Euclidean Distance Function Research Articles

Enhancing environmental sustainability: Optimal municipal-industrial waste burial site selection in Bandar Abbas, Iran using a fuzzy multi-criteria approach incorporating physical-geotechnical factor

Bandar Abbas, Iran faces a significant challenge with the disposal of approximately 500 tons of waste daily, often leading to severe environmental contamination due to improper disposal practices. Existing methodologies for waste management site selection in the region have largely overlooked critical physical-geotechnical factors, focusing primarily on ecological and structural criteria. This study aims to fill this gap by employing a novel fuzzy multi-criteria decision-making approach that integrates environmental, ecological, structural, and physical-geotechnical factors for optimal burial site identification. Using the Analytic Network Process (ANP) to weigh the criteria, this research develops information layers for each aspect. Euclidean distance mapping, combined with fuzzy operators (AND, OR, SUM, Product) and varied gamma values, generates a superposition map for site suitability analysis. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is then applied to rank potential sites and identify the most suitable locations. The results indicate that ecological criteria have the greatest influence on site selection, followed by structural, environmental, and physical-geotechnical factors. Key sub-criteria affecting site selection include proximity to sensitive habitats, residential areas, rivers, and fault lines. Based on the TOPSIS ranking model, five suitable burial locations in Bandar Abbas were identified, with Area 4 (west of Tal Siah Village) emerging as the optimal site. This study contributes to advancing waste management practices by offering a comprehensive, data-driven framework that balances environmental sustainability and geotechnical considerations, providing valuable insights for global application.

A product recommendation model based on online reviews: Improving PageRank algorithm considering attribute weights

Consumer reviews play a crucial role in evaluating products on online e-commerce platforms. Unlike numerical ratings, online reviews provide valuable information and sentiment. However, existing studies often overlook the unique interrelationships between products on e-commerce platforms, and fail to adequately capture the psychological behavior of consumers during online shopping. To address these gaps, this study presents a novel product recommendation model based on online reviews that evaluates products’ multi-attribute performances. The study first identifies the product attributes that are most important to consumers by analyzing review texts. Then, this study calculates the attribute performance scores of each product by considering consumer sentiment and the usefulness of online reviews. Next, it identifies competitors for the target product using a weighted Euclidean distance function and ranks all products employing an improved PageRank algorithm. Finally, to illustrate the validity of the proposed model, the study conducts a case study using a dataset of 41,352 online reviews obtained from Best Buy, and segments the data into three categories according to price. Comparative results with traditional MCDM models show that among the three categories, our results achieved a maximum improvement of 18.3% in the Spearman correlation coefficient.

Offline writer identification approach using moment features and high-order correlation functions

For text-independent writer identification(WI) tasks, this paper proposes a novel method based on semantic codebooks and two effective feature descriptors. This method requires constructing semantic codebooks consisting of various subimages and their labels. Moment features and high-order correlation functions are used to describe handwriting style, and the standard Euclidean distance function is used for distance measurement. Then, two-way analysis of variance(TW-ANOVA) is used to filter out character factors that affect identification accuracy. Finally, a combined measure obtained from multiple classifiers based on a fuzzy integral rule is used to determine the candidate writers. To verify the effectiveness of the system, experiments are performed using the Firemaker, IAM, CRUG-CN and Uyghur2016 databases. The evaluation results demonstrate better performance than most existing methods.

Quantification of shape parameters of Co-Cr-Mo-Si multiphase compounds and their role in the processing of metal injection molding feedstocks

