The Influence of Parent-Child Relationship on Pupils’ Learning Motivation: The Mediating Role of Teacher-Student Relationship

Objective: The study is to analyze the influence of parent-child relationship on pupils’ learning motivation, and to explore the mediating mechanism of teacher-student relationship in parent-child relationship and learning motivation. Method: This study conducted a questionnaire survey on 213 pupils in Grades 5 and 6 in two schools in Beijing using Pianta’s teacher-student relationship scale revised by Qu, Dornbush’s parent-child intimacy scale revised by Zhang and the learning motivation scale adapted by Hu. Results: Gender, grade, whether they are the only child and to be a class cadre or not show significant differences in some dimensions of parent-child relationship, teacherstudent relationship and learning motivation. The total scores of parent-child relationship, teacher-student relationship and learning motivation are positively correlated, and some sub dimensions are also significantly correlated. Parentchild relationship and teacher-student relationship have a significant positive predictive effect on learning motivation, and parent-child relationship has a significant positive predictive effect on teacher-student relationship. Teacher-student relationship plays a mediating role in the influence of parent-child relationship on learning motivation. Conclusions: Parent-child relationship can promote the relationship between teachers and students, and then enhance pupils’ learning motivation

A fiducial-aided data fusion method for the measurement of multiscale complex surfaces

Multiscale complex surfaces, possessing high form accuracy and geometric complexity, are widely used for various applications in fields such as telecommunications and biomedicines. Despite the development of multi-sensor technology, the stringent requirements of form accuracy and surface finish still present many challenges in their measurement and characterization. This paper presents a fiducial-aided data fusion method (FADFM), which attempts to address the challenge in modeling and fusion of the datasets from multiscale complex surfaces. The FADFM firstly makes use of fiducials, such as standard spheres, as reference data to form a fiducial-aided computer-aided design (FA-CAD) of the multiscale complex surface so that the established intrinsic surface feature can be used to carry out the surface registration. A scatter searching algorithm is employed to solve the nonlinear optimization problem, which attempts to find the global minimum of the transformation parameters in the transforming positions of the fiducials. Hence, a fused surface model is developed which takes into account both fitted surface residuals and fitted fiducial residuals based on Gaussian process modeling. The results of the simulation and measurement experiments show that the uncertainty of the proposed method was up to 3.97 × 10 −5 μm based on a surface with zero form error. In addition, there is a 72.5% decrease of the measurement uncertainty as compared with each individual sensor value and there is an improvement of more than 36.1% as compared with the Gaussian process-based data fusion technique in terms of root-mean-square (RMS) value. Moreover, the computation time of the fusion process is shortened by about 16.7%. The proposed method achieves final measuring results with better metrological quality than that obtained from each individual dataset, and it possesses the capability of reducing the measurement uncertainty and computational cost

Analyze the Entry Modes of Foreign Retailers in China: A case study on Sainsbury’s

With a fast-growing economy that support a population of 1.5 billion, China is believed to hold promise as both an economic superpower and an enormous market in the twenty-first century. Meanwhile, the speed of retailer internationalization has increased dramatically throughout the world. As a result, the study of international retailing has developed from a position of observation, through one of analysis, to one of conceptual exploitation. Selecting an optimal entry mode for the firm is the primary step to compete other international retailers in Chinese market. This dissertation examines various factors that influence the entry modes decision of retail MNEs when they are entering Chinese retail market. Furthermore, an optimal entry mode is discussed as well, by using the case of Sainsbury’s, one of the famous retailers in the UK market and have not expanded into China till now. This dissertation aims to offer a new perspective of the most appropriate entry mode used within the sector of retailing in present China. The best entry mode strategies for retailers to follow in China are suggested to enact a FDI plan or when shifting their current market strategy to better perform in the future

Bayesian Optimization with Hidden Constraints via Latent Decision Models

Bayesian optimization (BO) has emerged as a potent tool for addressing intricate decision-making challenges, especially in public policy domains such as police districting. However, its broader application in public policymaking is hindered by the complexity of defining feasible regions and the high-dimensionality of decisions. This paper introduces the Hidden-Constrained Latent Space Bayesian Optimization (HC-LSBO), a novel BO method integrated with a latent decision model. This approach leverages a variational autoencoder to learn the distribution of feasible decisions, enabling a two-way mapping between the original decision space and a lower-dimensional latent space. By doing so, HC-LSBO captures the nuances of hidden constraints inherent in public policymaking, allowing for optimization in the latent space while evaluating objectives in the original space. We validate our method through numerical experiments on both synthetic and real data sets, with a specific focus on large-scale police districting problems in Atlanta, Georgia. Our results reveal that HC-LSBO offers notable improvements in performance and efficiency compared to the baselines.Comment: 8 pages, 8 figures (exclude appendix

