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Background: Dyspnea and desaturation during exercise are essential assessment items for pulmonary rehabilitation. Characterizing patients using these 2 factors may be important for providing more effective pulmonary rehabilitation. This study aimed to categorize subjects with interstitial lung disease (ILD) using dyspnea and desaturation at the end of the 6-min walk test (6MWT). Methods: This was a retrospective study including 230 stable subjects with ILD who underwent 6MWT in our out-patient department at a general hospital in Japan. The modified Borg scale and oxygen saturation determined by SpO2 at the end of the 6MWT were used for cluster analysis using the k-means method with k = 4. Results: Subjects were classified into 4 characteristic clusters. SpO2 at the end of the 6MWT was lower in cluster 4 (80.5 ± 3.0%) than in clusters 1 (94.3 ± 2.0%), 2 (94.3 ± 1.9%), and 3 (87.9 ± 1.8%) and was lower in cluster 3 than in clusters 1 and 2. The modified Borg scale score at the end of the 6MWT was higher in clusters 2 (4 [3–8]), 3 (3 [0–9]), and 4 (4 [0–7]) than in cluster 1 (0.5 [0–2.0]) and was higher in cluster 2 than in cluster 3. Conclusions: Subjects with ILD were classified into 4 characteristic clusters using dyspnea and SpO2 at the end of the 6MWT. The 4 clusters are characterized as follows: Cluster 1 had mild desaturation and mild dyspnea; cluster 2 had mild desaturation and severe dyspnea; cluster 3 had both moderate desaturation and dyspnea, and cluster 4 had both severe desaturation and dyspnea. These classification data offer insight for individualized pulmonary rehabilitation for patients with ILD. [ABSTRACT FROM AUTHOR]

Condition monitoring and accurate fault diagnosis are always concerned for stable operating of mechanical equipment. The fault diagnosis based on supervised deep learning has been proved to be effective by their powerful capacities in feature extracting, but usually requiring large number of labeled data. Faced with the actual situation that labeled samples are often in short, data are imbalanced in category etc., accurate fault diagnosis based on deep learning is still challenging, so does to explore and explain the evolution of complex faults. A mechanical fault diagnosis model with Semi-Supervised Variational Autoencoder based on Long Short-Term Memory network (LSTM-SSVAE) is proposed in this paper. Through semi-supervised learning, LSTM-SSVAE uses unlabeled data to enhance the extraction of discriminant features of data, which make the model less dependent on only labeled data while giving improved fault diagnosis accuracy. The LSTM networks are applied as the encoder and decoder innovatively, and regularization constraints are added in loss function, to improve the clustering effect of the intermediate hidden variables, so that to achieve effective feature extraction and state detection. Based on open datasets, experimental results show that with the same number of labeled samples, the fault diagnosis accuracy obtained by using LSTM-SSVAE is higher than other typical semi-supervised learning models. Based on actual vibration data of working equipment in coal mining, the feasibility of clustering analysis of intermediate hidden variables also proves that the LSTM-SSVAE model is recommendable for fault evolution analysis and is potential for operating conditions prediction of mechanical equipment. [ABSTRACT FROM AUTHOR]

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ABSTRACT In this computer note I introduce software, PopCluster, that implements a new likelihood method for unsupervised population structure analysis from marker data. To infer a coarse population structure, it assumes the mixture model and adopts a simulated annealing algorithm to make a maximum likelihood clustering analysis, partitioning the sampled individuals into a predefined number of clusters. To deduce a fine population structure, it further assumes the admixture model and employs an expectation maximisation algorithm to estimate individual admixture proportions. PopCluster has many features. First, it is one of just a couple of model‐based methods that can handle both biallelic and multiallelic markers in the same framework. Second, it is the first population structure analysis method that uses both Message Passing Interface (MPI) and openMP to exploit multiple CPUs with both shared and distributed memories and has the capacity to handle genomic data with millions of individuals and millions of loci. Third, the algorithms for both mixture and admixture analyses are fast, rendering PopCluster favourably in computational efficiency over previous methods in analysing genomic data. Fourth, PopCluster is built for Windows, Linux and Mac platforms, and its Windows version has an integrated GUI that can conveniently visualise analysis results and facilitate data input. Fifth, its Windows version has a built‐in simulation module designed to simulate genotype data under admixture, hybridization or migration models. PopCluster provides a valuable toolset for researchers to simulate, infer and visualise individual admixture and population genetic structure, hybridization and migration using marker data. [ABSTRACT FROM AUTHOR]