Particle shape is one of the most important powder attributes affecting the properties of multiphase compounds intended for metal injection molding. This work reports the processability of gas (GA) and water (WA) atomized Co-Cr-Mo-Si alloy related to quantitative analyses of particle shape using a newly developed algorithm based on the Euclidean distance mapping, which results are compared with those of commercially available dynamic image analysis. Both methods reveal similar values for the parameters that quantify the shape of the powders, such as circularity, aspect ratio, and symmetry. The alloys were mixed with wax/high-density polyethylene (50/50 wt%) binder using a double sigma mixer to produce homogeneous feedstocks. The critical solid loading and flow performance corresponding to the differences in powder shapes were derived from the torque and capillary rheometers, respectively, to obtain the optimum molding parameters. A stable and repeatable viscosity was observed for the GA feedstock, whereas the compound containing WA was prone to phase separation during flow. The defect-free sintered specimens were examined by metallographic analysis; the GA powder compound could be sintered to a maximum of 99.2 % of the theoretical density, whereas the irregular WA powder reached a maximum of 98.8 % of the theoretical density, and the carbon residue was found to be higher for the GA feedstock. The GA samples had a significantly higher ultimate tensile strength owing to their larger symmetry, which resulted in a lower porosity and stress concentration.

Accelerating dynamic time warping for speech recognition with SSE

This study presents a significant enhancement to the Dynamic Time Warping (DTW) algorithm for real-time applications like speech recognition. Through integration of SIMD (Single Instruction Multiple Data) instructions to distance function, the research demonstrates how SSE accelerates DTW, markedly reducing computation time. The paper not only explores the theoretical aspects of DTW and this optimization but also provides empirical evidence of its effectiveness. Diverse dataset of 18 voice command classes was assembled, recorded in controlled settings to ensure audio quality. The audio signal of each speech sample was segmented into frames for detailed analysis of temporal dynamics. DTW search was performed on features set based on Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC), combined with delta features. A comprehensive set of 27 features was extracted from each frame to capture critical speech characteristics. The core of the study involved applying traditional DTW as a baseline for performance comparison with the SSE-optimized DTW. The evaluation, focusing on computational time, included measurements like minimum, maximum, average, and total computation times for both standard and SSE-optimized implementations. Experimental results, conducted on datasets ranging from 5 to 60 WAV files per class, revealed that the SSE-optimized DTW significantly outperformed the standard implementation across all dataset sizes. Particularly noteworthy was the consistent speed of the SSE-optimized Manhattan and Euclidean distance functions, which is crucial for real-time applications. The SSE-optimized DTW maintained a low average time, demonstrating remarkable stability and efficiency, especially with larger datasets. The study illustrates the potential of SSE optimizations in speech recognition, emphasizing the SSE-optimized DTW's capability to efficiently process large datasets.

Low-Power Analog Integrated Architecture of the Voting Classification Algorithm for Diabetes Disease Prediction.

A low-power (∼ 600nW), fully analog integrated architecture for a voting classification algorithm is introduced. It can effectively handle multiple-input features, maintaining exceptional levels of accuracy and with very low power consumption. The proposed architecture is based on a versatile Voting algorithm that selectively incorporates one of three key classification models: Bayes or Centroid, or, the Learning Vector Quantization model; all of which are implemented using Gaussian-likelihood and Euclidean distance function circuits, as well as a current comparison circuit. To evaluate the proposed architecture, a comprehensive comparison with popular analog classifiers is performed, using real-life diabetes dataset. All model architectures were trained using Python and compared with the software-based classifiers. The circuit implementations were performed using the TSMC 90 nm CMOS process technology and the Cadence IC Suite was utilized for the design, schematic and post-layout simulations. The proposed classifiers achieved sensitivity of ≥ 96.7% and specificity of ≥ 89.7%.

SABDTM: Security-first architecture-based dynamic trusted measurement scheme for operating system of the virtual computing node

