Search Results (75)

Since 2019, the Mexican Government has implemented the Sembrando Vida Program (SVP) to address environmental degradation and social marginalization in rural areas. This research analyzed the SVP’s targeting mechanisms to assess the fulfillment of its objectives in the central region of the state of Guerrero, Mexico. The methodology included an analysis of the Operating Rules; a review of the beneficiary registry; the construction of environmental, social, and economic indicators; proportional stratified sampling with a 90% confidence level; and an SVP evaluation using indicators and confidence intervals. Targeting mechanisms identified agrarian subjects aged ≥ 18 years with 2.5 hectares living below the poverty line. Changes in the indicator matrix highlight a focus on food self-sufficiency and poverty levels, among others. Key results from the indicators include 100% social recovery, 62.3% of beneficiaries increased their agricultural income, and 100% application of learned environmental techniques. The intervals showed that between 26.49–42.19% of individuals joined the agricultural sector; 55.68–71.58% diversified their crops; and 86.15–95.65% made improvements in health, housing, or education. The evaluation of the SVP demonstrated its impact on environmental preservation and the improvement of the socioeconomic well-being of the rural population in the study area. Full article

Background: Parkinson’s disease (PD) and frailty frequently co-occur and may interact bidirectionally through shared mechanisms of aging biology, mitochondrial dysfunction, inflammation, and reduced physiological reserve. Objective: We aimed to synthesize current evidence on prevalence, directionality, clinical overlap, adverse outcomes, and management implications of the PD–frailty nexus. Methods: A narrative review of epidemiologic, cohort, and interventional studies was performed, examining frailty in PD and PD risk in prefrail/frail populations, plus trials of multimodal interventions. Results: Frailty is common in PD, affecting approximately one-third of patients overall and becoming more prevalent as the disease advances. It independently predicts falls, cognitive decline, hospitalization, institutionalization, and mortality. Large cohorts suggest prefrailty/frailty is associated with incident PD risk, supporting a potential bidirectional association rather than direct causation. Diagnostic complexity arises because PD motor and non-motor features overlap with frailty constructs, risking misclassification. Management based on Comprehensive Geriatric Assessment (CGA) enhances personalized, multidisciplinary care. Exercise, particularly combined aerobic and resistance training reduces frailty and improves mobility, postural control, and quality of life. Complementary nutritional strategies, including muscle-targeted supplementation, can further strengthen rehabilitation outcomes, while careful attention to social determinants and polypharmacy remains essential to optimizing overall health and functional independence. Conclusions: Frailty is best understood as a clinical marker of vulnerability within PD and a correlate of more adverse trajectories rather than a proven causal determinant. Systematic frailty assessment integrated into PD care may help refine prognosis, individualize treatment, and support efforts to preserve independence. Priorities include PD-adapted frailty tools, CGA implementation, and rigorous trials of combined exercise–nutrition programs. Full article

Wildfires in the Wildland–Urban Interface (WUI) pose escalating threats to socio-ecological systems, challenging regional resilience and sustainable recovery. Understanding the compound impacts of such fires requires an integrated, data-driven assessment of both ecological disturbance and social response. This study develops a multi-dimensional framework combining multisource remote sensing data (Landsat/Sentinel-2 NDVI and VIIRS nighttime light) with socio-structural indicators. A Composite Disturbance Index (ImpactIndex) was constructed to quantify ecological, population, and socioeconomic disruption across six fire clusters in the January 2025 Southern California wildfires. Mechanism analysis was conducted using Fixed-Effects OLS (M2) and Geographically Weighted Regression (GWR, M3) models. The ImpactIndex revealed that Eaton and Palisades experienced the most severe compound disturbances, while Border 2 showed purely ecological impacts. During-disaster CNLI signals were statistically decoupled from ecological disturbance (ΔNDVI) and dominated by site-specific effects (p < 0.001). GWR results (Adj. R² = 0.354) confirmed asymmetric spatial heterogeneity: high-density clusters (Palisades, Kenneth) exhibited a significant “Structural Burden” effect, whereas low-density areas showed weak, nonsignificant recovery trends. This “Index-to-Mechanism” framework redefines the interpretation of nighttime light in disaster contexts and provides a robust, spatially explicit tool for targeted WUI resilience planning and post-fire recovery management. Full article

