Brain activity underlying visual search in depth when viewing volumetric multiplanar images.

The study investigated the cortical activity associated with 3D and 2D image perception on a volumetric multiplanar display by analyzing event-related potentials (ERPs) and power spectral density (PSD). In this study, we used a volumetric multiplanar display to present visual targets, and the brain signals were recorded via an EEG amplifier and analyzed using the EEGLAB toolbox on MATLAB. The study found no significant differences in amplitude between the 3D and 2D conditions across five occipital and parietal electrodes. However, there was a significant difference in latency of the P3 component on the Pz electrode. The analysis of PSD showed no significant differences between the two conditions, although there was a slightly higher alpha and beta activity observed in the 2D visualization. The study concluded that 3D image representation on a volumetric multiplanar display has no more sensory or cognitive load on the human brain than 2D representation, and that depth perception on a multiplanar display requires less brain activity.

Semiparametric inference for the scale-mixture of normal partial linear regression model with censored data.

In the censored data exploration, the classical linear regression model which assumes normally distributed random errors is perhaps one of the commonly used frameworks. However, practical studies have often criticized the classical linear regression model because of its sensitivity to departure from the normality and partial nonlinearity. This paper proposes to solve these potential issues simultaneously in the context of the partial linear regression model by assuming that the random errors follow a scale-mixture of normal (SMN) family of distributions. The postulated method allows us to model data with great flexibility, accommodating heavy tails and outliers. By implementing the B-spline approximation and using the convenient hierarchical representation of the SMN distributions, a computationally analytical EM-type algorithm is developed for obtaining maximum likelihood (ML) parameter estimates. Various simulation studies are conducted to investigate the finite sample properties, as well as the robustness of the model in dealing with the heavy tails distributed datasets. Real-world data examples are finally analyzed for illustrating the usefulness of the proposed methodology.

A theoretical framework for Landsat data modeling based on the matrix variate mean-mixture of normal model.

This paper introduces a new family of matrix variate distributions based on the mean-mixture of normal (MMN) models. The properties of the new matrix variate family, namely stochastic representation, moments and characteristic function, linear and quadratic forms as well as marginal and conditional distributions are investigated. Three special cases including the restricted skew-normal, exponentiated MMN and the mixed-Weibull MMN matrix variate distributions are presented and studied. Based on the specific presentation of the proposed model, an EM-type algorithm can be directly implemented for obtaining maximum likelihood estimate of the parameters. The usefulness and practical utility of the proposed methodology are illustrated through two conducted simulation studies and through the Landsat satellite dataset analysis.

Two distinct mechanisms of fluoride enrichment and associated health risk in springs' water near an inactive volcano, southeast Iran.

Groundwater fluoride contamination is a major issue of water pollution in the world with health hazards such as dental and skeletal fluorosis. This research focused on exposure to the high concentration of fluoride in the springs water in the Bazman volcanic area, southeast Iran. The combination of chemical/isotopic analysis, geochemical modeling, health risk assessment and multivariate statistical methods were applied to investigate the contamination and sources of fluoride in the samples. Groundwater samples were collected from cold and thermal springs. Major ions, fluoride, trace elements and stable isotopes δ18O and δD were measured in the samples using standard methods, ICP-MS and OA-ICOS, respectively. Fluoride content in springs varied from 0.5 to 3.75 mg/L with an average value of 1.66 mg/L. The highest fluoride concentrations were observed in the eastern cold springs while thermal springs showed the minimum fluoride contents. The majority of samples showed F contents higher than the calculated optimal concentration of fluoride (0.75 mg/L). Reaction of fluorite mineral with HCO3 and replacement of F in clay minerals and metal oxy-hydroxides with OH- in water were likely cause fluoride enrichment in the eastern springs. Whereas, in the western springs and thermal springs, origin of fluoride was related to weathering of muscovite, cryolite, apatite and fluoroapatite minerals. The δ18O and δ2H of the water samples displayed the impact on evaporation on fluoride enrichment in all spring water samples. The average value of contamination index (Cd) in the water samples was 1.94 categorizing medium risk level while springs S7, S8, S9 and S4 were above the threshold value of Cd index. The fluoride hazard quotient (HQ) showed that 25%, 44%, 56% and 0% of springs' water resources had high risk level for age group of adults, teenager, children and infants, respectively. Therefore, health risk of fluoride in drinking water resources were in the following order: children > teenager > adults > infants.

A skew factor analysis model based on the normal mean-variance mixture of Birnbaum-Saunders distribution.

This paper presents a robust extension of factor analysis model by assuming the multivariate normal mean-variance mixture of Birnbaum-Saunders distribution for the unobservable factors and errors. A computationally analytical EM-based algorithm is developed to find maximum likelihood estimates of the parameters. The asymptotic standard errors of parameter estimates are derived under an information-based paradigm. Numerical merits of the proposed methodology are illustrated using both simulated and real datasets.