Cluster Analysis Techniques Research Articles

Teleseismic Indication of Magmatic and Tectonic Activities at Slow- and Ultraslow-Spreading Ridges

Magmatic and tectonic processes in the formation of oceanic lithosphere at slow–ultraslow-spreading mid-ocean ridges (MORs) are more complicated relative to faster-spreading ridges, as their melt flux is overall low, with highly spatial and temporal variations. Here, we use the teleseismic catalog of magnitudes over 4 between 1995 and 2020 from the International Seismological Center to investigate the characteristics of magmatic and tectonic activities at the ultraslow-spreading Southwest Indian Ridge and Arctic Gakkel Ridge and the slow-spreading North Mid-Atlantic Ridge and Carlsberg Ridge (total length of 14,300 km). Using the single-link cluster analysis technique, we identify 78 seismic swarms (≥8 events), 877 sequences (2–7 events), and 3543 single events. Seismic swarms often occur near the volcanic center of second-order segments, presumably relating to relatively robust magmatism. By comparing the patterns of seismicity between ultraslow- and slow-spreading ridges, and between melt-rich and melt-poor regions of the Southwest Indian Ridge with distinct seafloor morphologies, we demonstrate that a lower spreading rate and a lower melt supply correspond to a higher seismicity rate and a higher potential of large volcano-induced seismic swarms, probably due to a thicker and colder lithosphere with a higher degree of along-axis melt focusing there.

A clinical prognostic model of oxidative stress-related genes linked to tumor immune cell infiltration and the prognosis of ovarian cancer patients

BackgroundAccording to statistics, ovarian cancer (OV) is the most prevalent type of gynecologic malignancy and has the highest mortality rate of all gynecologic tumors. Although several studies have shown that oxidative stress (OS) contributes significantly to the onset and progression of cancer, the role of OS in OV needs to be investigated further. Thus, it is critical to comprehend the function of OS-related genes in OV. MethodsIn this study, all data related to the transcriptome and clinical status of the patients were retrieved from “The Cancer Genome Atlas” (TCGA) and “Gene Expression Omnibus” (GEO) databases. Using the unsupervised cluster analysis technique, all patients with OV were classified into two different subtypes (categories) based on the OS gene. All hub genes were screened using the weighted gene co-expression network analysis (WGCNA). Since the hub genes and the differentially expressed genes (DEGs) in both categories were found to intersect, the univariate Cox regression analysis was implemented. A multivariate Cox analysis was also performed to construct a novel clinical prognosis model, which was validated using data from the GEO cohort. In addition, the relationship between risk score and immune cell infiltration level was evaluated using CIBERSORT. Finally, qRT-PCR was used to confirm the expression of the genes used to construct the model. ResultsTwo subtypes of OS were obtained. The findings indicated that OS-C1 had a better survival outcome than OS-C2. The results of WGCNA yielded 112 hub genes. For univariate COX regression analyses, 49 OS-related trait genes were obtained. Finally, a clinical prognostic model containing two genes was constructed. This model could differentiate between patients with OV having varying years of survival in the TCGA and GEO cohorts. The model risk score was verified as an independent prognostic indicator. According to the results of CIBERSORT, many tumor-infiltrating immune cells were found to be significantly related to the risk score. Furthermore, the results revealed that patients with low-risk OV in the CTLA4 treatment group had a high likelihood of benefiting from immunotherapy. qRT-PCR results also showed that the expression of MARVELD1 and VSIG4 was high in the OV samples. ConclusionsAnalysis of the results suggested that the newly developed model, which contained two characteristic OS-related genes, could successfully predict the survival outcomes of all patients with OV. The findings of this study could offer valuable information and insights into the refinement of personalized therapy and immunotherapy for OV in the future.

Clustering Functional Magnetic Resonance Imaging Time Series in Glioblastoma Characterization: A Review of the Evolution, Applications, and Potentials.

