Neighborhood System Research Articles

"Who You Are and Where You Live Matters": Hospice Care in New York City During COVID-19 Perspectives on Hospice and Social Determinants: A Rapid Qualitative Analysis.

Context: Social determinants of health (SDOH) impacted the quality of home hospice care provided during the COVID-19 pandemic. Perspectives from professionals who provided care identify challenges and lessons learned from their experience. Objective: To examine hospice professionals' perspectives of how SDOH affected the delivery of high-quality home hospice care in New York City (NYC) during the COVID-19 pandemic. Methods: We conducted semistructured interviews with 30 hospice professionals who delivered care to home hospice patients during the COVID-19 pandemic in NYC using a qualitative descriptive design. Purposive sampling was used to recruit professionals from a range of disciplines including physicians, advanced practice providers, nurses, social workers, chaplains, and hospice administration and management. Participants worked for one of two large NYC metro hospices and one outpatient palliative care practice serving the five boroughs of NYC and the surrounding suburbs. Rapid qualitative analysis was used to identify themes. Results: Thirty hospice professionals were interviewed, spanning a variety of clinical and administrative roles. Most (21 out of 30) reported that social determinants affected access and/or delivery of equitable hospice care. Two key themes emerged from interviews: (1) SDOH exist and affect the delivery of high-quality care and (2) disparities were exacerbated during the COVID-19 pandemic resulting in barriers to care. Subthemes outline barriers described by hospice professionals: decreased hospice enrollment, telehealth challenges, resulting in deficient patient/family education, shortages of nursing assistants in some neighborhoods, and diminished overall quality of hospice care for some patients. SDOH created barriers to hospice care through neighborhood factors, resource barriers, and system challenges. Conclusion: SDOH provide a context to understand disparity in the provision of hospice care. COVID-19 exacerbated these conditions. Addressing multidimensional barriers created by SDOH is key in creating high-quality and equitable hospice care, particularly during a crisis.

X-Ray Emission of Nearby Low-mass and Sunlike Stars with Directly Imageable Habitable Zones

Stellar X-ray and UV radiation can significantly affect the survival, composition, and long-term evolution of the atmospheres of planets in or near their host star’s habitable zone (HZ). Especially interesting are planetary systems in the solar neighborhood that may host temperate and potentially habitable surface conditions, which may be analyzed by future ground- and space-based direct-imaging surveys for signatures of habitability and life. To advance our understanding of the radiation environment in these systems, we leverage ∼3 Ms of XMM-Newton and Chandra observations in order to measure three fundamental stellar properties at X-ray energies for 57 nearby FGKM stellar systems: the shape of the stellar X-ray spectrum, the luminosity, and the timescales over which the stars vary (e.g., due to flares). These systems possess HZs that will be directly imageable to next-generation telescopes such as the Habitable Worlds Observatory and ground-based Extremely Large Telescopes. We identify 29 stellar systems with L X/L bol ratios similar to (or less than) that of the Sun; any potential planets in the HZs of these stars therefore reside in present-day X-ray radiation environments similar to (or less hostile than) modern Earth, though a broader set of these targets could host habitable planets. An additional 19 stellar systems have been observed with the Swift X-ray Telescope; in total, only ∼30% of potential direct imaging target stars has been observed with XMM-Newton, Chandra, or Swift. The data products from this work (X-ray light curves and spectra) are available via a public Zenodo repository (doi:10.5281/zenodo.11490574).

Complex network-based multistep forecasting model for hyperchaotic time series.

