Relative Distance Research Articles

Spatial Economic Correlations via Geographically Weighted Neural Network Regression with A New Dataset

The GDP and unemployment rates of two geographic units are influenced by their population density and economic characteristics, as well as their distance and spatial relationship. This work aim to use Geographically Weighted Regression (GWR), a classic and widely used method for modeling spatial heterogeneity, to analyze the correlation of these multiple factors. However, GWR does not precisely express its weighting kernel, making it insufficient to estimate complex geographic processes. Therefore, the work employed the Geographically Weighted Neural Network Regression (GNNWR) model, which combines Ordinary Least Squares (OLS) and neural networks, to estimate spatial heterogeneity based on GWR. This work collected various indicators, such as population density and GDP of first-level administrative units of the top 20 countries by GDP at the end of 2022, along with the Euclidean distance and geographic topology between these units. Using GNNWR, the work analyzed the correlation of their GDP growth. The results show that GNNWR outperforms OLS and GWR in fitting accuracy and provides more accurate predictions of the economic correlation between two geographic units.

'It’s become a theatre’: relational experiences of family carers and persons living with Amyotrophic lateral sclerosis (ALS) after cognitive impairment emerges

Background Amyotrophic lateral sclerosis (ALS) can lead to emotional and psychological distress between persons with ALS and their family carers. Many persons with ALS develop cognitive impairment, which limits their ability to process complex information, interact, and communicate. This cognitive decline adds to caregiver burden. Few studies have explored interpersonal relations between persons with ALS and their family carers. Aim To better understand how ALS-associated cognitive impairment influences close relations. Methods Individual semi-structured in-depth, interviews were conducted once, with four persons with ALS and four family carers. Reflexive thematic analysis was used. Results Increased distance of close relations was identified as the overarching theme. Differences and similarities within the data were identified according to the two main themes. The first main theme, Everyday life together but apart: a demanding role to play, was structured according to three subthemes: Keeping the façade of civility, Demanding daily interaction and communication, and Taken for granted changes in everyday roles. The second main theme, Coping with a lost future, was structured according to the two subthemes, Striving to live in the normal present, and Holding on to self. Conclusions and significance Cognitive impairment following ALS can lead to increased relational distance between persons with ALS and their family carers. To ease their burden, professionals should recognize person with ALS’ and family carers’ relational issues and grief at an early stage. Focusing on their occupational identity and valued occupations that are still-accessible may help persons with ALS and family carers regain meaning in everyday life.

Psychic distance and inter-organizational relationships: theoretical foundations, methodological approaches, and future research directions

PurposeThis paper presents a comprehensive and integrated review of the literature on the role of psychic distance in shaping inter-organizational relationships, by uncovering the theoretical, methodological, and thematic foundations of this research domain.Design/methodology/approachLeveraging a systematic literature review in combination of text mining techniques using Leximancer, the study identified 62 relevant articles published between 1980 and 2024. The analysis revealed four key areas of focus: (1) interfirm business relationships, (2) relational governance and performance, (3) relationship quality, and (4) relationship violation.FindingsThe findings indicate that this body of research is grounded in well-established theoretical frameworks, with social exchange theory, transaction cost analysis, relational exchange theory, and the resource-based view being the most frequently employed. The literature predominantly emphasizes the role of psychic distance in the formation and maintenance of interfirm business relationships, while the other three areas remain comparatively underexplored.Originality/valueBy offering a more robust framework and a holistic understanding of psychic distance in inter-organizational relationships context, this study paves the way for further theory-driven studies, which is a theoretical contribution towards advancing this research area

Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition.

