Combination Of Cluster Analysis Research Articles

Analysis and prediction of sputtering yield using combined hierarchical clustering analysis and artificial neural network algorithms

Sputtering is a crucial technology in fields such as electric propulsion, materials processing and semiconductors. Modeling of sputtering is significant for improving thruster design and designing material processing control algorithms. In this study we use the hierarchical clustering analysis algorithm to perform cluster analysis on 17 descriptors related to sputtering. These descriptors are divided into four fundamental groups, with representative descriptors being the mass of the incident ion, the formation energy of the incident ion, the mass of the target and the formation energy of the target. We further discuss the possible physical processes and significance involved in the classification process, including cascade collisions, energy transfer and other processes. Finally, based on the analysis of the above descriptors, several neural network models are constructed for the regression of sputtering threshold E th, maximum sputtering energy E max and maximum sputtering yield SY max. In the regression model based on 267 samples, the four descriptor attributes showed higher accuracy than the 17 descriptors (R 2 evaluation) in the same neural network structure, with the 5×5 neural network structure achieving the highest accuracy, having an R 2 of 0.92. Additionally, simple sputtering test data also demonstrated the generalization ability of the 5×5 neural network model, the error in maximum sputtering yield being less than 5%.

Landscape Character Assessment (LCA) in Historic Coal Mining Settings for Landscape Conservation: A Systematic Review

Landscape character assessment (LCA) is a crucial tool for conserving an area’s unique character. However, in our literature review, we found no data linking LCA to historic coal mining settings. This systematic review explores the ways in which the landscape character assessment (LCA) methodology has been applied, as well as the factors that influence it, in the conservation of historic coal mine landscapes. It focuses on three areas: analyzing the ways in which LCA has been applied in landscape conservation, proposing recommendations for the application of LCA in historic coal mine setting landscapes, and summarizing the factors that influence LCA in landscape conservation in historic coal mine settings. Methods: This study used the Meta-Analyses (PRISMA) method to perform the systematic review. The whole review was selected from 2030 potential articles; a total of 21 articles were included. Results: This study demonstrates that the LCA approach can be operationalized in the conservation of environmental landscapes in historic coal mines by combining cluster analysis and multi-scale assessment and incorporating other theories. The quality of the results can be affected by factors such as the accuracy and completeness of the data and the complexity and tractability of the model. Conclusions: Future research should focus on improving the data capture technology, model complexity, and design of actionable models. Additionally, we recommend the strategies of enhancing stakeholder engagement and raising public awareness.

Analisis Efektivitas Program Hibah Pokmas terhadap Pemberdayaan Masyarakat Desa

The objectives of this study are: 1) to find out how the impact of the POKMAS program on village community empowerment; and 2) to find out what are the factors that hamper the effectiveness of POKMAS in an effort to realize the empowerment of rural communities. Based on the combination of Cluster Analysis and Root Cause Anaysis methods, the POKMAS program has proven to have implications for the empowerment of rural communities as indicated through three things, namely: 1) an increase in income especially the poor; 2) the community is able to arrange village development planning and implementation; and 3) the community is able to take responsibility. However, the implementation of the program still leaves four problems, namely: 1) the limited amount of grant funds is adjusted to the needs of the village; 2) low participation of the poor and women; 3) the interests of individuals or small groups are still more dominant; and 4) limited administrative capacity. Overall, the POKMAS program has proven to have achieved village independence so it is important to hand over development to the village. This finding can be a proliferation of empowerment models that are based on the transfer of village authority.

Talent agglomeration research within digital economy development: Trend analysis and research prospects

Talent is important for development and innovation across fields. Further, in this era of digital economy, primarily driven by modern information networks, how to attract talent and effectively leverage talent’s agglomeration effect is a crucial research topic. Thus, this paper reviewed studies from the China Knowledge Network in the Chinese Social Sciences Citation Index and from the Web of Science Core Collection. By combining cluster analysis based on keyword co-occurrence with a review of highly relevant literature, this paper determines and compares mainstream directions in both domestic Chinese and international studies on talent agglomeration effects and digital economy talent research, thereby constructing a two-dimensional analytical framework. This paper recommends four research paths for the effects of talent agglomeration, namely, talent management, enterprise–industry development, industry–regional development, and industry–regional level of talent agglomeration. Meanwhile, considering the digital economy’s key role in future national economic strategies, its human resources studies should be included in these same paths for studying talent aggregation effects. Introducing public sector strategic manement theories into research on digital-economic talent aggregation could prove an extremely valuable subject matter in the future.

