Stochastic Averaging for Constrained Optimization With Application to Online Resource Allocation

Science.gov (United States)

Chen, Tianyi; Mokhtari, Aryan; Wang, Xin; Ribeiro, Alejandro; Giannakis, Georgios B.

2017-06-01

Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the underlying constrained optimization problem is formulated as a machine learning task involving both training and operational modes, with the goal of learning the sought multipliers in a fast and efficient manner. To this end, an order-optimal offline learning approach is developed first for batch training, and it is then generalized to the online setting with a procedure termed learn-and-adapt. The novel resource allocation protocol permeates benefits of stochastic approximation and statistical learning to obtain low-complexity online updates with learning errors close to the statistical accuracy limits, while still preserving adaptation performance, which in the stochastic network optimization context guarantees queue stability. Analysis and simulated tests demonstrate that the proposed data-driven approach improves the delay and convergence performance of existing resource allocation schemes.

Topology Optimization for Minimizing the Resonant Response of Plates with Constrained Layer Damping Treatment

Directory of Open Access Journals (Sweden)

Zhanpeng Fang

2015-01-01

Full Text Available A topology optimization method is proposed to minimize the resonant response of plates with constrained layer damping (CLD treatment under specified broadband harmonic excitations. The topology optimization problem is formulated and the square of displacement resonant response in frequency domain at the specified point is considered as the objective function. Two sensitivity analysis methods are investigated and discussed. The derivative of modal damp ratio is not considered in the conventional sensitivity analysis method. An improved sensitivity analysis method considering the derivative of modal damp ratio is developed to improve the computational accuracy of the sensitivity. The evolutionary structural optimization (ESO method is used to search the optimal layout of CLD material on plates. Numerical examples and experimental results show that the optimal layout of CLD treatment on the plate from the proposed topology optimization using the conventional sensitivity analysis or the improved sensitivity analysis can reduce the displacement resonant response. However, the optimization method using the improved sensitivity analysis can produce a higher modal damping ratio than that using the conventional sensitivity analysis and develop a smaller displacement resonant response.

Neuroevolutionary Constrained Optimization for Content Creation

DEFF Research Database (Denmark)

Liapis, Antonios; Yannakakis, Georgios N.; Togelius, Julian

2011-01-01

and thruster types and topologies) independently of game physics and steering strategies. According to the proposed framework, the designer picks a set of requirements for the spaceship that a constrained optimizer attempts to satisfy. The constraint satisfaction approach followed is based on neuroevolution...... and survival tasks and are also visually appealing....

Quasicanonical structure of optimal control in constrained discrete systems

Science.gov (United States)

Sieniutycz, S.

2003-06-01

This paper considers discrete processes governed by difference rather than differential equations for the state transformation. The basic question asked is if and when Hamiltonian canonical structures are possible in optimal discrete systems. Considering constrained discrete control, general optimization algorithms are derived that constitute suitable theoretical and computational tools when evaluating extremum properties of constrained physical models. The mathematical basis of the general theory is the Bellman method of dynamic programming (DP) and its extension in the form of the so-called Carathéodory-Boltyanski (CB) stage criterion which allows a variation of the terminal state that is otherwise fixed in the Bellman's method. Two relatively unknown, powerful optimization algorithms are obtained: an unconventional discrete formalism of optimization based on a Hamiltonian for multistage systems with unconstrained intervals of holdup time, and the time interval constrained extension of the formalism. These results are general; namely, one arrives at: the discrete canonical Hamilton equations, maximum principles, and (at the continuous limit of processes with free intervals of time) the classical Hamilton-Jacobi theory along with all basic results of variational calculus. Vast spectrum of applications of the theory is briefly discussed.

Effective Teaching of Economics: A Constrained Optimization Problem?

Science.gov (United States)

Hultberg, Patrik T.; Calonge, David Santandreu

2017-01-01

One of the fundamental tenets of economics is that decisions are often the result of optimization problems subject to resource constraints. Consumers optimize utility, subject to constraints imposed by prices and income. As economics faculty, instructors attempt to maximize student learning while being constrained by their own and students'…

Selection of magnetorheological brake types via optimal design considering maximum torque and constrained volume

International Nuclear Information System (INIS)

Nguyen, Q H; Choi, S B

2012-01-01

This research focuses on optimal design of different types of magnetorheological brakes (MRBs), from which an optimal selection of MRB types is identified. In the optimization, common types of MRB such as disc-type, drum-type, hybrid-types, and T-shaped type are considered. The optimization problem is to find the optimal value of significant geometric dimensions of the MRB that can produce a maximum braking torque. The MRB is constrained in a cylindrical volume of a specific radius and length. After a brief description of the configuration of MRB types, the braking torques of the MRBs are derived based on the Herschel–Bulkley model of the MR fluid. The optimal design of MRBs constrained in a specific cylindrical volume is then analysed. The objective of the optimization is to maximize the braking torque while the torque ratio (the ratio of maximum braking torque and the zero-field friction torque) is constrained to be greater than a certain value. A finite element analysis integrated with an optimization tool is employed to obtain optimal solutions of the MRBs. Optimal solutions of MRBs constrained in different volumes are obtained based on the proposed optimization procedure. From the results, discussions on the optimal selection of MRB types depending on constrained volumes are given. (paper)

Thermally-Constrained Fuel-Optimal ISS Maneuvers

Science.gov (United States)

Bhatt, Sagar; Svecz, Andrew; Alaniz, Abran; Jang, Jiann-Woei; Nguyen, Louis; Spanos, Pol

2015-01-01

Optimal Propellant Maneuvers (OPMs) are now being used to rotate the International Space Station (ISS) and have saved hundreds of kilograms of propellant over the last two years. The savings are achieved by commanding the ISS to follow a pre-planned attitude trajectory optimized to take advantage of environmental torques. The trajectory is obtained by solving an optimal control problem. Prior to use on orbit, OPM trajectories are screened to ensure a static sun vector (SSV) does not occur during the maneuver. The SSV is an indicator that the ISS hardware temperatures may exceed thermal limits, causing damage to the components. In this paper, thermally-constrained fuel-optimal trajectories are presented that avoid an SSV and can be used throughout the year while still reducing propellant consumption significantly.

Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems

Directory of Open Access Journals (Sweden)

Minggang Dong

2014-01-01

Full Text Available Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE for constrained optimization problems (COPs. More specifically, the original oracle penalty function approach is modified so as to satisfy the optimization criterion of COPs; then the modified oracle penalty function is incorporated in composite DE. Furthermore, in order to solve more complex COPs with discrete, integer, or binary variables, a discrete variable handling technique is introduced into MOCoDE to solve complex COPs with mix variables. This method is assessed on eleven constrained optimization benchmark functions and seven well-studied engineering problems in real life. Experimental results demonstrate that MOCoDE achieves competitive performance with respect to some other state-of-the-art approaches in constrained optimization evolutionary algorithms. Moreover, the strengths of the proposed method include few parameters and its ease of implementation, rendering it applicable to real life. Therefore, MOCoDE can be an efficient alternative to solving constrained optimization problems.

Optimization of an implicit constrained multi-physics system for motor wheels of electric vehicle

International Nuclear Information System (INIS)

Lei, Fei; Du, Bin; Liu, Xin; Xie, Xiaoping; Chai, Tian

2016-01-01

In this paper, implicit constrained multi-physics model of a motor wheel for an electric vehicle is built and then optimized. A novel optimization approach is proposed to solve the compliance problem between implicit constraints and stochastic global optimization. Firstly, multi-physics model of motor wheel is built from the theories of structural mechanics, electromagnetism and thermal physics. Then, implicit constraints are applied from the vehicle performances and magnetic characteristics. Implicit constrained optimization is carried out by a series of unconstrained optimization and verifications. In practice, sequentially updated subspaces are designed to completely substitute the original design space in local areas. In each subspace, a solution is obtained and is then verified by the implicit constraints. Optimal solutions which satisfy the implicit constraints are accepted as final candidates. The final global optimal solution is optimized from those candidates. Discussions are carried out to discover the differences between optimal solutions with unconstrained problem and different implicit constrained problems. Results show that the implicit constraints have significant influences on the optimal solution and the proposed approach is effective in finding the optimals. - Highlights: • An implicit constrained multi-physics model is built for sizing a motor wheel. • Vehicle dynamic performances are applied as implicit constraints for nonlinear system. • An efficient novel optimization is proposed to explore the constrained design space. • The motor wheel is optimized to achieve maximum efficiency on vehicle dynamics. • Influences of implicit constraints on vehicle performances are compared and analyzed.

Chance-constrained optimization of demand response to price signals

DEFF Research Database (Denmark)

Dorini, Gianluca Fabio; Pinson, Pierre; Madsen, Henrik

2013-01-01

within a recursive least squares (RLS) framework using data measurable at the grid level, in an adaptive fashion. Optimal price signals are generated by embedding the FIR models within a chance-constrained optimization framework. The objective is to keep the price signal as unchanged as possible from...

Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization

Directory of Open Access Journals (Sweden)

Weishang Gao

2013-01-01

Full Text Available Evolutionary algorithms (EAs were shown to be effective for complex constrained optimization problems. However, inflexible exploration-exploitation and improper penalty in EAs with penalty function would lead to losing the global optimum nearby or on the constrained boundary. To determine an appropriate penalty coefficient is also difficult in most studies. In this paper, we propose a bidirectional dynamic diversity evolutionary algorithm (Bi-DDEA with multiagents guiding exploration-exploitation through local extrema to the global optimum in suitable steps. In Bi-DDEA potential advantage is detected by three kinds of agents. The scale and the density of agents will change dynamically according to the emerging of potential optimal area, which play an important role of flexible exploration-exploitation. Meanwhile, a novel double optimum estimation strategy with objective fitness and penalty fitness is suggested to compute, respectively, the dominance trend of agents in feasible region and forbidden region. This bidirectional evolving with multiagents can not only effectively avoid the problem of determining penalty coefficient but also quickly converge to the global optimum nearby or on the constrained boundary. By examining the rapidity and veracity of Bi-DDEA across benchmark functions, the proposed method is shown to be effective.

Dynamic Convex Duality in Constrained Utility Maximization

OpenAIRE

Li, Yusong; Zheng, Harry

2016-01-01

In this paper, we study a constrained utility maximization problem following the convex duality approach. After formulating the primal and dual problems, we construct the necessary and sufficient conditions for both the primal and dual problems in terms of FBSDEs plus additional conditions. Such formulation then allows us to explicitly characterize the primal optimal control as a function of the adjoint process coming from the dual FBSDEs in a dynamic fashion and vice versa. Moreover, we also...

Optimal Power Constrained Distributed Detection over a Noisy Multiaccess Channel

Directory of Open Access Journals (Sweden)

Zhiwen Hu

2015-01-01

Full Text Available The problem of optimal power constrained distributed detection over a noisy multiaccess channel (MAC is addressed. Under local power constraints, we define the transformation function for sensor to realize the mapping from local decision to transmitted waveform. The deflection coefficient maximization (DCM is used to optimize the performance of power constrained fusion system. Using optimality conditions, we derive the closed-form solution to the considered problem. Monte Carlo simulations are carried out to evaluate the performance of the proposed new method. Simulation results show that the proposed method could significantly improve the detection performance of the fusion system with low signal-to-noise ratio (SNR. We also show that the proposed new method has a robust detection performance for broad SNR region.

A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.

Science.gov (United States)

Qin, Sitian; Feng, Jiqiang; Song, Jiahui; Wen, Xingnan; Xu, Chen

2018-03-01

In this paper, based on calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network.

A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

Directory of Open Access Journals (Sweden)

R. Venkata Rao

2014-01-01

Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

Volume-constrained optimization of magnetorheological and electrorheological valves and dampers

Science.gov (United States)

Rosenfeld, Nicholas C.; Wereley, Norman M.

2004-12-01

This paper presents a case study of magnetorheological (MR) and electrorheological (ER) valve design within a constrained cylindrical volume. The primary purpose of this study is to establish general design guidelines for volume-constrained MR valves. Additionally, this study compares the performance of volume-constrained MR valves against similarly constrained ER valves. Starting from basic design guidelines for an MR valve, a method for constructing candidate volume-constrained valve geometries is presented. A magnetic FEM program is then used to evaluate the magnetic properties of the candidate valves. An optimized MR valve is chosen by evaluating non-dimensional parameters describing the candidate valves' damping performance. A derivation of the non-dimensional damping coefficient for valves with both active and passive volumes is presented to allow comparison of valves with differing proportions of active and passive volumes. The performance of the optimized MR valve is then compared to that of a geometrically similar ER valve using both analytical and numerical techniques. An analytical equation relating the damping performances of geometrically similar MR and ER valves in as a function of fluid yield stresses and relative active fluid volume, and numerical calculations are provided to calculate each valve's damping performance and to validate the analytical calculations.

Solution for state constrained optimal control problems applied to power split control for hybrid vehicles

NARCIS (Netherlands)

Keulen, van T.A.C.; Gillot, J.; Jager, de A.G.; Steinbuch, M.

2014-01-01

This paper presents a numerical solution for scalar state constrained optimal control problems. The algorithm rewrites the constrained optimal control problem as a sequence of unconstrained optimal control problems which can be solved recursively as a two point boundary value problem. The solution

A one-layer recurrent neural network for constrained nonsmooth invex optimization.

Science.gov (United States)

Li, Guocheng; Yan, Zheng; Wang, Jun

2014-02-01

Invexity is an important notion in nonconvex optimization. In this paper, a one-layer recurrent neural network is proposed for solving constrained nonsmooth invex optimization problems, designed based on an exact penalty function method. It is proved herein that any state of the proposed neural network is globally convergent to the optimal solution set of constrained invex optimization problems, with a sufficiently large penalty parameter. In addition, any neural state is globally convergent to the unique optimal solution, provided that the objective function and constraint functions are pseudoconvex. Moreover, any neural state is globally convergent to the feasible region in finite time and stays there thereafter. The lower bounds of the penalty parameter and convergence time are also estimated. Two numerical examples are provided to illustrate the performances of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

Optimization and characterization of liposome formulation by mixture design.

Science.gov (United States)

Maherani, Behnoush; Arab-tehrany, Elmira; Kheirolomoom, Azadeh; Reshetov, Vadzim; Stebe, Marie José; Linder, Michel

2012-02-07

This study presents the application of the mixture design technique to develop an optimal liposome formulation by using the different lipids in type and percentage (DOPC, POPC and DPPC) in liposome composition. Ten lipid mixtures were generated by the simplex-centroid design technique and liposomes were prepared by the extrusion method. Liposomes were characterized with respect to size, phase transition temperature, ζ-potential, lamellarity, fluidity and efficiency in loading calcein. The results were then applied to estimate the coefficients of mixture design model and to find the optimal lipid composition with improved entrapment efficiency, size, transition temperature, fluidity and ζ-potential of liposomes. The response optimization of experiments was the liposome formulation with DOPC: 46%, POPC: 12% and DPPC: 42%. The optimal liposome formulation had an average diameter of 127.5 nm, a phase-transition temperature of 11.43 °C, a ζ-potential of -7.24 mV, fluidity (1/P)(TMA-DPH)((¬)) value of 2.87 and an encapsulation efficiency of 20.24%. The experimental results of characterization of optimal liposome formulation were in good agreement with those predicted by the mixture design technique.

MO-FG-CAMPUS-TeP2-01: A Graph Form ADMM Algorithm for Constrained Quadratic Radiation Treatment Planning

Energy Technology Data Exchange (ETDEWEB)

Liu, X; Belcher, AH; Wiersma, R [The University of Chicago, Chicago, IL (United States)

2016-06-15

Purpose: In radiation therapy optimization the constraints can be either hard constraints which must be satisfied or soft constraints which are included but do not need to be satisfied exactly. Currently the voxel dose constraints are viewed as soft constraints and included as a part of the objective function and approximated as an unconstrained problem. However in some treatment planning cases the constraints should be specified as hard constraints and solved by constrained optimization. The goal of this work is to present a computation efficiency graph form alternating direction method of multipliers (ADMM) algorithm for constrained quadratic treatment planning optimization and compare it with several commonly used algorithms/toolbox. Method: ADMM can be viewed as an attempt to blend the benefits of dual decomposition and augmented Lagrangian methods for constrained optimization. Various proximal operators were first constructed as applicable to quadratic IMRT constrained optimization and the problem was formulated in a graph form of ADMM. A pre-iteration operation for the projection of a point to a graph was also proposed to further accelerate the computation. Result: The graph form ADMM algorithm was tested by the Common Optimization for Radiation Therapy (CORT) dataset including TG119, prostate, liver, and head & neck cases. Both unconstrained and constrained optimization problems were formulated for comparison purposes. All optimizations were solved by LBFGS, IPOPT, Matlab built-in toolbox, CVX (implementing SeDuMi) and Mosek solvers. For unconstrained optimization, it was found that LBFGS performs the best, and it was 3–5 times faster than graph form ADMM. However, for constrained optimization, graph form ADMM was 8 – 100 times faster than the other solvers. Conclusion: A graph form ADMM can be applied to constrained quadratic IMRT optimization. It is more computationally efficient than several other commercial and noncommercial optimizers and it also

Gravitational waves in Fully Constrained Formulation in a dynamical spacetime with matter content

Energy Technology Data Exchange (ETDEWEB)

Cordero-Carrion, Isabel; Cerda-Duran, Pablo [Max-Planck-Institut fuer Astrophysik, Karl-Schwarzschild-Str. 1, D-85741, Garching (Germany); Ibanez, Jose MarIa, E-mail: chabela@mpa-garching.mpg.de, E-mail: cerda@mpa-garching.mpg.de, E-mail: jose.m.ibanez@uv.es [Departamento de AstronomIa y Astrofisica, Universidad de Valencia, C/ Dr. Moliner 50, E-46100 Burjassot, Valencia (Spain)

2011-09-22

We analyze numerically the behaviour of the hyperbolic sector of the Fully Constrained Formulation (FCF) (Bonazzola et al. 2004). The numerical experiments allow us to be confident in the performances of the upgraded version of the CoCoNuT code (Dimmelmeier et al. 2005) by replacing the Conformally Flat Condition (CFC), an approximation of Einstein equations, by FCF. First gravitational waves in FCF in a dynamical spacetime with matter content will be shown.

New Exact Penalty Functions for Nonlinear Constrained Optimization Problems

Directory of Open Access Journals (Sweden)

Bingzhuang Liu

2014-01-01

Full Text Available For two kinds of nonlinear constrained optimization problems, we propose two simple penalty functions, respectively, by augmenting the dimension of the primal problem with a variable that controls the weight of the penalty terms. Both of the penalty functions enjoy improved smoothness. Under mild conditions, it can be proved that our penalty functions are both exact in the sense that local minimizers of the associated penalty problem are precisely the local minimizers of the original constrained problem.

Constrained multi-objective optimization of storage ring lattices

Science.gov (United States)

Husain, Riyasat; Ghodke, A. D.

2018-03-01

The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

A multi-fidelity analysis selection method using a constrained discrete optimization formulation

Science.gov (United States)

Stults, Ian C.

The purpose of this research is to develop a method for selecting the fidelity of contributing analyses in computer simulations. Model uncertainty is a significant component of result validity, yet it is neglected in most conceptual design studies. When it is considered, it is done so in only a limited fashion, and therefore brings the validity of selections made based on these results into question. Neglecting model uncertainty can potentially cause costly redesigns of concepts later in the design process or can even cause program cancellation. Rather than neglecting it, if one were to instead not only realize the model uncertainty in tools being used but also use this information to select the tools for a contributing analysis, studies could be conducted more efficiently and trust in results could be quantified. Methods for performing this are generally not rigorous or traceable, and in many cases the improvement and additional time spent performing enhanced calculations are washed out by less accurate calculations performed downstream. The intent of this research is to resolve this issue by providing a method which will minimize the amount of time spent conducting computer simulations while meeting accuracy and concept resolution requirements for results. In many conceptual design programs, only limited data is available for quantifying model uncertainty. Because of this data sparsity, traditional probabilistic means for quantifying uncertainty should be reconsidered. This research proposes to instead quantify model uncertainty using an evidence theory formulation (also referred to as Dempster-Shafer theory) in lieu of the traditional probabilistic approach. Specific weaknesses in using evidence theory for quantifying model uncertainty are identified and addressed for the purposes of the Fidelity Selection Problem. A series of experiments was conducted to address these weaknesses using n-dimensional optimization test functions. These experiments found that model

Dynamical computation of constrained flexible systems using a modal Udwadia-Kalaba formulation: Application to musical instruments.

Science.gov (United States)

Antunes, J; Debut, V

2017-02-01

Most musical instruments consist of dynamical subsystems connected at a number of constraining points through which energy flows. For physical sound synthesis, one important difficulty deals with enforcing these coupling constraints. While standard techniques include the use of Lagrange multipliers or penalty methods, in this paper, a different approach is explored, the Udwadia-Kalaba (U-K) formulation, which is rooted on analytical dynamics but avoids the use of Lagrange multipliers. This general and elegant formulation has been nearly exclusively used for conceptual systems of discrete masses or articulated rigid bodies, namely, in robotics. However its natural extension to deal with continuous flexible systems is surprisingly absent from the literature. Here, such a modeling strategy is developed and the potential of combining the U-K equation for constrained systems with the modal description is shown, in particular, to simulate musical instruments. Objectives are twofold: (1) Develop the U-K equation for constrained flexible systems with subsystems modelled through unconstrained modes; and (2) apply this framework to compute string/body coupled dynamics. This example complements previous work [Debut, Antunes, Marques, and Carvalho, Appl. Acoust. 108, 3-18 (2016)] on guitar modeling using penalty methods. Simulations show that the proposed technique provides similar results with a significant improvement in computational efficiency.

Adaptive Multi-Agent Systems for Constrained Optimization

Science.gov (United States)

Macready, William; Bieniawski, Stefan; Wolpert, David H.

2004-01-01

Product Distribution (PD) theory is a new framework for analyzing and controlling distributed systems. Here we demonstrate its use for distributed stochastic optimization. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. The updating of the Lagrange parameters in the Lagrangian can be viewed as a form of automated annealing, that focuses the MAS more and more on the optimal pure strategy. This provides a simple way to map the solution of any constrained optimization problem onto the equilibrium of a Multi-Agent System (MAS). We present computer experiments involving both the Queen s problem and K-SAT validating the predictions of PD theory and its use for off-the-shelf distributed adaptive optimization.

Metal artifact reduction in x-ray computed tomography (CT) by constrained optimization

International Nuclear Information System (INIS)

Zhang Xiaomeng; Wang Jing; Xing Lei

2011-01-01

Purpose: The streak artifacts caused by metal implants have long been recognized as a problem that limits various applications of CT imaging. In this work, the authors propose an iterative metal artifact reduction algorithm based on constrained optimization. Methods: After the shape and location of metal objects in the image domain is determined automatically by the binary metal identification algorithm and the segmentation of ''metal shadows'' in projection domain is done, constrained optimization is used for image reconstruction. It minimizes a predefined function that reflects a priori knowledge of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available metal-shadow-excluded projection data, with image non-negativity enforced. The minimization problem is solved through the alternation of projection-onto-convex-sets and the steepest gradient descent of the objective function. The constrained optimization algorithm is evaluated with a penalized smoothness objective. Results: The study shows that the proposed method is capable of significantly reducing metal artifacts, suppressing noise, and improving soft-tissue visibility. It outperforms the FBP-type methods and ART and EM methods and yields artifacts-free images. Conclusions: Constrained optimization is an effective way to deal with CT reconstruction with embedded metal objects. Although the method is presented in the context of metal artifacts, it is applicable to general ''missing data'' image reconstruction problems.

Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems

National Research Council Canada - National Science Library

Abramson, Mark A; Audet, Charles; Dennis, Jr, J. E

2004-01-01

.... This class combines and extends the Audet-Dennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPS-filter algorithms for general nonlinear constraints...

[Optimization of formulations for dietetic pastry products].

Science.gov (United States)

Villarroel, M; Uquiche, E; Brito, G; Cancino, M

2000-03-01

Optimized formulations of dietetic pastry products such as cake and sponge cake premixes were formulated using the surface response methodology. % Emulsifier agent and baking time were the selected independent variables for cake, as well as % emulsifier agent % chlorinated flour the variables selected for sponge cake. Three different level of each variable summing up thirteen experimental formulae of each product were assessed to optimize the variables that could have some influence in the sensory characteristics of these dietetic products. The total sensory quality was determined for both dietetic products using the composite scoring test and a panel of 18 trained judges. Looking at the contour graphic and considering economic aspects the best combination of variables for cake formulation was 2% emulsifier agent and 48 minutes for baking time, With respect to sponge cake, the best combination was 6% emulsifier agent and 48% chlorinated flour. Shelf life studies showed that both dietetic formulations remained stable during storage conditions of 75 days at 30 degrees C. During this period, significant differences in sensory characteristics were not found (p pastry products had good acceptability, and open up marketing opportunities for new products with potential health benefits to consumers.

A constrained optimization algorithm for total energy minimization in electronic structure calculations

International Nuclear Information System (INIS)

Yang Chao; Meza, Juan C.; Wang Linwang

2006-01-01

A new direct constrained optimization algorithm for minimizing the Kohn-Sham (KS) total energy functional is presented in this paper. The key ingredients of this algorithm involve projecting the total energy functional into a sequence of subspaces of small dimensions and seeking the minimizer of total energy functional within each subspace. The minimizer of a subspace energy functional not only provides a search direction along which the KS total energy functional decreases but also gives an optimal 'step-length' to move along this search direction. Numerical examples are provided to demonstrate that this new direct constrained optimization algorithm can be more efficient than the self-consistent field (SCF) iteration

A New Continuous-Time Equality-Constrained Optimization to Avoid Singularity.

Science.gov (United States)

Quan, Quan; Cai, Kai-Yuan

2016-02-01

In equality-constrained optimization, a standard regularity assumption is often associated with feasible point methods, namely, that the gradients of constraints are linearly independent. In practice, the regularity assumption may be violated. In order to avoid such a singularity, a new projection matrix is proposed based on which a feasible point method to continuous-time, equality-constrained optimization is developed. First, the equality constraint is transformed into a continuous-time dynamical system with solutions that always satisfy the equality constraint. Second, a new projection matrix without singularity is proposed to realize the transformation. An update (or say a controller) is subsequently designed to decrease the objective function along the solutions of the transformed continuous-time dynamical system. The invariance principle is then applied to analyze the behavior of the solution. Furthermore, the proposed method is modified to address cases in which solutions do not satisfy the equality constraint. Finally, the proposed optimization approach is applied to three examples to demonstrate its effectiveness.

Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes

Directory of Open Access Journals (Sweden)

Zunaira Nadeem

2018-04-01

Full Text Available In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO, genetic algorithm (GA, firefly algorithm (FA and optimal stopping rule (OSR theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.

Design optimization of shell-and-tube heat exchangers using single objective and multiobjective particle swarm optimization

International Nuclear Information System (INIS)

Elsays, Mostafa A.; Naguib Aly, M; Badawi, Alya A.

2010-01-01

The Particle Swarm Optimization (PSO) algorithm is used to optimize the design of shell-and-tube heat exchangers and determine the optimal feasible solutions so as to eliminate trial-and-error during the design process. The design formulation takes into account the area and the total annual cost of heat exchangers as two objective functions together with operating as well as geometrical constraints. The Nonlinear Constrained Single Objective Particle Swarm Optimization (NCSOPSO) algorithm is used to minimize and find the optimal feasible solution for each of the nonlinear constrained objective functions alone, respectively. Then, a novel Nonlinear Constrained Mult-objective Particle Swarm Optimization (NCMOPSO) algorithm is used to minimize and find the Pareto optimal solutions for both of the nonlinear constrained objective functions together. The experimental results show that the two algorithms are very efficient, fast and can find the accurate optimal feasible solutions of the shell and tube heat exchangers design optimization problem. (orig.)

Amodified probabilistic genetic algorithm for the solution of complex constrained optimization problems

OpenAIRE

Vorozheikin, A.; Gonchar, T.; Panfilov, I.; Sopov, E.; Sopov, S.

2009-01-01

A new algorithm for the solution of complex constrained optimization problems based on the probabilistic genetic algorithm with optimal solution prediction is proposed. The efficiency investigation results in comparison with standard genetic algorithm are presented.

A novel constrained H2 optimization algorithm for mechatronics design in flexure-linked biaxial gantry.

Science.gov (United States)

Ma, Jun; Chen, Si-Lu; Kamaldin, Nazir; Teo, Chek Sing; Tay, Arthur; Mamun, Abdullah Al; Tan, Kok Kiong

2017-11-01

The biaxial gantry is widely used in many industrial processes that require high precision Cartesian motion. The conventional rigid-link version suffers from breaking down of joints if any de-synchronization between the two carriages occurs. To prevent above potential risk, a flexure-linked biaxial gantry is designed to allow a small rotation angle of the cross-arm. Nevertheless, the chattering of control signals and inappropriate design of the flexure joint will possibly induce resonant modes of the end-effector. Thus, in this work, the design requirements in terms of tracking accuracy, biaxial synchronization, and resonant mode suppression are achieved by integrated optimization of the stiffness of flexures and PID controller parameters for a class of point-to-point reference trajectories with same dynamics but different steps. From here, an H 2 optimization problem with defined constraints is formulated, and an efficient iterative solver is proposed by hybridizing direct computation of constrained projection gradient and line search of optimal step. Comparative experimental results obtained on the testbed are presented to verify the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

On meeting capital requirements with a chance-constrained optimization model.

Science.gov (United States)

Atta Mills, Ebenezer Fiifi Emire; Yu, Bo; Gu, Lanlan

2016-01-01

This paper deals with a capital to risk asset ratio chance-constrained optimization model in the presence of loans, treasury bill, fixed assets and non-interest earning assets. To model the dynamics of loans, we introduce a modified CreditMetrics approach. This leads to development of a deterministic convex counterpart of capital to risk asset ratio chance constraint. We pursue the scope of analyzing our model under the worst-case scenario i.e. loan default. The theoretical model is analyzed by applying numerical procedures, in order to administer valuable insights from a financial outlook. Our results suggest that, our capital to risk asset ratio chance-constrained optimization model guarantees banks of meeting capital requirements of Basel III with a likelihood of 95 % irrespective of changes in future market value of assets.

Topology Optimization of Constrained Layer Damping on Plates Using Method of Moving Asymptote (MMA Approach

Directory of Open Access Journals (Sweden)

Zheng Ling

2011-01-01

Full Text Available Damping treatments have been extensively used as a powerful means to damp out structural resonant vibrations. Usually, damping materials are fully covered on the surface of plates. The drawbacks of this conventional treatment are also obvious due to an added mass and excess material consumption. Therefore, it is not always economical and effective from an optimization design view. In this paper, a topology optimization approach is presented to maximize the modal damping ratio of the plate with constrained layer damping treatment. The governing equation of motion of the plate is derived on the basis of energy approach. A finite element model to describe dynamic performances of the plate is developed and used along with an optimization algorithm in order to determine the optimal topologies of constrained layer damping layout on the plate. The damping of visco-elastic layer is modeled by the complex modulus formula. Considering the vibration and energy dissipation mode of the plate with constrained layer damping treatment, damping material density and volume factor are considered as design variable and constraint respectively. Meantime, the modal damping ratio of the plate is assigned as the objective function in the topology optimization approach. The sensitivity of modal damping ratio to design variable is further derived and Method of Moving Asymptote (MMA is adopted to search the optimized topologies of constrained layer damping layout on the plate. Numerical examples are used to demonstrate the effectiveness of the proposed topology optimization approach. The results show that vibration energy dissipation of the plates can be enhanced by the optimal constrained layer damping layout. This optimal technology can be further extended to vibration attenuation of sandwich cylindrical shells which constitute the major building block of many critical structures such as cabins of aircrafts, hulls of submarines and bodies of rockets and missiles as an

Pole shifting with constrained output feedback

International Nuclear Information System (INIS)

Hamel, D.; Mensah, S.; Boisvert, J.

1984-03-01

The concept of pole placement plays an important role in linear, multi-variable, control theory. It has received much attention since its introduction, and several pole shifting algorithms are now available. This work presents a new method which allows practical and engineering constraints such as gain limitation and controller structure to be introduced right into the pole shifting design strategy. This is achieved by formulating the pole placement problem as a constrained optimization problem. Explicit constraints (controller structure and gain limits) are defined to identify an admissible region for the feedback gain matrix. The desired pole configuration is translated into an appropriate cost function which must be closed-loop minimized. The resulting constrained optimization problem can thus be solved with optimization algorithms. The method has been implemented as an algorithmic interactive module in a computer-aided control system design package, MVPACK. The application of the method is illustrated to design controllers for an aircraft and an evaporator. The results illustrate the importance of controller structure on overall performance of a control system

A Simply Constrained Optimization Reformulation of KKT Systems Arising from Variational Inequalities

International Nuclear Information System (INIS)

Facchinei, F.; Fischer, A.; Kanzow, C.; Peng, J.-M.

1999-01-01

The Karush-Kuhn-Tucker (KKT) conditions can be regarded as optimality conditions for both variational inequalities and constrained optimization problems. In order to overcome some drawbacks of recently proposed reformulations of KKT systems, we propose casting KKT systems as a minimization problem with nonnegativity constraints on some of the variables. We prove that, under fairly mild assumptions, every stationary point of this constrained minimization problem is a solution of the KKT conditions. Based on this reformulation, a new algorithm for the solution of the KKT conditions is suggested and shown to have some strong global and local convergence properties

An algorithm for mass matrix calculation of internally constrained molecular geometries

International Nuclear Information System (INIS)

Aryanpour, Masoud; Dhanda, Abhishek; Pitsch, Heinz

2008-01-01

Dynamic models for molecular systems require the determination of corresponding mass matrix. For constrained geometries, these computations are often not trivial but need special considerations. Here, assembling the mass matrix of internally constrained molecular structures is formulated as an optimization problem. Analytical expressions are derived for the solution of the different possible cases depending on the rank of the constraint matrix. Geometrical interpretations are further used to enhance the solution concept. As an application, we evaluate the mass matrix for a constrained molecule undergoing an electron-transfer reaction. The preexponential factor for this reaction is computed based on the harmonic model

An algorithm for mass matrix calculation of internally constrained molecular geometries.