This study advances the literature on digital entrepreneurship by examining how Information Technology Culture (ITC) and Technology Orientation (TO) influence entrepreneurial intentions through the mediating role of Entrepreneurial Self-Efficacy (ESE) and the moderating role of Social Support (SS) within the context of Saudi Arabia’s Vision 2030. By integrating psychological, cultural, and technological constructs, the research offers a comprehensive framework for understanding the internal drivers of digital venture creation in youth. Data were collected via an online survey targeting Saudi youth and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Validated scales measured ITC, TO, ESE, SS, and Digital Entrepreneurship Intention (DEI), with a sample of 372 participants predominantly under age 30. Findings reveal that while ITC and TO do not directly predict DEI, both exert significant indirect effects through ESE, underscoring the central role of psychological self-belief in entrepreneurial motivation. The moderating effect of SS on the ESE–DEI relationship was non-significant, suggesting that internal efficacy may outweigh external validation in this context. The sample’s demographic skew90.9% male and 99.5% under 30limits generalizability, though it aligns with the most digitally active segment of the population. The cross-sectional design restricts causal inference, and future research should explore longitudinal and gender-balanced samples to validate and extend these findings. This study provides actionable insights for policymakers and educators aiming to foster digital entrepreneurship by enhancing ESE through targeted training, cultural alignment, and strategic technology exposure, especially among youth populations driving Saudi Arabia’s innovation agenda. Full article

This paper investigates the investigators behind the distinct ‘moral panic’ that targeted mixed-race families residing in Britain’s multiracial port communities during the interwar period. This period witnessed heightened social anxieties following the First World War, exacerbated by the economic downturn and the visible presence of multiracial populations, a consequence of wartime labour demands. The 1919–1920 ‘race riots’, erupting in various British port cities, served as a critical catalyst in the exposure of underlying racial prejudices and anxieties surrounding interracial relationships and mixed-race children. In our paper we explore how the ensuing ‘moral panic’ was not simply a spontaneous societal reaction fuelled by sensationalist and prejudiced reporting in the press, but was actively constructed and sustained through a confluence of official investigations and the actions of key individuals within government and society. These forces collectively contributed to a pathological legacy that profoundly impacted the treatment and perception of mixed-race families in Britain well beyond the interwar years. Our paper builds upon our collaborative work with Peter Aspinall, to whom this paper is dedicated as one of his last scholarly endeavours. Full article

Against the backdrop of carbon reduction and sustainable development, cities play a central role in carbon emissions. These emissions are interconnected through economic, demographic, technological, and other factors, forming a complex network. This study investigates the structural characteristics and driving factors of carbon emission linkages among Chinese cities, with the aim of providing theoretical support and practical guidance for the development of sound regional carbon reduction policies. An improved gravity model was used to measure both the presence and intensity of linkages between cities. Social Network Analysis (SNA) was applied to examine network features such as density, centrality, and hierarchical structure. In addition, the Quadratic Assignment Procedure (QAP) was employed to test the effects of geographical proximity, economic disparities, demographic differences, and technological gaps on carbon emission linkages. Based on these methods, the study constructs the Chinese Carbon Emission Correlation Network (CECN), which shows high connectivity, a clear hierarchical structure, and a strengthened role of core cities. Cities with extensive linkages are mainly located in the eastern coastal region and political centers, forming a spatial pattern with stronger connections in the east than in the west, and more along the coast than inland. The network can also be divided into five distinct sub-groups. Moreover, geographical proximity, population differences, economic affluence, and technological disparities were found to significantly shape the spatial correlation of carbon emissions. These findings offer valuable guidance for designing targeted carbon reduction policies, which are essential for fostering regional coordination and advancing sustainable urban development. Full article

