The literature has indicated that personal relative deprivation (PRD) results in anxiety disorders. Given that some cognitive models propose that attention bias toward a threat causes and maintains anxiety, relatively deprived individuals may have difficulty gating threat from working memory. To test this hypothesis, this study investigated the influence of PRD on the filtering ability of happy, angry, and neutral facial distractors from visual working memory using electroencephalography (EEG). Participants were randomly assigned to a PRD (n = 24) or a non-PRD group (n = 24). Filtering ability was reflected by comparing the contralateral delay activity (CDA) amplitude for one-target, one-target-one-distractor, and two-targets conditions. The CDA was measured as the difference in mean amplitudes between activity in the hemispheres contralateral and ipsilateral to the to-be-remembered information. Results indicated that individuals in the PRD group showed a reduced ability to filter out neutral and angry facial distractors, as reflected by similar CDA amplitudes for one-target-one-distractor and two-targets conditions for both angry and neutral distractors in the PRD group. However, PRD did not impair the ability to filter out happy facial distractors, as reflected by similar CDA amplitudes for one-target-one-distractor and one-target conditions for happy distractors in the PRD group. As neutral faces might then be taken as potentially threatening information by relatively deprived individuals, these results support the hypothesis that relatively deprived individuals might have difficulty filtering out threat-related information.
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http://dx.doi.org/10.1016/j.ijpsycho.2021.02.008 | DOI Listing |
Sensors (Basel)
January 2025
Beijing Aerospace Automatic Control Institute, Beijing 100854, China.
The traditional method is capable of detecting and tracking stationary and slow-moving targets in a sea surface environment. However, the signal focusing capability of such a method could be greatly reduced especially for those variable-speed targets. To solve this problem, a novel tracking algorithm combining range envelope alignment and azimuth phase filtering is proposed.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
This paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh generation, and dynamic traversability analysis to enhance the robot's terrain perception and navigation. The proposed framework was validated through rigorous simulation and experimental testing with humanoid robots, showcasing the potential of the proposed approach for use in applications/environments characterized by complex environmental features (navigation inside collapsed buildings).
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China.
Aiming at the control challenges faced by unmanned surface vessels (USVs) in complex environments, such as nonlinearities, parameter uncertainties, and environmental perturbations, we propose a non-singular terminal integral sliding mode control strategy based on an extended state observer (ESO). The strategy first employs a third-order linear extended state observer to estimate the total disturbances of the USV system, encompassing both external disturbances and internal nonlinearities. Subsequently, a backstepping sliding mode controller based on the Lyapunov theory is designed to generate the steering torque control commands for the USV.
View Article and Find Full Text PDFInt J Mol Sci
January 2025
Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independentei 296, 060031 Bucharest, Romania.
We test here the prediction capabilities of the new generation of deep learning predictors in the more challenging situation of multistate multidomain proteins by using as a case study a coiled-coil family of Nucleotide-binding Oligomerization Domain-like (NOD-like) receptors from and a few extra examples for reference. Results reveal a truly remarkable ability of these platforms to correctly predict the 3D structure of modules that fold in well-established topologies. A lower performance is noticed in modeling morphing regions of these proteins, such as the coiled coils.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mechanical Engineering, University of Ulsan, Ulsan, 44610, Republic of Korea.
This paper proposes an adaptive output feedback full state constrain (FSC) controller based on the adaptive neural disturbance observer (ANDO) for a nonlinear electro-hydraulic system (NEHS) with unmodeled dynamics. The Barrier Lyapunov Functions (BLFs) are utilized to ensure that all states of the system are specified within the constraints, and the approximation ability of radial basis function neural networks (RBFNNs) is used to cope with the unknown nonlinear functions. An adaptive neural compensation disturbance observer is elaborated to estimate the compound disturbance and oil leakage fault, effectively addressing these negative effects.
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