Interactions between patterns of multimorbidity and functional status among hospitalized older patients: a novel approach using cluster analysis and association rule mining

BackgroundMultimorbidity (MM) is generally defined as the presence of 2 or more chronic diseases in the same patient and seems to be frequently associated with frailty and poor quality of life. However, the complex interplay between MM and functional status in hospitalized older patients has not been fully elucidated so far. Here, we implemented a 2-step approach, combining cluster analysis and association rule mining to explore how patterns of MM and disease associations change as a function of disability.MethodsThis retrospective cohort study included 3366 hospitalized older patients discharged from acute care units of Ancona and Cosenza sites of Italian National Institute on Aging (INRCA-IRCCS) between 2011 and 2017. Cluster analysis and association rule mining (ARM) were used to explore patterns of MM and disease associations in the whole population and after stratifying by dependency in activities of daily living (ADL) at discharge. Sensitivity analyses in men and women were conducted to test for robustness of study findings.ResultsOut of 3366 included patients, 78% were multimorbid. According to functional status, 22.2% of patients had no disability in ADL (functionally independent group), 22.7% had 1 ADL dependency (mildly dependent group), and 57.4% 2 or more ADL impaired (moderately-severely dependent group). Two main MM clusters were identified in the whole general population and in single ADL groups. ARM revealed interesting within-cluster disease associations, characterized by high lift and confidence. Specifically, in the functionally independent group, the most significant ones involved atrial fibrillation (AF)-anemia and chronic kidney disease (CKD) (lift = 2.32), followed by coronary artery disease (CAD)-AF and heart failure (HF) (lift = 2.29); in patients with moderate-severe ADL disability, the most significant ARM involved CAD-HF and AF (lift = 1.97), thyroid dysfunction and AF (lift = 1.75), cerebrovascular disease (CVD)-CAD and AF (lift = 1.55), and hypertension-anemia and CKD (lift = 1.43).ConclusionsHospitalized older patients have high rates of MM and functional impairment. Combining cluster analysis to ARM may assist physicians in discovering unexpected disease associations in patients with different ADL status. This could be relevant in the view of individuating personalized diagnostic and therapeutic approaches, according to the modern principles of precision medicine.

Combining cluster analysis with synthetic control for evaluating economic impacts of the dam breach in Mariana, Brazil

Combining cluster analysis with synthetic control for evaluating economic impacts of the dam breach in Mariana, Brazil

Appropriate mowing can promote the growth of Anabasis aphylla through the auxin metabolism pathway

Anabasis aphylla (A. aphylla), a species of the Amaranthaceae family, is widely distributed in northwestern China and has high pharmacological value and ecological functions. However, the growth characteristics are poorly understood, impeding its industrial development for biopesticide development. Here, we explored the regenerative capacity of A. aphylla. To this end, different lengths of the secondary branches of perennial branches were mowed at the end of March before sprouting. The four treatments were no mowing (M0) and mowing 1/3, 2/3, and the entire length of the secondary branches of perennial branches (M1–M3, respectively). Next, to evaluate the compensatory growth after mowing, new assimilate branches’ related traits were recorded every 30 days, and the final biomass was recorded. The mowed plants showed a greater growth rate of assimilation branches than un-mowed plants. Additionally, with the increasing mowing degree, the growth rate and the final biomass of assimilation branches showed a decreasing trend, with the greatest growth rate and final biomass in response to M1. To evaluate the mechanism of the compensatory growth after mowing, a combination of dynamic (0, 1, 5, and 8 days after mowing) plant hormone-targeted metabolomics and transcriptomics was performed for the M0 and M1 treatment. Overall, 26 plant hormone metabolites were detected, 6 of which significantly increased after mowing compared with control: Indole-3-acetyl-L-valine methyl ester, Indole-3-carboxylic acid, Indole-3-carboxaldehyde, Gibberellin A24, Gibberellin A4, and cis (+)-12-oxo-phytodienoic acid. Additionally, 2,402 differentially expressed genes were detected between the mowed plants and controls. By combining clustering analysis based on expression trends after mowing and gene ontology analysis of each cluster, 18 genes related to auxin metabolism were identified, 6 of which were significantly related to auxin synthesis. Our findings suggest that appropriate mowing can promote A. aphylla growth, regulated by the auxin metabolic pathway, and lays the foundation for the development of the industrial value of A. aphylla.