We present a method for predicting hyperchaotic time series using a complex network-based forecasting model. We first construct a network from a given time series, which serves as a coarse-grained representation of the underlying attractor. This network facilitates multistep forecasting by capturing the local nonlinearity of the dynamics and offers superior accuracy over more extended periods than traditional methods. The network is formed by converting the patterns of local oscillations into sequences of numerical symbols, which are then used to create nodes and edges in a network, capturing the system's dynamical behavior at a reduced resolution. The network allows predictions up to several steps ahead without the exponential error increase usually associated with linear first-order methods. The improved predictions result from the unique ability of the network to collect identical pattern transitions in the orbit dynamics into a system of neighborhoods in the network. The effectiveness of this approach is demonstrated through its application to several high-dimensional hyperchaotic systems, where it outperforms both the linear first-order and other network-based methods in terms of prediction accuracy and horizon. Besides enhancing the predictability of chaotic systems, this methodology also outlines a procedure to develop a discrete model flow within an attractor.

Weighted-Neighborhood-Information-Network-Enabled Anomaly Detection Method for Electronic Sensors and Sensor Networks

As electronic sensors and sensor networks advance, perception data are increasingly characterized by mixed attributes. Traditional anomaly detection methods predominantly focus on numerical attributes. In this paper, we introduce a weighted neighborhood information network (WNIN)-enabled anomaly detection method tailored for mixed-attribute data from electronic sensors and sensor networks. Firstly, we employ the analytic hierarchy process (AHP) to analyze the security of sensor networks, leveraging a hierarchical electronic sensor network model to construct a hierarchical perception security architecture for anomaly detection. Subsequently, a neighborhood information system is established to ascertain the relationships between data objects with mixed attributes. We then develop the WNIN to encapsulate the relationships, and a state-transferring probability matrix based on data object similarity is derived. Ultimately, a random wandering process within the WNIN is executed, and the importance of data objects is evaluated using the steady-state distribution vector, thereby determining the anomaly data. Simulation outcomes reveal that our proposed method attains superior anomaly detection rates compared with existing methods.

On the Characterizations of Approach Groups

In this paper, we present several characterizations on approach groups, and ultra-approach groups. In doing so, we first give necessary and sufficient conditions for an approach structure to be compatible with group structure. We show that every ultra-approach group is ultra uniformizable. Secondly, starting with an approach space, and its natural neighborhood system on a group, we characterize the resulting neighborhood approach group. Finally, we show that the category of ultra-approach-Cauchy group is a topological category, and more importantly, we show that the category of ultra-approach -Cauchy groups and the category of strongly normal limit groups are isomorphic.

Use of poppers (nitrite inhalants) among young men who have sex with men with HIV: A clinic-based qualitative study

Nitrite inhalants (poppers) are associated with HIV transmission and commonly used among young men who have sex with men (YMSM), a group at increased risk for HIV. Significant research gaps exist in understanding the context in which YMSM use poppers. Qualitative interviews were conducted with 15 YMSM (22–31 years) with HIV to better understand the context in which poppers are used and their impacts on HIV care outcomes, such as care retention and antiretroviral adherence. The Social Ecological Model was applied to understand intrapersonal, interpersonal, community, and system level influences on popper use. Factors influencing popper use included: ubiquity of popper use in sexual settings, introduction to poppers by casual sexual partners, patient-HIV provider communication surrounding poppers, neighborhood, substance use and HIV care systems, and the legal status of poppers. Implications for clinical care, public health, policy, and future research are discussed.

The normality of L-coarse spaces

For L being a completely distributive lattice, we introduce a notion of L-coarse spaces, which can be regarded as a large-scale analogue of probabilistic uniform spaces. The main goal of this paper is to investigate the properties of L-coarse spaces that can induce L-valued coarse proximity spaces. First of all, we introduce a notion of L-valued coarse neighborhood systems, and show that the resulting category is isomorphic to that of L-valued coarse proximity spaces. Then we study the normality of coarsely connected L-coarse spaces and of asymptotically connected L-valued asymptotic resemblance spaces, and show that these two normality properties are coincident. More importantly, we show that a coarsely connected L-coarse space induces an L-valued coarse proximity if and only if it is coarsely normal. We conclude with noting that the previous result are also valid for asymptotically connected L-valued asymptotic resemblance spaces.

