SDH微波接力通信系统差错性能指标的分配和预测方法

本文根据ＩＴＵ－ＴＧ．８２６建议和ＩＴＵ－Ｒ有关文件，分析了Ｇ．８２６指标在ＳＤＨ微波接力通信系统中的分配方法；提出了ＳＤＨ微波接力通信系统差错性能指标的预测方法；介绍了在ＳＤＨ微波接力通信系统中主要的抗衰落措施及其对差错性能指标的改善效果。     

制丝线加香加料系统的问题及改进方法

针对目前卷烟企业制丝线加香、加料系统普遍存在的问题,从设备选型、控制算法进行分析,提出改进方法。     

支持状态机建模的工作流引擎研究

为了能清晰地描述存在较多审批环节和步骤跳转的业务流程,提出利用状态机为流程建模的方法。通过改进传统的关系结构工作流引擎,给出一个支持状态机建模的关系结构工作流引擎的设计方案,详细介绍该引擎的设计原理和实现柔性方法。最后通过一个示例,演示支持状态机建模的工作流引擎的工作机制和实例迁移的过程。原型已经应用到高校学费管理系统中。实践证明,该引擎可以有效地支持状态机建模,并缩短关键业务的开发周期。     

An Operator-integration-factor splitting method for time-dependent problems: Application to incompressible fluid flow

In this paper we present a simple, general methodology for the generation of high-order operator decomposition (splitting) techniques for the solution of time-dependent problems arising in ordinary and partial differential equations. The new approach exploits operator integration factors to reduce multiple-operator equations to an associated series of single-operator initial-value subproblems. Two illustrations of the procedure are presented: the first, a second-order method in time applied to velocity-pressure decoupling in the incompressible Stokes problem; the second, a third-order method in time applied to convection-Stokes decoupling in the incompressible Navier-Stokes equations. Critical open questions are briefly described.     

Volume functions of historical texts and the amplitude correlation principle

A new empirico-statistical model is suggested to distinguish dependent narrative texts from independent narrative texts by means of their volume functions. A regard for information principle and an amplitude correlation principle are formulated. The model and both principles are examined experimentally using specific historical texts.Anatoliy T. Fomenko (Dr. Sci. in mathematics, Lomonosov State University) is a Professor at Lomonosov State University, Moscow. He is author of twelve monographs and textbooks, and is co-author of the two-volume Modern Geometry (Springer-Verlag). He is also artist and historian.Svetlozar T. Rachev (Dr. Sci., Steklov Mathematical Institute) is a Professor at the University of California, Santa Barbara. He has published more than seventy papers and is co-author of the monographMathematical Models for Construction of Queuing Models (Moscow, 1988). His main interests are: stability of stochastic models, theory of probability metrics, queuing theory and survival models.     

Efficient removal of boundary-divergence errors in time-splitting methods

A normal mode analysis is presented and numerical tests are performed to assess the effectiveness of a new time-splitting algorithm proposed recently in Karniadakiset al. (1990) for solving the incompressible Navier-Stokes equations. This new algorithm employs high-order explicit pressure boundary conditions and mixed explicit/implicit stiffly stable time-integration schemes, which can lead to arbitrarily high-order accuracy in time. In the current article we investigate both the time accuracy of the new scheme as well as the corresponding reduction in boundary-divergence errors for two model flow problems involving solid boundaries. The main finding is that time discretization errors, induced by the nondivergent splitting mode, scale with the order of the accuracy of the integration rule employed if a proper rotational form of the pressure boundary condition is used; otherwise a first-order accuracy in time similar to the classical splitting methods is achieved. In the former case the corresponding errors in divergence can be completely eliminated, while in the latter case they scale asO(vt)_1/2.     

An analytical and numerical study of the two-dimensional Bratu equation

Bratu's problem, which is the nonlinear eigenvalue equationu+ exp(u)=0 withu=0 on the walls of the unit square and as the eigenvalue, is used to develop several themes on applications of Chebyshev pseudospectral methods. The first is the importance ofsymmetry: because of invariance under the C₄ rotation group and parity in bothx andy, one can slash the size of the basis set by a factor of eight and reduce the CPU time by three orders of magnitude. Second, the pseudospectral method is ananalytical as well as a numerical tool: the simple approximation3.2A exp(–0.64A), whereA is the maximum value ofu(x, y), is derived via collocation with but a single interpolation point, but is quantitatively accurate for small and moderateA. Third, the Newton-Kantorovich/Chebyshev pseudospectral algorithm is so efficient that it is possible to compute good numerical solutions—five decimal places—on amicrocomputer inbasic. Fourth, asymptotic estimates of the Chebyshev coefficients can be very misleading: the coefficients for moderately or strongly nonlinear solutions to Bratu's equations fall off exponentially rather than algebraically withv untilv is so large that one has already obtained several decimal places of accuracy. The corner singularities, which dominate the behavior of the Chebyshev coefficients in thelimit v, are so weak as to be irrelevant, and replacing Bratu's problem by a more complicated and realistic equation would merely exaggerate the unimportance of the corner branch points even more.     

Incorporating Machine Learning into Established Bioinformatics Frameworks

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.