Science.gov (United States)

Aryanpour, Masoud; Dhanda, Abhishek; Pitsch, Heinz

2008-01-28

Dynamic models for molecular systems require the determination of corresponding mass matrix. For constrained geometries, these computations are often not trivial but need special considerations. Here, assembling the mass matrix of internally constrained molecular structures is formulated as an optimization problem. Analytical expressions are derived for the solution of the different possible cases depending on the rank of the constraint matrix. Geometrical interpretations are further used to enhance the solution concept. As an application, we evaluate the mass matrix for a constrained molecule undergoing an electron-transfer reaction. The preexponential factor for this reaction is computed based on the harmonic model.

Fuzzy chance constrained linear programming model for scrap charge optimization in steel production

DEFF Research Database (Denmark)

Rong, Aiying; Lahdelma, Risto

2008-01-01

the uncertainty based on fuzzy set theory and constrain the failure risk based on a possibility measure. Consequently, the scrap charge optimization problem is modeled as a fuzzy chance constrained linear programming problem. Since the constraints of the model mainly address the specification of the product...

Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

Directory of Open Access Journals (Sweden)

Hailong Wang

2018-01-01

Full Text Available The backtracking search optimization algorithm (BSA is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed.

Workshop on Computational Optimization

CERN Document Server

2015-01-01

Our everyday life is unthinkable without optimization. We try to minimize our effort and to maximize the achieved profit. Many real world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2013. It presents recent advances in computational optimization. The volume includes important real life problems like parameter settings for controlling processes in bioreactor, resource constrained project scheduling, problems arising in transport services, error correcting codes, optimal system performance and energy consumption and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others.

Applications of an alternative formulation for one-layer real time optimization

Directory of Open Access Journals (Sweden)

Schiavon Júnior A.L.

2000-01-01

Full Text Available This paper presents two applications of an alternative formulation for one-layer real time structure for control and optimization. This new formulation have arisen from predictive controller QDMC (Quadratic Dynamic Matrix Control, a type of predictive control (Model Predictive Control - MPC. At each sampling time, the values of the outputs of process are fed into the optimization-control structure which supplies the new values of the manipulated variables already considering the best conditions of process. The variables of optimization are both set-point changes and control actions. The future stationary outputs and the future stationary control actions have both a different formulation of conventional one-layer structure and they are calculated from the inverse gain matrix of the process. This alternative formulation generates a convex problem, which can be solved by less sophisticated optimization algorithms. Linear and nonlinear economic objective functions were considered. The proposed approach was applied to two linear models, one SISO (single-input/single output and the other MIMO (multiple-input/multiple-output. The results showed an excellent performance.

Influence of cellulose derivative and ethylene glycol on optimization of lornoxicam transdermal formulation.

Science.gov (United States)

Shahzad, Yasser; Khan, Qalandar; Hussain, Talib; Shah, Syed Nisar Hussain

2013-10-01

Lornoxicam containing topically applied lotions were formulated and optimized with the aim to deliver it transdermally. The formulated lotions were evaluated for pH, viscosity and in vitro permeation studies through silicone membrane using Franz diffusion cells. Data were fitted to linear, quadratic and cubic models and best fit model was selected to investigate the influence of variables, namely hydroxypropyl methylcellulose (HPMC) and ethylene glycol (EG) on permeation of lornoxicam from topically applied lotion formulations. The best fit quadratic model revealed that low level of HPMC and intermediate level of EG in the formulation was optimum for enhancing the drug flux across silicone membrane. FT-IR analysis confirmed absence of drug-polymer interactions. Selected optimized lotion formulation was then subjected to accelerated stability testing, sensatory perception testing and in vitro permeation across rabbit skin. The drug flux from the optimized lotion across rabbit skin was significantly better that that from the control formulation. Furthermore, sensatory perception test rated a higher acceptability while lotion was stable over stability testing period. Therefore, use of Box-Wilson statistical design successfully elaborated the influence of formulation variables on permeation of lornoxicam form topical formulations, thus, helped in optimization of the lotion formulation. Copyright © 2013 Elsevier B.V. All rights reserved.

The Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem

Directory of Open Access Journals (Sweden)

Denis Pinha

2016-11-01

Full Text Available This paper presents the formulation and solution of the Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem. The focus of the proposed method is not on finding a single optimal solution, instead on presenting multiple feasible solutions, with cost and duration information to the project manager. The motivation for developing such an approach is due in part to practical situations where the definition of optimal changes on a regular basis. The proposed approach empowers the project manager to determine what is optimal, on a given day, under the current constraints, such as, change of priorities, lack of skilled worker. The proposed method utilizes a simulation approach to determine feasible solutions, under the current constraints. Resources can be non-consumable, consumable, or doubly constrained. The paper also presents a real-life case study dealing with scheduling of ship repair activities.

Design and optimization of self-nanoemulsifying formulations for lipophilic drugs

International Nuclear Information System (INIS)

Zhao, Tianjing; Maniglio, Devid; Motta, Antonella; Migliaresi, Claudio; Chen, Jie; Chen, Bin

2015-01-01

The purpose of the current study was to develop and optimize novel self-nanoemulsifying drug delivery systems (SNEDDS) with a high proportion of essential oil as carriers for lipophilic drugs. Solubility and droplet size as a function of the composition were investigated, and a ternary phase diagram was constructed in order to identify the self-emulsification regions. The optimized SNEDDS formulation consisted of lemon essential oil (oil), Cremophor RH40 (surfactant) and Transcutol HP (co-surfactant) in the ratio 50:30:20 (v/v). Ibuprofen was chosen as the model drug. The droplet size, ζ-potential and stability of the drug-loaded optimized formulations were determined. The stability of SNEDDS was proved after triple freezing/thawing cycles and storage at 4 °C and 25 °C for 3 months. In vitro drug release studies of optimized SNEDDS revealed a significant increase of the drug release and release rate in comparison to the Ibuprofen suspension (80% versus approximately 40% in 2 h). The results indicated that these SNEDDS formulations could be used to improve the bioavailability of lipophilic drugs. (paper)

Optimization Formulations for the Maximum Nonlinear Buckling Load of Composite Structures

DEFF Research Database (Denmark)

Lindgaard, Esben; Lund, Erik

2011-01-01

This paper focuses on criterion functions for gradient based optimization of the buckling load of laminated composite structures considering different types of buckling behaviour. A local criterion is developed, and is, together with a range of local and global criterion functions from literature......, benchmarked on a number of numerical examples of laminated composite structures for the maximization of the buckling load considering fiber angle design variables. The optimization formulations are based on either linear or geometrically nonlinear analysis and formulated as mathematical programming problems...... solved using gradient based techniques. The developed local criterion is formulated such it captures nonlinear effects upon loading and proves useful for both analysis purposes and as a criterion for use in nonlinear buckling optimization. © 2010 Springer-Verlag....

Workshop on Computational Optimization

CERN Document Server

2016-01-01

This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2014, held at Warsaw, Poland, September 7-10, 2014. The book presents recent advances in computational optimization. The volume includes important real problems like parameter settings for controlling processes in bioreactor and other processes, resource constrained project scheduling, infection distribution, molecule distance geometry, quantum computing, real-time management and optimal control, bin packing, medical image processing, localization the abrupt atmospheric contamination source and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks.

Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure.

Science.gov (United States)

Luo, Biao; Liu, Derong; Wu, Huai-Ning

2018-06-01

Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition . To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

Nonlinear Chance Constrained Problems: Optimality Conditions, Regularization and Solvers

Czech Academy of Sciences Publication Activity Database

Adam, Lukáš; Branda, Martin

2016-01-01

Roč. 170, č. 2 (2016), s. 419-436 ISSN 0022-3239 R&D Projects: GA ČR GA15-00735S Institutional support: RVO:67985556 Keywords : Chance constrained programming * Optimality conditions * Regularization * Algorithms * Free MATLAB codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.289, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0460909.pdf

Topology optimization of acoustic-structure interaction problems using a mixed finite element formulation

DEFF Research Database (Denmark)

Yoon, Gil Ho; Jensen, Jens Stissing; Sigmund, Ole

2007-01-01

given during the optimization process. In this paper we circumvent the explicit boundary representation by using a mixed finite element formulation with displacements and pressure as primary variables (a u/p-formulation). The Helmholtz equation is obtained as a special case of the mixed formulation...... for the elastic shear modulus equating to zero. Hence, by spatial variation of the mass density, shear and bulk moduli we are able to solve the coupled problem by the mixed formulation. Using this modelling approach, the topology optimization procedure is simply implemented as a standard density approach. Several...... two-dimensional acoustic-structure problems are optimized in order to verify the proposed method....

Superalloy design - A Monte Carlo constrained optimization method

CSIR Research Space (South Africa)

Stander, CM

1996-01-01

Full Text Available optimization method C. M. Stander Division of Materials Science and Technology, CSIR, PO Box 395, Pretoria, Republic of South Africa Received 74 March 1996; accepted 24 June 1996 A method, based on Monte Carlo constrained... successful hit, i.e. when Liow < LMP,,, < Lhiph, and for all the properties, Pj?, < P, < Pi@?. If successful this hit falls within the ROA. Repeat steps 4 and 5 to find at least ten (or more) successful hits with values...

CLFs-based optimization control for a class of constrained visual servoing systems.

Science.gov (United States)

Song, Xiulan; Miaomiao, Fu

2017-03-01

In this paper, we use the control Lyapunov function (CLF) technique to present an optimized visual servo control method for constrained eye-in-hand robot visual servoing systems. With the knowledge of camera intrinsic parameters and depth of target changes, visual servo control laws (i.e. translation speed) with adjustable parameters are derived by image point features and some known CLF of the visual servoing system. The Fibonacci method is employed to online compute the optimal value of those adjustable parameters, which yields an optimized control law to satisfy constraints of the visual servoing system. The Lyapunov's theorem and the properties of CLF are used to establish stability of the constrained visual servoing system in the closed-loop with the optimized control law. One merit of the presented method is that there is no requirement of online calculating the pseudo-inverse of the image Jacobian's matrix and the homography matrix. Simulation and experimental results illustrated the effectiveness of the method proposed here. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

KAUST Repository

Reyes, Juan Carlos De los

2013-11-01

We propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.

Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

KAUST Repository

Reyes, Juan Carlos De los; Schö nlieb, Carola-Bibiane

2013-01-01

We propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.

Subspace Barzilai-Borwein Gradient Method for Large-Scale Bound Constrained Optimization

International Nuclear Information System (INIS)

Xiao Yunhai; Hu Qingjie

2008-01-01

An active set subspace Barzilai-Borwein gradient algorithm for large-scale bound constrained optimization is proposed. The active sets are estimated by an identification technique. The search direction consists of two parts: some of the components are simply defined; the other components are determined by the Barzilai-Borwein gradient method. In this work, a nonmonotone line search strategy that guarantees global convergence is used. Preliminary numerical results show that the proposed method is promising, and competitive with the well-known method SPG on a subset of bound constrained problems from CUTEr collection

Formulation and optimization of solid lipid nanoparticle formulation for pulmonary delivery of budesonide using Taguchi and Box-Behnken design.

Science.gov (United States)

Emami, J; Mohiti, H; Hamishehkar, H; Varshosaz, J

2015-01-01

Budesonide is a potent non-halogenated corticosteroid with high anti-inflammatory effects. The lungs are an attractive route for non-invasive drug delivery with advantages for both systemic and local applications. The aim of the present study was to develop, characterize and optimize a solid lipid nanoparticle system to deliver budesonide to the lungs. Budesonide-loaded solid lipid nanoparticles were prepared by the emulsification-solvent diffusion method. The impact of various processing variables including surfactant type and concentration, lipid content organic and aqueous volume, and sonication time were assessed on the particle size, zeta potential, entrapment efficiency, loading percent and mean dissolution time. Taguchi design with 12 formulations along with Box-Behnken design with 17 formulations was developed. The impact of each factor upon the eventual responses was evaluated, and the optimized formulation was finally selected. The size and morphology of the prepared nanoparticles were studied using scanning electron microscope. Based on the optimization made by Design Expert 7(®) software, a formulation made of glycerol monostearate, 1.2 % polyvinyl alcohol (PVA), weight ratio of lipid/drug of 10 and sonication time of 90 s was selected. Particle size, zeta potential, entrapment efficiency, loading percent, and mean dissolution time of adopted formulation were predicted and confirmed to be 218.2 ± 6.6 nm, -26.7 ± 1.9 mV, 92.5 ± 0.52 %, 5.8 ± 0.3 %, and 10.4 ± 0.29 h, respectively. Since the preparation and evaluation of the selected formulation within the laboratory yielded acceptable results with low error percent, the modeling and optimization was justified. The optimized formulation co-spray dried with lactose (hybrid microparticles) displayed desirable fine particle fraction, mass median aerodynamic diameter (MMAD), and geometric standard deviation of 49.5%, 2.06 μm, and 2.98 μm; respectively. Our results provide fundamental data for the

Optimized formulation of solid self-microemulsifying sirolimus delivery systems

Directory of Open Access Journals (Sweden)

Cho W

2013-04-01

Full Text Available Wonkyung Cho,1,2 Min-Soo Kim,3 Jeong-Soo Kim,2 Junsung Park,1,2 Hee Jun Park,1,2 Kwang-Ho Cha,1,2 Jeong-Sook Park,2 Sung-Joo Hwang1,4 1Yonsei Institute of Pharmaceutical Sciences, Yonsei University, Incheon, Republic of Korea; 2College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea; 3Department of Pharmaceutical Engineering, Inje University, Gimhae, Republic of Korea; 4College of Pharmacy, Yonsei University, Incheon, Republic of Korea Background: The aim of this study was to develop an optimized solid self-microemulsifying drug delivery system (SMEDDS formulation for sirolimus to enhance its solubility, stability, and bioavailability. Methods: Excipients used for enhancing the solubility and stability of sirolimus were screened. A phase-separation test, visual observation for emulsifying efficiency, and droplet size analysis were performed. Ternary phase diagrams were constructed to optimize the liquid SMEDDS formulation. The selected liquid SMEDDS formulations were prepared into solid form. The dissolution profiles and pharmacokinetic profiles in rats were analyzed. Results: In the results of the oil and cosolvent screening studies, Capryol™ Propylene glycol monocaprylate (PGMC and glycofurol exhibited the highest solubility of all oils and cosolvents, respectively. In the surfactant screening test, D-α-tocopheryl polyethylene glycol 1000 succinate (vitamin E TPGS was determined to be the most effective stabilizer of sirolimus in pH 1.2 simulated gastric fluids. The optimal formulation determined by the construction of ternary phase diagrams was the T32 (Capryol™ PGMC:glycofurol:vitamin E TPGS = 30:30:40 weight ratio formulation with a mean droplet size of 108.2 ± 11.4 nm. The solid SMEDDS formulations were prepared with Sucroester 15 and mannitol. The droplet size of the reconstituted solid SMEDDS showed no significant difference compared with the liquid SMEDDS. In the dissolution study, the release amounts of

Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems.

Science.gov (United States)

Xu, Y; Li, N

2014-09-01

Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.

Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems

International Nuclear Information System (INIS)

Xu, Y; Li, N

2014-01-01

Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator–prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework. (paper)

Order-Constrained Solutions in K-Means Clustering: Even Better than Being Globally Optimal

Science.gov (United States)

Steinley, Douglas; Hubert, Lawrence

2008-01-01

This paper proposes an order-constrained K-means cluster analysis strategy, and implements that strategy through an auxiliary quadratic assignment optimization heuristic that identifies an initial object order. A subsequent dynamic programming recursion is applied to optimally subdivide the object set subject to the order constraint. We show that…

Constrained Burn Optimization for the International Space Station

Science.gov (United States)

Brown, Aaron J.; Jones, Brandon A.

2017-01-01

In long-term trajectory planning for the International Space Station (ISS), translational burns are currently targeted sequentially to meet the immediate trajectory constraints, rather than simultaneously to meet all constraints, do not employ gradient-based search techniques, and are not optimized for a minimum total deltav (v) solution. An analytic formulation of the constraint gradients is developed and used in an optimization solver to overcome these obstacles. Two trajectory examples are explored, highlighting the advantage of the proposed method over the current approach, as well as the potential v and propellant savings in the event of propellant shortages.

Improved Sensitivity Relations in State Constrained Optimal Control

International Nuclear Information System (INIS)

Bettiol, Piernicola; Frankowska, Hélène; Vinter, Richard B.

2015-01-01

Sensitivity relations in optimal control provide an interpretation of the costate trajectory and the Hamiltonian, evaluated along an optimal trajectory, in terms of gradients of the value function. While sensitivity relations are a straightforward consequence of standard transversality conditions for state constraint free optimal control problems formulated in terms of control-dependent differential equations with smooth data, their verification for problems with either pathwise state constraints, nonsmooth data, or for problems where the dynamic constraint takes the form of a differential inclusion, requires careful analysis. In this paper we establish validity of both ‘full’ and ‘partial’ sensitivity relations for an adjoint state of the maximum principle, for optimal control problems with pathwise state constraints, where the underlying control system is described by a differential inclusion. The partial sensitivity relation interprets the costate in terms of partial Clarke subgradients of the value function with respect to the state variable, while the full sensitivity relation interprets the couple, comprising the costate and Hamiltonian, as the Clarke subgradient of the value function with respect to both time and state variables. These relations are distinct because, for nonsmooth data, the partial Clarke subdifferential does not coincide with the projection of the (full) Clarke subdifferential on the relevant coordinate space. We show for the first time (even for problems without state constraints) that a costate trajectory can be chosen to satisfy the partial and full sensitivity relations simultaneously. The partial sensitivity relation in this paper is new for state constraint problems, while the full sensitivity relation improves on earlier results in the literature (for optimal control problems formulated in terms of Lipschitz continuous multifunctions), because a less restrictive inward pointing hypothesis is invoked in the proof, and because

Prolonged release matrix tablet of pyridostigmine bromide: formulation and optimization using statistical methods.

Science.gov (United States)

Bolourchian, Noushin; Rangchian, Maryam; Foroutan, Seyed Mohsen

2012-07-01

The aim of this study was to design and optimize a prolonged release matrix formulation of pyridostigmine bromide, an effective drug in myasthenia gravis and poisoning with nerve gas, using hydrophilic - hydrophobic polymers via D-optimal experimental design. HPMC and carnauba wax as retarding agents as well as tricalcium phosphate were used in matrix formulation and considered as independent variables. Tablets were prepared by wet granulation technique and the percentage of drug released at 1 (Y(1)), 4 (Y(2)) and 8 (Y(3)) hours were considered as dependent variables (responses) in this investigation. These experimental responses were best fitted for the cubic, cubic and linear models, respectively. The optimal formulation obtained in this study, consisted of 12.8 % HPMC, 24.4 % carnauba wax and 26.7 % tricalcium phosphate, had a suitable prolonged release behavior followed by Higuchi model in which observed and predicted values were very close. The study revealed that D-optimal design could facilitate the optimization of prolonged release matrix tablet containing pyridostigmine bromide. Accelerated stability studies confirmed that the optimized formulation remains unchanged after exposing in stability conditions for six months.

Constrained evolution in numerical relativity

Science.gov (United States)

Anderson, Matthew William

The strongest potential source of gravitational radiation for current and future detectors is the merger of binary black holes. Full numerical simulation of such mergers can provide realistic signal predictions and enhance the probability of detection. Numerical simulation of the Einstein equations, however, is fraught with difficulty. Stability even in static test cases of single black holes has proven elusive. Common to unstable simulations is the growth of constraint violations. This work examines the effect of controlling the growth of constraint violations by solving the constraints periodically during a simulation, an approach called constrained evolution. The effects of constrained evolution are contrasted with the results of unconstrained evolution, evolution where the constraints are not solved during the course of a simulation. Two different formulations of the Einstein equations are examined: the standard ADM formulation and the generalized Frittelli-Reula formulation. In most cases constrained evolution vastly improves the stability of a simulation at minimal computational cost when compared with unconstrained evolution. However, in the more demanding test cases examined, constrained evolution fails to produce simulations with long-term stability in spite of producing improvements in simulation lifetime when compared with unconstrained evolution. Constrained evolution is also examined in conjunction with a wide variety of promising numerical techniques, including mesh refinement and overlapping Cartesian and spherical computational grids. Constrained evolution in boosted black hole spacetimes is investigated using overlapping grids. Constrained evolution proves to be central to the host of innovations required in carrying out such intensive simulations.

[Formulation optimization of panax notoginsenoside orally fast disintegration tablets].

Science.gov (United States)

Wang, Zhi; Wei, Li; Chen, Ting

2008-07-01

To optimize the formulation of panax notoginsenoside orally fast disintegrating tablets. Mannitol, microcrystalline cellulose (PH 102) and lactose 80 were used as diluent. A polynomial regression algorithm was used to evaluate the relationship between the controlling factor, compacting pressure and diluent ratio, and disintegration time, tensile strength of tablets. Optimum formulation and process parameters could be determined by contrast the contour plot of tensile strength to that of disintegration time. The disintegration time and tensile strength of panax notoginsenoside oral disintegrating tablets were good, and the taste was satisfactory. Panax notoginsenoside oral disintegrating tablets achieve the goal of design and this method can be fairly used in formulation screening.

An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems

Directory of Open Access Journals (Sweden)

Vivek Patel

2012-08-01

Full Text Available Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO is one of the recently proposed population based algorithm which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.

Exploring the impact of constraints in quantum optimal control through a kinematic formulation

International Nuclear Information System (INIS)

Donovan, Ashley; Beltrani, Vincent; Rabitz, Herschel

2013-01-01

Highlights: • This work lays a foundation for studying constraints in quantum control simulations. • The underlying quantum control landscape in the presence of constraints is explored. • Constrained controls can encounter suboptimal traps in the landscape. • The controls are kinematic stand-ins for dynamic time-dependent controls. • A method is developed to transfer between constrained kinematic and dynamic controls. - Abstract: The control of quantum dynamics with tailored laser fields is finding growing experimental success. In practice, experiments will be subject to constraints on the controls that may prevent full optimization of the objective. A framework is presented for systematically investigating the impact of constraints in quantum optimal control simulations using a two-stage process starting with simple time-independent kinematic controls, which act as stand-ins for the traditional dynamic controls. The objective is a state-to-state transition probability, and constraints are introduced by restricting the kinematic control variables during optimization. As a second stage, the means to map from kinematic to dynamic controls is presented, thus enabling a simplified overall procedure for exploring how limited resources affect the ability to optimize the objective. A demonstration of the impact of imposing several types of kinematic constraints is investigated, thereby offering insight into constrained quantum controls

Formulation Optimization and In-vitro Evaluation of Oral Floating ...

African Journals Online (AJOL)

matrix tablets and to systematically optimize its drug release using varying levels of xanthan gum and hydroxypropyl ... stomach and improve oral bioavailability of drugs that have ... which can affect its sustained release formulation. [19].

Incorporating a Constrained Optimization Algorithm into Remote- Sensing/Precision Agriculture Methodology

Science.gov (United States)

Morgenthaler, George; Khatib, Nader; Kim, Byoungsoo

with information to improve their crop's vigor has been a major topic of interest. With world population growing exponentially, arable land being consumed by urbanization, and an unfavorable farm economy, the efficiency of farming must increase to meet future food requirements and to make farming a sustainable occupation for the farmer. "Precision Agriculture" refers to a farming methodology that applies nutrients and moisture only where and when they are needed in the field. The goal is to increase farm revenue by increasing crop yield and decreasing applications of costly chemical and water treatments. In addition, this methodology will decrease the environmental costs of farming, i.e., reduce air, soil, and water pollution. Sensing/Precision Agriculture has not grown as rapidly as early advocates envisioned. Technology for a successful Remote Sensing/Precision Agriculture system is now available. Commercial satellite systems can image (multi-spectral) the Earth with a resolution of approximately 2.5 m. Variable precision dispensing systems using GPS are available and affordable. Crop models that predict yield as a function of soil, chemical, and irrigation parameter levels have been formulated. Personal computers and internet access are in place in most farm homes and can provide a mechanism to periodically disseminate, e.g. bi-weekly, advice on what quantities of water and chemicals are needed in individual regions of the field. What is missing is a model that fuses the disparate sources of information on the current states of the crop and soil, and the remaining resource levels available with the decisions farmers are required to make. This must be a product that is easy for the farmer to understand and to implement. A "Constrained Optimization Feed-back Control Model" to fill this void will be presented. The objective function of the model will be used to maximize the farmer's profit by increasing yields while decreasing environmental costs and decreasing

A first-order multigrid method for bound-constrained convex optimization

Czech Academy of Sciences Publication Activity Database

Kočvara, Michal; Mohammed, S.

2016-01-01

Roč. 31, č. 3 (2016), s. 622-644 ISSN 1055-6788 R&D Projects: GA ČR(CZ) GAP201/12/0671 Grant - others:European Commission - EC(XE) 313781 Institutional support: RVO:67985556 Keywords : bound-constrained optimization * multigrid methods * linear complementarity problems Subject RIV: BA - General Mathematics Impact factor: 1.023, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/kocvara-0460326.pdf

A New Interpolation Approach for Linearly Constrained Convex Optimization

KAUST Repository

Espinoza, Francisco

2012-08-01

In this thesis we propose a new class of Linearly Constrained Convex Optimization methods based on the use of a generalization of Shepard\\'s interpolation formula. We prove the properties of the surface such as the interpolation property at the boundary of the feasible region and the convergence of the gradient to the null space of the constraints at the boundary. We explore several descent techniques such as steepest descent, two quasi-Newton methods and the Newton\\'s method. Moreover, we implement in the Matlab language several versions of the method, particularly for the case of Quadratic Programming with bounded variables. Finally, we carry out performance tests against Matab Optimization Toolbox methods for convex optimization and implementations of the standard log-barrier and active-set methods. We conclude that the steepest descent technique seems to be the best choice so far for our method and that it is competitive with other standard methods both in performance and empirical growth order.

A Framework for Constrained Optimization Problems Based on a Modified Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Biwei Tang

2016-01-01

Full Text Available This paper develops a particle swarm optimization (PSO based framework for constrained optimization problems (COPs. Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011 algorithm. Since the convergence of PSO is of great importance and significantly influences the performance of PSO, this paper first theoretically investigates the convergence of SASPSO 2011. Then, a parameter selection principle guaranteeing the convergence of SASPSO 2011 is provided. Subsequently, a SASPSO 2011-based framework is established to solve COPs. Attempting to increase the diversity of solutions and decrease optimization difficulties, the adaptive relaxation method, which is combined with the feasibility-based rule, is applied to handle constraints of COPs and evaluate candidate solutions in the developed framework. Finally, the proposed method is verified through 4 benchmark test functions and 2 real-world engineering problems against six PSO variants and some well-known methods proposed in the literature. Simulation results confirm that the proposed method is highly competitive in terms of the solution quality and can be considered as a vital alternative to solve COPs.

Design optimization of axial flow hydraulic turbine runner: Part II - multi-objective constrained optimization method

Science.gov (United States)

Peng, Guoyi; Cao, Shuliang; Ishizuka, Masaru; Hayama, Shinji

2002-06-01

This paper is concerned with the design optimization of axial flow hydraulic turbine runner blade geometry. In order to obtain a better design plan with good performance, a new comprehensive performance optimization procedure has been presented by combining a multi-variable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. With careful analysis of the inverse design of axial hydraulic turbine runner, the total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using the weight factors. Parameters of a newly proposed blade bound circulation distribution function and parameters describing positions of blade leading and training edges in the meridional flow passage are taken as optimization variables.The optimization procedure has been applied to the design optimization of a Kaplan runner with specific speed of 440 kW. Numerical results show that the performance of designed runner is successfully improved through optimization computation. The optimization model is found to be validated and it has the feature of good convergence. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function. Copyright

Mixed Integer PDE Constrained Optimization for the Control of a Wildfire Hazard

Science.gov (United States)

2017-01-01

Constrained Optimization for the Control of a Wildfire Hazard Herausgegeben von der Professor fur Angewandte Mathematik Professor Dr. rer. nat. Armin...and H.H. Tan . Finite difference methods for solving the two-dimensional advection-diffusion equation. Int. J. Numer. Meth. Fluids, 9:75-98, 1989. 6

A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

Science.gov (United States)

Liu, Qingshan; Guo, Zhishan; Wang, Jun

2012-02-01

In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Application of pattern search method to power system security constrained economic dispatch with non-smooth cost function

International Nuclear Information System (INIS)

Al-Othman, A.K.; El-Naggar, K.M.

2008-01-01

Direct search methods are evolutionary algorithms used to solve optimization problems. (DS) methods do not require any information about the gradient of the objective function at hand while searching for an optimum solution. One of such methods is Pattern Search (PS) algorithm. This paper presents a new approach based on a constrained pattern search algorithm to solve a security constrained power system economic dispatch problem (SCED) with non-smooth cost function. Operation of power systems demands a high degree of security to keep the system satisfactorily operating when subjected to disturbances, while and at the same time it is required to pay attention to the economic aspects. Pattern recognition technique is used first to assess dynamic security. Linear classifiers that determine the stability of electric power system are presented and added to other system stability and operational constraints. The problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Pattern search method is then applied to solve the constrained optimization formulation. In particular, the method is tested using three different test systems. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that pattern search (PS) is very applicable for solving security constrained power system economic dispatch problem (SCED). In addition, valve-point effect loading and total system losses are considered to further investigate the potential of the PS technique. Based on the results, it can be concluded that the PS has demonstrated ability in handling highly nonlinear discontinuous non-smooth cost function of the SCED. (author)

Probabilistic Constrained Load Flow Considering Integration of Wind Power Generation and Electric Vehicles

DEFF Research Database (Denmark)

Vlachogiannis, Ioannis (John)

2009-01-01

A new formulation and solution of probabilistic constrained load flow (PCLF) problem suitable for modern power systems with wind power generation and electric vehicles (EV) demand or supply is represented. The developed stochastic model of EV demand/supply and the wind power generation model...... are incorporated into load flow studies. In the resulted PCLF formulation, discrete and continuous control parameters are engaged. Therefore, a hybrid learning automata system (HLAS) is developed to find the optimal offline control settings over a whole planning period of power system. The process of HLAS...

A chance-constrained stochastic approach to intermodal container routing problems.

Science.gov (United States)

Zhao, Yi; Liu, Ronghui; Zhang, Xi; Whiteing, Anthony

2018-01-01

We consider a container routing problem with stochastic time variables in a sea-rail intermodal transportation system. The problem is formulated as a binary integer chance-constrained programming model including stochastic travel times and stochastic transfer time, with the objective of minimising the expected total cost. Two chance constraints are proposed to ensure that the container service satisfies ship fulfilment and cargo on-time delivery with pre-specified probabilities. A hybrid heuristic algorithm is employed to solve the binary integer chance-constrained programming model. Two case studies are conducted to demonstrate the feasibility of the proposed model and to analyse the impact of stochastic variables and chance-constraints on the optimal solution and total cost.

A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization

Directory of Open Access Journals (Sweden)

Zhijun Luo

2014-01-01

Full Text Available A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.

Optimal power flow: a bibliographic survey I. Formulations and deterministic methods

Energy Technology Data Exchange (ETDEWEB)

Frank, Stephen [Colorado School of Mines, Department of Electrical Engineering and Computer Science, Golden, CO (United States); Steponavice, Ingrida [University of Jyvaskyla, Department of Mathematical Information Technology, Agora (Finland); Rebennack, Steffen [Colorado School of Mines, Division of Economics and Business, Golden, CO (United States)

2012-09-15

Over the past half-century, optimal power flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of electric power generation, transmission, and distribution networks subject to system constraints and control limits. Within this framework, however, there is an extremely wide variety of OPF formulations and solution methods. Moreover, the nature of OPF continues to evolve due to modern electricity markets and renewable resource integration. In this two-part survey, we survey both the classical and recent OPF literature in order to provide a sound context for the state of the art in OPF formulation and solution methods. The survey contributes a comprehensive discussion of specific optimization techniques that have been applied to OPF, with an emphasis on the advantages, disadvantages, and computational characteristics of each. Part I of the survey (this article) provides an introduction and surveys the deterministic optimization methods that have been applied to OPF. Part II of the survey examines the recent trend towards stochastic, or non-deterministic, search techniques and hybrid methods for OPF. (orig.)

Canonical Duality Theory for Topology Optimization

OpenAIRE

Gao, David Yang

2016-01-01

This paper presents a canonical duality approach for solving a general topology optimization problem of nonlinear elastic structures. By using finite element method, this most challenging problem can be formulated as a mixed integer nonlinear programming problem (MINLP), i.e. for a given deformation, the first-level optimization is a typical linear constrained 0-1 programming problem, while for a given structure, the second-level optimization is a general nonlinear continuous minimization pro...

Optimization of a Functional Cookie Formulation by Using Response Surface Methodology

International Nuclear Information System (INIS)

Lee, L.Y.; Tan, K.S.; Liew, S.L.