In this paper, we discuss how the clustering analysis technique can be applied to analyze functional magnetic resonance imaging (fMRI) time-series data in the context of glioblastoma (GBM), a highly heterogeneous brain tumor. The precise characterization of GBM is challenging and requires advanced analytical approaches. We have synthesized the existing literature to provide an overview of how clustering algorithms can help identify unique patterns within the dynamics of GBM. Our review shows that the clustering of fMRI time series has great potential for improving the differentiation between various subtypes of GBM, which is pivotal for developing personalized therapeutic strategies. Moreover, this method proves to be effective in capturing temporal changes occurring in GBM, enhancing the monitoring of disease progression and response to treatment. By thoroughly examining and consolidating the current research, this paper contributes to the understanding of how clustering techniques applied to fMRI data can refine the characterization of GBM. This article emphasizes the importance of incorporating cutting-edge data analysis techniques into neuroimaging and neuro-oncology research. By providing a detailed perspective, this approach may guide future investigations and boost the development of tailored therapeutic strategies for GBM.

Combining user-centered design and behavioral theory to enhance health technologies: A personas-based approach for a primary-care based multifactorial falls risk assessment tool

IntroductionMultifactorial falls risk assessment tools (FRATs) can be an effective falls prevention method for older adults, but are often underutilized by health care professionals (HCPs). This study aims to enhance the use and implementation of multifactorial FRATs by combining behavioral theory with the user-centered design (UCD) method of personas construction. Specifically, the study aimed to (1) construct personas that are based on external (i.e., needs, preferences) and intrinsic user characteristics (i.e., behavioral determinants); and (2) use these insights to inform requirements for optimizing an existing Dutch multifactorial FRAT (i.e., the ‘Valanalyse’). MethodsSurvey data from HCPs (n = 31) was used to construct personas of the ‘Valanalyse.’ To examine differences between clusters on 68 clustering variables, a multivariate cluster analysis technique with non-parametric analyses and computational methods was used. The aggregated external and intrinsic user characteristics of personas were used to inform key design and implementation requirements for the ‘Valanalyse,’ respectively, whereby intrinsic user characteristics were matched with appropriate behavior change techniques to guide implementation. ResultsSignificant differences between clusters were observed in 20 clustering variables (e.g., behavioral beliefs, situations for use). These variables were used to construct six personas representing users of each cluster. Together, the six personas helped operationalize four key design requirements (e.g., guide treatment-related decision making) and 14 implementation strategies (e.g., planning coping responses) for optimizing the ‘Valanalyse’ in Dutch geriatric, primary care settings. ConclusionThe findings suggest that theory- and evidence-based personas that encompass both external and intrinsic user characteristics are a useful method for understanding how the use and implementation of multifactorial FRATs can be optimized with and for HCPs, providing important implications for developers and eHealth interventions with regards to encouraging technology adoption.

Identifying and Marketing the Spiritual Tourism Destination: A Case Study on Gokarna, Karnataka

Purpose : India has an elusive history as a spiritual destination. The study investigates if a beach destination exists to be a contested site for ‘pilgrim tourists’ and spiritual tourists or could it be the destination which facilitates co-existence. Methodology: The research is undertaken at Gokarna, a significant religious destination in the coastal Karnataka. The study privileges quantitative techniques of descriptive statistics and Cluster Analysis to recognize statistically evident and discriminant visitor types backed by qualitative analysis and interpretation. Findings: The study presents statistically significant clusters of Religious Adherents, Health/Lifestyle seekers, New Age Spiritually Inclined Tourists and Recreational Tourists. The study establishes the elusive connect the new age tourists share with the well-established religious destinations. Implications : The study convincingly recommends that Gokarna could be promoted as a spiritual destination while being acclaimed as beach tourism destination for the new age tourists who seek philosophical and life experiences. Study assists the destination to retain its core offerings and the policy makers to devise well informed development plan to sustainably manage the place and its aura.

Multidimensional typologies of faulty medical device events: analysis, risk characterisation, and stakeholder mapping

ABSTRACT This study uses the publicly available International Medical Devices Database in three-phase research to present empirically grounded multi-dimensional typologies of events involving faulty medical devices. In the first phase, considering three major categorical data, i.e. event actions, event types, and countries, and, by employing descriptive analysis, we identify that perhaps ‘Field safety notice’ is the most dominant event type under which 66 event actions exist. In the second phase, employing the two-step clustering analysis technique, we identify that four distinct clusters exist. Finally, in the last phase, employing the focus group-driven content analysis approach using event reason description in conjunction with the SHELL (software-hardware-equipment-liveware) model, we classify the top 10 event actions. We also identify corresponding risk levels considering the top 10 sources of device problems. Further, the focus group employing the interest-influence framework in conjunction with pertinent self-addressed questions ascertained that the greatest number of top 10 event actions lies at the quadrant wherein the major stakeholder has a high degree of interest and influence in mitigating those medical events. Our study bridges the extant research gap about the lack of comprehensive, data-driven, and evidence-based themes around medical device events, the associated risk characteristics, and stakeholder mapping.

