Cluster Groups Research Articles

Research on Assessing Comprehensive Competitiveness of Tourist Destinations Within Cities, Based on Field Theory and Competitiveness Theory

The question of how to assess the comprehensive competitiveness of tourist destinations within cities is an important aspect for determining the potential of a city’s tourism development and its ranking among peers in the field. There are four main parts to the content of this article, which consist of the analysis of competition formation motives based on “Field Theory”, the selection of influencing factors by drawing on Porter’s theory of competitiveness, the construction of an assessment model based on the multi-factors weighted comprehensive evaluation method, and an empirical analysis using Nanjing as the research area. The conclusions are as follows: Firstly, the tourist destination field within a city is composed of three interrelated elements, which are actors, rules, and competition. Under the influence of mainstream social and cultural trends, each tourist destination occupies a certain “position” by relying on the attractiveness formed by various types of capital, and then participates in peer competition within the field. Secondly, the three major influencing aspects of the competitiveness of tourist destinations are element conditions, demand characteristics, and supporting conditions. The key points involved in the three aspects can be summarized into four categories of factors, namely, quality evaluation, popularity level, spatial attractiveness, and emotional cognition, which together constitute the indicator system. Thirdly, there are thirteen tourist destinations in Nanjing that are rated above the average, accounting for about 43% of all the popular destinations. The variation coefficient of competitiveness results is about 35%, indicating a moderate to relatively weak degree of dispersion. Finally, the competitiveness of the thirty hot tourist destinations generally presents a spatial order that gradually weakens in an outward direction from the center zone of the city, forming an overall pattern of cluster groups of well-known tourist destinations in the core of the city, relatively random small clusters in the new main city area, and scattered point distribution in the suburbs.

Long-Chain Polysaturated Fatty Acid in Atrial Fibrillation-Associated Stroke: Lipidomic-GWAS Study.

Background and Purpose This study aimed to explore the relationship between lipidomic domains, particularly free fatty acids (FFAs), and the presence of AF in patients with acute stroke, and to identify mechanisms of AF-associated stroke through genetic studies. Methods A total of 483 patients of stroke in patients without AF (n = 391) and with AF (n = 92) were selected from a prospectively collected stroke registry. Lipidomic profiling was conducted and the lipid components associated with AF were explored using fold-change analyses and clustering. Genotyping was conducted through trait comparison. Colocalization was also performed. Results Among the lipidomic domains, the free fatty acid (FFA) class was positively associated with AF. Long-chain fatty acids with 14-24 carbons and unsaturated FFAs distinguished AF. Clustering analysis based on FFAs revealed differences in AF proportion across groups. GWAS identified two loci associated with clustered groups of FFA metabolites: near MIR548F3 associated with FFA 20:1, FFA 20:2, FFA 22:5, and FFA 22:6; and near RPL37A associated with FFA 22:5 and FFA 22:6. These loci were associated with increased fibrinogen levels. In the GWAS for the FFA metabolite-quantitative trial locus analysis, loci near rs28456 and rs3770088, and FFA 20:4-QTLs were co-localized with the eQTLs of FADS2, a gene involved in the peroxisome proliferative-activated receptor gamma-related signaling pathway, in the whole blood, left ventricle, and atrial appendage tissue. Conclusion Elevated FFA levels, especially those of long-chain unsaturated FFAs, are strongly associated with AF-associated stroke. This relationship is regulated by the peroxisome proliferator-activated receptor gamma-related signaling pathway.

Directing the Linkage of Small Polyoxometalate Building Blocks Using (Benzene)ruthenium Cations.

Oligomerization of monomeric molybdate and tungstate oxyanions in the presence of organometallic cations produces a group of organometallic-polyoxometalate clusters, which have diverse structures based on cubane-like {M4O4} units resembling the structure of oxide surfaces. This work investigated the oligomerization of [MoO4]2- and [WO4]2- oxyanions in aqueous solutions in the presence of {Ru(C6H6)}2+ as the organometallic structure-directing agents. The reactions produced a mixture of several species, and fractional crystallization by adjusting crystallization temperature and molar ratios of Ru:Mo or Ru:W allowed the isolation of seven types of (benzene)ruthenium-polyoxometalate complexes: [{Ru(C6H6)}4Mo4O16] (1), [{Ru(C6H6)(OH)}3HMoO4][MoO4] (2), [{Ru(C6H6)}4H2Mo5O20] (3), [{Ru(C6H6)}4W2O10] (4), Na6[{Ru(C6H6)}2H2W8O30] (5), [{Ru(C6H6)}5H2W6O24] (6), and [{Ru(C6H6)(OH)}6W2O7][{Ru(C6H6)}2{Ru(C6H6)(OH2)}2H2W8O30]2 (7). They comprise small, reactive iso-polyoxometalate building blocks that are capped by {Ru(C6H6)}2+ cations, resulting in cubane-like {Ru3MO4}, {Ru2M2O4}, or {RuM3O4} (M = Mo or W) structural motifs. Moreover, the molecular crystal of 1 contains extensive C-H···O hydrogen bonds, and it undergoes a reversible crystal-to-amorphous-to-crystal transition upon dehydration-hydration cycles.