The Transformer model has received significant attention in Human Activity Recognition (HAR) due to its self-attention mechanism that captures long dependencies in time series. However, for Inertial Measurement Unit (IMU) sensor time-series signals, the Transformer model does not effectively utilize the a priori information of strong complex temporal correlations. Therefore, we proposed using multi-layer convolutional layers as a Convolutional Feature Extractor Block (CFEB). CFEB enables the Transformer model to leverage both local and global time series features for activity classification. Meanwhile, the absolute position embedding (APE) in existing Transformer models cannot accurately represent the distance relationship between individuals at different time points. To further explore positional correlations in temporal signals, this paper introduces the Vector-based Relative Position Embedding (vRPE), aiming to provide more relative temporal position information within sensor signals for the Transformer model. Combining these innovations, we conduct extensive experiments on three HAR benchmark datasets: KU-HAR, UniMiB SHAR, and USC-HAD. Experimental results demonstrate that our proposed enhancement scheme substantially elevates the performance of the Transformer model in HAR.

Evaluation of the genetic diversity and population structure of 5 pheasant breeds in Shanghai.

The genetics of pheasant breeds in Chinese farms has not been investigated yet. Understanding their genetic diversity and population structure is important for future advancements in pheasant breeding. In this study, the whole-genome resequencing was used to analyze a total of 352 samples from 5 pheasant species (American pheasant, White pheasant, Green pheasant, Shenhong pheasant, and Fengxian blue pheasant). The average effective population size (Ne) was 45.82. The average of expected heterozygosity (He) and observed heterozygosity (Ho) was 0.28514 and 0.27938, respectively. The Green pheasant had the lowest values of He (0.2730) and Ho (0.2692), whereas Fengxian blue pheasant had the highest values of He (0.2885) and Ho (0.2937), respectively. In addition, the 5 pheasant breeds could be divided into four different genetic populations. A similar genetic structure was also observed between American pheasant and Shenhong pheasant, whereas the other three pheasant breeds (White pheasant, Green pheasant, and Fengxian blue pheasant) exhibited obviously different genetic structures. Further analysis of population structure showed that some individuals among all 5 pheasant breeds had a high genetic distance and weak genetic relationships. A certain degree of inbreeding might exist in the population of White pheasant. Thus, effective breeding and conservation plans should be conducted to retain the genetic distinctiveness for White pheasant. Our data is of great significance for promoting the conservation and development of pheasant genetic resources.

The effects of environmental heterogeneity on the morphological similarity of toothed-leafed red oak species (Acutifoliae; Quercus) in Mexico and Central America

Background: In plants, environmental gradients can promote similar morphophysiological traits in species that inhabit areas with similar climatic conditions. Here, to evaluate possible causes of morphological similarity, we analyzed leaf morphological and functional variation in toothed-leaf oaks and its relationships with environmental and geographic variables. Hypotheses: We expected that species with a greater degree of environmental overlap would exhibit similar leaf morphology, and those with a broader range of environmental tolerance would exhibit greater leaf morphological variation. Studied species: Acutifoliae group (Fagaceae, Quercus) Study sites and dates: Leaf samples were collected from adult trees distributed in diverse forest types between 495 and 2,566 meters above sea level (m asl) in Mexico and Guatemala. Methods: We compared patterns of leaf variation in a total of 2,934 leaves from 640 individuals corresponding to 68 populations of ten oak species using univariate and multivariate analyses. We also characterized the environmental conditions where each of the species occurred using the available geographical records and bioclimatic variables. Results: We found morphological overlap in leaf traits in oak species occurring in similar climatic environments. Additionally, oak species with a greater range of environmental distributions showed greater leaf shape morphological variation. Conclusions: The environment influenced the generation of similar leaf morphologies among oak species despite geographical distance and phylogenetic relationships.

Unmanned aerial vehicles for human detection and recognition using neural-network model.

