Set Of Neighbors Research Articles

Structure correspondence searching of CAD model using local feature-based description and indexing

The CAD model retrieval has played a significant role in various applications, including product development and knowledge mining. However, most existing retrieval methods compare 3D shape similarity from a global perspective, while detecting similar structures automatically for CAD models remains a challenging problem. Consequently, this study proposes a structure correspondence searching framework for CAD models to address the issues. According to the boundary representation (B-rep) information, the proposed method first segments a CAD model into a set of local features denoted as structural cells. Then, the descriptor of each structural cell is extracted using a weighted shape distribution vector and neighbor set. In order to speed up the matching of structural cells, an indexing and filtering mechanism is constructed based on the shape clustering and topological analysis. The matched structural cells determine the boundary of similar structures. Finally, similarity measurement is conducted to generate a ranking list by analyzing the quality of the matched structural cells. The rationality and efficiency of the proposed approach are demonstrated via an analysis of experimental results.

SARW: Similarity-Aware Random Walk for GCN

Graph Convolutional Network (GCN) is an important method for learning graph representations of nodes. For large-scale graphs, the GCN could meet with the neighborhood expansion phenomenon, which makes the model complexity high and the training time long. An efficient solution is to adopt graph sampling techniques, such as node sampling and random walk sampling. However, the existing sampling methods still suffer from aggregating too many neighbor nodes and ignoring node feature information. Therefore, in this paper, we propose a new subgraph sampling method, namely, Similarity-Aware Random Walk (SARW), for GCN with large-scale graphs. A novel similarity index between two adjacent nodes is proposed, describing the relationship of nodes with their neighbors. Then, we design a sampling probability expression between adjacent nodes using node feature information, degree information, neighbor set information, etc. Moreover, we prove the unbiasedness of the SARW-based GCN model for node representations. The simplified version of SARW (SSARW) has a much smaller variance, which indicates the effectiveness of our subgraph sampling method in large-scale graphs for GCN learning. Experiments on six datasets show our method achieves superior performance over the state-of-the-art graph sampling approaches for the large-scale graph node classification task.

Protocol for the PATHOME study: a cohort study on urban societal development and the ecology of enteric disease transmission among infants, domestic animals and the environment

IntroductionGlobal morbidity from enteric infections and diarrhoea remains high in children in low-income and middle-income countries, despite significant investment over recent decades in health systems and water and sanitation infrastructure....

Solar neighborhoods: the impact of urban layout on a large-scale solar strategies application

The article addresses the challenges of evaluating energy performance in different neighborhood settings under various energy efficiency measures and proposes a methodology for selecting appropriate solar strategies on a neighborhood scale. The study selects five representative neighborhoods from various climatic zones with different building and street layouts. The proposed methodology involves a systematic three-step multi-domain workflow for implementing energy efficiency measures and solar strategies in the existing neighborhoods. The first step involves typical energy performance simulation, the second step involves energy simulation using high performance building envelope, and the third step involves the addition of solar strategies in combination with retrofitting materials to achieve net-zero status. The results of the study show that modifying the building envelope leads to a significant reduction in energy consumption, with up to 60% reduction observed. The study also finds that the optimal mix of solar strategies depends strongly on the type of neighborhood, its street layouts, and the type of buildings. The article highlights the importance of considering these factors when implementing solar strategies on a neighborhood scale to achieve energy efficiency and net-zero status. It provides urban planners with a systematic decision-making approach to evaluate and optimize neighborhoods to achieve net-zero energy status.

