Number Of Clusters Research Articles

Nanoscale organization of cardiac calcium channels is dependent on thyroid hormone status.

Thyroid hormone dysfunction is frequently observed in patients with chronic illnesses including heart failure which increases risk of adverse events. This study examined effects of thyroid hormones (TH) on cardiac T-tubule (TT) integrity, Ca2+ sparks, and nanoscale organization of ion channels in excitation-contraction (EC)-coupling, including L-type calcium channel (Cav1.2), ryanodine receptor-type 2 (RyR2), and junctophilin-2 (Jph2). TH deficiency was established in adult female rats by propyl-thiouracil (PTU) ingestion for 8 weeks; followed by randomization to continued PTU without or with oral triiodo-L-thyronine (T3; 10 ug/kg/d) for two additional weeks (PTU+T3). Confocal microscopy of isolated cardiomyocytes (CM) showed significant misalignment of TTs, and increased Ca2+ sparks in thyroid-deficient CMs. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analysis of STochastic Optical Reconstruction Microscopy (STORM) images showed decreased (p<0.0001) RyR2 cluster number per cell area in PTU CMs compared to euthyroid (EU) control myocytes, and this was normalized by T3-treatment. Cav1.2 channels and Jph2 localized within 210 nm radius of the RyR2 clusters were significantly reduced in PTU myocytes, and these values were increased with T3 treatment. A significant percentage of the RyR2 clusters in the PTU myocytes had neither Cav1.2 or Jph2, suggesting fewer functional clusters in EC-coupling. Nearest neighbor distances between RyR2 clusters were greater (p<0.001) in PTU cells compared to EU and T3-treated CMs that corresponds to disarray of TTs at the sarcomere z-discs. These results support a regulatory role of T3 in the nanoscale organization of RyR2 clusters and co-localization of Cav1.2 and Jph2 in optimizing EC-coupling.

Agro-morphological genetic diversity assessment of Amaranthus genotypes from Ethiopia based on qualitative traits

Amaranths are a promising plant in the family of Amaranthaceae because of their nutritional and functional properties, such as their high antioxidant content and dietary fiber content. However, it's being disregarded for several reasons, such as ignorance, a lack of in-depth research, and the plant's long-term genetic growth in Ethiopia, among other factors. In the current work, we described the genetic diversity of 120 amaranth genotypes using qualitative criteria. The experiment, which was configured with an alpha lattice design and duplicated twice, was run for two seasons in 2020 and 2021. Twenty qualitative descriptors were looked for in the gathered data. Among the 20 qualitative variables that were assessed, the chi-square test result indicated the presence of prevailing phenotypic variation. The results of the agro-morphological characterization also revealed a significant amount of variance. The overall mean of the Shannon diversity indices (H') was 0.61. The indices for germination rate, leaf margin, prominence of leaf veins, and the existence of auxiliary inflorescence varied from 0.12 to 0.99. The estimated diversity indices showed more intra-regional diversity (0.66) than inter-regional diversity (0.34), demonstrating the existence of gene flow between growing regions. Shannon–Weaver Diversity Index, ranged from 0.00 for auxiliary inflorescence to 1.94 for leaf coloration, with an overall mean of 19 characters (95%) that were found to have high diversity (> 0.76) while auxiliary inflorescence was invariant. Except for auxiliary inflorescence, all qualitative features showed a lot of variation. Additionally, Amaranthus hybridus L. subsp. cruentus (L.) Theil recorded the greatest Shannon diversity index (0.47) while Amaranthus spinosus L. recorded the lowest (0.00). The hierarchical clustering grouped all the genotypes into three clusters. The first cluster included the most genotypes (58), followed by the second (47), and the third cluster contained the fewest (15). Principal component analysis showed that the first six principal components with eigenvalues greater than one contributed 72% of the variability among genotypes. The study unequivocally demonstrated that, even when the genotypes were grouped into a small number of clusters, there was still enough divergence within the clusters to demonstrate the genotypes of amaranth to have a high genetic diversity. These results indicate that there is substantial genetic diversity among Ethiopian amaranth genotypes, which should be safeguarded and may be utilized in breeding in the future.

