Prevalence of the metabolic syndrome in Chinese adolescents

Since national figures on the occurrence of metabolic syndrome among Chinese adolescents are lacking, this study aims to estimate its prevalence and distribution among Chinese youngsters. The 2002 China National Nutrition and Health Survey is a nationally representative cross-sectional study. Applying the criteria for US adolescents, we estimated the prevalence of metabolic syndrome among 2761 adolescents aged 15 to 19 years. The prevalence of metabolic syndrome among Chinese adolescents overall was 3·7% (10% in US adolescents). It was 35·2 %, 23·4% and 2·3% among adolescents who were overweight (BMI $95th percentile), at risk of overweight (BMI between 85th and 95th percentile) and normal weight (BMI below the 85th percentile), respectively. Urban boys had the highest rate (5·8$85th percentile and one or two parent(s) with metabolic syndrome, the prevalence was 46·4 %. A total of 96% of overweight adolescents had at least one and 74·1% overweight adolescents had at least two abnormalities of metabolic syndrome. Based on these figures, it is estimated that more than three million Chinese adolescents have metabolic syndrome. Both overweight and metabolic syndrome prevalence among adolescents are still relatively low in China, but the prevalence of metabolic syndrome among Chinese overweight adolescents is similar to those living in the USA

Data analytic approach for manipulation detection in stock market

The term “price manipulation” is used to describe the actions of “rogue” traders who employ carefully designed trading tactics to incur equity prices up or down to make profit. Such activities damage the proper functioning, integrity, and stability of the financial markets. In response to that, the regulators proposed new regulatory guidance to prohibit such activities on the financial markets. However, due to the lack of existing research and the implementation complexity, the application of those regulatory guidance, i.e. MiFID II in EU, is postponed to 2018. The existing studies exploring this issue either focus on empirical analysis of such cases, or propose detection models based on certain assumptions. The effective methods, based on analysing trading behaviour data, are not yet studied. This paper seeks to address that gap, and provides two data analytics based models. The first one, static model, detects manipulative behaviours through identifying abnormal patterns of trading activities. The activities are represented by transformed limit orders, in which the transformation method is proposed for partially reducing the non-stationarity nature of the financial data. The second one is hidden Markov model based dynamic model, which identifies the sequential and contextual changes in trading behaviours. Both models are evaluated using real stock tick data, which demonstrate their effectiveness on identifying a range of price manipulation scenarios, and outperforming the selected benchmarks. Thus, both models are shown to make a substantial contribution to the literature, and to offer a practical and effective approach to the identification of market manipulation

Forecasting Water Demand and Supply of China for 2025

Water resource is the most critical resource; it is the basic condition of human survival, production and maintaining a good ecological environment. Water is a kind of renewable resources, but China is faced with a serious shortage of freshwater because of its huge population. In this paper, two models are built to make a forecast of water demand and supply of China in 2025. These two forecasting models for water demand and supply based on the algorithm of Double Exponential Smoothing. Based on the results of these models, the water amount of the nation and every province is obvious and two advices are proposed for China government makes any water strategy to meet the water demand of China in 2025, such as building desalination plants in the coastal provinces which are lack of water, and meanwhile, more water diversion projects should be constructed between different provinces in the middle of China.Key words: Double Exponential Smoothing, Demand forecast, Math models, Water Strateg

First-principles study on the effective masses of zinc-blend-derived Cu_2Zn-IV-VI_4 (IV = Sn, Ge, Si and VI = S, Se)

The electron and hole effective masses of kesterite (KS) and stannite (ST) structured Cu_2Zn-IV-VI_4 (IV = Sn, Ge, Si and VI = S, Se) semiconductors are systematically studied using first-principles calculations. We find that the electron effective masses are almost isotropic, while strong anisotropy is observed for the hole effective mass. The electron effective masses are typically much smaller than the hole effective masses for all studied compounds. The ordering of the topmost three valence bands and the corresponding hole effective masses of the KS and ST structures are different due to the different sign of the crystal-field splitting. The electron and hole effective masses of Se-based compounds are significantly smaller compared to the corresponding S-based compounds. They also decrease as the atomic number of the group IV elements (Si, Ge, Sn) increases, but the decrease is less notable than that caused by the substitution of S by Se.Comment: 14 pages, 6 figures, 2 table