Not all ‘greenness’ is equal: Influence of perceived neighborhood environments on psychological well-being in Chicago

Neighborhood greenness is of growing importance to understand the quality of urban life, including outcomes tied to health and well-being. However, all greenness may not lead to the same outcomes in racially and ethnically segregated cities. There are many factors associated with the influence of greenness, including perceptions of the physical and social environments. By using multilevel logistic regression analysis, this study investigated differences in psychological well-being associated with neighborhood greenness, park use frequency, and perceived neighborhood physical and social settings in Chicago, IL. We used individual-level health data of 4517 Chicago respondents in 77 neighborhoods, which were aggregated into three distinct regions of the city and drawn from the 2020 Healthy Chicago Survey (HCS); neighborhood greenness was measured using the Normalized Difference Vegetation Index (NDVI). Results suggest that: (1) greenness was significantly associated with psychological well-being in Chicago’s North region but not in the other regions; (2) more frequent park use was associated with psychological well-being in the North region but not in the other regions; (3) neighborhood safety was the strongest predictor of psychological well-being in all three regions of Chicago. This study offers theoretical and practical insights on the need for comprehensive assessment that can help disentangle and differentiate the levels of care and maintenance in urban green spaces. By coupling NDVI with geographic differences, researchers can better capture the complex interplay between different aspects of greenness and urban residents’ well-being.

Short- and long-term outcomes of laparoscopic versus open gastrectomy after neoadjuvant chemotherapy: A case-control study using a propensity score matching method

BackgroundNeoadjuvant chemotherapy (NACT) is increasingly becoming the recommended treatment for locally advanced gastric cancer (LAGC) with promising results. According to previous reports, few studies have evaluated the benefits of laparoscopic gastrectomy (LG) after NACT. Methods135 patients from our center who underwent gastrectomy with NACT were available, including 41 patients of LG and 94 OG between July 2018 and July 2022. To reduce selection bias, we used the nearest neighbor method and set caliper = 0.2 for 3:1 matching between LG and OG groups for propensity score matching method (PSM). After PSM, the matched 41 patients with LG and 80 patients with OG formed the cohort, respectively. Univariate and multivariate Cox analyses were performed on all variables to determine independent risk factors associated with survival. ResultsLG had a longer operating time compared to OG [260.00 min (220.00 min, 300.00 min) vs. 200.00 min (160.00 min, 260 min), P < 0.001]. The estimated blood loss, metastatic lymph nodes (LN), total LN examined, postoperative hospital stays, blood transfusion (P>0.05) and the incidence of postoperative complications did not show statistical differences from the OG group (P = 0.084). The type of surgery (LG vs. OG) did not show a significant risk propensity in the univariate and multivariate Cox analysis (HR = 0.69, P = 0.36, 95 % CI: 0.31–1.53). Through the Kaplan-Meier curves, a certain trend showed that the LG group had a better long-term survival outcomes than the OG group, although there was no statistical difference between two groups (P>0.05). ConclusionLG is a promising treatment option for LAGC patients receiving NACT and had an acceptable safety and efficacy compared to OG.

Distributed model predictive control of vehicle platoons under switching communication topologies

Abstract This paper studies the control problem for vehicle platoons under switching communication topologies, where the Predecessor-Following (PF)-failure topology is allowed. Firstly, we design a local optimization problem for each vehicle by using the state information of itself and its neighbouring vehicles, and then present Algorithm 1 based on Distributed Model Predictive Control (DMPC). By constructing a common Lyapunov function defined by the joint neighbourhood set, we derive sufficient conditions with respect to the weight matrices in the open-loop optimization problem and the switching topologies for ensuring the stability of the closed-loop system under Algorithm 1. Furthermore, we propose Algorithm 2 by introducing novel constraints into the local optimization problem for each vehicle, which utilize shrinking historical position errors to cope with PF-link failures, such that the string stability of the vehicle platoon is ensured. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed DMPC algorithms for the vehicel platoon under switching communication topologies.

Influencer recommendation system: choosing the right influencer using a network analysis approach