An efficient unsupervised classification model for galaxy morphology: Voting clustering based on coding from ConvNeXt large model

By combining unsupervised and supervised machine learning methods, we have proposed a framework, called USmorph to carry out automatic classifications of galaxy morphologies. In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into three key aspects as follows: (1) a convolutional autoencoder is used for image denoising and reconstruction and the rotational invariance of the model is improved by polar coordinate extension; (2) utilizing a pre-trained convolutional neural network (CNN) named ConvNeXt for encoding the image data. The features were further compressed via a principal component analysis (PCA) dimensionality reduction; (3) adopting a bagging-based multi-model voting classification algorithm to enhance robustness. We applied this model to I-band images of a galaxy sample with $I_ mag < 25$ in the COSMOS field. Compared to the original unsupervised method, the number of clustering groups required by the new method is reduced from 100 to 20. Finally, we managed to classify about 53<!PCT!> galaxies, significantly improving the classification efficiency. To verify the validity of the morphological classification, we selected massive galaxies with $M_ M_ sun $ for morphological parameter tests. The corresponding rules between the classification results and the physical properties of galaxies on multiple parameter surfaces are consistent with the existing evolution model. Our method has demonstrated the feasibility of using large model encoding to classify galaxy morphology, which not only improves the efficiency of galaxy morphology classification, but also saves time and manpower. Furthermore, in comparison to the original UML model, the enhanced classification performance is more evident in qualitative analysis and has successfully surpassed a greater number of parameter tests. The enhanced UML method will support the Chinese space station telescope in the future.

Application of Two-Step Entropy–TOPSIS Method and Complete Linkage Clustering for Water-Pumping Windmill Investment on Thailand Peninsula

This study focuses on identifying suitable areas for the installation of water-pumping windmills in Thailand, which require wind speeds of at least 4 m/s to operate efficiently. A simple combined approach is introduced, integrating the Entropy–TOPSIS method complete linkage clustering to prioritize and categorize potential locations. Out of 271 initial areas, 28 have been selected based on their ability to meet the 4 m/s wind speed threshold. The Entropy–TOPSIS method first evaluates these areas based on monthly wind speed and agricultural area. The analysis reveals that regions with higher wind speeds generally score better for wind energy potential, while areas with larger agricultural spaces tend to score higher for farming suitability. The final integrated scores show that agricultural area is more significant, with a weight of 0.7788, compared to the wind speed weight of 0.2212. The areas are then ranked, and complete linkage clustering groups them into six categories, from the most to the least suitable for windmill installation. A sensitivity analysis confirms the robustness of the clustering method, as the group composition remains stable despite minor changes in weight adjustments. This approach simplifies decision-making for sustainable energy investments in Thailand agriculture sector.

Dynamic spectrum resource allocations in wireless senor networks for improving packet transmission

Spectrum allocation has gained a lot of attention in cognitive wireless networks and research as one of the key problems for enhancing spectrum quality in the communication processes in the contemporary communication-dependent wireless environment. By effectively managing the restricted spectrum resources, adjusting to dynamic network conditions, lowering interference, and increasing energy efficiency, the study on dynamic spectrum allocation in wireless sensor networks seeks to improve packet transmission. In a variety of applications, this improves network performance, dependability, and quality of service. This is the reason we apply the dynamic source allocation to the sensor nodes in the network visualization for the packet transmission performance study in the 5G spectrum region. The primary goal of the study conducted for this article is to improve packet delivery ratios and data packet throughputs from the source to the destinations. Compared to previous research, this work has achieved 100% of its aims. In this context, certain DRL concerns are also addressed. The spectrum is allotted such that, in the event of phishing or malicious nodal assaults on the cluster groups of the wireless sensor nodal points in the WSN, efficient packet transmission will occur, beginning at the source and terminating at the sink. The simulation results demonstrate the efficacy of the approach described in the research paper and its application to data transmission.