The vulnerability of older adults in rural mountainous regions presents a critical challenge for sustainable development, particularly in the context of smart city and digital town construction. In this study, we develop a comprehensive analytical framework and evaluation index to assess Vulnerability to Elderly Poverty (VEP) and adaptive capacity, with a focus on its integration with smart infrastructure and age-friendly rural built environment strategies. Using Shizhu County in Chongqing, China, as a case study, we explore spatial disparities in VEP and apply quantile regression to identify the driving factors of adaptability. Our findings indicate that subsidy-dependent, middle-aged, and empty-nest older adults are the most vulnerable groups, with limited capacity to adapt to changing environments. A geographically alternating “high–low–high–low” VEP pattern reflects uneven development in infrastructure, accessibility, and public service construction. These disparities highlight the need for targeted planning and building interventions in rural settings. The key factors influencing adaptability include individual attributes, intergenerational support, and macro-level conditions such as policy design and digital infrastructure deployment. The integration of aging-friendly building strategies, smart infrastructure, and digital tools significantly enhances older adults’ resilience and social inclusion. Based on our results, we propose four adaptation models for aging populations in rural areas, emphasizing the construction of inclusive digital infrastructure, aging-sensitive building design, and community-based support systems. Strategic recommendations include promoting digital literacy through built environment interventions, enhancing intergenerational living arrangements, and embedding elderly-responsive features into smart construction planning. This research offers new insights into construction management practices that support aging in place and poverty alleviation through inclusive and resilient built environments. Full article

Urban growth often surpasses the actual needs of the population, leading to inefficient land use and long-term environmental challenges. This study provides an integrated perspective on urban landscape transformation by linking socio-demographic dynamics with ecological consequences, notably vegetation loss and increased impervious surfaces. The study area is Năvodari Administrative-Territorial Unit (ATU), a coastal tourist city located along the Black Sea in Romania. By integrating geospatial datasets such as Urban Atlas and Corine Land Cover with population- and construction-related statistics, the analysis reveals a disproportionate increase in urbanized land compared to population growth. Time-series analyses based on the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) from 1990 to 2022 highlight significant ecological degradation, including vegetation loss and increased built-up density. The findings suggest that real estate investment and tourism-driven development play a more substantial role than demographic dynamics in shaping land use change. Understanding urban expansion as a coupled social–ecological process is essential for promoting sustainable planning and enhancing environmental resilience. While this study is focused on the coastal city of Năvodari, its insights are relevant to a broader international context, particularly for rapidly developing tourist destinations facing similar urban and ecological pressures. The findings support efforts toward more inclusive, balanced, and environmentally responsible urban development, aligning with the core principles of Sustainable Development Goal 11, particularly Target 11.3, which emphasizes sustainable urbanization and efficient land use. Full article

Scholars and journalists typically trace the diffusion of breed-specific legislation (BSL) in the U.S. to the surge in negative media portraits of pit bull-type dogs (PBTDs) during the late twentieth century. Yet, while news coverage still portrays these dogs unfavorably, we document a sharp rise in countervailing sources of “pit bull positivity” over the past two decades. Drawing on insights from the respective social science research on changes in attitudes and public policy, we argue that this influx of positivity should powerfully impact opinions and policies towards PBTDs. Our data and analyses consistently support that argument. We analyze two different series of repeated cross-sectional surveys to show that public support for “pit bulls” grew considerably from 2014 to 2024. We also show that voters’ support for ballot measures overturning local “pit bull bans” increased substantially during that same ten-year period. Finally, our analysis of the frames and narratives deployed in recent state and local policy debates shows how this growing pit bull positivity has helped overturn over 300 discriminatory laws against these dogs since 2012. We conclude with a discussion of how shifts in portraits and opinions of PBTDs will likely continue eroding breed-specific legislation going forward. Full article

