Application of EEG multipole model in the diagnosis of Alzheimer''''s disease

In order to find the electroencephalogram （EEG） characteristic parameters typical of Alzheimer＇s disease （AD） and explore an effective diagnostic method, a new mobile current multipole model was proposed to simulate the AD patient＇s cortical dipole source activities. The indicators of goodness of fit （GOF） and DtononD （DD） were calculated from EEG samples to evaluate the performance of this model. Relevant results showed that this multipole model with higher GOFvalues and larger DD change well fitted the pathological electrical activities of cortical neurons aroused by AD＇s extended sulcus and gyrus in the cerebral cortex. Meanwhile, the products of DD mean ＆ standard variance were found in a clear linear correlation with the diagnostic data of mini-mental state examination （MMSE） used in AD clinics. Furthermore, by tracing this multipole model＇s indicators in typical patients and contrasting with the functional magnetic resonance image （fMRI） as AD progressed, we suggested that the DD index may be suitable for monitoring the AD developments as a new diagnostic parameter.     

逆向工程中NURBS曲面重构技术研究

该文提出一种利用数据点特征的非均匀有理B样条(NURBS)曲面重构方法。针对三坐标测量机测得数据点的特点，自动建立散乱点的拓扑信息，简化繁琐的数据前期处理工作。在曲面重构方法上，采用NURBS曲面插值方法，针对数据点的分布不均匀性，通过累积弦长法构造非均匀节点矢量，保证曲面的插值精度。B样条求值的快速算法，利用，临时多项式替代递归调用，能提高运算效率。     

Solving Multivariate Polynomial Matrix Diophantine Equations with Gr?bner Basis Method

Different from previous viewpoints, multivariate polynomial matrix Diophantine equations are studied from the perspective of modules in this paper, that is, regarding the columns of matrices as elements in modules. A necessary and sufficient condition of the existence for the solution of equations is derived. Using powerful features and theoretical foundation of Gr?bner bases for modules, the problem for determining and computing the solution of matrix Diophantine equations can be solved. Meanwh...     

Rapid reductive degradation of azo dyes by a unique structure of amorphous alloys

An Fe-based amorphous alloy was found to significantly enhance the reductive degradation of an azo dye in aqueous solution. The surface-area normalized rate constant was up to 2.0 L m-2 min-1 at room temperature,an order of magnitude larger than any previously reported value. The activation energy of the degradation process was calculated according to the Arrhenius equation,and a much lower value than those previously reported was obtained. Valence band measurement indicated that a depressed valence band maximum and a widened empty band were established in the amorphous ribbon. The unique structure of the amorphous alloy is important-it may reduce the activation energy of the degradation process and enhance the activity of the electrons,thus accelerating the degradation process.     

Attentional negativity bias moderated by positive mood arousal

Automatically allocation of more attention to negative stimuli is called emotional negativity bias. An event-related potentials (ERPs) experiment investigated whether or not this bias was altered by positive mood arousal. The results suggested that the attention bias towards negative stimuli was attenuated when positive information was accessible.     

含未知输入非线性系统的扩展平方根容积卡尔曼滤波算法

针对工程实际应用中存在的未知输入会导致经典的非线性滤波器状态估计精度下降甚至滤波发散的问题,提出了一种基于最小方差无偏估计(minimum variance unbiased estimation, MVUE)准则的扩展平方根容积卡尔曼滤波(extended square-root cubature Kalman filter, ESRCKF)算法。首先,结合上一时刻未知输入估计值对状态一步预测值进行修正,得到含未知输入条件下的状态预测值。其次,设计新息并采用加权最小二乘(weighted least squares, WLS)法获取当前时刻未知输入的无偏估计。最后,通过最小化协方差矩阵的迹,同时采用拉格朗日乘子法和舒尔补引理得到系统状态的最小方差无偏估计。仿真结果表明,相比于现有的非线性滤波算法,ESRCKF算法提高了在处理含未知输入非线性系统时的状态估计精度,并能同时实现系统状态和未知输入的最优估计,验证了该算法的有效性。     

