Neighborhood Relations Research Articles

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.

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.

Domesticating Property: Moral Economies of Post‐Socialist Homeownership Through Rental and Neighbour Relations

This article analyses property relations in post‐socialism through the analytical lens of housing studies and moral economy, specifically in the context of rental and neighbour relations in urban apartment buildings. It draws on 50 in‐depth residential biographies of residents of St. Petersburg, Russia, collected between 2017 and 2021. The interviews represent a diversity of tenures, as well as direct and indirect voices of homeowners and non‐owners. The article begins by introducing the socio‐historical context of the privatisation and commodification of housing in post‐socialist Russia. Then, based on the stories of the origin of property and the intensity of attachment to it, I analyse owners’ homemaking through daily interactions with other owners and non‐owners who act as their tenants and neighbours. Focusing on privatised, mortgaged, and inherited residential property, I identify three trajectories of complex relationships between owners and non‐owners in urban buildings and modes of homemaking, at the intersection of monetary and non‐monetary relations and imaginaries of counterparts. I argue that despite everyday interactions in the housing market and in apartment blocks, both the owners of privatised Soviet property and new owner‐occupiers tend to avoid the total commercialisation of the home and to challenge the dominance of homeownership as the only socially sanctioned tenure.

Social determinants of health, blood pressure classification, and incident stroke in community-dwelling adults: a prospective cohort study

Abstract Objective To determine whether the association of blood pressure (BP) categories, defined by the 2017 American College of Cardiology (ACC)/American Heart Association (AHA) BP guideline, with stroke differ among Chinese adults with low or high burden of unfavorable social determinants of health (SDOH). Design and Methods: Analyses were conducted in the prospective sub-cohort of the China Patient-Centered Evaluative Assessment of Cardiac Events Million Persons Project, started in 1, July 2017 and followed stroke events until 31, June 2023. Participants were divided into groups with low and high burden of unfavorable SDOH, which consisted of educational attainment, economic stability, health care access, social support, and neighborhood relationship. Within each group, participants were categorized as having normal BP (systolic BP [SBP] <120 mm Hg and diastolic BP [DBP] <80 mm Hg), elevated BP (SBP 120-129 mm Hg and DBP <80 mm Hg), stage 1 hypertension (SBP 130-139 mm Hg or DBP 80-89 mm Hg), and stage 2 hypertension (SBP ≥140 mm Hg or DBP ≥90mm Hg). The association between BP categories and stroke risk were examined within each SDOH group, separately, using multivariable-adjusted Cox proportional hazards models. Results Of the 96,686 adults included in this study (mean [SD] age, 53.8 [10.1] years; 59,080 [61.1%] women), 64,823 (67.0%) were categorized as having high burden of unfavorable SDOH. Over a median follow-up of 5.0 years, 4,673 (4.8%) incident stroke events occurred. In the group of low burden of SDOH, hazard ratios (95% CI) for stroke events for elevated BP, stage 1 hypertension, and stage 2 hypertension vs normal BP were 1.09 (0.86-1.37), 1.14 (0.94-1.37), and 1.59 (1.33-1.90), respectively. In the group of high burden of SDOH, hazard ratios for stroke events for elevated BP, stage 1 hypertension, and stage 2 hypertension vs normal BP were 1.20 (1.04-1.38), 1.45 (1.29-1.62), and 1.71 (1.53-1.91), respectively. Results were similar after excluding those taking antihypertensive medications. Conclusion Lower BP categories were closely associated with increased risk of stroke in the context of high burden of SDOH. Early counseling and surveillance of high BP should be emphasized within socioeconomically disadvantaged populations.

