Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.     

Risk factors associated with online game addiction: A hierarchical model

Online gaming addiction has been increasingly recognized as a mental disorder. However, the predictive factors that lead to online gaming addiction are not well established. The aim of this study was to identify factors that may influence the development of online gaming addiction. A total of 263 patients with problematic online gaming addiction (255 males (97%) and 8 females (3%), age: mean = 20.4 ± 5.8 years) and153 healthy comparison subjects (118 males (77%) and 35 females (23%), age: 21.2 ± 5.5 years, range) were recruited for participation in the current study. Hierarchical logistic regression analyses among each set of variables were conducted. Individual factors (sex and age), cognitive factors (IQ and perseverative errors), psychopathological conditions (ADHD, depression, anxiety, and impulsivity), and social interaction factors (family environment, social anxiety, and self-esteem) were evaluated in a stepwise fashion. All four factors were associated with online gaming addiction, with psychopathological conditions being the strongest risk factors for the addiction. Individual factors, psychological factors, and social interactions were associated with the development of pure online gaming addiction. As before, psychological factors (attention, mood, anxiety and impulsivity) were the strongest risk factors for online gaming addiction in patients with pure online gaming addiction. Psychopathologies, including ADHD and depression, were the strongest factors associated with the development of online gaming addiction in individuals.     

CMF300M355NU型质量流量计在油库汽车装车中的应用

介绍了质量流量计的工作原理有应用效果。这种新型质量流量计精度高，稳定性好，示值计量简便，可直接显示质量、流量、介质温度、密度等，维护量低，但必须正确安装、使用，才能发挥其应有的作用。     

系统备份及其误区分析

文章对系统备份的几个主要方面进行了讨论，并在此基础上，对系统备分的若干认识误区进行了分析。     

Alpha induced reactions on Sn: An experimental study of excitation functions and possible production pathways

In the frame of a systematic study of light ion induced threshold reactions on natural tin (Sn) excitation functions for alpha particles induced reactions are presented in a 12–38 MeV energy domain. Using a stacked foil activation method the following radioisotopes were identified: ¹¹⁶Te, ¹¹⁷Te, ¹¹⁸Te, ¹¹⁹Te, ¹²¹Te, ¹²³Te, ¹¹⁷Sb, ¹¹⁸Sb, ¹²⁰Sb, ¹²²Sb, ¹²⁴Sb, ¹²⁶Sb, ¹¹⁷Sn, ¹¹¹In. The experimental cross sections for these isotopes are presented for the first time in this energy range and a direct comparison with values calculated with the ALICE-IPPE code is discussed. Possible use of these data for production of some isotopes relevant in nuclear medicine is suggested.     

Alcohol tolerance in humans is enhanced by prior caffeine antagonism of alcohol-induced impairment.

The author tested the hypothesis that a history of drug-induced antagonism of alcohol impairment would enhance alcohol tolerance in humans. Groups of participants (N = 21) repeatedly performed a psychomotor task under different drug treatments: 0.65 g/kg alcohol, 4 mg/kg caffeine, or both drugs combined. Tolerance to a 0.65 g/kg alcohol dose challenge was then tested. Results showed that a history of combined alcohol and caffeine administrations increased alcohol tolerance compared with an exposure history to either drug alone. The findings contribute to the understanding of the complexities of polydrug use history and provide a useful model to examine how alcohol tolerance might be affected by a history of coadministration with other drugs (e.g., cocaine and nicotine). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

广义频率响应函数的鲁棒建模新方法

针对基于非线性谱分析的故障诊断技术的需要,研究了非线性系统的广义频率响应函数的鲁棒在线建模方 法。首先将次元分析算法应用于Volterra级数模型的辨识,并通过对此算法的修正,得到了一种改进的Volterra系统的鲁 棒辨识算法。对其鲁棒性能进行的分析表明,改进算法的鲁棒抗噪性能被明显提高。在此基础上,提出了一种广义频率响 应函数的两步鲁棒建模新方法。仿真实验表明,该方法具有良好的鲁棒收敛性能,能够有效解决GFRF的在线建模问题。