Nanoscale Suppression of Magnetization at Atomically Assembled Manganite Interfaces

Using polarized X-rays, we compare the electronic and magnetic properties of a La(2/3)Sr(1/3)MnO(3)(LSMO)/SrTiO(3)(STO) and a modified LSMO/LaMnO(3)(LMO)/STO interface. Using the technique of X-ray resonant magnetic scattering (XRMS), we can probe the interfaces of complicated layered structures and quantitatively model depth-dependent magnetic profiles as a function of distance from the interface. Comparisons of the average electronic and magnetic properties at the interface are made independently using X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD). The XAS and the XMCD demonstrate that the electronic and magnetic structure of the LMO layer at the modified interface is qualitatively equivalent to the underlying LSMO film. From the temperature dependence of the XMCD, it is found that the near surface magnetization for both interfaces falls off faster than the bulk. For all temperatures in the range of 50K - 300K, the magnetic profiles for both systems always show a ferromagnetic component at the interface with a significantly suppressed magnetization that evolves to the bulk value over a length scale of ~1.6 - 2.4 nm. The LSMO/LMO/STO interface shows a larger ferromagnetic (FM) moment than the LSMO/STO interface, however the difference is only substantial at low temperature.Comment: 4 pages, 4 figure

Computational intelligent hybrid model for detecting disruptive trading activity

The term “disruptive trading behaviour” was first proposed by the U.S. Commodity Futures Trading Commission and is now widely used by US and EU regulation (MiFID II) to describe activities that create a misleading appearance of market liquidity or depth or an artificial price movement upward or downward according to their own purposes. Such activities, identified as a new form of financial fraud in EU regulations, damage the proper functioning and integrity of capital markets and are hence extremely harmful. While existing studies have explored this issue, they have, in most cases, either focused on empirical analysis of such cases or proposed detection models based on certain assumptions of the market. Effective methods that can analyse and detect such disruptive activities based on direct studies of trading behaviours have not been studied to date. There exists, accordingly, a knowledge gap in the literature. This paper seeks to address that gap and provides a hybrid model composed of two data-mining-based detection modules that effectively identify disruptive trading behaviours. The hybrid model is designed to work in an on-line scheme. The limit order stream is transformed, calculated and extracted as a feature stream. One detection module, “Single Order Detection,” detects disruptive behaviours by identifying abnormal patterns of every single trading order. Another module, “Order Sequence Detection,” approaches the problem by examining the contextual relationships of a sequence of trading orders using an extended hidden Markov model, which identifies whether sequential changes from the extracted features are manipulative activities (or not). Both models were evaluated using huge volumes of real tick data from the NASDAQ, which demonstrated that both are able to identify a range of disruptive trading behaviours and, furthermore, that they outperform the selected traditional benchmark models. Thus, this hybrid model is shown to make a substantial contribution to the literature on financial market surveillance and to offer a practical and effective approach for the identification of disruptive trading behaviour

Data analytics enhanced component volatility model

Volatility modelling and forecasting have attracted many attentions in both finance and computation areas. Recent advances in machine learning allow us to construct complex models on volatility forecasting. However, the machine learning algorithms have been used merely as additional tools to the existing econometrics models. The hybrid models that specifically capture the characteristics of the volatility data have not been developed yet. We propose a new hybrid model, which is constructed by a low-pass filter, the autoregressive neural network and an autoregressive model. The volatility data is decomposed by the low-pass filter into long and short term components, which are then modelled by the autoregressive neural network and an autoregressive model respectively. The total forecasting result is aggregated by the outputs of two models. The experimental evaluations using one-hour and one-day realized volatility across four major foreign exchanges showed that the proposed model significantly outperforms the component GARCH, EGARCH and neural network only models in all forecasting horizons