2011-01-01

A functional cookie formulation containing oligo fructose, dietary fibre and lower calorie, fat and sugar contents than conventional cookies was optimized using Response Surface Methodology (RSM). Instant N-Oil II was used as a fat replacer, while Raftilose P95 was used as a sugar substitute with the addition of fructose to enhance sweetness. Selection of the optimal formulation was based on caloric content. An optimized formulation, V1, was obtained from the model Y = 4927.70 - 152.34X 1 - 155.42X 3 + 104.20X 3 2 + 151.71X 3 3 - 95.08X 3 4 , where Instant N-Oil II replaced 30 % of butter and 24.4 %, w/w (30.5 g) fructose replaced 40.0 %, w/w (50.0 g) sucrose. Two additional optimized formulations, S1 and S2, were proposed which contained the same ingredients as V1, but both contained 19.0 %, w/w (23.8 g) Raftilose P95. Also, S2 had a higher fat replacement level (42 %). A reference cookie prepared from a conventional recipe received significantly higher scores (P < 0.05) than the functional cookies V1, S1 and S2 in the sensory evaluation. However, when health benefits of the functional cookies were explained to the panel after the sensory evaluation had concluded, majority of the panelists stated that they would prefer S1, had they known of its health benefits. S1 contained 19.04 % fat, 8.62 % fructose and 0.74 % sucrose, namely, significantly lower fat and sucrose levels and higher fructose content than the conventional cookie. (author)

Block-triangular preconditioners for PDE-constrained optimization

KAUST Repository

Rees, Tyrone

2010-11-26

In this paper we investigate the possibility of using a block-triangular preconditioner for saddle point problems arising in PDE-constrained optimization. In particular, we focus on a conjugate gradient-type method introduced by Bramble and Pasciak that uses self-adjointness of the preconditioned system in a non-standard inner product. We show when the Chebyshev semi-iteration is used as a preconditioner for the relevant matrix blocks involving the finite element mass matrix that the main drawback of the Bramble-Pasciak method-the appropriate scaling of the preconditioners-is easily overcome. We present an eigenvalue analysis for the block-triangular preconditioners that gives convergence bounds in the non-standard inner product and illustrates their competitiveness on a number of computed examples. Copyright © 2010 John Wiley & Sons, Ltd.

Block-triangular preconditioners for PDE-constrained optimization

KAUST Repository

Rees, Tyrone; Stoll, Martin

2010-01-01

In this paper we investigate the possibility of using a block-triangular preconditioner for saddle point problems arising in PDE-constrained optimization. In particular, we focus on a conjugate gradient-type method introduced by Bramble and Pasciak that uses self-adjointness of the preconditioned system in a non-standard inner product. We show when the Chebyshev semi-iteration is used as a preconditioner for the relevant matrix blocks involving the finite element mass matrix that the main drawback of the Bramble-Pasciak method-the appropriate scaling of the preconditioners-is easily overcome. We present an eigenvalue analysis for the block-triangular preconditioners that gives convergence bounds in the non-standard inner product and illustrates their competitiveness on a number of computed examples. Copyright © 2010 John Wiley & Sons, Ltd.

An L∞/L1-Constrained Quadratic Optimization Problem with Applications to Neural Networks

International Nuclear Information System (INIS)

Leizarowitz, Arie; Rubinstein, Jacob

2003-01-01

Pattern formation in associative neural networks is related to a quadratic optimization problem. Biological considerations imply that the functional is constrained in the L ∞ norm and in the L 1 norm. We consider such optimization problems. We derive the Euler-Lagrange equations, and construct basic properties of the maximizers. We study in some detail the case where the kernel of the quadratic functional is finite-dimensional. In this case the optimization problem can be fully characterized by the geometry of a certain convex and compact finite-dimensional set

A technique for determining the optimum mix of logistics service providers of a make-to-order supply chain by formulating and solving a constrained nonlinear cost optimization problem

Directory of Open Access Journals (Sweden)

Mrityunjoy Roy

2013-04-01

Full Text Available In this paper, a technique has been developed to determine the optimum mix of logistic service providers of a make-to-order (MTO supply chain. A serial MTO supply chain with different stages/ processes has been considered. For each stage different logistic service providers with different mean processing lead times, but same lead time variances are available. A realistic assumption that for each stage, the logistic service provider who charges more for his service consumes less processing lead time and vice-versa has been made in our study. Thus for each stage, for each service provider, a combination of cost and mean processing lead time is available. Using these combinations, for each stage, a polynomial curve, expressing cost of that stage as a function of mean processing lead time is fit. Cumulating all such expressions of cost for the different stages along with incorporation of suitable constraints arising out of timely delivery, results in the formulation of a constrained nonlinear cost optimization problem. On solving the problem using mathematica, optimum processing lead time for each stage is obtained. Using these optimum processing lead times and by employing a simple technique the optimum logistic service provider mix of the supply chain along with the corresponding total cost of processing is determined. Finally to examine the effect of changes in different parameters on the optimum total processing cost of the supply chain, sensitivity analysis has been carried out graphically.

Accelerated Optimization in the PDE Framework: Formulations for the Active Contour Case

KAUST Repository

Yezzi, Anthony; Sundaramoorthi, Ganesh

2017-01-01

Following the seminal work of Nesterov, accelerated optimization methods have been used to powerfully boost the performance of first-order, gradient-based parameter estimation in scenarios where second-order optimization strategies are either inapplicable or impractical. Not only does accelerated gradient descent converge considerably faster than traditional gradient descent, but it also performs a more robust local search of the parameter space by initially overshooting and then oscillating back as it settles into a final configuration, thereby selecting only local minimizers with a basis of attraction large enough to contain the initial overshoot. This behavior has made accelerated and stochastic gradient search methods particularly popular within the machine learning community. In their recent PNAS 2016 paper, Wibisono, Wilson, and Jordan demonstrate how a broad class of accelerated schemes can be cast in a variational framework formulated around the Bregman divergence, leading to continuum limit ODE's. We show how their formulation may be further extended to infinite dimension manifolds (starting here with the geometric space of curves and surfaces) by substituting the Bregman divergence with inner products on the tangent space and explicitly introducing a distributed mass model which evolves in conjunction with the object of interest during the optimization process. The co-evolving mass model, which is introduced purely for the sake of endowing the optimization with helpful dynamics, also links the resulting class of accelerated PDE based optimization schemes to fluid dynamical formulations of optimal mass transport.

Accelerated Optimization in the PDE Framework: Formulations for the Active Contour Case

KAUST Repository

Yezzi, Anthony

2017-11-27

Following the seminal work of Nesterov, accelerated optimization methods have been used to powerfully boost the performance of first-order, gradient-based parameter estimation in scenarios where second-order optimization strategies are either inapplicable or impractical. Not only does accelerated gradient descent converge considerably faster than traditional gradient descent, but it also performs a more robust local search of the parameter space by initially overshooting and then oscillating back as it settles into a final configuration, thereby selecting only local minimizers with a basis of attraction large enough to contain the initial overshoot. This behavior has made accelerated and stochastic gradient search methods particularly popular within the machine learning community. In their recent PNAS 2016 paper, Wibisono, Wilson, and Jordan demonstrate how a broad class of accelerated schemes can be cast in a variational framework formulated around the Bregman divergence, leading to continuum limit ODE\\'s. We show how their formulation may be further extended to infinite dimension manifolds (starting here with the geometric space of curves and surfaces) by substituting the Bregman divergence with inner products on the tangent space and explicitly introducing a distributed mass model which evolves in conjunction with the object of interest during the optimization process. The co-evolving mass model, which is introduced purely for the sake of endowing the optimization with helpful dynamics, also links the resulting class of accelerated PDE based optimization schemes to fluid dynamical formulations of optimal mass transport.

Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

International Nuclear Information System (INIS)

Salazar, Daniel; Rocco, Claudio M.; Galvan, Blas J.

2006-01-01

This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature

Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

Energy Technology Data Exchange (ETDEWEB)

Salazar, Daniel [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain) and Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: danielsalazaraponte@gmail.com; Rocco, Claudio M. [Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: crocco@reacciun.ve; Galvan, Blas J. [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain)]. E-mail: bgalvan@step.es

2006-09-15

This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature.

Preventive Security-Constrained Optimal Power Flow Considering UPFC Control Modes

Directory of Open Access Journals (Sweden)

Xi Wu

2017-08-01

Full Text Available The successful application of the unified power flow controller (UPFC provides a new control method for the secure and economic operation of power system. In order to make the full use of UPFC and improve the economic efficiency and static security of a power system, a preventive security-constrained power flow optimization method considering UPFC control modes is proposed in this paper. Firstly, an iterative method considering UPFC control modes is deduced for power flow calculation. Taking into account the influence of different UPFC control modes on the distribution of power flow after N-1 contingency, the optimization model is then constructed by setting a minimal system operation cost and a maximum static security margin as the objective. Based on this model, the particle swarm optimization (PSO algorithm is utilized to optimize power system operating parameters and UPFC control modes simultaneously. Finally, a standard IEEE 30-bus system is utilized to demonstrate that the proposed method fully exploits the potential of static control of UPFC and significantly increases the economic efficiency and static security of the power system.

Comparison of preconditioned Krylov subspace iteration methods for PDE-constrained optimization problems

Czech Academy of Sciences Publication Activity Database

Axelsson, Owe; Farouq, S.; Neytcheva, M.

2017-01-01

Roč. 74, č. 1 (2017), s. 19-37 ISSN 1017-1398 Institutional support: RVO:68145535 Keywords : PDE-constrained optimization problems * finite elements * iterative solution methods * preconditioning Subject RIV: BA - General Mathematics OBOR OECD: Applied mathematics Impact factor: 1.241, year: 2016 https://link.springer.com/article/10.1007%2Fs11075-016-0136-5

Energy efficient LED layout optimization for near-uniform illumination

Science.gov (United States)

Ali, Ramy E.; Elgala, Hany

2016-09-01

In this paper, we consider the problem of designing energy efficient light emitting diodes (LEDs) layout while satisfying the illumination constraints. Towards this objective, we present a simple approach to the illumination design problem based on the concept of the virtual LED. We formulate a constrained optimization problem for minimizing the power consumption while maintaining a near-uniform illumination throughout the room. By solving the resulting constrained linear program, we obtain the number of required LEDs and the optimal output luminous intensities that achieve the desired illumination constraints.

Solving Multi-Resource Constrained Project Scheduling Problem using Ant Colony Optimization

Directory of Open Access Journals (Sweden)

Hsiang-Hsi Huang

2015-01-01

Full Text Available This paper applied Ant Colony Optimization (ACO to develop a resource constraints scheduling model to achieve the resource allocation optimization and the shortest completion time of a project under resource constraints and the activities precedence requirement for projects. Resource leveling is also discussed and has to be achieved under the resource allocation optimization in this research. Testing cases and examples adopted from the international test bank were studied for verifying the effectiveness of the proposed model. The results showed that the solutions of different cases all have a better performance within a reasonable time. These can be obtained through ACO algorithm under the same constrained conditions. A program was written for the proposed model that is able to automatically produce the project resource requirement figure after the project duration is solved.

Constrained Optimization Based on Hybrid Evolutionary Algorithm and Adaptive Constraint-Handling Technique

DEFF Research Database (Denmark)

Wang, Yong; Cai, Zixing; Zhou, Yuren

2009-01-01

A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...

Comparison of preconditioned Krylov subspace iteration methods for PDE-constrained optimization problems

Czech Academy of Sciences Publication Activity Database

Axelsson, Owe; Farouq, S.; Neytcheva, M.

2017-01-01

Roč. 74, č. 1 (2017), s. 19-37 ISSN 1017-1398 Institutional support: RVO:68145535 Keywords : PDE-constrained optimization problems * finite elements * iterative solution method s * preconditioning Subject RIV: BA - General Mathematics OBOR OECD: Applied mathematics Impact factor: 1.241, year: 2016 https://link.springer.com/article/10.1007%2Fs11075-016-0136-5

Coordinated trajectory planning of dual-arm space robot using constrained particle swarm optimization

Science.gov (United States)

Wang, Mingming; Luo, Jianjun; Yuan, Jianping; Walter, Ulrich

2018-05-01

Application of the multi-arm space robot will be more effective than single arm especially when the target is tumbling. This paper investigates the application of particle swarm optimization (PSO) strategy to coordinated trajectory planning of the dual-arm space robot in free-floating mode. In order to overcome the dynamics singularities issue, the direct kinematics equations in conjunction with constrained PSO are employed for coordinated trajectory planning of dual-arm space robot. The joint trajectories are parametrized with Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for coordinated trajectory planning of two kinematically redundant manipulators mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.

Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

Science.gov (United States)

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645

Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

Science.gov (United States)

Bacanin, Nebojsa; Tuba, Milan

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Minimum Time Trajectory Optimization of CNC Machining with Tracking Error Constraints

Directory of Open Access Journals (Sweden)

Qiang Zhang

2014-01-01

Full Text Available An off-line optimization approach of high precision minimum time feedrate for CNC machining is proposed. Besides the ordinary considered velocity, acceleration, and jerk constraints, dynamic performance constraint of each servo drive is also considered in this optimization problem to improve the tracking precision along the optimized feedrate trajectory. Tracking error is applied to indicate the servo dynamic performance of each axis. By using variable substitution, the tracking error constrained minimum time trajectory planning problem is formulated as a nonlinear path constrained optimal control problem. Bang-bang constraints structure of the optimal trajectory is proved in this paper; then a novel constraint handling method is proposed to realize a convex optimization based solution of the nonlinear constrained optimal control problem. A simple ellipse feedrate planning test is presented to demonstrate the effectiveness of the approach. Then the practicability and robustness of the trajectory generated by the proposed approach are demonstrated by a butterfly contour machining example.

Quality by design approach for formulation, evaluation and statistical optimization of diclofenac-loaded ethosomes via transdermal route.

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Jain, Shashank; Patel, Niketkumar; Madan, Parshotam; Lin, Senshang

2015-06-01

The objective of this study was to fabricate and understand ethosomal formulations of diclofenac (DF) for enhanced anti-inflammatory activity using quality by design approach. DF-loaded ethosomal formulations were prepared using 4 × 5 full-factorial design with phosphatidylcholine:cholesterol (PC:CH) ratios ranging between 50:50 and 90:10, and ethanol concentration ranging between 0% and 30% as formulation variables. These formulations were characterized in terms of physicochemical properties and skin permeation kinetics. The interaction of formulation variables had a significant effect on both physicochemical properties and permeation kinetics. The results of multivariate regression analysis illustrated that vesicle size and elasticity of ethosomes were the dominating physicochemical properties affecting skin permeation, and could be suitably controlled by manipulation of formulation variables to optimize the formulation and enhance the skin permeation of DF-loaded ethosomes. The optimized formulation had ethanol concentration of 22.9% and PC:CH ratio of 88.4:11.6, with vesicle size of 144 ± 5 nm, zeta potential of -23.0 ± 3.76 mV, elasticity of 2.48 ± 0.75 and entrapment efficiency of 71 ± 4%. Permeation flux for the optimized formulation was 12.9 ± 1.0 µg/h cm(2), which was significantly higher than the drug-loaded conventional liposome, ethanolic or aqueous solution. The in vivo study indicated that optimized ethosomal hydrogel exhibited enhanced anti-inflammatory activity compared with liposomal and plain drug hydrogel formulations.

Chance-constrained programming approach to natural-gas curtailment decisions

Energy Technology Data Exchange (ETDEWEB)

Guldmann, J M

1981-10-01

This paper presents a modeling methodology for the determination of optimal-curtailment decisions by a gas-distribution utility during a chronic gas-shortage situation. Based on the end-use priority approach, a linear-programming model is formulated, that reallocates the available gas supply among the utility's customers while minimizing fuel switching, unemployment, and utility operating costs. This model is then transformed into a chance-constrained program in order to account for the weather-related variability of the gas requirements. The methodology is applied to the East Ohio Gas Company. 16 references, 2 figures, 3 tables.

Order-constrained linear optimization.

Science.gov (United States)

Tidwell, Joe W; Dougherty, Michael R; Chrabaszcz, Jeffrey S; Thomas, Rick P

2017-11-01

Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order-constrained linear least-squares (OCLO) optimization algorithm that maximizes the linear least-squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out-of-sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat-tailed distributions), OCLO shows less bias and variance, and substantially better out-of-sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data. © 2017 The British Psychological Society.

A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application

Science.gov (United States)

Zhang, Yongsheng; Wei, Heng; Zheng, Kangning

2017-01-01

Considering that metro network expansion brings us with more alternative routes, it is attractive to integrate the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling. Therefore, the formulation, estimation and application of a constrained multinomial probit (CMNP) route choice model in the metro network are carried out in this paper. The utility function is formulated as three components: the compensatory component is a function of influencing factors; the non-compensatory component measures the impacts of routes set on utility; following a multivariate normal distribution, the covariance of error component is structured into three parts, representing the correlation among routes, the transfer variance of route, and the unobserved variance respectively. Considering multidimensional integrals of the multivariate normal probability density function, the CMNP model is rewritten as Hierarchical Bayes formula and M-H sampling algorithm based Monte Carlo Markov Chain approach is constructed to estimate all parameters. Based on Guangzhou Metro data, reliable estimation results are gained. Furthermore, the proposed CMNP model also shows a good forecasting performance for the route choice probabilities calculation and a good application performance for transfer flow volume prediction. PMID:28591188

Mixture experiment methods in the development and optimization of microemulsion formulations.

Science.gov (United States)

Furlanetto, S; Cirri, M; Piepel, G; Mennini, N; Mura, P

2011-06-25

Microemulsion formulations represent an interesting delivery vehicle for lipophilic drugs, allowing for improving their solubility and dissolution properties. This work developed effective microemulsion formulations using glyburide (a very poorly-water-soluble hypoglycaemic agent) as a model drug. First, the area of stable microemulsion (ME) formations was identified using a new approach based on mixture experiment methods. A 13-run mixture design was carried out in an experimental region defined by constraints on three components: aqueous, oil and surfactant/cosurfactant. The transmittance percentage (at 550 nm) of ME formulations (indicative of their transparency and thus of their stability) was chosen as the response variable. The results obtained using the mixture experiment approach corresponded well with those obtained using the traditional approach based on pseudo-ternary phase diagrams. However, the mixture experiment approach required far less experimental effort than the traditional approach. A subsequent 13-run mixture experiment, in the region of stable MEs, was then performed to identify the optimal formulation (i.e., having the best glyburide dissolution properties). Percent drug dissolved and dissolution efficiency were selected as the responses to be maximized. The ME formulation optimized via the mixture experiment approach consisted of 78% surfactant/cosurfacant (a mixture of Tween 20 and Transcutol, 1:1, v/v), 5% oil (Labrafac Hydro) and 17% aqueous phase (water). The stable region of MEs was identified using mixture experiment methods for the first time. Copyright © 2011 Elsevier B.V. All rights reserved.

A constrained maximization formulation to analyze deformation of fiber reinforced elastomeric actuators

Science.gov (United States)

Singh, Gaurav; Krishnan, Girish

2017-06-01

Fiber reinforced elastomeric enclosures (FREEs) are soft and smart pneumatic actuators that deform in a predetermined fashion upon inflation. This paper analyzes the deformation behavior of FREEs by formulating a simple calculus of variations problem that involves constrained maximization of the enclosed volume. The model accurately captures the deformed shape for FREEs with any general fiber angle orientation, and its relation with actuation pressure, material properties and applied load. First, the accuracy of the model is verified with existing literature and experiments for the popular McKibben pneumatic artificial muscle actuator with two equal and opposite families of helically wrapped fibers. Then, the model is used to predict and experimentally validate the deformation behavior of novel rotating-contracting FREEs, for which no prior literature exist. The generality of the model enables conceptualization of novel FREEs whose fiber orientations vary arbitrarily along the geometry. Furthermore, the model is deemed to be useful in the design synthesis of fiber reinforced elastomeric actuators for general axisymmetric desired motion and output force requirement.

A First-order Prediction-Correction Algorithm for Time-varying (Constrained) Optimization: Preprint

Energy Technology Data Exchange (ETDEWEB)

Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Simonetto, Andrea [Universite catholique de Louvain

2017-07-25

This paper focuses on the design of online algorithms based on prediction-correction steps to track the optimal solution of a time-varying constrained problem. Existing prediction-correction methods have been shown to work well for unconstrained convex problems and for settings where obtaining the inverse of the Hessian of the cost function can be computationally affordable. The prediction-correction algorithm proposed in this paper addresses the limitations of existing methods by tackling constrained problems and by designing a first-order prediction step that relies on the Hessian of the cost function (and do not require the computation of its inverse). Analytical results are established to quantify the tracking error. Numerical simulations corroborate the analytical results and showcase performance and benefits of the algorithms.

Enhanced Ungual Permeation of Terbinafine HCl Delivered Through Liposome-Loaded Nail Lacquer Formulation Optimized by QbD Approach.

Science.gov (United States)

Shah, Viral H; Jobanputra, Amee

2018-01-01

The present investigation focused on developing, optimizing, and evaluating a novel liposome-loaded nail lacquer formulation for increasing the transungual permeation flux of terbinafine HCl for efficient treatment of onychomycosis. A three-factor, three-level, Box-Behnken design was employed for optimizing process and formulation parameters of liposomal formulation. Liposomes were formulated by thin film hydration technique followed by sonication. Drug to lipid ratio, sonication amplitude, and sonication time were screened as independent variables while particle size, PDI, entrapment efficiency, and zeta potential were selected as quality attributes for liposomal formulation. Multiple regression analysis was employed to construct a second-order quadratic polynomial equation and contour plots. Design space (overlay plot) was generated to optimize a liposomal system, with software-suggested levels of independent variables that could be transformed to desired responses. The optimized liposome formulation was characterized and dispersed in nail lacquer which was further evaluated for different parameters. Results depicted that the optimized terbinafine HCl-loaded liposome formulation exhibited particle size of 182 nm, PDI of 0.175, zeta potential of -26.8 mV, and entrapment efficiency of 80%. Transungual permeability flux of terbinafine HCl through liposome-dispersed nail lacquer formulation was observed to be significantly higher in comparison to nail lacquer with a permeation enhancer. The developed formulation was also observed to be as efficient as pure drug dispersion in its antifungal activity. Thus, it was concluded that the developed formulation can serve as an efficient tool for enhancing the permeability of terbinafine HCl across human nail plate thereby improving its therapeutic efficiency.

Improved Formulation for the Optimization of Wind Turbine Placement in a Wind Farm

Directory of Open Access Journals (Sweden)

Zong Woo Geem

2013-01-01

Full Text Available As an alternative to fossil fuels, wind can be considered because it is a renewable and greenhouse gas-free natural resource. When wind power is generated by wind turbines in a wind farm, the optimal placement of turbines is critical because different layouts produce different efficiencies. The objective of the wind turbine placement problem is to maximize the generated power while minimizing the cost in installing the turbines. This study proposes an efficient optimization formulation for the optimal layout of wind turbine placements under the resources (e.g., number of turbines or budget limit by introducing corresponding constraints. The proposed formulation gave users more conveniences in considering resources and budget bounds. After performing the optimization, results were compared using two different methods (branch and bound method and genetic algorithm and two different objective functions.

Preparation of finasteride capsules-loaded drug nanoparticles: formulation, optimization, in vitro, and pharmacokinetic evaluation

Directory of Open Access Journals (Sweden)

Ahmed TA

2016-02-01

Full Text Available Tarek A Ahmed1,2 1Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia; 2Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Al-Azhar University, Cairo, Egypt Abstract: In this study, optimized freeze-dried finasteride nanoparticles (NPs were prepared from drug nanosuspension formulation that was developed using the bottom–up technique. The effects of four formulation and processing variables that affect the particle size and solubility enhancement of the NPs were explored using the response surface optimization design. The optimized formulation was morphologically characterized using transmission electron microscopy (TEM. Physicochemical interaction among the studied components was investigated. Crystalline change was investigated using X-ray powder diffraction (XRPD. Crystal growth of the freeze-dried NPs was compared to the corresponding aqueous drug nanosuspension. Freeze-dried NPs formulation was subsequently loaded into hard gelatin capsules that were examined for in vitro dissolution and pharmacokinetic behavior. Results revealed that in most of the studied variables, some of the quadratic and interaction effects had a significant effect on the studied responses. TEM image illustrated homogeneity and shape of the prepared NPs. No interaction among components was noticed. XRPD confirmed crystalline state change in the optimized NPs. An enhancement in the dissolution rate of more than 2.5 times from capsules filled with optimum drug NPs, when compared to capsules filled with pure drug, was obtained. Crystal growth, due to Ostwald ripening phenomenon and positive Gibbs free energy, was reduced following lyophilization of the nanosuspension formulation. Pharmacokinetic parameters from drug NPs were superior to that of pure drug and drug microparticles. In conclusion, freeze-dried NPs based on drug nanosuspension formulation is a successful

[Optimization of Formulation and Process of Paclitaxel PEGylated Liposomes by Box-Behnken Response Surface Methodology].

Science.gov (United States)

Shi, Ya-jun; Zhang, Xiao-feil; Guo, Qiu-ting

2015-12-01

To develop a procedure for preparing paclitaxel encapsulated PEGylated liposomes. The membrane hydration followed extraction method was used to prepare PEGylated liposomes. The process and formulation variables were optimized by "Box-Behnken Design (BBD)" of response surface methodology (RSM) with the amount of Soya phosphotidylcholine (SPC) and PEG2000-DSPE as well as the rate of SPC to drug as independent variables and entrapment efficiency as dependent variables for optimization of formulation variables while temperature, pressure and cycle times as independent variables and particle size and polydispersion index as dependent variables for process variables. The optimized liposomal formulation was characterized for particle size, Zeta potential, morphology and in vitro drug release. For entrapment efficiency, particle size, polydispersion index, Zeta potential, and in vitro drug release of PEGylated liposomes was found to be 80.3%, (97.15 ± 14.9) nm, 0.117 ± 0.019, (-30.3 ± 3.7) mV, and 37.4% in 24 h, respectively. The liposomes were found to be small, unilamellar and spherical with smooth surface as seen in transmission electron microscopy. The Box-Behnken response surface methodology facilitates the formulation and optimization of paclitaxel PEGylated liposomes.

Formulation and Optimization of Multiparticulate Drug Delivery System Approach for High Drug Loading.

Science.gov (United States)

Shah, Neha; Mehta, Tejal; Gohel, Mukesh

2017-08-01

The aim of the present work was to develop and optimize multiparticulate formulation viz. pellets of naproxen by employing QbD and risk assessment approach. Mixture design with extreme vertices was applied to the formulation with high loading of drug (about 90%) and extrusion-spheronization as a process for manufacturing pellets. Independent variables chosen were level of microcrystalline cellulose (MCC)-X 1 , polyvinylpyrrolidone K-90 (PVP K-90)-X 2 , croscarmellose sodium (CCS)-X 3 , and polacrilin potassium (PP)-X 4 . Dependent variables considered were disintegration time (DT)-Y 1 , sphericity-Y 2 , and percent drug release-Y 3 . The formulation was optimized based on the batches generated by MiniTab 17 software. The batch with maximum composite desirability (0.98) proved to be optimum. From the evaluation of design batches, it was observed that, even in low variation, the excipients affect the pelletization property of the blend and also the final drug release. In conclusion, pellets with high drug loading can be effectively manufactured and optimized systematically using QbD approach.

Optimization and formulation design of carbopol loaded Piroxicam gel using novel penetration enhancers.

Science.gov (United States)

Chaudhary, Hema; Rohilla, Ajay; Rathee, Permender; Kumar, Vikash

2013-04-01

The aim of the study was to develop and optimize Piroxicam transdermal gel formulation using three-factor, three-level Box-Behnken design by deriving a second-order polynomial equation to construct contour plots for prediction of responses as three selected independent variables with ratio of carbopol 974 (X1), ratio of propylene glycol (PG) (X2) and ratio of ethanol (X3). The dependent variables studied were the skin permeation rate of piroxicam (Y1), viscosity of the gel (Y2) and pH of the gel (Y3). Response surface plots were drawn, statistical validity of the polynomials was established to find the compositions of optimized formulation which was evaluated using the vertical Franz-type diffusion cell. The permeation rate of piroxicam increased proportionally with ethanol concentration but decreased with polymer concentration. The design demonstrated the role of the derived polynomial equation and contour plots in predicting the values of dependent variables for the preparation and optimization of gel formulation. Copyright © 2013 Elsevier B.V. All rights reserved.

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

Science.gov (United States)

Ono, Masahiro; Kuwata, Yoshiaki

2013-01-01

A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

On projection methods, convergence and robust formulations in topology optimization

DEFF Research Database (Denmark)

Wang, Fengwen; Lazarov, Boyan Stefanov; Sigmund, Ole

2011-01-01

alleviated using various projection methods. In this paper we show that simple projection methods do not ensure local mesh-convergence and propose a modified robust topology optimization formulation based on erosion, intermediate and dilation projections that ensures both global and local mesh-convergence.......Mesh convergence and manufacturability of topology optimized designs have previously mainly been assured using density or sensitivity based filtering techniques. The drawback of these techniques has been gray transition regions between solid and void parts, but this problem has recently been...

Optimal dispatch in dynamic security constrained open power market

International Nuclear Information System (INIS)

Singh, S.N.; David, A.K.

2002-01-01

Power system security is a new concern in the competitive power market operation, because the integration of the system controller and the generation owner has been broken. This paper presents an approach for dynamic security constrained optimal dispatch in restructured power market environment. The transient energy margin using transient energy function (TEF) approach has been used to calculate the stability margin of the system and a hybrid method is applied to calculate the approximate unstable equilibrium point (UEP) that is used to calculate the exact UEP and thus, the energy margin using TEF. The case study results illustrated on two systems shows that the operating mechanisms are compatible with the new business environment. (author)

Comparison of phase-constrained parallel MRI approaches: Analogies and differences.

Science.gov (United States)

Blaimer, Martin; Heim, Marius; Neumann, Daniel; Jakob, Peter M; Kannengiesser, Stephan; Breuer, Felix A

2016-03-01

Phase-constrained parallel MRI approaches have the potential for significantly improving the image quality of accelerated MRI scans. The purpose of this study was to investigate the properties of two different phase-constrained parallel MRI formulations, namely the standard phase-constrained approach and the virtual conjugate coil (VCC) concept utilizing conjugate k-space symmetry. Both formulations were combined with image-domain algorithms (SENSE) and a mathematical analysis was performed. Furthermore, the VCC concept was combined with k-space algorithms (GRAPPA and ESPIRiT) for image reconstruction. In vivo experiments were conducted to illustrate analogies and differences between the individual methods. Furthermore, a simple method of improving the signal-to-noise ratio by modifying the sampling scheme was implemented. For SENSE, the VCC concept was mathematically equivalent to the standard phase-constrained formulation and therefore yielded identical results. In conjunction with k-space algorithms, the VCC concept provided more robust results when only a limited amount of calibration data were available. Additionally, VCC-GRAPPA reconstructed images provided spatial phase information with full resolution. Although both phase-constrained parallel MRI formulations are very similar conceptually, there exist important differences between image-domain and k-space domain reconstructions regarding the calibration robustness and the availability of high-resolution phase information. © 2015 Wiley Periodicals, Inc.

JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language.

Science.gov (United States)

Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D

2017-01-25

Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open

Optimization of carboxylate-terminated poly(amidoamine) dendrimer-mediated cisplatin formulation.

Science.gov (United States)

Kulhari, Hitesh; Pooja, Deep; Singh, Mayank K; Chauhan, Abhay S

2015-02-01

Abstract Cisplatin is mainly used in the treatment of ovarian, head and neck and testicular cancer. Poor solubility and non-specific interactions causes hurdles in the development of successful cisplatin formulation. There were few reports on poly(amidoamine) (PAMAM) dendrimer-cisplatin complexes for anticancer treatment. But the earlier research was mainly focused on therapeutic effect of PAMAM dendrimer-cisplatin complex, with less attention paid on the formulation development of these complexes. Objective of the present study is to optimize and validate the carboxylate-terminated, EDA core PAMAM dendrimer-based cisplatin formulation with respect to various variables such as dendrimer core, generation, drug entrapment, purification, yield, reproducibility, stability, storage and in-vitro release. Dendrimer-cisplatin complex was prepared by an efficient method which significantly increases the % platinum (Pt) content along with the product yield. Dendrimers showed reproducible (∼27%) platinum loading by weight. Variation in core and generations does not produce significant change in the % Pt content. Percentage Pt content of dendrimeric formulation increases with increase in drug/dendrimer mole ratio. Formulation with low drug/dendrimer mole ratio showed delayed release compared to the higher drug/dendrimer mole ratio; these dendrimer formulations are stable in room temperature. In vitro release profiles of the stored dendrimer-cisplatin samples showed comparatively slow release of cisplatin, which may be due to formation of strong bond between cisplatin and dendrimer. This study will contribute to create a fine print for the formulation development of PAMAM dendrimer-cisplatin complexes.

Automated beam steering using optimal control

Energy Technology Data Exchange (ETDEWEB)

Allen, C. K. (Christopher K.)

2004-01-01

We present a steering algorithm which, with the aid of a model, allows the user to specify beam behavior throughout a beamline, rather than just at specified beam position monitor (BPM) locations. The model is used primarily to compute the values of the beam phase vectors from BPM measurements, and to define cost functions that describe the steering objectives. The steering problem is formulated as constrained optimization problem; however, by applying optimal control theory we can reduce it to an unconstrained optimization whose dimension is the number of control signals.

Strong diffusion formulation of Markov chain ensembles and its optimal weaker reductions

Science.gov (United States)

Güler, Marifi

2017-10-01

Two self-contained diffusion formulations, in the form of coupled stochastic differential equations, are developed for the temporal evolution of state densities over an ensemble of Markov chains evolving independently under a common transition rate matrix. Our first formulation derives from Kurtz's strong approximation theorem of density-dependent Markov jump processes [Stoch. Process. Their Appl. 6, 223 (1978), 10.1016/0304-4149(78)90020-0] and, therefore, strongly converges with an error bound of the order of lnN /N for ensemble size N . The second formulation eliminates some fluctuation variables, and correspondingly some noise terms, within the governing equations of the strong formulation, with the objective of achieving a simpler analytic formulation and a faster computation algorithm when the transition rates are constant or slowly varying. There, the reduction of the structural complexity is optimal in the sense that the elimination of any given set of variables takes place with the lowest attainable increase in the error bound. The resultant formulations are supported by numerical simulations.

