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Predicting the adult height of children accurately has great social value for the selection of outstanding athlete as well as early detection of children’s growth disorders. Currently, the mainstream method used to predict adult height in China has three problems: its standards are not uniform; it is stale for current Chinese children; its accuracy is not satisfactory. This article uses the data collected by the Chinese Children and Adolescents’ Physical Fitness and Growth Health Project in Zhejiang primary and secondary schools. We put forward a new multidimensional and high-precision youth growth curve prediction model, which is based on multilayer perceptron. First, this model uses multidimensional growth data of children as predictors and then utilizes multilayer perceptron to predict the children’s adult height. Second, we find the Table of Height Standard Deviation of Chinese Children and fit the data of zero standard deviation to obtain the curve. This curve is regarded as Chinese children’s mean growth curve. Third, we use the least-squares method and the mean curve to calculate the individual growth curve. Finally, the individual curve can be used to predict children’s state height. Experimental results show that this adult height prediction model’s accuracy (between 2 cm) of boys and girls reached 90.20% and 88.89% and the state height prediction accuracy reached 77.46% and 74.93%. Compared with Bayley–Pinneau, the adult height prediction is improved 19.61% for boys and 13.33% for girls. Compared with BoneXpert, the adult height prediction is improved 25.49% for boys and 6.67% for girls. Compared with the method based on the bone age growth map, the adult height prediction is improved 15.69% for boys and 24.45% for girls.

The prediction of children's adult height is a common procedure in childhood endocrinology. Through the prediction of children's adult height, it is possible to find abnormalities in children's growth and development. Many jobs in today's society have certain requirements for height, so the accuracy of children adulthood height prediction is important for children. Current methods for predicting adult height of children have some shortcomings such as inaccurate accuracy. To deal with these problems, this paper analyzes the data collected by the Chinese children and adolescents' physical and growth health projects in primary and secondary schools in Zhejiang Province, and proposes a method for predicting adult height based on back propagation neural network (BPNN) with the body composition of children and adolescents as input. Since the BP algorithm has the risk of falling into local optimization, and we propose LSALO-BP model that incorporates the ant lion optimizer (LSALO) into the BP algorithm as location strategy to avoid local optimization. The improvements achieved by the ant lion algorithm are mainly reflected in: improving the ant's walk mode, and enhancing the global search ability of the LSALO algorithm. The comparison experiment of 10 benchmark functions proves the feasibility and effectiveness of the location strategy. The LSALO-BP model is applied to the prediction of adult height of children and adolescents. The experimental results show that compared with other models, the LSALO-BP prediction model has increased the prediction accuracy by 6.67%~16.08% for boys and 4.67%~6.6% for girls, which can more accurately predict the adult height of children and adolescents.

Accurate height prediction has important reference significance for the development of children and adolescents and the selection of athletes. The current mainstream height prediction methods include the B-P (Bayley–Pinneau) method and the TW2 (Tanner–Whitehouse) method. A large number of documents show that the B-P method and the TW2 method have relatively large deviations in the lifelong height prediction results of Chinese children and adolescents. Based on the data collected by the Chinese Adolescent Students’ Physical Fitness and Growth and Development Health Project in Zhejiang’s primary and secondary schools, this paper proposes a graph of height growth trends based on bone age. The height map of age has more reference value. Aiming at the feasibility of the height data in the statistical results, the interpolation prediction method is used to verify the data, and the height growth trend graph is drawn through the method of fitting. Validation results with actual data show that the average error of the lifetime height prediction of the height growth trend map proposed in this paper is 2.1 cm, which is 1.4 cm lower than the 3.5 cm error predicted by the B-P method and 0.4 cm lower than the 2.5 cm error predicted by the TW2 method.

To enhance their economic engagement in Africa, both China and India have institutionalized their interactions with African countries through multilateral leaders’ summits. The Forum on China-Africa Cooperation (FOCAC) and India-Africa Forum Summit (IAFS) held successively in late 2015 reveal many interesting trends of both countries’ relationships with Africa. Although India has intended to differentiate itself from China in diplomatic rhetoric, both countries actually share a number of vital commonalities in their trade relations with Africa. Following their respective summits, China’s and India’s engagement in the vast continent is likely to demonstrate distinctive styles, as the two countries possess different sets of political and economic resources at their disposal and tend to prioritize contrasting dimensions of economic engagement in Africa. In the long run, however, it is possible to establish a trilateral partnership that combines India’s remarkable “soft power” with China’s economic advantages to generate lasting industrialization and economic progress of Africa.

Localization of sensors nodes is a key and fundamental issue in wireless sensor networks due to random deployment. In this paper, we propose a tree based clustering (TBC) multidimensional scaling algorithm for wireless sensor networks with the purpose of overcoming the shortage of classical MDS algorithms in its localization accuracy and computing complexity. Clustering is adopted to degrade the problem scale in our approach and a moderate number of common nodes between clusters are kept during clustering. Inner cluster local coordinates are calculated and then mapped into global coordinates according to the tree structure formed by clustering. Simulations on MATLAB are conducted and the results show that the proposed algorithm has better localization coverage and higher accuracy than the traditional MDS based algorithms.

Wireless Sensor Network (WSN) is widely used in environmental monitoring such as Tailings Dams, because of its low cost, high accuracy, and can remotely get monitoring information. We focuses on Tailings Dams monitoring application, proposed a MAC protocol with sensors non-autonomous moving. This protocol can quickly response events and fully considers about energy consumption. To develop network connectivity, this paper also developed the drift trajectory generation algorithm. At last, we make a simulation about the algorithm in MATLAB platform, and make a comparison between visual trajectory and real trajectory. The result shows that both trajectories are highly flt in speed and direct, there is an internal consistency between them.

Localization is a key technology in wireless sensor network and localization in three dimensional environments is very popular and will be leading the future development of localization technology. This paper proposed a DCP3D (the degree of coplanarity three dimensional) algorithm which used the degree of coplanarity and multiple threshold constraint strategy to improve the accuracy by analyzing the beacon node topology. Simulation results show that the improved method has a good performance in accuracy, bad node control and stability.

Node’s localization is a key technology in Wireless Sensor Networks. There are only quite a few studies on 3D localization which is suffered in slow progress, actually, is one of the main difficulties in WSN localization. Based on the study of the application of terrain modeling, localization algorithm for sensor nodes in 3D condition has been focus on as well as the application of terrain model. The paper proposed a new algorithm named Multi-hop 3D AOA With Space-based Angle Transmission, and then combined the new algorithm with Delaunay triangulation algorithm for terrain modeling. The results of simulation have shown that as the ratio of beacon nodes and their communication radius changes, the algorithm’s localization error rate range from 7% to 27%, and has a good terrain modeling accuracy, range from 59% to 98%.