Neighborhood Relations Research Articles

Multifaceted impacts of double-aging neighborhood’s built environments on SAIP: a deep dive into Chinese rapidly aging urban society

IntroductionThe healthy aging of older adults in dual-older adult communities is influenced by multiple factors, and understanding its underlying mechanisms can promote healthy aging among the older adults in a wide range of developing countries. This comprehensive study delves into the intricate interplay between multifaceted built environmental factors, and their direct and indirect effects on the successful AIP residing in double-aging neighborhoods.MethodsApplying a series of HLM, the research meticulously explores the intricate links between SAIP and multi-scale aging spaces, including home space, community social participation, and built environments.ResultsThe results show that: (1) Older adults people’s need for spiritual comfort derived from home space exceed the need for financial support and family care, becoming a major positive factor for SAIP; (2) The neighborhood based on acquaintance society, partly replace the role of home-based care in influencing SAIP. Especially, community participation has a positive impact, serving as an extension of the home space, such as college for senior citizens and outdoor activity space; (3) The built environment of double-aging neighborhoods has a significant positive effect, with a sense of place identity replacing the reliance on family members and acquaintances to facilitate SAIP; (4) In high-density old district, the distribution of public facilities is saturated, and the proper utilizes of these facilities becomes an important factor affecting SAIP.DiscussionWe provide a multi-factorial perspective of SAIP, demonstrating the compensatory and substitutional roles of community-based older adults care services and friendly neighborhood relationships in fulfilling home-based older adults care functions. This approach better promotes the construction of age-friendly communities and supports SAIP.

Spatiotemporal dynamics of fetal liver hematopoietic niches.

Embryonic hematopoietic cells develop in the fetal liver (FL), surrounded by diverse non-hematopoietic stromal cells. However, the spatial organization and cytokine production patterns of the stroma during FL development remain poorly understood. Here, we characterized and mapped the hematopoietic and stromal cell populations at early (E12.5-14.5) FL stages, revealing that while hepatoblasts were the primary source of hematopoietic growth factors, other stromal cells-including mesenchymal, mesothelial, and endothelial cells-also contributed to this signaling network. Using a dedicated image analysis pipeline, we quantified cell distances to tissue structures and defined neighbor relationships, uncovering that different hematopoietic progenitors exhibit distinct preferences for neighboring stromal cells and show developmental changes in spatial distribution. Notably, our data suggest that the sub-mesothelium region plays a prominent role in early fetal hematopoiesis. This approach offers a valuable tool for studying complex cellular interactions in biological systems, providing new insights into hematopoietic niche organization during development.

Health Literacy and Internet Use Among Japanese Older Adults: A Gender-Stratified Cross-Sectional Analysis of the Moderating Effects of Neighborhood Relationships.

Background/Objectives: Internet use positively impacts mental health in older adults, with health literacy (HL) playing a key role. While social networks may complement individual HL, the role of neighborhood relationships in this association, particularly by gender, remains unclear. This study examined how the association between HL and Internet use among older adults was modified by neighborhood relationships. Methods: Using baseline data from the Chofu-Digital-Choju project, a cross-sectional analysis was conducted on 1955 community-dwelling adults aged 65-84 (889 men and 1066 women). HL was assessed using the Communicative and Critical Health Literacy scale and dichotomized at four points. Neighborhood relationships were categorized as high (visiting/chatting with neighbors) or low (exchanging greetings/no relationship). Gender-stratified logistic regression analyses were performed with Internet use as the dependent variable, with HL, neighborhood relationships, and their interaction as independent variables. Results: Internet user proportion was 55.6% for men and 41.8% for women. HL was positively associated with Internet use in both genders, though patterns differed. Among men, the HL-Internet use association was consistent (OR = 3.09; 95% CI: 2.25-4.24) regardless of neighborhood relationship levels. For women, this association was significantly modified (interaction OR = 0.46, 95% CI: 0.24-0.87). Women with low HL but strong neighborhood relationships showed increased odds of Internet use (OR = 2.08, 95% CI: 1.32-3.26). Conclusions: Gender-specific patterns in HL and neighborhood relationships influence Internet use among older adults. Neighborhood relationships may compensate for low HL in women, underscoring the need for gender-sensitive strategies to promote digital HL.