Characterization of new functionalized calcium carbonate-polycaprolactone composite material for application in geometry-constrained drug release formulation development.

Science.gov (United States)

Wagner-Hattler, Leonie; Schoelkopf, Joachim; Huwyler, Jörg; Puchkov, Maxim

2017-10-01

A new mineral-polymer composite (FCC-PCL) performance was assessed to produce complex geometries to aid in development of controlled release tablet formulations. The mechanical characteristics of a developed material such as compactibility, compressibility and elastoplastic deformation were measured. The results and comparative analysis versus other common excipients suggest efficient formation of a complex, stable and impermeable geometries for constrained drug release modifications under compression. The performance of the proposed composite material has been tested by compacting it into a geometrically altered tablet (Tablet-In-Cup, TIC) and the drug release was compared to commercially available product. The TIC device exhibited a uniform surface, showed high physical stability, and showed absence of friability. FCC-PCL composite had good binding properties and good compactibility. It was possible to reveal an enhanced plasticity characteristic of a new material which was not present in the individual components. The presented FCC-PCL composite mixture has the potential to become a successful tool to formulate controlled-release dosage solid forms.

Transungual Gel of Terbinafine Hydrochloride for the Management of Onychomycosis: Formulation, Optimization, and Evaluation.

Science.gov (United States)

Thatai, Purva; Sapra, Bharti

2017-08-01

The present study was aimed to optimize, develop, and evaluate microemulsion and microemulsion-based gel as a vehicle for transungual drug delivery of terbinafine hydrochloride for the treatment of onychomycosis. D-optimal mixture experimental design was adopted to optimize the composition of microemulsion having amount of oil (X 1 ), Smix (mixture of surfactant and cosurfactant; X 2 ), and water (X 3 ) as the independent variables. The formulations were assessed for permeation (micrograms per square centimeter per hour; Y 1 ), particle size (nanometer; Y 2 ), and solubility of the drug in the formulation (milligrams per milliliter; Y 3 ). The microemulsion containing 3.05% oil, 24.98% Smix, and 71.96% water was selected as the optimized formulation. The microemulsion-based gel showed better penetration (∼5 folds) as well as more retention (∼9 fold) in the animal hoof as compared to the commercial cream. The techniques used to screen penetration enhancers (hydration enhancement factor, ATR-FTIR, SEM, and DSC) revealed the synergistic effect of combination of urea and n-acetyl cysteine in disruption of the structure of hoof and hence, leading to enhanced penetration of drug.

Systematic evaluation of common lubricants for optimal use in tablet formulation.

Science.gov (United States)

Paul, Shubhajit; Sun, Changquan Calvin

2018-05-30

As an essential formulation component for large-scale tablet manufacturing, the lubricant preserves tooling by reducing die-wall friction. Unfortunately, lubrication also often results in adverse effects on tablet characteristics, such as prolonged disintegration, slowed dissolution, and reduced mechanical strength. Therefore, the choice of lubricant and its optimal concentration in a tablet formulation is a critical decision in tablet formulation development to attain low die-wall friction while minimizing negative impact on other tablet properties. Three commercially available tablet lubricants, i.e., magnesium stearate, sodium stearyl fumerate, and stearic acid, were systematically investigated in both plastic and brittle matrices to elucidate their effects on reducing die-wall friction, tablet strength, tablet hardness, tablet friability, and tablet disintegration kinetics. Clear understanding of the lubrication efficiency of commonly used lubricants as well as their impact on tablet characteristics would help future tablet formulation efforts. Copyright © 2018 Elsevier B.V. All rights reserved.

Mesh dependence in PDE-constrained optimisation an application in tidal turbine array layouts

CERN Document Server

Schwedes, Tobias; Funke, Simon W; Piggott, Matthew D

2017-01-01

This book provides an introduction to PDE-constrained optimisation using finite elements and the adjoint approach. The practical impact of the mathematical insights presented here are demonstrated using the realistic scenario of the optimal placement of marine power turbines, thereby illustrating the real-world relevance of best-practice Hilbert space aware approaches to PDE-constrained optimisation problems. Many optimisation problems that arise in a real-world context are constrained by partial differential equations (PDEs). That is, the system whose configuration is to be optimised follows physical laws given by PDEs. This book describes general Hilbert space formulations of optimisation algorithms, thereby facilitating optimisations whose controls are functions of space. It demonstrates the importance of methods that respect the Hilbert space structure of the problem by analysing the mathematical drawbacks of failing to do so. The approaches considered are illustrated using the optimisation problem arisin...

Formulation development and optimization of Lamivudine 300 mg and Tenofovir Disoproxil Fumarate (TDF 300 mg FDC tablets by D-optimal mixture design

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Prosper Tibalinda

2016-12-01

Full Text Available The usage of fixed dose combination (FDC tablets of Lamivudine and Tenofovir Disoproxil Fumarate (TDF is increasing due to increased incidences of HIV/Hepatitis B and HIV/TB co-infections. This is likely to increase the financial crisis due to limited resources for funding procurement of ready-made products from the pharmaceuticals manufacturing leading countries. Therefore, production of local oral tablets containing Lamivudine and TDF FDC is inevitable. Lamivudine 300 mg/TDF 300 mg tablets were developed and optimized by D-optimal mixture design and produced by direct compression technique. Twenty trial formulations with independent variables, including PVP-CL 1–12.00%, PVP-K30 1–10.00%, starch-1500 2.5–12.5% and Avicel-PH102 2–19.25% were prepared by direct compression technique. The formulations were assessed on assay, dissolution, friability, weight variation and disintegration time. It was found that assay ranged from 98.13–101.95% for Lamivudine, 98.25–102.84 for TDF, both were within the in-house assay specification of 95 to 105%. Dissolution at single point was above 80% for Lamivudine 93.96–100.55% and 95.85–103.15% for TDF, disintegration time was between 1.92–66.33 min and friability 0.06–12.56%. Out of twenty formulation trials, eight formulations had all parameters in proven acceptable range. On optimization, one formulation with independent variables, PVP-CL 5.67%, PVP-K30 1.00%, Starch-1500 5.76% was selected. The optimized formulation was comparable to the reference product on the market with similarity factor (f2 and difference factor (f1 within the acceptable range for both Lamivudine and TDF.

Stability Constrained Efficiency Optimization for Droop Controlled DC-DC Conversion System

DEFF Research Database (Denmark)

Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.

2013-01-01

implementing tertiary regulation. Moreover, system dynamic is affected when shifting VRs. Therefore, the stability is considered in optimization by constraining the eigenvalues arising from dynamic state space model of the system. Genetic algorithm is used in searching for global efficiency optimum while....... As the efficiency of each converter changes with output power, virtual resistances (VRs) are set as decision variables for adjusting power sharing proportion among converters. It is noteworthy that apart from restoring the voltage deviation, secondary control plays an important role to stabilize dc bus voltage when...

Test Summary Report Vitrification Demonstration of an Optimized Hanford C-106/AY-102 Waste-Glass Formulation

International Nuclear Information System (INIS)

Goles, Ronald W.; Buchmiller, William C.; Hymas, Charles R.; MacIsaac, Brett D.

2002-01-01

In order to further the goal of optimizing Hanford?s HLW borosilicate flowsheet, a glass formulation effort was launched to develop an advanced high-capacity waste form exhibiting acceptable leach and crystal formation characteristics. A simulated C-106/AY-102 waste envelop inclusive of LAW pretreatment products was chosen as the subject of these nonradioactive optimization efforts. To evaluate this optimized borosilicate waste formulation under continuous dynamic vitrification conditions, a research-scale Joule-heated ceramic melter was used to demonstrate the advanced waste form?s flowsheet. The main objectives of this melter test was to evaluate (1) the processing characteristics of the newly formulated C-106/AY-102 surrogate melter-feed stream, (2) the effectiveness of sucrose as a glass-oxidation-state modifier, and (3) the impact of this reductant upon processing rates

Fast optimization of statistical potentials for structurally constrained phylogenetic models

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Rodrigue Nicolas

2009-09-01

Full Text Available Abstract Background Statistical approaches for protein design are relevant in the field of molecular evolutionary studies. In recent years, new, so-called structurally constrained (SC models of protein-coding sequence evolution have been proposed, which use statistical potentials to assess sequence-structure compatibility. In a previous work, we defined a statistical framework for optimizing knowledge-based potentials especially suited to SC models. Our method used the maximum likelihood principle and provided what we call the joint potentials. However, the method required numerical estimations by the use of computationally heavy Markov Chain Monte Carlo sampling algorithms. Results Here, we develop an alternative optimization procedure, based on a leave-one-out argument coupled to fast gradient descent algorithms. We assess that the leave-one-out potential yields very similar results to the joint approach developed previously, both in terms of the resulting potential parameters, and by Bayes factor evaluation in a phylogenetic context. On the other hand, the leave-one-out approach results in a considerable computational benefit (up to a 1,000 fold decrease in computational time for the optimization procedure. Conclusion Due to its computational speed, the optimization method we propose offers an attractive alternative for the design and empirical evaluation of alternative forms of potentials, using large data sets and high-dimensional parameterizations.

Formulation, optimization, and evaluation of self-emulsifying drug delivery systems of nevirapine.

Science.gov (United States)

Chintalapudi, Ramprasad; Murthy, T E G K; Lakshmi, K Rajya; Manohar, G Ganesh

2015-01-01

The aim of the present study was to formulate and optimize the self-emulsifying drug delivery systems (SEDDS) of nevirapine (NVP) by use of 2(2) factorial designs to enhance the oral absorption of NVP by improving its solubility, dissolution rate, and diffusion profile. SEDDS are the isotropic mixtures of oil, surfactant, co-surfactant and drug that form oil in water microemulsion when introduced into the aqueous phase under gentle agitation. Solubility of NVP in different oils, surfactants, and co-surfactants was determined for the screening of excipients. Pseudo-ternary phase diagrams were constructed by the aqueous titration method, and formulations were developed based on the optimum excipient combinations with the help of data obtained through the maximum micro emulsion region containing combinations of oil, surfactant, and co-surfactant. The formulations of SEDDS were optimized by 2(2) factorial designs. The optimum formulation of SEDDS contains 32.5% oleic acid, 44.16% tween 20, and 11.9% polyethylene glycol 600 as oil, surfactant, and co-surfactant respectively. The SEDDS was evaluated for the following drug content, self-emulsification time, rheological properties, zeta potential, in vitro diffusion studies, thermodynamic stability studies, and in vitro dissolution studies. An increase in dissolution was achieved by SEDDS compared to pure form of NVP. Overall, this study suggests that the dissolution and oral bioavailability of NVP could be improved by SEDDS technology.

A simple boundary element formulation for shape optimization of 2D continuous structures

International Nuclear Information System (INIS)

Luciano Mendes Bezerra; Jarbas de Carvalho Santos Junior; Arlindo Pires Lopes; Andre Luiz; Souza, A.C.

2005-01-01

For the design of nuclear equipment like pressure vessels, steam generators, and pipelines, among others, it is very important to optimize the shape of the structural systems to withstand prescribed loads such as internal pressures and prescribed or limiting referential values such as stress or strain. In the literature, shape optimization of frame structural systems is commonly found but the same is not true for continuous structural systems. In this work, the Boundary Element Method (BEM) is applied to simple problems of shape optimization of 2D continuous structural systems. The proposed formulation is based on the BEM and on deterministic optimization methods of zero and first order such as Powell's, Conjugate Gradient, and BFGS methods. Optimal characterization for the geometric configuration of 2D structure is obtained with the minimization of an objective function. Such function is written in terms of referential values (such as loads, stresses, strains or deformations) prescribed at few points inside or at the boundary of the structure. The use of the BEM for shape optimization of continuous structures is attractive compared to other methods that discretized the whole continuous. Several numerical examples of the application of the proposed formulation to simple engineering problems are presented. (authors)

Generation and reserve dispatch in a competitive market using constrained particle swarm optimization

International Nuclear Information System (INIS)

Azadani, E. Nasr; Hosseinian, S.H.; Moradzadeh, B.

2010-01-01

Competitive bidding for energy and ancillary services is increasingly recognized as an important part of electricity markets. In addition, the transmission capacity limits should be considered to optimize the total market cost. In this paper, a new approach based on constrained particle swarm optimization (CPSO) is developed to deal with the multi-product (energy and reserve) and multi-area electricity market dispatch problem. Constraint handling is based on particle ranking and uniform distribution. CPSO method offers a new solution for optimizing the total market cost in a multi-area competitive electricity market considering the system constraints. The proposed technique shows promising performance for smooth and non smooth cost function as well. Three different systems are examined to demonstrate the effectiveness and the accuracy of the proposed algorithm. (author)

Formulation and Optimization of Lansoprazole Pellets Using Factorial Design Prepared by Extrusion-Spheronization Technique Using Carboxymethyl Tamarind Kernel Powder.

Science.gov (United States)

Muley, Sagar Sopanrao; Nandgude, Tanaji; Poddar, Sushilkumar

2017-01-01

In the present study, Lansoprazole pellets were prepared employing a novel excipient Carboxymethyl tamarind kernel powder (CMTKP) using extrusion-spheronization technique. Various research studies including patents have been carried out on this polymer. Pellet formulation was optimized for formulation parameters (concentration of microcrystalline cellulose, CMTKP, croscarmellose sodium and isopropyl alcohol). Process parameters (speed and duration of spheronization) were optimized using factorial design. The pellets were evaluated for yield, bulk and tapped density, particle size, hardness, drug content, disintegration time and drug release. The optimized batch showed 93.53% yield, 0.307 kg/cm2 hardness, 2.15 mm average particle size, 292 sec disintegration time and 90.46% drug content. Drug release of the optimized batch (2F7) and marketed formulation (LANZOL cap) was found to be 82.33% and 80.07%, respectively. An accelerated study indicated that optimized formulation was stable. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

Science.gov (United States)

Liu, Qingshan; Wang, Jun

2011-04-01

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

Memory and Energy Optimization Strategies for Multithreaded Operating System on the Resource-Constrained Wireless Sensor Node

Directory of Open Access Journals (Sweden)

Xing Liu

2014-12-01

Full Text Available Memory and energy optimization strategies are essential for the resource-constrained wireless sensor network (WSN nodes. In this article, a new memory-optimized and energy-optimized multithreaded WSN operating system (OS LiveOS is designed and implemented. Memory cost of LiveOS is optimized by using the stack-shifting hybrid scheduling approach. Different from the traditional multithreaded OS in which thread stacks are allocated statically by the pre-reservation, thread stacks in LiveOS are allocated dynamically by using the stack-shifting technique. As a result, memory waste problems caused by the static pre-reservation can be avoided. In addition to the stack-shifting dynamic allocation approach, the hybrid scheduling mechanism which can decrease both the thread scheduling overhead and the thread stack number is also implemented in LiveOS. With these mechanisms, the stack memory cost of LiveOS can be reduced more than 50% if compared to that of a traditional multithreaded OS. Not is memory cost optimized, but also the energy cost is optimized in LiveOS, and this is achieved by using the multi-core “context aware” and multi-core “power-off/wakeup” energy conservation approaches. By using these approaches, energy cost of LiveOS can be reduced more than 30% when compared to the single-core WSN system. Memory and energy optimization strategies in LiveOS not only prolong the lifetime of WSN nodes, but also make the multithreaded OS feasible to run on the memory-constrained WSN nodes.

Inexact Multistage Stochastic Chance Constrained Programming Model for Water Resources Management under Uncertainties

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Hong Zhang

2017-01-01

Full Text Available In order to formulate water allocation schemes under uncertainties in the water resources management systems, an inexact multistage stochastic chance constrained programming (IMSCCP model is proposed. The model integrates stochastic chance constrained programming, multistage stochastic programming, and inexact stochastic programming within a general optimization framework to handle the uncertainties occurring in both constraints and objective. These uncertainties are expressed as probability distributions, interval with multiply distributed stochastic boundaries, dynamic features of the long-term water allocation plans, and so on. Compared with the existing inexact multistage stochastic programming, the IMSCCP can be used to assess more system risks and handle more complicated uncertainties in water resources management systems. The IMSCCP model is applied to a hypothetical case study of water resources management. In order to construct an approximate solution for the model, a hybrid algorithm, which incorporates stochastic simulation, back propagation neural network, and genetic algorithm, is proposed. The results show that the optimal value represents the maximal net system benefit achieved with a given confidence level under chance constraints, and the solutions provide optimal water allocation schemes to multiple users over a multiperiod planning horizon.

Global optimization for overall HVAC systems - Part I problem formulation and analysis

International Nuclear Information System (INIS)

Lu Lu; Cai Wenjian; Chai, Y.S.; Xie Lihua

2005-01-01

This paper presents the global optimization technologies for overall heating, ventilating and air conditioning (HVAC) systems. The objective function of global optimization and constraints are formulated based on mathematical models of the major components. All these models are associated with power consumption components and heat exchangers for transferring cooling load. The characteristics of all the major components are briefly introduced by models, and the interactions between them are analyzed and discussed to show the complications of the problem. According to the characteristics of the operating components, the complicated original optimization problem for overall HVAC systems is transformed and simplified into a compact form ready for optimization

A generalized fuzzy credibility-constrained linear fractional programming approach for optimal irrigation water allocation under uncertainty

Science.gov (United States)

Zhang, Chenglong; Guo, Ping

2017-10-01

The vague and fuzzy parametric information is a challenging issue in irrigation water management problems. In response to this problem, a generalized fuzzy credibility-constrained linear fractional programming (GFCCFP) model is developed for optimal irrigation water allocation under uncertainty. The model can be derived from integrating generalized fuzzy credibility-constrained programming (GFCCP) into a linear fractional programming (LFP) optimization framework. Therefore, it can solve ratio optimization problems associated with fuzzy parameters, and examine the variation of results under different credibility levels and weight coefficients of possibility and necessary. It has advantages in: (1) balancing the economic and resources objectives directly; (2) analyzing system efficiency; (3) generating more flexible decision solutions by giving different credibility levels and weight coefficients of possibility and (4) supporting in-depth analysis of the interrelationships among system efficiency, credibility level and weight coefficient. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. Therefore, optimal irrigation water allocation solutions from the GFCCFP model can be obtained. Moreover, factorial analysis on the two parameters (i.e. λ and γ) indicates that the weight coefficient is a main factor compared with credibility level for system efficiency. These results can be effective for support reasonable irrigation water resources management and agricultural production.

A New Optimization Strategy to Improve Design of Hydrogen Network Based Formulation of Hydrogen Consumers

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M. R. S. Birjandi

2018-03-01

Full Text Available This paper describes a shortcut model for formulating hydrogen consumers in hydrogen network based on inlet/outlet flow rate and inlet/outlet hydrogen purity. The formulation procedure is obtained using nonlinear regression of industrial data and represents the relationship between the flow rate and purity of outlet and inlet streams. The proposed model can estimate outlet flow rate and purity of hydrogen by changing inlet flow rate and purity of hydrogen. The shortcut model is used to achieve optimal operation of consumers and it optimizes hydrogen network design.

Pareto-optimal estimates that constrain mean California precipitation change

Science.gov (United States)

Langenbrunner, B.; Neelin, J. D.

2017-12-01

Global climate model (GCM) projections of greenhouse gas-induced precipitation change can exhibit notable uncertainty at the regional scale, particularly in regions where the mean change is small compared to internal variability. This is especially true for California, which is located in a transition zone between robust precipitation increases to the north and decreases to the south, and where GCMs from the Climate Model Intercomparison Project phase 5 (CMIP5) archive show no consensus on mean change (in either magnitude or sign) across the central and southern parts of the state. With the goal of constraining this uncertainty, we apply a multiobjective approach to a large set of subensembles (subsets of models from the full CMIP5 ensemble). These constraints are based on subensemble performance in three fields important to California precipitation: tropical Pacific sea surface temperatures, upper-level zonal winds in the midlatitude Pacific, and precipitation over the state. An evolutionary algorithm is used to sort through and identify the set of Pareto-optimal subensembles across these three measures in the historical climatology, and we use this information to constrain end-of-century California wet season precipitation change. This technique narrows the range of projections throughout the state and increases confidence in estimates of positive mean change. Furthermore, these methods complement and generalize emergent constraint approaches that aim to restrict uncertainty in end-of-century projections, and they have applications to even broader aspects of uncertainty quantification, including parameter sensitivity and model calibration.

Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways.

Science.gov (United States)

Mitsos, Alexander; Melas, Ioannis N; Morris, Melody K; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Alexopoulos, Leonidas G

2012-01-01

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

Non Linear Programming (NLP formulation for quantitative modeling of protein signal transduction pathways.

Directory of Open Access Journals (Sweden)

Alexander Mitsos

Full Text Available Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i excessive CPU time requirements and ii loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

A Continuous Formulation for Logical Decisions in Differential Algebraic Systems using Mathematical Programs with Complementarity Constraints

Directory of Open Access Journals (Sweden)

Kody M. Powell

2016-03-01

Full Text Available This work presents a methodology to represent logical decisions in differential algebraic equation simulation and constrained optimization problems using a set of continuous algebraic equations. The formulations may be used when state variables trigger a change in process dynamics, and introduces a pseudo-binary decision variable, which is continuous, but should only have valid solutions at values of either zero or one within a finite time horizon. This formulation enables dynamic optimization problems with logical disjunctions to be solved by simultaneous solution methods without using methods such as mixed integer programming. Several case studies are given to illustrate the value of this methodology including nonlinear model predictive control of a chemical reactor using a surge tank with overflow to buffer disturbances in feed flow rate. Although this work contains novel methodologies for solving dynamic algebraic equation (DAE constrained problems where the system may experience an abrupt change in dynamics that may otherwise require a conditional statement, there remain substantial limitations to this methodology, including a limited domain where problems may converge and the possibility for ill-conditioning. Although the problems presented use only continuous algebraic equations, the formulation has inherent non-smoothness. Hence, these problems must be solved with care and only in select circumstances, such as in simulation or situations when the solution is expected to be near the solver’s initial point.

SU-E-I-23: A General KV Constrained Optimization of CNR for CT Abdominal Imaging

International Nuclear Information System (INIS)

Weir, V; Zhang, J

2015-01-01

Purpose: While Tube current modulation has been well accepted for CT dose reduction, kV adjusting in clinical settings is still at its early stage. This is mainly due to the limited kV options of most current CT scanners. kV adjusting can potentially reduce radiation dose and optimize image quality. This study is to optimize CT abdomen imaging acquisition based on the assumption of a continuous kV, with the goal to provide the best contrast to noise ratio (CNR). Methods: For a given dose (CTDIvol) level, the CNRs at different kV and pitches were measured with an ACR GAMMEX phantom. The phantom was scanned in a Siemens Sensation 64 scanner and a GE VCT 64 scanner. A constrained mathematical optimization was used to find the kV which led to the highest CNR for the anatomy and pitch setting. Parametric equations were obtained from polynomial fitting of plots of kVs vs CNRs. A suitable constraint region for optimization was chosen. Subsequent optimization yielded a peak CNR at a particular kV for different collimations and pitch setting. Results: The constrained mathematical optimization approach yields kV of 114.83 and 113.46, with CNRs of 1.27 and 1.11 at the pitch of 1.2 and 1.4, respectively, for the Siemens Sensation 64 scanner with the collimation of 32 x 0.625mm. An optimized kV of 134.25 and 1.51 CNR is obtained for a GE VCT 64 slice scanner with a collimation of 32 x 0.625mm and a pitch of 0.969. At 0.516 pitch and 32 x 0.625 mm an optimized kV of 133.75 and a CNR of 1.14 was found for the GE VCT 64 slice scanner. Conclusion: CNR in CT image acquisition can be further optimized with a continuous kV option instead of current discrete or fixed kV settings. A continuous kV option is a key for individualized CT protocols

SU-E-I-23: A General KV Constrained Optimization of CNR for CT Abdominal Imaging

Energy Technology Data Exchange (ETDEWEB)

Weir, V; Zhang, J [University of Kentucky, Lexington, KY (United States)

2015-06-15

Purpose: While Tube current modulation has been well accepted for CT dose reduction, kV adjusting in clinical settings is still at its early stage. This is mainly due to the limited kV options of most current CT scanners. kV adjusting can potentially reduce radiation dose and optimize image quality. This study is to optimize CT abdomen imaging acquisition based on the assumption of a continuous kV, with the goal to provide the best contrast to noise ratio (CNR). Methods: For a given dose (CTDIvol) level, the CNRs at different kV and pitches were measured with an ACR GAMMEX phantom. The phantom was scanned in a Siemens Sensation 64 scanner and a GE VCT 64 scanner. A constrained mathematical optimization was used to find the kV which led to the highest CNR for the anatomy and pitch setting. Parametric equations were obtained from polynomial fitting of plots of kVs vs CNRs. A suitable constraint region for optimization was chosen. Subsequent optimization yielded a peak CNR at a particular kV for different collimations and pitch setting. Results: The constrained mathematical optimization approach yields kV of 114.83 and 113.46, with CNRs of 1.27 and 1.11 at the pitch of 1.2 and 1.4, respectively, for the Siemens Sensation 64 scanner with the collimation of 32 x 0.625mm. An optimized kV of 134.25 and 1.51 CNR is obtained for a GE VCT 64 slice scanner with a collimation of 32 x 0.625mm and a pitch of 0.969. At 0.516 pitch and 32 x 0.625 mm an optimized kV of 133.75 and a CNR of 1.14 was found for the GE VCT 64 slice scanner. Conclusion: CNR in CT image acquisition can be further optimized with a continuous kV option instead of current discrete or fixed kV settings. A continuous kV option is a key for individualized CT protocols.

Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis.

Science.gov (United States)

Chen, Jun; Bushman, Frederic D; Lewis, James D; Wu, Gary D; Li, Hongzhe

2013-04-01

Motivated by studying the association between nutrient intake and human gut microbiome composition, we developed a method for structure-constrained sparse canonical correlation analysis (ssCCA) in a high-dimensional setting. ssCCA takes into account the phylogenetic relationships among bacteria, which provides important prior knowledge on evolutionary relationships among bacterial taxa. Our ssCCA formulation utilizes a phylogenetic structure-constrained penalty function to impose certain smoothness on the linear coefficients according to the phylogenetic relationships among the taxa. An efficient coordinate descent algorithm is developed for optimization. A human gut microbiome data set is used to illustrate this method. Both simulations and real data applications show that ssCCA performs better than the standard sparse CCA in identifying meaningful variables when there are structures in the data.

Discrete Material and Thickness Optimization of laminated composite structures including failure criteria

DEFF Research Database (Denmark)

Lund, Erik

2017-01-01

This work extends the Discrete Material and Thickness Optimization approach to structural optimization problems where strength considerations in the form of failure criteria are taken into account for laminated composite structures. It takes offset in the density approaches applied for stress...... constrained topology optimization of single-material problems and develops formulations for multi-material topology optimization problems applied for laminated composite structures. The method can be applied for both stress- and strain-based failure criteria. The large number of local constraints is reduced...

Quality by design approach for optimizing the formulation and physical properties of extemporaneously prepared orodispersible films

NARCIS (Netherlands)

Visser, J. Caroline; Dohmen, Willem M. C.; Hinrichs, Wouter L. J.; Breitkreutz, Joerg; Frijlink, Henderik W.; Woerdenbag, Herman J.

2015-01-01

The quality by design (QbD) approach was applied for optimizing the formulation of extemporaneously prepared orodispersible films (ODFs) using Design-Expert Software. The starting formulation was based on earlier experiments and contained the film forming agents hypromellose and carbomer 974P and

Development of transethosomes formulation for dermal fisetin delivery: Box-Behnken design, optimization, in vitro skin penetration, vesicles-skin interaction and dermatokinetic studies.

Science.gov (United States)

Moolakkadath, Thasleem; Aqil, Mohd; Ahad, Abdul; Imam, Syed Sarim; Iqbal, Babar; Sultana, Yasmin; Mujeeb, Mohd; Iqbal, Zeenat

2018-05-07

The present study was conducted for the optimization of transethosomes formulation for dermal fisetin delivery. The optimization of the formulation was carried out using "Box-Behnken design". The independent variables were Lipoid S 100, ethanol and sodium cholate. The prepared formulations were characterized for vesicle size, entrapment efficiency and in vitro skin penetration study. The vesicles-skin interaction, confocal laser scanning microscopy and dermatokinetic studies were performed with optimized formulation. Results of the present study demonstrated that the optimized formulation presented vesicle size of 74.21 ± 2.65 nm, zeta potential of -11.0 mV, entrapment efficiency of 68.31 ± 1.48% and flux of 4.13 ± 0.17 µg/cm 2 /h. The TEM image of optimized formulation exhibited sealed and spherical shape vesicles. Results of thermoanalytical techniques demonstrated that the prepared transethosomes vesicles formulation had fluidized the rigid membrane of rat's skin for smoother penetration of fisetin transethosomes. The confocal study results presented well distribution and penetration of Rhodamine B loaded transethosomes vesicles formulation up to deeper layers of the rat's skin as compared to the Rhodamine B-hydro alcoholic solution. Present study data revealed that the developed transethosomes vesicles formulation was found to be a potentially useful drug carrier for fisetin dermal delivery.

Mini-batch optimized full waveform inversion with geological constrained gradient filtering

Science.gov (United States)

Yang, Hui; Jia, Junxiong; Wu, Bangyu; Gao, Jinghuai

2018-05-01

High computation cost and generating solutions without geological sense have hindered the wide application of Full Waveform Inversion (FWI). Source encoding technique is a way to dramatically reduce the cost of FWI but subject to fix-spread acquisition setup requirement and slow convergence for the suppression of cross-talk. Traditionally, gradient regularization or preconditioning is applied to mitigate the ill-posedness. An isotropic smoothing filter applied on gradients generally gives non-geological inversion results, and could also introduce artifacts. In this work, we propose to address both the efficiency and ill-posedness of FWI by a geological constrained mini-batch gradient optimization method. The mini-batch gradient descent optimization is adopted to reduce the computation time by choosing a subset of entire shots for each iteration. By jointly applying the structure-oriented smoothing to the mini-batch gradient, the inversion converges faster and gives results with more geological meaning. Stylized Marmousi model is used to show the performance of the proposed method on realistic synthetic model.

Electrochemomechanical constrained multiobjective optimization of PPy/MWCNT actuators

International Nuclear Information System (INIS)

Khalili, N; Naguib, H E; Kwon, R H

2014-01-01

Polypyrrole (PPy) conducting polymers have shown a great potential for the fabrication of conjugated polymer-based actuating devices. Consequently, they have been a key point in developing many advanced emerging applications such as biomedical devices and biomimetic robotics. When designing an actuator, taking all of the related decision variables, their roles and relationships into consideration is of pivotal importance to determine the actuator’s final performance. Therefore, the central focus of this study is to develop an electrochemomechanical constrained multiobjective optimization model of a PPy/MWCNTs trilayer actuator. For this purpose, the objective functions are designed to capture the three main characteristics of these actuators, namely their tip vertical displacement, blocking force and response time. To obtain the optimum range of the designated decision variables within the feasible domain, a multiobjective optimization algorithm is applied while appropriate constraints are imposed. The optimum points form a Pareto surface on which they are consistently spread. The numerical results are presented; these results enable one to design an actuator with consideration to the desired output performances. For the experimental analysis, a multilayer bending-type actuator is fabricated, which is composed of a PVDF layer and two layers of PPy with an incorporated layer of multi-walled carbon nanotubes deposited on each side of the PVDF membrane. The numerical results are experimentally verified; in order to determine the performance of the fabricated actuator, its outputs are compared with a neat PPy actuator’s experimental and numerical counterparts. (paper)

Constrained multi-objective optimization of radial expanders in organic Rankine cycles by firefly algorithm

International Nuclear Information System (INIS)

Bahadormanesh, Nikrouz; Rahat, Shayan; Yarali, Milad

2017-01-01

Highlights: • A multi-objective optimization for radial expander in Organic Rankine Cycles is implemented. • By using firefly algorithm, Pareto front based on the size of turbine and thermal efficiency is produced. • Tension and vibration constrains have a significant effect on optimum design points. - Abstract: Organic Rankine Cycles are viable energy conversion systems in sustainable energy systems due to their compatibility with low-temperature heat sources. In the present study, one dimensional model of radial expanders in conjunction with a thermodynamic model of organic Rankine cycles is prepared. After verification, by defining thermal efficiency of the cycle and size parameter of a radial turbine as the objective functions, a multi-objective optimization was conducted regarding tension and vibration constraints for 4 different organic working fluids (R22, R245fa, R236fa and N-Pentane). In addition to mass flow rate, evaporator temperature, maximum pressure of cycle and turbo-machinery design parameters are selected as the decision variables. Regarding Pareto fronts, by a little increase in size of radial expanders, it is feasible to reach high efficiency. Moreover, by assessing the distribution of decision variables, the variables that play a major role in trending between the objective functions are found. Effects of mechanical and vibration constrains on optimum decision variables are investigated. The results of optimization can be considered as an initial values for design of radial turbines for Organic Rankine Cycles.