PurposeThe rise of social media has led to the emergence of influencers and influencer marketing (IM) domains, which have become important areas of academic inquiry. However, despite its prominence as an area for study, several significant challenges must be addressed. One significant challenge involves identifying, assessing and recommending social media influencers (SMIs). This study proposes a semantic network model capable of measuring an influencer's performance on specific topics or subjects to address this issue. This study can assist managers in identifying suitable SMIs based on their estimated reach.Design/methodology/approachData from popular YouTube influencers and publicly available performance measures (views and likes) are extracted. Second, the titles of the past videos made by the influencer are used to develop a semantic network connecting all the videos to other videos based on similarity measures. Third, the nearest neighbor approach extracts the neighbors of the target title video. Finally, based on the set of neighbors, a range prediction is made for the views and likes of the target video with the influencer.FindingsThe results show that the model can predict an accurate range of views and likes based on the suggested video titles and the content creator, with 69–78% accuracy across different influencers on YouTube.Research limitations/implicationsThe current study introduces a novel and innovative approach that exploits the textual association between a SMI's previous content to forecast the outcome of their future content. Although the findings are encouraging, this research recognizes various constraints that upcoming researchers may tackle. Forecasting views of posts concerning novel subjects and precisely adjusting video view counts based on their age constitute two primary limitations of this study.Practical implicationsManagers interested in hiring influencers can employ the suggested approach to evaluate an influencer's potential performance on a specific topic. This research aids managers in making informed decisions regarding influencer selection, utilizing data-based metrics that are simple to comprehend and explain.Originality/valueThe study contributes to outreach evaluation and better estimating the impact of SMIs using a novel semantic network approach.

Approaching an Optimal Bitcoin Mining Overlay

Bitcoin builds upon an unstructured peer-to-peer overlay network to disseminate transactions and blocks. Broadcast in such a network is slow and brings inconsistencies, i.e. peers have different views of the system state. Due to the delayed block propagation and the competition of mining, forking, i.e. the blockchain temporarily diverges into two or more branches, occurs, which wastes computation power and causes security issues. This paper proposes an autonomous and distributed topology optimization mechanism to reduce block propagation delay and hence reduce the occurrence of blockchain forks. In the proposed mechanism, a node can autonomously update his neighbor set using the information provided by his current neighbors, since each neighbor will recommend a peer from his own neighbor set, i.e. a neighbor’s neighbor, to this node. Each recommendation is based on a peer’s propagation ability, which is characterized as a criteria function obtained through a combination of empirical analysis and machine learning. We further propose some metrics to evaluate a Bitcoin network topology. Experiment results reflect the effectiveness of the proposed mechanism and indicate the correlation between block propagation time and fork rate. Thus, we analyze the relation between block propagation time and fork rate by applying an epidemic model to capture the block propagation process. We prove that a Bitcoin network topology with a relatively small network delay variance among all nodes produces a lower fork rate than another topology if its average block propagation time to 84% of the entire network is shorter.

Improved Hybrid Collaborative Fitering Algorithm Based on Spark Platform

An improved Hybrid Collaborative Filtering algorithm (H-CF) is proposed, addressing the issues of data sparsity, low recommendation accuracy, and poor scalability present in traditional collaborative filtering algorithms. The core of H-CF is a linear weighted hybrid algorithm based on the Latent Factor Model (LFM) and the Improved Item Clustering and Similarity Calculation Collaborative Filtering Algorithm (ITCSCF). To begin with, the items are clustered based on their attribute dimension, which accelerates the computation of the nearest neighbor set. Subsequently, H-CF enhances the formula for scoring similarity by penalizing popular items and optimizing unpopular items. This improvement enhances the rationality of scoring similarity and reduces the impact of data sparseness. Furthermore, a weighting function is employed to combine the various improved algorithms. The balance factor of the weighting function is dynamically adjusted to attain the optimal recommendation list. To address the real-time and scalability concerns, the algorithm leverages the Spark big data distributed cluster computing framework. Experiments were conducted using the public dataset MovieLens, where the improved algorithm's performance was compared against the algorithm before enhancement and the algorithm running on a single machine. The experimental results demonstrate that the improved algorithm outperforms in terms of data sparsity, recommendation personalization, accuracy, recall, and efficiency.