Potential mechanisms of metabolic reprogramming induced by ischemia-reperfusion injury in diabetic myocardium.

This study aimed to explore metabolic reprogramming in diabetic myocardium subjected to ischemia-reperfusion injury (I/RI) and potential mechanisms. Increased vulnerability after I/RI in diabetic myocardium is a major cause of the high prevalence of perioperative adverse cardiac events, and the specific alterations in energy metabolism after I/RI in diabetic myocardium and the impact on increased vulnerability are not fully understood. Metabolomic methods were used to explore the differences and characteristics of metabolites in the heart tissues of four groups, and then, single-cell RNA sequencing (ScRNA-seq) was used to explore the potential mechanism of metabolic reprogramming. It was found that the fatty acid metabolism of db/db mouse I/RI (DMI) showed a significant upward trend, especially the metabolites of ultra-long and medium-long-chain fatty acids; the metabolic flow analysis found that the U-13C glucose M + 6 was significantly higher in the C57BL mouse sham operation (NM) group than in the db/db mouse sham operation (DM) group, and in the C57BL mouse I/RI (NMI) than in the DMI group. Compared with the NMI group, the intermediate metabolites of glycolysis and tricarboxylic acid (TCA) cycle were significantly reduced in the DMI group; all comparisons were statistically significant (p < 0.05), indicating that the glucose uptake of diabetic myocardetis, the ability of glucose glycolysis after I/RI, and the contribution of glucose to TCA were significantly reduced. The results of ScRNA-seq revealed that the number of Cluster 0 myocardial isoforms was significantly increased in diabetic myocardium, and the differential genes were mainly enriched in fatty acid metabolism, and the PPARA signaling pathway was found to be over-activated and involved in the regulation of metabolic reprogramming of diabetic myocardial I/RI. Metabolic reprogramming of diabetic myocardial I/RI may be the main cause of increased myocardial vulnerability. The number of myocardial subtype Cluster 0 increased significantly, and PPARA PPARA is a ligand-activated receptor of the nuclear hormone receptor family that plays a central regulatory role in lipid metabolism.signaling pathway activation may be a potential mechanism for reprogramming metabolism in diabetic myocardium.

Quantitative Analysis of White Matter Hyperintensities as a Predictor of 1-Year Risk for Ischemic Stroke Recurrence.

This study evaluates the role of quantitative characteristics of white matter hyperintensities (WMHs) in predicting the 1-year recurrence risk of ischemic stroke. We conducted a retrospective analysis of 1061 patients with ischemic stroke from January 2018 to April 2021. WMHs were automatically segmented using a cluster-based method to quantify their volume and number of clusters (NoC). Additionally, two radiologists independently rated periventricular and deep WMHs using the Fazekas scale. The cohort was divided into a training set (70%) and a testing set (30%). We employed Cox proportional hazards models to develop predictors based on quantitative WMH characteristics, Fazekas scores, and clinical factors, and compared their performance using the concordance index (C-index). A total of 180 quantitative variables related to WMHs were extracted. A higher NoC in deep white matter and brainstem, advanced age (>90years old), specific stroke subtypes, and absence of discharge antiplatelets showed stronger associations with the risk of ischemic stroke recurrence within 1year. The nomogram incorporating quantitative WMHs data showed superior discrimination compared to those based on the Fazekas scale or clinical factors alone, with C-index values of 0.709 versus 0.647 and 0.648, respectively, in the testing set. Notably, a combined model including both WMHs and clinical factors achieved the highest predictive accuracy, with a C-index of 0.735 in the testing set. Quantitative assessment of WMHs provides a valuable neuro-imaging tool for enhancing the prediction of ischemic stroke recurrence risk.

Machine Learning-Based Clustering Using a 12-Lead Electrocardiogram in Patients With a Implantable Cardioverter Defibrillator to Identify Future Ventricular Arrhythmia.