Formulation and optimization of mucoadhesive microemulsion containing mirtazapine for intranasal delivery

Directory of Open Access Journals (Sweden)

Hetal P Thakkar

2014-01-01

Full Text Available Background: Mirtazapine, an antidepressant drug, has absolute bioavailability of only 50% due to high first pass metabolism. Aim: The purpose of this study was to develop and optimize mucoadhesive microemulsion containing mirtazapine for intranasal delivery. Materials and Methods: Based on solubility study, Capmul Medium chain Monoglyceride, Tween 80 and polyethylene glycol (PEG 400 were selected as oil, surfactant and co surfactant respectively. Microemulsions were prepared using water titration method. 3:1% w/w ratio (Tween 80: PEG 400 was selected for formulation development. The prepared microemulsions were optimized for globule size, zeta potential, % transmittance and polydispersity index. The optimized batch was further characterized for % drug content, conductivity and transmission electron microscopy. Results and Conclusion: All the parameters showed the suitability of microemulsion of mirtazapine for intranasal delivery. Chitosan (0.5% w/w was used as a polymer for the preparation of mucoadhesive microemulsion to enhance the retention time in the nasal mucosa. Results of nasal toxicity study using excised sheep nasal mucosa showed comparatively no damage to epithelium and so formulation was considered safe for nasal administration. mirtazapine mucoadhesive microemulsion showed the highest percentage of diffusion (57.11 ± 0.710% after 210 min during in-vitro drug diffusion study through sheep nasal mucosa, followed by mirtazapine microemulsion (46.08 ± 0.674% and finally by mirtazapine solution (17.63 ± 0.612%.

Formulation and Evaluation of Optimized Oxybenzone Microsponge Gel for Topical Delivery

Directory of Open Access Journals (Sweden)

Atmaram P. Pawar

2015-01-01

Full Text Available Background. Oxybenzone, a broad spectrum sunscreen agent widely used in the form of lotion and cream, has been reported to cause skin irritation, dermatitis, and systemic absorption. Aim. The objective of the present study was to formulate oxybenzone loaded microsponge gel for enhanced sun protection factor with reduced toxicity. Material and Method. Microsponge for topical delivery of oxybenzone was successfully prepared by quasiemulsion solvent diffusion method. The effects of ethyl cellulose and dichloromethane were optimized by the 32 factorial design. The optimized microsponges were dispersed into the hydrogel and further evaluated. Results. The microsponges were spherical with pore size in the range of 0.10–0.22 µm. The optimized formulation possesses the particle size and entrapment efficiency of 72 ± 0.77 µm and 96.9 ± 0.52%, respectively. The microsponge gel showed the controlled release and was nonirritant to the rat skin. In creep recovery test it had shown highest recovery indicating elasticity. The controlled release of oxybenzone from microsponge and barrier effect of gel result in prolonged retention of oxybenzone with reduced permeation activity. Conclusion. Evaluation study revealed remarkable and enhanced topical retention of oxybenzone for prolonged period of time. It also showed the enhanced sun protection factor compared to the marketed preparation with reduced irritation and toxicity.

Application of hanging drop technique to optimize human IgG formulations.

Science.gov (United States)

Li, Guohua; Kasha, Purna C; Late, Sameer; Banga, Ajay K

2010-01-01

The purpose of this work is to assess the hanging drop technique in screening excipients to develop optimal formulations for human immunoglobulin G (IgG). A microdrop of human IgG and test solution hanging from a cover slide and undergoing vapour diffusion was monitored by a stereomicroscope. Aqueous solutions of IgG in the presence of different pH, salt concentrations and excipients were prepared and characterized. Low concentration of either sodium/potassium phosphate or McIlvaine buffer favoured the solubility of IgG. Addition of sucrose favoured the stability of this antibody while addition of NaCl caused more aggregation. Antimicrobial preservatives were also screened and a complex effect at different buffer conditions was observed. Dynamic light scattering, differential scanning calorimetry and size exclusion chromatography studies were performed to further validate the results. In conclusion, hanging drop is a very easy and effective approach to screen protein formulations in the early stage of formulation development.

Multivariate constrained shape optimization: Application to extrusion bell shape for pasta production

Science.gov (United States)

Sarghini, Fabrizio; De Vivo, Angela; Marra, Francesco

2017-10-01

Computational science and engineering methods have allowed a major change in the way products and processes are designed, as validated virtual models - capable to simulate physical, chemical and bio changes occurring during production processes - can be realized and used in place of real prototypes and performing experiments, often time and money consuming. Among such techniques, Optimal Shape Design (OSD) (Mohammadi & Pironneau, 2004) represents an interesting approach. While most classical numerical simulations consider fixed geometrical configurations, in OSD a certain number of geometrical degrees of freedom is considered as a part of the unknowns: this implies that the geometry is not completely defined, but part of it is allowed to move dynamically in order to minimize or maximize the objective function. The applications of optimal shape design (OSD) are uncountable. For systems governed by partial differential equations, they range from structure mechanics to electromagnetism and fluid mechanics or to a combination of the three. This paper presents one of possible applications of OSD, particularly how extrusion bell shape, for past production, can be designed by applying a multivariate constrained shape optimization.

Constrained Null Space Component Analysis for Semiblind Source Separation Problem.

Science.gov (United States)

Hwang, Wen-Liang; Lu, Keng-Shih; Ho, Jinn

2018-02-01

The blind source separation (BSS) problem extracts unknown sources from observations of their unknown mixtures. A current trend in BSS is the semiblind approach, which incorporates prior information on sources or how the sources are mixed. The constrained independent component analysis (ICA) approach has been studied to impose constraints on the famous ICA framework. We introduced an alternative approach based on the null space component (NCA) framework and referred to the approach as the c-NCA approach. We also presented the c-NCA algorithm that uses signal-dependent semidefinite operators, which is a bilinear mapping, as signatures for operator design in the c-NCA approach. Theoretically, we showed that the source estimation of the c-NCA algorithm converges with a convergence rate dependent on the decay of the sequence, obtained by applying the estimated operators on corresponding sources. The c-NCA can be formulated as a deterministic constrained optimization method, and thus, it can take advantage of solvers developed in optimization society for solving the BSS problem. As examples, we demonstrated electroencephalogram interference rejection problems can be solved by the c-NCA with proximal splitting algorithms by incorporating a sparsity-enforcing separation model and considering the case when reference signals are available.

A Mixed Integer Linear Programming Approach to Electrical Stimulation Optimization Problems.

Science.gov (United States)

Abouelseoud, Gehan; Abouelseoud, Yasmine; Shoukry, Amin; Ismail, Nour; Mekky, Jaidaa

2018-02-01

Electrical stimulation optimization is a challenging problem. Even when a single region is targeted for excitation, the problem remains a constrained multi-objective optimization problem. The constrained nature of the problem results from safety concerns while its multi-objectives originate from the requirement that non-targeted regions should remain unaffected. In this paper, we propose a mixed integer linear programming formulation that can successfully address the challenges facing this problem. Moreover, the proposed framework can conclusively check the feasibility of the stimulation goals. This helps researchers to avoid wasting time trying to achieve goals that are impossible under a chosen stimulation setup. The superiority of the proposed framework over alternative methods is demonstrated through simulation examples.

Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

Science.gov (United States)

Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong

2015-11-01

The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Racing Sampling Based Microimmune Optimization Approach Solving Constrained Expected Value Programming

Directory of Open Access Journals (Sweden)

Kai Yang

2016-01-01

Full Text Available This work investigates a bioinspired microimmune optimization algorithm to solve a general kind of single-objective nonlinear constrained expected value programming without any prior distribution. In the study of algorithm, two lower bound sample estimates of random variables are theoretically developed to estimate the empirical values of individuals. Two adaptive racing sampling schemes are designed to identify those competitive individuals in a given population, by which high-quality individuals can obtain large sampling size. An immune evolutionary mechanism, along with a local search approach, is constructed to evolve the current population. The comparative experiments have showed that the proposed algorithm can effectively solve higher-dimensional benchmark problems and is of potential for further applications.

Formulation and Optimization of Eudragit RS PO-Tenofovir Nanocarriers Using Box-Behnken Experimental Design

Directory of Open Access Journals (Sweden)

Kefilwe Matlhola

2015-01-01

Full Text Available The objective of present study was to develop an optimized polymeric nanoparticle system for the antiretroviral drug tenofovir. A modified nanoprecipitation method was used to prepare Eudragit RS PO nanoparticles of the drug. The effect of amount of polymer, surfactant concentration, and sonication time on particle size, particle distribution, encapsulation efficiency (EE, and zeta potential were assessed and optimized utilizing a three-factor, three-level Box-Behnken Design (BBD of experiment. Fifteen formulations of nanoparticles were prepared as per BBD and evaluated for particle size, polydispersity index (PDI, EE, and zeta potential. The results showed that the measured mean particle sizes were in the range of 233 to 499 nm, PDI ranged from 0.094 to 0.153, average zeta potential ranged from −19.9 to −45.8 mV, and EE ranged between 98 and 99%. The optimized formulation was characterized for in vitro drug release and structural characterization. The mean particle size of this formulation was 233 nm with a PDI of 0.0107. It had a high EE of 98% and average zeta potential of −35 mV, an indication of particle stability. The FTIR showed some noncovalent interactions between the drug and polymer but a sustained release was observed in vitro for up to 80 hours.

8th Workshop on Computational Optimization

CERN Document Server

2016-01-01

This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2015. It presents recent advances in computational optimization. The volume includes important real life problems like parameter settings for controlling processes in bioreactor, control of ethanol production, minimal convex hill with application in routing algorithms, graph coloring, flow design in photonic data transport system, predicting indoor temperature, crisis control center monitoring, fuel consumption of helicopters, portfolio selection, GPS surveying and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics, medicine and other domains can be formulated as optimization problems. .

Multi-Objective Trajectory Optimization of a Hypersonic Reconnaissance Vehicle with Temperature Constraints

Science.gov (United States)

Masternak, Tadeusz J.

This research determines temperature-constrained optimal trajectories for a scramjet-based hypersonic reconnaissance vehicle by developing an optimal control formulation and solving it using a variable order Gauss-Radau quadrature collocation method with a Non-Linear Programming (NLP) solver. The vehicle is assumed to be an air-breathing reconnaissance aircraft that has specified takeoff/landing locations, airborne refueling constraints, specified no-fly zones, and specified targets for sensor data collections. A three degree of freedom scramjet aircraft model is adapted from previous work and includes flight dynamics, aerodynamics, and thermal constraints. Vehicle control is accomplished by controlling angle of attack, roll angle, and propellant mass flow rate. This model is incorporated into an optimal control formulation that includes constraints on both the vehicle and mission parameters, such as avoidance of no-fly zones and coverage of high-value targets. To solve the optimal control formulation, a MATLAB-based package called General Pseudospectral Optimal Control Software (GPOPS-II) is used, which transcribes continuous time optimal control problems into an NLP problem. In addition, since a mission profile can have varying vehicle dynamics and en-route imposed constraints, the optimal control problem formulation can be broken up into several "phases" with differing dynamics and/or varying initial/final constraints. Optimal trajectories are developed using several different performance costs in the optimal control formulation: minimum time, minimum time with control penalties, and maximum range. The resulting analysis demonstrates that optimal trajectories that meet specified mission parameters and constraints can be quickly determined and used for larger-scale operational and campaign planning and execution.

Mathematical Modeling of Constrained Hamiltonian Systems

NARCIS (Netherlands)

Schaft, A.J. van der; Maschke, B.M.

1995-01-01

Network modelling of unconstrained energy conserving physical systems leads to an intrinsic generalized Hamiltonian formulation of the dynamics. Constrained energy conserving physical systems are directly modelled as implicit Hamiltonian systems with regard to a generalized Dirac structure on the

Reliability analysis for cementless hip prosthesis using a new optimized formulation of yield stress against elasticity modulus relationship

International Nuclear Information System (INIS)

Kharmanda, G.

2015-01-01

Highlights: • We develop a new formulation between the yield stress and Young’s modulus of bone. • We validate the optimized formulation for cortical and trabecular bone. • We integrate the reliability analysis into artificially hip replacement design. - Abstract: Using classical design optimization methods for implant-bone studies does not completely guarantee a safety and satisfactory performance, due in part to the randomness of bone properties and loading. Here, the material properties of the different bone layers are considered as uncertain parameters. So their corresponding yield stress values will not be deterministic, that leads to integrate variable limitations into the optimization process. Here there is a strong need to find a reliable mathematical relationship between yield stress and material properties of the different bone layers. In this work, a new optimized formulation for yield stress against elasticity modulus relationship is first developed. This model is based on some experimental results. A validation of the proposed formulation is next carried out to show its accuracy for both bone layers (cortical and cancellous). A probabilistic sensitivity analysis is then carried out to show the role of each input parameter with respect to the limit state function. The new optimized formulation is next integrated into a reliability analysis problem in order to assess the reliability level of the stem–bone study where we deal with variable boundary limitations. An illustrative application is considered as a bi-dimensional example (contains only two variables) in order to present the results in an illustrative 2D space. Finally, a multi-variable problem considering several daily loading cases on a hip prosthesis shows the applicability of the proposed strategy

Constrained Optimal Transport

Science.gov (United States)

Ekren, Ibrahim; Soner, H. Mete

2018-03-01

The classical duality theory of Kantorovich (C R (Doklady) Acad Sci URSS (NS) 37:199-201, 1942) and Kellerer (Z Wahrsch Verw Gebiete 67(4):399-432, 1984) for classical optimal transport is generalized to an abstract framework and a characterization of the dual elements is provided. This abstract generalization is set in a Banach lattice X with an order unit. The problem is given as the supremum over a convex subset of the positive unit sphere of the topological dual of X and the dual problem is defined on the bi-dual of X. These results are then applied to several extensions of the classical optimal transport.

Oil Reservoir Production Optimization using Optimal Control

DEFF Research Database (Denmark)

Völcker, Carsten; Jørgensen, John Bagterp; Stenby, Erling Halfdan

2011-01-01

Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using the adjo...... reservoir using water ooding and smart well technology. Compared to the uncontrolled case, the optimal operation increases the Net Present Value of the oil field by 10%.......Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using...

Design of a Circularly Polarized Galileo E6-Band Textile Antenna by Dedicated Multiobjective Constrained Pareto Optimization

Directory of Open Access Journals (Sweden)

Arnaut Dierck

2015-01-01

Full Text Available Designing textile antennas for real-life applications requires a design strategy that is able to produce antennas that are optimized over a wide bandwidth for often conflicting characteristics, such as impedance matching, axial ratio, efficiency, and gain, and, moreover, that is able to account for the variations that apply for the characteristics of the unconventional materials used in smart textile systems. In this paper, such a strategy, incorporating a multiobjective constrained Pareto optimization, is presented and applied to the design of a Galileo E6-band antenna with optimal return loss and wide-band axial ratio characteristics. Subsequently, different prototypes of the optimized antenna are fabricated and measured to validate the proposed design strategy.

A new experimental design method to optimize formulations focusing on a lubricant for hydrophilic matrix tablets.

Science.gov (United States)

Choi, Du Hyung; Shin, Sangmun; Khoa Viet Truong, Nguyen; Jeong, Seong Hoon

2012-09-01

A robust experimental design method was developed with the well-established response surface methodology and time series modeling to facilitate the formulation development process with magnesium stearate incorporated into hydrophilic matrix tablets. Two directional analyses and a time-oriented model were utilized to optimize the experimental responses. Evaluations of tablet gelation and drug release were conducted with two factors x₁ and x₂: one was a formulation factor (the amount of magnesium stearate) and the other was a processing factor (mixing time), respectively. Moreover, different batch sizes (100 and 500 tablet batches) were also evaluated to investigate an effect of batch size. The selected input control factors were arranged in a mixture simplex lattice design with 13 experimental runs. The obtained optimal settings of magnesium stearate for gelation were 0.46 g, 2.76 min (mixing time) for a 100 tablet batch and 1.54 g, 6.51 min for a 500 tablet batch. The optimal settings for drug release were 0.33 g, 7.99 min for a 100 tablet batch and 1.54 g, 6.51 min for a 500 tablet batch. The exact ratio and mixing time of magnesium stearate could be formulated according to the resulting hydrophilic matrix tablet properties. The newly designed experimental method provided very useful information for characterizing significant factors and hence to obtain optimum formulations allowing for a systematic and reliable experimental design method.

Affine Lie algebraic origin of constrained KP hierarchies

International Nuclear Information System (INIS)

Aratyn, H.; Gomes, J.F.; Zimerman, A.H.

1994-07-01

It is presented an affine sl(n+1) algebraic construction of the basic constrained KP hierarchy. This hierarchy is analyzed using two approaches, namely linear matrix eigenvalue problem on hermitian symmetric space and constrained KP Lax formulation and we show that these approaches are equivalent. The model is recognized to be generalized non-linear Schroedinger (GNLS) hierarchy and it is used as a building block for a new class of constrained KP hierarchies. These constrained KP hierarchies are connected via similarity-Backlund transformations and interpolate between GNLS and multi-boson KP-Toda hierarchies. The construction uncovers origin of the Toda lattice structure behind the latter hierarchy. (author). 23 refs

Thermo economical optimization of sugar plants with environmental constraints

Energy Technology Data Exchange (ETDEWEB)

Colombo, Mauricio; Mele, Fernando Daniel; Hernandez, Maria Rosa [Universidad Nacional de Tucuman (UNT), Tucuman (Argentina). Facultad de Ciencias Exactas y Tecnologia], Email: macolombo@herrera.unt.edu.ar; Gatica, Jorge [Cleveland State University (CSU), Cleveland, OH (United States). Dept. of Chemical and Biomedical Engineering], Email: j.gatica@csuohio.edu; Silveira, Jose Luz [Universidade Estadual Paulista (FEG/UNESP), Guaratingueta, SP (Brazil). Faculdade de Engenharia. Dept. de Energia], Email: joseluz@feg.unesp.br

2009-07-01

This paper highlights the need for analysis and optimization techniques which can be applied to new energy systems and include considerations for environmental issues. These techniques have proven indispensable in dealing with the constrained optimization problem of finite natural resources and growing demands of energy. Within this framework, thermo economical optimization has gradually been brought to the forefront as a powerful tool in assisting the decision-making process. This work uses the technique of Life Cycle Analysis (LCA) as a means to include environmental indexes in the optimization process. While most of the environmental approaches formulate the optimization problem aiming to reduce residue generation without assessing the impact of this reduction on related processes, LCA considers environmental issues as an integral part of the optimization problem. A sugar cane processing plant located in Tucuman (Argentina) is selected as a case study. This example serves to highlight the importance of formulating solutions that ensure an efficient use of a common fuel to meet useful heat, shaft power, and electricity demands. (author)

Formulation and optimization of chronomodulated press-coated tablet of carvedilol by Box–Behnken statistical design

Directory of Open Access Journals (Sweden)

Satwara RS

2012-08-01

Full Text Available Rohan S Satwara, Parul K PatelDepartment of Pharmaceutics, Babaria Institute of Pharmacy, Vadodara, Gujarat, IndiaObjective: The primary objective of the present investigation was to formulate and optimize chronomodulated press-coated tablets to deliver the antihypertensive carvedilol at an effective quantity predawn, when a blood pressure spike is typically observed in most hypertensive patients.Experimental work: Preformulation studies and drug excipient compatibility studies were carried out for carvedilol and excipients. Core tablets (6 mm containing carvedilol and 10-mm press-coated tablets were prepared by direct compression. The Box–Behnken experimental design was applied to these press-coated tablets (F1–F15 formula with differing concentrations of rate-controlling polymers. Hydroxypropyl methyl cellulose K4M, ethyl cellulose, and K-carrageenan were used as rate-controlling polymers in the outer layer. These tablets were subjected to various precompression and postcompression tests. The optimized batch was derived both by statistically (using desirability function and graphically (using Design Expert® 8; Stat-Ease Inc. Tablets formulated using the optimized formulas were then evaluated for lag time and in vitro dissolution.Results and discussion: Results of preformulation studies were satisfactory. No interaction was observed between carvedilol and excipients by ultraviolet, Fourier transform infrared spectroscopy, and dynamic light scattering analysis. The results of precompression studies and postcompression studies were within limits. The varying lag time and percent cumulative carvedilol release after 8 h was optimized to obtain a formulation that offered a release profile with 6 h lag time, followed by complete carvedilol release after 8 h. The results showed no significant bias between predicted response and actual response for the optimized formula.Conclusion: Bedtime dosing of chronomodulated press-coated tablets may offer a

Closedness type regularity conditions in convex optimization and beyond

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Sorin-Mihai Grad

2016-09-01

Full Text Available The closedness type regularity conditions have proven during the last decade to be viable alternatives to their more restrictive interiority type counterparts, in both convex optimization and different areas where it was successfully applied. In this review article we de- and reconstruct some closedness type regularity conditions formulated by means of epigraphs and subdifferentials, respectively, for general optimization problems in order to stress that they arise naturally when dealing with such problems. The results are then specialized for constrained and unconstrained convex optimization problems. We also hint towards other classes of optimization problems where closedness type regularity conditions were successfully employed and discuss other possible applications of them.

Joint Optimal Production Planning for Complex Supply Chains Constrained by Carbon Emission Abatement Policies

OpenAIRE

He, Longfei; Xu, Zhaoguang; Niu, Zhanwen

2014-01-01

We focus on the joint production planning of complex supply chains facing stochastic demands and being constrained by carbon emission reduction policies. We pick two typical carbon emission reduction policies to research how emission regulation influences the profit and carbon footprint of a typical supply chain. We use the input-output model to capture the interrelated demand link between an arbitrary pair of two nodes in scenarios without or with carbon emission constraints. We design optim...

Optimization of primaquine diphosphate tablet formulation for controlled drug release using the mixture experimental design.

Science.gov (United States)

Duque, Marcelo Dutra; Kreidel, Rogério Nepomuceno; Taqueda, Maria Elena Santos; Baby, André Rolim; Kaneko, Telma Mary; Velasco, Maria Valéria Robles; Consiglieri, Vladi Olga

2013-01-01

A tablet formulation based on hydrophilic matrix with a controlled drug release was developed, and the effect of polymer concentrations on the release of primaquine diphosphate was evaluated. To achieve this purpose, a 20-run, four-factor with multiple constraints on the proportions of the components was employed to obtain tablet compositions. Drug release was determined by an in vitro dissolution study in phosphate buffer solution at pH 6.8. The polynomial fitted functions described the behavior of the mixture on simplex coordinate systems to study the effects of each factor (polymer) on tablet characteristics. Based on the response surface methodology, a tablet composition was optimized with the purpose of obtaining a primaquine diphosphate release closer to a zero order kinetic. This formulation released 85.22% of the drug for 8 h and its kinetic was studied regarding to Korsmeyer-Peppas model, (Adj-R(2) = 0.99295) which has confirmed that both diffusion and erosion were related to the mechanism of the drug release. The data from the optimized formulation were very close to the predictions from statistical analysis, demonstrating that mixture experimental design could be used to optimize primaquine diphosphate dissolution from hidroxypropylmethyl cellulose and polyethylene glycol matrix tablets.

Recent advances in computational optimization

CERN Document Server

2013-01-01

Optimization is part of our everyday life. We try to organize our work in a better way and optimization occurs in minimizing time and cost or the maximization of the profit, quality and efficiency. Also many real world problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization. This book presents recent advances in computational optimization. The volume includes important real world problems like parameter settings for con- trolling processes in bioreactor, robot skin wiring, strip packing, project scheduling, tuning of PID controller and so on. Some of them can be solved by applying traditional numerical methods, but others need a huge amount of computational resources. For them it is shown that is appropriate to develop algorithms based on metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming etc...

Systematic Development of Transethosomal Gel System of Piroxicam: Formulation Optimization, In Vitro Evaluation, and Ex Vivo Assessment.

Science.gov (United States)

Garg, Varun; Singh, Harmanpreet; Bhatia, Amit; Raza, Kaisar; Singh, Sachin Kumar; Singh, Bhupinder; Beg, Sarwar

2017-01-01

Piroxicam is used in the treatment of rheumatoid arthritis, osteoarthritis, and other inflammatory diseases. Upon oral administration, it is reported to cause ulcerative colitis, gastrointestinal irritation, edema and peptic ulcer. Hence, an alternative delivery system has been designed in the form of transethosome. The present study describes the preparation, optimization, characterization, and ex vivo study of piroxicam-loaded transethosomal gel using the central composite design. On the basis of the prescreening study, the concentration of lipids and ethanol was kept in the range of 2-4% w/v and 0-40% v/v, respectively. Formulation was optimized by measuring drug retention in the skin, drug permeation, entrapment efficiency, and vesicle size. Optimized formulation was incorporated in hydrogel and compared with other analogous vesicular (liposomes, ethosomes, and transfersomes) gels for the aforementioned responses. Among the various lipids used, soya phosphatidylcholine (SPL 70) and ethanol in various percentages were found to affect drug retention in the skin, drug permeation, vesicle size, and entrapment efficiency. The optimized batch of transethosome has shown 392.730 μg cm -2 drug retention in the skin, 44.312 μg cm -2  h -1 drug permeation, 68.434% entrapment efficiency, and 655.369 nm vesicle size, respectively. It was observed that the developed transethosomes were found superior in all the responses as compared to other vesicular formulations with improved stability and highest elasticity. Similar observations were noted with its gel formulation.

Phase-Field Relaxation of Topology Optimization with Local Stress Constraints

DEFF Research Database (Denmark)

Stainko, Roman; Burger, Martin

2006-01-01

inequality constraints. We discretize the problem by finite elements and solve the arising finite-dimensional programming problems by a primal-dual interior point method. Numerical experiments for problems with local stress constraints based on different criteria indicate the success and robustness......We introduce a new relaxation scheme for structural topology optimization problems with local stress constraints based on a phase-field method. In the basic formulation we have a PDE-constrained optimization problem, where the finite element and design analysis are solved simultaneously...

Event-triggered decentralized robust model predictive control for constrained large-scale interconnected systems

Directory of Open Access Journals (Sweden)

Ling Lu

2016-12-01

Full Text Available This paper considers the problem of event-triggered decentralized model predictive control (MPC for constrained large-scale linear systems subject to additive bounded disturbances. The constraint tightening method is utilized to formulate the MPC optimization problem. The local predictive control law for each subsystem is determined aperiodically by relevant triggering rule which allows a considerable reduction of the computational load. And then, the robust feasibility and closed-loop stability are proved and it is shown that every subsystem state will be driven into a robust invariant set. Finally, the effectiveness of the proposed approach is illustrated via numerical simulations.

Optimizing the taste-masked formulation of acetaminophen using sodium caseinate and lecithin by experimental design.

Science.gov (United States)

Hoang Thi, Thanh Huong; Lemdani, Mohamed; Flament, Marie-Pierre

2013-09-10

In a previous study of ours, the association of sodium caseinate and lecithin was demonstrated to be promising for masking the bitterness of acetaminophen via drug encapsulation. The encapsulating mechanisms were suggested to be based on the segregation of multicomponent droplets occurring during spray-drying. The spray-dried particles delayed the drug release within the mouth during the early time upon administration and hence masked the bitterness. Indeed, taste-masking is achieved if, within the frame of 1-2 min, drug substance is either not released or the released amount is below the human threshold for identifying its bad taste. The aim of this work was (i) to evaluate the effect of various processing and formulation parameters on the taste-masking efficiency and (ii) to determine the optimal formulation for optimal taste-masking effect. Four investigated input variables included inlet temperature (X1), spray flow (X2), sodium caseinate amount (X3) and lecithin amount (X4). The percentage of drug release amount during the first 2 min was considered as the response variable (Y). A 2(4)-full factorial design was applied and allowed screening for the most influential variables i.e. sodium caseinate amount and lecithin amount. Optimizing these two variables was therefore conducted by a simplex approach. The SEM and DSC results of spray-dried powder prepared under optimal conditions showed that drug seemed to be well encapsulated. The drug release during the first 2 min significantly decreased, 7-fold less than the unmasked drug particles. Therefore, the optimal formulation that performed the best taste-masking effect was successfully achieved. Copyright © 2013 Elsevier B.V. All rights reserved.

Free and constrained symplectic integrators for numerical general relativity

International Nuclear Information System (INIS)

Richter, Ronny; Lubich, Christian

2008-01-01

We consider symplectic time integrators in numerical general relativity and discuss both free and constrained evolution schemes. For free evolution of ADM-like equations we propose the use of the Stoermer-Verlet method, a standard symplectic integrator which here is explicit in the computationally expensive curvature terms. For the constrained evolution we give a formulation of the evolution equations that enforces the momentum constraints in a holonomically constrained Hamiltonian system and turns the Hamilton constraint function from a weak to a strong invariant of the system. This formulation permits the use of the constraint-preserving symplectic RATTLE integrator, a constrained version of the Stoermer-Verlet method. The behavior of the methods is illustrated on two effectively (1+1)-dimensional versions of Einstein's equations, which allow us to investigate a perturbed Minkowski problem and the Schwarzschild spacetime. We compare symplectic and non-symplectic integrators for free evolution, showing very different numerical behavior for nearly-conserved quantities in the perturbed Minkowski problem. Further we compare free and constrained evolution, demonstrating in our examples that enforcing the momentum constraints can turn an unstable free evolution into a stable constrained evolution. This is demonstrated in the stabilization of a perturbed Minkowski problem with Dirac gauge, and in the suppression of the propagation of boundary instabilities into the interior of the domain in Schwarzschild spacetime

Application of mixture experimental design in the formulation and optimization of matrix tablets containing carbomer and hydroxy-propylmethylcellulose.

Science.gov (United States)

Petrovic, Aleksandra; Cvetkovic, Nebojsa; Ibric, Svetlana; Trajkovic, Svetlana; Djuric, Zorica; Popadic, Dragica; Popovic, Radmila

2009-12-01

Using mixture experimental design, the effect of carbomer (Carbopol((R)) 971P NF) and hydroxypropylmethylcellulose (Methocel((R)) K100M or Methocel((R)) K4M) combination on the release profile and on the mechanism of drug liberation from matrix tablet was investigated. The numerical optimization procedure was also applied to establish and obtain formulation with desired drug release. The amount of TP released, release rate and mechanism varied with carbomer ratio in total matrix and HPMC viscosity. Increasing carbomer fractions led to a decrease in drug release. Anomalous diffusion was found in all matrices containing carbomer, while Case - II transport was predominant for tablet based on HPMC only. The predicted and obtained profiles for optimized formulations showed similarity. Those results indicate that Simplex Lattice Mixture experimental design and numerical optimization procedure can be applied during development to obtain sustained release matrix formulation with desired release profile.

An optimized formulation for Deprit-type Lie transformations of Taylor maps for symplectic systems

International Nuclear Information System (INIS)

Shi, Jicong

1993-01-01

An optimized iterative formulation is presented for directly transforming a Taylor map of a symplectic system into a Deprit-type Lie transformation, which is a composition of a linear transfer matrix and a single Lie transformation, to an arbitrary order

Optimal shutdown management

International Nuclear Information System (INIS)

Bottasso, C L; Croce, A; Riboldi, C E D

2014-01-01

The paper presents a novel approach for the synthesis of the open-loop pitch profile during emergency shutdowns. The problem is of interest in the design of wind turbines, as such maneuvers often generate design driving loads on some of the machine components. The pitch profile synthesis is formulated as a constrained optimal control problem, solved numerically using a direct single shooting approach. A cost function expressing a compromise between load reduction and rotor overspeed is minimized with respect to the unknown blade pitch profile. Constraints may include a load reduction not-to-exceed the next dominating loads, a not-to-be-exceeded maximum rotor speed, and a maximum achievable blade pitch rate. Cost function and constraints are computed over a possibly large number of operating conditions, defined so as to cover as well as possible the operating situations encountered in the lifetime of the machine. All such conditions are simulated by using a high-fidelity aeroservoelastic model of the wind turbine, ensuring the accuracy of the evaluation of all relevant parameters. The paper demonstrates the capabilities of the novel proposed formulation, by optimizing the pitch profile of a multi-MW wind turbine. Results show that the procedure can reliably identify optimal pitch profiles that reduce design-driving loads, in a fully automated way

Optimal shutdown management

Science.gov (United States)

Bottasso, C. L.; Croce, A.; Riboldi, C. E. D.

2014-06-01

The paper presents a novel approach for the synthesis of the open-loop pitch profile during emergency shutdowns. The problem is of interest in the design of wind turbines, as such maneuvers often generate design driving loads on some of the machine components. The pitch profile synthesis is formulated as a constrained optimal control problem, solved numerically using a direct single shooting approach. A cost function expressing a compromise between load reduction and rotor overspeed is minimized with respect to the unknown blade pitch profile. Constraints may include a load reduction not-to-exceed the next dominating loads, a not-to-be-exceeded maximum rotor speed, and a maximum achievable blade pitch rate. Cost function and constraints are computed over a possibly large number of operating conditions, defined so as to cover as well as possible the operating situations encountered in the lifetime of the machine. All such conditions are simulated by using a high-fidelity aeroservoelastic model of the wind turbine, ensuring the accuracy of the evaluation of all relevant parameters. The paper demonstrates the capabilities of the novel proposed formulation, by optimizing the pitch profile of a multi-MW wind turbine. Results show that the procedure can reliably identify optimal pitch profiles that reduce design-driving loads, in a fully automated way.

Optimization and characterization of spray-dried IgG formulations: a design of experiment approach.

Science.gov (United States)

Faghihi, Homa; Najafabadi, Abdolhosein Rouholamini; Vatanara, Alireza

2017-10-24

The purpose of the present study is to optimize a spray-dried formulation as a model antibody regarding stability and aerodynamic property for further aerosol therapy of this group of macromolecules. A three-factor, three-level, Box-Behnken design was employed milligrams of Cysteine (X 1 ), Trehalose (X 2 ), and Tween 20 (X 3 ) as independent variables. The dependent variables were quantified and the optimized formulation was prepared accordingly. SEC-HPLC and FTIR-spectroscopy were conducted to evaluate the molecular and structural status of spray-dried preparations. Particle characterization of optimized sample was performed with the aid of DSC, SEM, and TSI examinations. Experimental responses of a total of 17 formulations resulted in yield values, (Y 1 ), ranging from 21.1 ± 0.2 to 40.2 ± 0.1 (%); beta-sheet content, (Y 2 ), from 66.22 ± 0.19 to 73.78 ± 0.26 (%); amount of aggregation following process, (Y 3 ), ranging from 0.11 ± 0.03 to 0.95 ± 0.03 (%); and amount of aggregation upon storage, (Y 4 ), from 0.81 ± 0.01 to 3.13 ± 0.64 (%) as dependent variables. Results-except for those of the beta sheet content-were fitted to quadratic models describing the inherent relationship between main factors. Co-application of Cysteine and Tween 20 preserved antibody molecules from molecular degradation and improved immediate and accelerated stability of spry-dried antibodies. Validation of the optimization study indicated high degree of prognostic ability of response surface methodology in preparation of stable spray-dried IgG. Graphical abstract Spray drying of IgG in the presence of Trehalose, Cysteine and Tween 20.

