The spatiotemporal characteristics of basic attention are important for understanding attending behaviours in real-life situations, and they are useful for evaluating the accessibility of visual information. However, although people are encircled by their 360-degree surroundings in real life, no study has addressed the general characteristics of attention to 360-degree surroundings. Here, we conducted an experiment using virtual reality technology to examine the spatiotemporal characteristics of attention in a highly controlled basic visual context consisting of a 360-degree surrounding. We measured response times and gaze patterns during the 360-degree search task and examined the spatial distribution of attention and its temporal variations in a 360-degree environment based on the participants' physical position. Data were collected from both younger adults and older adults to consider age-related differences. The results showed the fundamental spatiotemporal characteristics of 360-degree attention, which can be used as basic criteria to analyse the structure of exogenous effects on attention in complex 360-degree surroundings in real-life situations. For practical purposes, we created spherical criteria maps of 360-degree attention, which are useful for estimating attending behaviours to 360-degree environmental information or for evaluating visual information design in living environments, workspaces, or other real-life contexts.
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http://dx.doi.org/10.1038/s41598-019-52313-3 | DOI Listing |
Sensors (Basel)
December 2024
College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.
Early detection of autism spectrum disorder (ASD) is particularly important given its insidious qualities and the high cost of the diagnostic process. Currently, static functional connectivity studies have achieved significant results in the field of ASD detection. However, with the deepening of clinical research, more and more evidence suggests that dynamic functional connectivity analysis can more comprehensively reveal the complex and variable characteristics of brain networks and their underlying mechanisms, thus providing more solid scientific support for computer-aided diagnosis of ASD.
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December 2024
College of Civil Engineering, Xiangtan University, Xiangtan 411105, China.
Bridge expansion joints are critical components that accommodate the movement of a bridge caused by temperature fluctuations, concrete shrinkage, and vehicular loads. Analyzing the spatiotemporal deformation of these expansion joints is essential for monitoring bridge safety. This study investigates the deformation characteristics of Hongtang Bridge in Fuzhou, China, using synthetic aperture radar interferometry (InSAR).
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December 2024
Liaoning Engineering and Technology Research Center for Insect Resources, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang 110866, China.
Chitin deacetylases (CDAs) are carbohydrate esterases associated with chitin metabolism and the conversion of chitin into chitosan. Studies have demonstrated that chitin deacetylation is essential for chitin organization and compactness and therefore influences the mechanical and permeability properties of chitinous structures, such as the peritrophic membrane (PM) and cuticle. In the present study, two genes ( and ) encoding CDA protein isoforms were identified and characterized in Chinese oak silkworm () larvae.
View Article and Find Full Text PDFPLoS One
January 2025
Institute of Ocean Engineering, Ningbo University, Ningbo, Zhejiang, China.
Hydrological prediction in ungauged basins often relies on the parameter transplant method, which incurs high labor costs due to its dependence on expert input. To address these issues, we propose a novel hydrological prediction model named STH-Trans, which leverages multiple spatiotemporal views to enhance its predictive capabilities. Firstly, we utilize existing geographic and topographic indicators to identify and select watersheds that exhibit similarities.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Department of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Tsinghua University, Beijing, 100084, China.
Single nanoparticle analysis is crucial for various applications in biology, materials, and energy. However, precisely profiling and monitoring weakly scattering nanoparticles remains challenging. Here, it is demonstrated that deep learning-empowered plasmonic microscopy (Deep-SM) enables precise sizing and collision detection of functional chemical and biological nanoparticles.
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