Number Of Clusters Research Articles

Associations of modifiable dementia risk factors with dementia and cognitive decline: evidence from three prospective cohorts

ObjectiveThis study aims to assess the relationship between modifiable dementia risk factors and both dementia and cognitive decline.MethodsData were obtained from the Health and Retirement Study (HRS) [2008–2020], the China Health and Retirement Longitudinal Study (CHARLS) [2011–2020], and the English Longitudinal Study of Ageing (ELSA) [2010–2020]. After adjusting for confounding factors, multivariable logistic regression was utilized to analyze the relationship between modifiable dementia risk factors and dementia, while multivariable linear regression was employed to examine the relationship between these risk factors and cognitive decline. Additionally, the Cox proportional hazards model was used to assess the relationship between the number of risk factor events, clusters, and dementia risk.ResultsA total of 30,113 participants from HRS, CHARLS, and ELSA were included (44.6% male, mean age 66.04 years), with an average follow-up period of 7.29 years. A low education level was significantly associated with an increased risk of dementia and accelerated cognitive decline (Overall, OR = 2.93, 95% CI: 2.70–3.18; Overall, β = −0.25, 95% CI: −0.60 to-0.55). The presence of multiple dementia risk factors correlated with a higher dementia risk; Specifically, compared with more than 5 risk factor events, both having no dementia risk factors and having only one dementia risk factor were associated with a significantly lower risk of dementia (Overall, HR = 0.15, 95% CI: 0.11–0.22, HR = 0.22, 95% CI: 0.18–0.25). Compared to the group with no coexistence of risk factors, the clusters of excessive alcohol, diabetes, vision loss, and hearing loss (HR = 4.11; 95% CI = 3.42–4.95; p < 0.001); excessive alcohol, vision loss, smoking, and hearing loss (HR = 5.18; 95% CI = 4.30–6.23; p < 0.001); and excessive alcohol, obesity, diabetes, and smoking (HR = 5.96; 95% CI = 5.11–6.95; p < 0.001) were most strongly associated with dementia risk.ConclusionAmong the 11 risk factors, educational attainment has the greatest impact on dementia risk and cognitive decline. A dose–response relationship exists between the number of modifiable risk factor events and dementia risk. The coexistence of multiple risk factors is associated with dementia risk, and these associations vary by risk factor cluster.

Unsupervised Semantic Segmentation of Urban Scenes via Cross-Modal Distillation

Abstract Semantic image segmentation models typically require extensive pixel-wise annotations, which are costly to obtain and prone to biases. Our work investigates learning semantic segmentation in urban scenes without any manual annotation. We propose a novel method for learning pixel-wise semantic segmentation using raw, uncurated data from vehicle-mounted cameras and LiDAR sensors, thus eliminating the need for manual labeling. Our contributions are as follows. First, we develop a novel approach for cross-modal unsupervised learning of semantic segmentation by leveraging synchronized LiDAR and image data. A crucial element of our method is the integration of an object proposal module that examines the LiDAR point cloud to generate proposals for spatially consistent objects. Second, we demonstrate that these 3D object proposals can be aligned with corresponding images and effectively grouped into semantically meaningful pseudo-classes. Third, we introduce a cross-modal distillation technique that utilizes image data partially annotated with the learnt pseudo-classes to train a transformer-based model for semantic image segmentation. Fourth, we demonstrate further significant improvements of our approach by extending the proposed model using a teacher-student distillation with an exponential moving average and incorporating soft targets from the teacher. We show the generalization capabilities of our method by testing on four different testing datasets (Cityscapes, Dark Zurich, Nighttime Driving, and ACDC) without any fine-tuning. We present an in-depth experimental analysis of the proposed model including results when using another pre-training dataset, per-class and pixel accuracy results, confusion matrices, PCA visualization, k-NN evaluation, ablations of the number of clusters and LiDAR’s density, supervised finetuning as well as additional qualitative results and their analysis.