Optimization of gluten-free formulations for French-style breads.

Science.gov (United States)

Mezaize, S; Chevallier, S; Le Bail, A; de Lamballerie, M

2009-04-01

The formulation of gluten-free bread, which will be suitable for patients with coeliac disease, was optimized to provide bread similar to French bread. The effects of the presence of hydrocolloids and the substitution of the flour basis by flour or proteins from different sources were studied. The added ingredients were (1) hydrocolloids (carboxymethylcellulose [CMC], guar gum, hydroxypropylmethylcellulose [HPMC], and xanthan gum), and (2) substitutes (buckwheat flour, whole egg powder, and whey proteins). The bread quality parameters measured were specific volume, dry matter of bread, crust color, crumb hardness, and gas cell size distribution. Specific volume was increased by guar gum and HPMC. Breads with guar gum had color characteristics similar to French bread. Hardness decreased with the addition of hydrocolloids, especially HPMC and guar. Breads with guar gum had the most heterogeneous cell size distribution, and guar gum was therefore selected for further formulations. Bread prepared with buckwheat flour had improved quality: an increased specific volume, a softer texture, color characteristics, and gas-cell size distribution similar to French bread. Bread with 1.9% guar gum (w/w, total flour basis) and 5% buckwheat flour (of all flours and substitutes) mimicked French bread quality attributes.

Modified Covariance Matrix Adaptation – Evolution Strategy algorithm for constrained optimization under uncertainty, application to rocket design

Directory of Open Access Journals (Sweden)

Chocat Rudy

2015-01-01

Full Text Available The design of complex systems often induces a constrained optimization problem under uncertainty. An adaptation of CMA-ES(λ, μ optimization algorithm is proposed in order to efficiently handle the constraints in the presence of noise. The update mechanisms of the parametrized distribution used to generate the candidate solutions are modified. The constraint handling method allows to reduce the semi-principal axes of the probable research ellipsoid in the directions violating the constraints. The proposed approach is compared to existing approaches on three analytic optimization problems to highlight the efficiency and the robustness of the algorithm. The proposed method is used to design a two stage solid propulsion launch vehicle.

Assessment of oscillatory stability constrained available transfer capability

International Nuclear Information System (INIS)

Jain, T.; Singh, S.N.; Srivastava, S.C.

2009-01-01

This paper utilizes a bifurcation approach to compute oscillatory stability constrained available transfer capability (ATC) in an electricity market having bilateral as well as multilateral transactions. Oscillatory instability in non-linear systems can be related to Hopf bifurcation. At the Hopf bifurcation, one pair of the critical eigenvalues of the system Jacobian reaches imaginary axis. A new optimization formulation, including Hopf bifurcation conditions, has been developed in this paper to obtain the dynamic ATC. An oscillatory stability based contingency screening index, which takes into account the impact of transactions on severity of contingency, has been utilized to identify critical contingencies to be considered in determining ATC. The proposed method has been applied for dynamic ATC determination on a 39-bus New England system and a practical 75-bus Indian system considering composite static load as well as dynamic load models. (author)

Design, formulation and optimization of novel soft nano-carriers for transdermal olmesartan medoxomil delivery: In vitro characterization and in vivo pharmacokinetic assessment.

Science.gov (United States)

Kamran, Mohd; Ahad, Abdul; Aqil, Mohd; Imam, Syed Sarim; Sultana, Yasmin; Ali, Asgar

2016-05-30

Olmesartan is a hydrophobic antihypertensive drug with a short biological half-life, and low bioavailability, presents a challenge with respect to its oral administration. The objective of the work was to formulate, optimize and evaluate the transdermal potential of novel vesicular nano-invasomes, containing above anti-hypertensive agent. To achieve the above purpose, soft carriers (viz. nano-invasomes) of olmesartan with β-citronellene as potential permeation enhancer were developed and optimized using Box-Behnken design. The physicochemical characteristics e.g., vesicle size, shape, entrapment efficiency and skin permeability of the nano-invasomes formulations were evaluated. The optimized formulation was further evaluated for in vitro drug release, confocal microscopy and in vivo pharmacokinetic study. The optimum nano-invasomes formulation showed vesicles size of 83.35±3.25nm, entrapment efficiency of 65.21±2.25% and transdermal flux of 32.78±0.703 (μg/cm(2)/h) which were found in agreement with the predicted value generated by Box-Behnken design. Confocal laser microscopy of rat skin showed that optimized formulation was eventually distributed and permeated deep into the skin. The pharmacokinetic study presented that transdermal nano-invasomes formulation showed 1.15 times improvement in bioavailability of olmesartan with respect to the control formulation in Wistar rats. It was concluded that the response surfaces estimated by Design Expert(®) illustrated obvious relationship between formulation factors and response variables and nano-invasomes were found to be a proficient carrier system for transdermal delivery of olmesartan. Copyright © 2016 Elsevier B.V. All rights reserved.

Crystallization Formulation Lab

Data.gov (United States)

Federal Laboratory Consortium — The Crystallization Formulation Lab fills a critical need in the process development and optimization of current and new explosives and energetic formulations. The...

Constrained Optimization Methods in Health Services Research-An Introduction: Report 1 of the ISPOR Optimization Methods Emerging Good Practices Task Force.

Science.gov (United States)

Crown, William; Buyukkaramikli, Nasuh; Thokala, Praveen; Morton, Alec; Sir, Mustafa Y; Marshall, Deborah A; Tosh, Jon; Padula, William V; Ijzerman, Maarten J; Wong, Peter K; Pasupathy, Kalyan S

2017-03-01

Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of "regular" and "severe" patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Learning optimal embedded cascades.

Science.gov (United States)

Saberian, Mohammad Javad; Vasconcelos, Nuno

2012-10-01

The problem of automatic and optimal design of embedded object detector cascades is considered. Two main challenges are identified: optimization of the cascade configuration and optimization of individual cascade stages, so as to achieve the best tradeoff between classification accuracy and speed, under a detection rate constraint. Two novel boosting algorithms are proposed to address these problems. The first, RCBoost, formulates boosting as a constrained optimization problem which is solved with a barrier penalty method. The constraint is the target detection rate, which is met at all iterations of the boosting process. This enables the design of embedded cascades of known configuration without extensive cross validation or heuristics. The second, ECBoost, searches over cascade configurations to achieve the optimal tradeoff between classification risk and speed. The two algorithms are combined into an overall boosting procedure, RCECBoost, which optimizes both the cascade configuration and its stages under a detection rate constraint, in a fully automated manner. Extensive experiments in face, car, pedestrian, and panda detection show that the resulting detectors achieve an accuracy versus speed tradeoff superior to those of previous methods.

Enhanced Solubility and Dissolution Rate of Lacidipine Nanosuspension: Formulation Via Antisolvent Sonoprecipitation Technique and Optimization Using Box-Behnken Design.

Science.gov (United States)

Kassem, Mohamed A A; ElMeshad, Aliaa N; Fares, Ahmed R

2017-05-01

Lacidipine (LCDP) is a highly lipophilic calcium channel blocker of poor aqueous solubility leading to poor oral absorption. This study aims to prepare and optimize LCDP nanosuspensions using antisolvent sonoprecipitation technique to enhance the solubility and dissolution of LCDP. A three-factor, three-level Box-Behnken design was employed to optimize the formulation variables to obtain LCDP nanosuspension of small and uniform particle size. Formulation variables were as follows: stabilizer to drug ratio (A), sodium deoxycholate percentage (B), and sonication time (C). LCDP nanosuspensions were assessed for particle size, zeta potential, and polydispersity index. The formula with the highest desirability (0.969) was chosen as the optimized formula. The values of the formulation variables (A, B, and C) in the optimized nanosuspension were 1.5, 100%, and 8 min, respectively. Optimal LCDP nanosuspension had particle size (PS) of 273.21 nm, zeta potential (ZP) of -32.68 mV and polydispersity index (PDI) of 0.098. LCDP nanosuspension was characterized using x-ray powder diffraction, differential scanning calorimetry, and transmission electron microscopy. LCDP nanosuspension showed saturation solubility 70 times that of raw LCDP in addition to significantly enhanced dissolution rate due to particle size reduction and decreased crystallinity. These results suggest that the optimized LCDP nanosuspension could be promising to improve oral absorption of LCDP.

Comparison of preconditioned Krylov subspace iteration methods for PDE-constrained optimization problems - Poisson and convection-diffusion control

Czech Academy of Sciences Publication Activity Database

Axelsson, Owe; Farouq, S.; Neytcheva, M.

2016-01-01

Roč. 73, č. 3 (2016), s. 631-633 ISSN 1017-1398 R&D Projects: GA MŠk ED1.1.00/02.0070 Institutional support: RVO:68145535 Keywords : PDE-constrained optimization problems * finite elements * iterative solution methods Subject RIV: BA - General Mathematics Impact factor: 1.241, year: 2016 http://link.springer.com/article/10.1007%2Fs11075-016-0111-1

A Mathematical Formulation of the SCOLE Control Problem. Part 2: Optimal Compensator Design

Science.gov (United States)

Balakrishnan, A. V.

1988-01-01

The study initiated in Part 1 of this report is concluded and optimal feedback control (compensator) design for stability augmentation is considered, following the mathematical formulation developed in Part 1. Co-located (rate) sensors and (force and moment) actuators are assumed, and allowing for both sensor and actuator noise, stabilization is formulated as a stochastic regulator problem. Specializing the general theory developed by the author, a complete, closed form solution (believed to be new with this report) is obtained, taking advantage of the fact that the inherent structural damping is light. In particular, it is possible to solve in closed form the associated infinite-dimensional steady-state Riccati equations. The SCOLE model involves associated partial differential equations in a single space variable, but the compensator design theory developed is far more general since it is given in the abstract wave equation formulation. The results thus hold for any multibody system so long as the basic model is linear.

Incorporating Charging/Discharging Strategy of Electric Vehicles into Security-Constrained Optimal Power Flow to Support High Renewable Penetration

Directory of Open Access Journals (Sweden)

Kyungsung An

2017-05-01

Full Text Available This research aims to improve the operational efficiency and security of electric power systems at high renewable penetration by exploiting the envisioned controllability or flexibility of electric vehicles (EVs; EVs interact with the grid through grid-to-vehicle (G2V and vehicle-to-grid (V2G services to ensure reliable and cost-effective grid operation. This research provides a computational framework for this decision-making process. Charging and discharging strategies of EV aggregators are incorporated into a security-constrained optimal power flow (SCOPF problem such that overall energy cost is minimized and operation within acceptable reliability criteria is ensured. Particularly, this SCOPF problem has been formulated for Jeju Island in South Korea, in order to lower carbon emissions toward a zero-carbon island by, for example, integrating large-scale renewable energy and EVs. On top of conventional constraints on the generators and line flows, a unique constraint on the system inertia constant, interpreted as the minimum synchronous generation, is considered to ensure grid security at high renewable penetration. The available energy constraint of the participating EV associated with the state-of-charge (SOC of the battery and market price-responsive behavior of the EV aggregators are also explored. Case studies for the Jeju electric power system in 2030 under various operational scenarios demonstrate the effectiveness of the proposed method and improved operational flexibility via controllable EVs.

A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality

Science.gov (United States)

Cheung, KW; So, HC; Ma, W.-K.; Chan, YT

2006-12-01

The problem of locating a mobile terminal has received significant attention in the field of wireless communications. Time-of-arrival (TOA), received signal strength (RSS), time-difference-of-arrival (TDOA), and angle-of-arrival (AOA) are commonly used measurements for estimating the position of the mobile station. In this paper, we present a constrained weighted least squares (CWLS) mobile positioning approach that encompasses all the above described measurement cases. The advantages of CWLS include performance optimality and capability of extension to hybrid measurement cases (e.g., mobile positioning using TDOA and AOA measurements jointly). Assuming zero-mean uncorrelated measurement errors, we show by mean and variance analysis that all the developed CWLS location estimators achieve zero bias and the Cramér-Rao lower bound approximately when measurement error variances are small. The asymptotic optimum performance is also confirmed by simulation results.

Optimization of polynomials in non-commuting variables

CERN Document Server

Burgdorf, Sabine; Povh, Janez

2016-01-01

This book presents recent results on positivity and optimization of polynomials in non-commuting variables. Researchers in non-commutative algebraic geometry, control theory, system engineering, optimization, quantum physics and information science will find the unified notation and mixture of algebraic geometry and mathematical programming useful. Theoretical results are matched with algorithmic considerations; several examples and information on how to use NCSOStools open source package to obtain the results provided. Results are presented on detecting the eigenvalue and trace positivity of polynomials in non-commuting variables using Newton chip method and Newton cyclic chip method, relaxations for constrained and unconstrained optimization problems, semidefinite programming formulations of the relaxations and finite convergence of the hierarchies of these relaxations, and the practical efficiency of algorithms.

Chance constrained uncertain classification via robust optimization

NARCIS (Netherlands)

Ben-Tal, A.; Bhadra, S.; Bhattacharayya, C.; Saketha Nat, J.

2011-01-01

This paper studies the problem of constructing robust classifiers when the training is plagued with uncertainty. The problem is posed as a Chance-Constrained Program (CCP) which ensures that the uncertain data points are classified correctly with high probability. Unfortunately such a CCP turns out

OpenMDAO: Framework for Flexible Multidisciplinary Design, Analysis and Optimization Methods

Science.gov (United States)

Heath, Christopher M.; Gray, Justin S.

2012-01-01

The OpenMDAO project is underway at NASA to develop a framework which simplifies the implementation of state-of-the-art tools and methods for multidisciplinary design, analysis and optimization. Foremost, OpenMDAO has been designed to handle variable problem formulations, encourage reconfigurability, and promote model reuse. This work demonstrates the concept of iteration hierarchies in OpenMDAO to achieve a flexible environment for supporting advanced optimization methods which include adaptive sampling and surrogate modeling techniques. In this effort, two efficient global optimization methods were applied to solve a constrained, single-objective and constrained, multiobjective version of a joint aircraft/engine sizing problem. The aircraft model, NASA's nextgeneration advanced single-aisle civil transport, is being studied as part of the Subsonic Fixed Wing project to help meet simultaneous program goals for reduced fuel burn, emissions, and noise. This analysis serves as a realistic test problem to demonstrate the flexibility and reconfigurability offered by OpenMDAO.

SU-G-BRA-08: Diaphragm Motion Tracking Based On KV CBCT Projections with a Constrained Linear Regression Optimization

Energy Technology Data Exchange (ETDEWEB)

Wei, J [City College of New York, New York, NY (United States); Chao, M [The Mount Sinai Medical Center, New York, NY (United States)

2016-06-15

Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associated algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately

SU-G-BRA-08: Diaphragm Motion Tracking Based On KV CBCT Projections with a Constrained Linear Regression Optimization

International Nuclear Information System (INIS)

Wei, J; Chao, M

2016-01-01

Purpose: To develop a novel strategy to extract the respiratory motion of the thoracic diaphragm from kilovoltage cone beam computed tomography (CBCT) projections by a constrained linear regression optimization technique. Methods: A parabolic function was identified as the geometric model and was employed to fit the shape of the diaphragm on the CBCT projections. The search was initialized by five manually placed seeds on a pre-selected projection image. Temporal redundancies, the enabling phenomenology in video compression and encoding techniques, inherent in the dynamic properties of the diaphragm motion together with the geometrical shape of the diaphragm boundary and the associated algebraic constraint that significantly reduced the searching space of viable parabolic parameters was integrated, which can be effectively optimized by a constrained linear regression approach on the subsequent projections. The innovative algebraic constraints stipulating the kinetic range of the motion and the spatial constraint preventing any unphysical deviations was able to obtain the optimal contour of the diaphragm with minimal initialization. The algorithm was assessed by a fluoroscopic movie acquired at anteriorposterior fixed direction and kilovoltage CBCT projection image sets from four lung and two liver patients. The automatic tracing by the proposed algorithm and manual tracking by a human operator were compared in both space and frequency domains. Results: The error between the estimated and manual detections for the fluoroscopic movie was 0.54mm with standard deviation (SD) of 0.45mm, while the average error for the CBCT projections was 0.79mm with SD of 0.64mm for all enrolled patients. The submillimeter accuracy outcome exhibits the promise of the proposed constrained linear regression approach to track the diaphragm motion on rotational projection images. Conclusion: The new algorithm will provide a potential solution to rendering diaphragm motion and ultimately

Modeling and operation optimization of a proton exchange membrane fuel cell system for maximum efficiency

International Nuclear Information System (INIS)

Han, In-Su; Park, Sang-Kyun; Chung, Chang-Bock

2016-01-01

Highlights: • A proton exchange membrane fuel cell system is operationally optimized. • A constrained optimization problem is formulated to maximize fuel cell efficiency. • Empirical and semi-empirical models for most system components are developed. • Sensitivity analysis is performed to elucidate the effects of major operating variables. • The optimization results are verified by comparison with actual operation data. - Abstract: This paper presents an operation optimization method and demonstrates its application to a proton exchange membrane fuel cell system. A constrained optimization problem was formulated to maximize the efficiency of a fuel cell system by incorporating practical models derived from actual operations of the system. Empirical and semi-empirical models for most of the system components were developed based on artificial neural networks and semi-empirical equations. Prior to system optimizations, the developed models were validated by comparing simulation results with the measured ones. Moreover, sensitivity analyses were performed to elucidate the effects of major operating variables on the system efficiency under practical operating constraints. Then, the optimal operating conditions were sought at various system power loads. The optimization results revealed that the efficiency gaps between the worst and best operation conditions of the system could reach 1.2–5.5% depending on the power output range. To verify the optimization results, the optimal operating conditions were applied to the fuel cell system, and the measured results were compared with the expected optimal values. The discrepancies between the measured and expected values were found to be trivial, indicating that the proposed operation optimization method was quite successful for a substantial increase in the efficiency of the fuel cell system.

A novel experimental design method to optimize hydrophilic matrix formulations with drug release profiles and mechanical properties.

Science.gov (United States)

Choi, Du Hyung; Lim, Jun Yeul; Shin, Sangmun; Choi, Won Jun; Jeong, Seong Hoon; Lee, Sangkil

2014-10-01

To investigate the effects of hydrophilic polymers on the matrix system, an experimental design method was developed to integrate response surface methodology and the time series modeling. Moreover, the relationships among polymers on the matrix system were studied with the evaluation of physical properties including water uptake, mass loss, diffusion, and gelling index. A mixture simplex lattice design was proposed while considering eight input control factors: Polyethylene glycol 6000 (x1 ), polyethylene oxide (PEO) N-10 (x2 ), PEO 301 (x3 ), PEO coagulant (x4 ), PEO 303 (x5 ), hydroxypropyl methylcellulose (HPMC) 100SR (x6 ), HPMC 4000SR (x7 ), and HPMC 10(5) SR (x8 ). With the modeling, optimal formulations were obtained depending on the four types of targets. The optimal formulations showed the four significant factors (x1 , x2 , x3 , and x8 ) and other four input factors (x4 , x5 , x6 , and x7 ) were not significant based on drug release profiles. Moreover, the optimization results were analyzed with estimated values, targets values, absolute biases, and relative biases based on observed times for the drug release rates with four different targets. The result showed that optimal solutions and target values had consistent patterns with small biases. On the basis of the physical properties of the optimal solutions, the type and ratio of the hydrophilic polymer and the relationships between polymers significantly influenced the physical properties of the system and drug release. This experimental design method is very useful in formulating a matrix system with optimal drug release. Moreover, it can distinctly confirm the relationships between excipients and the effects on the system with extensive and intensive evaluations. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

A Sequential Quadratically Constrained Quadratic Programming Method of Feasible Directions

International Nuclear Information System (INIS)

Jian Jinbao; Hu Qingjie; Tang Chunming; Zheng Haiyan

2007-01-01

In this paper, a sequential quadratically constrained quadratic programming method of feasible directions is proposed for the optimization problems with nonlinear inequality constraints. At each iteration of the proposed algorithm, a feasible direction of descent is obtained by solving only one subproblem which consist of a convex quadratic objective function and simple quadratic inequality constraints without the second derivatives of the functions of the discussed problems, and such a subproblem can be formulated as a second-order cone programming which can be solved by interior point methods. To overcome the Maratos effect, an efficient higher-order correction direction is obtained by only one explicit computation formula. The algorithm is proved to be globally convergent and superlinearly convergent under some mild conditions without the strict complementarity. Finally, some preliminary numerical results are reported

Lower irritation microemulsion-based rotigotine gel: formulation optimization and in vitro and in vivo studies.

Science.gov (United States)

Wang, Zheng; Mu, Hong-Jie; Zhang, Xue-Mei; Ma, Peng-Kai; Lian, Sheng-Nan; Zhang, Feng-Pu; Chu, Sheng-Ying; Zhang, Wen-Wen; Wang, Ai-Ping; Wang, Wen-Yan; Sun, Kao-Xiang

2015-01-01

Rotigotine is a potent and selective D1, D2, and D3 dopaminergic receptor agonist. Due to an extensive first-pass effect, it has a very low oral bioavailability (approximately 0.5% in rats). The present investigation aimed to develop a microemulsion-based hydrogel for transdermal rotigotine delivery with lower application site reactions. Pseudoternary phase diagrams were constructed to determine the region of oil in water (o/w)-type microemulsion. Central composite design was used to support the pseudoternary phase diagrams and to select homogeneous and stable microemulsions with an optimal amount of rotigotine permeation within 24 hours. In vitro skin permeation experiments were performed, using Franz diffusion cells, to compare rotigotine-loaded microemulsions with rotigotine solutions in oil. The optimized formulation was used to prepare a microemulsion-based hydrogel, which was subjected to bioavailability and skin irritancy studies. The selected formulations of rotigotine-loaded microemulsions had enhanced flux and permeation coefficients compared with rotigotine in oil. The optimum microemulsion contained 68% water, 6.8% Labrafil(®), 13.44% Cremophor(®) RH40, 6.72% Labrasol(®), and 5.04% Transcutol(®) HP; the drug-loading rate was 2%. To form a microemulsion gel, 1% Carbomer 1342 was added to the microemulsion. The bioavailability of the rotigotine-loaded microemulsion gel was 105.76%±20.52% with respect to the marketed rotigotine patch (Neupro(®)). The microemulsion gel irritated the skin less than Neupro. A rotigotine microemulsion-based hydrogel was successfully developed, and an optimal formulation for drug delivery was identified. This product could improve patient compliance and have broad marketability.

A model for optimal constrained adaptive testing

NARCIS (Netherlands)

van der Linden, Willem J.; Reese, Lynda M.

2001-01-01

A model for constrained computerized adaptive testing is proposed in which the information on the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum

A model for optimal constrained adaptive testing

NARCIS (Netherlands)

van der Linden, Willem J.; Reese, Lynda M.

1997-01-01

A model for constrained computerized adaptive testing is proposed in which the information in the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum

Quality by design approach for optimizing the formulation and physical properties of extemporaneously prepared orodispersible films.

Science.gov (United States)

Visser, J Carolina; Dohmen, Willem M C; Hinrichs, Wouter L J; Breitkreutz, Jörg; Frijlink, Henderik W; Woerdenbag, Herman J

2015-05-15

The quality by design (QbD) approach was applied for optimizing the formulation of extemporaneously prepared orodispersible films (ODFs) using Design-Expert® Software. The starting formulation was based on earlier experiments and contained the film forming agents hypromellose and carbomer 974P and the plasticizer glycerol (Visser et al., 2015). Trometamol and disodium EDTA were added to stabilize the solution. To optimize this formulation a quality target product profile was established in which critical quality attributes (CQAs) such as mechanical properties and disintegration time were defined and quantified. As critical process parameters (CPP) that were evaluated for their effect on the CQAs the percentage of hypromellose and the percentage of glycerol as well as the drying time were chosen. Response surface methodology (RMS) was used to evaluate the effects of the CPPs on the CQAs of the final product. The main factor affecting tensile strength and Young's modulus was the percentage of glycerol. Elongation at break was mainly influenced by the drying temperature. Disintegration time was found to be sensitive to the percentage of hypromellose. From the results a design space could be created. As long as the formulation and process variables remain within this design space, a product is obtained with desired characteristics and that meets all set quality requirements. Copyright © 2015 Elsevier B.V. All rights reserved.

Energy optimization for upstream data transfer in 802.15.4 beacon-enabled star formulation

Science.gov (United States)

Liu, Hua; Krishnamachari, Bhaskar

2008-08-01

Energy saving is one of the major concerns for low rate personal area networks. This paper models energy consumption for beacon-enabled time-slotted media accessing control cooperated with sleeping scheduling in a star network formulation for IEEE 802.15.4 standard. We investigate two different upstream (data transfer from devices to a network coordinator) strategies: a) tracking strategy: the devices wake up and check status (track the beacon) in each time slot; b) non-tracking strategy: nodes only wake-up upon data arriving and stay awake till data transmitted to the coordinator. We consider the tradeoff between energy cost and average data transmission delay for both strategies. Both scenarios are formulated as optimization problems and the optimal solutions are discussed. Our results show that different data arrival rate and system parameters (such as contention access period interval, upstream speed etc.) result in different strategies in terms of energy optimization with maximum delay constraints. Hence, according to different applications and system settings, different strategies might be chosen by each node to achieve energy optimization for both self-interested view and system view. We give the relation among the tunable parameters by formulas and plots to illustrate which strategy is better under corresponding parameters. There are two main points emphasized in our results with delay constraints: on one hand, when the system setting is fixed by coordinator, nodes in the network can intelligently change their strategies according to corresponding application data arrival rate; on the other hand, when the nodes' applications are known by the coordinator, the coordinator can tune the system parameters to achieve optimal system energy consumption.

THE DUBINS TRAVELING SALESMAN PROBLEM WITH CONSTRAINED COLLECTING MANEUVERS

Directory of Open Access Journals (Sweden)

Petr Váňa

2016-11-01

Full Text Available In this paper, we introduce a variant of the Dubins traveling salesman problem (DTSP that is called the Dubins traveling salesman problem with constrained collecting maneuvers (DTSP-CM. In contrast to the ordinary formulation of the DTSP, in the proposed DTSP-CM, the vehicle is requested to visit each target by specified collecting maneuver to accomplish the mission. The proposed problem formulation is motivated by scenarios with unmanned aerial vehicles where particular maneuvers are necessary for accomplishing the mission, such as object dropping or data collection with sensor sensitive to changes in vehicle heading. We consider existing methods for the DTSP and propose its modifications to use these methods to address a variant of the introduced DTSP-CM, where the collecting maneuvers are constrained to straight line segments.

Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems

Directory of Open Access Journals (Sweden)

R. Venkata Rao

2016-01-01

Full Text Available The teaching-learning-based optimization (TLBO algorithm is finding a large number of applications in different fields of engineering and science since its introduction in 2011. The major applications are found in electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics, chemistry, biotechnology and economics. This paper presents a review of applications of TLBO algorithm and a tutorial for solving the unconstrained and constrained optimization problems. The tutorial is expected to be useful to the beginners.

Optimal Control of a Fed-Batch Fermentation Involving Multiple Feeds

Directory of Open Access Journals (Sweden)

Chongyang Liu

2012-01-01

Full Text Available A nonlinear dynamical system, in which the feed rates of glycerol and alkali are taken as the control functions, is first proposed to formulate the fed-batch culture of 1,3-propanediol (1,3-PD production. To maximize the 1,3-PD concentration at the terminal time, a constrained optimal control model is then presented. A solution approach is developed to seek the optimal feed rates based on control vector parametrization method and improved differential evolution algorithm. The proposed methodology yielded an increase by 32.17% of 1,3-PD concentration at the terminal time.

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

Science.gov (United States)

Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

Lycopene in Beverage Emulsions: Optimizing Formulation Design and Processing Effects for Enhanced Delivery

Directory of Open Access Journals (Sweden)

Erika Meroni

2018-02-01

Full Text Available Lycopene is a desired ingredient in food formulations, yet its beneficial effects on human health remain largely underexploited due to its poor chemical stability and bioavailability. Oil-in-water emulsions may offer multiple advantages for the incorporation and delivery of this carotenoid species. Engineering and processing aspects for the development of emulsion-based delivery systems are of paramount importance for maintaining the structural integrity of lycopene. The selection of emulsifiers, pH, temperature, oil phase, particle size, homogenization conditions and presence of other antioxidants are major determinants for enhancing lycopene stability and delivery from a food emulsion. Process and formulation optimization of the delivery system is product-specific and should be tailored accordingly. Further research is required to better understand the underlying mechanisms of lycopene absorption by the human digestive system.

Loading pattern optimization with maximum utilization of discharging fuel employing adaptively constrained discontinuous penalty function

International Nuclear Information System (INIS)

Park, T. K.; Joo, H. G.; Kim, C. H.

2010-01-01

In order to find the most economical loading pattern (LP) considering multi-cycle fuel loading, multi-objective fuel LP optimization problems are examined by employing an adaptively constrained discontinuous penalty function (ACDPF) method. This is an improved method to simplify the complicated acceptance logic of the original DPF method in that the stochastic effects caused by the different random number sequence can be reduced. The effectiveness of the multi-objective simulated annealing (SA) algorithm employing ACDPF is examined for the reload core LP of Cycle 4 of Yonggwang Nuclear Unit 4. Several optimization runs are performed with different numbers of objectives consisting of cycle length and average burnup of fuels to be discharged or reloaded. The candidate LPs obtained from the multi-objective optimization runs turn out to be better than the reference LP in the aspects of cycle length and utilization of given fuels. It is note that the proposed ACDPF based MOSA algorithm can be a practical method to obtain an economical LP considering multi-cycle fuel loading. (authors)

Thermodynamic optimization of mixed refrigerant Joule- Thomson systems constrained by heat transfer considerations

International Nuclear Information System (INIS)

Hinze, J F; Klein, S A; Nellis, G F

2015-01-01

Mixed refrigerant (MR) working fluids can significantly increase the cooling capacity of a Joule-Thomson (JT) cycle. The optimization of MRJT systems has been the subject of substantial research. However, most optimization techniques do not model the recuperator in sufficient detail. For example, the recuperator is usually assumed to have a heat transfer coefficient that does not vary with the mixture. Ongoing work at the University of Wisconsin-Madison has shown that the heat transfer coefficients for two-phase flow are approximately three times greater than for a single phase mixture when the mixture quality is between 15% and 85%. As a result, a system that optimizes a MR without also requiring that the flow be in this quality range may require an extremely large recuperator or not achieve the performance predicted by the model. To ensure optimal performance of the JT cycle, the MR should be selected such that it is entirely two-phase within the recuperator. To determine the optimal MR composition, a parametric study was conducted assuming a thermodynamically ideal cycle. The results of the parametric study are graphically presented on a contour plot in the parameter space consisting of the extremes of the qualities that exist within the recuperator. The contours show constant values of the normalized refrigeration power. This ‘map’ shows the effect of MR composition on the cycle performance and it can be used to select the MR that provides a high cooling load while also constraining the recuperator to be two phase. The predicted best MR composition can be used as a starting point for experimentally determining the best MR. (paper)

Mucoadhesive Hydrogel Films of Econazole Nitrate: Formulation and Optimization Using Factorial Design

Directory of Open Access Journals (Sweden)

Balaram Gajra

2014-01-01

Full Text Available The mucoadhesive hydrogel film was prepared and optimized for the purpose of local drug delivery to oral cavity for the treatment of oral Candidiasis. The mucoadhesive hydrogel film was prepared with the poly(vinyl alcohol by freeze/thaw crosslinking technique. 32 full factorial design was employed to optimize the formulation. Number of freeze/thaw cycles (4, 6, and 8 cycles and the concentration of the poly(vinyl alcohol (10, 15, and 20% were used as the independent variables whereas time required for 50% drug release, cumulative percent of drug release at 8th hour, and “k” of zero order equation were used as the dependent variables. The films were evaluated for mucoadhesive strength, in vitro residence time, swelling study, in vitro drug release, and effectiveness against Candida albicans. The concentration of poly(vinyl alcohol and the number of freeze/thaw cycles both decrease the drug release rate. Mucoadhesive hydrogel film with 15% poly(vinyl alcohol and 7 freeze/thaw cycles was optimized. The optimized batch exhibited the sustained release of drug and the antifungal studies revealed that the drug released from the film could inhibit the growth of Candida albicans for 12 hours.

Formulation and Pharmacokinetic Evaluation of Controlled-Release ...

African Journals Online (AJOL)

A coating layer was then applied with a mixture of HPMC, ethylcellulose, shellac, and HPMC phthalate. The effect of several formulation variables on in vitro drug release was studied; furthermore, the drug release kinetics of the optimized formulation was evaluated. The in vivo pharmacokinetics of the optimized formulation ...

Optimal Financing Decisions of Two Cash-Constrained Supply Chains with Complementary Products

Directory of Open Access Journals (Sweden)

Yuting Li

2016-04-01

Full Text Available In recent years; financing difficulties have been obsessed small and medium enterprises (SMEs; especially emerging SMEs. Inter-members’ joint financing within a supply chain is one of solutions for SMEs. How about members’ joint financing of inter-supply chains? In order to answer the question, we firstly employ the Stackelberg game to propose three kinds of financing decision models of two cash-constrained supply chains with complementary products. Secondly, we analyze qualitatively these models and find the joint financing decision of the two supply chains is the most optimal one. Lastly, we conduct some numerical simulations not only to illustrate above results but also to find that the larger are cross-price sensitivity coefficients; the higher is the motivation for participants to make joint financing decisions; and the more are profits for them to gain.