Veri Madenciliğinde Kümeleme Teknikleri: Yöntemler, Zorluklar ve Uygulamalar Üzerine Bir Araştırma

Clustering is a crucial technique in both research and practical applications of data mining. It has traditionally functioned as a pivotal analytical technique, facilitating the organization of unlabeled data to extract meaningful insights. The inherent complexity of clustering challenges has led to the development of a variety of clustering algorithms. Each of these algorithms is tailored to address specific data clustering scenarios. In this context, this paper provides a thorough analysis of clustering techniques in data mining, including their challenges and applications in various domains. It also undertakes an extensive exploration of the strengths and limitations characterizing distinct clustering methodologies, encompassing distance-based, hierarchical, grid-based, and density-based algorithms. Additionally, it explains numerous examples of clustering algorithms and their empirical results in various domains, including but not limited to healthcare, image processing, text and document clustering, and the field of big data analytics.

Characterisation of night-time outdoor lighting in urban centres using cluster analysis of remotely sensed light emissions

Evidence of the negative impact of light pollution on ecosystems is increasing every year. Its monitoring and study requires the identification, characterisation and control of the emitting sources. This is the case of urban centres with outdoor lighting that spills light outside the place it is intended to illuminate. The quantity and nature of the pollutant (artificial light at night) depends on the lamps used and how they are positioned, which is important because a greater proportion of blue light means a greater scattering effect. In this study, we analysed the emissions of 100 urban centres in the north of Granada province (Spain), using images from International Space Station (ISS) in 2012 and 2021, and compared the results with public lighting inventories and with data from the VIIRS instrument. Using inference and cluster analysis techniques, we confirmed an overall increase in emissions and a shift in their colour towards blue, consistent with the results of the lighting inventory analysis. However, there is a discrepancy with the results obtained from VIIRS images, which is explained by the lack of sensitivity of this instrument to blue light. We concluded that the analysis of ISS images is a powerful tool for the study and characterisation of street lighting and its evolution, especially when the changes occur in the blue band.

The variation in preventable hospitalization in patients with type 2 diabetes in Kentucky before and after the Medicaid expansion.

Hospitalizations are more resource intensive and expensive than outpatient care. Therefore, type 2 diabetes-related preventable hospitalization are a major topic of research efficiency in the healthcare system. Analyze county level variation in type 2 diabetes-related preventable hospitalization rates in Kentucky before the Medicaid expansion (2010-2013) and after the Medicaid expansion (2014-2017). Geographic mapping and cluster analysis. Data for a state of the United States of America. We used the KID data to generate geographic mapping for type 2 diabetes-related preventable hospitalizations to visualize rates. We included all Kentucky discharges of age 18 years and older with the ICD9/10 principal diagnosis code for type 2 diabetes. Then, we conducted cluster analysis techniques to compare county-level variation in type 2 diabetes-related preventable hospitalization rates across Kentucky counties pre- and post-Medicaid expansion. County type 2 diabetes-related preventable hospitalization pre- and post-Medicaid expansion. From 2010-2017, type 2 diabetes-related preventable hospitalization discharge rates reduced significantly in the period of the post-Medicaid expansion (P=.001). The spatial statistics analysis revealed a significant spatial clustering of counties with similar rates of type 2 diabetes-related preventable hospitalization in the south, east, and southeastern Kentucky pre- and post-Medicaid expansion (positive z-score and positive Moran's Index value (P>.05). Also, there was a significant clustering of counties with low type 2 diabetes-related preventable hospitalization rates in the north, west, and central regions of the state pre-Medicaid expansion and post-Medicaid expansion (positive z-score and positive Moran's Index value (P>.05). Kentucky counties in the southeast have experienced a significant clustering of highly avoidable hospitalization rates during both periods. Focusing on the vulnerable counties and the economic inequality in Kentucky could lead to efforts to lowering future type 2 diabetes-related preventable hospitalization rates. We used de-identified data which does not provide insights into the frequency of hospitalizations per patient. An individual patient may be hospitalized several times and counted as several individuals.

