Search Results (1,412)

Background: Topological Data Analysis (TDA) captures multi-scale geometric features of data as persistence diagrams, yet no principled information-theoretic framework quantifies how much information those features carry, how efficiently they compress, or when they are informationally irreducible. Methods: We construct a measure-theoretic probability space over persistence diagram space using a Poisson-process reference measure, and define topological entropy (H-T), topological mutual information (I-T), and a topological rate–distortion function as the core objects of a new theory. Results: Four theorems with full proofs establish finite stability, axiomatic uniqueness, a Topological Data Processing Inequality, and a Rate–Distortion Theorem with explicit Poisson-model closed-form formula. A Renyi generalization of topological entropy is also established. Computational and practical implementation aspects—including finite-sample estimation, multi-parameter extension, and algorithmic realization—are addressed inline throughout the paper. Conclusions: This framework provides a rigorous measure-theoretic information-theoretic foundation for persistent homology, demonstrated on simulated brain connectivity and point cloud data, with applications to threshold selection, genomic classification bounds, and compressed sensing. Full article

The joint probability distribution of observable variables from a system is constrained by the underlying causal structure. In the presence of hidden variables, untestable independencies that involve hidden variables lead to testable causally-imposed inequality constraints for observable variables, whose violation can reject the compatibility of a causal structure with data. One type of causally informative inequalities is entropic inequalities, which appear in the space of entropic terms associated with the distribution of observable variables. We derive a new type of minimum information (minInf) entropic inequalities that substantially increases causal inference power. These new entropic inequalities appear when considering the constraints that the causal structure imposes on entropic terms determined by information minimization within families of distributions that preserve sets of marginals shared with the original distribution. We introduce a new family of minInf data processing inequalities and a procedure to recursively combine different types of data processing inequalities to create tighter testable entropic inequalities. We extensively illustrate the applicability of this procedure in the instrumental causal scenario, integrating the new inequalities with standard instrumental entropic inequalities constructed with multivariate instrumental sets. We also provide additional examples with other types of entropic inequalities, such as the Information Causality and Groups-Decomposition inequalities. Full article

Educational trajectories developed by students throughout their schooling are shaped by experiences across multiple domains, where learning opportunities coexist with factors that may hinder academic achievement and the development of successful trajectories. The aim of this study was to analyze the personal, family, school, and contextual factors associated with educational trajectories and academic achievement among primary and secondary school students. A systematic review of the literature was conducted based on quantitative longitudinal studies published between 2022 and 2025 and identified through the Web of Science, Scopus, and Education Resources Information Center databases. The results indicate that educational trajectories linked to academic achievement tend to begin in primary education and show relative stability throughout secondary education, with variations over time associated with the interaction of individual, family, school, and contextual factors. These findings have practical implications for behaviorally informed interventions aimed at strengthening self-regulation, teacher support, socioemotional competencies, and family engagement to promote more equitable academic pathways. Overall, the evidence underscores the need to implement comprehensive and differentiated educational interventions articulated across multiple levels to reduce inequalities and foster sustainable academic development. Full article

Background: Adults with disabilities living in low-resource communities experience persistent inequities in access to healthcare, mental health services, and community participation. However, qualitative data capturing lived experiences in the Deep South remain limited. This study aimed to identify priority needs among adults with mobility disabilities residing in economically distressed communities near Birmingham, Alabama, to inform future telehealth programming. Methods: Fifteen adults (mean age = 60 ± 10 years), predominantly African American and female, completed semi-structured phone interviews exploring basic needs, neighborhood accessibility, health priorities, and perceived supports. Interviews were audio-recorded, transcribed verbatim, and analyzed using Braun and Clarke’s six-phase thematic analysis. Results: Five themes emerged: (1) seeking stability amid severe mental health strain and inadequate supports; (2) constrained food environments shaped by cost, location, and safety; (3) feeling forgotten: systemic neglect and restricted participation in community life; (4) physical health deprioritized by competing needs and structural barriers; and (5) remote support as a viable but unrealized option. Participants described how safety concerns, transportation barriers, and rising food costs constrained daily functioning, while unmet mental health needs compounded isolation. Despite widespread cardiometabolic disease, immediate needs related to mental health, food, and housing consistently superseded physical health. Mental health support was identified as the most feasible area for remote delivery, though poor awareness of available resources limited engagement with any service model. Conclusions: Findings demonstrate that disability-related disparities in low-resource communities are driven largely by structural and environmental factors rather than individual choice. Telehealth and mobile-based services may provide a feasible access strategy for mental health and supportive care in under-resourced settings, particularly when integrated with broader community supports. Addressing foundational needs is essential for advancing health equity among people with disabilities in the Southeast. Full article

