Search Results (131)

Over the past decade, Central and Eastern Europe has emerged as a key region shaping contemporary labour migration patterns within the European Union. Poland and Hungary occupy a particularly distinctive position within this landscape, combining increasingly restrictive political narratives on migration particularly regarding asylum and irregular migration, with a growing reliance on third-country workers. This article offers a comparative analysis of labour migration governance in Poland and Hungary, focusing on the interaction between regulatory frameworks, institutional practices and public discourse. Drawing on legal acts, policy documents, statistical data and academic literature, the study examines how both countries respond to demographic decline, labour shortages and economic competitiveness while simultaneously articulating security-driven or migration-sceptical narratives. The analysis demonstrates that, despite notable differences in political communication and public justification, Poland and Hungary increasingly converge in their practical regulatory solutions, expanding access to labour markets for third-country nationals through selective and sector-specific admission mechanisms. This article argues that both states represent variants of a shared functional model of labour migration governance, characterised by a persistent policy–rhetoric gap. While migration control remains central to political messaging, economic imperatives drive a gradual but consistent opening of labour markets. By adopting a focused two-country comparison, this article highlights broader regional patterns of regulatory adaptation and narrative tension that are likely to shape the future of labour migration governance in Central and Eastern Europe. Full article

Intergenerational memories, migration histories, and the lasting influence of colonial linguistic systems profoundly shape heritage language maintenance in Afro-descendant communities of Central America. This study examines how genealogy functions as a pedagogical tool for sustaining English-based Creole languages among Afro-descendant populations in Costa Rica, Panama, and Belize, three nations linked by Afro-Caribbean migration yet shaped by distinct colonial and educational systems. Drawing on scholarship documenting oral histories, family narratives, and community-based linguistic practices, the study advances a genealogy-as-pedagogy framework to explain how families transmit language, identity, and belonging across generations through ancestral memory, positioning family-based knowledge transmission as curriculum. In Costa Rica and Panama, where Spanish colonial and post-independence language ideologies marginalize English-based Creole varieties, genealogical practices operate as primary mechanisms of linguistic continuity in the absence of sustained institutional support. In Belize, by contrast, British colonial legacies and the national recognition of Belizean Kriol create a distinct sociolinguistic environment in which state institutions, the media, and educational policy reinforce genealogical memory. Through comparative analysis, the study argues for integrating genealogical knowledge into multilingual education, community revitalization initiatives, and heritage language policy to strengthen Afro-descendant linguistic continuity in Costa Rica, Panama, and Belize. Full article

This study examines healthcare system strains in rapidly aging societies through a comparative analysis of Puerto Rico, Cuba, Japan, the Philippines, and South Korea. While existing research documents global aging and physician migration trends, few studies explore how these challenges manifest in conjunction with each other. Puerto Rico presents a critical case, with 24% of its population aged 65+, severe physician migration, and systemic underfunding under U.S. Medicaid structures. Using a structured comparative case methodology, we analyze policy responses across four nations with divergent approaches: Cuba, Japan, the Philippines, and South Korea. Data from government reports, academic literature, and World Health Organization (WHO) datasets show that (1) proactive medical education investments outperform reactive measures, (2) dedicated long-term care financing is essential but structurally unavailable in Puerto Rico, and (3) territorial status in the case of Puerto Rico, constrains policy innovation. Conventional aging frameworks are challenged by revealing how high-income territories can exhibit low systemic adaptability. Proposed are targeted reforms for Puerto Rico, including Medicaid restructuring and workforce incentives, with broader implications for aging societies under constrained sovereignty. This study fills a critical space in understanding how geopolitical contexts shape healthcare system vulnerabilities. Full article

Large organizations face a critical systems integration challenge when executing multiple concurrent security modernization programs. This paper examines the U.S. Department of Defense’s simultaneous implementation of three transformational initiatives—post-quantum cryptography (PQC) migration, Zero Trust Architecture (ZTA) deployment, and AI security assurance—each operating under separate governance structures, timelines, and compliance frameworks. Through systematic evidence synthesis of 59 sources (47 policy/standards documents and 12 performance benchmarks), we identify cross-program dependencies that create integration failures when programs operate in isolation. We propose a shared modernization substrate—a four-layer infrastructure architecture (Cryptographic Services, Identity Management, Analytics Pipeline, Policy Orchestration) that enables coordinated execution while preserving program independence. The framework addresses the fundamental systems challenge of achieving interoperability across programs with misaligned schedules and competing resource demands. We introduce a five-level Triad Convergence Maturity Model (TCMM) with operationalized indicators enabling repeatable organizational assessment. Illustrative application to three DoD modernization contexts demonstrates the framework’s ability to differentiate maturity levels. Performance analysis synthesizes published benchmark data: enterprise PQC latency overhead is modest (measured), while tactical environment estimates of 158–383% overhead are derived from benchmark extrapolation under packet-loss assumptions (modeled). Scenario modeling suggests that coordinated incident response through the substrate architecture could substantially reduce risk exposure windows compared to siloed approaches (modeled). The framework transforms fragmented program execution into synchronized systems modernization, offering practical guidance for chief information officers, program managers, and enterprise architects managing concurrent technology transitions. Full article