SPT-SZ MCMF: an extension of the SPT-SZ catalogue over the DES region

ABSTRACT We present an extension to a Sunyaev–Zel’dovich Effect (SZE) selected cluster catalogue based on observations from the South Pole Telescope (SPT); this catalogue extends to lower signal to noise than the previous SPT–SZ catalogue and therefore includes lower mass clusters. Optically derived redshifts, centres, richnesses, and morphological parameters together with catalogue contamination and completeness statistics are extracted using the multicomponent matched filter (MCMF) algorithm applied to the S/N > 4 SPT–SZ candidate list and the Dark Energy Survey (DES) photometric galaxy catalogue. The main catalogue contains 811 sources above S/N = 4, has 91 per cent purity, and is 95 per cent complete with respect to the original SZE selection. It contains in total 50 per cent more clusters and twice as many clusters above z = 0.8 in comparison to the original SPT-SZ sample. The MCMF algorithm allows us to define subsamples of the desired purity with traceable impact on catalogue completeness. As an example, we provide two subsamples with S/N > 4.25 and S/N > 4.5 for which the sample contamination and cleaning-induced incompleteness are both as low as the expected Poisson noise for samples of their size. The subsample with S/N > 4.5 has 98 per cent purity and 96 per cent completeness and is part of our new combined SPT cluster and DES weak-lensing cosmological analysis. We measure the number of false detections in the SPT-SZ candidate list as function of S/N, finding that it follows that expected from assuming Gaussian noise, but with a lower amplitude compared to previous estimates from simulations.

Semantic sensor data annotation method for industrial scene efficiency optimization to enable digital economy

AbstractIn the digital economy era, efficiently leveraging the vast amount of sensor data generated by the Industrial Internet of Things (IIoT) is essential. This paper presents an innovative semantic annotation method for industrial sensor data, designed to optimize data processing and enhance system efficiency. Our method combines cluster analysis, ontology development, and rule‐based reasoning to automatically annotate IIoT sensory data. By utilizing data aggregation and filtering mechanisms, which incorporate the DBSCAN (Density‐Based Spatial Clustering of Applications with Noise) algorithm and a rule engine, we significantly reduce the data volume required for annotation. The Semantic Web Rule Language aids in naming new concepts and properties identified through clustering, contributing further to the automation of data processing. Experimental results, using public datasets, validate the effectiveness of our method, showing a reduction in data volume by about 20% and underscoring its potential in enhancing industrial systems' automation and overall efficiency.

Solo traveler typology based on personal value: Incorporating cluster analysis into means-end chain

In recent years, the number of solo travelers has been steadily increasing, attracting more attention from the tourism market. However, both the tourism industry and academia have limited understanding of this group. This paper aims to delve into the decision-making process of solo travelers and establish a typology of solo travelers based on personal values. This research combines cluster analysis with means-end chain analysis to categorize solo travelers into four types: pleasure-seeking solo travelers, social solo travelers, exploratory solo travelers, and escape-oriented solo travelers. Each type of solo traveler emphasizes different attributes, consequences, and values in their solo travel experiences, and they also hold different views and attitudes towards solo travel phenomenon. By synthesizing the opinions of different types of solo travelers, this research reveals the key characteristics of solo travel, namely, “departing alone” and “enjoying one's own company.” These findings may provide insights for market segmentation and marketing strategies in the solo travel industry.

Bivariate regional drought frequency analysis using multivariate approaches: a case study in southwestern Iran

Abstract Bivariate approaches in Regional Frequency Analysis (RFA) address two issues: first, to evaluate the homogeneity of regions, and second, to estimate the joint return periods. This study was conducted to investigate the joint return period of a severe historical drought in southwestern Iran. Fifty-nine rain gauges were first clustered into three, four, and five regions using the fuzzy c-means clustering (FCM) algorithm. Then bivariate discordancy and homogeneity tests were applied to adjust the initial clusters. The results showed that only in the case of three clusters were all the regions homogeneous. Therefore, it can be inferred that combining clustering analysis and discordancy test is insufficient to form homogeneous regions. Finally, the joint return period, by choosing Generalized Logistic and Wakeby as marginal distributions and Clayton as a copula, was estimated for all the sites in the three regions. Since no three-parameter distribution function fitted well to the variable severity, the bivariate homogeneity index does not necessarily attest to region homogeneity regarding the marginal distribution functions. It is also deduced that sites with higher mean annual precipiataion (MAP) and, correspondingly, higher elevation are more likely to experience shorter return periods of same drought events, in contrast to sites with lower MAP or lower elevation.

