Cluster Groups Research Articles

Comparative Analysis of Inorganic and Organic Sources on Economic Viability Productivity of Turmeric (Curcuma Longa L.)

Turmeric (Curcuma longa L.) (Family: Zingiberaceae), Known as "Indian Saffron," a long-used and revered spice, is a significant commercial spice crop farmed in India. Known by many as the "Golden Spice of life," it is one of the most vital spices and a staple in cuisine all over the world. A tropical perennial plant native to Indonesia and India, turmeric is grown all over the world's tropics. India grows this significant commercial spice. It is ingrained in Indian tradition. The world's best turmeric is thought to come from India. In many Asian nations, Indian colloquial names are known as pasupu, haldi, manjal, and kunyit. Just 6% of India's land is used for growing spices and condiments, despite the country being the world's top producer, exporter, and supplier of 78% of the world's supply of turmeric. Furthermore, among Indian spices, turmeric is the second-biggest earner of foreign cash. India being one of the major producers of turmeric, contributes 80% to global production. In the year 2018-19, turmeric production was 389 thousand tonnes, with area and productivity 246 thousand hectares and 5646.34 kg per hectare respectively. The growth pattern of the area, production, and productivity of turmeric over the period of time indicate the growing contribution of production over the area expansion to the increased yield. India uses almost 80% of the world's turmeric. It makes up 80% of global production. With the largest proportion of 38% of all India's turmeric area, Andhra Pradesh is referred to as the "Turmeric Bowl of India," with Orissa coming in second. Another important policy strep i.e. “One District One Product” under Centrally Sponsored Scheme PMFME (PM Formalization of Micro food processing Enterprises Scheme), Turmeric is listed in the selected product in all states, this is an additional scope under which states can be encouraged to adopt cluster approach and group approaches such as FPOs, SHGs (Self Help Groups) and producer cooperatives. This will help to bring the win-win situation to both farmers and the microenterprises. Also, except Assam, the spread of formal/organized food processing units are scanty, the innovation center for developing value-added products from the traditional knowledge can be encouraged so that in the long run the product can be easily scaled up.

Long-term characterization of behavior phenotypes in children with seizures: Analytic approach matters.

Behavioral problems in children with new onset epilepsies have been well established in the literature. More recently, the literature indicates the presence of unique behavioral patterns or phenotypes in youth with epilepsy that vary significantly in vulnerability and resilience to behavioral problems. This study contrasts the interpretation of behavioral risk as inferred from cross-sectional versus latent group analytic perspectives, as well as the presence, consistency, stability, and progression of behavioral phenotypes in youth with new onset epilepsy and sibling controls over 3 years. Three hundred twelve participants (6-16 years old) were recruited within 6 weeks of their first recognized seizure along with 223 unaffected siblings. Each child's behavior was recorded by parents and teachers frequently over 36 months using the Child Behavior Checklist (CBCL), and each child completed self-report measures of depression symptoms over 36 months. Measures were evaluated cross-sectionally and longitudinally to identify clusters with prototypical behavioral trajectories. Cross-sectional analyses exhibited a pattern of generalized and undifferentiated behavioral problems compared to sibling controls at baseline and prospectively. In contrast, latent trajectory modeling identified three distinct behavior phenotype clusters across all raters (parents, teachers, and youth) over baseline and longitudinal assessments. CBCL Cluster 1 (~30% of youth with epilepsy) exhibited behavior similar to/better than controls, Cluster 2 (~50%) exhibited moderate behavior issues, and Cluster 3 (~20%) exhibited the most pronounced/problematic behavior, falling into Achenbach's clinically relevant behavior range. Behavior within clusters remained stable and consistent. Teachers' and children's behavior assessments corresponded to these cluster groupings consistently over 36 months. Predictors of cluster membership include seizure syndrome type and social determinants of health. This study demonstrates the varying public health perspectives of behavioral risk in youth with epilepsy that result as a function of analytic approach as well as the presence of distinct latent behavioral trajectory phenotypes over time in youth with new onset epilepsy.