Bibliometric Evaluation of Open Access Publishing in Intellectual Property Rights

This study conducts a bibliometric evaluation of open access publishing in the realm of intellectual property rights (IPR), aiming to provide insights into the landscape, trends, and potential research directions within this domain. Utilizing data spanning from 1970 to 2024, encompassing 1000 papers with 9684 citations, the analysis employs network visualization, cluster analysis, and trend examination techniques. The results highlight the collaborative nature of scholarly endeavors in IPR, with key themes emerging around the economic implications of IPR, its intersection with global trade, biotechnology, and human rights. These findings offer valuable insights for researchers, policymakers, and practitioners, facilitating evidence-based decision-making and promoting transparency and accessibility in the dissemination of IPR-related knowledge.

Research on Relationship Between Urbanization and Coal Consumption Based on Cluster Analysis Techniques

Since the initiation of the reform and opening-up policy, urbanization in China has seen rapid growth. At the same, the consumption of coal has also been on the rise year by year. This study selects the coal consumption of 30 provinces as the research subject. By employing the cluster analysis method, this paper compares the coal consumption in different regions and summarizes the correlation between the level of urbanization and coal consumption. The research reveals a nonlinear correlation between coal consumption and the level of urbanization: at lower urbanization rates, there is a positive correlation between urbanization and coal consumption. However, once the urbanization rate reaches a certain threshold, the relationship becomes inversely correlated. This paper offers insights for addressing industrial relations and energy level regulation during the urbanization process.

Innovation-driven clustering for better national innovation benchmarking

PurposeThis research paper aims to contribute to the field of innovation performance benchmarking by identifying appropriate benchmarking groups and exploring learning opportunities and integration directions.Design/methodology/approachThe study employs a multi-dimensional innovation-driven clustering methodology to analyze data from the 2019 edition of the Global Innovation Index (GII). Hierarchical and K-means Cluster Analysis techniques are applied using various sets of distance matrices to uncover and analyze distinct innovation patterns.FindingsThis study classifies 129 countries into four clusters: Specials, Advanced, Intermediates and Primitives. Each cluster exhibits strengths and weaknesses in terms of innovation performance. Specials excel in the areas of institutions and knowledge commercialization, while the Advanced cluster demonstrates strengths in education and ICT-related services but shows weakness in patent commercialization. Intermediates show strengths in venture-capital and labour productivity but display weaknesses in R&D expenditure and the higher education quality. Primitives exhibit strength in creative activities but suffer from weaknesses in digital skills, education and training. Additionally, the study has identified 35 indicators that have negligible variance contributions across countries.Originality/valueThe study contributes to finding the relevant countries’ grouping for the enhancement of communication, integration and learning. To this end, this study highlights the innovation structural differences among countries and provides tailored innovation policies.

Analyzing Wintertime Extreme Winds over Türkiye and Their Relationships with Synoptic Patterns Using Cluster Analysis

This study investigates the patterns of extreme winds and the correlation between synoptic patterns in Türkiye throughout the winter season, using the cluster analysis technique. We utilized the k-means algorithm to detect the surface patterns of extreme winds. Additionally, we deployed the Self-Organizing Map (SOM) technique to identify clusters of geopotential height at the 500 hPa level, average temperature at the 850 hPa level, and mean sea level pressure. We adopted the dataset from the New European Wind Atlas (NEWA) project for analyzing surface-level weather conditions and the ERA5 datasets for studying upper-level weather conditions. The k-means algorithm identifies six distinct clusters when applied to the ground-level data in Türkiye. These clusters are predominantly located around the Taurus Mountain ranges, which stretch in an east-west and northeastern direction along the Black Sea coast. The formation of these clusters is controlled by the characteristics of the land and its physical features. The higher-level clusters, consisting of nine SOM nodes, are unaffected by terrain and weather systems, which are characteristic of the macro-Mediterranean climate. These clusters are detected in the Eastern Mediterranean, Black Sea, and inner Aegean areas, emphasizing the impact of topography on surface patterns.

