In-home unintentional injuries (IUIs) seriously threatened children's safety. Three factors, including risky behaviors, parental supervision, and home environmental risks, have been identified as major causes for IUIs. Studies considering the interrelations between the three were limited and no relative studies has been carried out among Chinese children. The purpose of this study is to fully explore the influences of behavioral, supervisory and environmental risk factors on IUIs and their associations among Chinese children on the bases of our self-developed scales.Through stratified cluster sampling, a cross-sectional survey was conducted with 798 parents of children aged 0 ~ 6 years in Changsha, China. Social demographics and IUIs history in the past year were collected by self-administered questionnaires. Three IUI-related scales, which had been developed and validated by our team, aimed to measure risks from children behavior, parental supervision and in-home environment. Structural equation models were constructed to analyze the relationship of these factors and their influences on IUIs using SPSS 26.0 and AMOS 22.0.Seven hundred ninety-eight parents were surveyed in total, and 33.58% of them reported with IUIs history of their children. X2/df, goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI) and the root-mean-square error of approximation (RMSEA) for the model of the whole children were 4.832, 0.879, 0.856 and 0.069 respectively, indicating an acceptable level of model fit. Direct influences were discovered between risky behaviors and children's IUIs. Home environmental risks indirectly exerted impacts on IUIs by the mediating effect of risky behaviors, while the significant effect of parental supervision only existed in children aged 4-6 and girls.Risky behaviors played a mediating role in IUIs among children. Supervision and environmental risks affected IUIs indirectly by the exposure to risky behaviors. Parental supervision may not be able to offset the risks posed by the environmental and behavioral factors, so effective IUIs prevention strategies should focus on behavioral and environmental interventions, with appropriate supervision strategies based on the age and sex characteristics of the child.
Abstract In recent years, with the completion of the new library of Qinghai University, the collection of books in the library has greatly increased. The library has a total collection of 880,000 volumes, covering a dozen disciplines such as science, engineering, agriculture, literature, history, economics, philosophy, law, education, management and medicine. It is difficult for users to find the books they are interested in among the numerous materials. Based on the actual situation of the library of Qinghai University, the differences of different professional users and their personal interests, this paper chooses the item-based collaborative filtering algorithm to realize personalized recommendation. First of all, in the calculation of book similarity, the traditional user score data is not chosen to calculate the similarity, but to calculate the similarity between books and books according to the feature vector of book name. Secondly, in order to avoid the problem of cold start, the system recommends the users who have no borrowing record, but the most borrowed books in their department. The combination of the two realized the personalized recommendation of books. By comparing with other traditional recommendation algorithms, it is found that the algorithm adopted in this paper has better recommendation effect.
Surface symmetry breaking and disorder have been recently explored to overcome operation bandwidth, unwanted diffraction, and polarization dependence issues in the conventional metasurface designs thanks to their increasing degrees of design freedom. However, efficient full‐wave simulation and optimization of electrically large electromagnetic structures have been a longstanding problem. Herein, an interactive learning approach is developed to build new meta‐atom datasets which include the effect of mutual coupling. A deep learning‐based model is developed to extract features of incident/reflection waves and their neighboring interaction responses from a limited number of known meta‐atoms. Finally, the deep neural network is incorporated with optimization algorithms to design, as an example, large‐scale metasurfaces for beam manipulation and wideband scattering reduction. The results demonstrate that the proposed architecture can be successfully applied to rapidly design aperture‐efficient metasurfaces or metalenses at large scales of over tens of thousands of meta‐atoms.
short-paper Study on the Interaction Between The Shanghai Tennis Masters and the City Image of Shanghai Share on Author: Yihan Ma Shanghai Polytechnic University Shanghai Polytechnic UniversityView Profile Authors Info & Claims IPEC2021: 2021 2nd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2021 Pages 832–835https://doi.org/10.1145/3452446.3452645Online:25 April 2021Publication History 0citation14DownloadsMetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Tongue diagnosis is one of the important topics in the field of Chinese traditional medicine (TCM), and color is the basic element of tongue image, it has important diagnostic value. This paper presents a novel approach to extract color feature of tongue images. First, we use iterative method to extract initial main color and initial number of main color, then we adopt GLA(Generalized Loyd Algorithm) algorithm to get the histogram of main color. Experimental results show that the proposed method can improve the detection rate of lesion in tongue image relative to single feature retrieval.
The design of a metasurface array consisting of different unit cells with the objective of minimizing its radar cross-section is a popular research topic. Currently, this is achieved by conventional optimisation algorithms such as genetic algorithm (GA) and particle swarm optimisation (PSO). One major concern of such algorithms is the extreme time complexity, which makes them computationally forbidden, particularly at large metasurface array size. Here, we apply a machine learning optimisation technique called active learning to significantly speed up the optimisation process while producing very similar results compared to GA. For a metasurface array of size 10 × 10 at a population size of 106, active learning took 65 min to find the optimal design compared to genetic algorithm, which took 13,260 min to return an almost similar optimal result. The active learning optimisation strategy produced an optimal design for a 60 × 60 metasurface array 24× faster than the approximately similar result generated by GA technique. Thus, this study concludes that active learning drastically reduces computational time for optimisation compared to genetic algorithm, particularly for a larger metasurface array. Active learning using an accurately trained surrogate model also contributes to further lowering of the computational time of the optimisation procedure.
Wheat is one of the most important global crops, understanding the drivers of wheat yield has significant societal benefits. Climate variables are particularly important in determining interannual variations in wheat yield, either as primary factors which directly influence the stages of wheat growth, or as secondary factors through their influence on pests, diseases and soil conditions. Here we present a new approach to model wheat yield; an empirical method based on nonlinear complex systems identification, known as NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs model). We deploy the NARMAX analytical approach for a specific site, Rothamsted, UK, where detailed meteorological variables are available, together with specific information on site conditions and crop growth stages. NARMAX yield forecasts are compared with those from the WOFOST crop model and nine state-of-the-art machine learning (ML) models; experimental results show that NARMAX outperforms all the compared methods in both prediction accuracy and model interpretability. We also develop regional wheat yield forecasts derived from a new gridded meteorological data product. The NARMAX approach produces skillful forecasts (r = 0.78) of Rothamsted wheat yield for a validation period, with small errors. The NARMAX regional forecasts, based on less specific information than WOFOST, also show a high degree of skill (r = 0.73). In addition, the predictor terms chosen for the model are identifiable and can help to give insight into potential key processes involved in the determination of wheat yield at a specific location. This approach can be extended in principle to other crop types and locations. It is straightforward and inexpensive to implement, using a limited number of meteorological predictor variables, which can be taken from site-based observations, or from gridded meteorological datasets. The method is a new tool to understand the environmental drivers of wheat yields on an annual basis.
The quality of the data determines the quality of the model. In this paper, the grassland degradation data in the Headwaters of the Three Rivers were processed in the early stage and labeled with multiple classification. Guided clustering and semi-supervised clustering were used for comparison. The two methods were combined to classify and label the data, so as to improve the accuracy and completeness of the classification data.
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