Service area delineation to support on-demand mobility for the transport disadvantaged

Given the significance of technology to enhance public transit services and its potential for improving access to and the delivery of transportation services, this paper addresses strategic neighborhood mobility improvements to aid the transportation-disadvantaged. A spatial optimization model is presented to identify underserved areas for which transit can be better tailored to meet their mobility needs. The introduction of mobility-on-demand in the Cincinnati metropolitan area to augment and complement existing fixed-route transit is reported. The use of spatial analytics to support targeted neighborhood system design is detailed. The study illustrates how new mobility-on-demand services will substantially enhance travel options for the transport disadvantaged. Further, the use of spatial analytics offers insights for transit service plan development and implementation by more precisely targeting mobility needs in service area delineation and operationalization.

Strong [formula omitted]-continuity and weakly [formula omitted]-closed functions on [formula omitted] spaces

In this research article, we present the procedure of generating GPT spaces in two different ways: using the generalized neighbourhood system and the monotonic operator. Then, we introduce several types of generalized primal continuous functions. Some characteristics have been dissected, and the relationships among them have been studied. We use the technique of Császár, which changes the “generalized topology” to other “generalized topologies” weaker than it, to show some important results. Furthermore, we show that the notion of “strong GP-continuity” coincides with the notion “GP-continuity” under some conditions. We present these results on a simple graph to make it easier for the reader. Finally, study the preservation of the notions of “GP-connected” and “GP-hyperconnected” by different types of generalized primal continuous functions.

Sociodemographic factors and Child Opportunity Index disparities associated with missed care opportunities in pediatric patients with lymphoma and leukemia referred for FDG-PET/CT.

Little data exists on the association of missed care opportunities (MCOs) in children referred for nuclear medicine/nuclearoncology imaging examinations and socioeconomic disparities. To determine the prevalence of MCOs in children with lymphoma/leukemia scheduled for fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) and the impact of sociodemographic factors and Child Opportunity Index (COI). Retrospective analysis of MCOs in children with lymphoma/leukemia scheduled for FDG-PET/CT (2012 to 2022)was performed. In univariate analysis, patient, neighborhood, and appointment data were assessed across MCOs and completed appointments. Logistic regression evaluated independent effects of patient-, neighborhood-, and appointment-level factors with MCOs. Two-sided P-value < .05 was considered statistically significant. In 643 FDG-PET/CT appointments (n = 293 patients; median age 15years (IQR 11.0-17.0years); 37.9% female), there were 20 MCOs (3.1%) involving 16 patients. Only 8.2% appointments involved Black/African American non-Hispanic/Latino patients, yet they made up a quarter of total MCOs. Patients living in neighborhoods with very low or low COI experienced significantly higher MCOs versus zip codes with very high COI (6.9% vs. 0.8%; P = 0.02). Logistic regression revealed significantly increased likelihood of MCOs for patients aged 18 to 21 [odds ratio (OR) 4.50; 95% CI 1.53-13.27; P = 0.007], Black/African American non-Hispanic/Latino (OR 3.20; 95% CI 1.08-9.49; P = 0.04), zip codes with very low or low COI (OR 9.60; 95% CI 1.24-74.30; P = 0.03), and unknown insurance status. Children with lymphoma/leukemia, living in zip codes with very low or low COI, and who identified as Black/African American non-Hispanic/Latino experienced more MCOs. Our study supports the need to address intersecting sociodemographic, neighborhood, and health system factors that will improve equitable access to necessary healthcare imaging for children.

Chains with Connections of Diffusion and Advective Types

The local dynamics of a system of oscillators with a large number of elements and with diffusive- and advective-type couplings containing a large delay are studied. Critical cases in the problem of the stability of the zero equilibrium state are singled out, and it is shown that all of them have infinite dimensions. Applying special methods of infinite normalization, we construct quasinormal forms, namely, nonlinear boundary value problems of the parabolic type, whose nonlocal dynamics determine the behavior of the solutions of the initial system in a small neighborhood of the equilibrium state. These quasinormal forms contain either two or three spatial variables, which emphasizes the complexity of the dynamical properties of the original problem.