Cultivation mode of mechatronics professionals in higher vocational institutions under “1+X” certificate system

Abstract The cosine similarity approach of the collaborative filtering customized recommendation algorithm is used in this work to first compute the trust degree among users. Next, it identifies the closest neighbor set for users as an addition to the similarity degree, then predicts users’ ratings of items through an optimized item prediction rating strategy based on the ratings of users’ nearest neighbor set and recommends the items with higher prediction ratings to users. Then, we examine the goals and standards for talent development under the “1+X” hybrid certificate and modern apprenticeship systems and explore and build a suitable talent training model for higher vocational mechatronics technology majors. Finally, the research sample is selected from the electromechanical majors of higher vocational institutions, and a collaborative filtering, personalized recommendation algorithm is used to analyze the study sample data set’s data. The findings indicate that T=15 is a preferable option to guarantee a specific level of accuracy since the MAE of the collaborative filtering customized recommendation algorithm at T=30 and T=60 is lowered very little compared to that at T=15. This study provides informative opinions for universities to innovate and reform the talent cultivation mode and solve a series of problems in the current talent cultivation mode so that the quality of talent cultivation in Chinese universities can be improved continuously.

A Novel Dual-Center-Based Intuitionistic Fuzzy Twin Bounded Large Margin Distribution Machines

Intuitionistic fuzzy (IF) set theory combined with twin support vector machines (TSVM) has shown highly advantageous performance in robust and fast classification. However, the existing methods have some inherent flaws: i) The inaccurate confidence caused by the single-center based IF strategy degrades the classification performance of the model. ii) The non-membership degree is time-consuming and the threshold is difficult to determine, as it depends on sample's local neighborhood set. iii) Recent theoretical studies have demonstrated that optimizing the margin distribution achieve better generalization performance than maximizing the minimum margin in SVM-based models (SVMs). In view of the aforementioned shortcomings, we propose a novel dual-center based IF (DC-IF) function, and introduce the margin distribution into IFTSVM, forming a DC-IF twin bounded large margin distribution machines (DC-IFTBLDM). Initially, the DC-IF strategy references the contribution of one sample to both categories, which is time efficient and more accurately reflects the credibility of the samples. Moreover, the margin mean and variance are used to optimize the margin distribution, and a regularization term is utilized to minimize structural risk. Therefore, DC-IFTBLDM has a good performance in noise immunity and classification capability. Comparative experiments based on synthetic datasets, UCI benchmark datasets, and datasets with different noise ratios verify the effectiveness of the proposed model. The parameter sensitivity analysis further proves the high accuracy and stability of IFTBLDM.

Split Neighborhood Number in Fuzzy Graphs

In the present manuscript, we present and analyze the notion of a split neighborhood set in the context of fuzzy graphs. Additionally, we delve into the connections involving the split neighborhood number with the other known parameters in . Furthermore, we derive numerous inequalities for the parameter . Additionally, we provide a result akin to the Nordhaus-Gaddum theorem pertaining to this parameter.

Some conditions implying stability of graphs

AbstractA graph is said to be unstable if the direct product (also called the canonical double cover of ) has automorphisms that do not come from automorphisms of its factors and . It is nontrivially unstable if it is unstable, connected, nonbipartite, and distinct vertices have distinct sets of neighbours. In this paper, we prove two sufficient conditions for stability of graphs in which every edge lies on a triangle, revising an incorrect claim of Surowski and filling in some gaps in the proof of another one. We also consider triangle‐free graphs, and prove that there are no nontrivially unstable triangle‐free graphs of diameter 2. An interesting construction of nontrivially unstable graphs is given and several open problems are posed.

A Note on Distance Irregular Labeling of Graphs

Let us consider a~simple connected undirected graph G = ( V , E ) . For a~graph G we define a~ k -labeling ϕ : V ( G ) → { 1 , 2 , … , k } to be a~distance irregular vertex k -labeling of the graph G if for every two different vertices u and v of G , one has w t ( u ) ≠ w t ( v ) , where the weight of a~vertex u in the labeling ϕ is w t ( u ) = ∑ v ∈ N ( u ) ϕ ( v ) , where N ( u ) is the set of neighbors of u . The minimum k for which the graph G has a~distance irregular vertex k -labeling is known as distance irregularity strength of G , it is denoted as d i s ( G ) . In this paper, we determine the exact value of the distance irregularity strength of corona product of cycle and path with complete graph of order 1 , friendship graph, Jahangir graph and helm graph. For future research, we suggest some open problems for researchers of the same domain of study.

