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Childhood socioeconomic disadvantage is a well established determinant of health in later life. Less is known about how early-life deprivation unfolds when individuals experience major institutional transformation and migration in adulthood. Cohorts socialized under Soviet institutions provide a useful setting to examine life-course divergence under systemic change. This study uses harmonized data from the Survey of Health, Ageing and Retirement in Europe (SHARE) on older adults residing in Estonia, Latvia, and Israel to examine the association between retrospectively reported childhood deprivation and multiple health outcomes in later life, including poor self-rated health, chronic disease burden, functional limitation, depression, and a composite multifrailty indicator. Logistic regression models and predicted probabilities assess whether childhood deprivation predicts late-life health across different adult institutional contexts and whether associations vary by linguistic affiliation. Higher levels of childhood deprivation are consistently associated with poorer health outcomes across all three countries. Individuals in the highest deprivation quintile show substantially higher

Open-source social robots offer accessibility, repairability, and student empowerment, yet the build itself often presents a barrier. Existing platforms either ship pre-assembled, foreclosing hands-on learning, or expose students to unfamiliar fasteners, opaque wiring, and inaccessible service points that erode engagement. Whether targeted mechanical redesign can lower this barrier whilst maintaining structural integrity remains untested. Here we show that Design for Assembly (DfA) and Design for Disassembly (DfD) interventions reshape how a build feels before they shorten how long it takes. Working with university students in Guyana and Estonia, we applied the Double Diamond framework to co-create the Robot Study Companion (RSC) v4.1: mapping pain points, then redesigning its chassis around twist-lock fasteners, snap-fit joints, and tool-free service latches. Across two studies with developers and first-time builders, system usability climbed from Poor to Excellent (SUS 59.4 to 89.4), perceived workload trended downward (NASA-TLX 4.29 to 4.00), and mean assembly time trended downward (21.4 to 13.7 minutes, with juniors' learning effect), whilst orientation cues and navigation cont

The growing integration of power electronic-based technologies has increased the necessity of power quality (PQ) monitoring in transmission systems. Although large datasets are collected by operators, their use is typically limited to compliance assessment. Medium- to long-term forecasting can enhance the value of these datasets by enabling proactive asset management and trend detection, despite challenges related to data heterogeneity and seasonality. This paper systematically evaluates individual and ensemble forecasting approaches for PQ parameters in transmission systems. More than 700 weekly time series from measurement campaigns in Germany and Estonia are analysed to assess various models and aggregation strategies within a structured ensemble framework. The results show that ensemble forecasts consistently outperform individual models in terms of accuracy and robustness, achieving significant improvements over seasonal naive benchmarks and the best-performing single models. Ensemble forecasting is therefore confirmed as a robust and scalable approach for long-term PQ prediction in transmission systems.

Large language models (LLMs) enable rapid and consistent automated evaluation of open-ended exam responses, including dimensions of content and argumentation that have traditionally required human judgment. This is particularly important in cases where a large amount of exams need to be graded in a limited time frame, such as nation-wide graduation exams in various countries. Here, we examine the applicability of automated scoring on two large datasets of trial exam essays of two full national cohorts from Estonia. We operationalize the official curriculum-based rubric and compare LLM and statistical natural language processing (NLP) based assessments with human panel scores. The results show that automated scoring can achieve performance comparable to that of human raters and tends to fall within the human scoring range. We also evaluate bias, prompt injection risks, and LLMs as essay writers. These findings demonstrate that a principled, rubric-driven, human-in-the-loop scoring pipeline is viable for high-stakes writing assessment, particularly relevant for digitally advanced societies like Estonia, which is about to adapt a fully electronic examination system. Furthermore, the s

The rapid proliferation of artificial intelligence (AI) across industry, government, and education highlights the urgent need for robust frameworks to conceptualise and guide engagement. This paper introduces the Hierarchy of Engagement with AI model, a novel maturity framework inspired by Maslow's hierarchy of needs. The model conceptualises AI adoption as a progression through eight levels, beginning with initial exposure and basic understanding and culminating in ecosystem collaboration and societal impact. Each level integrates technical, organisational, and ethical dimensions, emphasising that AI maturity is not only a matter of infrastructure and capability but also of trust, governance, and responsibility. Initial validation of the model using four diverse case studies (General Motors, the Government of Estonia, the University of Texas System, and the African Union AI Strategy) demonstrate the model's contextual flexibility across various sectors. The model provides scholars with a framework for analysing AI maturity and offers practitioners and policymakers a diagnostic and strategic planning tool to guide responsible and sustainable AI engagement. The proposed model demons