Recognizing human actions is crucial for allowing machines to understand and recognize human behavior, with applications spanning video based surveillance systems, human-robot collaboration, sports analysis systems, and entertainment. The immense diversity in human movement and appearance poses a significant challenge in this field, especially when dealing with drone-recorded (RGB) videos. Factors such as dynamic backgrounds, motion blur, occlusions, varying video capture angles, and exposure issues greatly complicate recognition tasks. In this study, we suggest a method that addresses these challenges in RGB videos captured by drones. Our approach begins by segmenting the video into individual frames, followed by preprocessing steps applied to these RGB frames. The preprocessing aims to reduce computational costs, optimize image quality, and enhance foreground objects while removing the background. This results in improved visibility of foreground objects while eliminating background noise. Next, we employ the YOLOv9 detection algorithm to identify human bodies within the images. From the grayscale silhouette, we extract the human skeleton and identify 15 important locations, such as the head, neck, shoulders (left and right), elbows, wrists, hips, knees, ankles, and hips (left and right), and belly button. By using all these points, we extract specific positions, angular and distance relationships between them, as well as 3D point clouds and fiducial points. Subsequently, we optimize this data using the kernel discriminant analysis (KDA) optimizer, followed by classification using a deep neural network (CNN). To validate our system, we conducted experiments on three benchmark datasets: UAV-Human, UCF, and Drone-Action. On these datasets, our suggested model produced corresponding action recognition accuracies of 0.68, 0.75, and 0.83.

The relationship of anogenital distance with fertility and anti-müllerian hormone in Awassi and Cukurova meat ewes

The aim of the present study was to determine the relationship of anogenital distance (AGD) with fertility and the serum concentrations of estrogen and anti-müllerian hormone in Awassi and Cukurova meat ewes. Fifty five Cukurova meat ewes and 46 Awassi ewes were used in the study. Before sponge insertion, AGD of all ewes were measured with a digital caliper. For each breed, they were divided into two AGD categories: short AGD group and long AGD group. A sponge containing 60mg medroxyprogesterone acetate was applied intravaginally on day 0, and remained in situ for 12 days. On the 12th day, sponges were removed and eCG (500 IU per ewe) was administered intramuscularly. Blood samples were collected from all ewes on day 0 and at the onset of estrus. Estrous rate, conception rate, pregnancy rate, lambing rate, multiple birth rate, litter size and male rate were calculated in all groups and breeds. No significant difference were detected in the parameters between the groups (P>0.05). It is stated that AGD was found not to affect AMH or estrogen levels (P>0.05); however linear regression showed a positive correlation (P=0.03) between AGD and estrogen on the day of estrus in the Awassi ewes. It is stated that AGD is not associated with fertility in Awassi and Cukurova meat ewes, but there is a relationship between AGD and estrogen level in Awassi ewes.

Absolute, not perceived, delay modulates agency judgement: Evidence for cognitive impenetrability of sense of agency.

The sense of agency, which refers to awareness of causing events, is consistently influenced by the time interval between actions and their outcomes such that longer delays diminish the perceived strength of the agency. This study investigated whether the sense of agency is modulated by the distance between experienced delays or by their subjective discriminability, which is known to be subject to Weber's law (discriminability being a function of ratios rather than absolute differences between time intervals). To this end, participants executed keypress actions leading to outcomes at varying delays. In one experiment, delays were equidistant on a logarithmic scale (constant ratio relationship), while in the other experiment, they were equidistant on a linear scale (constant distance relationship). Our results showed that judgments of the agency were predicted better by actual temporal proximity between actions and outcomes compared with their subjective discriminability. Beyond providing a more complete picture regarding the effect of outcome delays on the sense of agency, these findings have broader implications for the mechanistic underpinnings of the sense of agency. They imply that even explicit judgments of agency can be influenced by certain factors transcending conscious experience.

Adaptive gravitational clustering algorithm integrated with noise detection

Clustering analysis is frequently used in data mining, image processing, artificial intelligence, and so on. Traditional approaches heavily rely on manually configured parameters, of which the initial selection exerts a profound influence on the clustering outcomes. In addition, they usually only consider the relationship between two individual samples when calculating distances, neglecting the overall structure of the dataset, which can negatively affect clustering performance. At the same time, many contemporary algorithms are tailored to specific datasets, posing challenges in achieving optimal clustering performance for intricate, noisy datasets. To address these limitations, we propose an Adaptive Gravitational Clustering Algorithm Integrated with Noise Detection called GCIND. Inspired by the law of gravitation, GCIND takes into account the natural neighborhood structure of the entire dataset, adaptively computing the gravitation between data points by leveraging shared neighbors and Euclidean distance relationships. Our algorithm initially identifies and eliminates outliers or edge points in the dataset. It subsequently uses gravitation to autonomously cluster the remaining core data. Finally, the removed data are reallocated to their respective clusters. GCIND has four notable advantages: (1) it uses gravitation to build the neighborhood graph, reflecting the overall dataset structure; (2) it demonstrates stronger robustness in handling noisy datasets; (3) it uses adaptive gravitational neighborhood graph clustering, removing manual parameter tuning; (4) it adapts to complex manifold-structured datasets, offering broad applicability. Experiments have shown that GCIND, without requiring any parameter settings, demonstrates slightly better performance than the algorithms compared in the study, especially when dealing with complex manifold datasets.