Equitable access to rehabilitation services is essential for individuals living with a disability, yet geographic disparities in outpatient rehabilitation care remain understudied. This study examined spatial accessibility to outpatient physical and occupational therapy services across Texas to identify regional inequities and inform workforce and policy planning. A descriptive cross-sectional geospatial analysis was conducted using outpatient clinic location data from the Texas Health and Human Services database (2022) and population data from the 2020 U.S. Census. Clinic addresses were verified and geocoded. Accessibility was measured using an origin–destination cost matrix to estimate the travel time to the nearest clinic, and the two-step floating catchment area (2SFCA) method to calculate an accessibility index. Spatial clustering of access was assessed using the Getis-Ord Gi* statistic to identify hot and cold spots. The analysis included 2255 outpatient rehabilitation clinics across 6896 census tracts. Travel times varied substantially, with rural areas experiencing the longest travel burdens. The 2SFCA analysis revealed pronounced disparities, with low-accessibility clusters concentrated in rural and border regions and high-accessibility clusters in urban metropolitan areas. These findings demonstrate persistent geographic disparities in outpatient rehabilitation access across Texas, suggesting the need for targeted workforce placement, transportation investment, and policy interventions to improve equitable access. Full article

Air pollution remains a critical environmental and public health threat, particularly in highly populated urban areas such as the Lisbon Metropolitan Area (LMA). This study provides a refined and detailed assessment of the spatial distribution of air pollution and associated attributable mortality across the LMA. High-resolution (1 km²) annual mean concentrations of key pollutants (PM_2.5, PM₁₀ and NO₂) for 2022 and 2023 were estimated by integrating outputs from the URBAIR dispersion model with ground-based monitoring observations using advanced geostatistical data-fusion techniques. Air pollutant concentrations were combined with gridded population data and age-stratified baseline mortality rates within a Geographic Information System framework to quantify spatial variations in health impacts. Using the World Health Organization AirQ+ framework and established concentration–response functions, we estimated a total of 3195 air-pollution-attributable deaths across the Lisbon Metropolitan Area (LMA) in 2022, increasing to 4010 deaths in 2023. Fine particulate matter (PM_2.5) was identified as the dominant contributor, accounting for more than 40% of the total health burden. At a high spatial resolution (1 km² grid), estimated mortality exhibited substantial variability, ranging from 0 to 29 deaths per cell in 2022 and from 0 to 36 deaths per cell in 2023. These results highlight the importance of fine-scale spatial analysis, revealing intra-urban disparities that are not captured by aggregated estimates of total attributable mortality. The proposed methodological framework, integrating dispersion modelling, data fusion, and spatially explicit health impact assessment at fine spatial scales, provides a robust and transferable approach to support evidence-based air quality management and urban health policy development in European metropolitan contexts. This integrated approach enhances comparability, improves exposure assessment accuracy, and strengthens the scientific basis for designing targeted mitigation strategies that could prevent hundreds of premature deaths annually while addressing documented spatial inequalities in pollution exposure. Full article

The sustainability of information poses an ever-greater challenge in the digital age, particularly within local media ecosystems, where access to technology does not necessarily lead to informed participation or stronger ties with institutions. In contexts such as Ecuador, persistent inequalities shape the way people access, use and trust information, reinforcing complex forms of the digital divide. This study analyses how the sustainability of information is reflected in media consumption patterns and levels of institutional engagement within a regional context. Based on a survey of 1784 people in the province of Imbabura, the study applies a combined approach using cluster analysis and random forest models to identify distinct audience profiles. The results reveal four distinct groups, demonstrating that the intensity and diversity of media use are more relevant than mere digital access. High levels of digital use do not guarantee greater institutional engagement; instead, hybrid patterns emerge that combine traditional, digital and institutional media in different ways. The findings show that digital access alone is not sufficient to ensure information sustainability or the formation of institutional opinion. From a public policy perspective, universities and public institutions should promote digital literacy, build trust and design more targeted communication strategies to reduce information inequalities and foster informed participation. Full article

In this paper, we introduce, for the first time, a novel class of (center-radius order $(P,\mathrm{m})$-superquadratic interval-valued functions) $\mathfrak{cr}$-$(P,\mathrm{m})$-superquadratic $\mathcal{IVF}$s, and systematically investigate their fundamental structural properties. Building upon these properties, we establish new Jensen and Hermite–Hadamard ($\mathcal{HH}$) type inequalities, together with their fractional extensions formulated via Riemann–Liouville ($\mathcal{RL}$) fractional integral operators within the setting of interval calculus. The validity and sharpness of the derived results are illustrated through numerical examples and graphical representations. Moreover, the theoretical developments are further enriched by applications in information theory, leading to meaningful generalizations and notable improvements over several existing results reported in the literature. Full article

