B$lt;sub$gt;$lt;i$gt;n$lt;/i$gt;$lt;/sub$gt;Y($lt;i$gt;n$lt;/i$gt;=1–11)团簇的结构和电子性质

利用密度泛函理论TPSSh方法对B采用6-311+G(d), 对Y采用Lanl2dz相对论有效势基组, 研究了B_nY (n=1–11)团簇的平均结合能、二阶能量差分、最高分子占据轨道和最低空轨道之间的能级间隙、极化率和第一静态超极化率等物理化学性质. 结果表明, 随着尺寸的增大, B_nY (n=1–11)团簇的最低能量结构从平面逐步演变为立体结构. 随硼原子数n的增加, 团簇的平均结合能表明了较好的热力学稳定性, 有利于Y掺杂B团簇形成较大的块体材料.二阶能量差分表明基态B₃Y, B₅Y和B₇Y团簇较相邻团簇稳定. 能隙表明了基态B₃Y, B₅Y, B₇Y和B₉Y的化学稳定性较高. 综合说明B_nY (n=1–11)硼团簇中, 基态B₃Y, B₅Y和B₇Y具有较好的稳定性. 极化率表明基态B_nY团簇的电子结构随B原子的增加趋于紧凑, 第一静态超极化率表明基态B₅Y, B₄Y, B₃Y和B₆Y平面结构的团簇具有明显的非线性光学性质, 为寻找性能优异的非线性光学材料提供了一定的参考. 关键词： 密度泛函TPSSh方法 nY (n=1–')" href="#">B_nY (n=1– 11)团簇 几何结构 电子性质     

A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy

The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.     

On mean flow universality of turbulent wall flows. II. Asymptotic flow analysis

Understanding of the structure of turbulent flows at extreme Reynolds numbers (Re) is relevant because of several reasons: almost all turbulence theories are only valid in the high Re limit, and most turbulent flows of practical relevance are characterized by very high Re. Specific questions about wall-bounded turbulent flows at extreme Re concern the asymptotic laws of the mean velocity and turbulence statistics, their universality, the convergence of statistics towards their asymptotic profiles, and the overall physical flow organization. In extension of recent studies focusing on the mean flow at moderate and relatively high Re, the latter questions are addressed with respect to three canonical wall-bounded flows (channel flow, pipe flow, and the zero-pressure gradient turbulent boundary layer). Main results reported here are the asymptotic logarithmic law for the mean velocity and corresponding scale-separation laws for bulk flow properties, the Reynolds shear stress, the turbulence production and turbulent viscosity. A scaling analysis indicates that the establishment of a self-similar turbulence state is the condition for the development of a strict logarithmic velocity profile. The resulting overall physical flow structure at extreme Re is discussed.     

Biomagnetic measurements utilising a superparamagnetic marker: a feasibility study

Summary A preliminary study is here reported on a new potential marker for biomagnetic measurements. The marker consists of superparamagnetic polymer microspheres which were detected in the presence of external steady magnetic fields by means of an r.f.-SQUID magnetometer. The particles were prepared in samples differing in the concentration value and immersed in a homogeneous magnetic field of variable intensity. A simple model was taken into account for the distribution of the microspheres in the samples, so that the theoretical values were compared to the marker field values measured by the biomagnetic sensor. The overall sensitivity of the experimental apparatus and the minimum concentration value of the marker were then estimated.     

A biomagnetic method for studying gastro-intestinal activity

Summary A novel method to investigate the gastro-intestinal activity by means of a superconducting instrumentation based on SQUID sensors developed for biomagnetic measurements is presented. The magnetic field generated by a small marker ingested by the subject is measured over the abdomen. The localization of the marker,i.e. a magnetic dipole, is carried out by means of a standard algorithm used in biomagnetic studies. In this way it is possible to noninvasively study the activity of the gastro-intestinal system. This new approach may lead to important clinical applications.     

Consciousness Detection in a Complete Locked-in Syndrome Patient through Multiscale Approach Analysis

Completely locked-in state (CLIS) patients are unable to speak and have lost all muscle movement. From the external view, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to still be conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is important to find alternative ways to re-establish communication with these patients during periods of awareness, and one such alternative is through a brain–computer interface (BCI). In this study, multiscale-based methods (multiscale sample entropy, multiscale permutation entropy and multiscale Poincaré plots) were applied to analyze electrocorticogram signals from a CLIS patient to detect the underlying consciousness level. Results from these different methods converge to a specific period of awareness of the CLIS patient in question, coinciding with the period during which the CLIS patient is recorded to have communicated with an experimenter. The aim of the investigation is to propose a methodology that could be used to create reliable communication with CLIS patients.