Mobilization, self-expression or argument? A computational method for identifying language styles in political discussion on Twitter

PurposeThis study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.Design/methodology/approachThis study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.FindingsThrough the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.Practical implicationsThis study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.Social implicationsThis study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.Originality/valueThis study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

Law Case Teaching Combining Big Data Environment With SPSS Statistics

This paper proposes an online learning platform learner DM method based on the improved fuzzy C clustering (FCM) algorithm, constructs a learner feature database, and combines clustering analysis and SPSS statistical methods to statistically summarize the big data of law, thus improving the deficiencies of static and absolute classification of students in the student model. In the experiment paper, the improved algorithm is implemented and the experimental data is analyzed. The results show that the learner behavior feature extraction model in this paper has fewer errors and higher recall rate. Compared with the traditional CF algorithm, the error rate is reduced by 19.64% and the recall rate is increased by 22.85%. This study provides better targeted teaching programs and case resources for legal case teaching and promotes the innovation of legal case teaching mode.

Combining Cluster Analysis with Multiple Linear Regression Analysis to Create the Most Accurate Prediction Model for Evaporation in the Kurdistan Region of

Combining Cluster Analysis with Multiple Linear Regression Analysis to Create the Most Accurate Prediction Model for Evaporation in the Kurdistan Region of

Combining Cluster Analysis with Multiple Linear Regression Analysis to Create the Most Accurate Prediction Model for Evaporation in the Kurdistan Region of

Combining Cluster Analysis with Multiple Linear Regression Analysis to Create the Most Accurate Prediction Model for Evaporation in the Kurdistan Region of

MRI phenotypes of the brain are related to long‐term dementia outcome in community‐dwelling older adults

AbstractBackgroundIndividual brain MRI markers only show at best a modest association with long‐term occurrence of dementia. Therefore, it is challenging to accurately identify individuals at increased risk for dementia. We aimed to identify different brain MRI phenotypes by combined hierarchical clustering analysis based on neurodegenerative and neurovascular brain MRI markers in community‐dwelling individuals. Within each of these brain MRI phenotype subgroups, we determined the long‐term dementia risk.MethodA hierarchical clustering analysis based on combined brain MRI markers in community‐dwelling individuals of the Age‐Gene/Environment Susceptibility (AGES) Reykjavik Study (n = 3056) was applied to identify different brain MRI phenotypes. Brain MRI markers used in the hierarchical clustering analysis were brain volumes for the estimation of brain atrophy, WMH markers (volume and shape), brain infarcts, microbleeds, and enlarged perivascular spaces.ResultWe identified 14 distinct subgroups of individuals with different brain MRI phenotypes (figure 1 and figure 2). The brain MRI phenotypes of the subgroups ranged from limited burden (subgroup (S) 12), mostly atrophy and infarcts (S14), mostly irregularly shaped WMH and atrophy (S3) to a multi‐burden subgroup (S2). These subgroups had a different long‐term risk for dementia (mean time to follow‐up: 9.9 ± 2.6 years; hazard ratio ranging from 4.1 (95%‐CI: 3.19‐13.40) to 13.8 (95%‐CI: 4.28‐44.37); see figure 3); especially the multi‐burden brain MRI phenotype showed an increased risk compared to the reference group (HR: 13.8 (95%‐CI: 4.28‐44.37)).ConclusionOur results indicate that distinct brain MRI phenotypes are related to different long‐term risks of developing dementia. Brain MRI phenotypes may assist in an improved understanding of the structural correlates of dementia predisposition. These findings may in the future be useful to determine patient prognosis and may aid in patient selection for future treatment studies.

A clustering-enhanced potential-based reduced order homogenization framework for nonlinear heterogeneous materials

This paper proposes a data-driven approach to improve the efficiency of computational homogenization for nonlinear hyperelastic materials with different microstructures in a small strain context. By combining clustering analysis and Proper Orthogonal Decomposition (POD) with efficient sampling, a reduced order model is established to accurately predict elastoplasticity under monotonic loadings. The microscopic RVE is spatially divided into multiple clusters using the k-means clustering algorithm during the offline phase. As suggested in Kunc and Fritzen (2019a), the reduced order model is constructed using reduced bases of deformation gradient fluctuations on the microscale. In contrast to the conventional displacement-based approach, deformation gradient fluctuations are employed to generate the POD snapshots. To improve the prediction accuracy and reduce the cost of offline computation, the energy minimum point set generation method proposed by Kunc and Fritzen (2019b) is employed. Numerical results show a acceleration factor in the order of 10-100 compared to a purely POD-based model can be archived, which significantly improves the applicability for structural analysis, while maintaining a sufficient accuracy level.