In the cloud environment, the virtual computing node has become the dominant form of cloud services used by users. Hence, it is increasingly critical to guarantee the trusted operation of the virtual computing node's operating system (VCNOS). However, previous schemes suffer a lot, such as insufficient consideration of the comprehensive of the measured objects, ignoring the dynamic of trusted, and the trusted measurement mechanisms rarely consider their security. Thus, a three-dimensional dynamic trusted measurement model SABDTM, which integrates the integrity measurement of kernel static data, trusted evaluation of operating system behavior (OSB), and the feedback trust of interacting nodes, is proposed. First, SABDTM divided OSB into multiple atomic behaviors and introduced the Bayesian decision theory to predict trusted expectations of OSBs. Second, the feedback trust of interacting nodes is considered to improve the comprehensiveness of the trusted measurement and evaluate its value based on the Euclidean distance function to reduce the impact of inaccurate feedback from malicious nodes. Subsequently, we set the appropriate weight for trusted measurement values of different moments based on Induced Ordered Weighted Averaging to accurately portray the actual state of VCNOS. Moreover, we designed a lightweight and independent subsystem to perform the trusted measurement, which guarantees the security of the measurement service. The security of our model is proved rigorously based on the non-interference theory. Finally, the experiments and comparative analysis demonstrated our model has better functionality and superiority.

Online educational video engagement prediction based on dynamic graph neural networks

PurposeWith the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.Design/methodology/approachThe quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.FindingsModels with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.Research limitations/implicationsA limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.Originality/valueIn this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

Local progression kinetics of macular atrophy in recessive Stargardt disease

ABSTRACT Background To determine the effect of lesion topography on progression in Stargardt disease (STGD1). Methods Fundus autofluoresence (excitation 488 nm) images of 193 eyes in patients with proven ABCA4 mutation were semi-automatically segmented for autofluoresence changes: (DDAF) and questionably decreased autofluoresence (QDAF), which are proxies for retinal pigment epithelial (RPE) atrophy. We calculated topographic incidence of DDAF and DDAF + QDAF, as well as velocity of progression of the border of lesions using Euclidean distance mapping. Results Incidence of atrophy was highest near the fovea, then decreased in incidence with increased foveal eccentricity. However, the rate of atrophy progression followed the opposite pattern; rate of atrophy increased with distance from foveal center. The mean growth rate 500 microns from the foveal center for DDAF + QDAF was 39 microns per year (95% CI = 28–49), whereas the mean growth rate 3000 microns from the foveal center was 342 microns per year (95% CI = 194–522). No difference in growth rate was noted by axis around the fovea. Conclusions Incidence and progression of atrophy by fundus autofluorescence follow opposite patterns in STGD1. Further, atrophy progression increases significantly with distance from foveal center, which should be taken into consideration in clinical trials.

An improved fuzzy C-means clustering algorithm using Euclidean distance function

The fuzzy c-mean (FCM) clustering algorithm is a typical algorithm using Euclidean distance for data clustering and it is also one of the most popular fuzzy clustering algorithms. However, FCM does not perform well in noisy environments due to its possible constraints. To improve the clustering accuracy of item varieties, an improved fuzzy c-mean (IFCM) clustering algorithm is proposed in this paper. IFCM uses the Euclidean distance function as a new distance measure which can give small weights to noisy data and large weights to compact data. FCM, possibilistic C-means (PCM) clustering, possibilistic fuzzy C-means (PFCM) clustering and IFCM are run to compare their clustering effects on several data samples. The clustering accuracies of IFCM in five datasets IRIS, IRIS3D, IRIS2D, Wine, Meat and Apple achieve 92.7%, 92.0%, 90.7%, 81.5%, 94.2% and 88.0% respectively, which are the highest among the four algorithms. The final simulation results show that IFCM has better robustness, higher clustering accuracy and better clustering centers, and it can successfully cluster item varieties.

Differentiable Curl-Noise

We present Differentiable Curl-Noise, a C1 procedural method to animate strictly incompressible fluid flows in two dimensions. While both the original Curl-Noise method of Bridson et al. [2007] and a recent modification by Chang et al. [2022] have been used to design incompressible flow fields, they often suffer from non-smoothness in their handling of obstacles, owing in part to properties of the underlying Euclidean distance function or closest point function. We therefore propose a differentiable scheme that modulates the background potential in a manner that respects arbitrary solid simple polygonal objects placed at any location, without introducing discontinuities. We demonstrate that our new method yields improved flow fields in a set of two dimensional examples, including when obstacles are in close proximity or possess concavities.