Emotions play a significant role in how we perceive and interact with products. Thoughtfully designed emotionally appealing products can evoke strong user responses, making them more attractive. Color, as a crucial attribute of products, is a significant aspect to consider in the process of emotional product design. However, users' emotional perception of product colors is highly intricate and challenging to define. To address this, this research proposes a product color design concept that considers human emotion perception based on deep learning and cluster analysis. First, for a given product, a color style is chosen for rerendering, which is an emotional color image. Different emotional color images have distinct RGB color representations. Second, clustering methods are employed to establish relationships between various emotional color images and different colors, selecting emotionally close style images. Subsequently, through transfer learning techniques, specific grid structures are used to retrain network weights, allowing for the fusion design of style and content images. This process ultimately achieves emotional color rendering design based on emotional color clustering and transfer learning. Multiple sets of emotional color design examples demonstrate that the method proposed in this study can accurately fulfill the emotional color design requirements of products, thereby, offering practical applicability. The satisfaction survey shows that the proposed method has certain guiding significance for clothing emotional color design. [ABSTRACT FROM AUTHOR]

AbstractThe estimation of large precision matrices is crucial in modern multivariate analysis. Traditional sparsity assumptions, while useful, often fall short of accurately capturing the dependencies among features. This paper addresses this limitation by focusing on precision matrix estimation for multivariate data characterized by a flexible yet unknown group structure. We introduce a novel approach that begins with the detection of this unknown group structure, clustering features within the low-dimensional space defined by the leading eigenvectors of the sample covariance matrix. Following this, we employ group-wise multivariate response linear regressions, guided by the identified group memberships, to estimate the precision matrix. We rigorously establish the theoretical foundations of our proposed method for both group detection and precision matrix estimation. The superior numerical performance of our approach is demonstrated through comprehensive simulation experiments and a comparative analysis with established methods in the field. Additionally, we apply our method to a real breast cancer dataset, showcasing its practical utility and effectiveness. [ABSTRACT FROM AUTHOR]

Accurate characterization of carbonate reservoirs remains a significant challenge due to complex facies variations and the substantial effects of wave propagation. We propose a facies-constrained reflectivity inversion strategy. The method establishes a relationship between logging data and seismic waveforms, applies clustering analysis using the Self-Organizing Map (SOM) technique, and utilizes the clustering results to constrain the construction of an initial model with realistic lateral variations. Based on this initial model, a Bayesian-based reflectivity inversion is performed, incorporating a modified Cauchy prior distribution to enhance inversion accuracy and stability. The reflectivity method offers a one-dimensional analytical solution to the wave equation, tacking thin layer thicknesses and wave propagation effects into consideration, thereby significantly alleviating inversion problems encountered in marl reservoirs. Compared to traditional inversion methods based on the Zoeppritz equation, the facies-constrained reflectivity inversion delivers higher accuracy and resolution. The application of this technique to identify marl reservoirs in the Lei32 sub-member of the Sichuan Basin has yielded promising results, effectively delineating favorable reservoir areas of approximately 210 km2 and offering strong support for future exploration and development. [ABSTRACT FROM AUTHOR]

