Grain-size distribution dataset of lagoonal and riverine coastal placer deposits along the southeastern coast of Sri Lanka.

The dataset contains grain size data of placer and non-placer sediments in lagoonal and riverine beaches of southeastern part of Sri Lanka. A total of 124 swash sediment samples were collected from a 70 km long coastline with an interval of 500 m. Placer sediments in the area mainly have mineralogy of ilmenite, zircon and almandine while non-placers are quartz, albite and calcite. After dry sieving, the grain size distribution (GSD) analyses were carried out on each sample using the Gradistat Excel template. Placer deposits result coarse-skewed leptokurtic to platykurtic fine sand distributions while non-placers are medium sand-grained. The dataset can be used to interpret the deposition environment and transportation dynamics. Further, they can be used to study the southwestern coastline of the Bay of Bengal, juvenile crust sediments of Grenvillian age, alongshore and fluvial sediment dynamics, depositional and erosion processes, geohazards assessments and heavy mineral deposits.

Detecting Trivariate Associations in High-Dimensional Datasets.

Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold: (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC.

Trivariate Theory of Mind Data Analysis with a Conditional Joint Modeling Approach.

Theory of mind (ToM) is an essential social-cognitive ability to understand one's own and other people's mental states. Neural data as well as behavior data have been utilized in ToM research, but the two types of data have rarely been analyzed together, creating a large gap in the literature. In this paper, we propose and apply a novel joint modeling approach to analyze brain activations with two types of behavioral data, response times and response accuracy, obtained from a multi-item ToM assessment, with the intention to shed new light on the nature of the underlying process of ToM reasoning. Our trivariate data analysis suggested that different levels or kinds of processes might be involved during the ToM assessment, which seem to differ in terms of cognitive efficiency and sensitivity to ToM items and the correctness of item responses. Additional details on the trivariate data analysis results are provided with discussions on their implications for ToM research.

Refinable tri-variate C 1 splines for box-complexes including irregular points and irregular edges.

C 1 splines over box-complexes generalize C 1 degree 3 (cubic) tensor-product splines. A box-complex is a collection of 3-dimensional boxes forming an unstructured hexahedral mesh that can include irregular points and irregular edges where the layout deviates from the tensor-product grid layout. For example, an edge shared and enclosed by five boxes is irregular. Where the mesh is locally regular, the restriction of the space to each box is a polynomial piece of the C 1 tri-cubic tensor-product spline, by default initialized as a C 2 tri-cubic. Boxes containing irregularities have their polynomials binarily split into 23 pieces to isolate the irregularity. The pieces join with matching derivatives. The derivatives are zero at irregularities, but these singularities are removable by a local change of variables. The space consists of 23 linearly independent functions per box and is refinable.

Hydrocarbon reservoir characterization and discrimination using well-logs over "AIB-EX" Oil Field, Niger Delta.

A computerized advanced statistical analysis which involves the characterization of reservoir elements involving mapping of lithofacies and pore fluids through crossplots of basic seismic variables in both bi-variate and tri-variate domains and functional transformations including rotation of axes have been used as discriminant tools over "AIB-EX" Oil Field, Niger Delta. The methodology encompasses reconstruction of geologic lithofacies information from geophysical logs. Reservoir characterization, rock physics analysis and log inversion were carried out using IHS Kingdom Advanced and Origin software. Three reservoir zones namely A, B, and C were analyzed. The obtained results characterized the reservoir elements as: shale, sandy-shale, shaly-sand and sand (with respective GR counts and P-wave velocity of 105-125 API and 2400-3600 m/s, 75-105 API and 2100-5000 m/s, 45-75 API and 2200-4750 m/s, and 10-45 API and 2000-4600 m/s) which represents seismic scale sedimentary units called lithofacies. Also, the results of both the bi-variate crossplots (GR and P-wave velocity) and tri-variate crossplots (GR, P-wave velocity, and resistivity) have not only differentiated the different lithology but have discriminated the saturating fluid (water or hydrocarbon). The pore fluids were further characterized as either brine or oil based on powerful discriminant tools such as plots of acoustic impedance versus porosity and elastic impedance versus porosity. Conclusively, the result of the research confirmed that hydrocarbon reservoirs can be discriminated with varying degree of effectiveness in various domains using the adopted approach. The obtained result, which can also be used to calibrate seismic inversion, yielded a reliable seismic lithofacies map in the presence of high resolution 3-D seismic data.

Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification.