Implantable cardioverter defibrillators (ICDs) reduce mortality associated with ventricular arrhythmia in high-risk patients with cardiovascular disease. Machine learning (ML) approaches are promising tools in arrhythmia research; however, their application in predicting ventricular arrhythmias in patients with ICDs remains unexplored. We aimed to predict and stratify ventricular arrhythmias requiring ICD therapy using 12-lead electrocardiograms (ECGs) in patients with an ICD. This retrospective analysis included 200 adult patients who underwent ICD implantation at a single center. Patient demographics, clinical features, and 12-lead ECG data were collected. Unsupervised learning techniques, including K-means and hierarchical clustering, were used to stratify patients based on 12-lead ECG features. Dimensionality reduction methods were also used to optimize clustering accuracy. The silhouette coefficient was used to determine the optimal method and number of clusters. Of the 200 patients, 59 (29.5%) received appropriate therapy. The mean age of patients was 62.3 years, and 81.0% were male. The mean follow-up period was 2,953 days, with no significant intergroup differences. Hierarchical clustering into 3 clusters proved to be the most accurate (silhouette coefficient=0.585). Kaplan-Meier curves for these 3 clusters revealed significant differences (P=0.026). We highlight the potential of ML-based clustering using 12-lead ECGs to help in the risk stratification of ventricular arrhythmia. Future research in a larger multicenter setting may provide further insights and refine ICD indications.

JWST’s PEARLS: 119 multiply imaged galaxies behind MACS0416, lensing properties of caustic crossing galaxies, and the relation between halo mass and number of globular clusters at z = 0.4

We present a new lens model for the ɀ = 0.396 galaxy cluster MACS J0416.1 –2403 based on a previously known set of 77 spectroscop-ically confirmed, multiply imaged galaxies plus an additional set of 42 candidate multiply imaged galaxies from past HST and new JWST data. The new galaxies lack spectroscopic redshifts but have geometric and/or photometric redshift estimates that are presented here. The new model predicts magnifications and time delays for all multiple images. The full set of constraints totals 343, constituting the largest sample of multiple images lensed by a single cluster to date. Caustic-crossing galaxies lensed by this cluster are especially interesting. Some of these galaxies show transient events, most of which are interpreted as micro-lensing of stars at cosmological distances. These caustic-crossing arcs are expected to show similar events in future, deeper JWST observations. We provide time delay and magnification models for all these arcs. The time delays and the magnifications for different arcs are generally anti-correlated. In the major sub-halos of the cluster, the dark-matter mass from our lens model correlates well with the observed number of globular clusters, as expected from N-body simulations. This confirms earlier results, derived at lower redshifts, which suggest that globular clusters can be used as powerful mass proxies for the halo masses when lensing constraints are scarce or not available.

Utilization law of shale gas in hydraulic fracturing with the Changning–Weyuan block in China as an example

The process of extracting shale gas, particularly the study of the utilization law of shale reservoirs modified by hydraulic fracturing technology, is crucial for understanding the effective development of shale gas. This understanding can significantly influence the estimated ultimate recovery of reserves. Our study focused on Longmaxi Formation shale in the Changning–Weiyuan block. To investigate the hydraulic fracturing expansion mechanism and utilization law, we employed shale reservoir physical and mechanical characterization, indoor triaxial hydraulic fracturing experiments, and numerical simulations. The findings are as follows. The rupture pressure of hydraulic fractures increases with the peripheral pressure, and high stress is conducive to the formation of internal microfractures. Additionally, a high rate of water injection enhances fracture extensions along the direction of fluid injection. Under varying injection pressures, the length of crack extensions and number of bond damages in shale are positively correlated with the injection pressure. Conversely, under different differential ground stresses, the length of crack extensions and number of bond damages are negatively correlated with the injection pressure. The six main factors affecting the effective utilization of shale gas are as follows: the stimulated reservoir volume, number of hydraulic fracture clusters, fracture length, fracture height, number of fracture stages, and cluster spacing, accounting for 27.13%, 14.74%, 10.31%, 9.58%, 9.53%, and 9.29%, respectively, with a cumulative contribution rate of 80.58%. This study clarifies the hydraulic fracturing mechanisms of shale reservoirs and the laws governing shale gas production, providing technical support for the development of shale gas reservoirs.