Acceptance of advance care planning and influencing factors from the perspective of the life cycle: a cross-sectional study

BackgroundAdvance care planning is an important part of palliative care. Public acceptance is a prerequisite for the widespread development and implementation of advance care planning. However, little is known about the level of public’s acceptance and influencing factors of advance care planning across different life cycles.MethodsA cross-sectional study in mainland China was conducted from June 20 to August 31, 2022. We used multi-stage sampling strategy to recruit participators. A stepwise linear regression analysis was used to examine the influencing factors in different life cycles (nonage, mature age, middle age and old age).ResultsThe final sample size was 18,002. The average acceptance score of advance care planning of the public throughout the entire life cycle was 64.03. The average score in nonage was 67.13, which is the highest. The average score in mature age was 63.87, in middle age was 63.51, and in old age was 63.54. Multiple linear stepwise regression results indicated that death education support level, well-being index, neighbor relations, health literacy, family social status, and siblings were influencing factors in nonage. Medical insurance, injury events, multiple properties, death education support level, health literacy, family social status, neighbor relation, social support, family health, media contact, and well-being index were influencing factors in mature age. In middle age, region, living alone, depression, debt, houses, death education support level, health literacy, social support, and family social status were influencing factors. In old age, injury event, death education support level, neighbor relation, well-being index, siblings and children were influencing factors.ConclusionsThis study is the first to compare the Chinese people with different life cycles. It found that the public’s acceptance and influencing factors of advance care planning varied across different life cycles. Governments and health care personnel should emphasize autonomy and initiate advance care planning based on different life cycles and individual approaches, then introduce appropriate public health policies into newer and broader fields.

Unveiling the Social Fabric: The Impact of China’s Rural Subsistence Allowance System on Community Isolation

This study investigates welfare stigma and its underlying mechanisms using data from the China Family Panel Studies Project. By employing propensity score matching, the research examines how indicators such as neighborhood relationships and interactions with relatives reflect social isolation within rural households. The analysis reveals critical determinants affecting rural families’ access to subsistence allowance assistance and explores the resultant social isolation effects within China’s rural subsistence allowance system. Our findings indicate that several factors—including family income, housing conditions, employment status, age demographics, health status, and village characteristics (such as landform and population density)—significantly impact the likelihood of receiving subsistence allowances. Additionally, the study highlights that the rural subsistence allowance system contributes to diminished neighborhood cohesion and fewer interactions with relatives among beneficiaries. The research further identifies a pronounced targeting bias within the district targeting mechanism of the allowance program, which is corroborated through robustness testing. Overall, this study provides novel insights into the relationship between welfare stigma and social isolation, offering valuable empirical evidence and policy recommendations to enhance the effectiveness of rural subsistence allowance policies in China.

Spatial Inequalities in the Centre of Gravity in Central-Eastern Europe Based on the GVA and the Employment Rate

The issue of territorial disparities is an interesting subject of study in Europe, as there are significant socio-economic disparities between regions, which do not disappear over time, despite substantial levels of funding. The Central-Eastern European region is lagging significantly behind the EU as a whole and the Western regions, with significant intra-country differences. This study aims to examine six countries in the Central-Eastern European region in terms of county-level gross value added and employment rates, examining the convergence processes in the region. In addition to the growth rates of the regions, the study examines the emergence of sigma and beta convergence phenomena, as well as the importance of spatial spillover effects and neighbourhood relations in catching up. I investigate the probability that the spatial distribution of each indicator is heterogeneous, which I also illustrate by the change in the spatial centre of gravity. Convergence studies highlight the internal convergence of the region but also draw attention to the impact of different crises on inequalities.

