Squared Euclidean Distance Research Articles

Detection and Characterization of Marine Ecotones Using Satellite-Derived Environmental Indicators

The delimitation of an ecotone is an important reference for ecosystem conservation; however, the assessment of a marine ecotone from an ecological point of view represents a knowledge gap. The Yellow River Estuary (YRE) serves as both spawning and feeding grounds for numerous economically important organisms. Delineating the boundary of YRE and assessing the boundary change have great importance in maintaining its ecosystem health. This study attempts to apply a Moving Split Window (MSW) to determine marine boundary in YRE. Level 2 remote sensing satellite data spanning from 2012 to 2020 sourced from the Geostationary Ocean Color Imager (GOCI) were utilized. Chlorophyll-a, Chromophoric Dissolved Organic Matter (CDOM), and Total Suspended Solids (TSS) were employed as variables, with Squared Euclidean Distance (SED) serving as the determinant for identifying the marine ecological ecotone within the Yellow Estuary and its adjacent waters. Results indicate the following: (1) SED values exhibit distinct peaks and valleys, facilitating the accurate identification of marine ecotones via MSW. (2) Evident ecotones are observable in both the gate and coastal regions. (3) The influence range of TSS on the gate spans between 10 km and 14 km. In synthesis, the ensuing conclusions are drawn: MSW proves to be a reliable method for quantitatively determining ecotones in marine environments. Furthermore, MSW introduces a novel approach to the delineation of marine ecotones.

Cluster analysis applied to obtaining reference models for building thermal performance studies

Cluster analysis applied to obtaining reference models for building thermal performance studies

Publication Activity of Public Higher Education Institutions in Poland Corresponding to the Idea of Sustainable Energy Development – Results of a Cluster Analysis

Abstract Objective The aim of the article is to present the involvement of public Higher Education Institutions in Poland in popularizing the idea of sustainable development within the framework of their publishing activities. Methodology In this study, agglomerative clustering approach, which is a representative of the hierarchical method, was used. The distance between selected public Higher Education Institutions in Poland was determined based on the Squared Euclidean Distance. In turn, to estimate the distance between clusters, the Ward method was used. The study used three parameters, constructed from data taken from SciVal: (1) total number of publications, (2) citation count, and (3) field-weighted citation impact. Findings Three groups of Polish public HEIs were distinguished as a result of the cluster analysis that was performed (based on: the total number of publications, the citation count, and the field-weighted citation impact). The most publication-productive were seven technical universities, i.e., Warsaw University of Technology, Gdansk University of Technology, Silesian University of Technology, AGH University of Science and Technology, Lodz University of Technology, Poznan University of Technology, and Wroclaw University of Science and Technology. The publications prepared by the employees of the universities of technology were also characterized by the highest number of citations. Value Added This article is one of the first to present (in quantitative terms) the involvement of universities in popularizing sustainable energy development concept. The methodology used in it can be applied to the other Sustainable Development Goals. Recommendations Given the increasing importance of the concept of sustainability in the functioning of universities, it seems necessary to create tools to measure the degree of their involvement in popularizing this idea. Among other things, universities are obliged to share their knowledge in this area. Bibliometric data provided by the SciVal system may be helpful in this regard. It is necessary to conduct research taking into account both domestic and foreign universities.

A typology of climate adaptation costs for a smallholder maize farming system

A typology of climate adaptation costs for a smallholder maize farming system

Distance Analysis Measuring for Clustering using K-Means and Davies Bouldin Index Algorithm

The purpose of this research is to analyze mapping results in the form of clusters formed using clustering method measures. This is done to determine the connections that the existing clusters create. Some of the measurements used are mixed measurements, Bregman differences, and number measurements (Mixed Euclidean Distance, Generalized Divergence, Squared Euclidean Distance, Mahalanobis Distance, and Euclidean Distance). Distance measurement shall be applied on number with primary school facilities in Indonesia. The Davies Bouldin Index (DBI) is different from the cluster number test (k = 2-10) for each Distance Measure. The average DBI value in the type of measure (mixed measure) and numerical measurement (Mixed Euclidean Distance) is 0.54. The average DBI value in the type of measure (Bregman divergences) and numeric measurements (generalized IDivergence) is 0.66. The average DBI value is 0.77 for the measurement type (Bregman divergences) and numerical measurement (Squared Euclidean Distance). From the results, the measurement of distance with mixed measurement and the mixed Euclidean distance with the cluster number (k = 2), namely 0.269, have the best DBI value.

