Predicting Driver's mental workload using physiological signals: A functional data analysis approach.

This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers' mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands.

Multivariate probit linear mixed models for multivariate longitudinal binary data.

When analyzing multivariate longitudinal binary data, we estimate the effects on the responses of the covariates while accounting for three types of complex correlations present in the data. These include the correlations within separate responses over time, cross-correlations between different responses at different times, and correlations between different responses at each time point. The number of parameters thus increases quadratically with the dimension of the correlation matrix, making parameter estimation difficult; the estimated correlation matrix must also meet the positive definiteness constraint.

Machine learning approach for study on subway passenger flow.

We investigate regional features nearby the subway station using the clustering method called the funFEM and propose a two-step procedure to predict a subway passenger transport flow by incorporating the geographical information from the cluster analysis to functional time series prediction. A massive smart card transaction dataset is used to analyze the daily number of passengers for each station in Seoul Metro. First, we cluster the stations into six categories with respect to their patterns of passenger transport.

Syntactic Complexity as a Linguistic Marker to Differentiate Mild Cognitive Impairment From Normal Aging.

Purpose In this study, we sought to identify critical linguistic markers that can differentiate sentence processing of individuals with mild cognitive impairment (MCI) from the sentence processing of normal-aging populations by manipulating sentences' linguistic complexity. We investigated whether passive sentences, as linguistically complex structures, can serve as linguistic markers that can contribute to diagnoses that distinguish MCI from normal aging. Method In total, 52 participants, including 26 adults with amnestic MCI and 26 cognitively unimpaired adults, participated in the study.

Experimental method for measuring color appearance shifts in high-dynamic-range luminance conditions.

We present an experimental method to determine color appearance shifts under high-dynamic-range conditions. A couple of light booths with variable luminance provide high-dynamic-range luminance conditions, and a perceptual color shift between the two booths is determined using color appearance matching. For red, green, yellow, and blue groups of four surface color samples, color shifts were measured for nine subjects under a dual illumination at background luminance levels of $100\,\,{{\rm cd/m}^2}$100cd/m and $4700\,\,{{\rm cd/m}^2}$4700cd/m.

Analysis of microarray right-censored data through fused sliced inverse regression.

Sufficient dimension reduction (SDR) for a regression pursue a replacement of the original p-dimensional predictors with its lower-dimensional linear projection. The so-called sliced inverse regression (SIR; [5]) arguably has the longest history in SDR methodologies, but it is still one of the most popular one. The SIR is known to be easily affected by the number of slices, which is one of its critical deficits.

Verifying Identities of Plant-Based Multivitamins Using Phytochemical Fingerprinting in Combination with Multiple Bioassays.

Sales of multivitamins have been growing rapidly and the concept of natural multivitamin, plant-based multivitamin, or both has been introduced in the market, leading consumers to anticipate additional health benefits from phytochemicals that accompany the vitamins. However, the lack of labeling requirements might lead to fraudulent claims. Therefore, the objective of this study was to develop a strategy to verify identity of plant-based multivitamins.

Effects of age, working memory, and word order on passive-sentence comprehension: evidence from a verb-final language.

Background: The purpose of the current study was to investigate the effects of working-memory (WM) capacity on age-related changes in abilities to comprehend passive sentences when the word order was systematically manipulated.

Methods: A total of 134 individuals participated in the study. The sentence-comprehension task consisted of the canonical and non-canonical word-order conditions.

Spatial uniformity inspection apparatus for solar cells using a projection display.

We demonstrate a measurement apparatus to inspect spatial uniformity of quantum efficiency of solar cells using a beam projector. Deviation of irradiance from the used beam projector over the area of 1.5×0.

Modeling microRNA-mRNA interactions using PLS regression in human colon cancer.

Background: Changes in microRNA (miRNA) expression patterns have been extensively characterized in several cancers, including human colon cancer. However, how these miRNAs and their putative mRNA targets contribute to the etiology of cancer is poorly understood. In this work, a bioinformatics computational approach with miRNA and mRNA expression data was used to identify the putative targets of miRNAs and to construct association networks between miRNAs and mRNAs to gain some insights into the underlined molecular mechanisms of human colon cancer.

Model-free predictor tests in survival regression through sufficient dimension reduction.

In this article, we test the effects of predictors in survival regression through two well-known sufficient dimension reduction methods. Since the usual sufficient dimension reduction methods do not require pre-specified models, the predictor effect tests can be considered model-free. All of the test statistics have χ (2) distributions.

Marginalized random effects models for multivariate longitudinal binary data.

Generalized linear models with random effects are often used to explain the serial dependence of longitudinal categorical data. Marginalized random effects models (MREMs) permit likelihood-based estimations of marginal mean parameters and also explain the serial dependence of longitudinal data. In this paper, we extend the MREM to accommodate multivariate longitudinal binary data using a new covariance matrix with a Kronecker decomposition, which easily explains both the serial dependence and time-specific response correlation.