Arbed (Luxembourg)

Abstract The risk of mental health problems is high in older adults. Older immigrants may be at even higher risk due to social and socioeconomic disparities. However, little is known about the timing of their first diagnosis compared to native-born peers. This study examined whether older immigrants have a higher risk of earlier mental health diagnosis compared to non-immigrants and the contribution of social determinants. Data came from SHARE waves 5–9, including 48,154 participants (55.1% women) aged 55+ with no prior mental health diagnosis. The outcome was first self-reported diagnosis of affective or emotional disorders. Stepwise hierarchical Cox mixed-effects models with country-level random intercepts sequentially adjusted for sociodemographic, socioeconomic, family, community engagement, and health care satisfaction factors. In the basic model, migrant status was associated with an 18% increased hazard of diagnosis (HR = 1.18, 95%CI: 1.08–1.29). All included covariates were statistically significant (p < .002). After full adjustment, the HR decreased to 1.16 (95%CI: 1.05–1.27), suggesting that community engagement and health care satisfaction partially account for the higher risk among migrants. Individuals from low-income countries showed a higher risk (HR > 1.30, p < .08) and substantial between-country variation was identified (p = .02). These results suggest that older immigrants were diagnosed earlier, with factors such as social engagement and satisfaction with health care partly explaining this difference, highlighting the importance of culturally sensitive interventions that enhance engagement and participation, as well as policy measures to reduce disparities in mental health outcomes among aging immigrant populations across Europe.

Abstract Subjective age, that is the age a person feels compared to their chronological age, is indicative of a variety of aging processes. Studies that investigate multi-variate, dynamic, longitudinal relations of subjective age with potential determinants and mechanisms of these relations have rarely been employed. In the current study, we focus on loneliness as a potential subjective age determinant, as loneliness affects a variety of psychosocial and health outcomes across life and is stereotypically perceived as a feature of old age. We investigate whether loneliness is related with levels and changes in subjective age and test whether this association is mediated via self-reported stress. N = 5,594 adults aged 18 – 93 years (Mage = 50.41, SD = 15.99) who participated in a longitudinal survey comprising up to three measurement occasions over a time span of 2.5 years reported their loneliness, subjective age, and stress as well as sociodemographic and health-related covariates. We employed latent growth modeling and found that, when controlling for sociodemographic and health-related covariates, higher loneliness was related to an older subjective age cross-sectionally and a to steeper increase in subjective age over time. These relations were mediated via stress; however, the relation between stress and subjective age was no longer statistically significant when including the covariates. All associations were qualified by significant interactions with chronological age, albeit in different directions. Our findings attest to the associations between loneliness, stress and subjective aging experiences and highlight the need for an age-informed approach when planning further studies and interventions.

Integrating demand-responsive mobility services with transit systems is recognized as a practical and effective strategy to mitigate their impact on traffic congestion and the environment. This paper develops an efficient hybrid metaheuristic to solve the corresponding integrated dial-a-ride problem utilizing electric vehicles to minimize both operational costs and customer travel time. The system aims to improve customer convenience by limiting a maximum intermodal transfer time to synchronize demand-responsive buses’ arrival and transit departures. The proposed metaheuristic addresses the challenges of optimizing the integrated demand-responsive vehicle routing and charging operations with fixed-route transit systems with capacitated charging stations and partial recharge. The algorithm is tested on instances with up to 50 customers, outperforming an 8-hour state-of-the-art solver by 23% in solution quality, with an average runtime of 136 seconds.

The integrity of time series data in smart grids is often compromised by missing values due to sensor failures, transmission errors, or disruptions. Gaps in smart meter data can bias consumption analyses and hinder reliable predictions, causing technical and economic inefficiencies. As smart meter data grows in volume and complexity, conventional techniques struggle with its nonlinear and nonstationary patterns. In this context, Generative Artificial Intelligence offers promising solutions that may outperform traditional statistical methods.In this paper, we evaluate two general-purpose Large Language Models and five Time Series Foundation Models for smart meter data imputation, comparing them with conventional Machine Learning and statistical models. We introduce artificial gaps (30 minutes to one day) into an anonymized public dataset to test inference capabilities. Results show that Time Series Foundation Models, with their contextual understanding and pattern recognition, could significantly enhance imputation accuracy in certain cases. However, the trade-off between computational cost and performance gains remains a critical consideration.

This article presents a novel methodological approach to territorial impact assessment in border regions: Dynamical Territorial Impact Assessment (DyTIA). Grounded in dynamical systems theory, DyTIA offers a framework for modelling complex and interdependent relationships between economic, social, environmental, and institutional dimensions of territorial development. In contrast to conventional static methodologies, DyTIA focuses on identifying feedback loops, time lags, threshold effects, and network interactions, thereby enabling a more accurate representation of the dynamic and often non-linear nature of border regions. The methodology was tested using data from the European Court of Auditors covering 23 INTERREG V-A programmes (2014–2020). The analysis revealed significant heterogeneity in funding allocations and thematic priorities across different border regions. Notably, the largest share of funding (23.2%) was directed towards the thematic objective of environmental protection and resource efficiency, highlighting the increasing importance of sustainability within EU cohesion policy. A case study based on the Greater Region programme demonstrated DyTIA’s capacity to uncover complex territorial effect chains and to support optimized resource allocation, especially under the constraints of a projected 18.6% budget reduction in the INTERREG NEXT framework for the 2021–2027 period. Particularly insightful was the analysis of interactions between different thematic objectives, which revealed synergistic effects that enhance overall territorial impact. Beyond its analytical utility, DyTIA also serves a strategic governance function. In the context of Ukraine’s European integration, DyTIA gains relevance as a policy tool for supporting the inclusion of Ukrainian border regions into the European space. The EGTC Tisza is examined as a pilot project for adapting DyTIA to the specific challenges of post-war recovery and EU accession preparation. Based on these findings, the article offers practical recommendations for applying DyTIA in national and regional strategic planning processes, for strengthening the institutional capacity of European Groupings of Territorial Cooperation (EGTCs), and for establishing a network of “territorial development and security laboratories” along Ukraine’s western border. Special attention is given to incorporating the security dimension into territorial impact assessments, in response to emerging geopolitical challenges. Ultimately, DyTIA is not only a methodological innovation but also part of a broader rethinking of how territorial development in border regions is conceptualized and governed. In a time of profound geopolitical changes, dynamic models such as DyTIA are essential for designing effective, sustainable, and inclusive strategies—strategies that contribute to integration and resilience, and help shape a more stable and secure European space, with Ukraine as an integral participant.

Abstract Scientific reasoning has emerged as a powerful social force, particularly due to the institutionalization of science, a process that has significantly accelerated since the late 20th century. This phenomenon, known as scientization in the social sciences, encapsulates how scientific reasoning has become the dominant medium for shaping science-society relationships. Despite its growing prominence, the mechanisms and outcomes of scientization remain less well understood. A systematic search of Elsevier’s Scopus database reveals a sharp increase in references to scientization since the late 1970s, with 296 publications doing so by 2020. This study examines how mechanisms such as rationalization, professionalization, technologization, commercialization, and actorhood have driven the institutionalization of science, impacting culture, academic disciplines, and policy-making. Our findings highlight the critical need for more nuanced understandings of how scientization influences modern societies, particularly within the policy sphere, where the interplay between science and policy has substantial and far-reaching consequences.

peer reviewed