AN APPROACH TO CULTURAL TOURISTS’ SEGMENTATION IN POST-UNESCO COIMBRA

This paper falls within the scope of cultural tourism studies, focusing on a World Heritage Site and intends to contribute to tourism literature by trying to better understand the differences between the tourists visiting it. A survey was conducted by self-administered questionnaires designed to identify different types of cultural tourists in post-UNESCO Coimbra (Portugal) so as to understand what they look for, the importance they assign to motivational factors, whether they are interested in participatory activities which call for cognitive, intellectual or affective engagement and their degree of satisfaction. A hierarchical cluster analysis was carried out based on age, education, family income, nationality, and length of stay. To perform this analysis of identification of homogeneous groups, Ward's method and the Squared Euclidean distance were used as a measure of similarity. The variables were standardized to avoid bias due to the different scales. The results of this analysis allowed the identification of two groups. Chi-square analyses and Mann-Whitney U tests were also carried out for better characterizing the clusters. Results seem to be useful for destination managers to provide what tourists seek, to enhance their experiences and satisfaction, and strengthen this destination competitiveness and value creation.

Impact on outcomes in Europe: a cluster analysis from the ESC-EHRA EORP AF general long-term registry

Abstract Funding Acknowledgements Type of funding sources: None. OnBehalf ESC-EHRA EORP AF General Long-Term Registry Investigators Introduction Data derived from recent observational studies in atrial fibrillation (AF) show how the complexity of the clinical phenotype, beyond baseline thromboembolic risk, can increase risk of major adverse outcomes. Importantly, risk factors tend to occur in clusters, rather than occur individually in isolation. Aims To describe AF patients’ clinical phenotypes among a large contemporary European AF cohort and to analyse the differential impact of these clinical phenotypes on the occurrence of major adverse outcomes. Methods We performed a hierarchical cluster analysis based on Ward’s Method and using Squared Euclidean Distance using 22 clinical covariates. All variables were considered as binary. Examining the distances between cluster coefficients and by visual inspection of the dendrogram produced we identified the optimal number of clusters. Patients with data available for all 22 variables were included. We considered occurrence of cardiovascular events and all-cause death. Results Among the original 11096 patients included, 9363 (84.4%) were available for this analysis. The cluster analysis identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients with prevalent noncardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients mainly admitted for first detected and paroxysmal AF with low prevalence of concomitant conditions; Cluster 3 (n = 2955; 31.6%) included patients with high prevalence of permanent AF, cardiac risk factors and comorbidities. Thromboembolic and bleeding risks were higher in Cluster 3 and progressively lower in Cluster 1 and Cluster 2 (both p < 0.001). Use of oral anticoagulant was significantly lower for Cluster 2 (83.2% vs. 86.5% and 86.7% in Cluster 1 and Cluster 3, respectively; p < 0.001). Over a mean follow-up of 22.5 (SD5.5) months, Cluster 3 had the highest rate of both cardiovascular events (10.0%) and all-cause death (13.2%), compared with Cluster 1 (6.6% and 9.4%, respectively) and Cluster 2 (3.7% and 3.8%, respectively) (both p < 0.001). Kaplan-Meier curves (Figure) show that Cluster 2 (green line) had the lowest cumulative risk of outcomes; risk was progressively higher in Cluster 1 (orange line) and Cluster 3 (yellow line). A Cox multivariable regression analysis, adjusted for type of AF, symptomatic status, CHA2DS2-VASc score and use of oral anticoagulants, showed that both Cluster 3 and Cluster 1 were associated with a significantly increased risk of cardiovascular events (HR: 1.80, 95%CI: 1.39-2.33 and HR: 1.40, 95%CI: 1.09-1.80, respectively) and all-cause death (HR: 1.80, 95%CI: 1.40-2.30 and HR: 1.66, 95%CI: 1.30-2.11) compared to Cluster 2. Conclusions In European AF patients, three main clinical clusters were identified, those with non-cardiac comorbidities, low risk and cardiac comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of cardiovascular events and all-cause death. Abstract Figure. Kaplan-Meier Curves for Outcomes

