Search Results (92)

Background/Objectives: Obstructive sleep apnea (OSA) and related sleep disorders affect a substantial proportion of hospitalized patients, with an estimated 48% pooled prevalence of undiagnosed OSA in cardiac inpatients and up to 80% of moderate-to-severe community OSA cases carrying no formal diagnosis at the time of hospital admission. In parallel, frailty—a state of heightened physiological vulnerability arising from cumulative multi-system biological decline—is present in 40–80% of inpatients and shares deep, bidirectional neurobiological pathways with sleep-disordered breathing through circadian dysregulation, intermittent hypoxia, hypothalamic–pituitary–adrenal axis activation, and chronic low-grade inflammation. Despite this convergence, no prior study has integrated validated, administratively computable frailty phenotyping with a machine learning framework specifically designed to predict inpatient sleep disorder diagnosis—and OSA in particular—at the point of hospital admission. The present study addresses this gap by developing an admission-time, explainable machine learning framework for the prediction of inpatient sleep disorder diagnoses (ICD-10 G47.x, encompassing OSA G47.3, insomnia G47.0, hypersomnia, and circadian rhythm disorders) and of insomnia specifically (ICD-10 G47.00). Methods: We developed and evaluated a suite of five binary classification models—XGBoost, Random Forest, LightGBM, CatBoost, and Decision Tree—using 9682 balanced hospitalization episodes from the MIMIC-IV (version 2.2) database. The predictor set comprised 23 admission-time structured features across three domains: (i) frailty and comorbidity burden, including the Hospital Frailty Risk Score (HFRS) derived from ICD-10 codes, the Elixhauser comorbidity index, prior admission history, and six binary disease flags (obesity, hypertension, type 2 diabetes, heart failure, COPD, and depression/anxiety); (ii) physiological and laboratory biomarkers from the first 24 h of care, including minimum SpO₂, heart rate variability, hemoglobin, creatinine, albumin, and arterial blood gas parameters; and (iii) sociodemographic and administrative variables encompassing age, sex, ethnicity, insurance type, and admission acuity. Model performance was assessed through five-fold stratified cross-validation and bootstrap confidence intervals (n = 1000 iterations), with predictor importance quantified using SHapley Additive exPlanations (SHAP). Results: XGBoost achieved the strongest aggregate performance across all evaluation metrics, attaining an area under the receiver operating characteristic curve (AUC) of 0.871 (95% CI: 0.856–0.887), accuracy of 79.6%, F1-score of 0.820, and sensitivity of 94.9%, correctly identifying 903 of 952 true positive cases in the held-out test set; all gradient boosting frameworks substantially outperformed the Decision Tree baseline (AUC 0.836). SHAP analysis identified the HFRS and Elixhauser index as the two dominant predictors, followed by depression/anxiety, obesity, hypertension, and minimum SpO₂—a hierarchy that recapitulates the canonical clinical phenotype of obstructive sleep apnea in frail inpatients rather than that of primary insomnia, indicating that the model is preferentially capturing the OSA–frailty axis within the broader G47.x outcome. The predicted probability outputs were well-calibrated across all risk deciles. Conclusions: Frailty-derived features, in combination with admission-time clinical and physiological data, can predict inpatient sleep disorder diagnoses—predominantly OSA—with high sensitivity and well-calibrated risk estimates. The deployable, interpretable nature of the XGBoost model makes it directly suitable for integration into clinical decision support systems, offering a screening tool that requires no dedicated instrumentation beyond routine admission data. By flagging high-risk patients at the moment of admission, the framework provides a concrete mechanism for accelerating referral for definitive diagnostic confirmation (overnight oximetry, polysomnography) and earlier initiation of CPAP and related therapies, with direct implications for reducing the persistent diagnostic gap, perioperative risk, and preventable adverse outcomes in frail hospitalized populations. Full article

