous low-thrust maneuvers remains a significant challenge, particularly for large satellite constellations like Starlink. This paper presents a method that integrates the unscented transformation into a batch filtering framework with an optimized rho-minimum sigma points sampling strategy. The proposed approach uses a reduced dynamics model that considers Earth’s non-spherical gravity and models the combined effects of low-thrust and atmospheric drag as an equivalent along-track acceleration. Numerical simulations under different measurement noise levels, initial state uncertainties, and across multiple satellites confirm the method’s reliable convergence and favorable accuracy, even in the absence of prior knowledge of the along-track acceleration. The method consistently converges within 10 iterations and achieves 24 h position predictions with root mean square errors of less than 3 km under realistic noise conditions. Additional validation using a higher-fidelity model that explicitly accounts for atmospheric drag demonstrates improved accuracy and robustness. The proposed method can provide accurate orbit knowledge for space situational awareness associated with continuously maneuvering Starlink satellites. Full article

Although food seems abundant in the European Union, challenges related to specific aspects of food security continue to exist and require ongoing attention. A country’s food security depends on various economic, social, environmental, and institutional factors, which are studied using several scientific research methodologies. The role of institutions in determining national success and failure has been increasingly emphasized in recent academic discourse. Our research makes a novel contribution to the literature on institutions and food security by integrating New Institutional Economics with food security metrics. It aims to examine the relationships between food security dimensions and country-specific institutional matrices in the twenty EU member states from 2012 to 2019. How strong were those relationships, and how did they differ between the new and old member states? Food security is proxied by the Global Food Security Index and its three pillars (economic accessibility, physical availability, and quality and safety). The institutional quality of a country is represented by the Worldwide Governance Indicators (regulatory quality, rule of law, and control of corruption). Using the food security indices as the dependent variables, we apply multiple regression models to identify which institutions determined national food security over time. The study revealed that between 2012 and 2019, there was no evidence of sigma convergence or reduction in the dispersion of institutional quality (except for control of corruption) and overall food security within the EU20. The domestic institutions were generally statistically significantly positively related to the GFSI and its elements. The weakest correlations for the EU20 were those linking institutional variables with food quality and safety. The rule of law, incorporating such formal institutions as the quality of contract enforcement and property rights, positively affected food security within the EU20, with the mostgreatest impact on food quality, safety, and availability. The dependence of food security on national institutional factors was stronger in new member states from Central and Eastern Europe. The exploratory results shed some light on the role of institutions in shaping food security. However, further research is required to gain a more detailed understanding of this phenomenon. The research findings suggest that policymakers in the EU countries could enhance national institutions to promote food security and, consequently, achieve the Sustainable Development Goals more effectively. Full article

This paper aims to investigate the sustainability performance trajectories of the Visegrád Group (V4) countries since their accession to the European Union. Given their shared historical, geopolitical, and socio-economic backgrounds, this study explores common evolutionary patterns and convergence within the sustainable development goal framework. This research employs the Kml3D non-parametric clustering algorithm and standard beta and sigma convergence tests. The analysis covers the 2004–2023 data sourced from the European Sustainable Development Report 2023/24 by the Sustainable Development Solutions Network. The findings demonstrate that while each country follows a distinct path, Czechia and Slovakia exhibit notable similarities in the evolution of individual sustainability dimensions. This underscores the influence of path dependence, institutional inertia, and cross-border policy coordination in sustainable development. The results also suggest that although the V4 countries generally converge in socio-economic and infrastructural dimensions of sustainability, divergence persists in education, gender equality, nature preservation, and institutional performance. Full article

The purpose of this study is to examine the economic and sectoral convergence of 32 countries in Latin America and the Caribbean (LAC) region from 1980 to 2022. The economic convergence hypothesis suggests that two economies with similar structural characteristics but different per capita income levels can tend to equalize in terms of income level in the long run. Confirming economic convergence has led to the development of various methodologies, among which dynamic and static disparity measures stand out. To achieve the objective of this research, both types of measures were calculated, determining beta and sigma convergence for dynamic disparity and gamma convergence for static disparity. This was accomplished by adopting the methodological approaches proposed by Sala-I-Martin and Marchante, Ortega, and Sánchez. The results show a gradual but steady evolution towards economic and sectoral convergence in LAC region during the 1980–2022 period. However, inequalities and divergences persist, requiring less developed countries to strengthen their institutions, implement sound macroeconomic policies, and diversify their economies. These measures are essential to driving economic growth and fostering more balanced and sustainable development across the region. Full article

Battery energy storage systems (BESSs) play a crucial role in integrating renewable energy sources into microgrids. However, robust BESS controllers are needed to carry out this function properly. Existing controllers suffer from overshoots and slow convergence issues. Moreover, as electrical grid networks become increasingly connected, the risk of cyberattacks grows, and traditional physics-based anomaly detection methods face challenges such as reliance on predefined models, high computational demands, and limited scalability for complex, large-scale data. To address the limitations of the existing approaches, this paper first proposes a novel sigma-delta modulation (SDM) controller for BESSs in solar photovoltaic (PV)-connected microgrids. The performance of SDM has been compared with those of the proportional–integral (PI) controller and fuzzy logic controller (FLC). Also, this paper proposes an improved ensemble-based method to detect the false data injection (FDI) and denial-of-service (DoS) attacks on the BESS controller. The performance of the proposed detection method has been compared with that of the traditional ensemble-based method. Four PV-connected microgrid systems, namely the solar DC microgrid, grid-connected solar AC microgrid, hybrid AC microgrid with two BESSs, and hybrid AC microgrid with a single BESS, have been considered to show the effectiveness of the proposed control and detection methods. The MATLAB/Simulink-based results show the effectiveness and better performance of the proposed controller and detection methods. Numerical results demonstrate the improved performance of the proposed SDM controller, with a 35% reduction in AC bus voltage error compared to the conventional PI controller and FLC. Similarly, the proposed SAMME AdaBoost detection method achieves superior accuracy with an F1 score of 95%, outperforming the existing ensemble approaches. Full article

Achieving precise target localization for UAVs is a complex problem that is often discussed. In order to achieve precise spatial localization of targets by UAVs and to solve the problems of premature convergence and easy to fall into local optimum in the original dung beetle algorithm, an error handling method based on the coordinate transformation of an airborne measurement system and the dung beetle optimization with crisscross and 3 Sigma Rule optimization (CCDBO) is proposed. Firstly, the total standard deviation is calculated by integrating the carrier position, the attitude angle, the pod azimuth, the pitch angle, and the given alignment error of the pod’s orientation. Subsequently, the Taylor series expansion method is adopted to linearize the approximated coordinate transformation process and simplify the error propagation model. Finally, in order to further improve the positioning accuracy, a target position correction strategy with the improved dung beetle optimization algorithm is introduced. The simulation and flight experiment results show that this method can significantly reduce the target positioning error of UAVs and improve the positioning accuracy by 20.42% on average compared with that of the original dung beetle algorithm, which provides strong support for the high-precision target observation and identification of UAVs in complex environments. Full article