Longitudinal Study of Transcriptomic Changes Occurring over Six Weeks of CHOP Treatment in Canine Lymphoma Identifies Prognostic Subtypes

The majority of canine lymphoma patients treated with the standard of care, the CHOP chemotherapy protocol, initially achieve remission but eventually relapse with a multi-drug-resistant phenotype. This study assesses gene expression profiles of canine lymphoma tumor cell populations using RNA-Seq data from 15 matched patient samples taken prior to treatment and again six weeks into treatment with CHOP. Two distinct clusters were present in the t-SNE dimensionality reduction of the gene expression profiles. There was a significant difference in progression-free survival (PFS) between the cluster groups, with a median of 43.5 days in a group of six patients and 185 days in another group of nine patients. Comparing the group with shorter PFS to the group with longer PFS, we identified 265 significantly enriched GO:BP terms in 3874 significantly up-regulated genes and 740 significantly enriched GO:BP terms in 3236 significantly down-regulated genes. Comparing the six-week timepoint against the initial timepoint, in the group with longer PFS, we identified 277 significantly enriched GO:BP terms in 413 significantly up-regulated genes and 222 significantly enriched GO:BP terms in 267 significantly down-regulated genes. In the group with shorter PFS, we only identified 27 significantly differentially expressed genes, for this comparison. We found DNA damage response genes to be enriched in the down-regulated genes in both comparisons. These results identify and characterize two transcriptionally distinct groups of canine lymphoma patients with significantly different responses to CHOP chemotherapy.

Analysis of the Effect of Bone Cement Distribution Patterns on Surgical Outcomes of Percutaneous Vertebroplasty for Osteoporotic Vertebral Compression Fractures

Objective: To evaluate the impact of bone cement distribution patterns on the surgical outcomes of percutaneous vertebroplasty (PVP) for osteoporotic vertebral compression fractures (OVCF). Methods: Sixty-four patients with OVCF treated at our hospital from January 2022 to December 2023 were included and divided into two groups based on bone cement distribution patterns: the clustered group and the diffuse group. Both groups underwent PVP. We compared the incidence of bone cement leakage, operative time, blood loss, changes in Visual Analog Scale (VAS) pain scores, and Oswestry Disability Index (ODI) scores before and after treatment between the two groups. Results: The number of cases with bone cement leakage was significantly higher in the diffuse group compared to the clustered group (P < 0.05). There were no significant differences between the two groups in terms of volume of bone cement injected, intraoperative blood loss, or surgical time (P > 0.05). Post-treatment, both VAS and ODI scores were lower than pre-treatment scores in both groups, with the diffuse group showing significantly lower scores than the clustered group (P < 0.05). No postoperative adverse complications were observed in either group. Conclusion: PVP is highly effective for treating OVCF. A more uniform distribution of bone cement is associated with a greater reduction in postoperative pain and improved functional outcomes. However, careful attention is needed to prevent bone cement leakage.

Monitoring Metacognitive Strategies Use During Interaction Collaborative Groups

This study of the use of metacognitive strategies during learning interaction in collaborative groups is considered referential for understanding the development of such strategies. The use of metacognitive strategies is related to better learning outcomes. We monitored the interaction in five collaborative groups at three points in time (initial, intermediate and final). These groups consisted of undergraduate health science (n = 9) and biomedical engineering students (n = 10). The aims were the following: (1) To check whether there were significant differences in the use of metacognitive strategies between the groups of students monitored depending on the type of degree or the point in time measurement. (2) To test whether there were significant differences in students’ learning outcomes depending on whether or not they had participated in the interaction monitoring during collaborative interaction. (3) To test clustering without a pre-assignment variable against learning outcomes in collaborative groups. Significant differences were detected in the use of some of the metacognitive strategies of orientation, planning and elaboration in favor of the collaborative groups of the biomedical engineering students. Also, students who participated in monitoring collaborative groups in both clusters obtained better learning outcomes. Lastly, the assignment groups were exactly related to the collaboration groups with respect to the use of metacognitive strategies in the final measurement.