This study examines the social, economic and cultural impacts that Latin American women face due to climate-induced displacement, considering these impacts as arenas of conflict and negotiation. Using an intersectional framework, the study analyses how climate disasters exacerbate structural inequalities rooted in patriarchal systems, thereby constraining women’s adaptive capacity while simultaneously catalysing resistance strategies. Through a comparative analysis of Bangladesh and the Dry Corridor in Central America using a Gender Vulnerability Index (GVI), the study reveals that displaced women navigate contested spaces, disputing access to resources, legal recognition and territorial belonging, while constructing transnational solidarity networks and cooperative economies. The emergence of women climate refugees challenges international legal frameworks, exposing critical gaps in protection regimes. The findings emphasise the need for gender-responsive policies that recognise women as transformative agents who negotiate power asymmetries in contexts of environmental crisis, not merely as vulnerable populations. This research contributes to our understanding of the nexus between climate change, gender and migration by foregrounding the dialectic of domination and agency in Latin American displacement processes. Full article

As one of the fastest growing economies in the world, it is critical for India to build climate-resilient infrastructure. Its rapidly increasing population growth and the consequent migration to urban areas, coupled with climate risks, have made infrastructure development a key priority area in India. Thus, ensuring that existing infrastructure and future constructions integrate strategies to address climate-related risks is vital. Despite the growing recognition of the importance of legal and policy landscapes on climate-resilient infrastructure, there is a very limited number of studies on this topic, especially in India. Thus, this study sought to bridge this gap by evaluating the influence of the Indian legal and policy framework on climate-resilient infrastructure. To this end, an analytical, comparative, and evaluative approach was adopted, employing benchmarks from international legal provisions and best practices from Japan’s legal and policy systems. In addition to doctrinal legal analysis using primary and secondary sources, case studies on the Mumbai Coastal Road Project and the Chandigarh–Manali Highway were performed to assess how the extant laws operate in the field. The findings indicate that while India’s legal system is gradually incorporating climate-risk considerations into its infrastructure sector, the effects of these considerations remain constrained due to institutional co-ordination challenges and limited enforceable obligations. Therefore, streamlining climate-resilient infrastructure governance requires more robust climate change legislation and improved implementation mechanisms. The findings of this study provide useful insights for legislators and policymakers in strengthening climate resilience integration within India’s infrastructure governance framework. Full article

Population migration is an important indicator for measuring the interactions and connections between cities. Analyzing the spatiotemporal distribution pattern of the migration flows between cities is highly important to understanding urban development relationships and regional structures. From a spatiotemporal perspective, we conduct STFlowLISA (Space-Time Flow Local Indicator of Spatial Association) spatiotemporal autocorrelation analysis using population migration data from Hubei Province from 2018 to 2023 and, on this basis, calculate the spatiotemporal hub index and identify spatiotemporal clusters. The research aims to reveal the regional spatial structure shaped by migration flows and compare it with that of existing town system planning to evaluate deviations and provide a decision-making basis for the regional synergistic development of Hubei Province. The key findings include: (1) the population migration flows between Wuhan and its surrounding cities mostly exhibit a spatiotemporal distribution pattern of HH (high-value agglomeration), whereas the long-distance migration flows between eastern and western Hubei mostly follow a pattern of LL (low-value agglomeration), and these urban connections show improvement after the epidemic; (2) the spatiotemporal hubs of Hubei Province demonstrate a “core-periphery” structure with Wuhan as the absolute core, while Xiangyang’s role as a subcenter does not meet planning expectations; and (3) urban spatiotemporal clusters are dense in the east and sparse in the west, with Enshi and Shiyan showing disconnection from the main network, which deviates from the planned polycentric spatial pattern. By examining the spatiotemporal autocorrelation of migration flows, this study enriches the empirical understanding of regional spatial structure in Hubei Province within the framework of classical spatial theory and highlights the necessity of incorporating dynamic flow analysis into regional planning and policy-making. Full article