Charting the Path of Technology-Integrated Competence in Industrial Design during the Era of Industry 4.0

The fusion of emerging technologies with industrial design has catalyzed a fundamental shift in the aesthetics, user experiences, and service frameworks of products in the Industry 4.0 era. Simultaneously, this convergence has heightened the demands placed on the technological integration competencies of designers. Consequently, there exists a necessity to articulate a precise developmental trajectory for proficiency in industrial design that incorporates these novel technologies. This study initiates with a bibliometric analysis to quantify the scholarly literature relevant to this research domain. Subsequently, leveraging the insights from this analysis, semi-structured interviews were conducted with 15 experts spanning the United States, Europe, South Korea, and China. Our conclusions show the following: (1) Co-word analysis and cluster analysis techniques are applied to identify 80 technologies and four technological clusters that demonstrate strong associations with industrial design in the Industry 4.0 era. (2) Employing coding techniques and thematic analysis, four distinct skill domains emerge for technology-integrated industrial design: Industrial Design Skills, Industrial Design Knowledge, Ethical Considerations in Industrial Design, and Industrial Design Industry Insight. Furthermore, a limitation that affects these competencies is identified. (3) A recommended methodology for assessing these competencies is proposed. This study represented an expansion upon existing industrial design competencies. The empirical data generated herein serves as a valuable resource for practitioners and educators within the field of industrial design. Furthermore, it provides a theoretical groundwork for future models addressing technology-infused industrial design capabilities.

How are poor sleepers with other clinical conditions affected by maladaptive personality traits? A neural network-based analysis.

Psychopathology research mainly focused on the cross-sectional and longitudinal associations between personality and psychiatric disorders without considering the moment-to-moment dynamics of personality in response to environmental situations. The present study aimed to both cluster a young sample according to three mixed clinical conditions (poor sleep quality, depression, and somatization) and to predict the derived clusters by maladaptive personality traits and sex differences using a deep machine learning approach. A sample of 839 adults aged 18-40 years (64% female) from the west of Iran were clustered according to the mixed clinical conditions using the cluster analysis techniques. An Artificial Neural Network (ANN) modeling is used to predict the derived clusters by maladaptive personality traits and biological gender. A receiver operating characteristic (ROC) curve was used to identify independent variables with high sensitivity specific to the derived clusters. The cluster analysis techniques suggested a fully stable and acceptable four-cluster solution for Depressed Poor Sleepers, Nonclinical Good Sleepers, Subclinical Poor Sleepers, and Clinical Poor Sleepers. The ANN model led to the identification of one hidden layer with two hidden units. The results of Area under the ROC Curve were relatively to completely acceptable, ranging from.726 to.855. Anhedonia, perceptual dysregulation, depressivity, anxiousness, and unusual beliefs are the most valuable traits with importance higher than 70%. The machine learning approach can be well used to predict mixed clinical conditions by maladaptive personality traits. Future research can test the complexity of normal personality traits connected to mixed clinical conditions.

SPILLOVER EFFECTS OF COVID-19 ON USA EDUCATION GROUP STOCKS

In this paper, we explore the price dynamics of 16 representative records of USA Education Group stocks, encompassing two non-overlapping periods (before, during, and after COVID-19). Based on information theory and cluster analysis techniques, our study provides insights into disorder, predictability, efficiency, similarity, and resilience/weakness, considering the most diverse financial stakeholders. Our findings contribute significantly to understanding price dynamics in the education sector, encompassing non-crisis and crisis periods, and provide valuable information for financial risk management, highlighting the importance of considering financial assets’ efficiency and resilience/weakness during economic turbulence, such as the COVID-19 crisis.