Understanding forest-cover change and its drivers is vital for global forest management and policy development. This study analyzed perceptions of historical drivers behind land-use/land-cover change (LULCC) and forest change in gazetted forests from 1966 to 2022 to evaluate the impact of human activities around the gazetted forest reserves, comparing three forests in Nasarawa State, North Central Nigeria. Data were collected through questionnaires, interviews, and focus group discussions. Three gazetted forests (Doma, Risha, and Odu) were sampled to represent the three geopolitical zones of the state. SPSS IBM version 29, NVivo 1.7, and Python 3 were used for data analyses to generate statistics and identify coherent themes across the forests. Results show that changes were perceived to be triggered by sixteen drivers (direct and indirect) related to social, economic, environmental, policy/institutional, and technological elements. Agricultural expansion, lumbering, and charcoal production were the most reported direct drivers, while population growth, poverty, and government policies were the most perceived indirect drivers. The results showed variations in human activities across forest sites. For example, agricultural expansion, lumbering, and grazing were more widespread, while construction and settlement activities differed between forests. The Risha forest community saw agriculture expansion ahead of other drivers, Doma forest people saw population growth above other drivers, and the Odu forest community saw lumbering aiding other drivers that led to change. Implementation of policies focusing on these key drivers must match local perceptions and priorities to engage people in forest conservation. These efforts could ensure effective forest protection that is vital for achieving global biodiversity and climate targets and safeguarding local livelihoods. The specific drivers of changes in each forest need to be targeted in conservation efforts. Full article

The opioid drug overdose death rate remains a significant public health crisis in the U.S., where an opioid epidemic has led to a dramatic rise in overdose deaths over the past two decades. Since 1999, opioids have been implicated in approximately 75% of the nearly one million drug-related deaths. Research indicates that the epidemic is caused by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and social isolation. Impeding this research is the lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolution. To address this issue, we sourced data from Reddit, where people share self-reported experiences with opioid substances, specifically using opioid drugs through different routes of administration. To achieve this objective, an opioid overdose dataset is created and manually annotated in binary and multi-classification, along with detailed annotation guidelines. In traditional manual investigations, the route of administration is determined solely through biological laboratory testing. This study investigates the efficacy of an automated tool leveraging natural language processing and transformer model, such as RoBERTa, to analyze patterns of substance use. By systematically examining these patterns, the model contributes to public health surveillance efforts, facilitating the identification of at-risk populations and informing the development of targeted interventions. This approach ultimately aims to enhance prevention and treatment strategies for opioid misuse through data-driven insights. The findings show that our proposed methodology achieved the highest cross-validation score of 93% for binary classification and 91% for multi-class classification, demonstrating performance improvements of 9.41% and 10.98%, respectively, over the baseline model (XGB, 85% in binary class and 81% in multi-class). Full article

Since China’s reform and opening up over 40 years ago, rapid urbanization has led to significant progress, but also severe air pollution. Understanding how air pollution affects the sustainable development of different city types is crucial for formulating targeted mitigation strategies. Thus, we propose the hypothesis that air pollution affects urban sustainable development through the synergistic interactions of population, industry, space, and society. To test this hypothesis, we construct a simultaneous equation system incorporating pollution, population, industry, spatial, and social factors, utilizing the Three-Stage Least Squares method for estimation. The robustness of our results is rigorously verified. Additionally, to explore deeper mechanisms, we perform a multi-dimensional heterogeneity analysis. Our results indicate that: (1) Air pollution negatively impacts urban sustainable development by reducing urban population size, with a coefficient of −0.950; (2) Air pollution hinders urban sustainable development by reducing manufacturing agglomeration, with a coefficient of −0.962; (3) Air pollution induces disordered urban spatial expansion, undermining sustainable development, with a coefficient of 2.596; and (4) Air pollution leads to stricter environmental rules, with a coefficient of 17.428, and these rules boost urban sustainability in the long term. Heterogeneity analysis further reveals that the intensity, direction, and statistical significance of air pollution’s effects vary across cities with different regional characteristics, pollution levels, population scales, industrial structures, spatial patterns, and regulatory intensities. Full article