To address the scattered protection efforts and uneven effectiveness of traditional villages in southwestern Hubei, this study focuses on 92 nationally recognized traditional villages in Enshi Prefecture. By integrating literature research, field investigation, and multi-source data fusion, we developed an innovative model that combines the Analytic Network Process (ANP), entropy weight, and fuzzy comprehensive evaluation, thereby integrating subjective and objective weighting to improve evaluation accuracy. A quantitative evaluation was conducted across 13 criteria and 32 indicators, including traffic conditions, intangible cultural heritage resources, and industrial foundation. The results reveal that traditional villages in Enshi Prefecture exhibit a significant spatial pattern of “overall dispersion with local concentration,” accompanied by a high concentration index. Traffic conditions, intangible cultural heritage, and infrastructure emerge as the core factors affecting protection effectiveness, and a spatial differentiation pattern of “two cores and one corridor” is identified within the region. Based on the quantitative evaluation, we propose targeted cluster protection strategies, including a “dual-core multi-node” transportation network, “three-industry linkage” industrial collaboration, and a living heritage approach that integrates cultural relics with intangible cultural heritage. These strategies were validated in pilot villages such as Yejiaoyuan Village, resulting in significant increases in village satisfaction and tourist volume. The findings provide methodological support and practical paradigms for the systematic protection and sustainable development of traditional villages in southwestern ethnic minority areas. Full article

Background/Objectives: Estimate the prevalence of myopia among children aged 6–17 years in county and rural areas across seven geographically diverse provinces of China, and identify demographic, behavioral, and geographic factors associated with myopia, with particular focus on urban–rural and ethnic differences. Methods: A multi-stage stratified cluster sampling design was employed. Seven provinces were randomly selected, one from each of seven geographical regions of China (Southeast, North, Central, South, Southwest, Northwest, and Northeast). In each province, one rural county was randomly chosen. Within each county, one urban survey site (county town) and one rural survey site (village) were selected. From each site, one primary school and one junior high school were included. In each school, approximately 20 ± 2 students per grade (grades 1–9) were recruited. Uncorrected visual acuity and non-cycloplegic autorefraction were measured. Multivariable generalized linear mixed models (GLMM) with random intercepts at the class level were used to identify factors associated with myopia, accounting for the cluster sampling design. Results: The overall myopia prevalence was 42.9% (urban 49.6%, rural 36.0%). In the multivariable GLMM, educational stage was the strongest risk factor (grades 7–9 vs. 1–3: OR = 5.54). A significant district × ethnicity interaction was found only for Mongolian children: rural residence was strongly protective (OR = 0.19) compared to Han (OR = 0.65), and the ethnic advantage disappeared in county towns. Only 14.2% of myopic students had adequate correction. Conclusions: In conclusion, myopia is highly prevalent and severely under-corrected in rural China. Educational pressure is the main risk factor, and the rural protective effect is strongest in Mongolians but erodes with urbanization. Urgent public health actions, including vision screening, affordable spectacles, and lifestyle preservation, are needed to address this growing burden. Full article

Exploring the spatial patterns and associated mechanisms of Rural Tourism Demonstration Villages (RTDVs) and Potential Rural Tourism Villages (PRTVs) is crucial for rural tourism planning and regional coordination. This study focuses on the arid region of Northwest China. Based on national and provincial official directories, it selects villages listed under tourism-oriented categories as RTDVs, while designating other villages categorized for their ecological, cultural, and agricultural characteristics as PRTVs. Multiple geospatial analyses were conducted to identify spatial distribution characteristics and differences between RTDVs and PRTVs, while the optimal-parameter geographical detector model quantified the influences and interactions of natural, socioeconomic, locational, and cultural–tourism factors. Results show that rural tourism is concentrated in the Ili River Valley, the mid-Hexi Corridor, and the Urumqi–Turpan area. RTDVs follow this pattern but display stronger hierarchical differentiation. Cultural Potential Rural Tourism Villages (C-PRTVs) cluster in multi-ethnic areas. Ecological Potential Rural Tourism Villages (E-PRTVs) occur mainly in mountain oases, and agricultural Potential Rural Tourism Villages (A-PRTVs) agglomerate near provincial capitals and major transport corridors. Overall, influencing factors exhibit interactive enhancement, suggesting that spatial patterns are associated with multidimensional synergy. The findings provide empirical support for differentiated planning and sustainable development in arid regions. Full article