Handling Exponentially Growing Strategies in Spatial Cooperative Games: The Case of the European Union

This paper introduces a comprehensive cooperative game theory framework to measure the significance of location and neighborhood relations in conjunction with the magnitude of players/parties. The significances of these relations are measured over the EU geography. In this case, there are (i) the test of availability of a core solution that satisfies all associated parties/players; (ii) the measurement of players’/parties’ rational minimal and maximal return expectations from the grand coalition regarding their all individual and sub-group strategies and associated return rationalities; (iii) the determination of the critical players/parties in the grand coalition. The study’s main contributions are the provision of a methodology that identifies spatially/geographically critical players/parties and the design of an algorithm for handling exponentially growing strategies alongside increasing numbers of players/parties. In sum, a comprehensive cooperative game theory framework is introduced to measure the significance of location and neighborhood relations in conjunction with the magnitude of the players/parties. The case of the EU has revealed the union’s geographically critical countries, with Germany being found to be the most influential.

MGNR: A Multi-Granularity Neighbor Relationship and Its Application in KNN Classification and Clustering Methods.

In the real world, data distributions often exhibit multiple granularities. However, the majority of existing neighbor-based machine-learning methods rely on manually setting a single-granularity for neighbor relationships. These methods typically handle each data point using a single-granularity approach, which severely affects their accuracy and efficiency. This paper adopts a dual-pronged approach: it constructs a multi-granularity representation of the data using the granular-ball computing model, thereby boosting the algorithm's time efficiency. It leverages the multi-granularity representation of the data to create tailored, multi-granularity neighborhood relationships for different task scenarios, resulting in improved algorithmic accuracy. The experimental results convincingly demonstrate that the proposed multi-granularity neighbor relationship effectively enhances KNN classification and clustering methods.

GQEO: Nearest neighbor graph-based generalized quadrilateral element oversampling for class-imbalance problem.

The class imbalance problem is one of the difficult factors affecting the performance of traditional classifiers. The oversampling technique is the most common way to solve the class imbalance problem. They alleviate the performance impact of the class imbalance problem on traditional machine learning by augmenting minority instance feature representation. However, many SMOTE-based oversampling techniques perform linear interpolation on the line segment between the anchor instance and its nearest neighbor. This type of method only uses local information and ignores the impact of the global neighborhood relationship on the anchor instance. Therefore, inspired by finite element interpolation, a novel generalized quadrilateral element oversampling technique (GQEO) based on k-nearest neighbor graphs is proposed. First, GQEO uses the k-nearest neighbor to search the global neighbor relationship and build the global neighbor relationship graph. Then, the global neighbor graph is searched for nodes forming generalized quadrilateral elements, using planar quadrilaterals as constraints. Finally, in generalized quadrilateral elements, we use one-dimensional shape functions to synthesize minority instances in quadrilateral elements. Experimental results on 30 imbalanced datasets show that GQEO can alleviate the impact of the class imbalance problem and prevent noise from participating in the synthesis process. GQEO obtains competitive results compared to state-of-the-art oversampling techniques that consider minority noises.

U-Match: Exploring Hierarchy-Aware Local Context for Two-View Correspondence Learning.

Rejecting outlier correspondences is one of the critical steps for successful feature-based two-view geometry estimation, and contingent heavily upon local context exploration. Recent advances focus on devising elaborate local context extractors whereas typically adopting explicit neighborhood relationship modeling at a specific scale, which is intrinsically flawed and inflexible, because 1) severe outliers often populated in putative correspondences and 2) the uncertainty in the distribution of inliers and outliers make the network incapable of capturing adequate and reliable local context from such neighborhoods, therefore resulting in the failure of pose estimation. This prospective study proposes a novel network called U-Match that has the flexibility to enable implicit local context awareness at multiple levels, naturally circumventing the aforementioned issues that plague most existing studies. Specifically, to aggregate multi-level local context implicitly, a hierarchy-aware graph representation module is designed to flexibly encode and decode hierarchical features. Moreover, considering that global context always works collaboratively with local context, an orthogonal local-and-global information fusion module is presented to integrate complementary local and global context in a redundancy-free manner, thus yielding compact feature representations to facilitate correspondence learning. Thorough experimentation across relative pose estimation, homography estimation, visual localization, and point cloud registration affirms U-Match's remarkable capabilities.

