Global spatial optimization with hydrological systems simulation: appliication to land-use allocation and peak runoff minimization

A general methodology is presented to integrate complex simulation models of hydrological systems into optimization models, as an alternative to scenario-based approaches. A gradient-based hill climbing algorithm is proposed to reach locally optimal solutions from distinct starting points. The gradient of the objective function is estimated numerically with the simulation model. A statistical procedure based on the Weibull distribution is used to build a confidence interval for the global optimum. The methodology is illustrated by an application to a small watershed in Ohio, where the decision variables are related to land-use allocations and the objective is to minimize peak runoff. The results suggest that this specific runoff function is convex in terms of the land-use variables, and that the global optimum has been reached. Modeling extensions and areas for further research are discussed

Variational Level Set Segmentation using Shape Prior

SUMMARY We proposed a new level set segmentation model with statistical shape prior using a variational approach. The image attraction force is derived from the interactions of gradient vectors across the whole image domain. This gives the active contour a global representation of the geometric configuration, making it more robust to image noise, weak edges and initial configurations. Statistical shape information is incorporated using a nonparametric technique is used to model the shape distribution, which allows the model to handle relatively large shape variations

The position profiles of order cancellations in an emerging stock market

Order submission and cancellation are two constituent actions of stock trading behaviors in order-driven markets. Order submission dynamics has been extensively studied for different markets, while order cancellation dynamics is less understood. There are two positions associated with a cancellation, that is, the price level in the limit-order book (LOB) and the position in the queue at each price level. We study the profiles of these two order cancellation positions through rebuilding the limit-order book using the order flow data of 23 liquid stocks traded on the Shenzhen Stock Exchange in the year 2003. We find that the profiles of relative price levels where cancellations occur obey a log-normal distribution. After normalizing the relative price level by removing the factor of order numbers stored at the price level, we find that the profiles exhibit a power-law scaling behavior on the right tails for both buy and sell orders. When focusing on the order cancellation positions in the queue at each price level, we find that the profiles increase rapidly in the front of the queue, and then fluctuate around a constant value till the end of the queue. These profiles are similar for different stocks. In addition, the profiles of cancellation positions can be fitted by an exponent function for both buy and sell orders. These two kinds of cancellation profiles seem universal for different stocks investigated and exhibit minor asymmetry between buy and sell orders. Our empirical findings shed new light on the order cancellation dynamics and pose constraints on the construction of order-driven stock market models.Comment: 17 pages, 6 figures and 6 table

Trends in Tissue Engineering for Blood Vessels

Over the years, cardiovascular diseases continue to increase and affect not only human health but also the economic stability worldwide. The advancement in tissue engineering is contributing a lot in dealing with this immediate need of alleviating human health. Blood vessel diseases are considered as major cardiovascular health problems. Although blood vessel transplantation is the most convenient treatment, it has been delimited due to scarcity of donors and the patient's conditions. However, tissue-engineered blood vessels are promising alternatives as mode of treatment for blood vessel defects. The purpose of this paper is to show the importance of the advancement on biofabrication technology for treatment of soft tissue defects particularly for vascular tissues. This will also provide an overview and update on the current status of tissue reconstruction especially from autologous stem cells, scaffolds, and scaffold-free cellular transplantable constructs. The discussion of this paper will be focused on the historical view of cardiovascular tissue engineering and stem cell biology. The representative studies featured in this paper are limited within the last decade in order to trace the trend and evolution of techniques for blood vessel tissue engineering

Liquid-to-liquid phase transition in pancake vortex systems

We study the thermodynamics of a model of pancake vortices in layered superconductors. The model is based on the effective pair potential for the pancake vortices derived from the London approximation of a version of the Lawrence-Doniach model which is valid for extreme type-II superconductors. Using the hypernetted-chain (HNC) approximation, we find that there is a temperature below which multiple solutions to the HNC equations exist. By explicitly evaluating the free energy for each solution we find that the system undergoes a first-order transition between two vortex liquid phases. The low-temperature phase has larger correlations along the field direction than the high-temperature phase. We discuss the possible relation of this phase transition to the liquid-to-liquid phase transition recently observed in Y-Ba-Cu-O superconductors in high magnetic fields in the presence of disorder.Comment: 7 pages, 6 figure

First order transition from correlated electron semiconductor to ferromagnetic metal in single crystalline FeSi1-xGex

The phase diagram of FeSi1-xGex, obtained from magnetic, thermal and transport measurements on single crystals, shows a first-order transition from a correlated electron semiconductor to a ferromagnetic metal at a critical concentration, x ~ 0.25. The gap of the insulating phase strongly decreases with x. The specific heat coefficient appears to track the density of states of a Kondo insulator. The phase diagram is consistent with a correlation induced insulator-metal transition in conjunction with disorder on the Si/Ge ligand site

Parquet Graph Resummation Method for Vortex Liquids

We present in detail a nonperturbative method for vortex liquid systems. This method is based on the resummation of an infinite subset of Feynman diagrams, the so-called parquet graphs, contributing to the four-point vertex function of the Ginzburg-Landau model for a superconductor in a magnetic field. We derive a set of coupled integral equations, the parquet equations, governing the structure factor of the two-dimensional vortex liquid system with and without random impurities and the three-dimensional system in the absence of disorder. For the pure two-dimensional system, we simplify the parquet equations considerably and obtain one simple equation for the structure factor. In two dimensions, we solve the parquet equations numerically and find growing translational order characterized by a length scale $R_c$ as the temperature is lowered. The temperature dependence of $R_c$ is obtained in both pure and weakly disordered cases. The effect of disorder appears as a smooth decrease of $R_c$ as the strength of disorder increases.Comment: 15 pages, 12 PostScript figures, uses multicols.sty and epsf.st

Nonparametric nonlinear model predictive control

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC

Slow Control Systems of the Reactor Experiment for Neutrino Oscillation

The RENO experiment has been in operation since August 2011 to measure reactor antineutrino disappearance using identical near and far detectors. For accurate measurements of neutrino mixing parameters and efficient data taking, it is crucial to monitor and control the detector in real time. Environmental conditions also need to be monitored for stable operation of detectors as well as for safety reasons. In this article, we report the design, hardware, operation, and performance of the slow control system