CopyMix: Mixture model based single-cell clustering and copy number profiling using variational inference

Investigating tumor heterogeneity using single-cell sequencing technologies is imperative to understand how tumors evolve since each cell subpopulation harbors a unique set of genomic features that yields a unique phenotype, which is bound to have clinical relevance. Clustering of cells based on copy number data obtained from single-cell DNA sequencing provides an opportunity to identify different tumor cell subpopulations. Accordingly, computational methods have emerged for single-cell copy number profiling and clustering; however, these two tasks have been handled sequentially by applying various ad-hoc pre- and post-processing steps; hence, a procedure vulnerable to introducing clustering artifacts. We avoid the clustering artifact issues in our method, CopyMix, a Variational Inference for a novel mixture model, by jointly inferring cell clusters and their underlying copy number profile. Our probabilistic graphical model is an improved version of the mixture of hidden Markov models, which is designed uniquely to infer single-cell copy number profiling and clustering. For the evaluation, we used likelihood-ratio test, CH index, Silhouette, V-measure, total variation scores. CopyMix performs well on both biological and simulated data. Our favorable results indicate a considerable potential to obtain clinical impact by using CopyMix in studies of cancer tumor heterogeneity.

Toward data efficient anomaly detection in heterogeneous edge-cloud environments using clustered federated learning

Anomaly detection in edge-cloud scenarios stands as a critical means to ensure the security of network environment. Federated learning (FL)-based anomaly detection combines multiple data sources and ensures data privacy, making it a promising distributed detection method. However, FL-based anomaly detection system is usually affected by data heterogeneity and data bias, resulting in the inefficiency of data used for FL and the decline of detection performance. We propose an iterative federated clustering ensemble algorithm named IFCEA, in which we (1) establish a committee on the devices, and select the optimal participation for each device based on the evaluations of committee; (2) filter the clusters based on committee results, and exclude the biased clusters; (3) design an aggregation weight that reflects the degree of local distribution balance; (4) present a novel cluster initialization method, OneBiPartition, which adapts to the number of clusters and commences clustering federated task efficiently. IFCEA enhances the data quality used in FL-based anomaly detection system from two perspectives: device selection and participation weights, effectively addressing the issues of data heterogeneity and data bias faced during the FL training phase. Extensive experimental results on five network traffic datasets (the UNSW-NB15, CIC-IDS2017, CIC-IDS2018, CIC-DDoS2019 and BCCC-DDoS2024 datasets) demonstrate that our proposed framework outperforms in terms of detection metrics and convergence performance.

Penerapan Algoritma Clustering untuk Segmentasi Pelanggan E-commerce berdasarkan Data Pembelian dan Aktivitas

In the current digital era, e-commerce has become one of the main pillars of global trade. With the ever-increasing amount of transaction and user activity data, e-commerce companies are faced with the challenge of understanding and managing diverse customer segments more effectively. This paper discusses the application of clustering algorithms for e-commerce customer segmentation based on purchasing data and user activity. The aim of this research is to identify homogeneous customer groups to support more targeted marketing strategies and increase customer retention. The problem faced is how to process big data originating from user transactions and activities on e-commerce platforms, as well as how to identify patterns that are useful for customer segmentation. The data used in this research includes purchase history, frequency of visits, length of time spent on the site, and interactions with certain products. The solution method applied in this research is the clustering algorithm, especially K-Means and DBSCAN. K-Means is used to group data into a predetermined number of clusters based on the Euclidean distance between data points. Meanwhile, DBSCAN is used to identify clusters with high density and separate data that is considered noise or outliers. Data preprocessing is carried out to clean and normalize the data before being applied to the clustering algorithm. Validation of clustering results is carried out using metrics such as Silhouette Score and Davies-Bouldin Index. The research results show that by applying the clustering algorithm, customers can be grouped into several segments that have similar characteristics. For example, we found groups of customers with high purchase frequency but low transaction value, as well as other groups with high transaction value but low purchase frequency. This information is very useful for companies to design more effective marketing strategies, such as special offers for customers with high transaction values ​​or loyalty programs for customers with high purchasing frequency. The conclusion of this research is that clustering algorithms can be a very effective tool in e-commerce customer segmentation, allowing companies to understand customer behavior patterns and develop more targeted and effective marketing strategies. Thus, implementing this method is expected to improve business performance and overall customer satisfaction.