As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere is used to estimate the local mean. However, the unbalanced data distribution in high-dimensional space often conflicts with the uniform samples and its performance is sensitive to the noise components. Considering the common fact that the vibration signal is generated by three sensors located in different measuring positions in the domain of the structural health monitoring for the key equipment, thus a novel trivariate empirical mode decomposition via convex optimization was proposed for rolling bearing condition identification in this paper. For the trivariate data matrix, the low-rank matrix approximation via convex optimization was firstly conducted to achieve the denoising. It is worthy to note that the non-convex penalty function as a regularization term is introduced to enhance the performance. Moreover, the non-uniform sample scheme was determined by applying singular value decomposition (SVD) to the obtained low-rank trivariate data and then the approach used in conventional MEMD algorithm was employed to estimate the local mean. Numerical examples of synthetic defined by the fault model and real data generated by the fault rolling bearing on the experimental bench are provided to demonstrate the fruitful applications of the proposed method.

Nonparametric estimation of the multivariate survivor function: the multivariate Kaplan-Meier estimator.

The Dabrowska (Ann Stat 16:1475-1489, 1988) product integral representation of the multivariate survivor function is extended, leading to a nonparametric survivor function estimator for an arbitrary number of failure time variates that has a simple recursive formula for its calculation. Empirical process methods are used to sketch proofs for this estimator's strong consistency and weak convergence properties. Summary measures of pairwise and higher-order dependencies are also defined and nonparametrically estimated. Simulation evaluation is given for the special case of three failure time variates.

Predicting Time to Reflux of Children With Antenatal Hydronephrosis: A Competing Risks Approach.

The aim of this study was describing methodological aspects and applying a trivariate Weibull survival model using the competing risks concept to predict time to occurrence different types of reflux (unilateral (left, right) or bilateral) in children with antenatal hydronephrosis. Data from 333 children in Pediatric Urology Research Center of Children's Hospital Medical Center, affiliated with Tehran University of Medical Sciences was used. The effect of some demographic and clinical factors on child's reflux was studied. The assumption of independent between times of different types of reflux was evaluated. Of infants 80.5% were boy. The percentage of children experienced right, left and bilateral reflux or have been censored are 15.3%, 14.1%, 60.4% and 10.2% respectively. For the time of left reflux, variables, Week of diagnosis ANH, UC, UA, HUN, HN, APD_Right, Direction of ANH, CA19-9 baby, Urethra were significant. For the time of right reflux, variables, constipation, UC, UA, HUN, APD_Right, Direction and Severity of ANH, Bladder, and finally for the time of bilateral reflux, variables, Week of diagnosis ANH, Gender, UA, HUN, HN, APD_Left, Urethra, and Bladder were significant P<0.05. In the presence of competing risks, it is inappropriate to use the Kaplan-Meier method and standard Cox model which do not take competing risks into account. Trivariate Weibull survival model using competing risks not only is able to calculate the hazard rate of variables with different type of events but also it will be able to compare the hazard rate within the same type of event with different covariates.

Penalized likelihood estimation of a trivariate additive probit model.

This article proposes a penalized likelihood method to estimate a trivariate probit model, which accounts for several types of covariate effects (such as linear, nonlinear, random, and spatial effects), as well as error correlations. The proposed approach also addresses the difficulty in estimating accurately the correlation coefficients, which characterize the dependence of binary responses conditional on covariates. The parameters of the model are estimated within a penalized likelihood framework based on a carefully structured trust region algorithm with integrated automatic multiple smoothing parameter selection. The relevant numerical computation can be easily carried out using the SemiParTRIV() function in a freely available R package. The proposed method is illustrated through a case study whose aim is to model jointly adverse birth binary outcomes in North Carolina.

The Effects of Exercise and Relaxation on Health and Wellbeing.

Better management by individuals of their long-term conditions is promoted to improve health and reduce healthcare expenditure. However, there is limited evidence on the determinants and consequences of self-management activity. We investigate the determinants of two forms of self-management, exercise and relaxation, and their impact on the health and wellbeing of 3472 individuals with long-term health conditions over a 1-year period. We use simultaneous recursive trivariate models to estimate the effects of these two inputs on three health and wellbeing outcomes: the EuroQol five-dimensional (EQ-5D) score, self-assessed health and happiness. We reflect the opportunity cost of time and knowledge with employment status and education and find that employment reduces relaxation and education increases exercise. We find that neither exercise nor relaxation affects the EuroQol five-dimensional score, but exercise increases self-assessed health and relaxation increases happiness. Our findings show that individuals tailor their self-management activities to their economic constraints, with effects on different aspects of their utility. Interventions to encourage self-management should take account of heterogeneous effects and constraints. © 2017 The Authors. Health Economics Published by John Wiley & Sons, Ltd.

Cross-Disorder Genetic Analysis of Tic Disorders, Obsessive-Compulsive, and Hoarding Symptoms.