This paper introduces a novel similarity function that evaluates both the quantitative and qualitative similarities between data instances, named QQ-Means (Qualitative and Quantitative-Means). The values are naturally scaled to fall within the range of − 1 to 1. The magnitude signifies the extent of quantitative similarity, while the sign denotes qualitative similarity. The effectiveness of the QQ-Means for cluster analysis is tested by incorporating it into the K-means clustering algorithm. We compare the results of the proposed distance measure with commonly used distance or similarity measures such as Euclidean distance, Hamming distance, Mutual Information, Manhattan distance, and Chebyshev distance. These measures are also applied to the classic K-means algorithm or its variations to ensure consistency in the experimental procedure and conditions. The QQ-Means similarity metric was evaluated on gene-expression datasets and real-world complex datasets. The experimental findings demonstrate the effectiveness of the novel similarity measurement method in extracting valuable information from the data. [ABSTRACT FROM AUTHOR]

Focusing on the classification of business data resources within a unified business semantic environment is an important method to simplify the data environment and a crucial approach to studying data intelligence. A multi-value chain data space is a typical business semantic heterogeneous complex data environment. This paper summarizes the characteristics of multi-value chain business data resources and proposes a study on their classification using business semantic logic. By constructing a semantic-based relational model for multi-value chain business data resources and a multi-value chain business lexicon, this paper unifies the semantics of business data resources. This creates conditions for their classification according to business logic. Based on the feature transformation of business data resources, this paper proposes a clustering algorithm for multi-value chain business data resources (Business data resource classification algorithm for multi-value chain data space, BDRCA4MVCDS) aimed at a data space, completing the classification of business data resources. Finally, comparative experiments with KMeans and KABSA demonstrate the clustering effectiveness of the proposed algorithm (BDRCA4MVCDS) as well as its good stability and adaptability. [ABSTRACT FROM AUTHOR]

To evaluate the genetic diversity of 58 sesame local germplasm resources collected from 38 counties in Shanxi Province during the Third Crop Germplasm Resource Census and Collection Action in 2020, field investigations were conducted on 41 phenotypic traits of 57 resources. The study was conducted using coefficient of variation, genetic diversity index, correlation coefficient, and cluster analysis methods, and excellent germplasm was screened. The results revealed abundant genetic variations in quantitative traits, with an average genetic diversity index of 1.826 for 27 traits. 1000-grain weight showed the highest genetic diversity index (2.09), and calyx length exhibited the lowest (1.23); The height of the initial branch exhibited the highest coefficient of variation (72.05%), and the growth period showed the lowest (4.01%). The length of the corolla, the height of the initial branch, the length of the main stem fruit axis, the length and thickness of the capsule, the length and width of the basal and middle leaves are significantly positively correlated with the yield per plant. Cluster analysis divided the resource into five groups: materials with multiple branches and high yield; dwarf type; a long growth period with multiple branches but low yield type; high stem, high yield, and large grain; and those has moderate comprehensive traits and has potential for increasing yield after improvement. Excellent germplasm resources were selected based on comprehensive phenotypic traits, including P141028023, P141032019, P140502022, and P140830014. This study provides theoretical reference for the excavation and utilization of sesame local germplasm, and provides excellent parental materials for new variety breeding. [ABSTRACT FROM AUTHOR]

This study aims to use clustering analysis techniques to identify different immune subtypes in sepsis patients and explore their clinical relevance and prognosis. Methods: The study included 236 patients from the EICU at Shanghai Tenth People's Hospital, who met the Sepsis 3.0 diagnostic criteria. Blood samples were collected to measure lymphocyte subsets and cytokine levels, along with demographic and clinical data. K-means clustering analysis was used to categorize patients into three groups based on immune and inflammatory markers. Results: Three immune subtypes were identified: the high immune activation subtype (Cluster 1), characterized by high levels of CRP and WBC, high levels of T cells, NK cells, and B cells, and low levels of IL-6, IL-8, and IL-10; the moderate immune activation subtype (Cluster 2), characterized by moderate levels of CRP, WBC, T cells, NK cells, B cells, IL-6, IL-8, and IL-10; and the high inflammation and immune suppression subtype (Cluster 3), characterized by very high levels of IL-6, IL-8, and IL-10, low levels of T cells, NK cells, and B cells, and relatively lower CRP levels. Patients in Cluster 3 had a significantly increased 28-day mortality risk compared to those in Cluster 1 (HR = 21.65, 95% CI: 7.46– 62.87, p < 0.001). Kaplan-Meier survival curves showed the lowest survival rates for Cluster 3 and the highest for Cluster 1, with the differences among the three groups being highly statistically significant (p < 0.0001). Conclusion: This study identified three immune subtypes of sepsis that are significantly associated with clinical outcomes. These findings provide evidence for personalized treatment strategies to improve patient outcomes. [ABSTRACT FROM AUTHOR]