Testing the sources of the peculiar abundances in globular clusters

This work aims to analyze some of the polluters proposed in the self-enrichment scenarios put forward to explain the multiple populations in globular clusters (GCs), extending previous studies. Three scenarios with different polluter stars were tested: asymptotic giant branch stars (AGBs), high-mass interacting binaries (IBs), and fast rotating massive stars (FRMSs). With abundance data available from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey and ΔY estimates from precise Hubble Space Telescope (HST) photometry, twenty-six clusters were studied, increasing the number of clusters in previous studies by more than a factor of three. We also included the study of the abundances of N, C, Mg, and Al, extending previous studies that mainly focused on the abundances of He, O, and Na. In addition, we constructed an empirical model to test whether one could explain the chemical signatures of the “enriched” population of GC stars with a fixed source and dilution process based on empirical data. In agreement with work by other authors, we found that the proposed polluters can generally predict the qualitative abundance patterns in GC stars and in some cases quantitatively predict some elements, but in most cases when we compare the model yields with the observations, we find that they cannot explain the entire set of observed abundance patterns. The empirical model succeeds in reproducing the abundances of Al for a given ΔY (and vice versa), showing that there is a direct relationship between Al and He, with one increasing proportionally to the other. However, the empirical model fails to reproduce the observed abundances of Na and N, in agreement with the results of previous works. The observed decoupling between the maximum abundances of CNO-cycle elements such as N and Na with those of Al and He provides new information and constraints for future models and could take us a step closer to understanding the origin of the peculiar abundance variations of GC stars.

Clinical phenotype of ARDS based on K-means cluster analysis: a study from the eICU database

PurposeTo explore the characteristics of the clinical phenotype of ARDS based on Machine Learning. MethodsThis is a study on Machine Learning. Screened cases of acute respiratory distress syndrome (ARDS) in the eICU database collected basic information in the cases and clinical data on the Day 1, Day 3, and Day 7 after the diagnosis of ARDS, respectively. Using the Calinski-Harabasz criterion, Gap Statistic, and Silhouette Coefficient, we determine the optimal clustering number k value. By the K-means cluster analysis to derive clinical phenotype, we analyzed the data collected within the first 24 hours. We compared it with the survival of cases under the Berlin standard classification, and also examined the phenotypic conversion within the first 24 hours, on day 3, and on day 7 after the diagnosis of ARDS. ResultsWe collected 5,054 cases and derived three clinical phenotypes using K-means cluster analysis. Phenotype-I is characterized by fewer abnormal laboratory indicators, higher oxygen partial pressure, oxygenation index, APACHE IV score, systolic and diastolic blood pressure, and lower respiratory rate and heart rate. Phenotype-II is characterized by elevated white blood cell count, blood glucose, creatinine, temperature, heart rate, and respiratory rate. Phenotype-III is characterized by elevated age, partial pressure of carbon dioxide, bicarbonate, GCS score, albumin. The differences in ICU length of stay and in-hospital mortality were significantly different between the three phenotypes (P<0.05), with phenotype I having the lowest in-hospital mortality (10%) and phenotype II having the highest (31.8%). To compare the survival analysis of ARDS patients classified by phenotype and those classified according to Berlin criteria. The results showed that the differences in survival between phenotypes were statistically significant (P<0.05) under phenotypic classification. ConclusionsThe clinical classification of ARDS based on K-means clustering analysis is beneficial for further identifying ARDS patients with different characteristics. Compared to the Berlin standard, the new clinical classification of ARDS provides a clearer display of the survival status of different types of patients, which helps to predict patient prognosis.