Hoarding, obsessive-compulsive disorder (OCD), and Tourette's disorder (TD) are psychiatric disorders that share symptom overlap, which might partly be the result of shared genetic variation. Population-based twin studies have found significant genetic correlations between hoarding and OCD symptoms, with genetic correlations varying between 0.1 and 0.45. For tic disorders, studies examining these correlations are lacking. Other lines of research, including clinical samples and GWAS or CNV data to explore genetic relationships between tic disorders and OCD, have only found very modest if any shared genetic variation. Our aim was to extend current knowledge on the genetic structure underlying hoarding, OC symptoms (OCS), and lifetime tic symptoms and, in a trivariate analysis, assess the degree of common and unique genetic factors contributing to the etiology of these disorders. Data have been gathered from participants in the Netherlands Twin Register comprising a total of 5293 individuals from a sample of adult monozygotic (n = 2460) and dizygotic (n = 2833) twin pairs (mean age 33.61 years). The data on Hoarding, OCS, and tic symptoms were simultaneously analyzed in Mplus. A liability threshold model was fitted to the twin data, analyzing heritability of phenotypes and of their comorbidity. Following the criteria for a probable clinical diagnosis in all phenotypes, 6.8% of participants had a diagnosis of probable hoarding disorder (HD), 6.3% of OCS, and 12.8% of any probable lifetime tic disorder. Genetic factors explained 50.4, 70.1, and 61.1% of the phenotypic covariance between hoarding-OCS, hoarding-tics, and OCS-tics, respectively. Substantial genetic correlations were observed between hoarding and OCS (0.41), hoarding and tics (0.35), and between OCS and tics (0.37). These results support the contribution of genetic factors in the development of these disorders and their comorbidity. Furthermore, tics were mostly influenced by specific environmental factors unshared with OCS and HD.

Trivariate Modeling of Interparental Conflict and Adolescent Emotional Security: An Examination of Mother-Father-Child Dynamics.

Recognizing the significance of interacting family subsystems, the present study addresses how interparental conflict is linked to adolescent emotional security as a function of parental gender. A total of 272 families with a child at 12.60 years of age (133 boys, 139 girls) were invited to participate each year for three consecutive years. A multi-informant method was used, along with trivariate models to test the associations among mothers, fathers, and their adolescent children's behaviors. The findings from separate models of destructive and constructive interparental conflict revealed intricate linkages among family members. In the model of destructive interparental conflict, mothers and fathers predicted each other's conflict behaviors over time. Moreover, adolescents' exposure to negativity expressed by either parent dampened their emotional security. Consistent with child effects models, adolescent emotional insecurity predicted fathers' destructive conflict behaviors. As for the model of constructive interparental conflict, fathers predicted mothers' conflict behaviors over time. Adolescents' exposure to fathers' constructive conflict behaviors also enhanced their sense of emotional security. Consistent with child effects models, adolescent emotional security predicted mothers' and fathers' constructive conflict behaviors. These findings extended the family and the adolescent literature by indicating that family processes are multiidirectional, involving multiple dyads in the study of parents' and adolescents' functioning. Contributions of these findings to the understanding of interparental conflict and emotional security in adolescence are discussed.

Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional approach.

Objective measurement of physical activity using wearable devices such as accelerometers may provide tantalizing new insights into the association between activity and health outcomes. Accelerometers can record quasi-continuous activity information for many days and for hundreds of individuals. For example, in the Baltimore Longitudinal Study on Aging physical activity was recorded every minute for [Formula: see text] adults for an average of [Formula: see text] days per adult. An important scientific problem is to separate and quantify the systematic and random circadian patterns of physical activity as functions of time of day, age, and gender. To capture the systematic circadian pattern, we introduce a practical bivariate smoother and two crucial innovations: (i) estimating the smoothing parameter using leave-one-subject-out cross validation to account for within-subject correlation and (ii) introducing fast computational techniques that overcome problems both with the size of the data and with the cross-validation approach to smoothing. The age-dependent random patterns are analyzed by a new functional principal component analysis that incorporates both covariate dependence and multilevel structure. For the analysis, we propose a practical and very fast trivariate spline smoother to estimate covariate-dependent covariances and their spectra. Results reveal several interesting, previously unknown, circadian patterns associated with human aging and gender.

A Trivariate Extreme Value Distribution Applied to Flood Frequency Analysis.

A trivariate extreme value distribution has been derived from the logistic model for the multivariate extreme value distribution. The construction of its corresponding probability distribution and density function is described. In order to obtain the parameters of such a trivariate distribution, a generalized maximum likelihood estimation procedure is described to allow for the cases of samples with different record lengths. Furthermore the reliability of the estimated parameters of the irivariate extreme value distribution is measured through the use of relative information ratios. A region in Northern Mexico with six gauging stations has been selected to apply the trivariate model. Results produced by the proposed model have been compared with those obtained by general extreme value (GEV) distribution functions.