劳动力供给带弹性的Solow模型

本文给出了在劳动力供给带弹性条件下的Ｓｏ１ｏｗ增长模型，给出了经济增长的黄金律和均衡处资本稳定性的证明，并且与不带弹性的Ｓｏｌｏｗ模型作了比较．     

Comparison between Eulerian and Lagrangian semi-analytical models to simulate the pollutant dispersion in the PBL

In this work we display a numerical comparison, under statistical and computational point of view, between semi-analytical Eulerian and Lagrangian dispersion models to simulate the ground-level concentration values of a passive pollutant released from a low height source. The Eulerian approach is based on the solution of the advection–diffusion equation by the Laplace transform technique. The Lagrangian approach is based on solution of the Langevin equation through the Picard’s Iterative Method. Turbulence inputs are calculated according to a parameterization capable of generating continuous values in all stability conditions and in all heights of the Planetary Boundary Layer (PBL). Numerical simulations and comparisons show a good agreement between predicted and observed concentrations values. The comparison reveals the main advantages and disadvantages between the models.     

SDP diagonalizations and perspective cuts for a class of nonseparable MIQP

We present a new approach, requiring the solution of a SemiDefinite Program, for decomposing the Hessian of a nonseparable mixed-integer quadratic problem to permit using perspective cuts to improve its continuous relaxation bound. The new method favorably compares with a previously proposed one requiring a minimum eigenvalue computation.     

波罗的海运价指数与样本航线期租的协整关系研究

波罗的海运价指数BD I是综合反应干散货航运市场运价总水平的指标,在众多的运价指数中占有公认的主导地位。本文以BD I运价指数为研究对象,通过协整研究,证明了BD I运价指数与BC I4条样本航线期租价格之间稳定的联动关系,并且给出了它们的误差修正模型。     

40,48Ca+90,96Zr融合反应的动力学模型研究

应用改进的量子分子动力学模型,在严格挑选初始核考虑弹靶结构效应的基础上,研究了近垒和垒上融合反应^40,48Ca+^90,96Zr. 研究表明: 4个反应的理论计算截面与实验值很好符合; 丰中子反应⁴⁰Ca+⁹⁶Zr的垒下融合截面比其他3个反应有明显增强的现象.为了理解丰中子反应⁴⁰Ca+⁹⁶Zr与⁴⁰Ca+⁹⁰Zr相比垒下融合截面增强,而Ca+⁹⁶Zr垒下融合截面没有明显增强的原因, 进一步分析了484个反应的融合位垒,中子转移与融合位垒的关系、中子转移与Q值的关系,结果表明: 正反应Q值会引起核子(特别是中子)转移的增强,从而导致动力学融合位垒的下降和垒下融合截面增强.     

Entropic criterion for model selection

Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback–Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why use this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficult to get. Following the logic of inductive inference proposed by Caticha [Relative entropy and inductive inference, in: G. Erickson, Y. Zhai (Eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings, vol. 707, 2004 (available from arXiv.org/abs/physics/0311093)], we show relative entropy to be a unique criterion, which requires no prior information and can be applied to different fields. We examine this criterion by considering a physical problem, simple fluids, and results are promising.     

Electroencephalogram (EEG) time series classification: Applications in epilepsy

Epilepsy is among the most common brain disorders. Approximately 25–30% of epilepsy patients remain unresponsive to anti-epileptic drug treatment, which is the standard therapy for epilepsy. In this study, we apply optimization-based data mining techniques to classify the brain's normal and epilepsy activity using intracranial electroencephalogram (EEG), which is a tool for evaluating the physiological state of the brain. A statistical cross validation and support vector machines were implemented to classify the brain's normal and abnormal activities. The results of this study indicate that it may be possible to design and develop efficient seizure warning algorithms for diagnostic and therapeutic purposes. Research was partially supported by the Rutgers Research Council grant-202018, the NSF grants DBI-980821, CCF-0546574, IIS-0611998, and NIH grant R01-NS-39687-01A1.     

激光对生物分子的共振激发作用

在生物分子的“谐”和“非谐”振子势模型的基础上，用量子力学讨论了激光对生物分子的共振激作用；并用之对激光的生物激活、诱变机理的“共振吸收”定性理论作一解析分析。     

An evolutionary method for optimization of plate buckling resistance

Optimization of plate buckling resistance is very complicated, because the in-plane stress resultants in the prebuckled state of a plate are functions of thickness distribution. This paper discusses the problem of finding the optimum thickness distribution of isotropic plate structures, with a given volume and layout, that would maximise the buckling load. A simple numerical method using the finite-element analysis is presented to obtain the optimum thickness distribution. Optimum designs of compression-loaded rectangular plates with different boundary conditions and plate aspect ratios are obtained by using the proposed method. Optimum designs from earlier studies and the methods of buckling analysis used to attain these results are discussed and compared with the designs from the proposed method. This paper also examines the reliability of the optimality criterion generally used for plate buckling optimization, which is based on the uniform strain energy density.     

Optimization technique based on learning automata

Optimization techniques are finding increasingly numerous applications in process design, in parallel to the increase of computer sophistication. The process synthesis problem can be stated as a largescale constrained optimization problem involving numerous local optima and presenting a nonlinear and nonconvex character. To solve this kind of problem, the classical optimization methods can lead to analytical and numerical difficulties. This paper describes the feasibility of an optimization technique based on learning systems which can take into consideration all the prior information concerning the process to be optimized and improve their behavior with time. This information generally occurs in a very complex analytical, empirical, or know-how form. Computer simulations related to chemical engineering problems (benzene chlorination, distillation sequence) and numerical examples are presented. The results illustrate both the performance and the implementation simplicity of this method.Nomenclature c _i penalty probability - cp precision parameter on constraints - D variation domain of the variablex - f(·) objective function - g(·) constraints - i,j indexes - k iteration number - N number of actions - P probability distribution vector - p _i ith component of the vectorP as iterationk - r number of reactors in the flowsheet - u(k) discrete value or action chosen by the algorithm at iterationk - u _i discrete value of the optimization variable in [u _min,u _max] - u _min lowest value of the optimization variable - u _max largest value of the optimization variable - Z random number - x variable for the criterion function - xp precision parameter on criterion function - W(k) performance index unit output at iterationk - ₀, ₁ reinforcement scheme parameters - _p sum of the probability distribution vector components