Investigating effects of environmental physical attributes on neighborhood walkability

Studies have identified attributes and categories of attributes that relate to walkability, but as those studies are correlational, they do not establish causality. We used a within subject repeated measure controlled trials experiment tested the effects of seven street attributes on intention to walk in neighborhood settings. A national sample of 49 adults and 37 parents or guardians saw and rated each of 25 simulated streets on three items for either their intention to walk (the adults) or their intention to let their child walk (parents). The analyses found intention to walk for both groups highest for streets with the wide sidewalk, with no parking or with trees. In addition, parents or guardians reported higher intention to let their children walk on streets with either no parked cars or more vegetation. Research needs to test how well these interventions work on real streets. Designers could benefit the result for improve the quality of neighborhoods.

Neighbor group structure preserving based consensus graph learning for incomplete multi-view clustering

In the area of clustering, multi-view clustering has drawn a lot of research attention by making full use of information from different views. In many practical applications, collecting complete multi-view data without missing views is sometimes expensive and impossible. Therefore, the study in incomplete multi-view clustering has become a trend in the field of clustering analysis. Graph learning-based approach is one of the most effective tools. The essence of graph learning is how to construct the affinity graph or similarity matrix, whose elements depict the similarity of the corresponding sample pairs. In this paper, we propose a new method, called Neighbor Group Structure Preserving-based Consensus Graph Learning (NGSP_CGL), to learn a high-quality consensus graph for incomplete multi-view clustering. Different from the existing graph learning-based works which only focus on the relationship between isolated sample pairs, NGSP_CGL seeks to explore the neighbor group structure corresponding to the nearest neighbor sets of sample pairs and designs a novel but simple nearest neighbor group structure embedding constraint so as to enhance the quality of consensus graph. The experimental results on several datasets demonstrate the effectiveness of NGSP_CGL.

New Products on Undirected Graphs

In this paper we present some new products on undirected graphs, explain them by examples and introduce some properties of these products. Moreover, we define the neighborhood set of vertices in these products and examine their sizes.

Individual, Social and Environmental Influences on Sleep in Latino Pre-Adolescents: A Qualitative Analysis

A community-based qualitative study identified multilevel influences on sleep duration, quality, and timing in 10 to 12-year-old Latino pre-adolescents via 11 focus groups with 46 children and 15 interviews with parents. An iterative content analysis revealed three themes negatively and positively impacted sleep: (1) Individual-level; (2) Social-level; and (3) Environmental-level influences. At the individual level, use of technology (e.g., phones), activity levels (e.g., sitting all day), dietary intake (e.g., junk food) and emotions (e.g., stress/anxiety) were reported to impact children’s sleep. Social-level influences included interactions with peers and family members as well as time hanging out and arguing/fighting. Environmental-level influences were living in home and neighborhood settings with certain sounds (e.g., soothing music), uncomfortable temperatures, and items/things (e.g., stuffed animal) in the sleeping area. Parent reports indicated that some factors at each level were exacerbated by the COVID-19 pandemic. Findings that influences at various levels interacted to impact sleep illustrate the need to simultaneously account for multiple levels of influence to best inform intervention development. Thus, application of social ecological models of behavior change to interventions may enhance sleep duration, quality, and timing among Latino pre-adolescents, as these models account for single as well as interacting influences to explain behavior.

Entanglement of free fermions on Johnson graphs

Free fermions on Johnson graphs J(n, k) are considered, and the entanglement entropy of sets of neighborhoods is computed. For a subsystem composed of a single neighborhood, an analytical expression is provided by the decomposition in irreducible submodules of the Terwilliger algebra of J(n, k) embedded in two copies of su(2). For a subsystem composed of multiple neighborhoods, the construction of a block-tridiagonal operator that commutes with the entanglement Hamiltonian is presented, its usefulness in computing the entropy is stressed, and the area law pre-factor is discussed.