Generalized zero-sample industrial fault diagnosis with domain bias

Generalized zero-sample fault diagnosis (GZSFD) is a challenging task involving the diagnosis of all samples from both previously seen and unseen faults. However, the scarcity of unseen samples for training causes that existing methods are hindered by domain bias, where unseen faults are more likely to be misclassified as seen faults. In this article, an efficacious solution is proposed by constructing an unseen fault detector for test samples in GZSFD with domain bias, which utilizes the detected unseen-sample knowledge to enhance the diagnosis performance. Specifically, a ResNet-based one-dimensional convolutional neural network is designed for high-quality feature extraction. Also, a Kullback–Leibler divergence-based distribution threshold detector is constructed for the identification of test samples. Afterwards, test samples are detected and distinguished into seen or unseen classes. In detected unseen classes, a zero-sample fault diagnosis (ZSFD) problem is undertaken, while in detected seen classes, a sub-GZSFD problem is addressed. For ZSFD tasks, to leverage the unseen samples in the test set, a clustering-based scheme without a predefined cluster number is used for the detected unseen fault. For sub-GZSFD tasks, combined with classification results in the ZSFD task, two embedding strategies are proposed to further mitigate the domain bias. They learn a shared weight and the optimal weights of semantic attributes from the feature space to the semantic embedding space, respectively. Using the shared fine-grained semantic attribute descriptions as auxiliary information, the final fault category can be determined. Experimental results showcase that the proposed strategies effectively alleviate the domain bias in GZSFD tasks.

Firefly forest: A swarm iteration-free swarm intelligence clustering algorithm

The Firefly Forest algorithm is a novel bio-inspired clustering method designed to address key challenges in traditional clustering techniques, such as the need to set a fixed number of neighbors, predefine cluster numbers, and rely on computationally intensive swarm iterative processes. The algorithm begins by using an adaptive neighbor estimation, refined to filter outliers, to determine the brightness of each firefly. This brightness guides the formation of firefly trees, which are then merged into cohesive firefly forests, completing the clustering process. This approach allows the algorithm to dynamically capture both local and global patterns, eliminate the need for predefined cluster numbers, and operate with low computational complexity. Experiments involving 14 established clustering algorithms across 19 diverse datasets, using 8 evaluative metrics, demonstrate the Firefly Forest algorithm’s superior accuracy and robustness. These results highlight its potential as a powerful tool for real-world clustering applications. Our code will be open-sourced after the peer review.

Sustainable transformation of energy sector: Cluster analysis for the sustainable development strategies of selected European countries

Implementing measures by the European countries within the framework of the ratification of the Paris Climate Agreement should ensure an enlarged share in energy production from alternative energy sources and the introduction of new alternative energy capacities, which would provide an annual decrease in carbon dioxide emissions growth rate, reduce anthropogenic pressure on the natural environment and the use of non-renewable natural resources while preserving biodiversity. This paper aims at forming a system of indicators of sustainable transformation of the energy sector and studying their influence on the policy of environmental-oriented development. The paper applies Ward's method for determining the optimal cluster number and discriminant analysis for checking which variables significantly influence the formation of countries' clusters. The six clusters of countries obtained describe the different features of decarbonisation and climate change mitigation policies. Using discriminant analysis helped to single out the most relevant indicators of the energy sector's transformation in the direction of its decarbonisation and climate change mitigation. The most significant indicators (energy efficiency, electricity production capacities for renewables and wastes, and share of energy from renewable sources) largely determine the pace and features of forming sustainable development strategies for the energy sector. By highlighting the relevant cluster groups in the study, their characteristic features, and ways of transformational processes in the energy sector were discovered. This enabled the authors to form specific directions for strengthening and activating the processes of decarbonisation of the energy sector and climate change mitigation. There outcomes can become the basis for further research and the formation of appropriate strategies for sustainable development and transformation of the energy sector. The results stemming from this study could help scholars and other affiliated persons determine future strategies for research for implementing Sustainable Development Goals 2030.