Genetic Variability Studies in Early Segregating Generation of Cowpea [Vigna unguiculata (L.) Walp subsp unguiculata

Aims: In a self-pollinated crop like cowpea, most breeding approaches predominantly use different techniques of handling the segregating generations. It is important to select and increase the frequency of the desirable genotypes for efficient management of segregating generations and gradual improvement of important characters in the material. For the selection to be efficient, a complete knowledge of the features of the segregating material in terms of genetic variability with regard to important characters is crucial. So the objective of the study was to comprehend the genetic variability in segregating generations and to estimate the percentage of transgressive segregants. Study Design: The experiment was laid out in Augmented design II during kharif, 2023. Place and Duration of Study: The current experiment was undergone at the Botany garden, Department of Genetics and Plant Breeding, University of Agricultural Sciences, Dharwad, Karnataka to evaluate 36 F4 families and six checks during kharif 2023. Methodology: Observations were taken for ten yield and yield related traits viz., total yield (kg/ha), single plant yield (g), number of pods per plant, test weight (g), number of seeds per pod, pod length (cm), number of clusters per plant, number of pods per cluster, days to fifty per cent flowering and days to maturity. Analysis of variance (ANOVA) for yield and yield related traits of 36 F4 families of diverse crosses and pedigree along with checks in grain type cowpea was carried out in augmented design II as proposed by Federer and Raghavarao, 1975. Different genetic parameters were estimated to characterize the material for nature and amount of genetic variability. Percentage of transressive segregants was calculated using the mean±1 SD for all characters. Results: The variances between genotypes were shown to be highly significant among all the characters under study. All traits exhibited high heritability (>60%) except the trait number of pods per cluster. High heritability coupled with high genetic advance (>20%) was obtained in traits like total yield (kg/ha) (71.00% and 43.59%), single plant yield (g) (76.18% and 35.01%), number of pods per plant (71.98% and 33.49%), pod length (cm) (75.99% and 22.00%) and number of clusters per plant (64.90% and 30.02%), indicating the possibility of effective selection for these characters. Highest percentage of transgressive segregants was revealed for the traits total yield (19.44%) and days to fifty per cent flowering (19.44%). Conclusion: Sufficient amount of genetic variability present among the F4 families ensures profound scope of selection which in turn indicates possibility of improvement in the population.

IMPLEMENTATION OF K-MEDOIDS AND K-PROTOTYPES CLUSTERING FOR EARLY DETECTION OF HYPERTENSION DISEASE

Hypertension is a serious concern because of its significant impact on public health, especially in the context of lifestyle changes and specific health conditions. One method for grouping patients based on complex clinical data is the Clustering method. This research type is quantitative, namely taking or collecting the necessary data and then analyzing it using the K-Medoids and K-Prototypes methods. The K-Medoids method is more resistant to outliers and noise than the K-Means method, which is more suitable for this research. The K-Prototypes method can handle mixed numerical and categorical data, effectively grouping hypertensive patients based on different variable categories. This research used the K-Medoids and K-Prototypes grouping methods to categorize patients into risk categories based on gender, age, family history of hypertension, smoking status, pulse rate, and increased systolic and diastolic blood pressure. The Elbow and Silhouette Coefficient methods were applied to evaluate the data and determine the optimal number of clusters for dividing patients into low-risk and high-risk hypertension groups. The analysis revealed that two clusters are the optimal solution. The clustering results show K-Medoids' superiority in grouping data with higher Silhouette Coefficient values ​​compared to K-Prototypes. Overall, the K-Medoids and K-Prototypes algorithms can detect early hypertension risk by dividing patients into different risk groups. Although the clustering results are still weak, these two methods show potential in helping health institutions identify and treat hypertension risk in Indonesia.