The subsidence–distance relationship for land subsidence induced by groundwater abstraction

The subsidence–distance relationship for land subsidence induced by groundwater abstraction

The Analysis of Geospatial Factors Necessary for the Planning, Design, and Construction of Agricultural Biogas Plants in the Context of Sustainable Development

This spatial analysis focuses on the relevant elements regarding the use of renewable energy sources, in particular, biogas, taking into account sustainability. The characteristics of the processes of biogas formation are presented, including the technological and biological aspects of its production. The issues of agricultural biogas plant profitability are discussed using examples from Poland, analyzing the economic and financial aspects of investing in this type of plant. Geospatial analysis supported by geographic information systems (GISs) looked at factors affecting the relief of the land and also the analysis of local plans or zoning decisions for the selected site. In addition, distance relationships and geological aspects taking into account terrain, landslides, and forestation, as well as hydrological aspects relating to flood and flooding hazards, were examined. It analyzed the availability of utility networks and proximity to road infrastructure. It was also examined that the proposed project will not have a negative impact on environmental protection. The analyzed site meets favorable conditions for the construction of the proposed project, and the conclusion drawn from this study is to emphasize the importance of integrating renewable energy sources with information systems to achieve sustainable development.

Population genomics informs the management of harvested snappers across north-western Australia

Failure to consider population structure when managing harvested fishes increases the risk of stock depletion, yet empirical estimates of population structure are often lacking for important fishery species. In this study, we characterise genetic variation in single nucleotide polymorphisms (SNPs) to assess population structure for three harvested species of tropical snappers across the broad (up to 300 km wide) and extensive (~ 4000 km) continental shelf of north-western Australia. Comparisons across ~ 300 individuals per species, showed remarkably similar patterns of genetic structure among Lutjanus sebae (red emperor), L. malabaricus (saddletail snapper) and Pristipomoides multidens (goldband snapper) despite subtle differences in biological and ecological traits. Low levels of genetic subdivision were reflected in an isolation by distance relationship where genetic connectivity increased with geographic proximity. This indicates extensive but not unlimited dispersal across the north-western Australian shelf. Our findings provide evidence of connectivity between current management areas, violating the assumption of multiple independent stocks. Spatial stock assessment models may be more suitable for the management of these species however demographic connectivity rates cannot be accurately estimated from the conventional population genetic approaches applied in this study. We recommend that managers aim to maintain adequate spawning biomass across current management areas, and assess stocks at finer scales, where practical.

One-Shot Federated Clustering Based on Stable Distance Relationships

One-Shot Federated Clustering Based on Stable Distance Relationships

Bulkiness of goods and the gravity of international trade: Differential impact of trade barriers

This study investigates how the trade elasticity to trade barriers, or the lack thereof, varies with the bulkiness of goods, measured as the value-to-weight ratio (V/W ratio). The empirical analysis presented herein offers compelling evidence that lighter goods, characterized by higher V/W ratios, demonstrate reduced trade elasticity to distance, contiguity, and colonial relationships but heightened trade sensitivity to language. These findings emphasize the importance of accounting for the variations in V/W ratios when applying gravity models and reveal noteworthy heterogeneity in trade elasticity at the country and trade-pair levels.