K-means 알고리즘을 기반으로 한 야오족 자수 색상 추출 및 가상 캐릭터 의상디자인 응용

To promote the digital preservation and transmission of traditional cultural elements, this study focuses on Yao embroidery as a case study, combining digital technologies and algorithms to extract and analyze its color characteristics. These extracted color features were applied to the design of virtual character clothing. First, a bilateral filtering algorithm was used to preprocess the images of Yao embroidery, reducing noise while preserving intricate details. Subsequently, Euclidean distance was calculated in the Lab color space, and the OSTU adaptive binarization method was employed to separate the image from its background. Additionally, K-means clustering was applied multiple times to the color data, and the optimal result was achieved when the number of clusters was set to eight. Through this process, eight major colors of Yao embroidery were extracted. Based on these extracted colors, 3D point cloud analysis was conducted to study the three-dimensional spatial distribution of color points, and a color combination model based on hue, brightness, and saturation was constructed. The results revealed that the color composition of Yao embroidery predominantly consists of high-saturation colors, with strong color contrasts and distinct ethnic characteristics. Ultimately, this study demonstrated the applicability of integrating the color model and design patterns of Yao embroidery into virtual character clothing design, showcasing the potential for incorporating traditional embroidery into digital design. This contributes to the preservation and promotion of intangible cultural heritage and serves as a valuable reference for further research on the color systems of traditional ethnic costumes.

Magnetoencephalography Profile of Patients with Drug-Resistant Focal Epilepsy and Normal MRI.

Magnetoencephalography (MEG) could be a valuable tool in the presurgical evaluation of drug-resistant epilepsy (DRE), especially when the initial evaluation is inconclusive. In this retrospective study, we describe the profile of MEG in patients with DRE and normal magnetic resonance imaging (MRI). We included patients with focal epilepsy and normal MRI who underwent presurgical evaluation for DRE. MEG profiles of these patients, including the frequency of spikes, density of clusters, number of clusters, and concordance with video electroencephalography (VEEG), were analyzed. Of the 73 patients included, magnetic source imaging (MSI) provided localizing information in 51 (69.9%) patients. Among patients with localizing MEG findings, localizing information on VEEG too was noted in 42 (57.5% of the whole cohort). Thirty-one (42.5%) patients had concordant findings with region-specific localization, six (8.2%) patients had partial concordance, and five (6.8%) subjects showed discordant findings. There was a moderate agreement for the presumed epileptogenic zone in comparing findings derived from MEG and VEEG (kappa value of 0.451, P < 0.001). The agreement was lower when MEG localized to the frontal lobe (kappa value of 0.379, P = 0.001) than the temporal lobe (kappa value 0.442, P = 0.002). MEG can provide localizing information in most patients with a normal MRI. A moderate degree of agreement between localization by MEG and VEEG was noted. These findings highlight the usefulness of MSI in the presurgical evaluation of MRI-negative DRE.

K-means clustering-based young users kitchen needs analysis and personalized design strategy

Abstract Objectives: This study aimed to understand the kitchen use behaviors and design preferences that cater to the needs of young people, encouraging their intention to cook and promoting healthy living. Methods: This study conducted a survey with 310 young people (aged 18 to 40) and applied the K-means clustering method for analysis. The silhouette coefficient feature in R was used to automatically assess the impact of different K-values on the clustering outcome, thereby determining the ideal number of clusters. After identifying the optimal number of clusters, K-means clustering was performed in R to segment the survey data and identify the primary user personas. Results: The analysis identified two user categories: 1. Health-Convenience Advocates, that is, young people with a high level of education who seek efficient lifestyles but due to their busy schedules have a significant need for quick and healthy dietary solutions; 2. Cooking Explorers, typically modern, educated young individuals who are passionate about cooking, viewing it not only as a necessity but also as a personal interest and a realm for exploration. Conclusion: This study provides valuable guidance for kitchen designers and home product developers to better understand the actual needs and preferences of young users, highlighting the importance of excellent kitchen design in promoting a healthy lifestyle and improving quality of life.