ANALYSING MARKET DYNAMICS: REVEALING OBSCURED PATTERNS IN LQ45 STOCKS (2021-2023) USING WARD’S HIERARCHICAL CLUSTERING

This study aimed to address the instability of the Indonesian stock market from 2021 to 2023 by analyzing the LQ45 index, a critical indicator of economic robustness and corporate performance. Hierarchical Ward clustering was employed to categorize LQ45 stocks based on fundamental metrics such as Return, Volume, Price, Price-Earnings Ratio (PER), Earnings Per Share (EPS), and Dividends. Data preprocessing involved feature creation, Max-Abs scaling for normalization, and binary encoding of categorical variables. The optimal number of clusters was identified using dendrograms, revealing two primary clusters: one focusing on core materials and the other on financial services, alongside other industry-specific clusters. This method, characterized by its ability to minimize variance within clusters and determine natural groupings without predefined assumptions, provided valuable insights for financial advisors, policymakers, and investors. The findings offer practical guidance for optimizing decision-making, minimizing risks, and leveraging opportunities within the Indonesian stock market during a period of significant economic uncertainty. By employing this strategy, investors and traders can gain a comprehensive understanding of the current condition of the stock market, offering a thorough comprehension of the connections between equities and the operational and financial issues currently under scrutiny.

Automatic Grape Cluster Detection Combining YOLO Model and Remote Sensing Imagery

Precision agriculture has recently experienced significant advancements through the use of technologies such as unmanned aerial vehicles (UAVs) and satellite imagery, enabling more efficient and precise agricultural management. Yield estimation from these technologies is essential for optimizing resource allocation, improving harvest logistics, and supporting decision-making for sustainable vineyard management. This study aimed to evaluate grape cluster numbers estimated by using YOLOv7x in combination with images obtained by UAVs from a vineyard. Additionally, the capability of several vegetation indices calculated from Sentinel-2 and PlanetScope satellites to estimate grape clusters was evaluated. The results showed that the application of the YOLOv7x model to RGB images acquired from UAVs was able to accurately predict grape cluster numbers (R2 value and RMSE value of 0.64 and 0.78 clusters vine−1). On the contrary, vegetation indexes derived from Sentinel-2 and PlanetScope satellites were found not able to predict grape cluster numbers (R2 lower than 0.23), probably due to the fact that these indexes are more related to vegetation vigor, which is not always related to yield parameters (e.g., cluster number). This study suggests that the combination of high-resolution UAV images, multispectral satellite images, and advanced detection models like YOLOv7x can significantly improve the accuracy of vineyard management, resulting in more efficient and sustainable agriculture.

Klasterisasi Peserta KB Aktif di Desa Kalirejo Lawang Menggunakan Metode K-Means

The Family Planning (KB) program in Kalirejo Lawang Village faces challenges in the process of clustering active participants, which is time-consuming and prone to errors. Based on these challenges, a clustering solution using the K-Means algorithm was proposed. Experiments were conducted by testing the number of clusters from 2 to 8 and evaluating them using the silhouette score. The results of the study showed that the optimal number of clusters is two, as indicated by a silhouette score of 0.447. This value represents the best clustering quality compared to other cluster numbers, where the scores for clusters 3 to 8 did not exceed this value. This demonstrates that clustering into two groups provides the most optimal results. Lower scores for clusters 3 to 8 indicate that dividing the clusters into more groups did not create clear separations or worsened the cohesion within clusters. The conclusion of this study shows that the K-Means method can be applied and is reliable for clustering active KB participants in Kalirejo Lawang Village. With its speed and accuracy, K-Means offers a significant solution to improving the efficiency of the KB program at the village level. The practical implication of this research is to provide a more structured basis for planning and decision-making in the KB program at the village level. These findings mark an important step in optimizing the management of KB program data, opening opportunities for broader implementation in other areas.

