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The debate surrounding the influence of parental nutritional knowledge on offspring’s susceptibility to overweight/obesity persists, with a dearth of research elucidating the mechanisms involved. This study aims to explore if and how parental nutrition knowledge affects adolescents' overweight and obesity, by highlighting the mediating effect of parental perceived severity of obesity.

A questionnaire was conducted in Zhejiang Province, China, with a sample of 2,298 students as well as their parents during November–December 2022. Logistic regression and propensity score matching methods was used to validate the relationship between parental nutrition knowledge and adolescents' overweight/obesity, while the multiplication coefficient method was used to test the mediation effect.

Parental nutrition knowledge significantly reduces the odds ratio for adolescent obesity, without affecting the likelihood of overweight. The perceived severity of obesity fully mediates the relationship between parental nutrition knowledge and adolescents' overweight and obesity.

By exploring mediating factors, the analysis offers an innovative explanation of the process by which nutritional knowledge influences health behaviors. Lack of relevant motivation or beliefs is an important reason for the failure of nutritional knowledge. If parents do not have sufficient awareness of the dangers of overweight or obesity, they will have inadequate motivation to apply nutritional knowledge in guiding dietary or exercise decisions. We also supplement the literature by demonstrating the health belief model in highlighting the mediating mechanism of perceived severity in the relationship between parental nutrition knowledge and adolescents' obesity.

Extending the research focus on subjective beliefs and cognitive motivation bears important policy implications for designing education campaigns to effectively restrain the obesity rate. Considering that many Chinese parents have insufficient awareness of the dangers of childhood overweight and obesity, even considering “chubby children” as symbols of “cuteness” and “health”. This can largely undermine their motivation to apply nutritional knowledge to restrain adolescents' overweight and obesity. Consequently, education campaigns, public health professionals and government agencies can be developed aiming to increase parental nutrition knowledge, especially on the hazards of obesity.

With the prevalence of overweight and obesity among adolescents, it is critical to control students' weight and health. Families, especially parents, are vital to the physical and mental health of adolescents. This research confirmed that improving parental nutrition knowledge is necessary for maintaining a healthy weight for adolescents. Results also emphasized that the association between parental nutritional knowledge and adolescents' overweight or obesity was mediated by parental perceived severity. This indicates that interventions designed to enhance parental nutritional knowledge should pay more attention to strengthening parental cognition of and beliefs in obesity.

This paper added to the debate on the impact of parental nutritional knowledge on adolescent’s overweight and obesity. Based on the health belief model, it also identified a novel pathway and mechanism by highlighting the importance of subjective motivations and beliefs such as the perceived severity of obesity in influencing overweight/obesity. Conclusions bear important policy implications for designing education campaigns to effectively restrain the obesity rate.

This study aims to explore the degree of Chinese consumers' trust and confidence in the Chinese dairy products supply chain and the relationships between trust and overall confidence in dairy products safety and quality.

This study collected data from 1,278 respondents by field survey from five provinces of China. The data were analyzed using ordered logit model.

This study shows the following results: (1) Chinese consumer confidence in domestic dairy products and trust in actors of the dairy chain are at a moderate-to-low level. (2) Government regulators are considered to take the most responsibility, with both an optimism-enhancing and a pessimism-reducing effect (the former effect is greater), while perceived trust in dairy farmers and retailers has little effect. (3) Perceived care has both an optimism-enhancing and a pessimism-reducing effect, and the former effect is stronger. Competence and openness have an optimism-enhancing effect and a pessimism-reducing effect, respectively. (4) The importance of the three dimensions of trust related to optimism-increasing and pessimism-reduction is limited, except in the case of government regulators.

This study contributes to a better understanding of consumer trust in food safety and also help demonstrate to the actors and institutions involved in the dairy supply chain the best way to improve the performance of their duties to meet the consumers' needs for safe and quality dairy products.

The purpose of this paper is to shed light on how technological innovation capabilities (TICs) influence the product competitiveness of Chinese manufacturing enterprises and identify the key technological innovation components.

Quantitative research setting was applied in Chinese Yangtze River Delta. Survey was carried out with 166 responses.

The study reveals that the firm’s strategies capabilities, knowledge resources, fundamental research, application R & D, and manufacturing capabilities have significant influence on the new product development performance and product competitiveness of Chinese manufacturing enterprises. Interestingly, firm’s organizational capabilities and human, finance, and material resource have no significant correlation with the product competitiveness.

From a practical perspective, the relationships among TICs enablers, processes, and product competitiveness may provide a clue regarding how firms can promote technological innovation to sustain their competitive advantage. Moreover, the key factors of TICs found in the study are useful for policy makers and managers of Chinese firms to make decision.

This study is one of the first studies to apply the structure equation model method to measure the relationship between TICs and product competitiveness under the background of Chinese manufacturing. The results provide a new framework on the how technological innovation capability influence product competitiveness of Chinese manufacturing firms. From a managerial perspective, this study identifies several crucial TICs factors to support product competitiveness, and discusses the implications of these factors for developing organizational strategies that encourage technological innovation.

The purpose of this paper is to present an efficient, interactive foreground/background image segmentation method using mean shift (MS) and graph cuts, in order to improve the segmentation performance with little user interaction.

By incorporating the advantages of the mean shift method and the graph cut algorithm, the proposed approach ensures the accuracy of segmentation results. First, the user marks certain pixels as foreground or background. Then the graph is constructed and the cost function composed of the boundary properties and the region properties is defined. To obtain the hidden information of user interaction, the foreground and background marks are clustered separately by the mean shift method. The region properties are determined by the minimum distances from the unmarked pixels to the foreground and background clusters. The boundary properties are determined by the relationship between the unmarked pixels and its neighbor pixels. Finally, using the graph cuts method solves the energy minimization problem to get the foreground which is of interest.

