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This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward’s method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward’s method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy.

The aim of this study is to detect changes in flow regime of rivers in Poland. On the basis of daily discharges recorded in 1951-2010 at 159 gauging stations located on 94 rivers regularities in the variability of the river flow characteristics in the multi-year period and in the annual cycle were identified and also their spatial uniformity was examined. In order to identify changes in the characteristics of river regime, similarities of empirical distribution functions of the 5-day sets (pentads) of discharges were analyzed and the percent shares of similar and dissimilar distributions of the 5-day discharge frequencies in the successive 20-year sub-periods were compared with the average values of discharges recorded in 1951-2010. Three alternative methods of river classification were employed and in the classification procedure use was made of the Ward’s hierarchical clustering method. This resulted in identification of groups of rivers different in terms of the degree of transformation of their hydrological regimes in the multi-year and annual patterns.

Job cycle time is the cycle time of a job or the time required to complete a job. Prediction of job cycle time is a critical task for a semiconductor fabrication factory. A predictive model must forecast job cycle time to pursue sustainable development, meet customer requirements, and promote downstream operations. To effectively predict job cycle time in semiconductor fabrication factories, we propose an effective hybrid approach combining the fuzzy c-means (FCM)-based genetic algorithm (GA) and a backpropagation network (BPN) to predict job cycle time. All job records are divided into two datasets: the first dataset is for clustering and training, and the other is for testing. An FCM-based GA classification method is developed to pre-classify the first dataset of job records into several clusters. The classification results are then fed into a BPN predictor. The BPN predictor can predict the cycle time and compare it with the second dataset. Finally, we present a case study using the actual dataset obtained from a semiconductor fabrication factory to demonstrate the effectiveness and efficiency of the proposed approach.

Deformability of rock masses influencing their behavior is an important geomechanical property for the rock structures design. Due to the problems in determining the deformability of jointed rock masses at the laboratory-scale, various in situ test methods such as plate loading tests, dilatometer etc. have been developed. Although these methods are currently the best techniques, they are expensive and time consuming, and present operational problems. Furthermore, the influence of the test volume on modulus of deformation depending on the technique used is also important. For these reasons, in this paper, the adaptive network-based fuzzy inference system (ANFIS) was used to build a prediction model for the indirect estimation of deformation modulus of a rock mass. Three ANFIS models were implemented by grid partitioning (GP), subtractive clustering method (SCM) and fuzzy c-means clustering method (FCM). The estimation abilities offered using three ANFIS models were presented by using field data of achieved from road and railway construction sites in Korea. In these models, rock mass rating (RMR), depth, uniaxial compressive strength of intact rock (UCS) and elastic modulus of intact rock (E i ) were utilized as the input parameters, while the deformation modulus of a rock mass was the output parameter. Various statistical performance indexes were utilized to compare the performance of those estimation models. The results achieved indicate that the ANFIS-SCM model has strong potential to indirect estimation of deformation modulus of a rock mass with high degree of accuracy and robustness.

Subpopulation identification, usually via some form of unsupervised clustering, is a fundamental step in the analysis of many single-cell RNA-seq data sets. This has motivated the development and application of a broad range of clustering methods, based on various underlying algorithms. Here, we provide a systematic and extensible performance evaluation of 14 clustering algorithms implemented in R, including both methods developed explicitly for scRNA-seq data and more general-purpose methods. The methods were evaluated using nine publicly available scRNA-seq data sets as well as three simulations with varying degree of cluster separability. The same feature selection approaches were used for all methods, allowing us to focus on the investigation of the performance of the clustering algorithms themselves. We evaluated the ability of recovering known subpopulations, the stability and the run time and scalability of the methods. Additionally, we investigated whether the performance could be improved by generating consensus partitions from multiple individual clustering methods. We found substantial differences in the performance, run time and stability between the methods, with SC3 and Seurat showing the most favorable results. Additionally, we found that consensus clustering typically did not improve the performance compared to the best of the combined methods, but that several of the top-performing methods already perform some type of consensus clustering. All the code used for the evaluation is available on GitHub ( https://github.com/markrobinsonuzh/scRNAseq_clustering_comparison ). In addition, an R package providing access to data and clustering results, thereby facilitating inclusion of new methods and data sets, is available from Bioconductor ( https://bioconductor.org/packages/DuoClustering2018 ).