In recent years, urban road collapse incidents have been occurring with increasing frequency, particularly in populous cities. To mitigate road collapses, geophysical prospecting plays a crucial role in urban road inspections. Ground Penetrating Radar (GPR), a non-destructive technology, is extensively employed for detecting urban road damage, with manual interpretation of GPR images typically used to identify buried objects. Nonetheless, manual interpretation methods are not only inefficient but also subjective, as they rely on the interpreter's experience, thereby affecting the interpreting reliability. This study investigates the distribution and causes of road collapses and develops a deep learning-based intelligent recognition model using GPR detection images of urban roads in cities of the South China as original samples. The finding reveal that road collapses are concentrated in the months of July and August, mainly caused by pipe leakage and rainfall. Common anomalies in urban road GPR detection comprise seven types of target objects, including cavity, pipeline, etc., with standard GPR images acquired through outdoor field experiments. Utilizing GPR forward simulation and image augmentation methods to expand the sample size, as well as generating anchor box dimensions through clustering analysis, have all been proven to effectively improve the model's performance. The urban road GPR image intelligent recognition model, based on the YOLOv4 algorithm, achieves a detection accuracy of up to 85%, proving effective in GPR detection of urban roads in cities of North China. This research offers valuable insights for the future application of deep learning-based image recognition algorithms in urban road GPR detection. [ABSTRACT FROM AUTHOR]

This study aims to bridge the fields of urban morphology and land use/land cover (LULC) mapping through a systematic analysis of their integration in recent research. The research employs systematic literature review (SLR) methodology combining quantitative and qualitative methods through four methodological steps: data search, data selection, data analysis, and data clustering. The analysis performed three distinct clustering patterns: (1) methods and tools, (2) data types, and (3) urban morphology aspects. The results reveal five distinct methodological approaches—Data-Driven Typological Decoding Approach, Quantitative Structural Metrics Approach, Predictive Spatiotemporal Transition Approach, Temporal Change Detection and Performance Approach, and Spatial Configuration and Density Analysis Approach—each contributing unique insights to urban form analysis. The findings demonstrate the multidimensional nature of urban form analysis, incorporating both social and temporal dimensions, while highlighting the essential role of change detection in understanding urban pattern evolution. This systematic review establishes a comprehensive framework for understanding the relationship between urban morphology and LULC mapping, providing valuable insights for future research integration. [ABSTRACT FROM AUTHOR]

Landslides exhibiting step-wise deformation characteristics are extensively dispersed throughout the Three Gorges Reservoir (TGR) region of China. Predicting the deformation state of landslides in TGR holds paramount significance in landslide early warning and risk management. Machine learning-based landslide deformation state prediction is a combination of clustering and imbalanced classification. This paper compares the efficacy of three prevalent clustering methods, namely K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Model (GMM), in the clustering analysis process. Furthermore, the paper evaluates the performance of three widely-used data sampling technologies, namely Synthetic Minority Oversampling Technique (SMOTE), SMOTE-Edited Nearest Neighbors (SMOTE-ENN), and ADAptive SYNthetic Sampling (ADASYN), in the imbalanced classification process. The Baijiabao and Bazimen landslides in the TGR region, which exhibit step-wise deformation characteristics, are used as case studies. Results indicate that DBSCAN and GMM exhibit significant advantages in the clustering process. Meanwhile, the mixture models that integrate oversampling technologies and classification algorithms perform exceptionally well in imbalanced classification. The aforementioned algorithms are recommended for predicting the deformation states of step-wise landslides in the TGR region. The machine learning-based predictive models can serve as potent instruments in facilitating the implementation of early warning systems aimed at mitigating landslide risks. [ABSTRACT FROM AUTHOR]