考虑地质分层约束的长短期记忆循环神经网络测井曲线重构

延长油田东部裸眼井区早期测井资料普遍只有自然电位（SP）、自然伽马（GR）及梯度电阻率(R2.5)三条曲线,因缺失声波（AC）、地层电阻率（RT）等测井曲线,难以满足精细油藏地质研究需求。东部裸眼井区开发时间长、单井产量低,重新测井缺乏可行性及经济性。采用长短期记忆循环神经网络(Long Short-Term Memory,LSTM)进行缺失测井曲线重构是一种经济有效方法,适用于地层测井序列数据。然而延长油田东部浅层油藏上覆黄土层段测井数据信号干扰大,直接应用模型精度较差。针对此问题,本文采用考虑地质分层约束的LSTM模型进行缺失测井曲线的重构,通过分层数据截取每口井长6层段测井数据作为样本数据,既保留了LSTM模型处理序列数据的优势,同时又避免了上覆黄土层测井数据对模型的干扰。利用裸眼井区完整测井数据进行模型训练优化和验证,讨论了考虑地质分层约束的LSTM测井曲线重构精度,结果表明通过引入地质分层约束,模型重构测井曲线精度更高。应用优化后模型实现裸眼井区50口仅有GR、SP、R2.5三条曲线数据井的AC、RT曲线重构,对50口井的142个射孔段进行二次解释,对比试油解释结论符合率达到89.4%,验证了该方法对测井曲线重构的实用性和有效性。     

级联失效模型下电力信息物理系统鲁棒性与能控性分析

电力网与信息网的深度融合在增强电力系统能控性的同时,也使得系统抵御外界干扰的能力下降。首先,根据提出的节点负载与容量的负载重分配分配概率建立电力信息物理系统级联失效模型,研究不同耦合策略下的电力信息物理系统级联失效过程中的鲁棒性与能控性变化。其次,利用幸存负荷百分比的概念来量化电力网抵御级联失效的能力,并分析不同耦合策略和容量参数对CPPS的鲁棒性和能控性的影响。基于网络能控性理论提出电力节点重要度的评估方法,并且在IEEE39系统中进行验证。研究结果表明,电力网高介数节点与信息网高介数节点耦合形成的电力信息物理网络抗干扰能力更强,电力网节点容量参数比信息网节点容量参数对系统影响更大。     

外用碳酸氢钠在高水平足球运动员高强度间歇训练期的试验

碳酸氢钠对提高运动员竞技表现水平和缓解运动疲劳有较好效果,但运动员口服碳酸氢钠会有碱中毒、肠胃反应、心律失常等副作用,从而无法在竞技体育中广泛应用。目的：研究碳酸氢钠经皮肤外涂给药对男子足球运动员的高强度间歇训练期间血清指标和延迟性肌肉酸痛的影响。方 法：选取16名高水平男子足球运动员为研究对象,每天为其分别涂抹碳酸氢钠外用乳剂和安慰剂乳剂后,分别在7天高强度间歇训练后对训练后的主观疲劳程度和下肢延迟性肌肉酸痛程度进行统计,对训练前后血乳酸、肌酸激酶、尿素氮、血红蛋白等血液生化指标、训练期的训练刺激量、最大心率、能量消耗、有氧和无氧训练效果指标,跑动距离、跑动速度、加速度、高速跑距离、总负荷等运动表现指标进行分析。结 果：在一次高强度间歇训练前后,实验组相比对照组,血乳酸升高数值显著降低（p< 0.05）,肌酸激酶升高数值增高,血尿素氮升高幅度降低;在7天训练期,相比于对照组,实验组的下肢DOMS和RPE总体水平显著降低（p< 0.05）,下肢DOMS数值在第2天和第6天显著下降（p< 0.05）;在反映训练强度和训练效果的TRIMP数值上,实验组的总体水平高于对照组,且变化幅度更小。实验组运动员训练的无氧训练效果、EPOC峰值、最大心率、能量消耗相比于对照组均有所提升,其中最大心率、无氧训练效果有显著性提升（p< 0.05）。结 论：碳酸氢钠的外涂给药可以显著降低足球运动员高强度间歇训练带来的肌肉延迟性酸痛,并显著降低高强度训练产生的血液血乳酸浓度升高,有利于高强度训练后的无氧训练效果提升,使得运动员心率储备能力和快速恢复能力的增强,从而促进专项运动能力的提升。     

Robust Stabilization Control for a Class of Nonlinear Measure Differential Systems with Delay

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