Elevated Expression of Cell Adhesion, Metabolic, and Mucus Secretion Gene Clusters Associated with Tumorigenesis, Metastasis, and Poor Survival in Pancreatic Ductal Adenocarcinoma.

Technological advances in identifying gene expression profiles are being applied to study an array of cancers. The goal of this study was to identify differentially expressed genes in pancreatic ductal adenocarcinoma (PDAC) and examine their potential role in tumorigenesis and metastasis. The transcriptomic profiles of PDAC and non-tumorous tissue samples were derived from the gene expression omnibus (GEO), which is a public repository. The GEO2R tool was used to further derive differentially expressed genes from those profiles. In this study, a total of 68 genes were derived from upregulated PDAC genes in three or more transcriptomic profiles and were considered PDAC gene sets. The identified PDAC gene sets were examined in the molecular signatures database (MSigDB) for ontological investigation, which revealed that these genes were involved in the extracellular matrix and associated with the cell adhesion process in PDAC tumorigenesis. The gene set enrichment analysis showed greater enrichment scores for the gene sets. Moreover, the identified gene sets were examined for protein-protein interaction using the STRING database. Based on functional k-means clustering, the following three functional cluster groups were identified in this study: extracellular matrix/cell adhesion, metabolic, and mucus secretion-related protein groups. The receiver operating characteristic (ROC) curve revealed greater specificity and sensitivity for these cluster genes in predicting PDAC tumorigenesis and metastases. In addition, the expression of the cluster genes affects the overall survival rate of PDAC patients. Using the cancer genome atlas (TCGA) database, the associations between expression levels and clinicopathological features were validated. Overall, the genes identified in this study appear to be critical in PDAC development and can serve as potential diagnostic and prognostic targets for pancreatic cancer treatment.

Non-target feature filtering for weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) utilizes weak labels to learn semantic segmentation models, significantly reducing reliance on pixel-level annotations. WSSS typically employs a multi-label classification network to extract image features for constructing localization maps. The quality of the localization map critically influences the performance of WSSS. However, non-target semantic noise within the features impedes the improvement of localization map quality. To address this issue, we propose a non-target feature filtering class activation mapping (NFF-CAM) for WSSS, which can reduce non-target semantic signals and generate higher-quality localization maps. Specifically, the class-constrained dual cosine clustering (CDCC) and channel identification (CI) modules are introduced in NFF-CAM. CDCC effectively addresses the issue of unsuitability in the clustering group relationships of the original features under specified class conditions. CI can efficiently identify channel features containing non-target semantic information. We conduct extensive evaluations of NFF-CAM on popular datasets, including PASCAL VOC 2012 and MS COCO 2014. Experimental results show that NFF-CAM can effectively improve the segmentation performance of off-the-shelf methods.

Arbuscular mycorrhizal fungi improve morphological and yield performance of Eragrostis tef genotypes in Tigray, Ethiopia

Arbuscular mycorrhizal fungi (AMF) form beneficial partnerships with most plant species, helping to improve crop resilience in tough environmental conditions. This paper analyzed how different genotypes responded to AMF inoculation, focusing on root colonization percentage (RCP) and the impact of AMF on above-ground agronomic traits. However, RCP alone may not fully explain genotype variation, and AMF effects cannot be judged solely on above-ground traits. This research aims to explore the variation in ninety tef genotypes under AMF conditions and assess how AMF and genotypes affect root and shoot morphology traits. Genotypes were sorted into six cluster groups, showing varied responsiveness to AMF, with RCP ranging from 25.03 to 72.29%. Despite similar RCP, variations in morphological traits were observed, and groups with lower RCP exhibited important traits not found in those with higher RCP, indicating RCP alone cannot indicate genotype variability. Wider Mahalanobis distance (D2) between clusters IV and VI, I and VI, and V and VI were crucial for developing different varieties and advancing root traits through hybridization. Among the tested genotypes, Wehni and Tsaeda zezew, followed by Gureaza, exhibited higher scores for plant height (PH), panicle length (PL), shoot biomass yield (SBY), root length (RL), and specific root length (SRL) compared to Simada. However, Wehni, Tsaeda zezew, and Gureaza showed similar results for days to maturity (DM), grain yield (GY), harvest index (HI), root dry weight (RDW), and root depth distribution (RDD) but differed from Simada genotype. Moreover, the inoculated Wehni genotype increased in days to panicle emergence (DPE) by 72%, DM by 84.11%, PH by 73.93%, PL by 73.68%, SBY by 144.17%, GY by 254.58%, HI by 133.33%, RL by 74.16%, RDW by 216.92%, SRL by 220%, and RDD by 93.28% as compared to the non-inoculated Simada. Improved performance of inoculated genotypes despite genotype variability could be because AMF enhances nutrient and water uptake by increasing root and shoot growth and the inherent growth strategy of the genotypes. Small-seeded crops planted shallowly benefit from AMF, which promotes deeper root growth for better nutrient and water uptake.