AI-Driven Customers Segmentation Using K-Means Clustering

Effective customer segmentation is essential for businesses to enhance marketing strategies and improve customer engagement. This study applies K-Means clustering to segment customers based on demographic and behavioral data collected through structured surveys and questionnaires. The dataset includes attributes such as age, income, purchase frequency, product preferences, and brand loyalty. Data preprocessing involved normalization, imputation, and encoding to ensure quality and suitability for clustering. The optimal number of clusters was determined using the Elbow Method and Davies-Bouldin Index, resulting in three distinct segments: High-Spending, Frequent Shoppers, Price-Sensitive, Infrequent Shoppers, and Brand-Loyal, Moderate Shoppers. These clusters provide actionable insights for businesses, enabling tailored strategies such as loyalty programs, discounts, and targeted promotions. The Davies-Bouldin Index score of 1.27 and visualization using Principal Component Analysis (PCA) validated the effectiveness and distinctiveness of the segmentation. This research highlights the potential of AI-driven methods like K-Means clustering in uncovering hidden patterns in customer behavior, offering a robust alternative to traditional segmentation approaches. Future studies could explore larger datasets, integrate additional behavioral attributes, and compare the performance of other clustering algorithms to further enhance segmentation outcomes.

K-Mean Clustering of Holstein Friesian Dairy Cattle using Genomic Breeding Values

Clustering refers to algorithms to uncover such clusters in unlabeled data. Data points belonging to the same cluster exhibit similar features, whereas data points from different clusters are dissimilar to each other. The identification of such clusters leads to segmentation of data points into a number of distinct groups. In this study it was aimed to classify the 492 Holstein Friesian dairy cattle with determining the optimum number of clusters using the genomic breeding values (GBVs) calculated with 13250 SNPs using GBLUP for milk yield (kg), milk fat (%), milk protein (%), milk lactose (%), and milk dry matter (%). Results showed that the optimum number cluster was determined as two for the genomic breeding values. Determining the most appropriate number of clusters, it provides great convenience in the selection of breeding animals after determining the animals that can provide optimum efficiency in the herd or the animals that need to be eliminated from the existing herd. As a result, it can be said that the k-means method can be used successfully in clustering animals for genomic breeding values, but for this, at first, the optimum number of clusters must be determined.

Toward mitigating the impact of non-bulk defects on describing water structure in salt aqueous solutions: Characterizing solution density with a network-based structural indicator.

Structural indicators, also known as structural descriptors, including order parameters, have been proposed to quantify the structural properties of water to account for its anomalous behaviors. However, these indicators, mainly designed for bulk water, are not naturally transferrable to the vicinity of ions due to disruptions in the immediate neighboring space and a resulting loss of feature completeness. To address these non-bulk defects, we introduced a structural indicator that draws on the concept of clique number from graph theory and the criterion in agglomerative clustering, denoted as the average cluster number. This structural indicator aims to discern intrinsic structural characteristics within the water molecules regardless of the ions occupying the neighboring space, without requiring additional corrections. From molecular dynamics simulation results for neat water and salt aqueous solutions utilizing the TIP4P/2005 water model and the Madrid-2019 force field, we characterized the variations in densities with temperature using this network-based indicator, thereby demonstrating its practical utility. The findings suggest that at lower temperatures, the addition of ions disrupts the intrinsic structure of water molecules, with this effect diminishing as the temperature rises. Cations with larger charge density tend to induce stronger disruptions. This study highlights the importance of mitigating the impact of non-bulk defects before applying the indicators to analyze water's intrinsic structural properties in solutions. By doing so, the relationship between changes in water structure and solution behaviors can be more accurately assessed.

Determination of Anatolian Plate’s tectonic block boundaries with clustering analysis using GNSS sites velocities

The Anatolian Plate is an important tectonic structure containing different types of faults. In this study, block boundary studies were performed by clustering analysis using horizontal velocity data for 841 GNSS sites located on the Anatolian Plate. Four algorithms were used to determine the most appropriate cluster number. Cluster analysis was performed with K-means analysis. Different to other studies, the analysis included the location parameter for the GNSS sites in the cluster analysis. Generally block boundaries followed active fault zones and surface ruptures forming as a result of earthquakes. In this block study, 3 previously undocumented block boundaries were defined based on GNSS data. When all parameters are assessed in cluster analysis, the most appropriate cluster number was determined to be 9.