This study analyzes OECD countries in the context of the energy trilemma index and clusters countries with similar characteristics. In the study, the k-means clustering technique is used. The optimum number of clusters was determined using the Elbow method in combination with the Silhouette Index. Moreover, all results are visualized to enhance comprehensibility. The results show that countries such as Austria, Canada, Finland, and Denmark are in the high energy trilemma group with index scores of 82.2, 82.3, 82.7, and 83.3, respectively. Countries in the high group have achieved a high level of balance between energy security, energy equity, and environmental sustainability. In addition, countries such as Belgium, Hungary, Australia, the Czech Republic, and Estonia are in the medium energy trilemma group with index scores of 76.4, 76.6, 77.1, 77.6, and 78.7, respectively. Countries in the medium group have made progress in balancing the dimensions of the energy trilemma but have not yet reached excellence. However, countries such as Mexico, Türkiye, Colombia, and Costa Rica are in the low energy trilemma group with index scores of 63.1, 64.1, 64.8, and 69.3, respectively. These lo

This article investigates crime patterns across European countries in 2022 using Compositional Data Analysis (CoDA) to address limitations of traditional statistical approaches in dealing with the relative nature of crime data. Recognizing crime types as components of a whole, we employ CoDA to explore relationships between different crime categories while respecting their inherent interdependencies. The study utilizes k-means clustering to group countries based on their crime profiles, identifying three distinct clusters largely aligning with geographical locations. This clustering is visualized through t-SNE and geographic mapping, revealing regional similarities. Further analysis using Robust Principal Component Analysis on identified crime clusters reveals insightful relationships between specific crime types, such as homicide, smuggling, and financial crimes, and how their prevalence varies across countries. The findings reveals distinct crime patterns across Europe, highlighting regional commonalities while also highlighting divergences like Norway and Latvia that deviate from their expected geographical classifications. Moreover, the study identifies specific crime groups; f

As artificial intelligence transforms public sector operations, governments struggle to integrate technological innovations into coherent systems for effective service delivery. This paper introduces the Algorithmic State Architecture (ASA), a novel four-layer framework conceptualising how Digital Public Infrastructure, Data-for-Policy, Algorithmic Government/Governance, and GovTech interact as an integrated system in AI-enabled states. Unlike approaches that treat these as parallel developments, ASA positions them as interdependent layers with specific enabling relationships and feedback mechanisms. Through comparative analysis of implementations in Estonia, Singapore, India, and the UK, we demonstrate how foundational digital infrastructure enables systematic data collection, which powers algorithmic decision-making processes, ultimately manifesting in user-facing services. Our analysis reveals that successful implementations require balanced development across all layers, with particular attention to integration mechanisms between them. The framework contributes to both theory and practice by bridging previously disconnected domains of digital government research, identifying cr

Heating of buildings represents a significant share of the energy consumption in Europe. Smart thermostats that capitalize on the data-driven analysis of heating patterns in order to optimize heat supply are a very promising part of building energy management technology. However, factors driving their acceptance by building inhabitants are poorly understood although being a prerequisite for fully tapping on their potential. In order to understand the driving forces of technology adoption in this use case, a large survey (N = 2250) was conducted in five EU countries (Austria, Belgium, Estonia, Germany, Greece). For the data analysis structural equation modelling based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was employed, which was extended by adding social beliefs, including descriptive social norms, collective efficacy, social identity and trust. As a result, performance expectancy, price value, and effort expectancy proved to be the most important predictors overall, with variations across countries. In sum, the adoption of smart thermostats appears more strongly associated with individual beliefs about their functioning, potentially reducing their adopti

This paper shows how measures of uncertainty can be applied to existing population forecasts using Estonia as a case study. The measures of forecast uncertainty are relatively easy to calculate and meet several important criteria used by demographers who routinely generate population forecasts. This paper applies the uncertainty measures to a population forecast based on the Cohort-Component Method, which links the probabilistic world forecast uncertainty to demographic theory, an important consideration in developing accurate forecasts. We applied this approach to world population projections and compared the results to the Bayesian-based probabilistic world forecast produced by the United Nations, which we found to be similar but with more uncertainty than found in the latter. We did a similar comparison in regard to sub-national probabilistic forecasts and found our results to be similar with Bayesian-based uncertainty measures. These results suggest that the probability forecasts produced using our approach for Estonia are consistent with knowledge about forecast uncertainty. We conclude that this new method appears to be well-suited for developing probabilistic world, national

The integration of Central and Eastern European (CEE) countries into the European Economic Area serves as a valuable experiment for the regional economic development theory. The long-lasting convergence of these economies with more advanced Western Europe exhibits a few standard features and varying policies implemented. Even the Baltic countries, which started from very similar starting positions, demonstrate their unique trajectories of development. We employ a panel data regression model that allows coefficients to vary over time to compare the contributions of a few macroeconomic factors to the GDP growth of CEE countries. In particular, we regress the annual change of GDP per capita in PPP terms as a function of achieved GDP, price, trade, investment, and debt levels. Time-varying common slope coefficients in this approach describe the external economic environment in which countries implement their own policies. The panel consists of 11 Central and Eastern European countries (Bulgaria, Czechia, Estonia, Croatia, Latvia, Lithuania, Hungary, Poland, Romania, Slovenia, and Slovakia), which have been observed annually from 1995 to 2024. While the main selected factors of this inv

This study aims to reveal different varieties of capitalism and to uncover new patterns of development that emerged between 2010 and 2020. A hybrid model is applied that quantifies three pillars of development (Future - F, Outside - O, Inside - I) using supply-side and demand-side indicators that measure norms, institutions, and policies. Investigating 34 OECD members, this study describes five varieties of capitalism: traditional, dualistic, government-led, open market-based, and human capital-based models. It is suggested that the most significant cut-off point in the development of OECD economies in this period was along the green growth dimension, where European countries with a tradition in coordinated markets outperform the rest. Using Israel and Estonia as an example, it is also suggested that institutional and policy changes that enhance the quality of governance and make coordination more effective are the way out of the middle-income trap.

