高寒草甸地区陆面过程观测及耦合模式研究

对海北高寒草甸地区水热传输过程进行了系统观测,特别考虑了叶片气孔为非饱和水汽条件下的交换情况,结合修正的根系吸水公式,发展了一个多层陆气耦合模式．利用该模式对中国科学院海北高寒草甸生态试验站地区矮嵩草草甸陆气水热交换进行了数值模拟,分析了湍流交换的物理过程,给出了沿高度分布的各物理量．模拟结果与实测值吻合较好．     

芳纶纤维平纹织布在爆炸载荷下的动态响应与失效行为的数值分析

为了研究柔性纤维织布的抗爆性能,通过数值模拟的方法对芳纶纤维织布在爆炸冲击下的响应与失效行为进行了分析。对国产芳纶纤维织布H1000D-AP220进行了力学性能试验,建立了柔性平纹织布的本构模型和爆炸冲击数值分析模型,对不同厚度不同铺层角度织布进行了爆炸冲击数值分析,获得了织布在不同爆炸载荷下的动态响应和失效模式。结果表明,织布在爆炸冲击载荷下主要表现为中心撕裂破孔和简支边界处拉伸撕裂2种典型的失效模式,并伴有明显褶皱,宽度方向出现织布向内侧收缩翻转现象;相比于中间层织布,迎爆面和背爆面吸能较多;在计算分析中改变了织布的层叠角度,获得了更好的抗爆效果。     

百强企业内审之策

经济责任审计的目标不仅注重真实陆和合法性,更应该注重效益陆,案例企业内审的具体操作与实施效果,为经济责任审计的实施与走向做出了分析。     

海-气耦合气候系统非线性扰动模式的周期正解

与ENSO相关的热带大尺度海-气相互作用是影响全球气候年际变化的主要过程之一.该文从一个海-气相互作用方程组出发,推广了一个具有一般形式的海 气耦合气候系统非线性扰动模式.运用拓扑度理论,从数学上严格证明了一定条件下该模式存在周期正解的结果,并分析了所得结果潜在的应用价值.海-气相互作用研究,有助于理解气候变化过程,为气候模拟和预报提供理论基础.     

碳纤维/树脂基复合材料曲壁蜂窝夹芯结构的三点弯曲性能北大核心CSCD

为研究碳纤维复合材料(CFRP)曲壁蜂窝结构在三点弯曲载荷作用下的承载特性与失效模式,对不同芯层高度、面板厚度的结构进行了理论预报、数值模拟及试验.首先,根据夹芯结构的主要失效模式,提出了相应的理论预报公式,并绘制了失效机制图;其次,建立了CFRP曲壁蜂窝夹芯结构的有限元仿真模型,对其在三点弯曲载荷作用下的典型失效行为进行模拟;最后,通过模压成型工艺制备了不同尺寸的CFRP曲壁蜂窝夹芯结构,并将试验结果与理论、模拟结果进行比较.结果表明,蜂窝夹芯结构承载能力与芯层高度、面板厚度密切相关,结构芯层及面板刚度随其尺寸的减小而下降,导致结构失效模式由面芯脱黏失效变为面板压溃失效.     

刚性挡墙绕底转动时的非极限主动土压力数值解北大核心CSCD

刚性挡土墙在下部受限时往往呈现绕墙底转动的位移模式,该模式下不同深度土体所处非极限状态不同,给土压力计算带来了困难.在已有研究基础上,推导了适用于绕墙底转动模式下土体强度参数与墙体位移的函数关系;假定墙后土体形成圆弧形土拱,滑裂面为不确定的曲面,将墙后土体按小步长水平分层,构建了绕墙底转动模式下非极限主动土压力的数值迭代格式,给出了该模式下非极限主动土压力的数值计算方法.该数值解既能确定墙后滑裂面的形状,又能计算非极限主动土压力的强度、合力及作用点.将数值解与模型试验结果、现有解析解进行了对比,发现墙后滑裂面为一曲面,该解计算结果与模型试验结果的契合度比现有解析解更高.这提供了刚性挡土墙绕底转动时非极限主动土压力的更精确解答,对这类挡土墙设计具有现实指导价值.     

