Impact of Personality Traits on Knowledge Hiding: A Comparative Study on Technology-Based Online and Physical Education

Knowledge hiding has been a variable of interest that has led to major intangible losses to organizations, especially in this pandemic era when everything has shifted to online platforms and social media. Knowledge hiding has taken a new turn into the field of knowledge management. Moreover, the major players in knowledge hiding are the personality characteristics of individuals that have now found a way of expression without coming into the spotlight. This study is a necessary one in this time of online working environments where the role of personality traits and psychological ownership has been explored to understand their impact on the knowledge hiding within the organizations of China, and furthermore, to understand what role social status plays in moderating these relationships. The sampling design used is convenient random sampling with a sample size of 298 managers. This study has used the software Smart-PLS 3.3.3 for analyzing the data. The data relied on and was validated using preliminary tests of reliability and discriminant and convergent validities using the measurement model algorithm. Further, the partial least square technique was used to find the equation modeling for the variables, with the help of a structural model algorithm using 500 iterations for bootstrapping. The findings of the current study show that the personality traits of the “BIG FIVE” model positively predict knowledge hiding, except for openness to experience. At the same time, psychological ownership plays a partial mediating role.

Impact of a strong temperature gradient on grain growth in films

The migration of grain boundaries and, therewith, the phenomenon of grain growth depend strongly on the annealing temperature. Generally, higher temperatures are associated with higher mobilities of the boundaries and therewith faster microstructural coarsening. In the present study, the influence of a strong temperature gradient on grain growth in thin films is investigated. To that aim, a modified three-dimensional Potts model algorithm is employed, where the annealing temperature changes with the thickness of the sample taking grain boundary mobility and energy into account. The resulting drag effect has serious consequences for the temporal and spatial evolution of the grain microstructure.

Fast Realization of Collision Risk Calculation in Power Line Patrol Flight Based on CUDA

In this paper, a low-altitude risk collision model based on CUDA is designed to avoid problems that may occur in the process of unmanned aerial power patrol. By collecting and analyzing the data related to the unmanned aerial power patrol task, the collision accident probability is extracted and the probability distribution model and the influence of weather factors on the collision risk are combined. The model validates the collision risk of unmanned aerial vehicles in different locations and verifies the reliability and computational efficiency of the model based on different operating systems. The model algorithm can effectively improve the response time to avoid collision risk during UAV patrol and reduce the risk level of UAV collision accidents.

Application Research on Roller Skater Detection, Tracking, and Trajectory Prediction Based on Video Stream

With the continuous progress of science and technology, the sport of roller skating has developed rapidly and the technical level of the game has become higher and higher. Its sport performance has been rapidly improved. However, China’s roller skating is relatively late, and there is still a certain gap compared with many Western developed countries. In order to improve the performance of China’s roller skating, this study takes the representative Chinese and foreign excellent speed skaters as the research object and compares the sprinting technology of Chinese and foreign excellent speed skaters by using image measurement and image analysis to obtain the kinematic parameters and data of the athletes’ sprinting technology in the competition state. In view of the problem that the current video target tracking algorithm is easy to follow multiple targets, a video multiobject detection and tracking algorithm with improved tracking learning detection (TLD) is studied with the skater in the video as the research object. For the lost target, the prediction function of Kalman filter algorithm is used to track the trajectory of the typical target in the video, and the trajectory tracked by Kalman filter algorithm is used to compensate the lost part of TLD algorithm, so as to obtain the complete trajectory of the typical target in the video to improve the accuracy of video multiobject tracking. Since the existing trajectory prediction algorithms have the limitation of poor accuracy, a social-long short-term memory (Social-LSTM) network-based video typical target trajectory prediction algorithm is proposed to predict the trajectory sequences of typical targets to be detected by incorporating the contextual environment information and the interaction relationship between multiple target trajectories into the Social-LSTM network. The simulation results show that the proposed trajectory prediction algorithm outperforms the traditional LSTM algorithm, Hidden Markov Model Algorithm, and Hybrid Gaussian model algorithm, which is helpful to improve the accuracy of video roller skater target trajectory prediction, and the tracking success rate is 0.98.