A fuzzy rough granular ensemble learning based on the feature selection with chi-square1

Neighborhood granulation is a classical granulation method. Although it is adequate for clustering and classification tasks, its granules are more complex, and the data representation is binary. This paper proposes a new granulation method based on the neighborhood granulation. Firstly, a detailed definition of the granular form is given with fuzzy rough set theory. Then, a modified fuzzy rough discriminant function is proposed based on neighborhood systems. The samples are globally granulated on single features to construct granules and on multiple features to construct granular vectors. Also, a feature selection technique based on the Chi-square, which strikingly reduces the complexity of the fuzzy rough granular vectors, is introduced to address the disadvantage of the fuzzy rough granular vectors. An ensemble model structure is also proposed in the paper for the mixed nature of fuzzy rough granular vectors. The paper makes a detailed comparison between the fuzzy rough granulation and the neighborhood granulation. The results show that fuzzy rough granulation has higher computational efficiency and classification performance. Finally, a detailed comparison is made between the fuzzy rough granular ensemble model and various classical ensemble algorithms. The final results show that the fuzzy rough granular ensemble model has better robustness and generalization.

Riemannian Manifold-Based Feature Space and Corresponding Image Clustering Algorithms.

Image feature representation is a key factor influencing the accuracy of clustering. Traditional point-based feature spaces represent spectral features of an image independently and introduce spatial relationships of pixels in the image domain to enhance the contextual information expression ability. Mapping-based feature spaces aim to preserve the structure information, but the complex computation and the unexplainability of image features have a great impact on their applications. To this end, we propose an explicit feature space called Riemannian manifold feature space (RMFS) to present the contextual information in a unified way. First, the Gaussian probability distribution function (pdf) is introduced to characterize the features of a pixel in its neighborhood system in the image domain. Then, the feature-related pdfs are mapped to a Riemannian manifold, which constructs the proposed RMFS. In RMFS, a point can express the complex contextual information of corresponding pixel in the image domain, and pixels representing the same object are linearly distributed. This gives us a chance to convert nonlinear image segmentation problems to linear computation. To verify the superiority of the expression ability of the proposed RMFS, a linear clustering algorithm and a fuzzy linear clustering algorithm are proposed. Experimental results show that the proposed RMFS-based algorithms outperform their counterparts in the spectral feature space and the RMFS-based ones without the linear distribution characteristics. This indicates that the RMFS can better express features of an image than spectral feature space, and the expressed features can be easily used to construct linear segmentation models.

Decomposition of Equations of Nonlinear Affine Control Systems and its Application to the Synthesis of Regulators

The article deals with the decomposition of nonlinear differential equations based on the group-theoretic approach. At the beginning, the decomposition of differential equations of linear systems using a transition matrix of state is presented, and then, based on the theory of continuous groups (Lie groups), the process of decomposition of differential equations of nonlinear systems is shown. The decomposition approach is based on the isomorphism theorem of the space of vector fields and Lie derivatives, which allows us to consider vector fields as differential operators of smooth functions. A formula is derived about the adjoin representation of a Lie group in its Lie algebra, which actually determines the finding of a vector field that characterizes the interaction of two or more vector fields. The Lie algebra of derivatives makes it possible to determine the infinitesimal action of the Lie group, i.e. the linearization of this action is carried out (transformation of the points of the trajectory space of the original system in a small neighborhood). Decomposition allows, as in the linear case, to separate the finding of an action (only locally) of a group of transformations from the transformed points themselves. For linear systems, this separation is global. It is also shown that the decomposition of linear equations is a particular case of the decomposition of nonlinear equations. An algorithm of the method of model predictive control with Gramian weighting using this decomposition is presented. A practical example of decomposition and application of the model predictive control for stabilization of a nonstationary nonlinear system is considered.