Optimization of elastic elements of a damping devices for cylindrical hinges in crane-manipulating installations of mobile machines

Directory of Open Access Journals (Sweden)

Lagerev I.A.

2016-03-01

Full Text Available The article considers the problems of designing an original damping devices worn for cylindrical hinges in crane-manipulating installations of mobile machines. These devices can significantly reduce the additional impact load on a steel structure manipulators due to the presence of increased gaps in the hinges. Formulated the general formulation of nonlinear constrained optimization of the sizes of the elastic elements of the damping devices. Considered a promising design variants of elastic elements. For circular and arc elastic elements with circular and rectangular cross-section for-mulated the problems of optimal design including criterion functions and systems of geometric, technological, stiffness and strength penalty constraints. Analysis of the impact of various operating and design parameters on the results of optimal design of elastic elements was performed. Were set to the recommended the use of the constructive types of elastic elements to generate the required stiffness of the damper devices.

New scale-down methodology from commercial to lab scale to optimize plant-derived soft gel capsule formulations on a commercial scale.

Science.gov (United States)

Oishi, Sana; Kimura, Shin-Ichiro; Noguchi, Shuji; Kondo, Mio; Kondo, Yosuke; Shimokawa, Yoshiyuki; Iwao, Yasunori; Itai, Shigeru

2018-01-15

A new scale-down methodology from commercial rotary die scale to laboratory scale was developed to optimize a plant-derived soft gel capsule formulation and eventually manufacture superior soft gel capsules on a commercial scale, in order to reduce the time and cost for formulation development. Animal-derived and plant-derived soft gel film sheets were prepared using an applicator on a laboratory scale and their physicochemical properties, such as tensile strength, Young's modulus, and adhesive strength, were evaluated. The tensile strength of the animal-derived and plant-derived soft gel film sheets was 11.7 MPa and 4.41 MPa, respectively. The Young's modulus of the animal-derived and plant-derived soft gel film sheets was 169 MPa and 17.8 MPa, respectively, and both sheets showed a similar adhesion strength of approximately 4.5-10 MPa. Using a D-optimal mixture design, plant-derived soft gel film sheets were prepared and optimized by varying their composition, including variations in the mass of κ-carrageenan, ι-carrageenan, oxidized starch and heat-treated starch. The physicochemical properties of the sheets were evaluated to determine the optimal formulation. Finally, plant-derived soft gel capsules were manufactured using the rotary die method and the prepared soft gel capsules showed equivalent or superior physical properties compared with pre-existing soft gel capsules. Therefore, we successfully developed a new scale-down methodology to optimize the formulation of plant-derived soft gel capsules on a commercial scale. Copyright © 2017 Elsevier B.V. All rights reserved.

Formulation Optimization and Ex Vivo and In Vivo Evaluation of Celecoxib Microemulsion-Based Gel for Transdermal Delivery.

Science.gov (United States)

Cao, Mengyuan; Ren, Lili; Chen, Guoguang

2017-08-01

Celecoxib (CXB) is a poorly aqueous solubility sulfonamide non-steroidal anti-inflammatory drug (NSAID). Hence, the formulation of CXB was selected for solubilization and bioavailability. To find out suitable formulation for microemulsion, the solubility of CXB in triacetin (oil phase), Tween 80 (surfactant), and Transcutol-P (co-surfactant) was screened respectively and optimized by using orthogonal experimental design. The Km value and concentration of oil, S mix , and water were confirmed by pseudo-ternary phase diagram studies and central composite design. One percent carbopol 934 was added to form CXB microemulsion-based gel. The final formulation was evaluated for its appearance, pH, viscosity, stability, drug content determination, globule size, and zeta potential. Its ex vivo drug permeation and the in vivo pharmacokinetic was investigated. Further research was performed to ensure the safety and validity by skin irritation study and in vivo anti-inflammatory activity study. Ex vivo permeation study in mice was designed to compare permeation and transdermal ability between microemulsion formulation and conventional gel. The results revealed that optimized microemulsion-based gel gained higher permeation based on smaller globule size and high drug loading of microemulsion. Transdermal ability was also greatly improved. Bioavailability was compared to market Celebrex® by the in vivo pharmacokinetic study in rabbits. The results indicated that CXB microemulsion-based gel had better bioavailability than Celebrex®.

Linearly constrained minimax optimization

DEFF Research Database (Denmark)

Madsen, Kaj; Schjær-Jacobsen, Hans

1978-01-01

We present an algorithm for nonlinear minimax optimization subject to linear equality and inequality constraints which requires first order partial derivatives. The algorithm is based on successive linear approximations to the functions defining the problem. The resulting linear subproblems...

A Variant of the Topkis-Veinott Method for Solving Inequality Constrained Optimization Problems

International Nuclear Information System (INIS)

Birge, J. R.; Qi, L.; Wei, Z.

2000-01-01

In this paper we give a variant of the Topkis-Veinott method for solving inequality constrained optimization problems. This method uses a linearly constrained positive semidefinite quadratic problem to generate a feasible descent direction at each iteration. Under mild assumptions, the algorithm is shown to be globally convergent in the sense that every accumulation point of the sequence generated by the algorithm is a Fritz-John point of the problem. We introduce a Fritz-John (FJ) function, an FJ1 strong second-order sufficiency condition (FJ1-SSOSC), and an FJ2 strong second-order sufficiency condition (FJ2-SSOSC), and then show, without any constraint qualification (CQ), that (i) if an FJ point z satisfies the FJ1-SSOSC, then there exists a neighborhood N(z) of z such that, for any FJ point y element of N(z) {z } , f 0 (y) ≠ f 0 (z) , where f 0 is the objective function of the problem; (ii) if an FJ point z satisfies the FJ2-SSOSC, then z is a strict local minimum of the problem. The result (i) implies that the entire iteration point sequence generated by the method converges to an FJ point. We also show that if the parameters are chosen large enough, a unit step length can be accepted by the proposed algorithm

A game-theoretic formulation of the homogeneous self-reconfiguration problem

KAUST Repository

Pickem, Daniel; Egerstedt, Magnus; Shamma, Jeff S.

2015-01-01

In this paper we formulate the homogeneous two- and three-dimensional self-reconfiguration problem over discrete grids as a constrained potential game. We develop a game-theoretic learning algorithm based on the Metropolis-Hastings algorithm that solves the self-reconfiguration problem in a globally optimal fashion. Both a centralized and a fully decentralized algorithm are presented and we show that the only stochastically stable state is the potential function maximizer, i.e. the desired target configuration. These algorithms compute transition probabilities in such a way that even though each agent acts in a self-interested way, the overall collective goal of self-reconfiguration is achieved. Simulation results confirm the feasibility of our approach and show convergence to desired target configurations.

A game-theoretic formulation of the homogeneous self-reconfiguration problem

KAUST Repository

Pickem, Daniel

2015-12-15

In this paper we formulate the homogeneous two- and three-dimensional self-reconfiguration problem over discrete grids as a constrained potential game. We develop a game-theoretic learning algorithm based on the Metropolis-Hastings algorithm that solves the self-reconfiguration problem in a globally optimal fashion. Both a centralized and a fully decentralized algorithm are presented and we show that the only stochastically stable state is the potential function maximizer, i.e. the desired target configuration. These algorithms compute transition probabilities in such a way that even though each agent acts in a self-interested way, the overall collective goal of self-reconfiguration is achieved. Simulation results confirm the feasibility of our approach and show convergence to desired target configurations.

Formulation optimization of transdermal meloxicam potassium-loaded mesomorphic phases containing ethanol, oleic acid and mixture surfactant using the statistical experimental design methodology.

Science.gov (United States)

Huang, Chi-Te; Tsai, Chia-Hsun; Tsou, Hsin-Yeh; Huang, Yaw-Bin; Tsai, Yi-Hung; Wu, Pao-Chu

2011-01-01

Response surface methodology (RSM) was used to develop and optimize the mesomorphic phase formulation for a meloxicam transdermal dosage form. A mixture design was applied to prepare formulations which consisted of three independent variables including oleic acid (X(1)), distilled water (X(2)) and ethanol (X(3)). The flux and lag time (LT) were selected as dependent variables. The result showed that using mesomorphic phases as vehicles can significantly increase flux and shorten LT of drug. The analysis of variance showed that the permeation parameters of meloxicam from formulations were significantly influenced by the independent variables and their interactions. The X(3) (ethanol) had the greatest potential influence on the flux and LT, followed by X(1) and X(2). A new formulation was prepared according to the independent levels provided by RSM. The observed responses were in close agreement with the predicted values, demonstrating that RSM could be successfully used to optimize mesomorphic phase formulations.

Incorporating a constrained optimization algorithm into remote sensing/precision agriculture methodology

Science.gov (United States)

Moreenthaler, George W.; Khatib, Nader; Kim, Byoungsoo

2003-08-01

For two decades now, the use of Remote Sensing/Precision Agriculture to improve farm yields while reducing the use of polluting chemicals and the limited water supply has been a major goal. With world population growing exponentially, arable land being consumed by urbanization, and an unfavorable farm economy, farm efficiency must increase to meet future food requirements and to make farming a sustainable, profitable occupation. "Precision Agriculture" refers to a farming methodology that applies nutrients and moisture only where and when they are needed in the field. The real goal is to increase farm profitability by identifying the additional treatments of chemicals and water that increase revenues more than they increase costs and do no exceed pollution standards (constrained optimization). Even though the economic and environmental benefits appear to be great, Remote Sensing/Precision Agriculture has not grown as rapidly as early advocates envisioned. Technology for a successful Remote Sensing/Precision Agriculture system is now in place, but other needed factors have been missing. Commercial satellite systems can now image the Earth (multi-spectrally) with a resolution as fine as 2.5 m. Precision variable dispensing systems using GPS are now available and affordable. Crop models that predict yield as a function of soil, chemical, and irrigation parameter levels have been developed. Personal computers and internet access are now in place in most farm homes and can provide a mechanism for periodically disseminating advice on what quantities of water and chemicals are needed in specific regions of each field. Several processes have been selected that fuse the disparate sources of information on the current and historic states of the crop and soil, and the remaining resource levels available, with the critical decisions that farmers are required to make. These are done in a way that is easy for the farmer to understand and profitable to implement. A "Constrained

Theoretical calculation of reorganization energy for electron self-exchange reaction by constrained density functional theory and constrained equilibrium thermodynamics.

Science.gov (United States)

Ren, Hai-Sheng; Ming, Mei-Jun; Ma, Jian-Yi; Li, Xiang-Yuan

2013-08-22

Within the framework of constrained density functional theory (CDFT), the diabatic or charge localized states of electron transfer (ET) have been constructed. Based on the diabatic states, inner reorganization energy λin has been directly calculated. For solvent reorganization energy λs, a novel and reasonable nonequilibrium solvation model is established by introducing a constrained equilibrium manipulation, and a new expression of λs has been formulated. It is found that λs is actually the cost of maintaining the residual polarization, which equilibrates with the extra electric field. On the basis of diabatic states constructed by CDFT, a numerical algorithm using the new formulations with the dielectric polarizable continuum model (D-PCM) has been implemented. As typical test cases, self-exchange ET reactions between tetracyanoethylene (TCNE) and tetrathiafulvalene (TTF) and their corresponding ionic radicals in acetonitrile are investigated. The calculated reorganization energies λ are 7293 cm(-1) for TCNE/TCNE(-) and 5939 cm(-1) for TTF/TTF(+) reactions, agreeing well with available experimental results of 7250 cm(-1) and 5810 cm(-1), respectively.

Formulation optimization of gentamicin loaded Eudragit RS100 microspheres using factorial design study.

Science.gov (United States)

Singh, Deependra; Saraf, Swarnlata; Dixit, Vinod Kumar; Saraf, Shailendra

2008-04-01

Gentamicin-Eudragit RS100 microspheres were prepared by modified double emulsion method. A 3(2) full factorial experiment was designed to study the effects of the composition of outer aqueous phase in terms of amount of glycerol (viscosity effect) and sodium chloride (osmotic pressure gradient effect) on the entrapment efficiency and % yield and microsphere size. The results of analysis of variance test for responses measured indicated that the test is significant (p>0.05). The contribution of sodium chloride concentration was found to be higher on entrapment efficiency and % yield, whereas glycerol produced significant effect on the mean diameter of microspheres. Microspheres demonstrated spherical particles in the size range of 33.24-60.43 microm. In vitro release profile of optimized formulation demonstrated sustained release for 24 h following Higuchi kinetics. Finally, drug bioactivity was found to remain intact after microencapsulation. Response surface graphs are presented to examine the effects of independent variables on the responses studied. Thus, by formulation design important parameters affecting formulation characteristics of gentamicin loaded Eudragit RS100 microspheres can be identified for controlled delivery with desirable characters in terms of maximum entrapment and yield.

Slope constrained Topology Optimization

DEFF Research Database (Denmark)

Petersson, J.; Sigmund, Ole

1998-01-01

The problem of minimum compliance topology optimization of an elastic continuum is considered. A general continuous density-energy relation is assumed, including variable thickness sheet models and artificial power laws. To ensure existence of solutions, the design set is restricted by enforcing...

Formulation and optimization of itraconazole polymeric lipid hybrid nanoparticles (Lipomer) using Box Behnken design.

Science.gov (United States)

Gajra, Balaram; Dalwadi, Chintan; Patel, Ravi

2015-01-21

The objective of the study was to formulate and to investigate the combined influence of 3 independent variables in the optimization of Polymeric lipid hybrid nanoparticles (PLHNs) (Lipomer) containing hydrophobic antifungal drug Itraconazole and to improve intestinal permeability. The Polymeric lipid hybrid nanoparticle formulation was prepared by the emulsification solvent evaporation method and 3 factor 3 level Box Behnken statistical design was used to optimize and derive a second order polynomial equation and construct contour plots to predict responses. Biodegradable Polycaprolactone, soya lecithin and Poly vinyl alcohol were used to prepare PLHNs. The independent variables selected were lipid to polymer ratio (X1) Concentration of surfactant (X2) Concentration of the drug (X3). The Box-Behnken design demonstrated the role of the derived equation and contour plots in predicting the values of dependent variables for the preparation and optimization of Itraconazole PLHNs. Itraconazole PLHNs revealed nano size (210 ± 1.8 nm) with an entrapment efficiency of 83 ± 0.6% and negative zeta potential of -11.7 mV and also enhance the permeability of itraconazole as the permeability coefficient (Papp) and the absorption enhancement ratio was higher. The tunable particle size, surface charge, and favourable encapsulation efficiency with a sustained drug release profile of PLHNs suggesting that it could be promising system envisioned to increase the bioavailability by improving intestinal permeability through lymphatic uptake, M cell of payer's patch or paracellular pathway which was proven by confocal microscopy.

Optimization of natural lipstick formulation based on pitaya (Hylocereus polyrhizus) seed oil using D-optimal mixture experimental design.

Science.gov (United States)

Kamairudin, Norsuhaili; Gani, Siti Salwa Abd; Masoumi, Hamid Reza Fard; Hashim, Puziah

2014-10-16

The D-optimal mixture experimental design was employed to optimize the melting point of natural lipstick based on pitaya (Hylocereus polyrhizus) seed oil. The influence of the main lipstick components-pitaya seed oil (10%-25% w/w), virgin coconut oil (25%-45% w/w), beeswax (5%-25% w/w), candelilla wax (1%-5% w/w) and carnauba wax (1%-5% w/w)-were investigated with respect to the melting point properties of the lipstick formulation. The D-optimal mixture experimental design was applied to optimize the properties of lipstick by focusing on the melting point with respect to the above influencing components. The D-optimal mixture design analysis showed that the variation in the response (melting point) could be depicted as a quadratic function of the main components of the lipstick. The best combination of each significant factor determined by the D-optimal mixture design was established to be pitaya seed oil (25% w/w), virgin coconut oil (37% w/w), beeswax (17% w/w), candelilla wax (2% w/w) and carnauba wax (2% w/w). With respect to these factors, the 46.0 °C melting point property was observed experimentally, similar to the theoretical prediction of 46.5 °C. Carnauba wax is the most influential factor on this response (melting point) with its function being with respect to heat endurance. The quadratic polynomial model sufficiently fit the experimental data.

Optimization of Natural Lipstick Formulation Based on Pitaya (Hylocereus polyrhizus Seed Oil Using D-Optimal Mixture Experimental Design

Directory of Open Access Journals (Sweden)

Norsuhaili Kamairudin

2014-10-01

Full Text Available The D-optimal mixture experimental design was employed to optimize the melting point of natural lipstick based on pitaya (Hylocereus polyrhizus seed oil. The influence of the main lipstick components—pitaya seed oil (10%–25% w/w, virgin coconut oil (25%–45% w/w, beeswax (5%–25% w/w, candelilla wax (1%–5% w/w and carnauba wax (1%–5% w/w—were investigated with respect to the melting point properties of the lipstick formulation. The D-optimal mixture experimental design was applied to optimize the properties of lipstick by focusing on the melting point with respect to the above influencing components. The D-optimal mixture design analysis showed that the variation in the response (melting point could be depicted as a quadratic function of the main components of the lipstick. The best combination of each significant factor determined by the D-optimal mixture design was established to be pitaya seed oil (25% w/w, virgin coconut oil (37% w/w, beeswax (17% w/w, candelilla wax (2% w/w and carnauba wax (2% w/w. With respect to these factors, the 46.0 °C melting point property was observed experimentally, similar to the theoretical prediction of 46.5 °C. Carnauba wax is the most influential factor on this response (melting point with its function being with respect to heat endurance. The quadratic polynomial model sufficiently fit the experimental data.

Improved helicopter aeromechanical stability analysis using segmented constrained layer damping and hybrid optimization

Science.gov (United States)

Liu, Qiang; Chattopadhyay, Aditi

2000-06-01

Aeromechanical stability plays a critical role in helicopter design and lead-lag damping is crucial to this design. In this paper, the use of segmented constrained damping layer (SCL) treatment and composite tailoring is investigated for improved rotor aeromechanical stability using formal optimization technique. The principal load-carrying member in the rotor blade is represented by a composite box beam, of arbitrary thickness, with surface bonded SCLs. A comprehensive theory is used to model the smart box beam. A ground resonance analysis model and an air resonance analysis model are implemented in the rotor blade built around the composite box beam with SCLs. The Pitt-Peters dynamic inflow model is used in air resonance analysis under hover condition. A hybrid optimization technique is used to investigate the optimum design of the composite box beam with surface bonded SCLs for improved damping characteristics. Parameters such as stacking sequence of the composite laminates and placement of SCLs are used as design variables. Detailed numerical studies are presented for aeromechanical stability analysis. It is shown that optimum blade design yields significant increase in rotor lead-lag regressive modal damping compared to the initial system.

WE-AB-209-12: Quasi Constrained Multi-Criteria Optimization for Automated Radiation Therapy Treatment Planning

Energy Technology Data Exchange (ETDEWEB)

Watkins, W.T.; Siebers, J.V. [University of Virginia, Charlottesville, VA (United States)

2016-06-15

Purpose: To introduce quasi-constrained Multi-Criteria Optimization (qcMCO) for unsupervised radiation therapy optimization which generates alternative patient-specific plans emphasizing dosimetric tradeoffs and conformance to clinical constraints for multiple delivery techniques. Methods: For N Organs At Risk (OARs) and M delivery techniques, qcMCO generates M(N+1) alternative treatment plans per patient. Objective weight variations for OARs and targets are used to generate alternative qcMCO plans. For 30 locally advanced lung cancer patients, qcMCO plans were generated for dosimetric tradeoffs to four OARs: each lung, heart, and esophagus (N=4) and 4 delivery techniques (simple 4-field arrangements, 9-field coplanar IMRT, 27-field non-coplanar IMRT, and non-coplanar Arc IMRT). Quasi-constrained objectives included target prescription isodose to 95% (PTV-D95), maximum PTV dose (PTV-Dmax)< 110% of prescription, and spinal cord Dmax<45 Gy. The algorithm’s ability to meet these constraints while simultaneously revealing dosimetric tradeoffs was investigated. Statistically significant dosimetric tradeoffs were defined such that the coefficient of determination between dosimetric indices which varied by at least 5 Gy between different plans was >0.8. Results: The qcMCO plans varied mean dose by >5 Gy to ipsilateral lung for 24/30 patients, contralateral lung for 29/30 patients, esophagus for 29/30 patients, and heart for 19/30 patients. In the 600 plans computed without human interaction, average PTV-D95=67.4±3.3 Gy, PTV-Dmax=79.2±5.3 Gy, and spinal cord Dmax was >45 Gy in 93 plans (>50 Gy in 2/600 plans). Statistically significant dosimetric tradeoffs were evident in 19/30 plans, including multiple tradeoffs of at least 5 Gy between multiple OARs in 7/30 cases. The most common statistically significant tradeoff was increasing PTV-Dmax to reduce OAR dose (15/30 patients). Conclusion: The qcMCO method can conform to quasi-constrained objectives while revealing

WE-AB-209-12: Quasi Constrained Multi-Criteria Optimization for Automated Radiation Therapy Treatment Planning

International Nuclear Information System (INIS)

Watkins, W.T.; Siebers, J.V.

2016-01-01

Purpose: To introduce quasi-constrained Multi-Criteria Optimization (qcMCO) for unsupervised radiation therapy optimization which generates alternative patient-specific plans emphasizing dosimetric tradeoffs and conformance to clinical constraints for multiple delivery techniques. Methods: For N Organs At Risk (OARs) and M delivery techniques, qcMCO generates M(N+1) alternative treatment plans per patient. Objective weight variations for OARs and targets are used to generate alternative qcMCO plans. For 30 locally advanced lung cancer patients, qcMCO plans were generated for dosimetric tradeoffs to four OARs: each lung, heart, and esophagus (N=4) and 4 delivery techniques (simple 4-field arrangements, 9-field coplanar IMRT, 27-field non-coplanar IMRT, and non-coplanar Arc IMRT). Quasi-constrained objectives included target prescription isodose to 95% (PTV-D95), maximum PTV dose (PTV-Dmax)< 110% of prescription, and spinal cord Dmax<45 Gy. The algorithm’s ability to meet these constraints while simultaneously revealing dosimetric tradeoffs was investigated. Statistically significant dosimetric tradeoffs were defined such that the coefficient of determination between dosimetric indices which varied by at least 5 Gy between different plans was >0.8. Results: The qcMCO plans varied mean dose by >5 Gy to ipsilateral lung for 24/30 patients, contralateral lung for 29/30 patients, esophagus for 29/30 patients, and heart for 19/30 patients. In the 600 plans computed without human interaction, average PTV-D95=67.4±3.3 Gy, PTV-Dmax=79.2±5.3 Gy, and spinal cord Dmax was >45 Gy in 93 plans (>50 Gy in 2/600 plans). Statistically significant dosimetric tradeoffs were evident in 19/30 plans, including multiple tradeoffs of at least 5 Gy between multiple OARs in 7/30 cases. The most common statistically significant tradeoff was increasing PTV-Dmax to reduce OAR dose (15/30 patients). Conclusion: The qcMCO method can conform to quasi-constrained objectives while revealing

Q-deformed systems and constrained dynamics

International Nuclear Information System (INIS)

Shabanov, S.V.

1993-01-01

It is shown that quantum theories of the q-deformed harmonic oscillator and one-dimensional free q-particle (a free particle on the 'quantum' line) can be obtained by the canonical quantization of classical Hamiltonian systems with commutative phase-space variables and a non-trivial symplectic structure. In the framework of this approach, classical dynamics of a particle on the q-line coincides with the one of a free particle with friction. It is argued that q-deformed systems can be treated as ordinary mechanical systems with the second-class constraints. In particular, second-class constrained systems corresponding to the q-oscillator and q-particle are given. A possibility of formulating q-deformed systems via gauge theories (first-class constrained systems) is briefly discussed. (orig.)

Scalable algorithms for optimal control of stochastic PDEs

KAUST Repository

Ghattas, Omar

2016-01-07

We present methods for the optimal control of systems governed by partial differential equations with infinite-dimensional uncertain parameters. We consider an objective function that involves the mean and variance of the control objective, leading to a risk-averse optimal control formulation. To make the optimal control problem computationally tractable, we employ a local quadratic approximation of the objective with respect to the uncertain parameter. This enables computation of the mean and variance of the control objective analytically. The resulting risk-averse optimization problem is formulated as a PDE-constrained optimization problem with constraints given by the forward and adjoint PDEs for the first and second-order derivatives of the quantity of interest with respect to the uncertain parameter, and with an objective that involves the trace of a covariance-preconditioned Hessian (of the objective with respect to the uncertain parameters) operator. A randomized trace estimator is used to make tractable the trace computation. Adjoint-based techniques are used to derive an expression for the infinite-dimensional gradient of the risk-averse objective function via the Lagrangian, leading to a quasi-Newton method for solution of the optimal control problem. A specific problem of optimal control of a linear elliptic PDE that describes flow of a fluid in a porous medium with uncertain permeability field is considered. We present numerical results to study the consequences of the local quadratic approximation and the efficiency of the method.

Scalable algorithms for optimal control of stochastic PDEs

KAUST Repository

Ghattas, Omar; Alexanderian, Alen; Petra, Noemi; Stadler, Georg

2016-01-01

We present methods for the optimal control of systems governed by partial differential equations with infinite-dimensional uncertain parameters. We consider an objective function that involves the mean and variance of the control objective, leading to a risk-averse optimal control formulation. To make the optimal control problem computationally tractable, we employ a local quadratic approximation of the objective with respect to the uncertain parameter. This enables computation of the mean and variance of the control objective analytically. The resulting risk-averse optimization problem is formulated as a PDE-constrained optimization problem with constraints given by the forward and adjoint PDEs for the first and second-order derivatives of the quantity of interest with respect to the uncertain parameter, and with an objective that involves the trace of a covariance-preconditioned Hessian (of the objective with respect to the uncertain parameters) operator. A randomized trace estimator is used to make tractable the trace computation. Adjoint-based techniques are used to derive an expression for the infinite-dimensional gradient of the risk-averse objective function via the Lagrangian, leading to a quasi-Newton method for solution of the optimal control problem. A specific problem of optimal control of a linear elliptic PDE that describes flow of a fluid in a porous medium with uncertain permeability field is considered. We present numerical results to study the consequences of the local quadratic approximation and the efficiency of the method.

Optimal design of MR shock absorber and application to vehicle suspension

International Nuclear Information System (INIS)

Nguyen, Quoc-Hung; Choi, Seung-Bok

2009-01-01

This paper presents an optimal design of a magnetorheological (MR) shock absorber based on finite element analysis. The MR shock absorber is constrained in a specific volume and the optimization problem identifies geometric dimensions of the shock absorber that minimize a multi-objective function. The objective function is proposed by considering the damping force, dynamic range and the inductive time constant of the shock absorber. After describing the configuration of the MR shock absorber, a quasi-static modeling of the shock absorber is performed based on the Bingham model of an MR fluid. The initial geometric dimensions of the shock absorber are then determined based on the assumption of constant magnetic flux density throughout the magnetic circuit. The objective function of the optimization problem is derived based on the solution of the initial shock absorber. An optimization procedure using a golden-section algorithm and a local quadratic fitting technique is constructed via a commercial finite element method parametric design language. Using the developed optimization tool, optimal solutions of the MR shock absorber, which is constrained in a specific cylindrical volume defined by its radius and height, are determined. Subsequently, a quarter-car suspension model with the optimized MR shock absorber is formulated and the vibration control performance of the suspension is evaluated under bump and sinusoidal road conditions

Sequential optimization of a terrestrial biosphere model constrained by multiple satellite based products

Science.gov (United States)

Ichii, K.; Kondo, M.; Wang, W.; Hashimoto, H.; Nemani, R. R.

2012-12-01

Various satellite-based spatial products such as evapotranspiration (ET) and gross primary productivity (GPP) are now produced by integration of ground and satellite observations. Effective use of these multiple satellite-based products in terrestrial biosphere models is an important step toward better understanding of terrestrial carbon and water cycles. However, due to the complexity of terrestrial biosphere models with large number of model parameters, the application of these spatial data sets in terrestrial biosphere models is difficult. In this study, we established an effective but simple framework to refine a terrestrial biosphere model, Biome-BGC, using multiple satellite-based products as constraints. We tested the framework in the monsoon Asia region covered by AsiaFlux observations. The framework is based on the hierarchical analysis (Wang et al. 2009) with model parameter optimization constrained by satellite-based spatial data. The Biome-BGC model is separated into several tiers to minimize the freedom of model parameter selections and maximize the independency from the whole model. For example, the snow sub-model is first optimized using MODIS snow cover product, followed by soil water sub-model optimized by satellite-based ET (estimated by an empirical upscaling method; Support Vector Regression (SVR) method; Yang et al. 2007), photosynthesis model optimized by satellite-based GPP (based on SVR method), and respiration and residual carbon cycle models optimized by biomass data. As a result of initial assessment, we found that most of default sub-models (e.g. snow, water cycle and carbon cycle) showed large deviations from remote sensing observations. However, these biases were removed by applying the proposed framework. For example, gross primary productivities were initially underestimated in boreal and temperate forest and overestimated in tropical forests. However, the parameter optimization scheme successfully reduced these biases. Our analysis

Formulation design and optimization for the improvement of nystatin-loaded lipid intravenous emulsion.

Science.gov (United States)

Marín-Quintero, Deiry; Fernández-Campos, Francisco; Calpena-Campmany, Ana C; Montes-López, María J; Clares-Naveros, Beatriz; Del Pozo-Carrascosa, Alfonso

2013-11-01

Nystatin (NYS) is a polyene macrolide with broad antifungal spectrum restricted to topical use owing to its toxicity upon systemic administration. The aims of this work were the design, development, and optimization of NYS-loaded lipid emulsion for intravenous administration. A closed circuit system was designed to apply ultrasound during the elaboration of the lipid intravenous emulsions (LIEs). Additionally, a comparison with the commercially available Intralipid(®) 20% was also performed. Manufacturing conditions were optimized by factorial design. Formulations were evaluated in terms of physicochemical parameters, stability, release profile, and antimicrobial activity. The average droplet size, polydispersity index, zeta-potential, pH, and volume distribution values ranged between 192.5 and 143.0 nm, 0.170 and 0.135, -46 and -44 mV, 7.11 and 7.53, 580 and 670 nm, respectively. The selected NYS-loaded LIE (NYS-LIE54) consisted of soybean oil (30%), soybean lecithin (2%), solutol HS(®) 15 (4%), and glycerol (2.25%) was stable for at least 60 days. In vitro drug release studies of this formulation suggested a sustained-release profile. Equally, NYS-LIE54 showed the best antimicrobial activity being higher than the free drug. Thus, it could be a promising drug delivery system to treat systemic fungal infections. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

Convex Relaxations of Chance Constrained AC Optimal Power Flow

DEFF Research Database (Denmark)

Venzke, Andreas; Halilbasic, Lejla; Markovic, Uros

2017-01-01

, reactive power, and voltage. We state a tractable formulation for two types of uncertainty sets. Using a scenario-based approach and making no prior assumptions about the probability distribution of the forecast errors, we obtain a robust formulation for a rectangular uncertainty set. Alternatively...

Optimally Stopped Optimization

Science.gov (United States)

Vinci, Walter; Lidar, Daniel

We combine the fields of heuristic optimization and optimal stopping. We propose a strategy for benchmarking randomized optimization algorithms that minimizes the expected total cost for obtaining a good solution with an optimal number of calls to the solver. To do so, rather than letting the objective function alone define a cost to be minimized, we introduce a further cost-per-call of the algorithm. We show that this problem can be formulated using optimal stopping theory. The expected cost is a flexible figure of merit for benchmarking probabilistic solvers that can be computed when the optimal solution is not known, and that avoids the biases and arbitrariness that affect other measures. The optimal stopping formulation of benchmarking directly leads to a real-time, optimal-utilization strategy for probabilistic optimizers with practical impact. We apply our formulation to benchmark the performance of a D-Wave 2X quantum annealer and the HFS solver, a specialized classical heuristic algorithm designed for low tree-width graphs. On a set of frustrated-loop instances with planted solutions defined on up to N = 1098 variables, the D-Wave device is between one to two orders of magnitude faster than the HFS solver.

Security constrained optimal power flow by modern optimization tools

African Journals Online (AJOL)

The main objective of an optimal power flow (OPF) functions is to optimize .... It is characterized as propagation of plants and this happens by gametes union. ... ss and different variables, for example, wind, nearby fertilization can have a critic.

SU-E-T-574: Novel Chance-Constrained Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

International Nuclear Information System (INIS)

An, Y; Liang, J; Liu, W

2015-01-01

Purpose: We propose to apply a probabilistic framework, namely chanceconstrained optimization, in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to hedge against the influence of uncertainties and improve robustness of treatment plans. Methods: IMPT plans were generated for a typical prostate patient. Nine dose distributions are computed — the nominal one and one each for ±5mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. These nine dose distributions are supplied to the solver CPLEX as chance constraints to explicitly control plan robustness under these representative uncertainty scenarios with certain probability. This probability is determined by the tolerance level. We make the chance-constrained model tractable by converting it to a mixed integer optimization problem. The quality of plans derived from this method is evaluated using dose-volume histogram (DVH) indices such as tumor dose homogeneity (D5% – D95%) and coverage (D95%) and normal tissue sparing like V70 of rectum, V65, and V40 of bladder. We also compare the results from this novel method with the conventional PTV-based method to further demonstrate its effectiveness Results: Our model can yield clinically acceptable plans within 50 seconds. The chance-constrained optimization produces IMPT plans with comparable target coverage, better target dose homogeneity, and better normal tissue sparing compared to the PTV-based optimization [D95% CTV: 67.9 vs 68.7 (Gy), D5% – D95% CTV: 11.9 vs 18 (Gy), V70 rectum: 0.0 % vs 0.33%, V65 bladder: 2.17% vs 9.33%, V40 bladder: 8.83% vs 21.83%]. It also simultaneously makes the plan more robust [Width of DVH band at D50%: 2.0 vs 10.0 (Gy)]. The tolerance level may be varied to control the tradeoff between plan robustness and quality. Conclusion: The chance-constrained optimization generates superior IMPT plan compared to the PTV-based optimization with

Formulation development and optimization of palm kernel oil esters-based nanoemulsions containing sodium diclofenac.