Novel resampling algorithms with maximal cliques for class-imbalance problems

The imbalance issue significantly deteriorates the performance of classifiers. While researchers proposed resampling methods to address this problem, it often struggles with class overlap and small disjuncts, and focuses only on one-way relationships among instances. Most of these techniques emphasize neighbor relationships between pairs of instances while ignoring the global relationships within the dataset. To address this, we represented instances as nodes in a graph, with edges based on neighbor relationships. We constructed a neighbor graph of the dataset and identify its maximal clique. Using this concept, we proposed two resampling techniques: the Maximal Clique-based Oversampling (MCSO) and Undersampling (MCSU) methods. MCSO employs the maximal clique method to tackle the small disjunct problem, while MCSU addresses overlapping issues from a global perspective. We tested these methods using three base classifiers-CART, RF, and GBDT-on 35 public datasets. Our results showed that MCSO and MCSU outperform state-of-the-art methods in terms of AUC and G-mean. Additionally, the Friedman test and Nemenyi post-hoc test showed that our MCSO and MCSU are significantly better than other algorithms, demonstrating their superior performance.

Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks

BackgroundRecent advancements in spatially resolved transcriptomics (SRT) have opened up unprecedented opportunities to explore gene expression patterns within spatial contexts. Deciphering spatial domains is a critical task in spatial transcriptomic data analysis, aiding in the elucidation of tissue structural heterogeneity and biological functions. However, existing spatial domain detection methods ignore the consistency of expression patterns and spatial arrangements between spots, as well as the severe gene dropout phenomenon present in SRT data, resulting in suboptimal performance in identifying tissue spatial heterogeneity.ResultsIn this paper, we introduce a novel framework, spatially regularized deep graph networks (SR-DGN), which integrates gene expression profiles with spatial information to learn spatially-consistent and informative spot representations. Specifically, SR-DGN employs graph attention networks (GAT) to adaptively aggregate gene expression information from neighboring spots, considering local expression patterns between spots. In addition, the spatial regularization constraint ensures the consistency of neighborhood relationships between physical and embedded spaces in an end-to-end manner. SR-DGN also employs cross-entropy (CE) loss to model gene expression states, effectively mitigating the impact of noisy gene dropouts.ConclusionsExperimental results demonstrate that SR-DGN outperforms state-of-the-art methods in spatial domain identification across SRT data from different sequencing platforms. Moreover, SR-DGN is capable of recovering known microanatomical structures, yielding clearer low-dimensional visualizations and more accurate spatial trajectory inferences.

Two relocation strategies: Preserving erstwhile neighbourhood relationships in Shenzhen

Relocation, both within China and other countries, typically results in a decline in neighbourhood relationships, especially for people who do not move willingly. For migrants, preserving neighbourhood relationships is particularly important as they often rely on these connections as their fitting-in strategy within cities. However, little empirical research has unravelled how migrants navigate relocation under massive urban redevelopment contexts and preserve pre-existing neighbourhood relationships. This paper examines to what extent and in what ways relocation willingness influences pre-existing neighbourhood relationships by applying multiple mediation effect models to a questionnaire survey dataset collected in 2022–2023. The results reveal that migrants who are more willing to relocate tend to stay in touch with former neighbours post-relocation. Additionally, relocation strategies (i.e., moving with former neighbours and short-distance moving) are important pathways through which relocation willingness can have positive effects on preserving pre-existing neighbourhood relationships. Interview data further indicates that employing relocation strategies not only aids in preserving pre-existing neighbourhood relationships but also in establishing new ones in destination neighbourhoods. The results suggest that extended eviction notice periods and phased redevelopment approaches could provide migrants with the opportunity to implement relocation strategies mitigating adverse impacts of relocation and better adapt to new neighbourhoods.