T-cell receptor and B-cell receptor repertoires profiling in pleural tuberculosis.

Tuberculosis (TB) is a leading cause of death worldwide from a single infectious agent. In China the most common extra-pulmonary TB (EPTB) is pleural tuberculosis (PLTB). An important clinical feature of PLTB is that the lymphocytes associated with TB will accumulate in the pleural fluid. The adaptive immune repertoires play important roles in Mycobacterium tuberculosis (Mtb) infection. In this study, 10 PLTB patients were enrolled, and their Peripheral Blood Mononuclear Cells(PBMCs) and Pleural Effusion Mononuclear Cells(PEMCs) were collected. After T cells were purified from PBMCs and PEMCs, high-throughput immunosequencing of the TCRβ chain (TRB), TCRγ chain(TRG), and B cell receptor(BCR) immunoglobulin heavy chain (IGH) were conducted on these samples. The TRB, TRG, and BCR IGH repertoires were characterized between the pleural effusion and blood in PLTB patients, and the shared clones were analyzed and collected. The binding activity of antibodies in plasma and pleural effusion to Mtb antigens was tested which indicates that different antibodies responses to Mtb antigens in plasma and pleural effusion in PLTB patients. Moreover, GLIPH2 was used to identify the specificity groups of TRB clusters and Mtb-specific TRB sequences were analyzed and collected by VJ mapping. We characterize the adaptive immune repertoires and identify the shared clones and Mtb-specific clones in pleural effusion and blood in PLTB patients which can give important clues for TB diagnosis, treatment, and vaccine development.

Phenotypic Differentiation of Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus Isolates Found in Yogurt Starter Cultures

The mutualistic relationship between Streptococcus thermophilus (S. thermophilus) and L. delbrueckii subsp. bulgaricus (L. bulgaricus) is responsible for milk coagulation, gel formation, and the flavour of yogurt. Under set-style yogurt processing conditions, the performance of a mixed culture composed of these species depends on key technological parameters such as the capacity for acidification and proteolytic activity. This study aimed to determine the extent of phenotypic diversity by comparing the key traits of acidification and proteolytic activity among isolates found in yogurt starter cultures. Seventy-two isolates from three industrial starter cultures were ranked by either their fast or slow acidification activity (time to reach pH 4.6, 16 h), proteolytic activity, cell envelope proteinase (CEP) activity, redox potential and titratable acidity. The integration of multiple phenotype measures by hierarchical clustering and non-metric dimensional scaling (NMDS) clustered groups of isolates by multifactor similarity. A significant difference (p-value < 0.05) was observed between the clusters regarding redox potential and the proteolytic activity of both S. thermophilus and L. bulgaricus. The integration of multiple phenotypes points to the diversification that may have occurred over repeated culturing of yogurt starter bacteria. The phenotypic diversity may explain the divergence in starter performance and be used to refine the formulation of new starter cultures. Future work will investigate the correlation between the activity of specific enzymes based on the phenotype to explain the separation between the fast and slow acidification of isolates.

Application of MRI imaging technology based on magnetic nanoparticles in diagnosis and prognosis evaluation of prostate cancer

Objective: Objective: Prostate cancer is one of the most common malignant tumors in men. Early diagnosis and prognosis evaluation are of great significance for the treatment and prevention of prostate cancer. The purpose of this study was to explore the application of magnetic nanoparticle-based MRI imaging technology in the diagnosis and prognosis assessment of prostate cancer. A total of 81 patients in our hospital from September 2018 to January 2021 were selected as the study objects, all suspected prostate cancer patients, and prostate detection was performed under the guidance of MRI and rectal ultrasound.According to the pathological results, the patients were divided into prostate cancer cluster group and benign prostatic hyperplasia group. Imaging of prostate cancer is achieved by the response of magnetic nanoparticles to magnetic fields. MRI images of patients were collected and analyzed using professional software. It can provide high-resolution images that enable accurate detection and localization of tumors, and the technology can also assess the severity of prostate cancer and predict a patient's prognosis.

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.

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

Objective.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.Approach.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 (FC) in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states.Main results. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar FC characteristics through clustering, thereby enhancing the classification process.Significance.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.

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.