Spinocerebellar ataxia type 10 and Huntington disease-like 2 in Venezuela: Further evidence of two different ancestral founder effects.

The American continent populations have a wide genetic diversity, as a product of the admixture of three ethnic groups: Amerindian, European, and African Sub-Saharan. Spinocerebellar ataxia type 10 (SCA10) and Huntington disease-like 2 (HDL2) have very ancient ancestral origins but are restricted to two populations: Amerindian and African Sub-Saharan, respectively. This study aimed to investigate the genetic epidemiological features of these diseases in Venezuela. In-phase haplotypes with the expanded alleles were established in seven unrelated index cases diagnosed with SCA10 and in 11 unrelated index cases diagnosed with HDL2. The origins of remote ancestors were recorded. The geographic origin of the ancestors showed grouping in clusters. SCA10 had a minimal general prevalence of 1:256,174 families in the country, but within the identified geographic clusters, the prevalence ranged from 5 per 100,000 to 43 per 100,000 families. HDL2 had a general prevalence of 1:163,016 families, however, within the clusters, the prevalence ranged from 31 per 100,000 to 60 per 100,000 families. The locus-specific haplotype shared by all families worldwide, including the Venezuelans, supports a single old ancestral origin in each case. Knowing the genetic ancestry and geographic origins of patients in Ibero-American mixed populations could have significant diagnostic implications; thus, both diseases in Venezuela should always be first explored in patients with a suggestive phenotype and ancestors coming from the same known geographic clusters.

Employing cluster analysis in defining groundwater wells patterns in Rafah

Clustering analysis techniques used in identifying homogeneity of groundwater wells patterns in terms of Chloride, Nitrate, and TDS. Data was collected through the laboratories of Palestinian Water Authority in the Rafah 2020. R-Programming is used for data analysis. Kolmogorov-Smirnov test is used to test data normality. Hierarchical clustering analysis applied to generate a cluster tree (r>0.75) in correlation matrix. Agglomerative Hierarchical Clustering is used to classify with “average” method and found that there are two clusters. First cluster with 28 wells and Second cluster with 9 wells. Test of homogeneity between the two clusters groups using T-test for the hierarchical clustering and found that there are significant differences in means for TDS and for Chloride between first cluster and second cluster with a significant level less than 0.01. While for Nitrates, also, there are significant differences in means between the first cluster and second cluster with a significant level less than 0.05. Different stability validation measures carried out for data concludes that for the APN and ADM measures, hierarchical clustering with two clusters again gives the best score. Three internal cluster validation measures are used. Concludes that Hierarchical clustering with two clusters performs the best in (Connective, Dunn, and Silhouette). Recall that the connectivity should be minimized, while both the Dunn Index and the Silhouette Width should be maximized. Thus, Hierarchical clustering outperforms the other clustering algorithms under each validation measure.

예비기술교사의 기술교과 교육과정 개념 형성과 변화: C대학 강좌를 중심으로

Objectives This study aims to identify the process of pre-service technology teachers' understanding of the cur-riculum through the formation and change of pre-service technology teachers' conceptions of the technology curriculum. Methods A total of 94 pre-service technology teachers taking the ‘Technology Curriculum’ course were surveyed to collect their conceptions of the technology curriculum before and after the course. The number of pre-service technology teachers studied was 48 in 2022 and 46 in 2023. The collected concepts were analyzed for frequency, centrality, clustering groups, structural hole, and ego network analysis using semantic network analysis. Results Before taking the course, pre-service technology teachers recognized the concept of achieving ‘educational goals’ as the main concept. In addition, it was possible to apply curriculum-related contents after the class, and changes in concepts occurred in curriculum-related specialized contents. In addition, it was possible to apply cur-riculum-related contents after the class, and changes in concepts occurred in curriculum-related specialized contents. In addition, it was possible to confirm that concepts were expanded and differentiated in terms of curric-ulum design and operation from the perspective of technical subjects teachers after the class. Conclusions Through this study, the concept of curriculum was materialized, and it can be used as a basis for fu-ture curriculum revision through changes to a cohesive conceptual structure and influence relationships with major curriculum variables.