Prediction of Floor Failure Depth Based on Dividing Deep and Shallow Mining for Risk Assessment of Mine Water Inrush

Understanding and predicting floor failure depth is crucial for both mitigating mine water inrush hazards and safeguarding groundwater resources. Mining activities can significantly disturb the geological strata, leading to shifts and damage that may result in floor cracks. These disruptions can extend to confined aquifers, thereby increasing the risk of water inrushes. Such events not only pose a threat to the safety of mining operations but also jeopardize the sustainability of surrounding groundwater systems. Therefore, accurately predicting floor failure depth to take effective coal seam floor management measures is the key to reducing the impact of coal seam mining on water resources. Seventy-eight sets of data on coal seam floor failure depth in China were collected, and the main controlling factors were considered: mining depth (D1), working face inclination length (D2), coal seam inclination (D3), and mining thickness (D4). Firstly, the distance evaluation function based on Euclidean distance was constructed as the clustering effectiveness index, and the optimal cluster number K = 3 was determined. The collected data were clustered into three categories using the K-means clustering algorithm. It was found that the clustering results were positively correlated with the size of D1, indicating that D1 played a dominant role in the clustering. The D1 dividing points of the three types of samples were between 407.7~414.9 m and 750~900 m. On this basis, the grey correlation analysis method was used to analyze the order of the influence weights of the main controlling factors of coal seam floor failure depth. For the first group, the order was D2 &gt; D1 &gt; D3 &gt; D4, while, in the other two, it was D1 &gt; D2 &gt; D3 &gt; D4. D1 emerged as the most influential factor, surpassing D2. Therefore, D1 between 407.7 and 414.9 m could be used as the boundary, the first group could be classified as shallow mining, and the second and third groups could be classified as deep mining. Based on this boundary, CatBoost prediction models for the depth of coal seam floor failure in deep and shallow parts were constructed and the prediction results of the model test set were compared with the calculation results of the empirical formula. These models exhibited superior accuracy with a lower mean squared error (MSE) and mean absolute error (MAE) and a higher R-squared (R2) compared to the empirical formula. This study helps to enhance the understanding of coal seam floor behavior, guide floor management, and protect groundwater resources by defining deep and shallow mining to accurately predict floor failure depth.

Descriptive Analysis Of K-Means and Apriori Methods To Find Promotion Strategies For University Bhayangkara.

The increasing number of higher education institutions in Indonesia has intensified competition between universities. Universitas Bhayangkara Jakarta Raya (Ubhara Jaya) must develop an effective marketing plan to stand out. This study used segmentation and associative analysis on 2023 student enrolment data. The Apriori algorithm identified patterns in student preferences for study programmes, while the K-Means method categorized students based on demographics and family income. Three income-based clusters were identified: C0 ‘Already stable’ (IDR 1,000,000 - 2,500,000), C1 ‘Focus on promotion strategy’ (IDR 20,000,000), and C2 ‘Maximise promotion again’ (IDR 5,000,000 - 10,000,000). The Davies-Bouldin Index (DBI) indicated k=5 as the optimal cluster number, but k=3 was adequate with a minimal score difference. The most popular programmes were Communication Science and Management, with high support and confidence values. This data helps Ubhara Jaya manage study programme demand and room availability. Combining K-Means and Apriori algorithms is expected to enhance data segmentation[1] and support effective marketing strategies, aiding strategic decisions in higher education marketing.