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Intuitive processing has a dominant effect under conditions of limited cognitive resources, because compared to analytical processing, it relies on cognitive resources to a much smaller extent. However, the effect of schema on intuitive processing has not been fully elucidated. Therefore, the current study aims to explore this issue through two experiments: Experiment 1 uses a Chinese character chunk decomposition task to investigate how schema affects the performance of intuitive and analytical processing under different cognitive loads. Experiment 2 further examines the effects of schema directly on two types of processing via the removal of load. The results revealed the following: in Experiment 1, the accuracy of intuitive and analytical material was significantly higher under low load than under high load in the schema condition, but there was no effect of varying cognitive load in the non-schema condition; in Experiment 2, the accuracy of intuitive processing was significantly higher than that of analytical processing under the schema condition but there were no differences between two types of processing under the non-schema condition. The results indicate that schema facilitates an intuitive dominance effect, with the implication of intuition maybe more effective when encountering familiar information., Author(s): Yunhong Wang [sup.1] , Wei Bao [sup.1] , Haijiang Li [sup.1] , Junlong Luo [sup.1] Author Affiliations: (1) https://ror.org/01cxqmw89, grid.412531.0, 0000 0001 0701 1077, Department of Psychology, Education College, [...]

Post-earthquake recovery simulation is crucial for resilient community planning, and different interconnected factors are required to be considered holistically. To identify the key infrastructural characteristics affecting the recovery of densely populated urban residential communities (URCs) in China, a comprehensive methodology for resilience assessment and analysis is established on a systematic integration of multiple analysis tools (e.g. population-based functionality indicators, post-earthquake recovery simulations, and infrastructural dependence analyses). The methodology can consider the dependence among residential buildings, supporting buildings, and utility networks, as well as the relationships between their functionalities and resident outmigration; it also includes infrastructural repair sequences to allow flexible repair plans to be simulated. A case study is employed to conduct a factor analysis to clarify the impacts of three important infrastructural characteristics. Results show that improvements on the seismic performance of residential buildings facilitate recovery more significantly than utility networks, and the use of redundant utility pipelines can hardly impact the recovery of URCs. In long-term community recoveries, utility networks play more important role due to the cascading effects arising from the extension of repair durations. The proposed methodology can promote the understanding of community recovery, and the results demonstrate the effectiveness of identifying key factors. [ABSTRACT FROM AUTHOR]

Previous studies found an association between mindfulness and forgiveness. However, the effects of self-construal on this association are still unclear. In the present study, self-reported forgiveness and a behavioral measure of forgiveness were used to explore the moderating effect of self-construal plays between mindfulness and forgiveness among 126 participants recruited based on their mindfulness scores. Results showed that participants with high level of mindfulness in the interdependent self-construal condition reported greater forgiveness and gave more money donations towards transgressors than those in the independent self-construal condition, while there is no significant difference between interdependent and independent self-construal groups among participants with low level of mindfulness. These findings suggest that self-construal moderates the relationship between mindfulness and interpersonal forgiveness. [ABSTRACT FROM AUTHOR]

The realism of ocean visual systems is a key challenge in developing maritime simulators within ocean engineering. Achieving high realism in turbulence simulation is crucial for enhancing the effectiveness of these simulators. Traditional spectrum-based methods lack realism and fail to generate turbulent interaction effects. To address this, an improved Hybrid Smoothed Particle Hydrodynamics method is proposed for simulating ocean scenes, incorporating advanced micropolar fluid model techniques to enhance detail realism. The proposed algorithm introduces a density constraint solver that directly adjusts particle distribution and couples it with a divergence-free velocity solver, aiming to construct a physical-based fluid simulation framework that enhances detail realism in ocean scene simulations. The results demonstrate that the proposed method effectively accelerates the convergence of constraint conditions, reduces simulation time, and improves overall incompressibility. Additionally, the introduced turbulence model addresses high-frequency detail loss caused by numerical dissipation in the Smoothed Particle Hydrodynamics method, enabling more complex navigation scenarios. This study provides theoretical and technical references for achieving realistic ocean scene simulations in maritime simulators. [ABSTRACT FROM AUTHOR]

Urban communities have long been pivotal in wealth creation and technological innovation. In the contemporary context, their modus operandi is intricately tied to a diverse array of critical infrastructure systems (CISs). These systems—encompassing utilities, transportation, communication, and more—are indispensable for daily life; however, historical lessons underscore that the ever-growing interdependence among modern CISs has sapped their robustness. Furthermore, this vulnerability is compounded by the intensifying natural hazards catalysed by climate change, leaving urban communities with eroded resilience. Against this backdrop, pilot studies have harnessed breakthroughs in artificial intelligence (AI) to chart a new course toward resilient urban communities. This paper illuminates AI-driven resilience by reviewing the latest research in key aspects including (1) the limitation of state-of-the-art resilience assessment frameworks; (2) emergency response as a novel blueprint featuring swift response following catastrophes; (3) efficient loss assessment of CISs using AI algorithms; and (4) machine-learning-enabled autonomous emergency response planning. The remaining challenges and hardships faced on the journey toward resilient urban communities are also discussed. The findings could contribute to the ongoing discourse on enhancing urban resilience in the face of increasingly frequent and destructive climate hazards. [ABSTRACT FROM AUTHOR]

Bridge maintenance is a highly intricate task that involves considering a wide range of factors in order to achieve optimal decisions that align with multiple objectives, criteria, and the entire lifecycle of the bridge. While physics-informed analysis, such as the finite element method (FEM), can simulate complex and closely coupled scenarios, such as bridge structural analysis, it cannot account for some loosely coupled discrete factors, which could be addressed by ontological reasoning. Therefore, this paper presents a knowledge-driven decision-making framework that combines static knowledge reasoning with dynamic FEM analysis results to support holistic bridge maintenance decisions. One significant contribution of this research is the development of a comprehensive bridge maintenance ontology that incorporates knowledge derived from bridge maintenance standards. Another key contribution is the ability to employ complex runtime rules-based reasoning to tackle intricate bridge maintenance scenarios. To enable automatic knowledge-driven reasoning, an integrated workflow is developed to orchestrate semantic modeling with numerical modeling through a Python-based Web Ontology Language application programming interface (OWL API). This integration facilitates the efficient orchestration of the framework. A case study is presented to demonstrate the potential for the developed framework in assisting with the complex holistic decisions required for bridge maintenance. [ABSTRACT FROM AUTHOR]