Recent studies in the Romanian Western Carpathians have revealed increasing socio-demographic fragility in rural areas and small towns, driven by depopulation, population aging, and declining living standards. These trends stem from the legacy of forced collectivization and industrialization (1950–1990) and the post-1990 transition, which triggered extensive out-migration and the erosion of local socio-economic structures. This study examines the fragility of human communities in the Trascău Mountains in order to evaluate spatial, demographic, and economic recovery dynamics and to assess settlement vulnerability as a major obstacle to sustainable regional development. Fragility was measured using indicators of population density and change, age structure, accessibility, and socio-demographic dynamics, based on comparative data for the interval of 1977–2021. These variables were integrated into a composite development index (Id), derived from twelve indicators covering demography, economy, infrastructure, and living standards, enabling the hierarchical classification of settlements by degree of vulnerability. The methodological framework combines empirical and analytical methods, statistical, cartographic, bibliographic, and field-based analyses within evolutionary, structural–functional, and typological perspectives. The results identify the main drivers of decline, quantify their impacts, and outline development prospects and policy directions for reducing territorial disparities. Overall, fragile settlements emerge as critical pressure points that undermine sustainability, intensify regional instability, and increase risks related to migration and social cohesion. Full article

In edge networks, hardware failures and resource pressure may disrupt Service Function Chains (SFCs) deployed on the failed node, making it necessary to efficiently migrate multiple Virtual Network Functions (VNFs) under limited resources. To address these challenges, this paper proposes an offline reinforcement learning-based parallel migration optimization algorithm (RL-PMO) to enable parallel migration of multiple VNFs. The method follows a two-stage framework: in the first stage, improved heuristic algorithms are used to generate high-quality migration trajectories and construct a multi-scenario dataset; in the second stage, the Decision Mamba model is employed to train the policy network. With its selective modeling capability for structured sequences, Decision Mamba can capture the dependencies between VNFs and underlying resources. Combined with a twin-critic architecture and CQL regularization, the model effectively mitigates distribution shift and Q-value overestimation. The simulation results show that RL-PMO maintains approximately a 95% migration success rate across different load conditions and improves performance by about 13% under low and medium loads and up to 17% under high loads compared with typical offline RL algorithms such as IQL. Overall, RL-PMO provides an efficient, reliable, and resource-aware solution for SFC migration in node failure scenarios. Full article

Recent policy reports and state briefs continue to highlight the trend of out-migration from California. This outflow has been pronounced over the last three years, revealing a substantial net loss (i.e., net migration) of approximately 740,000 residents. However, there has been comparatively less emphasis on new residents moving to California. Over the past decade, California has attracted substantial in-migration from both domestic and international sources with annual inflows often exceeding 300,000 individuals. As such, studying in-migration is noteworthy as it shapes economic, political, and social landscapes. In-migration can alter the demographic profiles of regions, thereby impacting community dynamics, cultural diversity, and the provision of social services. Using pooled data from the American Community Survey (ACS) from 2021 to 2023 and employing a two-way fixed effects regression framework, I study how temporal changes in racial and ethnic composition, age structure, educational attainment, and economic indicators influence in-migration rates per 1000 residents at the public use microdata level (PUMA). The analysis reveals that higher proportions of Asian and Hispanic populations, as well as an increased share of college-educated residents, are positively associated with in-migration. Notably, higher supplemental poverty rates are also associated with greater in-migration, a counterintuitive finding that may reflect mobility toward affordable housing markets. These findings emphasize the importance of recognizing demographic and intra-regional variability, which can aid policymakers and planners in assessing and delivering public services. Full article

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article

This paper examines the historical evolution and pathways of rural transformation (RT) at the regional level in four Asian countries—Bangladesh, China, Indonesia, and Pakistan. We conduct a comparative spatial analysis of regional level changes in high-value agricultural production (HVAP) and non-farm rural employment (NFRE). Using long-run data and bivariate mapping, we trace how rural economies have evolved over the past four decades and identify multiple transformation pathways. The results reveal both common upward trends and stark regional contrasts. China demonstrates the most rapid and synergic rise, integrating agricultural upgrading with widespread non-farm rural expansion. Bangladesh and Indonesia show more diverse trajectories, shaped by migration, urbanization, and agro-industrial linkages. Pakistan’s transformation is slower and more fragmented, marked by strong progress in some regions but persistent lags in others. The findings underscore that RT is not linear and can follow diverse pathways—synergic, HVAP-driven, NFRE-driven, remittance-based, or stagnant—depending on geography, natural endowments, policy, and local resource endowments. Our research highlights the need for regionally tailored strategies that link agricultural upgrading with rural labor diversification, strengthen rural–urban connectivity, and ensure that lagging regions are not left further behind. Full article