Assessment of Genetic Diversity of Niger Plant (Guizotia abyssinica L.) in Moiben Sub County, Kenya, Using Inter Simple Sequence Repeat Markers

Niger plant (Guizotia abyssinica), exhibits phenotypic plasticity in different environments. There is need to assess its genetic diversity since guizotia species has a high number of species which may be confused amongst themselves. To achieve this, inter simple sequence repeat (ISSR) markers were used to estimate genetic diversity among 12 wild populations of Niger plant from Moiben sub-county. Total genomic DNA was extracted as per the cetyltrimethylammonium bromide (Ctab) method and subjected to ISSR analysis using 20 primers. None of the primers produced unique banding patterns. ISSR data were used to calculate a squared-euclidean distance matrix. All the twenty primers (100%) gave polymorphic bands thus they were all considered for further analysis. The allele frequency of all the primers was below 0.95 indicating that they were all polymorphic in character. Gene diversity was high ranging from 0.3550 to 0.7337 with a mean value of 0.6302. The ISSR based upgma clustering produced four clusters. Niger plant within Moiben sub-county was found to be genetically diverse though heterozygosity was not noticed. The study recommends further analysis of Niger plant so as to form a basis for further development of the plant.

Genetic diversity of pomegranate genotypes for important horticultural traits

In the present study, genetic relatedness based on 12 phenotypic traits was accessed among 60 pomegranate genotypes which represented 20 indigenous and 40 exotic types. Per se performance was measured for different traits like plant height, number of branches, thorn length, rind thickness, fruit weight, fruit length and fruit breath, etc. The phenotypic correlation coefficients were found higher in magnitude than genotypic correlation coefficients. Using principal component analysis, the first principal component (PC1) explained 28.73% of the total variance that mainly consisted of fruit breadth, fruit length and fruit weight predominantly while, PC2 explained 21.28% variability that mainly included plant height and number of branches and fruit length upto some extent. Further, PC3 and PC4 showed remaining 15.16% and 11.48%, respectively for traits like, thorn length, number of stem and rind thickness. Phylogenetic tree constructed on the basis of phenotypic characteristics by applying Squared Euclidean Distance and group average clustering method showed two diverse groups but partially divided the genotypes on the basis of their type into two major groups or clusters. The present results could be further utilized for screening parents for a particular trait in future breeding programs.

Rail Fastener Positioning Based on Double Template Matching

For global template matching (GTM), which is commonly used in the positioning of rail fasteners, only the fastener template is used to search the global image in both two dimensions, which will result in errors in two dimensions, and the lower positioning accuracy will be caused. A positioning method for rail fasteners based on double template matching (DTM) is proposed in this paper, in which the double template contains the rail template and the fastener template. First, the rail template is used to scan the original image in horizontal dimension, and the squared Euclidean distance (SED) is used to obtain the rail positioning in the original image. Combining with the prior knowledge of the fastener template image, the image composed of the rail and the fastener can be obtained, which is called the Rail Area Map (RAM) in this paper. Then, after preprocessing the RAM and the fastener template image, the fastener template image is used to scan the RAM in vertical dimension, and the normalized correlation coefficient (NCC) is used to calculate the similarity between the template and the subgraph of the RAM to achieve precise positioning of the fastener. The proposed DTM method adopts a positioning strategy from coarse to fine, and two templates are used to complete different positioning tasks in their own dimension, respectively. Due to the rail can be precise positioned in horizontal dimension, the error of the fastener positioning in the horizontal dimension can be avoided, and thus, the positioning accuracy can be improved. Experiments on the on-site line fastener images prove that the proposed method can effectively achieve the precise positioning of fasteners.