Cardinality rough neighborhoods with applications

&lt;p&gt;Rough set theory serves as an effective method for managing complicated real-world data. Through rough approximation operators, it discerns both confirmed and possible data attainable through subsets. Earlier studies have presented several rough approximation models, drawing inspiration from neighborhood systems aimed at enhancing accuracy degree and satisfying the axioms of traditional approximation spaces (TAS) that were initiated by Pawlak. This article proposes an easy method to deal with information systems in most cases, wherein it introduces a new forming of generalized approximation spaces, namely, cardinality rough neighborhoods. It is defined depending on the cardinal number of the $ \mathcal{N}_\sigma $-neighborhoods of elements that are established under an arbitrary relation. Their main features are investigated and the connections between them, as well as their relationships with the preceding kinds of neighborhood systems, are uncovered with the aid of some examples. Then, novel rough set paradigms induced by cardinality rough neighborhoods are displayed that satisfy most properties of Pawlak's paradigm. Next, a topological method to study these paradigms is provided, wherein this method produces approximation operators similar to the given paradigms in six cases that are proved. Additionally, both paradigms in a practical example concerning books and the authors who authored them or participated in their authorship are applied. To illuminate the need for the current concepts, we elaborate on their advantages from different views. Finally, a summary of the obtained results and relationships and suggestions for some forthcoming work are offered.&lt;/p&gt;

Evidence gaps among systematic reviews examining the relationship of race, ethnicity, and social determinants of health with adult inpatient quality measures

Abstract Background: The field of healthcare epidemiology is increasingly focused on identifying, characterizing, and addressing social determinants of health (SDOH) to address inequities in healthcare quality. To identify evidence gaps, we examined recent systematic reviews examining the association of race, ethnicity, and SDOH with inpatient quality measures. Methods: We searched Medline via OVID for English language systematic reviews from 2010 to 2022 addressing race, ethnicity, or SDOH domains and inpatient quality measures in adults using specific topic questions. We imported all citations to Covidence (www.covidence.org, Veritas Health Innovation) and removed duplicates. Two blinded reviewers assessed all articles for inclusion in 2 phases: title/abstract, then full-text review. Discrepancies were resolved by a third reviewer. Results: Of 472 systematic reviews identified, 39 were included. Of these, 23 examined all-cause mortality; 6 examined 30-day readmission rates; 4 examined length of stay, 4 examined falls, 2 examined surgical site infections (SSIs) and one review examined risk of venous thromboembolism. The most evaluated SDOH measures were sex (n = 9), income and/or employment status (n = 9), age (n = 6), race and ethnicity (n = 6), and education (n = 5). No systematic reviews assessed medication use errors or healthcare-associated infections. We found very limited assessment of other SDOH measures such as economic stability, neighborhood, and health system access. Conclusion: A limited number of systematic reviews have examined the association of race, ethnicity and SDOH measures with inpatient quality measures, and existing reviews highlight wide variability in reporting. Future systematic evaluations of SDOH measures are needed to better understand the relationships with inpatient quality measures.

Topologically indistinguishable relations and separation axioms

&lt;abstract&gt;&lt;p&gt;This study focuses on defining separation axioms for sets without an inherent topological structure. By utilizing a mapping to relate such sets to a topological space, we first define a distinguishable relation over the universal set with respect to the neighborhood systems inspired by a topology of the co-domain set and elucidate its basic properties. To facilitate the way of discovering this distinguishable relation, we initiate a color technique for the equivalence classes inspired by a given topology. Also, we provide an algorithm to determine distinguishable members (or objects) under study. Then, we establish a framework for introducing separation properties within these structureless sets and examine their master characterizations. To better understand the obtained results and relationships, we display some illustrative instances.&lt;/p&gt;&lt;/abstract&gt;

IPCONV: Convolution with Multiple Different Kernels for Point Cloud Semantic Segmentation

The segmentation of airborne laser scanning (ALS) point clouds remains a challenge in remote sensing and photogrammetry. Deep learning methods, such as KPCONV, have proven effective on various datasets. However, the rigid convolutional kernel strategy of KPCONV limits its potential use for 3D object segmentation due to its uniform approach. To address this issue, we propose an Integrated Point Convolution (IPCONV) based on KPCONV, which utilizes two different convolution kernel point generation strategies, one cylindrical and one a spherical cone, for more efficient learning of point cloud data features. We propose a customizable Multi-Shape Neighborhood System (MSNS) to balance the relationship between these convolution kernel point generations. Experiments on the ISPRS benchmark dataset, LASDU dataset, and DFC2019 dataset demonstrate the validity of our method.