Facial Expression Recognition-You Only Look Once-Neighborhood Coordinate Attention Mamba: Facial Expression Detection and Classification Based on Neighbor and Coordinates Attention Mechanism

In studying the joint object detection and classification problem for facial expression recognition (FER) deploying the YOLOX framework, we introduce a novel feature extractor, called neighborhood coordinate attention Mamba (NCAMamba) to substitute for the original feature extractor in the Feature Pyramid Network (FPN). NCAMamba combines the background information reduction capabilities of Mamba, the local neighborhood relationship understanding of neighborhood attention, and the directional relationship understanding of coordinate attention. The resulting FER-YOLO-NCAMamba model, when applied to two unaligned FER benchmark datasets, RAF-DB and SFEW, obtains significantly improved mean average precision (mAP) scores when compared with those obtained by other state-of-the-art methods. Moreover, in ablation studies, it is found that the NCA module is relatively more important than the Visual State Space (VSS), a version of using Mamba for image processing, and in visualization studies using the grad-CAM method, it reveals that regions around the nose tip are critical to recognizing the expression; if it is too large, it may lead to erroneous prediction, while a small focused region would lead to correct recognition; this may explain why FER of unaligned faces is such a challenging problem.

A FIELD STUDY ON SOCIAL CAPITAL, CULTURE OF LIVING TOGETHER AND NEIGHBORHOOD RELATIONS

Turkey, which is the intersection of Europe, Asia and Africa, has been the main route of migration throughout human history. Forced migrants coming to Turkey have low-skilled labor and mostly consist of farmers engaged in agriculture in rural areas. Turkey settles migrants migrating from nearby geography in districts where agricultural production is carried out in rural areas and can employ these migrants in agricultural production. When the host community trusts the migrants, they can treat them warmly, neighborly relations begin and a culture of living together that is beneficial for both parties is formed. This study aimed to investigate on-site the contribution of social capital to the neighborhood relations and the culture of living together process established by Meskhetian Turks settled in Üzümlü District of Erzincan Province since 2016 with the residents of Üzümlü District. In line with this objective, a survey was conducted with 269 Meskhetian Turks migrating from the Russian Federation. In the analyses conducted with Structural Equation Modeling, it was determined that Meskhetian Turks developed very good neighborhood relations with the people of Üzümlü District of Erzincan Province and created a culture of living together. While the Bonding Social Capital contributed positively to this process, the Bridging Social Capital had a negative effect. The study approaches the issue of migration within the scope of “microeconomic migration theories”. The economic needs and desires of individuals are taken as basis.

A simulation data-driven semi-supervised framework based on MK-KNN graph and ESSGAT for bearing fault diagnosis

Current supervised intelligent fault diagnosis relies on abundant labeled data. However, collecting and labeling data are typically both expensive and time-consuming. Fault diagnosis with unlabeled data remains a significant challenge. To address this issue, a simulation data-driven semi-supervised framework based on multi-kernel K-nearest neighbor (MK-KNN) and edge self-supervised graph attention network (ESSGAT) is proposed. The novel MK-KNN establishes the neighborhood relationships between simulation data and real data. The developed multi-kernel function mitigates the risks of overfitting and underfitting, thereby enhancing the robustness of the simulation-real graphs. The designed ESSGAT employs two forms of self-supervised attention to predict the presence of edges, increasing the weights of crucial neighboring nodes in the MK-KNN graph. The performance of the proposed method is evaluated using a public bearing dataset and a self-constructed dataset of high-speed train axle box bearings. The results show that the proposed method achieves better diagnostic performance compared with other state-of-the-art graph construction methods and graph convolutional networks.

Resilient cooperative platoon control for leader-following uncertain vehicular systems subject to cyber-physical attacks

This paper develops a resilient cooperative platoon control protocol for vehicle following systems under unreliable switching communication networks with cyber composite attacks, which includes actuator and sensors attacks, as well as denial of service (DoS) attacks. In this framework, to achieve leader-following platoon task, a novel resilient distributed adaptive control algorithm is designed by exploiting relative neighborhood information. First, a switching resilient turning mechanism with controller parameter updating laws is introduced into platoon controller architecture, which can compensate sensor and actuator attacks. Second, a topology-dependent analysis shows that cooperative leader-following platoon objective is achieved under some sufficient conditions, then the robust stability of the closed-loop coordination tracking error system is built. Finally, the simulation studies on a platoon of autonomous vehicles are provided to testify the design.