Neutrosophic Likert Scale for Evaluating Environmental Sanctions

One of the fundamental tools to guarantee compliance with environmental laws is the sanctions, which act as deterrent mechanisms against possible violations. The present study focused on evaluating public perceptions towards environmental policies and sanctions, applying a methodology that integrates neutrosophy and indeterminate Likert scales to capture the complexity of human opinions on environmental legislation. By distributing a structured questionnaire to a representative sample, data was collected and subsequently analyzed using neutrosophic logic and cluster analysis techniques. The results underlined the importance of considering indeterminacy and uncertainty in the study of public perceptions towards environmental sanctions, providing valuable data for the improvement of environmental policies and communication in this area. This study demonstrates the relevance of addressing human attitudes and behaviors toward environmental challenges inclusively and effectively.

Research on Digital Management of Scientific and Technological Innovation Services in Universities Based on Artificial Intelligence Background

Abstract In this paper, the digital management of scientific and technological innovation services in universities based on the background of artificial intelligence is studied in depth, aiming to create a comprehensive and efficient management platform. By constructing a platform with a multi-level architecture, it realizes the informatization management of the whole process of university science and technology projects. Specifically, this study designed a functional structure of 10 subsystems, covering all stages from project declaration to results management. To improve the management efficiency, principal component analysis and cluster analysis techniques were introduced, and the effectiveness and feasibility of the platform were verified by analyzing 270 questionnaires. The average score is 3.8293, showing that users highly recognize the platform. In addition, by qualitatively and quantitatively analyzing the factors affecting management, the key factors promoting and inhibiting the management efficiency were clarified, providing a new perspective for the management of scientific and technological innovation services in universities.

MULTIVARIATE DATA ANALYSIS TO ASSESS GROUNDWATER HYDROCHEMICAL CHARACTERIZATION IN RAWADANAU BASIN, BANTEN INDONESIA

A multivariate statistical technique of principal component analysis (PCA) and hierarchical cluster analysis (HCA) has been applied to identify and classify the various water sources that comprise the Rawadanau Basin. The data collection includes 60 samples taken during the dry (29 samples) and the rainy season (31 samples) in tropical regions. Sources of sampled water include dug wells, rivers, cold springs, and hot springs. Water chemistry measurable variables include field data (T, pH, EC), major ions (Na+, K+, Ca2+, Mg2+, Cl-, HCO3 -, SO4 2-), SiO2 , Fetotal, Mn, and stable isotopes of water (δ2H, and δ18O). The correlation of the concentration of water chemistry shows changes in the rainy season to Fetotal and Mn. Interpretation based on HCA using the dendrogram based on the chemical elements of water produces two clusters. Cluster A reflects an unconfined aquifer and bicarbonate type. Meanwhile, cluster B is a chloride type from the confined aquifer and does not change in different seasons. The PCA results show that the three-component matrix accounts for 86.12% of the data structure describing the Rawadanau Basin water sources that volcanic rocks affect and strongly correlate with Na+, K+, Ca2+, and Mg2+. PC1 has a high positive value for hydrochemical composition, indicating that lithology influences the kind of water. PC2 has a positive value for the stable isotope (δ18O and δ2H), meaning it is the main water source in Rawadanau. PC3 has a positive value for elevation and a negative for longitude, indicating a recharge area influenced by geological factors and is correlated with geothermal influences and volcanic rocks. This multivariate analysis can identify components and clusters of hydrochemical variables that have not been determined in previous studies.

The Visual Psychological Space of Chinese Traditional Landscape Painting Creators in the New Media Background

Abstract Based on the connection between visual psychology and landscape painting, the article investigates the spatiality characteristics of images and the visual balance factors affecting the creation of landscape paintings. By applying a vector median filter, this study successfully removes noise from color landscape painting images and uses vectors to represent these noise-reduced images. Lab color model was constructed and combined with cluster analysis technique to calculate the color difference between image pixel points. Binarization of the image is achieved by preprocessing the segmented image. The study estimated and analyzed landscape paintings’ aesthetic characteristics and quantitative values. The results show that among the 30 selected landscape paintings, the percentage of white space area is more than 0.4 is 36.67%, while the rate of less than 0.4 is 63.33%. Among the landscape painting creators who participated in the study, the mean of the total scores of the experienced ones on the four visual psycho-spatial abilities was 83.39, which was 14.217 points higher than that of the inexperienced group. This indicates that the method proposed in this paper is very effective in assessing the visual psychospatial abilities of landscape painting creators, and that the creators can improve their creative skills after long-term training.