Voronoi treemap in Manhattan distance and Chebyshev distance

The ordinary Voronoi treemap generated based on the Euclidean distance function has the flexibility to slightly adjust the layout when visualizing time-varying hierarchical data for better visual quality, preserving neighborhood relationships, and preserving a stable layout. However, its layout formed by segments with arbitrary orientations has poor shape stability between successive layouts at different time indexes, which is not conducive for the users to understand the plot and track the same node. In this paper, we propose novel Voronoi treemaps in Manhattan distance and Chebyshev distance respectively, such that the segments in the new layouts only have four orientations (horizontal, vertical, and ±45° to the [Formula: see text]-axis). The new layouts can not only heritage the abilities of ordinary Voronoi treemap, but preserve good shape stability. To achieve this, we first focus on the weighted bisector between two sites in Manhattan distance and design a bisector generation method for different weight values of two sites, as the bisector is the foundation to form a diagram. Then a divide-and-conquer method is utilized to form the bisectors into a Voronoi diagram, and a Voronoi treemap layout can be finally obtained by using Lloyd’s method to iteratively adjust the diagram. Moreover, we prove that the treemap algorithm in Manhattan distance can be adjusted to also generate the Voronoi treemap in Chebyshev distance via linear transformation, avoiding designing additional algorithm. The computational properties of the proposed methods are first evaluated to check whether the layouts can be generated fast and accurately. Then the perceptual properties are evaluated quantitatively and qualitatively based on quality metrics and user studies, respectively. The results demonstrate that the proposed Voronoi treemaps preserve similar layout stability, but better visual quality and shape stability than the ordinary Voronoi treemap. Our algorithms are simple and resolution-independent, but also provide alternatives to the Voronoi treemaps.

Investigating phenotypic relationships in persimmon accessions through integrated proteomic and metabolomic analysis of corresponding fruits.

Together with phenological and genomic approaches, gel-based and label-free proteomic as well metabolomic procedures were separately applied to plants to highlight differences between ecotypes, to estimate genetic variability within/between organism populations, or to characterize specific mutants/genetically modified lines at metabolic level. To investigate the possible use of tandem mass tag (TMT)-based quantitative proteomics in the above-mentioned contexts and based on the absence of combined proteo-metabolomic studies on Diospyros kaki cultivars, we here applied integrated proteomic and metabolomic approaches to fruits from Italian persimmon ecotypes with the aim to characterize plant phenotypic diversity at molecular level. We identified 2255 proteins in fruits, assigning 102 differentially represented components between cultivars, including some related to pomological, nutritional and allergenic characteristics. Thirty-three polyphenols were also identified and quantified, which belong to hydroxybenzoic acid, flavanol, hydroxycinnamic acid, flavonol, flavanone and dihydrochalcone sub-classes. Heat-map representation of quantitative proteomic and metabolomic results highlighted compound representation differences in various accessions, whose elaboration through Euclidean distance functions and other linkage methods defined dendrograms establishing phenotypic relationships between cultivars. Principal component analysis of proteomic and metabolomic data provided clear information on phenotypic differences/similarities between persimmon accessions. Coherent cultivar association results were observed between proteomic and metabolomic data, emphasizing the utility of integrating combined omic approaches to identify and validate phenotypic relationships between ecotypes, and to estimate corresponding variability and distance. Accordingly, this study describes an original, combined approach to outline phenotypic signatures in persimmon cultivars, which may be used for a further characterization of other ecotypes of the same species and an improved description of nutritional characteristics of corresponding fruits.

LOCATION OF FUEL STATIONS USING FUZZY MODEL IN GEOSPATIAL INFORMATION SYSTEM (CASE STUDY: 7TH DISTRICT, TEHRAN CITY)

Abstract. In this study, by using the fuzzy method and geospatial information system (GIS), was attempted to locate the fuel station in the 7th district of Tehran. For this purpose, the information was digitized as a shape file by referring to the field and using the existing maps. The distance map was prepared using the Euclidean Distance function. Finally, using ArcGIS 10.8 software, this information was normalized by the fuzzy method and overlapped using the fuzzy gamma operator. It is presented as a map of prone areas for optimal positioning of fuel stations in the 7th district of Tehran. The most important criteria are the distance from the communication network, population density, incompatible users (commercial, industrial, administrative, residential, cultural and religious, health and medical), compatible users (park and green space, sports, fire department), and distance from fuel stations.