Climate change is an urgent global crisis requiring collaboration across sectors, including public health. In Canada, extreme heat is a leading cause of weather-related mortality, and cities play a central role in mitigating health impacts. This study examined the governance mechanisms shaping intersectoral extreme heat response in Vancouver, Toronto, and Montreal, Canada. Using a comparative case study methodology, we conducted semi-structured interviews (N = 28) and reviewed local heat response documents (N = 30) between November 2023 and December 2024. Thematic analysis informed cross-case comparisons of governance mechanisms shaping collaborative efforts. Across cases, legislative mandates, formal response plans, and coordinating structures for network engagement supported effective intersectoral collaboration. However, collaboration varied in terms of network governance leadership, intersectoral scope (i.e., the type and number of sectors involved), degree of engagement, and the roles of public health authorities. Co-leadership across sectors in Montreal seems to enable greater intersectoral engagement and integration of heat strategies. Areas for improvement include community-engaged heat response planning and enhanced capacity for conducting heat response outcome evaluations. Public health authorities may inform the strategic direction of future heat strategies by supporting the application of a population health lens and facilitating intersectoral collaboration to better address the upstream determinants of heat health inequities. Full article

Utilizing an integrated knowledge translation framework (iKT), the purpose of this study was to identify best practices for long-term care (LTC) staffing in British Columbia, Canada, based on learnings from the COVID-19 pandemic. Through multiple data sources, including an electronic survey provided to LTC operators and knowledge-generation forums held with LTC community members, four staffing recommendations were created. Our major findings emphasize how the pandemic exposed and further exacerbated LTC workforce shortcomings and provide rich, contextual information to help create efficacious and practical outcomes and enhance public health. Our recommendations include conducting contingency planning for potential crises, increasing the use of volunteers, implementing recruitment and retention strategies for the LTC workforce, and standardizing evaluations of staffing adequacy and resident outcomes. These investments can serve to strengthen LTC currently and to protect against potential future health crises. This project highlights how lived experience can be utilized to address health inequities and bolster public health outcomes. Full article

We formulate the Quantum Information Copy Time (QICT) framework for conserved charges under strictly local quantum dynamics and isolate its logically strongest consequence. The theorem-level core is a receiver-optimised variational speed-limit inequality: after projection away from the conserved zero mode, the copy time is bounded from below by the inverse square root of a Liouvillian-squared receiver susceptibility times a local encoding seminorm. This statement is written in a finite-volume operator framework and does not require a diffusive ansatz. We then examine what follows only after additional infrared assumptions. Under a single diffusive slow-mode hypothesis, the variational inequality reduces to the practical scaling relation used in the benchmark computations. That reduction is treated as conditional and is stress-tested numerically rather than promoted by rhetoric. Within the anomaly-free Abelian span relevant for one Standard-Model-like generation, hypercharge selection is elevated to theorem-level status; by contrast, minimal gauge-algebra uniqueness remains explicitly conditional on additional model-selection axioms. The remainder of the manuscript is organised as an explicitly documented closure programme built on top of this core. In that closure, a gauge-coded QCA construction, a microscopic benchmark for the transport normalisation, and an electroweak matching convention are combined to produce a resonance-centred Higgs-portal singlet-scalar mass band together with direct-detection, invisible-width, and relic-consistency checks. These latter results are presented as model-dependent consequences of an explicit closure ansatz rather than as deductions from locality alone. Full article

This article focuses on the fixed-time synchronization problem for fractional-order fuzzy cellular neural networks (FOFCNNs) with information interactions and time-varying delays. To capture the complex dynamics of practical networks, nonlinear activation functions along with fuzzy AND and OR operators are incorporated into the master–slave systems. To achieve fixed-time synchronization despite these complexities, a novel adaptive multi-module controller is proposed. This controller integrates three functionally distinct components to accelerate the convergence rate, eliminate the effects of delays, and introduce negative feedback during communication, respectively. By employing fractional calculus tools, inequality techniques, and the proposed control law, sufficient criteria for the synchronization of the considered systems are rigorously established. Compared with existing synchronization works, this paper has significant advantages in model generality and controller design. Additionally, an explicit settling-time estimate is derived, which depends solely on control parameters and is independent of the initial conditions. Full article