Social cohesion is a core dimension of social sustainability and a prerequisite for inclusive, resilient cities. Rapid rural-to-urban migration often exceeds the capacity of cities to accommodate newcomers, leading many immigrants to settle in informal neighborhoods. These areas, typically composed of diverse ethnic groups with distinct cultural, linguistic, and social backgrounds, frequently face challenges in building social cohesion. This study examines how physical elements of public spaces influence social cohesion in multiethnic informal settlements, using the Hesar Imam Khomeini neighborhood in Hamadan, Iran, as a case study. Hesar, with its rural origins and recent influx of Lor, Kurdish, Turkish, and Fars migrants, provides a unique setting to explore the relationship between the built environment and interethnic relations. A conceptual model was developed based on existing literature, and data were collected through a questionnaire survey using a Likert scale. Partial least squares structural equation modeling (PLS-SEM) was applied to test the hypothesized relationships. The findings demonstrate that physical factors shape social cohesion through a three-stage mechanism: they first foster social interactions among residents, which then contribute to the development of social capital, and ultimately lead to greater social cohesion and integration. These results highlight how inclusive public-space design can support community-based informal-settlement upgrading and sustainable urban development, by strengthening social sustainability outcomes such as cohesion, integration, and resilience. Full article

Amid accelerating global urbanization, the Qinghai–Tibet Plateau, as a repository of multi-ethnic architectural heritage, plays a crucial role in preserving plateau cultural diversity and sustaining harmonious human–environment relationships. A critical research gap persists, however, in the systematic, comparable, and quantitative assessment of urban architectural character across plateau towns, particularly in high-altitude, ecologically sensitive, and multi-ethnic regions such as Haixi Mongol and Tibetan Autonomous Prefecture. This study takes the Haixi Mongol and Tibetan Autonomous Prefecture as a case to address the specific paradox between the homogenization of urban architectural styles and the erosion of cultural authenticity in plateau towns. We develop and apply an innovative three-dimensional evaluation model—encompassing natural substrate, built environment, and cultural context—to 22 towns. For the first time in research on this region, a chained methodological approach integrating descriptive statistics, principal component analysis (PCA), and cluster analysis is employed to systematically examine the spatial differentiation of architectural character. The analysis reveals three key findings. First, it delineates a regional composite landscape characterized by mountain-basin enclosures, seasonal arid rivers and lakes, small-scale towns with expansive layouts, and multi-ethnic cultural fusion. Second, it identifies a clear ternary differentiation in urban style dominance: nine towns are nature-dominated, nine are human-made (built environment) dominated, and only four are culture-dominated, quantitatively highlighting a significant weakness in the cultural dimension. Third, cluster analysis objectively classifies the towns into eight distinct character groups—for instance, Category I towns exhibit strong architectural regionalism and traditional continuity, whereas Category V towns integrate modern relics with adjacent mountain-water features. Methodologically, this study contributes by providing a replicable, chained quantitative framework that addresses a critical gap in comparative urban studies of high-altitude, underdeveloped regions. Empirically, it reveals the specific “nature > human-made > culture” dominance pattern in Haixi and offers a scientific foundation for formulating differentiated conservation and development strategies tailored to distinct town types in the ecologically fragile areas of western China. Full article

Conventional tourism planning in ecologically fragile regions often adopts a reductionist perspective, failing to address the synergistic spatial interactions between ecological conservation, resource utilization, and infrastructure. To bridge this gap, this study develops a multi-constraint synergistic assessment framework for the dry-hot valley of Lujiang Dam (LJD) in China. Grounded in the understanding of rural tourism as a complex adaptive system, the framework innovatively integrates the InVEST model, kernel density estimation, and cumulative cost-distance algorithms to identify Natural Spatial Suitability for Tourism Development (NSSTD). Key findings include (1) pronounced spatial heterogeneity in habitat quality, with high-quality zones in the west/southeast requiring strict conservation; (2) a “barbell-shaped” clustering of natural/cultural resources at the valley’s northern and southern extremities, highly congruent with ethnic settlements; and (3) a “concentric layered” accessibility pattern where 88.08% of resources are within a 90 min drive. Crucially, the spatial overlay analysis revealed that NSSTD (54.74 km²) emerges not from single high-value zones but from areas of synergy, such as those with medium habitat quality coupled with high resource endowment and accessibility. These results provide a scientifically robust, spatially explicit layer for China’s “Multi-plan Integration” territorial spatial planning. They enable differentiated strategies—channeling development to southern corridors, implementing niche tourism in northern “structural hole” villages, and enforcing conservation in western habitats—thereby offering a replicable methodology to balance ecological integrity with sustainable rural development. Full article