Multistate model to estimate sex‐specific dispersal rates and distances for a wetland‐breeding amphibian population

AbstractHow animals move across space and time in heterogeneous landscapes has important implications for the conservation of imperiled and dispersal‐limited taxa, such as amphibians. Wetland‐breeding amphibians are thought to exhibit strong site fidelity, but there is growing evidence that many species are more vagile than previously assumed. Few studies have quantified breeding dispersal probabilities or distances while also accounting for observational uncertainty, which means that resultant estimates could be biased or confounded with sampling intensity and other demographic rates (e.g., site‐ and time‐specific detection and survival probabilities). Here, we analyze data from a 6‐year capture–mark–recapture study on adult spotted salamanders (Ambystoma maculatum) conducted at 12 wetlands in central Pennsylvania to estimate population dynamics and population structure at multiple scales. We used a multistate, hidden Markov estimator to quantify sex‐specific site fidelity and breeding dispersal as a function of Euclidean distance between wetlands while accounting for imperfect detection. We estimated short‐timescale movements (i.e., those that occur within a single breeding season) and longer timescale movements (i.e., those that occur among breeding seasons) to determine if dispersal rates and distances differed. We found that interannual site fidelity of males varied among wetlands and was positively associated with population density. Females exhibited higher interannual site fidelity and dispersed further than males between breeding seasons. Within breeding seasons, we found that up to 6% of males dispersed to a new wetland each day. Our study is the first to directly compare amphibian breeding dispersal probabilities and distances at multiple scales and provides a robust framework for improving inference on the spatial and temporal patterns of amphibian movement.

VULNERABILIDADE AMBIENTAL NO ENTORNO DO MONUMENTO NATURAL DO ITABIRA, ES, BRASIL

ABSTRACT The institution of biological value areas through the creation of Natural Protected Areas is an essential tool for protecting the country’s environmental resources to face advanced anthropic development in natural areas, either to increase agriculture production or for economic growth. Environmental vulnerability studies combined with geotechnological tools have been vital to identifying the most vulnerable areas and consequently help Conservation Units protection. This study aimed to analyze environmental vulnerability at the buffer zone of Itabira Natural Monument (MONAI) in Cachoeiro de Itapemirim, Brazil. The necessary methodological steps for this study were as follows: a) Photointerpretation of land use; b) Anthropic variables selection; c) Application of Euclidean distance function in variables vector images; d) Application of fuzzy membership function in variables raster images; e) Application of Analytical Hierarchical Process (AHP); and f) Spatial classification of environmental vulnerability around MONAI. Land use conflicts reveal a relevant presence of pasture areas, representing 49.80% of the buffer zone. This fact corroborates with its Euclidean distance value, which showed the lowest linear value, 836 meters. Eight anthropic variables have been incorporated, highlighting crops, urban areas, exposed soil and pasture. Jenks natural breaks defined five vulnerability classes. According to the results, 57.14% of the entire MONAI buffer zone is represented by the high and very high classes. The outdated law creation of MONAI and the need for a management plan result in a vulnerable buffer zone. The present study can support management plan formulation and assist MONAI’s buffer zone delimitation.