不同索力斜拉索的主共振瞬时相频特性

考虑拉索垂度及几何非线性,研究了不同索力拉索的瞬时相频特性。利用斜拉索面内分布激励下的运动控制方程,采用多尺度法对微分方程进行摄动求解,分别得到面内、外主共振响应的近似解析式,再采用Hilbert变换得到响应与激励的瞬时相位差及其幅值。研究了不同索力下,响应与激励的瞬时相位差的变化规律及其原因。研究表明:面外主共振响应与激励间保持恒定的相位差,而面内响应与激励的瞬时相位差与索弹性参数和垂度等有关,微小的索力变化可能导致瞬时相频特性的明显改变。主要原因是面内响应的近似解中存在两倍频项和漂移项,前者使响应瞬时相位在单个周期内出现两次正负交替,后者决定面内响应与激励瞬时相位差的最大值及其变化规律。     

软土地基固结变形的弹塑性分析

本文是十年来作者用有限单元法研究软土地基的固结和变形的耦合问题的总结。 第一部分介绍了作者提议的双屈服面弹塑性模式及相应的计算参数测定方法,第二部分叙述该模式在实际工程中的应用并与其它模式对比的情况。根据5个工程的计算与实测比较,该模式的计算沉降量在实测值的75—110％之间,计算孔隙压力也与实测值大体接近。相比之下,用英国剑桥模式计算的沉降量显著偏小,而用简单的弹性模式计算的沉降量则往往偏大。     

存在内共振的覆冰四分裂导线的非线性舞动

采用非线性有限元方法模拟研究存在内共振的覆冰四分裂导线的非线性舞动.通过稳定风场和随机风场中典型覆冰四分裂线路舞动过程的数值模拟,研究当覆冰四分裂导线的对称面内模态频率与面外模态频率之比接近于2∶1,即存在内共振条件时,导线的舞动特征.结果表明,存在内共振的覆冰四分裂导线在舞动过程中,其能量在竖直面内运动和横向水平面外运动之间不断交换,与不存在内共振线路的舞动特征差别明显.研究结果对舞动耦合机理的理解具有重要的理论意义.     

原油价格时间序列自回归子模式传输特征分析

为了更好地分析原油现货价格的波动规律,文章以大庆原油日现货价时间序列为研究对象,把计量经济模型与复杂网络方法结合起来,定量地定义自回归模式,运用时间滑动窗的思想设置合理的窗体长度和步长,将时间序列划分为多个子模块,建立了多个自回归子模式.将自回归子模式设置为节点,各子模式之间的传输设置为边,建立自回归子模式传输复杂网络,利用复杂网络的特征与性质,研究大庆原油现货价格时间序列传输特性.文章发现少数自回归子模式和传输模式驱动大庆原油现货价时间序列的振荡,在传输过程中出现对波动的聚类效应,并且某些非主要自回归子模式在原油时间序列中具有高中介能力等.这项研究不仅为分析原油价格时间序列提出了独特的视角,而且为投资者提供了重要信息.     

干旱区湖泊水质预测的数学模型

本文建立了干旱区湖泊水质的非线性数学模型 ,我们考虑了湖水的强烈蒸发因素 ,给出了适合干旱区的二维浅水湖的运动学方程组和污染物浓度场的扩散方程 ,作为水质分析的数学模型 ,对博斯腾湖两个典型水文年的矿化度浓度场进行了模拟计算 ,结果与实测值基本吻合 ,从而验证了该模型的准确性与可扩展性 .     

THE USE OF TELESCOPING SPATIAL SCALES TO CAPTURE INSHORE TO SLOPE DYNAMICS IN MARINE ECOSYSTEM MODELING

Abstract Ecosystem processes function at many scales, and capturing these processes is a challenge for ecosystem models. Nevertheless, it is a necessary step for considering many management issues pertaining to shelf and coastal systems. In this paper, we explore one method of modeling large areas with a focus at a range of scales. We develop an ecosystem model that can be used for strategic management decision support by modeling the waters off southeastern Australia using a polygon telescoping approach, which incorporates fine‐scale detail at the coastal zone, increasing in scale to a very coarse scale in the offshore areas. This telescoping technique is a useful tool for incorporating a wide range of habitats at different scales into a single model.     

种内竞争时滞对植被周期振荡模式的影响研究

考虑到干旱、半干旱地区的幼年植被与成年植被之间存在竞争水资源的现象,该文构建一个具有种内竞争时滞的植被-土壤水动力学模型。分析出系统存在植被灭绝平衡点和植被生存平衡点,并给出了平衡点局部稳定的条件,分别给出非空间系统和空间系统产生Hopf分支周期解的条件。通过数值模拟展示出两种系统对应的植被随时间演化做周期振荡模式,并通过参数敏感性分析发现降雨量和植被的增长率对这种模式的产生和模式的振幅、周期有显著影响,但蒸发量的影响效果最不显著,表明降雨量和植被本身的特征对干旱、半干旱地区植被的演化发展产生了深刻的影响。同时发现空间扩散的引入会抑制这种模式的发生,但对振幅和周期没有任何影响。所获得的结果解释了自然界中广泛观察到的植被周期振荡现象,为植被系统的可持续发展提供了理论支撑。     

Neural network modeling of hydrological systems: A review of implementation techniques

This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers

• Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies.

• Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems.

• More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.

     

Sharpness versus robustness of the percolation transition in 2d contact processes

We study versions of the contact process with three states, and with infections occurring at a rate depending on the overall infection density. Motivated by a model described in Kéfi et al. (2007) for vegetation patterns in arid landscapes, we focus on percolation under invariant measures of such processes. We prove that the percolation transition is sharp (for one of our models this requires a reasonable assumption). This is shown to contradict a form of ‘robust critical behaviour’ with power law cluster size distribution for a range of parameter values, as suggested in Kéfi et al. (2007).     

Ornstein–Uhlenbeck processes time changed with additive subordinators and their applications in commodity derivative models

We characterize Ornstein–Uhlenbeck processes time changed with additive subordinators as time-inhomogeneous Markov semimartingales, based on which a new class of commodity derivative models is developed. Our models are tractable for pricing European, Bermudan and American futures options. Calibration examples show that they can be better alternatives than those developed in Li and Linetsky (2012)  [6]. Our method can be applied to many other processes popular in various areas besides finance to develop time-inhomogeneous Markov processes with desirable features and tractability.     

市场化、城镇化与城乡收入差距——基于空间动态面板模型的实证分析

采用1997-2007年我国城乡收入差距的省际面板数据,通过构建空间动态模型,采用SCBB估计方法,分析了市场化和城镇化进程对城乡收入差距的影响效应.实证结果表明,现阶段城镇化、市场化有利于缩小城乡收入差距,对缩小城乡收入差距的作用明显.研究结论认为市场化、城镇化对缩小城乡收入差距有积极意义,相邻省份城乡收入差距在空间关系作用下有趋同发展之势.在实证研究基础上本文对十八大以来政府主导城镇化进程提出方向性政策建议.     

Comparison of combined stochastic risk processes and its applications

In this article we compare combined stochastic risk processes and consider its applications to various fields of relevance. Initially, the problem is formulated in terms of optimal transportation under fatal risks which may cause the failure of the transportation. Various transportation policies are considered and the problem of determining optimal policy maximizing the probability of success of transportation is suggested. Then the suggested problem is reformulated in the context of reliability modelling under more general settings and the main results are derived. Applications of the results to many different areas are discussed.     

Nonlinear Dynamical Systems and Adaptive Filters in Biomedicine

In this paper we present the application of a method of adaptive estimation using an algebra–geometric approach, to the study of dynamic processes in the brain. It is assumed that the brain dynamic processes can be described by nonlinear or bilinear lattice models. Our research focuses on the development of an estimation algorithm for a signal process in the lattice models with background additive white noise, and with different assumptions regarding the characteristics of the signal process. We analyze the estimation algorithm and implement it as a stochastic differential equation under the assumption that the Lie algebra, associated with the signal process, can be reduced to a finite dimensional nilpotent algebra. A generalization is given for the case of lattice models, which belong to a class of causal lattices with certain restrictions on input and output signals. The application of adaptive filters for state estimation of the CA3 region of the hippocampus (a common location of the epileptic focus) is discussed. Our areas of application involve two problems: (1) an adaptive estimation of state variables of the hippocampal network, and (2) space identification of the coupled ordinary equation lattice model for the CA3 region.     

Individual-based modelling and simulation of microbial processes: yeast fermentation and multi-species composting

Controlled microbial activity is the core of many industrial processes. Such dynamic microbial processes must be carefully studied to optimize their application. They are usually tackled by means of continuous mathematical modelling at the population level (top-down). An alternative approach is individual-based modelling (IbM) (bottom-up). INDISIM is a discrete and spatially explicit IbM. It sets the rules that govern each microbe and its interaction with its local environment, as well as the significant environmental processes. Then it performs simulations that include a large number of microbes, and the behaviour of the whole system emerges. The rules are changed to reproduce the behaviour of microbes depending on the system to be studied. Two adaptations of INDISIM to study yeast fermentations and multi-species composting are presented in this article (INDISIM-YEAST and INDISIM-COMP), proof of INDISIM's versatility. A few representative results are also shown.