P- and S-wave impedances are accounted as two significant parameters conventionally inverted from seismic amplitudes for evaluation of gas and oil reservoirs. They may not be the final goal of interpretation studies; however, they play an important role in many methods such as reservoir characterization, rock physical modeling, geostatistical simulation, fluid detection. Bayesian inversion is a conventional method used by many researchers and even by industry to invert these parameters. To compare this method with intelligent methods, the adaptive network-based fuzzy inference system (ANFIS) was utilized to construct a model for the prediction of P- and S-wave impedances. Two ANFIS models were implemented, subtractive clustering method (SCM) and fuzzy c-means clustering method. The prediction capabilities offered by ANFIS models were shown by using field data obtained from a carbonate reservoir in Iran. Unlike other studies, input parameters, in this study, are pre-stack seismic data and attributes, while the P- and S-wave impedances are the output parameters in all methods. Mean square error was used for comparison of the performance of those models. The obtained results show that the ANFIS-SCM model generates the best indirect estimation of P- and S-wave impedances with high degree of accuracy and robustness.

k -means clustering method is an iterative partition-based method which for finite data-sets converges to a solution in a finite time. The running time of this method grows linearly with respect to the size of the data-set. Many variants have been proposed to speed-up the conventional k -means clustering method. In this paper, we propose a prototype-based hybrid approach to speed-up the k -means clustering method. The proposed method, first partitions the data-set into small clusters (grouplets), which are of varying sizes. Each grouplet is represented by a prototype. Later, the set of prototypes is partitioned into k clusters using the modified k -means method. The modified k -means clustering method is similar to the conventional k -means method but it avoids empty clusters (the clusters to which no pattern is assigned) in the iterative process. In each cluster of prototypes, each prototype is replaced by its corresponding set of patterns (which formed the grouplet) to derive a partition of the data-set. Since this partition of the data-set can deviate from the partition obtained using the conventional k -means method over the entire data-set, a correcting step is proposed. Both theoretically and experimentally, the conventional k -means method and the proposed hybrid method (augmented with the correcting step) are shown to yield the same result (provided, the initial k seed points are same). But, the proposed method is much faster than the conventional one. Experimentally, the proposed method is compared with the conventional method and the other recent methods that are proposed to speed-up the k -means method.

Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.

Background Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Objective Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG‐fMRI fusion technology to reveal brain neural activity related with feedback stimuli. Methods In this study, an EEG‐informed fMRI analysis was applied to process simultaneous EEG‐fMRI data. First, relative power‐spectrum analysis and K‐means clustering method were performed separately to extract EEG‐fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. Results The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG‐fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback‐related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta‐band power was used as a regressor compared with the other two fusion results. Conclusions The EEG‐based fMRI analysis can more accurately depict the whole‐brain activation map and analyze emotional decision problems. In this work, we applied an EEG‐informed fMRI analysis to process simultaneous EEG‐fMRI data during the monetary gambling task, which is most widely adopted in the study of emotional decision concerning “hot” components of executive function. EEG trial‐by‐trial amplitudes of the feedback‐related negativity (FRN) and the power of alpha‐ and theta‐band related with feedback were separately used as regressors in general linear models (GLMs), and the fusion results with different regressors were compared.

PurposeAn intersectionality framework has been increasingly incorporated into quantitative study of health inequity, to incorporate social power in meaningful ways. Researchers have identified “person-centered” methods that cluster within-individual characteristics as appropriate to intersectionality. We aimed to review their use and match with theory.MethodsWe conducted a multidisciplinary systematic review of English-language quantitative studies wherein authors explicitly stated an intersectional approach, and used clustering methods. We extracted study characteristics and applications of intersectionality.Results782 studies with quantitative applications of intersectionality were identified, of which 16 were eligible: eight using latent class analysis, two latent profile analysis, and six clustering methods. Papers used cross-sectional data (100.0%) primarily had U.S. lead authors (68.8%) and were published within psychology, social sciences, and health journals. While 87.5% of papers defined intersectionality and 93.8% cited foundational authors, engagement with intersectionality method literature was more limited. Clustering variables were based on social identities/positions (e.g., gender), dimensions of identity (e.g., race centrality), or processes (e.g., stigma). Results most commonly included four classes/clusters (60.0%), which were frequently used in additional analyses. These described sociodemographic differences across classes/clusters, or used classes/clusters as an exposure variable to predict outcomes in regression analysis, structural equation modeling, mediation, or survival analysis. Author rationales for method choice included both theoretical/intersectional and statistical arguments.ConclusionLatent variable and clustering methods were used in varied ways in intersectional approaches, and reflected differing matches between theory and methods. We highlight situations in which these methods may be advantageous, and missed opportunities for additional uses.