Identifying Opportunities for Developing Nature-Based Sport Tourism to Improve Health in Papua: A Bibliometric Analysis of Research Over Ten Years

Objectives. This study aimed to examine research trends around nature-based tourism, event-based sports tourism, and their relationship with government policies to identify potential opportunities for sustainable sports tourism development in Papua. Materials and methods. The methodological approach comprised using a bibliometric analysis by reviewing 997 scientific articles sourced from Scopus, PubMed, and Web of Science databases from 2014-2024. Harzing’s publish or Perish software was used to sort the articles, and VOSViewer software was used to analyze the articles. The review included the number of cluster groups, research themes related to sports tourism in the year groups, research trends around nature-based sports tourism, and gaps in sports tourism development. Results. There were eight cluster groups and five groups. The current research trends in the field of sports tourism comprise event-based sports tourism, nature tourism, the tourism industry, and social media. The research trends related to nature-based sports tourism include six keywords, namely nature-based tourism, nature connectedness, nature sport, nature-based events, nature-based recreation, and nature. Visitors, managers, communities, and governments benefit from the health and economic advantages of nature-based sports tourism. In developing nature-based sports tourism, it is essential to consider the sustainability of nature, cultural characteristics, and the form of activities offered. Local governments need to take part in efforts to develop and preserve nature-based sports tourism. Conclusions. The development of nature-based sports tourism with traditional sports activities has a great opportunity to be enhanced. This innovation certainly needs careful planning from determining goals, human resources, risk factors, and presenting an effective promotional strategies. The involvement of local government can facilitate the implementation of this planning.

Triple-oxygen isotopes of stony micrometeorites by secondary ion mass spectrometry (SIMS): Olivine, basaltic glass and iron oxide matrix effects for sensitive high-mass resolution ion microprobe-stable isotope (SHRIMP-SI).

Micrometeorites are extraterrestrial particles smaller than ~2mm in diameter, most of which melted during atmospheric entry and crystallised or quenched to form 'cosmic spherules'. Their parentage among meteorite groups can be inferred from triple-oxygen isotope compositions, for example, by secondary ion mass spectrometry (SIMS). This method uses sample efficiently, preserving spherules for other investigations. While SIMS precisions are improving steadily, application requires assumptions about instrumental mass fractionation, which is controlled by sample chemistry and mineralogy (matrix effects). We have developed a generic SIMS method using sensitive high-mass resolution ion micro probe-stable isotope (SHRIMP-SI) that can be applied to finely crystalline igneous textures as in cosmic spherules. We correct for oxygen isotope matrix effects using the bulk chemistry of samples obtained by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and model bulk chemical compositions as three-component mixtures of olivine, basaltic glass and Fe-oxide (magnetite), finding a unique matrix correction for each target. Our first results for cosmic spherules from East Antarctica compare favourably with established micrometeorite groups defined by precise and accurate but consumptive bulk oxygen isotope methods. The Fe-oxide content of each spherule is the main control on magnitude of oxygen isotope ratio bias, with effects on δ18O up to ~6‰. Our main peak in compositions closely coincides with so-called 'Group 1' objects identified by consumptive methods. The magnitude of SIMS matrix effects we find is similar to the previous intraspherule variations, which are now the limiting factor in understanding their compositions. The matrix effect for each spherule should be assessed quantitatively and individually, especially addressing Fe-oxide content. We expect micrometeorite triple-oxygen isotope compositions obtained by SIMS to converge on the main clusters (Groups 1 to 4) after correction firstly for magnetite content and secondarily for other phases (e.g., basaltic glass) in each target.

The importance of analysing a single common window of a concentric and eccentric dynamic muscle contraction: unveiling the components of EMG data and motor unit pool

This study proposes a method for analysing decomposed electromyographic signals to enhance the characterization of motor units (MU). It involves two steps: (i) clustering groups of motor units based on firing rate (FR), recruitment threshold, and MU action potential amplitude, and (ii) segmentation of data at the time of interest. Such method, capable of distinguishing different groups of MU, could help understanding muscle force production. FR of MU in Vastus Lateralis and Rectus Femoris during knee extension was investigated. The findings distinguish MU groups and reveals differences in: FR between both contractions types; clustered groups; and segmented MU data in both contraction types.