Introduction: The PROMOTE cohort study is a prospective pregnancy cohort study that seeks to improve the understanding of cardiometabolic risk and determinants, such as diet, during pregnancy in a multi-ethnic population. Increasing age and obesity has resulted in an increased risk of cardiometabolic complications during pregnancy, including gestational diabetes. Trials of lifestyle interventions have so far produced mixed results, partly due to a wide variation in the methods, duration, adherence and type of dietary intervention. There is a need for high quality data about dietary habits in pregnancy, particularly in multi-ethnic populations. Objectives: In this study, we report the dietary habits of women in early pregnancy in the population of interest. We report early data seeking to assess the relationship between dietary patterns and risks of gestational diabetes. Methods and analysis: The PROMOTE cohort study is a prospective pregnancy cohort study recruiting pregnant participants with <16 weeks gestation in an area of high social and cultural diversity in western Sydney, Australia. The participants are surveyed about their physical activity levels, diet quality, emotional wellbeing and sociodemographic status using validated tools. Participants have consented to the use of routinely collected clinical and social data, including medical conditions, body mass index (BMI), blood pressure (BP) and glycaemia. The follow-up is from routinely collected data. This paper presents dietary data. Results: A total of 459 participants were recruited (n = 459), including 416 with GDM data available, at the conclusion of the first 2 years of recruitment. No participants met national dietary guideline recommendations. Fifty-six participants (n = 56, 13%) met a pragmatic composite standard of favourable diet, defined as two servings of vegetables and two servings of fruit per day, with a maximum of one discretionary serving per day. Over half the participants (n = 215, 51%) reported an adequate daily fruit intake. In total, 7 participants ate at least five servings of vegetables per day (n = 7, 1.7%), 61 participants (14.7%) ate three or more servings of vegetables per day and 212 (51.2%) participants reported one discretionary item per day. The data suggest that few women meet dietary recommendations in pregnancy. The association between dietary habits and GDM was unable to be assessed. The study was underpowered to detect an association due to the highly skewed distribution of dietary patterns in our population. Conclusions: The uptake of dietary recommendations was very low in our sample. This represents a major population health concern. Multi-level approaches are urgently needed to address poor dietary habits in pregnancy. Full article

As a form of living cultural heritage, local residential landscapes manifest the essence of long-term, resilient human–land interactions. The Wuling Corridor, a vital ethnic and cultural passage connecting the Central Plains with Southwest China in Chinese history, serves as a crucial region for the mixed residence and cultural exchange of Tujia, Miao, Dong, Han, and other ethnic groups. Within this region, Ganlan stands as both the most representative vernacular architectural heritage and a residential form that is still extensively used, constituting a continuous and unique residential landscape. The spatial distribution patterns of Ganlan are the physical witness of the history of ethnic groups adapting to the complex topographic and cultural conditions. Current research focuses on the case description of single Ganlan forms, failing to systematically investigate the spatial formation mechanisms of Ganlan as a residential landscape from a geographical continuum perspective. Therefore, this study establishes a geographical database encompassing 9425 Ganlan samples from the Wuling Corridor. It integrates the geographic information system (GIS) with clustering algorithms to systematically identify the distribution patterns of Ganlan within specific geographic–cultural units and their coupling relationships with natural environments. It conducts quantitative analysis on the key driving factors concerning the emergence and evolution of Ganlan in the study area; the findings reveal the following: (1) Ganlan buildings exhibit a spatially aggregated distribution pattern along major water systems, demonstrating characteristics of multi-ethnic sharing and spatial interweaving. (2) Their distribution is constrained by natural geographical factors and influenced by the transmission pathways of construction techniques during ancient ethnic migrations to the southwest China. (3) Within multi-ethnic settlement structures, inter-ethnic cultural interactions (particularly with Central Plains culture) serve as a key driving force for the typological evolution of Ganlan. (4) The evolutionary lineage of “full-Ganlan,” “semi-Ganlan,” and “courtyard-style Ganlan” systematically demonstrates the dynamic adaptive capacity of local residential systems. Additionally, by integrating massive Ganlan heritage data with multiple spatial analysis methods, the study serves as a typical case study illuminating the adaptive strategies and resilience mechanisms of Ganlan as a local residential landscape formed in response to the environmental conditions and social changes. Also, it provides a scientific basis for the holistic conservation of architectural heritages shared by multiple ethnic groups and the integrated development of local cultural tourism industries. Full article