Collective Rural Resettlement: the Vulnerabilities of Families Affected by a Hydroelectric Dam in Southern Brazil

Objective: To evaluate the ways in which affected families cope with health vulnerabilities and environmental changes after compulsory displacement, considering the Rural Collective Resettlement (RCC) service modality. Theoretical Framework: The vulnerability in health and environment proposed by José Ricardo Ayres is observed in the affected population as the chance of people being exposed to illness, taking into account the environment, individual, social and health service aspects. Method: 11 questionnaires were administered to those affected by the hydroelectric plant, who were served by the RCC modality. Results and Discussion: Subjective aspects involving the compulsory displacement of the affected population, such as the networks of social, cultural and neighborhood relations, are supplanted by the prospect of economic gains for the municipalities, based on the supposed regional development caused by the movement brought about by the start of construction and, later, by the payment of royalties to the affected municipalities. However, the losses due to profound changes, especially for the population affected, the compulsory displacement and, with it, the reduction in the local population and, consequently, the economy, are not properly accounted for. Research Implications: The RCCs are the least incentivized and most time-consuming form of care Originality/Value: The sense of belonging and identification as a community, the promotion of collective work and the organization of the health and education care network have contributed to making the health and environmental vulnerability caused by the hydroelectric dam less significant.

The Tropical and Zonotopal Geometry of Periodic Timetables

AbstractThe Periodic Event Scheduling Problem (PESP) is the standard mathematical tool for optimizing periodic timetables in public transport. A solution to a PESP instance consists of three parts: a periodic timetable, a periodic tension, and integer offset values. While the space of periodic tensions has received much attention in the past, we explore geometric properties of the other two components. The general aim of this paper is to establish novel connections between periodic timetabling and discrete geometry. Firstly, we study the space of feasible periodic timetables as a disjoint union of polytropes. These are polytopes that are convex both classically and in the sense of tropical geometry. We then study this decomposition and use it to outline a new heuristic for PESP, based on neighbourhood relations of the polytropes. Secondly, we recognize that the space of fractional cycle offsets is in fact a zonotope, and then study its zonotopal tilings. These are related to the hyperrectangle of fractional periodic tensions, as well as the polytropes of the periodic timetable space, and we detail their interplay. To conclude, we also use this new understanding to give tight lower bounds on the minimum width of an integral cycle basis.

Mesoscopic structure graphs for interpreting uncertainty in non-linear embeddings

Probabilistic-based non-linear dimensionality reduction (PB-NL-DR) methods, such as t-SNE and UMAP, are effective in unfolding complex high-dimensional manifolds, allowing users to explore and understand the structural patterns of data. However, due to the trade-off between global and local structure preservation and the randomness during computation, these methods may introduce false neighborhood relationships, known as distortion errors and misleading visualizations. To address this issue, we first conduct a detailed survey to illustrate the design space of prior layout enrichment visualizations for interpreting DR results, and then propose a node-link visualization technique, ManiGraph. This technique rethinks the neighborhood fidelity between the high- and low-dimensional spaces by constructing dynamic mesoscopic structure graphs and measuring region-adapted trustworthiness. ManiGraph also addresses the overplotting issue in scatterplot visualization for large-scale datasets and supports examining in unsupervised scenarios. We demonstrate the effectiveness of ManiGraph in different analytical cases, including generic machine learning using 3D toy data illustrations and fashion-MNIST, a computational biology study using a single-cell RNA sequencing dataset, and a deep learning-enabled colorectal cancer study with histopathology-MNIST.

A Hybrid Segmentation Algorithm for Rheumatoid Arthritis Diagnosis Using X-ray Images

Rheumatoid Arthritis (RA) is a chronic autoimmune illness that occurs in the joints, resulting in inflammation, pain, and stiffness. X-ray examination is one of the most common diagnostic procedures for RA, but manual X-ray image analysis has limitations because it is a time-consuming procedure and is prone to errors. A specific algorithm aims to a lay stable and accurate segmenting of carpal bones from hand bone images, which is vitally important for identifying rheumatoid arthritis. The algorithm demonstrates several stages, starting with Carpal bone Region of Interest (CROI) specification, dynamic thresholding, and Gray Level Co-occurrence Matrix (GLCM) application for texture analysis. To get the clear edges of the image, the component is first converted to the greyscale function and thresholding is carried out to separate the hand from the background. The pad region is identified to obtain the contours of it, and the CROI is defined by the bounding box of the largest contour. The threshold value used in the CROI method is given a dynamic feature that can separate the carpal bones from the surrounding tissue. Then the GLCM texture analysis is carried out, calculating the number of pixel neighbors, with the specific intensity and neighbor relations of the pixels. The resulting feature matrix is then employed to extract features such as contrast and energy, which are later used to categorize the images of the affected carpal bone into inflamed and normal. The proposed technique is tested on a rheumatoid arthritis image dataset, and the results show its contribution to diagnosis of the disease. The algorithm efficiently divides carpal bones and extracts the signature parameters that are critical for correct classification of the inflammation in the cartilage images.