Determination of the distinctness, uniformity and stability (DUS) of plant cultivars is an important means of protecting the original innovations of breeders, increasing the enthusiasm of breeders and promoting the development of plant breeding. In this study, combinations of simple sequence repeat (SSR) markers and DUS morphological characteristics were used to identify cultivars. The genetic diversity of 30 flax plant cultivars was analyzed using 20 SSR markers and 18 DUS morphological characteristics. The results of the morphological characteristic analysis showed that the 30 flax cultivars had high genetic diversity. Ninety allelic variations were detected in 20 polymorphic SSR molecular markers, and 2–10 allelic variations were detected in each pair of primers. The diversity index (H) of Nei's gene range was 0.10–0.39, with a mean value of 0.23; the Shannon information index (I) range was 0.19–0.57, with a mean value of 0.37; and the polymorphism information content (PIC) range was 0.25–0.82, with a mean value of 0.51. Cluster analysis revealed that there were correlations between the SSR markers and morphological characteristics, and there were also some differences. These results show that these two methods have good potential for future research, especially for DUS analysis of hundreds of cultivars. Therefore, the use of molecular markers can reduce the cost and duration of resource consumption, and more importantly, identify and protect highly specific plant cultivars. [ABSTRACT FROM AUTHOR]

One of the most common causes of maternal death during pregnancy is preeclampsia. A deeper understanding of the patient's features can aid in the hospital's clinical care distribution. However, at the IESS Los Ceibos Hospital, these types of studies have not been carried out for preeclampsia. Therefore, in this work, we describe the application of descriptive and clustering analysis to characterize preeclamptic patients. Preeclamptic patients treated at the IESS Los Ceibos Hospital in Guayaquil comprised the dataset used in this study. Descriptive and clustering analysis allowed us to find that severe preeclampsia (O141) is the most common diagnosis when preeclamptic patients arrive at the hospitalization unit, representing 79.5% of the cases. Moreover, women whose maternal age falls between 26 and 35 years have the highest prevalence of preeclampsia, representing 55.4% of the cases. Finally, adult patients in their late 30s or older are often diagnosed with severe preeclampsia (O141) and often require many hours of hospital care during the first two visits. These findings will help to generate care and prevention policies, such as the use of a low dose of aspirin, in these age groups to avoid the complications that preeclampsia can cause. [ABSTRACT FROM AUTHOR]

Moving objects detection has always been a prominent research topic for the computer vision and image processing field. In recent years, many methods of moving objects detection in optical imaging have been proposed. However, the high-precision detection and recognition of multiple moving objects is still quite challenging. Many key problems that restrict the performance of multiple moving objects detection, such as dynamic background, complex environment, different objects classification, and detection accuracy, have not been effectively solved. This study proposes a dense optical flow multiple moving objects recognition and extraction method to overcome these problems. Unlike most moving objects detection methods based on sparse optical flow, we propose a multiple moving objects detection and recognition method which combines an optical flow histogram and K-means clustering analysis based on dense optical flow. First, we utilize an enhanced optical flow calculation and feature threshold processing to segment the moving objects foreground region. Subsequently, according to the histogram of optical flow amplitude and directions, K-means iterative clustering is utilized to cluster the foreground optical flow. Different moving objects can be recognized and segmented using different optical flow amplitudes and direction. The implementation results indicate that the proposed method exhibits more optimal results for both rigid and non-rigid multiple targets with small internal optical flow difference in static and dynamic environments. [ABSTRACT FROM AUTHOR]