Improving subsurface structural interpretation in complex geological settings through geophysical imaging and machine learning

This study employs seismic refraction tomography (SRT) and electrical resistivity tomography (ERT) to assess subsurface geological conditions along the proposed Porsgrunn Highway in Norway. The primary objective is to analyze SRT and ERT tomograms to identify subsurface geological structures. However, interpreting tomograms is often limited by smoothed boundaries and reduced resolution. To address these challenges, we apply k-means clustering, a machine learning technique that groups data based on similarities in physical properties, to post-process the geophysical tomograms. This study pioneers the use of k-means clustering to interpret tomograms from SRT and ERT data in complex geological settings. We first evaluate the effectiveness of clustering techniques using numerical modeling for two geological scenarios: a horizontally layered case and a layered case with undulation and a fault structure. Utilizing automated methods (Elbow and Silhouette), we objectively determine the optimal number of clusters for each geophysical tomogram. Subsequently, we compare the performance of the k-means clustering algorithm with subjective expert interpretations and the Laplacian edge detection method. Borehole data validate the clustering results and confirm the effectiveness of optimal cluster selection techniques. The findings of this study demonstrate that k-means clustering significantly enhances the detection of geological structures by establishing clearer boundaries and minimizing noise interference, enabling more accurate fault zone delineation. Compared to traditional edge detection and subjective interpretation methods, k-means clustering offers a systematic and objective approach that improves consistency and reliability across diverse geological settings. Moreover, its automated classification of geophysical data into meaningful clusters enables efficient analysis of large datasets. This study underscores the value of integrating machine learning techniques with geophysical methods such as SRT and ERT to improve interpretability and accurately identify subsurface geological structures, particularly in fault zone identification.

Identification of cell type-specific cell-penetrating peptides through in vivo phage display leveraged by next generation sequencing.

Vascular anomalies (VA) refer to abnormal blood or lymphatic vessel architecture, most often as a result of dysregulated growth. Venous malformations (VM), a subgroup of VAs, are triggered by activating mutations in the Angiopoietin/TIE2-PI3K/AKT/mTOR signaling pathway with TIE2 L914F (gene name TEK) being one of the most frequent mutations in patients with VMs. Although systemic targeting of the overactivated pathway is possible, it would be a therapeutic advantage to restrict treatment to only the affected lesions. To identify peptides with potential selective binding to TIE2 L914F lesions we applied in vivo phage display to TIE2 L914F-overexpressing endothelial cells (ECs) in a subcutaneous matrigel xenograft mouse model of VMs. By panning for lesion-targeting phages in combination with subcellular fractionation, a screen for cell-penetrating candidate phages was established. Employing Next Generation Sequencing (NGS) and a refined bioinformatic analysis we were able to identify many novel cell-penetrating peptides (CPPs). To pinpoint the most selective and viable CCP candidates a hierarchical clustering algorithm was utilized. This method aggregated CPPs with highly similar sequences into a small number of clusters from which consensus sequences could be derived. Selected candidate CPPs exhibited uptake in TIE2 L914F-expressing human umbilical vein endothelial cells (HUVEC) in culture and were able to deliver siRNA into these cells. In conclusion, our NGS bioinformatic-supported approach led to the identification of novel and selective CPPs capable of transporting a siRNA cargo into targeted cells.