Prescribed-Time Network-Based Deployment of Nonlinear Multiagent Systems: A Discrete-Space PDE Method.

This article attempts to design the prescribed-time time-varying deployment schemes for first-order and second-order nonlinear multiagent systems (MASs). We assume that all agents can obtain the information of their current and final relative positions with their neighbors, and the final absolute velocities (as well as their current and final relative velocities, the final absolute accelerations for the second-order MASs) through a communication network, whereas two boundary agents are able to obtain their current and final absolute positions (as well as their current and final absolute velocities for the second-order MASs). The neighbor relationship of all agents is described by a spatial variable and two static-feedback controllers are introduced, which can be expressed as a second-order space difference of the spatial variable. Then, the deployment of MASs can be transformed into the stabilization of discrete-space partial differential equation (PDE) systems. Three virtual agents are introduced to constitute the Dirchlet and Neumann boundary conditions. Several algebraic inequality criteria are derived to guarantee that the prescribed-time time-varying deployment can be achieved within a prescribed time under the Dirchlet and mixed boundary conditions. Unlike the published results, our results are derived based on the discrete-space PDE systems instead of continuous-space PDE systems, which is consistent with the discrete spatial distribution of agents. Finally, two numerical examples are given to illustrate the effectiveness of our results.

제주지역 노인의 지역사회 계속거주에 미치는 영향요인 연구

This study examined the factors influencing Aging-in-Place(AIP) in older adults in the Jeju region. Also, the study tried to find supporting ways of AIP for older adults. The study used data from the 2020 Ministry of Health and Welfare Survey on Korea Older Adults and analyzed 375 cases of older adults in Jeju. The influencing factors of AIP consisted of functional, structural, phenomenological, and ecological factors. The main results are as follows: First, 98.2% of older adults in a healthy situation chose AIP, but this number drops to 59.5% when their health becomes weak and unhealthy. Second, the factors influencing the AIP of older adults in the Jeju area were age as a functional factor, household type and economic satisfaction as a structural factor, housing and residential environment satisfaction variables with a phenomenological factor, and satisfaction with neighborhood relationships as an ecological factor. Based on the above results, this study suggested the policy to support older adults in the Jeju area as they lead their daily lives and face old age. In particular, the willingness to AIP decreases significantly when the health of older people in the Jeju region deteriorates; the study emphasized and discussed the importance of creating a residential environment.

Superclass-aware visual feature disentangling for generalized zero-shot learning

Zero-shot learning (ZSL) aims to learn a model trained on seen samples with the ability to recognize samples from unseen classes, while generalized ZSL (GZSL) takes a step closer to realistic scenarios by recognizing both of seen and unseen samples. The existing methods rely on the semantic descriptions as the side-information and conduct tight alignment between the visual and semantic spaces. However, the tight modality alignment may result in incomplete representations, leading to the loss of originally detailed and discriminative information. In this paper, we propose a simple yet effective superclass-aware visual feature disentangling method termed as SupVFD for GZSL. We use the neighbor relations of the semantic descriptions to define superclass and with the guide of superclass, our method disentangles visual features into discriminative and transferable factors. To this end, the semantic descriptions are used as implicit supervision, which preserves the valuable detailed and discriminative information in the visual features. The extensive experiments in both ZSL and GZSL settings prove our method outperforms the state-of-the-art methods for image object classification as well as video action recognition. Code is available at our github: https://github.com/changniu54/SupVFD-Master.

Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models

Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. The statistical properties of parameter and forecast estimates strongly depend on the structure of such matrices. The least squares (LS) method is the most flexible and can estimate systems of large dimensions; however, it is biased in the presence of multilateral (sparse) matrices. Instead, the unilateral specification of SAR models provides triangular weight matrices that allow consistent LS estimates and sequential prediction functions. These two properties are strictly related and depend on the linear and recursive nature of the system. In this paper, we show the better performance in out-of-sample forecasting of unilateral SAR (estimated with LS), compared to multilateral SAR (estimated with maximum likelihood, ML). This conclusion is supported by numerical simulations and applications to real geological data, both on regular lattices and irregularly distributed points.

Neighborhood margin rough set: Self-tuning neighborhood threshold

The neighborhood threshold in the neighborhood rough set has a significant impact on the neighborhood relation. When the neighborhood threshold of an object exceeds the critical value, the labels of objects in the neighborhood are not completely consistent, and the critical value of each object often differs. Most existing neighborhood rough set models cannot adaptively regulate the neighborhood threshold. In this paper, we introduce a novel neighborhood rough set model that incorporates a self-tuning mechanism for the neighborhood threshold, taking into account the distribution of objects across different areas. The neighborhood margin is a measure proposed to assess the condition of neighborhoods, and it is calculated by subtracting the neighborhood threshold from the closest distance between heterogeneous elements. The neighborhood margin accurately represents the local state of the neighborhood, taking into account decision information. The margin neighborhood is proposed with a self-tuning the neighborhood threshold. Finally, we introduce the margin neighborhood rough set model and margin neighborhood-based attribute reduction algorithm, and explore the relationship between the proposed model and the neighborhood rough set model. The experiment examines the performance of reducts under various measures, and demonstrates that the neighborhood margin rough set reduces the uncertainty of neighborhood granules effectively, leading to excellent classification performance compared to other neighborhood-based SOTA models.

The KIND Challenge community intervention to reduce loneliness and social isolation, improve mental health, and neighbourhood relationships: an international randomized controlled trial.

Loneliness and social isolation are risk factors for poor health, but few effective interventions are deployable at scale. This study was conducted to determine whether acts of kindness can reduce loneliness and social isolation, improve mental health, and neighbourhood social cohesion. Three randomized controlled trials (RCTs) were conducted in the USA, UK, and Australia, involving a total of 4284 individuals aged 18-90years old, randomized to the KIND challenge intervention or a waitlist control group. Participants allocated to the intervention were asked to do at least one act of kindness per week within a four-week period. The primary outcome was loneliness and secondary outcomes included measures of social isolation, mental health, and neighbourhood social cohesion. There was a significant, albeit small, intervention effect after four weeks for reduced loneliness in the USA and the UK, but not for Australia. Relative to controls, KIND challenge participants also showed significantly reduced social isolation and social anxiety in the USA, and reduced stress in Australia. There was also reduced neighbourhood conflict in the USA, increased number of neighbourhood contacts in the USA and Australia, greater neighbourhood stability and feelings of neighbourhood importance in the UK, and better neighbourhood social relationships in Australia. Promoting the provision of social support through small acts of kindness to neighbours has the potential to reduce loneliness, social isolation and social anxiety, and promote neighbourhood relationships, suggesting a potential strategy for public health campaigns. Clinical Trials Registry. NCT04398472. Registered 21st May 2020.

Mondrian Abstraction and Language Model Embeddings for Differential Pathway Analysis.

In this study, we introduce the Mondrian Map, an innovative visualization tool inspired by Piet Mondrian's abstract art, to address the complexities inherent in visualizing biological networks. By converting intricate biological data into a structured and intuitive format, the Mondrian Map enables clear and meaningful representations of biological pathways, facilitating a deeper understanding of molecular dynamics. Each pathway is represented by a square whose size corresponds to fold change, with color indicating the direction of regulation (up or down) and statistical significance. The spatial arrangement of pathways is derived from language model embeddings, preserving neighborhood relationships and enabling the identification of clusters of related pathways. Additionally, colored lines highlight potential crosstalk between pathways, with distinctions between short- and long-range functional interactions. In a case study of glioblastoma multiforme (GBM), the Mondrian Map effectively revealed distinct pathway patterns across patient profiles at different stages of disease progression. These insights demonstrate the tool's potential to enhance downstream bioinformatics analysis by providing a more comprehensive and visually accessible overview of pathway interactions, offering new avenues for therapeutic exploration and personalized medicine.