Robust semi-supervised nonnegative matrix factorization for image clustering

Robust semi-supervised nonnegative matrix factorization for image clustering

Pythagorean fuzzy linear programming technique for multidimensional analysis of preference using a squared-distance-based approach for multiple criteria decision analysis

Pythagorean fuzzy linear programming technique for multidimensional analysis of preference using a squared-distance-based approach for multiple criteria decision analysis

FCM using squared euclidean distance for e-commerce classification in Indonesia

Clustering is a method of grouping data into several clusters so that the data in one cluster has a high level of similarity while the data between other clusters have a low level of similarity. One method used in clustering is Fuzzy C-Means (FCM) which is a data clustering technique in which the existence of each data point in a cluster is determined by the degree of membership in each cluster. The FCM algorithm has an objective function that requires distance. The distance used in this study is Squared Euclidean distance. The clustering conducted is the clustering of the popularity of e-commerce in Indonesia in 2019 using the variable average number of monthly visitors, number of website visitors, number of social media followers (Twitter, Instagram, and Facebook) as well as the number of workers. The result of this method is the level of popularity of e-commerce in Indonesia, which is divided into gold, silver, and bronze. Clustering results were tested with the Partition Entropy Index (PEI) and Classification Entropy (CE) if the results are getting closer to 0, the results are getting better. The result of PEI is 2.9697e-0, and CE is 2.5710e-04. So, based on the two indexes It can be concluded that FCM using Squared Euclidean distance is good to clustering.

Multiple criteria group decision analysis using a Pythagorean fuzzy programming model for multidimensional analysis of preference based on novel distance measures

Multiple criteria group decision analysis using a Pythagorean fuzzy programming model for multidimensional analysis of preference based on novel distance measures

A time-series clustering methodology for knowledge extraction in energy consumption data

A time-series clustering methodology for knowledge extraction in energy consumption data

A novel risk analysis approach of casing under complex conditions using copulas

A novel risk analysis approach of casing under complex conditions using copulas

Correlation analysis between cocaine samples seized in China by the rapid detection of organic impurities using direct analysis in real time coupled with high-resolution mass spectrometry

Correlation analysis between cocaine samples seized in China by the rapid detection of organic impurities using direct analysis in real time coupled with high-resolution mass spectrometry

Why health policies should be transnational: A case for East Asia Pacific countries

This paper argues that health policies should transcend national boundaries yet should not reach the supranational level. Along with multinational global health efforts, such cross-national health policies are essential to leverage joint efforts by countries learning from their peers that experience similar health system challenges. In our analysis, we used World Bank Health, Nutrition, and Population (HNP) data from 1995 to 2014 for East Asia Pacific (EAP) countries to explore health system comparability across member nations. We applied a hierarchical cluster analysis using Ward's method and a squared Euclidean distance approach to classify 24 EAP countries into four relatively stable clusters based on their (dis)similarities over nine selected health expenditure and health system performance related indicators. One-way analysis of variance (ANOVA) was used to assess the discreteness of the formed clusters. Each cluster had unique characteristics based on the included indicators and health system performance of the member countries. We present transnational health policy recommendations for the EAP region based on both our use of robust methodology and the resulting comparative clusters.

Facial image retrieval on semantic features using adaptive mean genetic algorithm

The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.

Facial Image Retrieval on Semantic Features Using Adaptive Genetic Algorithm

The emergence of larger databases has made image retrieval techniques an essential component, and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.