CLUSTER ANALYSIS OF RISK PERCEPTION TO IMPLEMENT TOURIST HEALTH SAFETY IN VIETNAM

Perceived risk refers to tourists' objective assessment of the negative consequences associated with travel. The COVID-19 pandemic has significantly impacted all areas of human activity, influencing personal behavior and organizational practices. In post-COVID-19, travellers' perceptions of disease risk and behaviour have evolved. This study aimed to evaluate tourists' risk perceptions while travelling, categorizing them into distinct groups to identify differences among them. The research uses a mixed-method approach with validated surveys to measure travelers' risk perceptions via Likert scales. Data was collected from representative samples at destinations. K-means clustering identifies distinct segments, with the optimal number of clusters determined using the Elbow Method. Using cluster analysis, tourists were divided into three groups: Cluster 1 (32.18%), Cluster 2 (16.56%), and Cluster 3 (51.26%). The perceived risks were ranked in descending order as safety and hygiene risks, health risks, time risks, and emergency response support concerns. The analysis highlights that tourists prioritize safety, hygiene, health risks, and time risks when considering future travel, emphasizing the need for improved safety measures and effective tourism marketing strategies to restore traveler confidence. This study offers original insights through cluster analysis, showcasing the diverse risk perceptions among different social clusters and customer segmentation.

Air temperature prediction models for pavement design: a gradient boosting-based approach

ABSTRACT This article presents a machine learning-based approach for the prediction of 7-day maximum and 1-day minimum air temperatures in India. In this approach, a Gradient-boosting model is developed using latitude, longitude and altitude as model features. Raw data for this study consisted of hourly air temperature data collected over 30 years across 20,168 grid points within India. As part of the predictive methodology, cluster analysis was performed initially, which helped in obtaining homogeneous regions. Three different approaches empirical clustering, statewise clustering and k-mean clustering, were used. The model features pertaining to individual clusters were used with a gradient-boosting approach. Statistical analysis was conducted to check the accuracy of model predictions based on different clustering techniques. In all cases, predictions made at the global level resulted in poor predictions. In most cases, the results obtained with k-mean clustering showed that increasing the number of clusters improved the predictive accuracy. Furthermore, predictive accuracy with statewise or k-mean clustering was dependent on several features involved. The proposed predictive models have a very simple structure that requires the least input (i.e. geographical indicators). Hence the same can be used for faster and accurate computation of 7-day maximum and 1-day minimum air temperature.

Soft Clustering Techniques: An In-Depth Analysis of GMM and FCM Algorithms and Comparative Performance

Clustering is one of the modern techniques that have been discovered to solve the problem of the degree of similarity and dissimilarity between data within the network. Clustering originates from unsupervised techniques whose main function is to organize data into subsets based on the degree of similarity between these data. The research conducted an analytical study on Fuzzy C-Means (FCM) and Gaussian Mixture Model (GMM), which are considered the most prominent clustering techniques and aims to compare them in terms of the time taken by each algorithm to cluster the data and the energy consumed. Experiments were conducted in four different scenarios. The experiments concluded that GMM showed variation in energy consumption when the number of clusters gradually increased, while FCM showed clear stability in most cases. In terms of time, GMM was generally faster with fluctuations in performance, while FCM's performance was stable but relatively slower. Ultimately, each algorithm is used in a specific environment. GMM is fast with fluctuations in performance, which is useful in applications that require speed in performance, unlike FCM, which is relatively stable but slower, which is useful in applications that require accuracy in results at the expense of time.

K-Means Clustering of Social Studies Performance at Junior High School

This study aims to optimize the use of technology in evaluating student performance by grouping students based on their abilities. The main issues include the underutilization of technology, the absence of an appropriate evaluation system for different levels of student ability, and ineffective methods for grouping students. The K-Means Clustering algorithm was chosen because it has proven effective in grouping academic data in various studies. The data used includes Daily Knowledge Scores (DKS), Daily skill scores (DSS), Mid-term Summative Scores (MSS), End-of-Year Summative Scores (ESS), and Grade Report (GR). The data was analyzed using the CRISP-DM methodology with the help of RapidMiner. The results showed that 28.63% of students were classified as having excellent performance, 50.21% as having good performance, and 21.16% as having moderate performance. The Davies-Bouldin Index score of 1.713 for K=3 was considered sufficient for distinguishing the different student performance groups. The results of this study are expected to help schools provide learning support that better aligns with student needs. Future research is recommended to focus on optimizing the number of clusters (K), applying this method to other subjects, and integrating it with e-learning platforms for real-time student performance monitoring.