Food insecurity (FI) is a major public health problem that disproportionately affects women, especially if they are migrants. In Chile, there is limited data on how gender and migration status intersect to explain vulnerability to FI. A cross-sectional analytical study was conducted among 2124 women of childbearing age (1062 Chilean and 1062 immigrants) residing in south-central Chile. Biosociodemographic variables were collected through a structured questionnaire, and FI was assessed using the Household Food Insecurity Access Scale (HFIAS). Multivariate logistic regression models were applied to estimate risk factors using odds ratios (OR). Overall, 39.2% of women experienced some degree of FI, with prevalence significantly higher among immigrants (49%) compared to Chileans (29%). Severe FI was twice as frequent in immigrant women (18.1% vs. 9.2%). The risk factor of FI in the total sample included immigrant status (OR = 2.61; 95% CI: 2.15–3.17), low socioeconomic status (OR = 2.25; 1.77–2.87), having children (OR = 1.82; 1.49–2.23), being head of household (OR = 1.53; 1.25–1.87), not having a job (OR = 1.27; 1.02–1.58), and suffering from depression (OR = 2.11; 1.66–2.67). Subgroup analyses confirmed similar determinants in both groups, with not having a job being relevant mainly for immigrants and age acting as a protective factor among Chileans. FI is highly prevalent among women in south-central Chile, particularly among immigrants. Structural determinants such as socioeconomic status, having children, being the head of the household, and depression increase vulnerability. Policies must integrate gender and migration perspectives, promoting access to adequate food, employment, childcare, and mental health support. Full article

Intergenerational contact is a key component of the informal support systems that contribute to the wellbeing of older adults. As societies age and migration patterns diversify family structures, understanding how contact is sustained across generations becomes increasingly relevant for health equity and the sustainability of care systems. In this study, we conceptualise sustainability not in environmental terms but as social and health-system sustainability—that is, the long-term ability of families and care systems to maintain intergenerational ties, ensure equitable access to support, and remain resilient under demographic and social pressures. Drawing on theories of intergenerational solidarity and social capital, this study situates contact as both a resource for individual wellbeing and a pillar of care sustainability in diverse societies. We examine the frequency of contact between parents and adult children among adults aged 50 and above, comparing migrant and non-migrant populations across 25 European countries. Using data from Waves 7, 8, and the COVID-19 wave of the Survey of Health, Ageing and Retirement in Europe (SHARE), we assess both in-person and digital forms of contact before and during the pandemic. Results show that older migrants have less frequent contact with parents but more frequent contact with children than do non-migrants, with similar patterns attested across repeated cross-sections (2017, 2019, 2021). The strong contact observed in each cross-section, facilitated by digital tools, implies resilient family ties under public-health stress. However, resilience is uneven: weaker contact with parents among migrant populations reflects structural barriers such as visa restrictions, caregiving responsibilities, discrimination, language barriers, and unequal digital access. Moreover, differences in access and proficiency with digital tools suggest that digital contact did not compensate equally across groups. These findings underscore the importance of sustainable and inclusive strategies in ageing and health policy. Specifically, targeted digital literacy programmes for older migrants, policies supporting transnational caregiving, affordable internet access, mobility solutions, and anti-discrimination measures in family visitation are crucial to reducing inequities. Full article

This study investigates factors influencing migrants’ decisions to enter Europe via Bulgaria or Greece along the Balkan route, using logistic regression and machine learning models on data from the International Organization for Migration (IOM) Flow Monitoring Survey (August 2022–June 2025, $n=5536$). We examine demographic variables (age), push factors (economic reasons, war/conflict, personal violence, limited access to services, and avoiding military service), and governance clusters derived from the World Bank’s Worldwide Governance Indicators (WGIs). An adapted migration gravity model incorporates corruption control as a key push–pull factor. Key findings indicate that younger migrants are significantly more likely to choose Bulgaria ($\beta \approx -0.021$, $p<0.001$), and governance clusters show that migrants from high-corruption origins (e.g., Syria and Afghanistan) prefer Bulgaria, likely due to governance similarities and facilitation costs. The Cluster Model achieves a slight improvement in fit (McFadden’s $R^2=0.008$, AIC = 7367) compared to the Base (AIC = 7374) and Interaction (AIC = 7391) models. Machine learning extensions using LASSO and Random Forests on a subset of data ($n=4429$) yield similar moderate performance (AUC: LASSO = 0.524, RF = 0.515). These insights highlight corruption’s role in route selection, offering policy recommendations for origin, transit, and destination phases. Full article