The dimensional optimization based on kinematic and dynamic performance indices is proven significant for improving the operational performance of parallel robots. As the pose-dependent performances vary with the dimensional parameters, there are still many challenges in the optimal design of parallel robots, such as the possible conflict amongst different performance indices and computational expensiveness. By considering a 4-DOF high-speed parallel robot as an illustrative example, this paper presents a framework for the optimal design of parallel robots by integrating skeleton modeling, CAD-CAE integration and multi-objective optimization techniques. In this approach, the models for kinematic and elasto-dynamic performance evaluation are first developed using the CAD and CAE techniques, respectively. After evaluating the performance distributions over the task workspace, several reference poses representative of multiple neighboring sample poses are determined to formulate corresponding local performance indices by using the Hard C-Means (HCM) clustering analysis algorithm. This consideration leads to the determination of the optimal dimensions by investigating the Pareto-optimal solutions of a multi-objective optimization problem, allowing the conflict amongst different performance indices to be appropriately handled and the computational time to be saved significantly. The results of the case study show that the proposed approach can significantly enhance the elastic dynamic performance while ensuring that the kinematic performance is not excessively compromised, when compared to the performance of existing physical prototype of the robot. A software package has been developed using the proposed framework, providing a highly accessible way for designing various types of parallel/hybrid robots based on CAD and CAE techniques. [ABSTRACT FROM AUTHOR]

Understanding earthquake nucleation is vital for predicting and mitigating seismic events, saving lives, and enhancing construction practices in earthquake-prone areas. Cascade triggering and preslip triggering are prevalent theories, posing challenges in differentiation based on field observations. Our study employs a novel unsupervised machine learning pipeline, integrating macroscopic- and grain-scale data from stick-slip experiments in a discrete element method (DEM) framework. Running 27 simulations, we cluster foreshocks and mainshocks separately and assess their correlation. The study supports the cascade triggering model on the macro-scale, as we did not observe any scaling between nucleation parameters and the mainshock size. On the other hand, further grain-scale analysis identifies that, separate from Coulomb stress transfer, there is an additional mechanism related to shear stress accumulation on the fault, which is likely the preslip triggering. Overall, while foreshocks may not directly influence the trend at which contact force evolves, they could prime the fault for dynamic rupture by increasing the proportion of contacts accumulating shear stresses. Our findings infer the possible coexistence of the two theorized mechanisms. [ABSTRACT FROM AUTHOR]

Background: Persistent airflow limitation (PAL) in childhood asthma is associated with a poor prognosis. The aim of this study was to categorize asthmatic children with PAL into distinct phenotypes and investigate the risk factors associated with each phenotype. Methods: We conducted a case-control study with a total of 119 PAL patients and 120 non-PAL (NPAL) individuals. To classify the patients into appropriate clusters, unsupervised cluster analysis using K-means clustering was employed. The clusters were then compared to explore different PAL phenotypes. Univariate and multivariate logistic regression analyses were performed to identify risk factors for PAL and calculate odds ratios (ORs) with 95% confidence intervals (95%CIs). Results: K-means clustering divided patients into three clusters: Cluster 0 included 120 NPAL patients, Cluster 1 characterized by elevated blood neutrophils included 66 PAL patients, and Cluster 2 exhibited elevated blood eosinophils and FeNO levels, containing 53 PAL patients. Independent risk factors for PAL included older age in both Cluster 1 (9 ∼ 11y: OR 12.67, 95%CI 3.30-55.74; ≥ 11y: OR 5.42, 95%CI 1.26-25.69) and Cluster 2 (9 ∼ 11y: OR 7.25, 95%CI 1.70-33.35; ≥ 11y: OR 11.28, 95%CI 2.79-51.89), as well as pneumonia history, with an OR of 6.41(95%CI 1.34-33.41) in Cluster 1 and an OR of 7.92(95%CI 1.83-37.44) in Cluster 2. Furthermore, specific factors associated with Cluster 1 included BMI above 22 kg/ m 2 (OR 12.28, 95%CI 2.68-70.45), asthma duration exceeding three years (OR 4.77, 95%CI 1.60-15.94), and a blood neutrophil percentage between 0.4 and 0.5 (OR 4.13, 95%CI 1.17-16.6). In Cluster 2, independent risk factors included a blood eosinophil percentage greater than 0.07 (OR 4.36, 95%CI 1.16-19.73) and a high FeNO level (OR 3.94, 95%CI 1.35-11.97). Conclusion: Our study identified two phenotypes of PAL in asthmatic children: non-eosinophilic and eosinophilic inflammation. Older age and a history of pneumonia were independent risk factors for both phenotypes. For non-eosinophilic inflammation PAL, specific contributing factors included higher BMI, long duration of asthma, and a blood neutrophil percentage between 0.4 and 0.5. Elevated FeNO levels and blood eosinophilic percentage were independently associated with eosinophilic inflammation PAL. [ABSTRACT FROM AUTHOR]