Research on the Construction of Big Data Classification System Based on Distributed Data Flow

In the context of the comprehensive popularization of network technical services and database construction system, more and more data are used by enterprises or individuals. It is difficult for the existing technology to meet the technical analysis requirements of the development of the era of big data. Therefore, in the development of practice, we should continue to explore new technologies and methods to reasonably use big data. Therefore, on the basis of understanding the current big data technology and its system operation status, this paper designs relevant algorithms according to the big data classification model, and verifies the effectiveness of the analysis model algorithm based on practice.

Optimization of regional transport networks based on a mathematical model of passenger preferences

This article presents the results of developing a model for a regional passenger transport network aimed at solving the logistical problem of constructing rational intermodal routes in a defined closed loop. The tools of graph theory and linear mathematical programming have been applied to build the model algorithm, which allows finding solutions for weighted graphs, in the absence of negative weight links, while keeping information about the sequence of hub points on the selected paths. The proposed solutions are versatile enough to be scalable for regions with different network topologies. The model is adapted to dynamically changing and extensible systems, allowing it to be practically applied to justify options for the future development of infrastructure in different modes of transport.

Classification of Spine Image from MRI Image Using Convolutional Neural Network

The spine tumor is a fast-growing abnormal cell in the spinal canal or vertebrae of the spine, it affected many people. Thousands of researchers have focused on this disease for better understanding of tumor classification to provide more effective treatment to the patients. The main objective of this paper is to form a methodology for classification of spine image. We proposed an efficient and effective method that helpful for classifying the spine image and identified tumor region without any human assistance. Basically, Contrast Limited Adaptive Histogram Equalization used to improve the contrast of spine images and to eliminate the effect of unwanted noise. The proposed methodology will classify spine images as Normal or Abnormal using Convolutional Neural Network (CNN) model algorithm. The CNN model can classify spine image as Normal or Abnormal with 99.4% Accuracy, 94.5% Sensitivity, 95.6% Precision, and 99.9% specificity. Compared with the previous existing methods, our proposed solution achieved the highest performance in terms of classification based on the spine dataset. From the experimental results performed on the different images, it is clear that the analysis for the spine tumor detection is fast and accurate when compared with the manual detection performed by radiologists or clinical experts, So, anyone can easily identify the tumor affected area also determine abnormal images.

Evaluation Model for Particulate Matter Emissions in Korean Construction Sites

Particulate matter (PM) has caused serious environmental issues in Asia, and various policies for systematic management of PM based on evaluation of the characteristics of emissions are being discussed. In Korea, where the damage of PM from construction sites is severe, only regulatory policies according to the concentration are being implemented; however, there is no policy for the quantitative management of PM. Therefore, this study aimed to derive and propose an emission evaluation model to be used for the establishment of management policies for construction site PM emissions in South Korea by assuming structures as manufactured products. Therefore, this study derived a method of calculating the PM10, PM2.5, NOx, SOx, and VOCs emission factors for each type of equipment in construction sites and then estimated annual total emissions. In addition, this paper put forth a method for offsetting emission permission standards as the criteria for evaluating the adequacy of the estimated emissions. Finally, a model algorithm was proposed for evaluating emissions in advance during the construction planning phase by comparing the PM10, PM2.5, NOx, SOx, and VOCs emissions in construction sites with established standards; the supplementary point of the algorithm is discussed for further studies.

Simulation of English Linguistic Phonetic and Lexical Variation Based on Sociology Perspective

Objectives: From the perspective of sociology, the speech and vocabulary variation simulation of English linguistics is discussed in depth. Firstly, the background of the research object and the significance of the research are elaborated, and then the research theory related to the English phonetic and lexical variation simulation is analyzed. Methods: Through the design of the English phonetic intonation network teaching system, the design ideas that conform to the development of each function of the platform are proposed. Results: Furthermore, the English linguistic speech and lexical variation simulation model algorithm based on sociological perspective is used to design and verify the function of the teaching system, and the effectiveness of the algorithm is verified by empirical analysis. Conclusion: The final results of the experiment show that by using the Internet of Things (IoT) technology to develop a system tool that conforms to the teaching method and put it into specific teaching work can improve students’ English linguistics pronunciation and vocabulary learning ability.