Cyclic shift short-distance attention for defect detection of fine etching mesh

Abstract Based on the deficiency of the existing Swin Transformer in the detection of surface defects in metal etched grids, i.e. the isolation of information transmission between different windows, and the limitations of image matrix size and network structure,which make it not suitable for long sequence data processing and dense prediction scenarios, a defect detection method for metal etching nets based on Cyclic shift Long Short Distance Attention (C-LSDA) is proposed. In the proposed method, cross-scale features are used to capture long and short distance relationships,and enhance the feature transfer between windows. Tracking position bias (TPB) is also used to improve relative position bias (RPB), in order to help the model to better understand the spatial relationships in the input data. In addition, the image is downsampled at various scales, so that the proposed method can be suitable for long sequence data processing and dense prediction scenarios. In order to improve the detection accuracy of small targets. a multi-feature fusion pyramid network (MFFPN) for feature fusion is also proposed. The experimental results show that the proposed method is significantly superior to the traditional CNN recognition method and the transformer recognition method. The proposed methodhas good identification accuracy and low computational cost.

Novel modified weights and cosine similarity based maximum marginal projection and its application in fault diagnosis

Abstract Facing the problem that the data generated in industrial processes have few labeled samples and the local manifold learning dimensionality reduction method ignores the local spatial structure of sample points and the distance relationship in constructing different weights. To solve the above problems, this paper presents a novel modified weights and cosine similarity based maximum marginal projection named MCMMP. In MCMMP, cosine similarity is used to consider the space feature of sample points, which enhances the performance of dimensionality reduction. The new modified weights are applied to measure the between-class and the within-class sample points, which enhance the divisibility of sample points. After MCMMP dimensionality reduction, the classifier is used to classify the dimensionality reduction sample points. Finally, the proposed new method is used in two cases Tennessee Eastman Process (TEP) and Three-phase Flow Facility (TFF) to test the fault diagnosis performance. The results of the simulation process indicated that the new fault diagnosis method based on MCMMP, compared with other related diagnosis methods, has good performance.

Quantitative Analysis about the Spatial Heterogeneity of Water Conservation Services Function Using a Space–Time Cube Constructed Based on Ecosystem and Soil Types

Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol’ method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies.

Different roads take me home: the nonlinear relationship between distance and flows during China’s Spring Festival

Human mobility modelling has attracted scholarly attention from physics-based methods and social science explanatory approaches. However, there is limited knowledge of the nonlinear relationship of flows and distance in intercity mobility and regional differences in the nonlinear relationship. Focusing on China’s long-distance and large-scale mobility during the Spring Festival, this paper develops a framework to explain the nonlinear relationship. Using the Gradient Boosting Decision Tree (GBDT) model and Tencent Big Data, we find that there are three types of nonlinear relationships, namely plateau (almost zero distance decay parameter), drop (decreasing distance decay parameter) and rebound (increasing distance decay parameter after decreasing). The provincial differences also reveal that the nonlinear relationships depend on the domestic relative location and the intra-provincial urban system. This result shows that the cities in the coastal province enjoy a more inclusive spatial structure, which supports the migration from the periphery of the province. In contrast, the inland cities are concerned with embracing the migrants and settling them down.

Velocity of ultrasound diffusion in heterogeneous media and its applications

This paper investigates the velocity of ultrasonic diffusion in heterogeneous media under the conditions where the diffuse wave approximation is valid (λ≤a). The diffusion velocity is defined as the moving speed of the peak of the energy evolution curve. The peak arrival time as a function of transport distance is calculated for infinite spaces and bounded domains. The results show that the peak arrival time is independent of the domain’s geometry, i.e. the size, shape, and boundary conditions. This is confirmed by comparing the arrival times in an infinite space and a bounded domain with a geometric feature – a surface-breaking crack of varying depth. Therefore, the velocity of ultrasonic diffusion is an intrinsic property of a medium, and the formula for the infinite three-dimensional space is sufficient to calculate the arrival time–transport distance relationship, given the diffuse properties of the medium. These findings eliminate the needs for the finite element numerical simulations in the applications to determine geometric parameters such as the crack depth using diffuse ultrasound. The diffusion velocity is a function of transport distance in general, while its far-field asymptotic value is constant regardless of the dimensionality and geometry of the domain.