Outcome and life expectancy associated with novel subgroups of atrial fibrillation: a data-driven cluster analysis

Abstract Background/Introduction Atrial fibrillation (AF) presents a significant global health burden, affecting millions and associated with elevated risks of mortality, heart failure, and stroke despite current treatment strategies. The current diagnosis of AF relies on episode characteristics, yet its heterogeneity calls for personalized assessment incorporating clinical features, biomarkers, and substrate determination for improved classification and management. Purpose This study aimed to develop a refined classification of AF according to comprehensive risk factors to identify patients at high-risk for poor prognosis and their life expectancy, aiming to facilitate personalized interventions. Methods A total of 7,391 participants aged 40-69 with AF at baseline, from UK Biobank, were recruited in the main analyses. Firstly, LASSO regression with Cox model and factor analysis were adopted for identification and integration of principal risk factors. Second, Consensus clustering analysis was employed to estimate the optimal cluster number and group all 7,391 participants. Difference in the risk of death and major complications, as well as reductions in life expectancy, among clusters were estimated. Replication was done in 2,399 participants with newly diagnosed AF within two years after baseline. Results Risk factors for AF were categorized into seven aspects (metabolic factors, respiratory factors, cardiovascular factors, renal and immunity diseases, mental health, acute illness, age), based on the correlation between them. Five distinct AF clusters were identified according to these seven characteristics: acute illness-related (Cluster 1), mental health-related (Cluster 2), cardiovascular disease-related (Cluster 3), immune-and-renal disease-related (Cluster 4), and respiratory-and-metabolism disease-related AF (Cluster 5). Patients with respiratory-and-metabolism disease-related AF had the highest risk of death, acute myocardial infarction, heart failure, and cerebral ischemic stroke, while those with acute illness-related AF had the lowest corresponding risk. In addition, compared with individuals with acute illness-related AF, those with respiratory-and-metabolism disease-related and mental health-related AF had the top 2 greatest loss of life expectancy. Furthermore, genetic variants for AF had different effect among the five clusters. Replication analysis confirmed the result stability. Conclusion A novel AF classification was developed, which provided insights into varying life expectancy and risks of death and complications among AF subgroups with distinct characteristics. It offers a practical approach for identifying high-risk patients, which may help to tailor precise interventions for AF management.Graphic abstractCluster features

Hydro-chemical characterization of groundwater using multivariate statistics in Hawassa City, Southern Ethiopia

ABSTRACT This study characterized the hydro-chemical characteristics of groundwater for assessing the possibility of managed aquifer recharge in Hawassa City. A total of 48 water samples were taken from hand-dug wells and boreholes and examined to determine the water type, critical metrics, and key determinants of water quality. Multivariate statistical techniques such as hierarchical cluster, principal component, and linear discriminant analysis were used. The samples were divided into four variable groups and four case cluster groups. The results depicted the water hardness group (C1), soil salinity group (C2), weak and strong acids forming group (C3), and pollution indicator group (C4). Four water types were identified, Na–HCO3 and Ca–Na–HCO3 (87.5%), Ca–HCO3, and Na–Cl. Na–HCO3 was the dominant in hand-dug wells than in deep boreholes, which may account for evaporation or contaminations. Seven principal components with a cumulative variance of 78.58% were also formed. The first two, hardness and salinity, contributed 25.4 and 11.4% variance, respectively. In linear discriminate analysis, three discriminate functions with eight variables were generated, namely pH, K+, Na+, Ca2+, HCO3−, Cl−, BOD5, and COD. Thus, it is revealed that the decline in water quality attributed to natural and anthropogenic causes that require regulation enforcement and higher recharge to reverse the situation.

