18 results match your criteria: "Zhengzhou University of Economics and Business[Affiliation]"

In the era of economic globalization, China attracts significant foreign direct investment (FDI) to accelerate economic prosperity. FDI inflows could have ramifications on environmental degradation (ED) despite the enactment of different environmental regulations (ERs) such as market-incentive, command-and-control as well as informal regulations. Though some studies have shown that FDI and ED have significant relationship, the moderating roles of different ERs on the environmental impact of FDI has not been empirically unraveled.

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Purpose: This study examines the relationship between social networking sites addiction and pro-social behavior, considering the increasing importance of social networking sites in daily life. It explores the mediating role of Fear of Missing Out in this relationship and investigates the moderating role of basic psychological need satisfaction.

Research Design Data And Methodology: This study employed a snowball sampling method to conduct an online survey among social network users in China.

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Driven by modern technological innovations (virtual reality, augmented reality, mixed reality and interactive 3D, etc.), digital museums open up new modes of user visitation through virtual exhibition halls and interactive technologies, thus bridging the gap between the museums and their users for in-depth communication. This study explores the determinants of users' continuance intention to use digital museums based on Bagozzi's self-regulation framework.

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In this study, the cooling effect below ambient air temperature, heat dissipation properties and heating energy efficacy of a superomniphobic self-cleaning (SSC) highly emissive (HE) coating were systematically investigated. Except at midday, the SSC-HE coating with an extremely high solar reflectance of 0.985 showed a better cooling effect than a 10-cm-thick polyurethane insulation layer.

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Background: Health risk perception is an important predictor of health-protective behaviors according to the health belief model. However, the underlying mechanism connecting health risk perception and exercise behaviors is not well understood. The current study investigates how health risk perception predicts college students' exercise intention in the post-pandemic era in China and analyzes the mediating effect of health anxiety and the moderating effect of lay theories of health.

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Article Synopsis
  • The study investigated the impact of 16 weeks of varied aerobic activities on physical fitness and executive functions in 90 right-handed primary school students in China.
  • Participants were divided into four groups: control, physical activity, intellectual activity, and a combination of both, with each group undergoing daily sessions for 16 weeks.
  • Results showed that while body composition remained unchanged, both physical and intellectual activities significantly enhanced physical fitness and executive functions, with the combination of both exercises yielding the most notable improvements.
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Salient object detection has emerged as a burgeoning area of interest within the realm of computer vision. However, prevailing algorithms exhibit diminished precision when tasked with detecting salient objects within intricate and multifaceted environments. In light of this pressing concern, this article presents an end-to-end deep neural network that aims to detect salient objects within complex environments.

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There has been recently a growing interest in L2 learners' engagement as a central factor in their success. However, not all L2 learners are engaged intellectually, passionately, and behaviorally in L2 learning and this can be due to their experience of burnout in language educational settings. On the other hand, L2 learners frequently encounter heightened degrees of burnout because of emotional and behavioral disengagement.

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Non-Standard Map Robot Path Planning Approach Based on Ant Colony Algorithms.

Sensors (Basel)

August 2023

Department of Mechanical Engineering, Kunsan National University, Gunsan 54150, Jeollabuk, Republic of Korea.

Robot path planning is an important component of ensuring the robots complete work tasks effectively. Nowadays, most maps used for robot path planning obtain relevant coordinate information through sensor measurement, establish a map model based on coordinate information, and then carry out path planning for the robot, which is time-consuming and labor-intensive. To solve this problem, a method of robot path planning based on ant colony algorithms after the standardized design of non-standard map grids such as photos was studied.

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Kernel Partial Least Squares Feature Selection Based on Maximum Weight Minimum Redundancy.

Entropy (Basel)

February 2023

School of Mathematics and Statistics, Xidian University, Xi'an 710071, China.

Feature selection refers to a vital function in machine learning and data mining. The maximum weight minimum redundancy feature selection method not only considers the importance of features but also reduces the redundancy among features. However, the characteristics of various datasets are not identical, and thus the feature selection method should have different feature evaluation criteria for all datasets.

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It is generally accepted that China is a significant cause of global warming and other climate change consequences. This paper applies panel cointegration tests and autoregressive distributed lag (ARDL) techniques to investigate the interactions among energy policy, technological innovation, economic development, trade openness, and sustainable development using panel data from China from 1990 to 2020. Results explain that renewable energy policy and technology innovation are negatively associated with sustainable development.

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Objective: The present study aims to investigate the underlying neurochemical mechanism of physical exercise on striatum synapsis and memory function in vascular dementia model.

Methods: 32 Sprague-Dawley (SD) rats were randomly divided into 4 groups: control group (C group, n = 6), vascular dementia group (Vascular dementia group, n = 7), physical exercise and vascular dementia group (Exe-VD group, n = 6), physical exercise and black group (Exe group, n = 6). 4 weeks of voluntary wheel running were used as pre-exercise training.

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The virtual community offers groups of consumers a knowledge-shared platform, wherein shared brand information influences the brand cognition of others. Using stimulus-organism-response (SOR) model, this study constructs a research framework for the influence of knowledge sharing on consumer-brand relationship in virtual communities. The empirical analysis shows that knowledge-sharing quality has significant positive effects on the sense of virtual community, as does the sense of virtual community on the consumer-brand relationships.

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Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature.

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Arrhythmia is a common cardiovascular disease that can threaten human life. In order to assist doctors in accurately diagnosing arrhythmia, an intelligent heartbeat classification system based on the selected optimal feature sets and AdaBoost + Random Forest model is developed. This system can acquire ECG signals through the Holter and transmit them to the cloud platform for preprocessing and feature extraction, and the features are input into AdaBoost + Random Forest for heartbeat classification.

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Automatic classification of ECG is very important for early prevention and auxiliary diagnosis of cardiovascular disease patients. In recent years, many studies based on ECG have achieved good results, most of which are based on single-label problems; one record corresponds to one label. However, in actual clinical applications, an ECG record may contain multiple diseases at the same time.

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Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules.

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