The manuscript aims to assess the scale of shadow economic processes within the non-observed economy, focusing on the self-employment sector, which is insufficiently reflected in national statistics. The research methodology includes an analysis of the conceptual foundations of the shadow economy, decomposition of its components, identification of factors negatively affecting the economy, development of an algorithm for sociological research, and the selection of appropriate models for evaluating the non-observed economy. The study formulates the concept of the shadow economy and shows that shadow business activity in Kazakhstan contributes to income inequality, hidden unemployment, and the exclusion of certain goods and services from official GDP statistics. Using statistical data from 2005 to 2024 and applying methods such as system and statistical analysis, modeling approaches, and the MIMIC (Multiple Indicator Multiple Cause) and DGE (Dynamic General Equilibrium) models, the study estimates the size of the shadow sector. The results reveal insufficient statistical data on shadow activities within self-employment and SMEs. The study concludes that the most reliable assessment of the shadow economy requires an integrated methodological approach, including targeted sociological research and models that account for the influence of multiple factors on informal self-employment. Full article

The rapid integration of artificial intelligence (AI) into educational systems is transforming how student performance is analysed and how educational policies are informed by large-scale data. Within this context, machine learning techniques are increasingly used to identify patterns associated with academic success and educational inequality. However, the use of predictive algorithms in education also raises important questions regarding transparency, fairness, and potential algorithmic bias. This study examines the predictive performance and fairness implications of machine learning models used to identify academically resilient students using data from the Programme for International Student Assessment (PISA) 2022. The analysis is based on a dataset containing more than 600,000 student observations across multiple national education systems. Academic resilience is operationalised following the OECD framework, identifying students who belong to the lowest quartile of the socioeconomic status index (ESCS) within their country while simultaneously achieving mathematics performance in the top quartile (PV1MATH). A predictive framework incorporating six supervised learning algorithms—Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost—was implemented. The modelling pipeline includes data preprocessing, missing value imputation, class imbalance correction using SMOTE, and model evaluation through multiple classification metrics, including accuracy, F1-score, and the area under the ROC curve (AUC). In addition, fairness diagnostics are conducted to examine potential disparities in prediction outcomes across gender groups, while feature importance analysis and SHAP-based explanations are used to interpret the contribution of key predictors. The results indicate that ensemble-based models achieve the highest predictive performance, particularly those based on gradient boosting techniques. At the same time, the analysis reveals that socioeconomic status, migration background, and school repetition constitute the most influential predictors of academic resilience. Although gender displays relatively low predictive importance, measurable differences in positive prediction rates across gender groups suggest the presence of potential algorithmic disparities. These findings highlight the importance of integrating fairness evaluation, transparency, and interpretability into educational data science workflows. The study contributes to ongoing discussions on the responsible use of artificial intelligence in education by emphasising the need for governance frameworks capable of ensuring that algorithmic systems support equity-oriented educational policies. Full article

Tuberculosis (TB) remains a major public health burden in Vietnam, yet few studies have examined pulmonary TB mortality trends at sub-national levels, where local epidemiological patterns may differ substantially from national averages and reveal high-risk populations requiring targeted interventions and inform resource allocation. Lang Son, Vietnam, is a mountainous border province with many ethnic minority residents, and extensive cross-border movement creates distinct challenges for TB surveillance and treatment adherence. Although mortality has declined in line with national trends, rates in this border province remain higher than those in Vietnam’s major urban centers. This disparity suggests a hidden burden where Lang Son’s unique geographic challenges and ethnic diversity create health inequities that are often obscured by favorable national-level averages. To better understand local epidemiological patterns, this study examined temporal trends and gender differences in pulmonary TB mortality in Lang Son Province over a 12-year period (2005–2008 and 2011–2018). Using data from a population-based mortality registration system, we calculated crude and age-standardized mortality rates (ASR) per 100,000 person-years. Temporal trends were assessed using Poisson regression. The overall ASR was 7.7 per 100,000 person-years among men (95% CI: 6.5–9.0) and 1.9 among women (95% CI: 1.3–2.7), yielding a male-to-female ASR ratio of 4.1. The age-standardized pulmonary TB mortality declined by approximately 49.2% (from 6.3 (95% CI: 4.1–9.2) to 3.2 (95% CI: 1.9–4.9) per 100,000 person-years; p = 0.025). Notably, 69.9% of deaths occurred in individuals under age 70. While declines were observed in both sexes, sex-specific temporal trends were not statistically significant (p > 0.05). Despite these improvements, persistently higher mortality among men and older adults highlights ongoing inequities in TB outcomes within the province. These pre-pandemic findings provide an essential epidemiological baseline for assessing COVID-19’s impact on TB control and underscore the need for age- and gender-targeted interventions at sub-national levels to accelerate Vietnam’s progress toward TB elimination. Full article