Reconnecting River-City: A Visibility and Accessibility Assessment of the Ping River’s View Characters in Chiang Mai City

A river’s landmarks have advantages in attracting people to engage with natural/historical settings. However, uncontrolled development can have negative effects on the visual and obstruct people’s access. In this context, researchers have highlighted obstacles to these landmarks and suggested features of the relationship between the visibility and accessibility to these landmarks, aiming to preserve heritage sites. This project evaluated riverscape landmarks to develop a strategic urban design that uses scientific and spatial toolkits to maintain the historic landmark on the riverside in Chiang Mai city. The methodology utilized for GIS included visibility and accessibility assessments as variables for analyzing interconnections and classification utilizing the statistical method of hierarchical clustering analysis (HCA). The findings indicate differences in the accessibility of angular step coverage (ASC) and walking distance coverage (WDC), which have an impact on landmark designation. The city-level landmarks have the highest ASC, while various religious landmarks display the highest visibility. These factors have a unique impact on the HCA results, which are categorized into six groups that are consistent with the historical content and purpose of the landmarks. The clustered groups represent the strategic design of the riverscape, which necessitates policies to enhance the potential for preserving these icons as part of the historic landscape.

Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features.

While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings. In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar functional connectivity characteristics through clustering, thereby enhancing the classification process. Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.&#xD.

Global Stochastic Comprehensive Sensitivity Analysis Based on Robustness Grouping and Improved Pelican Algorithm-Optimized Radial Basis Function Neural Network

Abstract Modeling analysis is one of the important means to analyze practical engineering, and as technology continues to evolve, various models are getting closer and closer to reality, while at the same time, there are more and more parameters in the models. It is important to analyse the impact of these parameters on the project to assist engineers in making plans or decisions. Sensitivity analysis (SA) can describe the effect of changes in these parameters on the model. However, complex models often have dozens or even hundreds of parameters, and most current SA methods struggle to deal reliably and effectively with these high-dimensional problems. In addition, it is difficult to obtain the sensitivity of continuous points in the parameter space with traditional SA methods. Therefore, this paper proposes a method that combines adaptive grouping and an Improved Pelican Optimization Algorithm for an optimal radial basis function (IPOA-RBF) agent model to solve these problems. Firstly, a clustering grouping method considering grouping robustness is established to obtain objective and stable parameter grouping results in high-dimensional sensitivity analysis. Secondly, a proxy model based on radial basis function neural network and an improved Pelican optimization algorithm are proposed to capture the logic of the proxy model to obtain the parameter sensitivity of continuous points in the parameter space. Finally, the superiority and applicability of this method is verified using an arch dam simulation model.

Molecular Typing of Pseudomonas aeruginosa Isolates Collected in Abidjan Hospitals (Côte d'Ivoire) Using the Multiple-Locus Variable Number of Tandem Repeats Method.

Background/objectives:Pseudomonas aeruginosa can cause community-acquired infections affecting various body sites. The present retrospective study investigated the genetic diversity of 173 isolates (166 clinical, 7 environmental) of P. aeruginosa collected from clinical pathology laboratories in Abidjan, Côte d'Ivoire (2001-2011). Methods: Multiple-Locus Variable Number of Tandem Repeats (VNTR) Analysis (MLVA) using 13 loci was applied to all isolates and compared to published MLVA data. The antibiotics status of the isolates was compiled when available and compared to published profiles. Results: Among 95 isolates analyzed for their antibiotics status, 14 displayed concerning resistance profiles: five multidrug-resistant (MDR) and nine extensively drug-resistant (XDR). MLVA typing revealed a high genetic diversity (>130 genotypes), with many genotypes represented by a single strain. Notably, thirteen clusters (≥4 related isolates) were observed. Some clusters displayed close genetic relatedness to isolates from France, Korea, and well-studied strains (ST560, LES and PA14). Comparative analysis suggested the presence of international high-risk MDR clones (CC233, CC111) in Côte d'Ivoire. Importantly, MLVA clustering revealed a close relationship of CC235-MDR strains with a locally identified cluster (group 9). Conclusions: These findings support MLVA as a reliable and cost-effective tool for low-resource settings, allowing the selection of relevant strains for future whole genome sequence analyses. This approach can improve outbreak investigations and public health interventions aimed at curbing MDR P. aeruginosa transmission within hospitals and at the national level.