Logical frameworks and meta-languages form a common substrate for representing, implementing and reasoning about a wide variety of deductive systems of interest in logic and computer science. Their design, implementation and their use in reasoning tasks, ranging from the correctness of software to the properties of formal systems, have been the focus of considerable research over the last three decades. The LFMTP workshop brought together designers, implementors and practitioners to discuss various aspects impinging on the structure and utility of logical frameworks, including the treatment of variable binding, inductive and co-inductive reasoning techniques and the expressiveness and lucidity of the reasoning process. The 2024 instance of LFMTP was organized by Florian Rabe and Claudio Sacerdoti Coen in Tallinn, Estonia, the 8th July, as a satellite event of the FSCD conference. The workshop received 8 submissions, of which 6 were presented at the workshop. Of these, 2 were work-in-progress presentations, and 4 were accepted for these formal proceedings. Additionally, Carsten Schürmann of IT University of Copenhagen gave an invited talk on Nominal State Separating Proofs.

Dynamic Line Rating (DLR) systems are crucial for renewable energy integration in transmission networks. However, traditional methods relying on sensor data face challenges due to the impracticality of installing sensors on every pole or span. Additionally, sensor-based approaches may struggle predicting DLR in rapidly changing weather conditions. This paper proposes a novel approach, leveraging machine learning (ML) techniques alongside hyper-local weather forecast data. Unlike conventional methods, which solely rely on sensor data, this approach utilizes ML models trained to predict hyper-local weather parameters on a full network scale. Integrating topographical data enhances prediction accuracy by accounting for landscape features and obstacles around overhead lines. The paper introduces confidence intervals for DLR assessments to mitigate risks associated with uncertainties. A case study from Estonia demonstrates the practical implementation of the proposed methodology, highlighting its effectiveness in real-world scenarios. By addressing limitations of sensor-based approaches, this research contributes to the discourse of renewable energy integration in transmission systems,

This paper presents the Nordic-Estonian Quantum Computing e-Infrastructure Quest - NordIQuEst - an international collaboration of scientific and academic organizations from Denmark, Estonia, Finland, Norway, and Sweden, working together to develop a hybrid High-Performance and Quantum Computing (HPC+QC) infrastructure. The project leverages existing and upcoming classical high-performance computing and quantum computing systems, facilitating the development of interconnected systems. Our effort pioneers a forward-looking architecture for both hardware and software capabilities, representing an early-stage development in hybrid computing infrastructure. Here, we detail the outline of the initiative, summarizing the progress since the project outset, and describing the framework established. Moreover, we identify the crucial challenges encountered, and potential strategies employed to address them.

This paper explores cost-efficient methods to adapt pretrained Large Language Models (LLMs) to new lower-resource languages, with a specific focus on Estonian. Leveraging the Llama 2 model, we investigate the impact of combining cross-lingual instruction-tuning with additional monolingual pretraining. Our results demonstrate that even a relatively small amount of additional monolingual pretraining followed by cross-lingual instruction-tuning significantly enhances results on Estonian. Furthermore, we showcase cross-lingual knowledge transfer from high-quality English instructions to Estonian, resulting in improvements in commonsense reasoning and multi-turn conversation capabilities. Our best model, named \textsc{Llammas}, represents the first open-source instruction-following LLM for Estonian. Additionally, we publish Alpaca-est, the first general task instruction dataset for Estonia. These contributions mark the initial progress in the direction of developing open-source LLMs for Estonian.

Open Government Data (OGD) plays a crucial role in transforming smart cities into sustainable and intelligent entities by providing data for analytics, real-time monitoring, and informed decision-making. This data is increasingly used in urban digital twins, enhancing city management through stakeholder collaboration. However, local administrative data remains underutilized even in digitally advanced countries like Estonia. This study explores barriers preventing Estonian municipalities from sharing OGD, using a qualitative approach through interviews with Estonian municipalities and drawing on the OGD-adapted Innovation Resistance Theory model (IRT). Interviews with local government officials highlight ongoing is-sues in data provision and quality. By addressing overlooked weaknesses in the Estonian open data ecosystem and providing actionable recommendations, this research contributes to a more resilient and sustainable open data ecosystem. Additionally, by validating the OGD-adapted Innovation Resistance Theory model and proposing a revised version tailored for local government contexts, the study advances theoretical frameworks for understanding data sharing resistance. Ultimat