The Genetic Dissection of Economically Important Traits in Chilli Genotypes (Capsicum annuum L.) through Multivariate Analysis

Chilli (Capsicum annuum L.) cultivar share a variety of different and intriguing growth and yield characteristics, for a plant breeder studies on genotypic and phenotypic relationship for horticulture cum vegetable spicy crop and its traits are useful to design, evaluate and to develop selection criteria for desired traits. With this background information the present study focused on identification of crucial characteristics required for boosting yield and to determine the parameters for the selection of superior genotypes. A total of 30 chilli genotypes were subjected to variability, divergence and mutual correlation studies for yield and yield component traits by using multivariate analysis.
 Results from the analysis of variance and variability revealed highly significant differences at (p≤0.01) and (p≤0.05) for all the studied traits. The highest genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were observed for FL fallowed by FYP, NFP, IFW, WPDM, FD, PH and NSF respectively, except for MC has revealed low GCV and PCV. All the traits were shown the presence of high heritability ( ) in broad sense (>60%) coupled with high GAM (>20%), except for MC, indicating the presence of positive additive gene action and effective transmission of characters to the next generation with selection. Highly significant and positive strong correlation was observed for IFW (r = 0.68**), WPDM (r = 0.54**), TW (r = 0.40*) and FD (r = 0.37*) with FYP, while the path analysis revealed highest direct effect for the traits such as FPT, FL and NFP. Principle component analysis revealed a 70.60% total variation accounted with first four PCAs with Eigen vector values more than one.
 Further cluster analysis grouped the 30 chilli genotypes into four major cluster (Cluster I, II, III and IV) and combined cluster analysis identified genotypes contributing to the maximum variability, viz., cultivars such as LIPSTICK, B.KADDI, B.DABBI, BHOOT JALOKIA, SUM- 17, BCH- 42, PBC- 80, LCA- 310, M- 262, KA2- L, S. SELECTION 1, JCH- 42 and 9608- U, so these genotypes serves as a donor for further breeding programme to develop superior cultivars.

Combined cluster and discriminant analysis based on hydrochemical and isotopic evidence: A case study of the Yellow River Delta in China

Groundwater serves as an indispensable resource, fundamental for both sustaining life and underpinning various human endeavors. However, escalating demands, coupled with climate change, have given rise to significant water scarcity in numerous areas, including the Yellow River Delta (YRD) in China. This study aims to characterize the hydrochemical composition and stable isotopes (δ2H and δ18O) of groundwater in YRD and identify spatial clustering patterns using combined Hierarchical Cluster Analysis (HCA) and Discriminant Analysis (DA). Comprehensive analyses were conducted on the major ions (i.e., K+, Na+, Ca2+, Mg2+, Cl-, SO42-, and HCO3-), water quality indicators (i.e., pH, electrical conductivity (EC), and oxidation-reduction potential (ORP)) and isotopic composition of groundwater samples. Applying HCA on a collection of 38 water samples, the study delineated six distinct clusters, each bearing unique hydrochemical characteristics. DA was used to establish a discriminant model, achieving a classification accuracy of 80% for new groundwater samples. The study underscores that there are localized variations in the hydrochemical characteristics of groundwater in YRD, attributed to different geological conditions and anthropogenic influences. Such insights are instrumental in crafting effective strategies for water resource management, environmental conservation, and sustainable development in YRD and other analogous regions confronted with groundwater challenges.

Prediction of maximum scour depth in river bends by the Stacking model

Abstract The accurate prediction of maximum erosion depth in riverbeds is crucial for early protection of bank slopes. In this study, K-means clustering analysis was used for outlier identification and feature selection, resulting in Plan 1 with six influential features. Plan 2 included features selected by existing methods. Regression models were built using Support Vector Regression, Random Forest Regression (RF Regression), and eXtreme Gradient Boosting on sample data from Plan 1 and Plan 2. To enhance accuracy, a Stacking method with a feed-forward neural network was introduced as the meta-learner. Model performance was evaluated using root mean squared error, mean absolute error, mean absolute percentage error, and R2 coefficients. The results demonstrate that the performance of the three models in Plan 1 outperformed that of Plan 2, with improvements in R2 values of 0.0025, 0.0423, and 0.0205, respectively. Among the three regression models in Plan 1, RF Regression performs the best with an R2 value of 0.9149 but still lower than the 0.9389 achieved by the Stacking fusion model. Compared to the existing formulas, the Stacking model exhibits superior predictive performance. This study verifies the effectiveness of combining clustering analysis, feature selection, and the Stacking method in predicting maximum scour depth in bends, providing a novel approach for bank protection design.