Co-management enhances social capital and recognition of protected area: Perspectives from indigenous rangers on the Qinghai-Tibet Plateau

Indigenous rangers play a critical role in achieving global biodiversity goals by facilitating effective conservation and community development. However, there is limited knowledge about rangers' livelihoods and responses to conservation policies. This study examined indigenous rangers' perceptions of livelihood impacts and attitudes toward national park management in Three-River-Source National Park (TRSNP). Utilizing a household questionnaire survey, data from 228 valid responses were analyzed. Results indicate that rangers had positive attitudes toward the impacts of TRSNP on their livelihood capitals, despite increased patrol costs. Since the establishment of TRSNP, average household income and the evenness of income sources have increased significantly, along with improved community relations, participation in public affairs and children's education. Rangers with higher incomes, better neighborhood relations, and a belief in increased conservation capacity held more positive views of national park management. In contrast, rangers who are older, those patrolling closer to home and with larger families expressed more negative feedback. This study adds new evidence on indigenous rangers' livelihood and their perceptions, and suggests the need to understand the impacts of biodiversity policies on indigenous and local rangers to achieve equitable and right-based conservation. Our findings emphasize the importance of inclusive and participatory approaches to enhance conservation and development goals, particularly for vulnerable demographic groups.

The Impact of Built Environment in Shanghai Neighborhoods on the Physical and Mental Health of Elderly Residents: Validation of a Chain Mediation Model Using Deep Learning and Big Data Methods

As urban aging issues intensify, the impact of the built environment in urban neighborhoods on the physical and mental health of elderly residents has garnered increasing attention. Previous studies have demonstrated that the built environment is related to various health outcomes; however, most empirical research typically focuses on the objective physical environment, lacking measurements of subjective environmental perceptions. This study, using 24 neighborhoods in Shanghai as case studies, employed deep learning, big data methods, and surveys to collect 462 valid questionnaires from elderly residents. Structural equation modeling was applied to explore the relationship between the built environment and the physical and mental health of elderly residents, incorporating respondents’ subjective perceptions, physical activity, and neighborhood relationships as chain mediation effects. The results indicate that although there is no direct relationship between the built environment and the mental health of elderly residents, the built environment positively impacts mental health through enhancing subjective.

The Different Aspects of the Housing Quality of Older Adults: Which Criteria Should Be Prioritized?

ABSTRACT Various studies have identified that older adults’ assessment of their housing quality differs from that deemed as good-quality housing by housing professionals. This has prompted the need to advance academic discourse beyond simply reporting high levels of satisfaction in older adult’s housing surveys. This study attempts to achieve this by using empirical data gathered through a mixed quantitative and qualitative research approach conducted with older adults in Slovenia. While the quantitative survey revealed generally high levels of satisfaction, the qualitative face-to-face interviews revealed numerous deficiencies, irrespective of whether older adults tended to express satisfaction with their dwellings. Therefore, our findings suggest that attributes such as ownership, period of residence, and neighborhood relations are far more important in determining housing satisfaction. Thus, we conclude that policies and programs for modifying housing for older adults must be based on a deeper understanding of their specific needs. During the policy formulation process and the implementation of specific housing improvement programs, emphasis should be placed on the social-historical aspects related to the lifestyle of each specific older adult.

A Sustainable urban renovation assessment model (SURAM) for enhancing elderly-friendly environments

The rapid increase in the aging population has raised significant concerns about the living conditions and well-being of elderly residents in old communities. This study addresses these concerns by proposing a Sustainable Urban Renovation Assessment Model (SURAM) specifically designed to enhance elderly-friendly environments in Chongqing City. The model encompasses multiple dimensions, including the comfort of public facilities, service safety and convenience, medical travel services, infrastructure security, life service convenience, neighbor relations, ambulance aid accessibility, commercial service facilities, privacy protection, elderly care facilities and service supply, and medical and health facilities. By employing factor analysis, the study reduces the dimensionality of the 49 indicator factors, allowing for a more focused and comprehensive evaluation of the effectiveness of aging-friendly renovation efforts. The main factors identified in the proposed model include community infrastructure security, elderly comfort of community public facilities, completeness and convenience of surrounding living services, and security and convenience of elderly care services. The results reveal that the age-appropriate comfort of public facilities plays a significant role in achieving successful aging-appropriate renovation outcomes. The findings demonstrate that by addressing specific needs such as safety, accessibility, and convenience, communities can significantly improve the quality of life for elderly residents. Moreover, the application of SURAM provides actionable insights for policymakers, urban planners, and community stakeholders, guiding them in implementing targeted initiatives for sustainable and inclusive urban development.

