Crash causation research has identified inattention as a major source of driver error leading to crashes. The series of experiments presented herein investigate the characteristics of an in-vehicle information system (IVIS) task that could hinder driving performance due to uncertainty buildup and cognitive capture. Three on-road studies were performed that used instrumented passenger and tractor-trailer vehicles to obtain real-world driving performance data. Participants included young, middle-aged, and older passenger vehicle drivers and middle-aged and older commercial vehicle operators. While driving, they were presented with IVIS tasks with various information densities, decision-making elements, presentation formats, and presentation modalities (visual or auditory). The experiments showed that, for both presentation modalities, the presence of multiple decision-making elements in a task had a substantial negative impact on driving performance of both automobile drivers and truck drivers when compared to conventional tasks or tasks with only one decision-making element. The results from these experiments can be used to improve IVIS designs, allowing for potential IVIS task phenomena such as uncertainty buildup and cognitive capture to be avoided.
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http://dx.doi.org/10.1016/j.aap.2006.02.015 | DOI Listing |
Chem Commun (Camb)
January 2025
Center for High Pressure Science and Technology Advanced Research (HPSTAR), Shanghai, China.
Functional materials, possessing specific properties and performing particular functions beyond their mechanical or structural roles, are the foundation of modern matter science including energy, environment, and quantum sciences. The atomic and electronic structures of these materials can be significantly altered by external stimuli such as pressure. High-pressure techniques have been extensively utilized to deepen our understanding of structure-property relationships of materials, while also enabling emergent or enhanced properties.
View Article and Find Full Text PDFSmall
January 2025
Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India.
Growing global population, escalating energy consumption, and climate change threaten future energy security. Fossil fuel combustion, primarily coal, oil, and natural gas, exacerbates the greenhouse effect driving global warming through CO emissions. To address such issues, research is focused on converting CO into valuable fuels and chemicals, which aims to reduce noxious CO and simultaneously bridge the gap between energy demands and sustainable supply.
View Article and Find Full Text PDFAdv Mater
January 2025
Center for Renewable Energy and Storage Technologies (CREST), Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
The sluggish anodic oxygen evolution reaction (OER) in proton exchange membrane (PEM) electrolysis necessitates applied bias to facilitate electron transfer as well as bond cleavage and formation. Traditional electrocatalysis focuses on analyzing the effects of electron transfer, while the role of charge accumulation induced by the applied overpotential has not been thoroughly investigated. To explore the influence mechanism of bias-driven charge accumulation, capacitive Mn is incorporated into IrO to regulate the local electronic structure and the adsorption behavior.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
Despite remarkable advancements in the organic synthesis field facilitated by the use of machine learning (ML) techniques, the prediction of reaction outcomes, including yield estimation, catalyst optimization, and mechanism identification, continues to pose a significant challenge. This challenge arises primarily from the lack of appropriate descriptors capable of retaining crucial molecular information for accurate prediction while also ensuring computational efficiency. This study presents a successful application of ML for predicting the performance of Ir-catalyzed allylic substitution reactions.
View Article and Find Full Text PDFJ Nanobiotechnology
January 2025
Department of Spinal Surgery, The First People's Hospital of Wenling, Affiliated Wenling Hospital, Wenzhou Medical University, Taizhou, Zhejiang, 317500, China.
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