In-Vehicle Information (IVI) features such as navigation assistance play an important role in the travel of drivers around the world. Frequent use of IVI, however, can easily increase the cognitive load of drivers. The interface design, especially the quantity of icons presented to the driver such as those for navigation, music, and phone calls, has not been fully researched. To determine the optimal number of icons, a systematic evaluation of the IVI Human Machine Interface (HMI) was examined using single-factor and multivariate analytical methods in a driving simulator. When one-way ANOVA was performed, the results showed that the 3-icon design scored best in subjective driver assessment, and the 4-icon design was best in the steering wheel angle. However, when a new method of analyzing the data that enabled a simultaneous accounting of changes observed in the dependent measures, 3 icons had the highest score (that is, revealed the overall best performance). This method is referred to as the fuzzy synthetic evaluation model (FSE). It represents the first use of it in an assessment of the HMI design of IVI. The findings also suggest that FSE will be applicable to various other HMI design problems.
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http://dx.doi.org/10.1016/j.aap.2022.106813 | DOI Listing |
Heliyon
November 2024
Department of Textile Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh.
This research evaluated ways for applying natural dyes, such as pomegranate peels, marigold flowers, and turmeric separately, to dye hybrid fabric made of cotton and jute. Natural dyes provide a harmless alternative to synthetic dyes, which support the sustainable properties of cotton and jute. Applying natural dyes, such as the dye extracted from pomegranate peels, can reduce wastes.
View Article and Find Full Text PDFChaos
October 2024
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia 30303, USA.
Phys Eng Sci Med
December 2024
Department of Medical Physics and Biomedical Engineering School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold.
View Article and Find Full Text PDFJ Mol Biol
November 2024
Butler University, Indianapolis, IN 46208, USA.
Activation domains (ADs) of eukaryotic gene activators remain enigmatic for decades as short, extremely variable sequences which often are intrinsically disordered in structure and interact with an uncertain number of targets. The general absence of specificity increasingly complicates the utilization of the widely accepted mechanism of AD function by recruitment of coactivators. The long-standing enigma at the heart of molecular biology demands a fundamental rethinking of established concepts.
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