The paper presents experimental results and compares the results to other methods. It can be seen from the comparison that this method can obtain a better segmentation performance in many cases.

The paper incorporates the advantages of the mean shift method and the graph cut algorithm to obtain better segmentation results, even though the scene is complex.

The purpose of this paper is to propose a novel registration method using Euclidean reconstruction and natural features tracking for AR‐based assembly guidance systems.

The method operates in two steps: offline Euclidean reconstruction and online tracking. Offline stage involves obtaining the structure of scene using Euclidean reconstruction technique. The classification trees are constructed using affine transform for online initialization. In tracking, the classification‐based wide baseline matching strategy and T_d,d test are used to get a fast and accurate initialization for the first frame after which a modified optical flow tracker is used to fulfill the task of feature tracking in the real‐time video sequences. The four specified points are transferred to the current image to compute the registration matrix for augmentation.

Firstly, Euclidean reconstruction was used instead of projective reconstruction to get the projections of predefined features. Compared with the six points needed in projective reconstruction‐based method, this method can run normally even when only four features are successfully tracked. Secondly, an adaptive strategy was proposed to adjust the classification trees using the tracked features in online stage by which one can initialize or reinitialize the system, even with large difference between the first and reference images.

Some indoor and outdoor experiments are provided to validate the performance of the proposed method.

The purpose of this paper is to provide a flexible registration method for markerless augmented reality (AR) systems.

The proposed method distinguishes itself as follows: firstly, the method is simple and efficient, as no man‐made markers are needed for both indoor and outdoor AR applications. Secondly, an adaptation method is presented to tune the particle filter dynamically. The result is a system which can achieve tolerance to fast motion and drift during tracking process. Thirdly, the authors use the reduced scale invariant feature transform (SIFT) and scale prediction techniques to match natural features. This method deals easily with the camera pose estimation problem in the case of large illumination and visual angle changes.

Some experiments are provided to validate the performance of the proposed method.

The paper proposes a novel camera pose estimation method based on adaptive particle filter and natural features matching techniques.

China's increasing income inequality might cause a series of problems, such as the slowdown of economic growth, social and economic tension, the decline of the ecological environment quality and the threat to citizens' health. Consequently, income inequality will inevitably affect the ecological well-being performance (EWP) level of China's provinces through the above aspects. Analyzing the impact of income inequality on EWP and its spatial spillover effects are conducive to improving the level of EWP in China. Therefore, the research purpose of this paper is to use China's provincial data from 2001 to 2017 to analyze the impact of income inequality on EWP and the spatial spillover effect based on the evaluation of the EWP value of each province.

At first, this study utilizes the super efficiency slacks-based measure model (Super-SBM model) to calculate the EWP values of 30 provinces in China, which can evaluate and rank the effective decision units in the SBM model and make up for the defect that the effective decision units cannot be distinguished. Then this study applies the spatial Durbin model and Tobit regression model (SDM-Tobit model) to explore the impact of income inequality and other influencing factors on EWP and the spatial spillover effects in adjacent areas.

Firstly, the average EWP in China fluctuated slightly and showed a downward trend from 2001 to 2017. In addition, the EWP values of the provinces in the western region are usually weaker than those in the eastern and central regions. Moreover, income inequality is negatively correlated with EWP, and the EWP has a spatial spillover effect, which means the EWP level in a region is affected by EWP values in the adjacent regions. Furthermore, the industrial structure and urbanization level are both negatively related to EWP, while technology level, investment openness, trade openness and education level are positively related to EWP.

Compared with the existing research, the possible contribution of this research is that it takes income inequality as one of the important influencing factors of EWP and adopts the SDM-Tobit model to analyze the impact mechanism of income inequality on EWP from the perspective of time and space, providing new ideas for improving the EWP of various provinces in China.

Statistics show that selecting the best investment program based on both cost and effectiveness can avoid financial losses. However, investment evaluation of a power transformer is full of uncertainty as it is hard to obtain accurate and useful cost-effectiveness results. Therefore, the purpose of the paper is to establish an investment evaluation model.

The cost-effectiveness evaluation model in this study used grey correlation analysis for the power transformer selection based on life cycle cost (LCC). Indices of cost and effectiveness factors were chosen to form a three-level index system including quantitative and qualitative factors. Evidential reasoning was applied to quantify the qualitative indexes. Grey correlation analysis was applied to select the best investment program.

The results from this study show that the proposed approach is effective and offers a new approach to evaluating transformer investment.

The model was applied to an investing decision-making problem of the transformer in a new substation in Wuhan, China.

It is very important to select the best transformer program in the candidate investment programs because the investment program decides almost 70 percent of the LCC of the power transformer.

The purpose of this paper is to present a novel registration method for augmented reality (AR) systems based on robust estimation of trifocal tensor using point and line correspondence simultaneously.

The proposed method distinguishes itself in following three ways: first, to establish the world coordinate system, the restriction that the four specified points must form an approximate square is relaxed, the only requirement is that these four points should not be collinear. Second, besides feature points, line segments are also used to calculate the needed trifocal tensors. The registration process can still be achieved even without the use of feature points. Third, to estimate trifocal tensors precisely, progressive sample consensus (PROSAC) is used instead of random sample consensus to remove outliers.

As shown in the experiments, the proposed method really enhances the usability of this system. To calculate trifocal tensor, a PROSAC based algebraic minimization algorithm is put forward which improves the accuracy and reduces the computation complexity.

In current system, it is stipulated that there is no large rotation of the user's head relative to the registration scenes, because the NCC will degrade when there is a large rotation between images.

A more robust feature matching strategy is needed. Treating feature matching as a classification problem may be a good choice.

This paper presents a novel registration approach for AR system.