Adaptive fuzzy multi-neighborhood feature selection with hybrid sampling and its application for class-imbalanced data

For imbalanced data, classification efficiency degrades significantly due to the missing information for the positive class, and existing sampling schemes do not consider the distributions of samples. Additionally, the global parameters of fuzzy neighborhoods are set manually. These defects affect the effectiveness of classifier. To address these problems, we offer an adaptive fuzzy multi-neighborhood feature selection methodology with intercluster distance-based hybrid sampling for class-imbalanced data. First, the number of clusters can be defined in terms of the number of samples in the negative or positive class. The initial centers of the clusters are determined according to the number of clusters, and the dissimilarity and similarity measures are calculated by using the intercluster distances between samples. Then, the cluster center, fuzzy membership matrix, and intercluster distance are studied, and then the optimization objective function is designed. The hybrid sampling scheme can be used to combine the generated positive class samples and negative class samples and obtain a class-balanced system. Second, according to the sample distribution, the standard deviation and a set of adaptive fuzzy multi-neighborhood radii are designed. A fuzzy multi-neighborhood similarity relation is defined by introducing a Gaussian kernel model to obtain a fuzzy multi-neighborhood granule, and an improved fuzzy multi-neighborhood rough set model is provided. Uncertain measures of fuzzy neighborhood systems are evaluated by the positive region and dependency. Third, by integrating fuzzy dependence with fuzzy complementary condition entropy, fuzzy multi-neighborhood complementary mutual information is provided on two viewpoints of algebra and information. Finally, a heuristic feature subset selection methodology for imbalanced classification with hybrid sampling using fuzzy c-means clustering is studied to obtain this excellent set of features. Experiments on 26 imbalanced datasets show the effectiveness of our designed algorithm.

A conceptual framework for early life determinants of future multimorbidity

Abstract Background Social, biological and environmental factors in early life, defined as the period from preconception until age 18, play a role in shaping the risk of Multiple Long-Term Condition Multimorbidity (MLTC-M). There is a need for conceptual framing of these factors to inform and shape future research on aetiology and for modelling prevention scenarios of MLTC-M. We developed a conceptual framework to characterise early life determinants of future MLTC-M risk. Methods The conceptualisation of determinant domains was shaped by reviewing existing research evidence and policy, and co-produced with public contributors living in the UK via two workshops, one with 8 contributors age 18-30 regardless of MLTC-M status, and one with 12 contributors age 40-65 who all self-reported MLTC-M. Results Domains included: 1.Prenatal, antenatal, neonatal and birth; 2.Adverse childhood experiences; 3.Child health, check-ups and screening; 4.Developmental attributes; 5.Child education and health literacy; 6.Demographics; 7. Parent health and health behaviours; 8.Socioeconomics; 9.Parental family factors; 10.Neighbourhood, physical environment and health care systems; 11.Health behaviours and diet; 12.Religion, spirituality and culture. Some domains identified by the public contributors, such as religion, health screening, check-ups, and diet, were not well represented within the research evidence or policy documents. Conclusions This co-produced conceptualisation can inform research to investigate the early life characteristics of population groups at risk of future MLTC-M, as well as policy directions towards prevention of early-onset MLTC-M. Additionally, it can shape the direction of further data collection, particularly with respect to the understudied domains identified by our public contributors. Key messages • We developed a conceptual framework to characterise early life determinants of multimorbidity. • This conceptualisation can inform future research and policy directions towards prevention of multimorbidity.