Exploring the impact of dry–wet change on the Manchu rise has important implications for revealing the impact of climate change on ethnic dynamics. In this study, we used tree rings of Carya cathayensis and historical data to study this dynamic in Northeast Asia using function fitting and step-by-step elimination analysis. The results show a mean reconstructed scPDSI_4–10 of 0.822 from 1583 to 1644, which is 0.287 higher than the mean from 1548 to 2022 (0.535), and during 25 slightly wet years. This indicates that dry–wet change provided a favorable natural environment for the Manchu rise, under which the group’s area continued to expand and change shape in complex ways, and the population increased rapidly in the control region. However, in some years, the closer the scPDSI_4–10 was to the multi-year mean (0.774) of the deviation from the mean (0.535) of the scPDSI_4–10, the faster the control region expanded and the more the population increased. These results provide a reference for understanding the relationship between ethnic groups’ dynamics and climate change. Full article

Under global warming, the frequency and intensity of extreme climate events have markedly increased. As one of the most climate-sensitive and ecologically fragile regions in the world, the Tibetan Plateau faces mounting environmental and demographic challenges. This study integrates multi-model ensemble simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) with population projection data from the Shared Socioeconomic Pathways (SSPs) under the high-emission scenario (SSP5-8.5). Three extreme climate indices—very wet days precipitation (R95p), warm days (TX90p), and consecutive dry days (CDDs)—were analyzed to assess future changes in climate extremes (2021–2100) and their relationships with demographic dynamics across Tibetan ethnic areas. The results indicate that, under high-emission conditions, both R95p and TX90p increase significantly, while CDDs slightly decreases, though drought risks remain pronounced in central regions. Over the same period, the total population is projected to decline by nearly 60%, with substantial differences in climate risk exposure across groups: working-age adults and less-educated individuals experience the highest exposure before mid-century, followed by a decline, whereas the elderly and highly educated populations will show continuously increasing exposure, stabilizing by the end of the century. The transformation of population patterns is reshaping socio-cultural structures, highlighting the need for culturally adaptive governance to ensure the sustainability of Tibetan ethnic communities. These findings enhance our understanding of the coupled interactions among climate change, population dynamics, and cultural transitions, providing a scientific basis for integrated adaptation strategies to promote sustainable development across the Tibetan Plateau. Full article