Grey Wolf Optimizer-Based Optimal Controller Tuning Method for Unstable Cascade Processes with Time Delay

This paper presents a controller design technique for cascade control systems based on the grey wolf optimization (GWO) algorithm. In the proposed control scheme, the proportional-integral-proportional-derivative (PI-PD) controller structure is used to control both the open-loop unstable process in the primary loop and the stable process in the secondary loop. To determine the optimal controller parameters, a new optimization algorithm is used in which the Euclidean distance function is used as a multi-objective function. A symmetry property of the Euclidean distance function is that its distance does not depend on the starting point and destination. The multi-objective function is designed according to system time response specifications such as settling time, overshoot, and steady-state error. Thus, the optimization algorithm allows the simultaneous determination of all controller parameters according to the desired output response. Three simulation studies are presented in the paper and the results are compared with studies using various methods based on internal model control, a modified Smith predictor, and a genetic algorithm. The simulation results reveal that the proposed method improves the performances of the systems in the control of cascade processes.

Mengiplementasikan Vector Space Model Similarity Euclidean Distance Menggunakan TFIDF Pada Klasifikasi Teks Bahasa Indonesia

Weighting based on the term with stemming techniques to get the basic word form term in question. This will the application of the Indonesian language text classification machine using the K-Nearest Neighbor algorithm and the Vector Space Model method on the TFIDF frequency weighting of the number of words and the Euclidean Distance function. comparison between the test documents and the test sample collection Using news documents as learning documents, a total of 10 (10) documents with 3 (three) categories, produces an Precision and Recall 90.00% for k = 5 using frequency weighting in words with the Euclidean Distance function.

The Joint Method of Triple Attention and Novel Loss Function for Entity Relation Extraction in Small Data-Driven Computational Social Systems

With the development of the social Internet of Things (IoT) and multimedia communications, our daily lives in computational social systems have become more convenient; for example, we can share shopping experiences and ask questions of people in an ad hoc network. Relation extraction focuses on supervised learning with adequate training data, and it helps to understand the knowledge behind the observed information. However, if only some social data in an unknown area can be used, how to obtain the related knowledge and information is a key topic for supporting social intelligence. This article proposes the joint method of triple attention and novel loss function for entity relation extraction by few-shot learning in computational social systems. We consider using a prototypical network as the base model to acquire support set prototypes and to compare queries with the prototypes for classification. First, triple attention is employed to make the query instances and support set share interactive information in a global and instancewise manner, highlighting the important features. Second, we combine a weighted Euclidean distance function with a multilayer perceptron (MLP) to perform class matching, which maps the generated features to their proper classifications, emphasizing the prominent dimensions in the feature space and relieving data sparsity. Third, triplet loss and uniformity regularization are used to solve the inconsistency problem faced by the support set, where the features of the support set in the same class are often far apart in different characteristic dimensions. Finally, the experimental results demonstrate the improved performance of our model on the FewRel dataset.

Probabilistic linguistic fuzzy cognitive maps: applications to the critical factors affecting the health of rural older adults

BackgroundAchieving healthy ageing has become the only way for China to alleviate the pressure of ageing, especially in rural areas. However, the factors affecting the health of rural older adults are numerous and complex. It is important to identify the critical factors that affecting the health of older adults in rural areas and provide decision-making support for targeted health interventions.MethodsTo overcome some limitations of existing works, an extended probabilistic linguistic fuzzy cognitive map model is proposed in this paper as a useful tool for modeling the cause-effect relationship between factors. The proposed model integrates the advantages of probabilistic linguistic term sets and fuzzy cognitive maps. In the end, to rank and identify the critical factors affecting the health, a novel similarity measure based on Euclidean distance and Z-mapping function is proposed.ResultsThe proposed model can effectively deal with the uncertainty of experts and reflect different opinions of groups well. In terms of representing uncertainty and ambiguity, the proposed method outperforms other models in modeling complex systems. In the real-world case analysis, we find that education is the most important factor affecting the health of rural older adults, followed by previous occupational experiences, psychology, and physical exercise, among other things. Intergenerational relationship has become another important factor affecting the health of rural older adults in China as the development of Chinese society.ConclusionsFrom a macro perspective, social economic status, living environment, lifestyle, and health management, are the variables that have the greatest impact on the health of rural older adults. As a result, providing more precise health interventions with the characteristics of factors influencing health is a crucial guarantee for preserving and improving the health of rural older adults in China.