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid's absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid's operation and management. [ABSTRACT FROM AUTHOR]

We develop a cluster‐based model order reduction (called C‐pRBMOR) approach for efficient homogenization of bones, compatible with a large variety of generalized standard material (GSM) models. To this end, the pRBMOR approach based on a mixed incremental potential formulation is extended to a clustered version for a significantly improved computational efficiency. The microscopic modeling of bones falls into a mixed incremental class of the GSM framework, originating from two potentials. An offline phase of the C‐pRBMOR approach includes both a clustering analysis spatially decomposing the micro‐domain within an RVE and a space–time decomposition of the microscopic plastic strain fields. A comparative study on two different clustering approaches and two algorithms for mode identification is additionally conducted. For an online analysis, a cluster‐enhanced version of evolution equations for the reduced variables is derived from an effective incremental variational formulation, rendering a very small set of nonlinear equations to be numerically solved. Several numerical examples show the effectiveness of the C‐pRBMOR approach. A striking acceleration rate beyond 104 against conventional FE computations and that beyond 103 against the original pRBMOR approach are observed. [ABSTRACT FROM AUTHOR]

Vehicular movement and driver behavior at unsignalized intersections are responsible for increasing the susceptibility to potential crashes. Among them, unsignalized blackspots need serious attention because they have records of several crashes occurring. At intersections, right-turning vehicles from major and minor roads take risky actions and undergo serious conflicts with high-speed through-moving vehicles. So, the study proposed crash severity levels intending to predict the chances of crashes at unsignalized intersection blackspots for crossing and rear-end conflicts using estimated surrogate safety measures (SSM), i.e., postencroachment time (PET) and modified time to collision (MTTC), along with indicators like critical speed and time to stop (Ts). The study also focused on determining the percentage of critical conflicts and vehicle combinations for crossing and rear-end conflicts. To achieve the outcomes of the study, video data and crash data from four unsignalized intersection blackspots (three-legged and four-legged) from Nagpur, India, were collected. The PET values were determined for right-turning and crossing conflicts, and MTTC values were used for rear-end conflicts. Based on the threshold values of these SSM parameters, a range of crash severity values was proposed using the k -mean clustering analysis method. The severity level was divided into four levels, from A being the most severe to D having the least severe values, indicating that drivers have less than 1 s to react to a situation of conflict. From the study, it was observed that the percentage of critical conflicts was between 20.67% and 35.22% for crossing conflicts and 66.67% to 89.47% for rear-end conflicts. The vehicle combinations of two wheelers–two wheelers (2W–2W), two wheelers–light motorized vehicles (2W–LMV), and two wheelers–three wheelers (2W–3W) were found to be critical for crossing conflicts, and for rear-end conflicts, 2W–2W, LMV–2W, and LMV–LMV were critical. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk. Methods: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation. Results: The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16–10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250–319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84–0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics. Conclusions/interpretation: Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables. [ABSTRACT FROM AUTHOR]