Understanding the spatial patterns of ethnic inter-embeddedness is essential for promoting sustainable development in multi-ethnic regions. This study develops a novel “Clustered-Boundary-Cellular” typological model to interpret the spatial differentiation of traditional villages in China’s Hehuang region. Using an integrated approach that combines GIS spatial analysis (Kernel Density Estimation, Ripley’s K-function, and Standard Deviational Ellipse), spatial statistics (Global Moran’s I), and other statistical tests (Kruskal–Wallis tests and multinomial logistic regression), we categorized and analyzed 153 nationally designated traditional villages. The results indicate the following: (1) The villages exhibit significant spatial differentiation, falling into three distinct scenarios. Clustered–Isolation villages (107/153, 69.9%) are predominantly located in topographically constrained areas and display strong spatial clustering; Boundary–Permeation villages (24/153, 15.7%) are distributed along transport corridors and show the highest road density (0.55 km/km²); Cellular–Symbiosis villages (22/153, 14.4%) occur in multi-ethnic cores areas and exhibit a relatively random spatial distribution. (2) This differentiation results from the synergistic effects of multidimensional drivers: natural environmental constraints (notably elevation and proximity to rivers), religious–cultural adaptation (Global Moran’s I analysis confirms the strong clustering of Tibetan and Salar groups, reflecting distinct religious spatial logics), and economic transition dynamics (transportation infrastructure serves as a key catalyst). This study demonstrates the value of the proposed model as an analytical tool for diagnosing ethnic spatial relations. The findings offer important insights and spatial guidance for formulating context-sensitive strategies for sustainable governance, cultural heritage preservation, and ethnic integration. Full article

To address the challenges of the loss of ethnic cultural carriers and the spatial fragmentation of landscape management due to rural population shrinkage, constructing heritage corridors has emerged as a crucial strategy for integrating fragmented resources, enhancing cultural landscape connectivity, and improving functional resilience. Using the Nanling Ethnic Corridor in China as a case study, this research proposes an integrated method combining Landscape Character Assessment (LCA), Morphological Spatial Pattern Analysis (MSPA), and the Minimum Cumulative Resistance (MCR) model, aiming to construct a landscape heritage corridor network for ethnic villages. Firstly, LCA was employed to identify 12 categories of landscape characters, followed by a multi-dimensional value evaluation to determine high-value landscape areas. Subsequently, MSPA was used to extract core landscape patches, and the importance of these patches was assessed by combining connectivity indices (dIIC, dPC), resulting in the identification of 48 key landscape source areas. Finally, the MCR model was applied to generate potential corridors, and a heritage corridor network was formed through the optimization of topological nodes. The results indicate that (1) the heritage network consists of 48 source areas and 151 corridors, forming a structure with “two vertical and one horizontal” main axes and a circular branch network; (2) spatial distribution of source areas and corridors exhibits aggregation in the central and western regions and sparsity in the southeast, closely aligning with the distribution of ethnic villages and high-value landscape areas; (3) the optimized corridor network significantly improves the connection efficiency and resilience of cultural nodes. This study provides a scientific foundation for the systematic conservation, spatial optimization, and sustainable development of cultural heritage in ethnic regions experiencing population shrinkage. Full article

Since the 1980s, several studies have reported a decline in all-White neighborhoods and a rising number of racially mixed neighborhoods, including what have been called multiethnic “global” neighborhoods. Previous research has shown that these changes between 1980 and 2010 partly reflected the rapidly rising shares of Hispanics and Asians in urban areas. However, they also showed that there had been a substantial change in the pattern of settlement, resulting in many transitions to greater diversity than could have been expected from this demographic shift. We update their analysis to 2020, comparing transitions in the 1980–2000 period to those in 2000–2020, to test whether the earlier observed trends have continued, intensified, or weakened. We also quantify the impact of residential changes on the numbers of persons in each racial/ethnic group who live in each type of neighborhood. Full article

The landscapes of China’s multi-ethnic areas are rich in natural and cultural value, but they are threatened by homogenization and urbanization. This study aims to establish a method for identifying and classifying the landscape characters in China’s multi-ethnic areas to support the protection and sustainable development of the landscape in these areas. Taking the Miao Frontier Corridor as an example, the study optimized a parameterization method of landscape character assessment (LCA), integrated relevant cultural and natural elements, and used the K-means clustering algorithm to determine the landscape character types and regions of the Miao Frontier Corridor. The results show that (1) the natural conditions, ethnic exchanges, and historical institutions of the Miao Frontier Corridor have had a significant impact on its overall landscape; and (2) using ethnic group culture as a cultural element in LCA helps to reveal the unique cultural value of areas with different landscape characters. This study expands the LCA framework and applies it to multi-ethnic areas in China, thereby establishing a database that can serve as the basis for cross-regional landscape protection, management, and development planning in these areas. The research methods can be widely used in other multi-ethnic areas in China. Full article