Research on online public opinion dissemination and emergency countermeasures of food safety in universities—take the rat head and duck neck incident in China as an example

In recent years, food safety accidents have occurred frequently in colleges and universities, and students are prone to emotional resonance with food safety. It triggered heated discussions among the whole society and gradually formed a unique online public opinion on food safety in universities. After food safety incidents broke out in universities, some universities deliberately avoided responsibility or made mistakes in handling the incidents, which will create greater risks of online public opinion. Therefore, this paper takes the “Rat Head and Duck Neck” incident at Jiangxi Institute of Technology in China as an example. The purpose is to study the dissemination of public opinion on food safety online in universities and propose emergency countermeasures. Above all, the food safety online public opinion is divided into five stages: incubation period, burst period, spreading period, recurring period and dissipation period. Then, methods such as text mining and cluster analysis were used to deeply analyze the influencing factors at each stage of the development of food safety online public opinion. And analyze the role of different subjects in the development of public opinion based on the perspective of stakeholders. Finally, this paper provides corresponding countermeasures for different stages of online public opinion on food safety in universities, which provides suggestions and references for university governance. This study found that: (1) The resonance effect of online public opinion media on food safety in universities is significant. (2) Public opinion on food safety in universities is repetitive. (3) Improper response to food safety incidents in universities can easily trigger negative secondary public opinion

On-machine surface defect detection using light scattering and deep learning

This paper presents an on-machine surface defect detection system using light scattering and deep learning. A supervised deep learning model is used to mine the information related to defects from light scattering patterns. A convolutional neural network is trained on a large dataset of scattering patterns that are predicted by a rigorous forward scattering model. The model is valid for any surface topography with homogeneous materials and has been verified by comparing with experimental data. Once the neural network is trained, it allows for fast, accurate and robust defect detection. The system capability is validated on micro-structured surfaces produced by ultra-precision diamond machining

Control of Citrus Post-harvest Green Molds, Blue Molds, and Sour Rot by the Cecropin A-Melittin Hybrid Peptide BP21

In this study, the activity of the cecropin A-melittin hybrid peptide BP21 (Ac-FKLFKKILKVL-NH2) in controlling of citrus post-harvest green and blue molds and sour rot and its involved mechanism was studied. The minimum inhibitory concentrations of BP21 against Penicillium digitatum, Penicillium italicum, and Geotrichum candidum were 8, 8, and 4 μmol L-1, respectively. BP21 could inhibit the growth of mycelia, the scanning electron microscopy results clearly showed that the mycelia treated with BP21 shrank, formed a rough surface, became distorted and collapsed. Fluorescent staining with SYTOX Green (SG) indicated that BP21 could disintegrate membranes. Membrane permeability parameters, including extracellular conductivity, the leakage of potassium ions, and the release of cellular constituents, visibly increased as the BP21 concentration increased. Gross and irreversible damage to the cytoplasm and membranes was observed. There was a positive correlation between hemolytic activity and the concentration of BP21. These results suggest peptide BP21 could be used to control citrus post-harvest diseases

Research on Nonpoint Source Pollution Assessment Method in Data Sparse Regions: A Case Study of Xichong River Basin, China

The NPS pollution is difficult to manage and control due to its complicated generation and formation mechanism, especially in the data sparse area. Thus the ECM and BTOPMC were, respectively, adopted to develop an easy and practical assessment method, and a comparison between the outputs of them is then conducted in this paper. The literature survey and field data were acquired to confirm the export coefficients of the ECM, and the loads of TN and TP were statistically analyzed in the study area. Based on hydrological similarity, runoff data from nearby gauged sites were pooled to compensate for the lack of at-site data and the water quality submodel of BTOPMC was then applied to simulate the monthly pollutant fluxes in the two sections from 2010 to 2012. The results showed agricultural fertilizer, rural sewage, and livestock and poultry sewage were the main pollution sources, and under the consideration of self-purification capacity of river, the outputs of the two models were almost identical. The proposed method with a main thought of combining and comparing an empirical model and a mechanistic model can assess the water quality conditions in the study area scientifically, which indicated it has a good potential for popularization in other regions