Background: Fatigue is a prevalent and complex condition with significant impacts on well-being. Existing fatigue assessments often lack comprehensiveness or practicality for general population studies. Methods: This study validated the REST Questionnaire, a novel fatigue assessment tool, in a sample of 268 adults. Psychometric properties, including internal consistency and construct validity, were evaluated. REST scores were correlated with WHO-5 well-being, BMI, self-rated health, and chronic conditions. Exploratory factor analysis identified underlying dimensions of fatigue. Results: The REST Questionnaire demonstrated excellent internal consistency (Cronbach’s alpha = 0.918) and construct validity. Higher fatigue scores were associated with lower well-being, female gender, and the presence of certain chronic conditions (cancer, kidney stones, gastric ulcers). Two distinct fatigue dimensions, “physical fatigue and functional impacts” and “emotional and social consequences”, were identified. Conclusions: The REST Questionnaire is a reliable and valid tool for assessing fatigue in the general population. Its multidimensional framework and sensitivity to comorbidities offer valuable insights for research and public health applications, with the potential to inform targeted interventions aimed at improving well-being. Full article

Background/Objectives: The evolution of digital technology enhances the broadening of a person’s intellectual growth. Research points out that implementing innovative applications of the digital world improves human social, cognitive, and metacognitive behavior. Artificial intelligence chatbots are yet another innovative human-made construct. These are forms of software that simulate human conversation, understand and process user input, and provide personalized responses. Executive function includes a set of higher mental processes necessary for formulating, planning, and achieving a goal. The present study aims to investigate executive function reinforcement through artificial intelligence chatbots, outlining potentials, limitations, and future research suggestions. Specifically, the study examined three research questions: the use of conversational chatbots in executive functioning training, their impact on executive-cognitive skills, and the duration of any improvements. Methods: The assessment of the existing literature was implemented using the systematic review method, according to the PRISMA 2020 Principles. The avalanche search method was employed to conduct a source search in the following databases: Scopus, Web of Science, PubMed, and complementary Google Scholar. This systematic review included studies from 2021 to the present using experimental, observational, or mixed methods. It included studies using AI-based chatbots or conversationalists to support executive functions, such as anxiety, stress, depression, memory, attention, cognitive load, and behavioral changes. In addition, this study included general populations with specific neurological conditions, all peer-reviewed, written in English, and with full-text access. However, the study excluded studies before 2021, the literature reviews, systematic reviews, non-AI-based chatbots or conversationalists, studies not targeting the range of executive skills and abilities, studies not written in English, and studies without open access. The criteria aligned with the study objectives, ensuring a focus on AI chatbots and the impact of conversational agents on executive function. The initial collection totaled n = 115 articles; however, the eligibility requirements led to the final selection of n = 10 studies. Results: The findings of the studies suggested positive effects of using AI chatbots to enhance and improve executive skills. Although, several limitations were identified, making it still difficult to generalize and reproduce their effects. Conclusions: AI chatbots are an innovative artificial intelligence tool that can function as a digital assistant for learning and expanding executive skills, contributing to the cognitive, metacognitive, and social development of the individual. However, its use in executive skills training is at a primary stage. The findings highlighted the need for a unified framework for reference and future studies, better study designs, diverse populations, larger sample sizes of participants, and longitudinal studies that observe the long-term effects of their use. Full article

This paper delves into the process of evangelization undertaken by the Spanish in the northern frontier of New Spain during the 16th century, specifically targeting the nomadic Indigenous populations known as Chichimecas. Missionaries encountered unique challenges due to the absence of religious infrastructure, robust political authorities, and the nomadic lifestyle of these groups. To overcome these hurdles, the Spanish implemented a strategy that intertwined evangelization with colonization. The text highlights the significance of constructing physical infrastructure in these frontier territories, such as churches, schools, and dwellings, to facilitate the process of evangelization and colonial control. Moreover, it emphasizes the need to impose a social and political structure on these nomadic communities, transforming them into obedient colonial subjects. Full article