A functional for the momentum equations of incompressible viscous flow

Vectorial mechanics and analytical mechanics are two time-honored forms of classical mechanics. Vectorial mechanics is mainly based on Newton’s l aws i n a c lear a nd simple mathematical form. It has achieved a high degree of sophistication and success in solid mechanics. Analytical mechanics is based on the principle of virtual work and D'Alembert’s principle, which is highly universal. Often, the term vectorial mechanics is applied to the form based on Newton's work, to contrast it with analytical mechanics which uses two scalar properties of motion, the kinetic and potential energies, instead of vector forces, to analyze the motions. Analytical mechanics was primarily developed to extend the scope of classical mechanics in a systematic, generalized and efficient way to solve problems using the concept of constraints on systems and path integrals. In this paper, we give a functional of fluid in Lagrangian form. Then we demonstrate that the momentum equations of incompressible viscous flow can be achieved after several mathematical operations. At last, we show the Eulerian approximation of the energy functional under some assumptions. Our work lays a good foundation for our numerical methods

Assessing perceptions of resilience: The understanding from network analysis

IntroductionPrevious studies have yet to reach a consensus on the construct of resilience perception, and how to enhance the effect of resilience intervention remains an urgent issue. In this consideration, this study examines the fundamental construct of resilience. It provides insight into the critical prevention goal for resilience intervention by utilizing the latest methods of psychological network analysis.MethodsThe sample is the graduate students enrolled in September 2021. Participants completed (1) the Connor-Davidson Resilience Scale, (2) the University of Washington Resilience Scale-8 Item, (3) the Brief Resilience Scale, and (4) the Resilience Scale for Adults, each representing different orientations of resilience.ResultsThe network analysis grants greater clarity to the resilience perception as a dynamic system that interacts between an individual's tendency to intrinsic capacity and response to external resources. This study has shown that a positive perception of external social resources is the most important for individuals' resilience cognition; the effect of resilience intervention can be achieved more quickly by changing the individual's sense of hope.DiscussionBased on the results, a psychometric instrument that integrates different orientations of resilience concepts and is based on time-varying needs to be developed

The Effect of Parental Involvement and Socioeconomic Status on Junior School Students’ Academic Achievement and School Behavior in China

A survey was conducted on 19,487 Chinese junior school students to elucidate the moderating role of socioeconomic status (SES) in the relationship between parental involvement (i.e., home-based involvement and academic socialization) and junior school students’ performance in school (i.e., academic achievement and school behavior). The data includes 10,042 males and 9,445 females (mean age = 14.52, SD = 1.24). It was taken from the 2013–2014 Chinese Educational Panel Survey (CEPS), that was administrated by the National Survey Research Center at Renmin University of China. The results demonstrate that SES negatively moderates both the relationship between academic socialization and academic achievement, and the relationship between home-based involvement and school behavior. Findings imply that parental involvement activities are highly beneficial for junior school students in families with low SES. Academic socialization is generally associated with academic success, whereas home-based involvement closely relates to school behavior. Future home-based interventions can be developed to promote parental involvement activities in low-SES families. The results also showed important implications for the development of family education in China

A pupil-positioning method based on the starburst model

Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction, disease diagnosis, and psychological and physiological studies. Gaze-tracking systems are an important research topic in the human-computer interaction field. As one of the core modules of the head-mounted gaze-tracking system, pupil positioning affects the accuracy and stability of the system. By tracking eye movements to better locate the center of the pupil, this paper proposes a method for pupil positioning based on the starburst model. The method uses vertical and horizontal coordinate integral projections in the rectangular region of the human eye for accurate positioning and applies a linear interpolation method that is based on a circular model to the reflections in the human eye. In this paper, we propose a method for detecting the feature points of the pupil edge based on the starburst model, which clusters feature points and uses the RANdom SAmple Consensus (RANSAC) algorithm to perform ellipse fitting of the pupil edge to accurately locate the pupil center. Our experimental results show that the algorithm has higher precision, higher efficiency and more robustness than other algorithms and excellent accuracy even when the image of the pupil is incomplete.Science and Technology Support Plan Project of Hebei Province (grant numbers 17210803D and 19273703D Science and Technology Spark Project of the Hebei Seismological Bureau (grant number DZ20180402056) Education Department of Hebei Province (grant number QN2018095) Polytechnic College of Hebei University of Science and Technolog

Gender, Age, and Cross-Cultural Invariance of Brief Inventory of Thriving Among Emerging Adults

This study tested factorial structure, reliability as well as gender, age, and cultural measurement invariance of the Brief Inventory of Thriving (BIT). Data were collected from 981 Italian, Portuguese, and Chinese emerging adults. Results showed that BIT scores were reliable, mono-dimensional, and suitable to assess thriving across different populations.info:eu-repo/semantics/publishedVersio

ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling

Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially accelerate autonomous driving research, especially for perception tasks such as 3D detection and trajectory forecasting. Since the driving logs in these datasets contain HD maps and detailed object annotations which accurately reflect the real-world complexity of traffic behaviors, we can harvest a massive number of complex traffic scenarios and recreate their digital twins in simulation. Compared to the hand-crafted scenarios often used in existing simulators, data-driven scenarios collected from the real world can facilitate many research opportunities in machine learning and autonomous driving. In this work, we present ScenarioNet, an open-source platform for large-scale traffic scenario modeling and simulation. ScenarioNet defines a unified scenario description format and collects a large-scale repository of real-world traffic scenarios from the heterogeneous data in various driving datasets including Waymo, nuScenes, Lyft L5, and nuPlan datasets. These scenarios can be further replayed and interacted with in multiple views from Bird-Eye-View layout to realistic 3D rendering in MetaDrive simulator. This provides a benchmark for evaluating the safety of autonomous driving stacks in simulation before their real-world deployment. We further demonstrate the strengths of ScenarioNet on large-scale scenario generation, imitation learning, and reinforcement learning in both single-agent and multi-agent settings. Code, demo videos, and website are available at https://metadriverse.github.io/scenarionet