Pengelompokan Provinsi di Indonesia Menggunakan Gaussian Mixture Model Berdasarkan Indikator Kemiskinan

Poverty is a multidimensional concept that can not only be seen from an economic perspective, but can also be seen from a social, cultural and political perspective. Poverty is generally expressed as a static concept with the amount of poverty as a description of welfare conditions at a certain time. In reality, poverty has a time period that is never cut off and has a continuous pattern over time. The Indonesian government is committed to efforts to eradicate poverty in order to reduce Indonesia's poverty rate. One way that can be done is by allocating aid to the community. Grouping is carried out by cluster analysis using the Gaussian Mixture Model method with the Expectation-Maximization (EM) algorithm. The optimal number of clusters in the Gaussian Mixture Model uses the Bayesian Information Criterion (BIC) method. Based on the research carried out, the results of the analysis of cluster grouping using the Gaussian Mixture Model method were obtained with the smallest BIC value, namely -397.6876 with a total of 2 clusters.

A Machine Learning-Based Clustering Using Radiomics of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for the Prediction of Prognosis in Patients with Intrahepatic Cholangiocarcinoma

Background: Intrahepatic cholangiocarcinoma (IHCC) is highly aggressive primary hepatic malignancy with an increasing incidence. Objective: This study aimed to develop machine learning-based radiomic clustering using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for predicting recurrence-free survival (RFS) and overall survival (OS) in IHCC. Methods: We retrospectively reviewed pretreatment F-18 FDG PET/CT scans of 60 IHCC patients who underwent surgery without neoadjuvant treatment between January 2008 and July 2020. Radiomic features such as first order, shape, and gray level were extracted from the scans of 52 patients and analyzed using unsupervised hierarchical clustering. Results: Of the 60 patients, 36 experienced recurrence and 31 died during follow-up. Eight patients with a negative FDG uptake were classified as Group 0. The unsupervised hierarchical clustering analysis divided the total cohort into three clusters (Group 1: n = 27; Group 2: n = 23; Group 3: n = 2). The Kaplan–Meier curves showed significant differences in RFS and OS among the clusters (p < 0.0001). Multivariate analyses showed that the PET radiomics grouping was an independent prognostic factor for RFS (hazard ratio (HR) = 3.03, p = 0.001) and OS (HR = 2.39, p = 0.030). Oxidative phosphorylation was significantly activated in Group 1, and the KRAS, P53, and WNT β-catenin pathways were enriched in Group 2. Conclusions: This study demonstrated that machine learning-based PET radiomics clustering can preoperatively predict prognosis and provide valuable information complementing the genomic profiling of IHCC.

Cultural heritage sports tourism design opportunities: a bibliometric analysis

The development of cultural heritage sports tourism requires collaboration from the government, communities around cultural heritage, academics, and stakeholders. The implementation of cultural heritage sports tourism has been carried out by many researchers around the world. This research was conducted to provide a review of cultural heritage tourism that has been carried out and to find out future research gaps related to cultural heritage sports tourism. Harzing's publish or perish software, Mendeley Desktop, and VosViewer were used to sort and identify gaps in this research. A total of 1069 articles from 2014 to 2024 from the Scopus, Web of Science, and PubMed databases were reviewed according to a predetermined review. The review included cluster information, impact factors, types of activities, research findings, and gaps for future research. The results showed that there were eight cluster groups in the resulting analysis with seven keywords that had a high impact factor. The keywords that have high impact factors are revisiting intention, digitalization, game-based learning, location-based applications, surveys, circular economy, and immersive technologies. The findings in this study are that the development of cultural heritage attractions needs to consider visitor expectations, the content presented, government involvement, and promotion. The development of cultural heritage sports tourism has great potential. Keywords: bibliometric analysis, sport tourism, cultural heritage, future research