Prediction of Potential miRNA-Disease Associations Based on a Masked Graph Autoencoder.

Biomedical evidence has demonstrated the relevance of microRNA (miRNA) dysregulation in complex human diseases, and determining the relationship between miRNAs and diseases can aid in the early detection and prevention of diseases. Traditional biological experimental methods have the disadvantages of high cost and low efficiency, which are well compensated by computational methods. However, many computational methods have the challenge of excessively focusing on the neighbor relationship, ignoring the structural information of the graph, and belittling the redundant information of the graph structure. This study proposed a computational model based on a graph-masking autoencoder named MGAEMDA. MGAEMDA is an asymmetric framework in which the encoder maps partially observed graphs into latent representations. The decoder reconstructs the masked structural information based on the edge and node levels and combines it with linear matrices to obtain the result. The empirical results on the two datasets reveal that the MGAEMDA model performs better than its counterparts. We also demonstrated the predictive performance of MGAEMDA using a case study of four diseases, and all the top 30 predicted miRNAs were validated in the database, providing further evidence of the excellent performance of the model.

Integrated Knowledge Graph and Drug Molecular Graph Fusion via Adversarial Networks for Drug-Drug Interaction Prediction.

The Co-administration of multiple drugs can enhance the efficacy of disease treatment by reducing drug resistance and side effects. However, it also raises the risk of adverse drug interactions, presenting a challenging problem in healthcare. Various approaches have been developed to predict drug-drug interactions (DDIs) by leveraging both knowledge graphs and drug attribute information. While these methods have shown promise, they often fail to effectively capture correlations between biomedical information in the knowledge graph and drug properties. This work introduces a novel end-to-end DDI predictor framework based on generative adversarial networks. This framework utilizes a message-passing neural network to capture molecular structure information while employing the knowledge-aware graph attention network to capture the representation of drugs in the knowledge graph through considering the importance of different multihop neighbor nodes and relationships. The dual generative adversarial networks employ two generators and two discriminators in knowledge graph embedding and molecular topology embedding for adversarial training to capture the interrelations and complementary knowledge between molecular structure information and semantic information from the knowledge graph. comprehensive experiments have demonstrated that the proposed method outperforms state-of-the-art algorithms in binary classification, with improvements of 1.0% in accuracy, 0.45% in area under the receiver operating characteristic curve (AUC), 0.24% in area under the precision-recall curve (AUPR), and 0.98% in F1 score. Furthermore, for multiclass classification tasks, improvements were observed across various evaluation metrics, including 0.9% in accuracy, 0.25% in macro precision, 0.2% in macro recall, and 0.28% in macro F1. Additionally, ablation studies were conducted to showcase the effectiveness and robustness of our method in DDI prediction tasks.

Research on Gating Fusion Algorithm for Power Grid Survey Data Based on Enhanced Mamba Spatial Neighborhood Relationship.

In power grid surveying, it is often necessary to fuse panchromatic and multispectral imagery for the design of power lines. Despite the abundance of deep learning networks for fusing these images, the results often suffer from spectral information loss or structural blurring. This study introduces a fusion model specifically tailored for power grid surveying that significantly enhances the representation of spatial-spectral features in remote sensing images. The model comprises three main modules: a TransforRS-Mamba module that integrates the sequence processing capabilities of the Mamba model with the attention mechanism of the Transformer to effectively merge spatial and spectral features; an improved spatial proximity-aware attention mechanism (SPPAM) that utilizes a spatial constraint matrix to greatly enhance the recognition of complex object relationships; and an optimized spatial proximity-constrained gated fusion module (SPCGF) that integrates spatial proximity constraints with residual connections to boost the recognition accuracy of key object features. To validate the effectiveness of the proposed method, extensive comparative and ablation experiments were conducted on GF-2 satellite images and the QuickBird (QB) dataset. Both qualitative and quantitative analyses indicate that our method outperforms 11 existing methods in terms of fusion effectiveness, particularly in reducing spectral distortion and spatial detail loss. However, the model's generalization performance across different data sources and environmental conditions has yet to be evaluated. Future research will explore the integration of various satellite datasets and assess the model's performance in diverse environmental contexts.