Genetic analysis of yield and yield components and powdery mildew resistance in F4 and F5 populations of blackgram

The present investigation aimed to evaluate 96 F4 and F5 genotypes, including two parental lines (VBN11 and LBG17) of blackgram, across two locations for yield, yield components, and resistance to powdery mildew. Pooled analysis, genetic variability assessment, correlation studies, parent-regression coefficient analysis, and the Multi-trait Genotype Ideotype Distance Index (MGIDI) were performed. Combined ANOVA revealed significant differences for all the traits studied. In the combined analysis, genotypes G3 and G1 exhibited desirable yields along with powdery mildew resistance. High heritability and genetic advance as a percentage of the mean (GAM) were observed for the traits such as single plant yield, the number of pods per plant, and percent disease index (PDI). Seed yield per plant recorded significant and positive correlations with the number of clusters per plant, number of pods per plant, number of seeds per pod, and 100-seed weight in both generations. Among the traits, PDI showed the highest regression coefficient. Principal component analysis exhibited four principal components (PCs) explaining the total variance in the F4 and F5 generations. The MGIDI index analysis revealed a total genetic gain for specific traits, including the number of pods per plant, single plant yield, and the number of capsules per plant in the F5 generation. Genotypes G18, G19, G4, and G38 were identified as valuable genetic resource through MGIDI, contributing significantly to blackgram improvement for high yield and powdery mildew resistance.

Unravelling the Genetic Complexity: Enhancing MYMV Resistance in Blackgram [Vigna mungo (L.) Hepper] Through Trait-based Selection

Background: Limited genetic diversity in blackgram, stemming from its common ancestry with greengram, presents challenges in combating Mungbean Yellow Mosaic Virus (MYMV), leading to yield stagnation. To address this, genetic screening and selection of segregating generations in F2 are essential for refining resistance traits with enhancing productivity. Methods: A two-season experiment was conducted in Panpoli, Tamil Nadu, during the Kharif 2021 and Kharif 2022 in a randomized block design using, F2 seeds from specific crosses (MDU 1 × Mash 114 and ADT 3 × PU 31), along with F1 and parent plants. MYMV disease scores were recorded using the infector row method for all the genotypes studied. Result: The study unveiled a complex genetic basis of MYMV resistance in blackgram, characterized by di-genic dominant genes displaying duplicative dominance type of epistatic interaction. This complexity underscores the insufficiency of simple selection methods. Estimates of genetic variability for “number of pods per plant” exhibited high genetic advance and moderate heritability, indicating control by additive genes. Positive correlations were observed between plant height, number of clusters per plant and number of pods per plant with yield. Path analysis revealed that traits like number of seeds per pod, 100 seed weight and number of pods per plant directly and positively influenced yield, which exhibit minimal influence from MYMV. These traits offer crucial selection criteria for enhancing MYMV resistance in blackgram.

Jointly Modeling and Clustering Tensors in High Dimensions

Tackling High-Dimensional Tensor Clustering In the paper “Jointly Modeling and Clustering Tensors in High Dimensions,” Cai, Zhang, and Sun address the challenge of jointly modeling and clustering tensors by introducing a high-dimensional tensor mixture model with heterogeneous covariances. The proposed mixture model exploits the intrinsic structures of tensor data. The authors develop a computationally efficient high-dimensional expectation conditional maximization (HECM) algorithm and show that the HECM iterates, with an appropriate initialization, converge geometrically to a neighborhood that is within statistical precision of the true parameter. The theoretical analysis is nontrivial because of the dual nonconvexity arising from both the expectation maximization-type estimation and the nonconvex objective function in the M step. They also study the convergence rate of the algorithm when the number of clusters is overspecified and when the signal-to-noise ratio diminishes with sample size. The efficacy of the proposed method is demonstrated through numerical experiments and a real-world medical data application.