Science.gov (United States)

Rezaee, Malahat; Basri, Mahiran; Rahman, Raja Noor Zaliha Raja Abdul; Salleh, Abu Bakar; Chaibakhsh, Naz; Karjiban, Roghayeh Abedi

2014-01-01

Response surface methodology was employed to study the effect of formulation composition variables, water content (60%-80%, w/w) and oil and surfactant (O/S) ratio (0.17-1.33), as well as high-shear emulsification conditions, mixing rate (300-3,000 rpm) and mixing time (5-30 minutes) on the properties of sodium diclofenac-loaded palm kernel oil esters-nanoemulsions. The two response variables were droplet size and viscosity. Optimization of the conditions according to the four variables was performed for preparation of the nanoemulsions with the minimum values of particle size and viscosity. The results showed that the experimental data could be sufficiently fitted into a third-order polynomial model with multiple regression coefficients (R(2) ) of 0.938 and 0.994 for the particle size and viscosity, respectively. Water content, O/S ratio and mixing time, quadrics of all independent variables, interaction between O/S ratio and mixing rate and between mixing time and rate, as well as cubic term of water content had a significant effect (Pdiclofenac nanoemulsions were predicted to be: 71.36% water content; 0.69 O/S ratio; 950 rpm mixing rate, and 5 minute mixing time. The optimized formulation showed good storage stability in different temperatures.

Advanced Computational Methods for Security Constrained Financial Transmission Rights: Structure and Parallelism

Energy Technology Data Exchange (ETDEWEB)

Elbert, Stephen T.; Kalsi, Karanjit; Vlachopoulou, Maria; Rice, Mark J.; Glaesemann, Kurt R.; Zhou, Ning

2012-07-26

Financial Transmission Rights (FTRs) help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, a novel non-linear dynamical system (NDS) approach is proposed to solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™ and tested on large-scale systems using data from the Western Electricity Coordinating Council (WECC). The NDS is demonstrated to outperform the widely used CPLEX algorithms while exhibiting superior scalability. Furthermore, the NDS based solver can be easily parallelized which results in significant computational improvement.

Demand-Side Energy Management Based on Nonconvex Optimization in Smart Grid

Directory of Open Access Journals (Sweden)

Kai Ma

2017-10-01

Full Text Available Demand-side energy management is used for regulating the consumers’ energy usage in smart grid. With the guidance of the grid’s price policy, the consumers can change their energy consumption in response. The objective of this study is jointly optimizing the load status and electric supply, in order to make a tradeoff between the electric cost and the thermal comfort. The problem is formulated into a nonconvex optimization model. The multiplier method is used to solve the constrained optimization, and the objective function is transformed to the augmented Lagrangian function without constraints. Hence, the Powell direction acceleration method with advance and retreat is applied to solve the unconstrained optimization. Numerical results show that the proposed algorithm can achieve the balance between the electric supply and demand, and the optimization variables converge to the optimum.

Evolutionary optimization methods for accelerator design

Science.gov (United States)

Poklonskiy, Alexey A.

Many problems from the fields of accelerator physics and beam theory can be formulated as optimization problems and, as such, solved using optimization methods. Despite growing efficiency of the optimization methods, the adoption of modern optimization techniques in these fields is rather limited. Evolutionary Algorithms (EAs) form a relatively new and actively developed optimization methods family. They possess many attractive features such as: ease of the implementation, modest requirements on the objective function, a good tolerance to noise, robustness, and the ability to perform a global search efficiently. In this work we study the application of EAs to problems from accelerator physics and beam theory. We review the most commonly used methods of unconstrained optimization and describe the GATool, evolutionary algorithm and the software package, used in this work, in detail. Then we use a set of test problems to assess its performance in terms of computational resources, quality of the obtained result, and the tradeoff between them. We justify the choice of GATool as a heuristic method to generate cutoff values for the COSY-GO rigorous global optimization package for the COSY Infinity scientific computing package. We design the model of their mutual interaction and demonstrate that the quality of the result obtained by GATool increases as the information about the search domain is refined, which supports the usefulness of this model. We Giscuss GATool's performance on the problems suffering from static and dynamic noise and study useful strategies of GATool parameter tuning for these and other difficult problems. We review the challenges of constrained optimization with EAs and methods commonly used to overcome them. We describe REPA, a new constrained optimization method based on repairing, in exquisite detail, including the properties of its two repairing techniques: REFIND and REPROPT. We assess REPROPT's performance on the standard constrained

Curcumin phytosomal softgel formulation: Development, optimization and physicochemical characterization.

Science.gov (United States)

Allam, Ahmed N; Komeil, Ibrahim A; Abdallah, Ossama Y

2015-09-01

Curcumin, a naturally occurring lipophilic molecule can exert multiple and diverse bioactivities. However, its limited aqueous solubility and extensive presystemic metabolism restrict its bioavailability. Curcumin phytosomes were prepared by a simple solvent evaporation method where free flowing powder was obtained in addition to a newly developed semisolid formulation to increase curcumin content in softgels. Phytosomal powder was characterized in terms of drug content and zeta potential. Thirteen different softgel formulations were developed using oils such as Miglyol 812, castor oil and oleic acid, a hydrophilic vehicle such as PEG 400 and bioactive surfactants such as Cremophor EL and KLS P 124. Selected formulations were characterized in terms of curcumin in vitro dissolution. TEM analysis revealed good stability and a spherical, self-closed structure of curcumin phytosomes in complex formulations. Stability studies of chosen formulations prepared using the hydrophilic vehicle revealed a stable curcumin dissolution pattern. In contrast, a dramatic decrease in curcumin dissolution was observed in case of phytosomes formulated in oily vehicles.

Curcumin phytosomal softgel formulation: Development, optimization and physicochemical characterization

Directory of Open Access Journals (Sweden)

Allam Ahmed N.

2015-09-01

Full Text Available Curcumin, a naturally occurring lipophilic molecule can exert multiple and diverse bioactivities. However, its limited aqueous solubility and extensive presystemic metabolism restrict its bioavailability. Curcumin phytosomes were prepared by a simple solvent evaporation method where free flowing powder was obtained in addition to a newly developed semisolid formulation to increase curcumin content in softgels. Phytosomal powder was characterized in terms of drug content and zeta potential. Thirteen different softgel formulations were developed using oils such as Miglyol 812, castor oil and oleic acid, a hydrophilic vehicle such as PEG 400 and bioactive surfactants such as Cremophor EL and KLS P 124. Selected formulations were characterized in terms of curcumin in vitro dissolution. TEM analysis revealed good stability and a spherical, self-closed structure of curcumin phytosomes in complex formulations. Stability studies of chosen formulations prepared using the hydrophilic vehicle revealed a stable curcumin dissolution pattern. In contrast, a dramatic decrease in curcumin dissolution was observed in case of phytosomes formulated in oily vehicles.

Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

Directory of Open Access Journals (Sweden)

Yongpeng Shen

2016-02-01

Full Text Available Auxiliary power units (APUs are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC emissions, carbon monoxide (CO emissions and nitric oxide (NO x emissions. Then, the multi-objective particle swarm optimization (MOPSO algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC, Federal test procedure (FTP and high way fuel economy test (HWFET driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency.

Solution of wind integrated thermal generation system for environmental optimal power flow using hybrid algorithm

Directory of Open Access Journals (Sweden)

Ambarish Panda

2016-09-01

Full Text Available A new evolutionary hybrid algorithm (HA has been proposed in this work for environmental optimal power flow (EOPF problem. The EOPF problem has been formulated in a nonlinear constrained multi objective optimization framework. Considering the intermittency of available wind power a cost model of the wind and thermal generation system is developed. Suitably formed objective function considering the operational cost, cost of emission, real power loss and cost of installation of FACTS devices for maintaining a stable voltage in the system has been optimized with HA and compared with particle swarm optimization algorithm (PSOA to prove its effectiveness. All the simulations are carried out in MATLAB/SIMULINK environment taking IEEE30 bus as the test system.

Constrained Optimization of MIMO Training Sequences

Directory of Open Access Journals (Sweden)

Coon Justin P

2007-01-01

Full Text Available Multiple-input multiple-output (MIMO systems have shown a huge potential for increased spectral efficiency and throughput. With an increasing number of transmitting antennas comes the burden of providing training for channel estimation for coherent detection. In some special cases optimal, in the sense of mean-squared error (MSE, training sequences have been designed. However, in many practical systems it is not feasible to analytically find optimal solutions and numerical techniques must be used. In this paper, two systems (unique word (UW single carrier and OFDM with nulled subcarriers are considered and a method of designing near-optimal training sequences using nonlinear optimization techniques is proposed. In particular, interior-point (IP algorithms such as the barrier method are discussed. Although the two systems seem unrelated, the cost function, which is the MSE of the channel estimate, is shown to be effectively the same for each scenario. Also, additional constraints, such as peak-to-average power ratio (PAPR, are considered and shown to be easily included in the optimization process. Numerical examples illustrate the effectiveness of the designed training sequences, both in terms of MSE and bit-error rate (BER.

Optimizing real power loss and voltage stability limit of a large transmission network using firefly algorithm

Directory of Open Access Journals (Sweden)

P. Balachennaiah

2016-06-01

Full Text Available This paper proposes a Firefly algorithm based technique to optimize the control variables for simultaneous optimization of real power loss and voltage stability limit of the transmission system. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained optimization problem with an objective function integrating both real power loss and voltage stability limit. Transformers taps, unified power flow controller and its parameters have been included as control variables in the problem formulation. The effectiveness of the proposed algorithm has been tested on New England 39-bus system. Simulation results obtained with the proposed algorithm are compared with the real coded genetic algorithm for single objective of real power loss minimization and multi-objective of real power loss minimization and voltage stability limit maximization. Also, a classical optimization method known as interior point successive linear programming technique is considered here to compare the results of firefly algorithm for single objective of real power loss minimization. Simulation results confirm the potentiality of the proposed algorithm in solving optimization problems.

Formulation optimization and evaluation of jackfruit seed starch-alginate mucoadhesive beads of metformin HCl.

Science.gov (United States)

Nayak, Amit Kumar; Pal, Dilipkumar

2013-08-01

The present study deals with the formulation optimization of jackfruit (Artocarpus heterophyllus Lam., family: Moraceae) seed starch (JFSS)-alginate mucoadhesive beads containing metformin HCl through ionotropic gelation using 3(2) factorial design. The effect of sodium alginate to JFSS ratio and CaCl2 concentration on the drug encapsulation efficiency (DEE, %), and cumulative drug release at 10h (R10h, %) was optimized. The optimized beads containing metformin HCl showed DEE of 97.48±3.92%, R10h of 65.70±2.22%, and mean diameter of 1.16±0.11mm. The in vitro drug release from these beads was followed controlled-release (zero-order) pattern with super case-II transport mechanism. The beads were also characterized by SEM and FTIR. The swelling and degradation of these beads were influenced by pH of the test medium. The optimized beads also exhibited good mucoadhesivity and significant hypoglycemic effect in alloxan-induced diabetic rats over prolonged period after oral administration. Copyright © 2013 Elsevier B.V. All rights reserved.

An Optimization-Based Impedance Approach for Robot Force Regulation with Prescribed Force Limits

Directory of Open Access Journals (Sweden)

R. de J. Portillo-Vélez

2015-01-01

Full Text Available An optimization based approach for the regulation of excessive or insufficient forces at the end-effector level is introduced. The objective is to minimize the interaction force error at the robot end effector, while constraining undesired interaction forces. To that end, a dynamic optimization problem (DOP is formulated considering a dynamic robot impedance model. Penalty functions are considered in the DOP to handle the constraints on the interaction force. The optimization problem is online solved through the gradient flow approach. Convergence properties are presented and the stability is drawn when the force limits are considered in the analysis. The effectiveness of our proposal is validated via experimental results for a robotic grasping task.

Level-Set Topology Optimization with Aeroelastic Constraints

Science.gov (United States)

Dunning, Peter D.; Stanford, Bret K.; Kim, H. Alicia

2015-01-01

Level-set topology optimization is used to design a wing considering skin buckling under static aeroelastic trim loading, as well as dynamic aeroelastic stability (flutter). The level-set function is defined over the entire 3D volume of a transport aircraft wing box. Therefore, the approach is not limited by any predefined structure and can explore novel configurations. The Sequential Linear Programming (SLP) level-set method is used to solve the constrained optimization problems. The proposed method is demonstrated using three problems with mass, linear buckling and flutter objective and/or constraints. A constraint aggregation method is used to handle multiple buckling constraints in the wing skins. A continuous flutter constraint formulation is used to handle difficulties arising from discontinuities in the design space caused by a switching of the critical flutter mode.

Dynamic Optimization of Constrained Layer Damping Structure for the Headstock of Machine Tools with Modal Strain Energy Method

Directory of Open Access Journals (Sweden)

Yakai Xu

2017-01-01

Full Text Available Dynamic stiffness and damping of the headstock, which is a critical component of precision horizontal machining center, are two main factors that influence machining accuracy and surface finish quality. Constrained Layer Damping (CLD structure is proved to be effective in raising damping capacity for the thin plate and shell structures. In this paper, one kind of high damping material is utilized on the headstock to improve damping capacity. The dynamic characteristic of the hybrid headstock is investigated analytically and experimentally. The results demonstrate that the resonant response amplitudes of the headstock with damping material can decrease significantly compared to original cast structure. To obtain the optimal configuration of damping material, a topology optimization method based on the Evolutionary Structural Optimization (ESO is implemented. Modal Strain Energy (MSE method is employed to analyze the damping and to derive the sensitivity of the modal loss factor. The optimization results indicate that the added weight of damping material decreases by 50%; meanwhile the first two orders of modal loss factor decrease by less than 23.5% compared to the original structure.

Quantization of the 2D effective gravity in the geometrical formulation

International Nuclear Information System (INIS)

Aoyama, S.

1992-01-01

There exist various formulations to discuss the 2d effective gravity: light-cone gauge formulation; geometrical formation; formulation by the constrained WZWN model; and conformal gauge formulation. In the formulations other than the last one, quantization of the 2d effective gravity is not complete in the sense that either the central charges of both sectors are not known, or one of them is known but not the other. In this paper, the authors will provide a thorough argument on quantization of the 2d effective gravity in the formulation. The argument will allow us to complete the quantization in the formation, and establish the relations among the formulations at the quantum level

Genetic optimization of steam multi-turbines system

International Nuclear Information System (INIS)

Olszewski, Pawel

2014-01-01

Optimization analysis of partially loaded cogeneration, multiple-stages steam turbines system was numerically investigated by using own-developed code (C++). The system can be controlled by following variables: fresh steam temperature, pressure, and flow rates through all stages in steam turbines. Five various strategies, four thermodynamics and one economical, which quantify system operation, were defined and discussed as an optimization functions. Mathematical model of steam turbines calculates steam properties according to the formulation proposed by the International Association for the Properties of Water and Steam. Genetic algorithm GENOCOP was implemented as a solving engine for non–linear problem with handling constrains. Using formulated methodology, example solution for partially loaded system, composed of five steam turbines (30 input variables) with different characteristics, was obtained for five strategies. The genetic algorithm found multiple solutions (various input parameters sets) giving similar overall results. In real application it allows for appropriate scheduling of machine operation that would affect equable time load of every system compounds. Also based on these results three strategies where chosen as the most complex: the first thermodynamic law energy and exergy efficiency maximization and total equivalent energy minimization. These strategies can be successfully used in optimization of real cogeneration applications. - Highlights: • Genetic optimization model for a set of five various steam turbines was presented. • Four various thermodynamic optimization strategies were proposed and discussed. • Operational parameters (steam pressure, temperature, flow) influence was examined. • Genetic algorithm generated optimal solutions giving the best estimators values. • It has been found that similar energy effect can be obtained for various inputs

Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint

Energy Technology Data Exchange (ETDEWEB)

Dall' Anese, Emiliano; Baker, Kyri; Summers, Tyler

2016-12-01

The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.

Adaptive Finite Element Method for Optimal Control Problem Governed by Linear Quasiparabolic Integrodifferential Equations

Directory of Open Access Journals (Sweden)

Wanfang Shen

2012-01-01

Full Text Available The mathematical formulation for a quadratic optimal control problem governed by a linear quasiparabolic integrodifferential equation is studied. The control constrains are given in an integral sense: Uad={u∈X;∫ΩUu⩾0, t∈[0,T]}. Then the a posteriori error estimates in L∞(0,T;H1(Ω-norm and L2(0,T;L2(Ω-norm for both the state and the control approximation are given.

Global optimization framework for solar building design

Science.gov (United States)

Silva, N.; Alves, N.; Pascoal-Faria, P.

2017-07-01

The generative modeling paradigm is a shift from static models to flexible models. It describes a modeling process using functions, methods and operators. The result is an algorithmic description of the construction process. Each evaluation of such an algorithm creates a model instance, which depends on its input parameters (width, height, volume, roof angle, orientation, location). These values are normally chosen according to aesthetic aspects and style. In this study, the model's parameters are automatically generated according to an objective function. A generative model can be optimized according to its parameters, in this way, the best solution for a constrained problem is determined. Besides the establishment of an overall framework design, this work consists on the identification of different building shapes and their main parameters, the creation of an algorithmic description for these main shapes and the formulation of the objective function, respecting a building's energy consumption (solar energy, heating and insulation). Additionally, the conception of an optimization pipeline, combining an energy calculation tool with a geometric scripting engine is presented. The methods developed leads to an automated and optimized 3D shape generation for the projected building (based on the desired conditions and according to specific constrains). The approach proposed will help in the construction of real buildings that account for less energy consumption and for a more sustainable world.

Optimizing Oral Bioavailability in Drug Discovery: An Overview of Design and Testing Strategies and Formulation Options.

Science.gov (United States)

Aungst, Bruce J

2017-04-01

For discovery teams working toward new, orally administered therapeutic agents, one requirement is to attain adequate systemic exposure after oral dosing, which is best accomplished when oral bioavailability is optimized. This report summarizes the bioavailability challenges currently faced in drug discovery, and the design and testing methods and strategies currently utilized to address the challenges. Profiling of discovery compounds usually includes separate assessments of solubility, permeability, and susceptibility to first-pass metabolism, which are the 3 most likely contributors to incomplete oral bioavailability. An initial assessment of absorption potential may be made computationally, and high throughput in vitro assays are typically performed to prioritize compounds for in vivo studies. The initial pharmacokinetic study is a critical decision point in compound evaluation, and the importance of the effect the dosing vehicle or formulation can have on oral bioavailability, especially for poorly water soluble compounds, is emphasized. Dosing vehicles and bioavailability-enabling formulations that can be used for discovery and preclinical studies are described. Optimizing oral bioavailability within a chemical series or for a lead compound requires identification of the barrier limiting bioavailability, and methods used for this purpose are outlined. Finally, a few key guidelines are offered for consideration when facing the challenges of optimizing oral bioavailability in drug discovery. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Constrained Dynamic Optimality and Binomial Terminal Wealth

DEFF Research Database (Denmark)

Pedersen, J. L.; Peskir, G.

2018-01-01

with interest rate $r \\in {R}$). Letting $P_{t,x}$ denote a probability measure under which $X^u$ takes value $x$ at time $t,$ we study the dynamic version of the nonlinear optimal control problem $\\inf_u\\, Var{t,X_t^u}(X_T^u)$ where the infimum is taken over admissible controls $u$ subject to $X_t^u \\ge e...... a martingale method combined with Lagrange multipliers, we derive the dynamically optimal control $u_*^d$ in closed form and prove that the dynamically optimal terminal wealth $X_T^d$ can only take two values $g$ and $\\beta$. This binomial nature of the dynamically optimal strategy stands in sharp contrast...... with other known portfolio selection strategies encountered in the literature. A direct comparison shows that the dynamically optimal (time-consistent) strategy outperforms the statically optimal (time-inconsistent) strategy in the problem....

Balancing emergency message dissemination and network lifetime in wireless body area network using ant colony optimization and Bayesian game formulation

Directory of Open Access Journals (Sweden)

R. Latha

Full Text Available Nowadays, Wireless Body Area Network (WBAN is emerging very fast and so many new methods and algorithms are coming up for finding the optimal path for disseminating emergency messages. Ant Colony Optimization (ACO is one of the cultural algorithms for solving many hard problems such as Travelling Salesman Problem (TSP. ACO is a natural behaviour of ants, which work stochastically with the help of pheromone trails deposited in the shortest route to find their food. This optimization procedure involves adapting, positive feedback and inherent parallelism. Each ant will deposit certain amount of pheromone in the tour construction it makes searching for food. This type of communication is known as stigmetric communication. In addition, if a dense WBAN environment prevails, such as hospital, i.e. in the environment of overlapping WBAN, game formulation was introduced for analyzing the mixed strategy behaviour of WBAN. In this paper, the ant colony optimization approach to the travelling salesman problem was applied to the WBAN to determine the shortest route for sending emergency message to the doctor via sensor nodes; and also a static Bayesian game formulation with mixed strategy was analysed to enhance the network lifetime. Whenever the patient needs any critical care or any other medical issue arises, emergency messages will be created by the WBAN and sent to the doctor's destination. All the modes of communication were realized in a simulation environment using OMNet++. The authors investigated a balanced model of emergency message dissemination and network lifetime in WBAN using ACO and Bayesian game formulation. Keywords: Wireless body area network, Ant colony optimization, Bayesian game model, Sensor network, Message latency, Network lifetime

Pricing and lot sizing optimization in a two-echelon supply chain with a constrained Logit demand function

Directory of Open Access Journals (Sweden)

Yeison Díaz-Mateus

2017-07-01

Full Text Available Decision making in supply chains is influenced by demand variations, and hence sales, purchase orders and inventory levels are therefore concerned. This paper presents a non-linear optimization model for a two-echelon supply chain, for a unique product. In addition, the model includes the consumers’ maximum willingness to pay, taking socioeconomic differences into account. To do so, the constrained multinomial logit for discrete choices is used to estimate demand levels. Then, a metaheuristic approach based on particle swarm optimization is proposed to determine the optimal product sales price and inventory coordination variables. To validate the proposed model, a supply chain of a technological product was chosen and three scenarios are analyzed: discounts, demand segmentation and demand overestimation. Results are analyzed on the basis of profits, lotsizing and inventory turnover and market share. It can be concluded that the maximum willingness to pay must be taken into consideration, otherwise fictitious profits may mislead decision making, and although the market share would seem to improve, overall profits are not in fact necessarily better.

Understanding and optimizing the dual excipient functionality of sodium lauryl sulfate in tablet formulation of poorly water soluble drug: wetting and lubrication.

Science.gov (United States)

Aljaberi, Ahmad; Chatterji, Ashish; Dong, Zedong; Shah, Navnit H; Malick, Waseem; Singhal, Dharmendra; Sandhu, Harpreet K

2013-01-01

To evaluate and optimize sodium lauryl sulfate (SLS) and magnesium stearate (Mg.St) levels, with respect to dissolution and compaction, in a high dose, poorly soluble drug tablet formulation. A model poorly soluble drug was formulated using high shear aqueous granulation. A D-optimal design was used to evaluate and model the effect of granulation conditions, size of milling screen, SLS and Mg.St levels on tablet compaction and ejection. The compaction profiles were generated using a Presster(©) compaction simulator. Dissolution of the kernels was performed using a USP dissolution apparatus II and intrinsic dissolution was determined using a stationary disk system. Unlike kernels dissolution which failed to discriminate between tablets prepared with various SLS contents, the intrinsic dissolution rate showed that a SLS level of 0.57% was sufficient to achieve the required release profile while having minimal effect on compaction. The formulation factors that affect tablet compaction and ejection were identified and satisfactorily modeled. The design space of best factor setting to achieve optimal compaction and ejection properties was successfully constructed by RSM analysis. A systematic study design helped identify the critical factors and provided means to optimize the functionality of key excipient to design robust drug product.

Assessment of electricity demand-supply in health facilities in resource-constrained settings : optimization and evaluation of energy systems for a case in Rwanda

NARCIS (Netherlands)

Palacios, S.G.

2015-01-01

In health facilities in resource-constrained settings, a lack of access to sustainable and reliable electricity can result on a sub-optimal delivery of healthcare services, as they do not have lighting for medical procedures and power to run essential equipment and devices to treat their patients.

Optimization of formulation and processing of Moringa oleifera and spirulina complex tablets.

Science.gov (United States)

Zheng, Yi; Zhu, Fan; Lin, Dan; Wu, Jun; Zhou, Yichao; Mark, Bohn

2017-01-01

Objective: To prepare a more comprehensive nutrition, more balanced proportion of natural nutritional supplement tablets with Moringa oleifera leaves and spirulina the two nutrients which have complementary natural food ingredients. Method: On the basis of research M. oleifera leaves with spirulina nutrient composition was determined on M. oleifera leaves and spirulina ratio of raw materials, and the choice of microcrystalline cellulose, sodium salt of caboxy methyl cellulose(CMC),magnesium stearate excipient, through single factor and orthogonal experiment, selecting the best formula tablets prepared by powder direct compression technology, for preparation of M. oleifera and spirulina complex tablets. Results: The best ratio of raw material for the M. oleifera leaves powder: spirulina powder was 7:3, the best raw materials for the tablet formulation was 88.5%, 8.0% microcrystalline cellulose, CMC 2.0%, stearin magnesium 1.5%, the optimum parameters for the raw material crushing 200-300 mesh particle size, moisture content of 7%, tableting pressure 40 kN. Conclusion: Through formulation and process optimization, we can prepare more comprehensive and balanced nutrition M. oleifera and spirulina complex tablets, its sheet-shaped appearance, piece weight variation, hardness, friability, disintegration and other indicators have reached the appropriate quality requirements.

Optimization of chlorphenesin emulgel formulation

OpenAIRE

Mohamed, Magdy I.

2004-01-01

This study was conducted to develop an emulgel formulation of chlorphenesin (CHL) using 2 types of gelling agents: hydroxypropylmethyl cellulose (HPMC) and Carbopol 934. The influence of the type of the gelling agent and the concentration of both the oil phase and emulsifying agent on the drug release from the prepared emulgels was investigated using a 23 factorial design. The prepared emulgels were evaluated for their physical appearance, rheological behavior, drug release, antifungal activi...

Quality by Design (QbD) approach to optimize the formulation of a bilayer combination tablet (Telmiduo®) manufactured via high shear wet granulation.

Science.gov (United States)

Lee, Ah Ram; Kwon, Seok Young; Choi, Du Hyung; Park, Eun Seok

2017-12-20

A bilayer tablet, which consisted of telmisartan and amlodipine besylate, was formulated based on a Quality by Design (QbD) approach. The control and response factors were determined based on primary knowledge and the target values of the control tablet (Twynsta ® ). A D-optimal mixture design was used to obtain the optimal formulations in terms of D-mannitol, crospovidone, and MCC for the telmisartan layer, and CCM-Na, PVP K25, and Prosolv for the amlodipine layer. The quantitative effects of the different formulation factors on the response factors were accurately predicted using the equations of best fit and a strong linearity was observed between the predicted and actual values of the response factors. The optimized bilayer tablet was obtained using a numeric optimization technique and was characterized compared with a control (Twynsta ® ) by using various physical evaluations and in vivo pharmacokinetic parameters. The physical stability of Telmiduo ® was greater than that of Twynsta ® owing to the improvement of formulation factors. The in vivo pharmacokinetic parameters suggested that Telmiduo ® might have pharmaceutical equivalence and bioequivalence with Twynsta ® . Therefore, the bilayer tablet that consisted of telmisartan and amlodipine besylate could be produced using a more economical and simpler method than that used to produce Twynsta ® . Moreover, the suitability of QbD for effective product development in the pharmaceutical industry was shown. Copyright © 2017 Elsevier B.V. All rights reserved.

Simulation of global oceanic upper layers forced at the surface by an optimal bulk formulation derived from multi-campaign measurements.

Science.gov (United States)

Garric, G.; Pirani, A.; Belamari, S.; Caniaux, G.

2006-12-01

order to improve the air/sea interface for the future MERCATOR global ocean operational system, we have implemented the new bulk formulation developed by METEO-FRANCE (French Meteo office) in the MERCATOR 2 degree global ocean-ice coupled model (ORCA2/LIM). A single bulk formulation for the drag, temperature and moisture exchange coefficients is derived from an extended consistent database gathering 10 years of measurements issued from five experiments dedicated to air-sea fluxes estimates (SEMAPHORE, CATCH, FETCH, EQUALANT99 and POMME) in various oceanic basins (from Northern to equatorial Atlantic). The available database (ALBATROS) cover the widest range of atmospheric and oceanic conditions, from very light (0.3 m/s) to very strong (up to 29 m/s) wind speeds, and from unstable to extremely stable atmospheric boundary layer stratification. We have defined a work strategy to test this new formulation in a global oceanic context, by using this multi- campaign bulk formulation to derive air-sea fluxes from base meteorological variables produces by the ECMWF (European Centre for Medium Range and Weather Forecast) atmospheric forecast model, in order to get surface boundary conditions for ORCA2/LIM. The simulated oceanic upper layers forced at the surface by the previous air/sea interface are compared to those forced by the optimal bulk formulation. Consecutively with generally weaker transfer coefficient, the latter formulation reduces the cold bias in the equatorial Pacific and increases the too weak summer sea ice extent in Antarctica. Compared to a recent mixed layer depth (MLD) climatology, the optimal bulk formulation reduces also the too deep simulated MLDs. Comparison with in situ temperature and salinity profiles in different areas allowed us to evaluate the impact of changing the air/sea interface in the vertical structure.

PSO Based Optimization of Testing and Maintenance Cost in NPPs

Directory of Open Access Journals (Sweden)

Qiang Chou

2014-01-01

Full Text Available Testing and maintenance activities of safety equipment have drawn much attention in Nuclear Power Plant (NPP to risk and cost control. The testing and maintenance activities are often implemented in compliance with the technical specification and maintenance requirements. Technical specification and maintenance-related parameters, that is, allowed outage time (AOT, maintenance period and duration, and so forth, in NPP are associated with controlling risk level and operating cost which need to be minimized. The above problems can be formulated by a constrained multiobjective optimization model, which is widely used in many other engineering problems. Particle swarm optimizations (PSOs have proved their capability to solve these kinds of problems. In this paper, we adopt PSO as an optimizer to optimize the multiobjective optimization problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Numerical results have demonstrated the efficiency of our proposed algorithm.

Solid self-nanoemulsifying drug delivery systems for oral delivery of polypeptide-k: Formulation, optimization, in-vitro and in-vivo antidiabetic evaluation.

Science.gov (United States)

Garg, Varun; Kaur, Puneet; Singh, Sachin Kumar; Kumar, Bimlesh; Bawa, Palak; Gulati, Monica; Yadav, Ankit Kumar

2017-11-15

Development of self-nanoemulsifying drug delivery systems (SNEDDS) of polypeptide-k (PPK) is reported with the aim to achieve its oral delivery. Box-Behnken design (BBD) was adopted to develop and optimize the composition of SNEDDS. Oleoyl polyoxyl-6 glycerides (A), Tween 80 (B), and diethylene glycol monoethyl ether (C) were used as oil, surfactant and co-surfactant, respectively as independent variables. The effect of variation in their composition was observed on the mean droplet size (y1), polydispersity index (PDI) (y2), % drug loading (y3) and zeta potential (y4). As per the optimal design, seventeen SNEDDS prototypes were prepared. The optimized composition of SNEDDS formulation was 25% v/v Oleoyl polyoxyl-6 glycerides, 37% v/v Tween 80, 38% v/v diethylene glycol monoethyl ether, and 3% w/v PPK. The optimized formulation revealed values of y1, y2, y3, and y4 as 31.89nm, 0.16, 73.15%, and -15.65mV, respectively. Further the optimized liquid SNEDDS were solidified through spray drying using various hydrophilic and hydrophobic carriers. Among the various carriers, Aerosil 200 was found to provide desirable flow, compression, disintegration and dissolution properties. Both, liquid and solid-SNEDDS have shown release of >90% within 10min. The formulation was found stable with change in pH, dilution, temperature variation and freeze thaw cycles in terms of droplet size, zeta potential, drug precipitation and phase separation. Crystalline PPK was observed in amorphous state in solid SNEDDS when characterized through DSC and PXRD studies. The biochemical, hematological and histopathological results of streptozotocin induced diabetic rats shown promising antidiabetic potential of PPK loaded in SNEDDS at its both the doses (i.e. 400mg/kg and 800mg/kg) as compared to its naïve form at both the doses. The study revealed successful formulation of SNEDDS for oral delivery of PPK. Copyright © 2017 Elsevier B.V. All rights reserved.

Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators

Directory of Open Access Journals (Sweden)

Kiran Teeparthi

2017-04-01

Full Text Available In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultaneously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of getting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large interconnected power system to solve MO-SCOPF problem with integration of wind and thermal generators.

On the optimal identification of tag sets in time-constrained RFID configurations.

Science.gov (United States)

Vales-Alonso, Javier; Bueno-Delgado, María Victoria; Egea-López, Esteban; Alcaraz, Juan José; Pérez-Mañogil, Juan Manuel

2011-01-01

In Radio Frequency Identification facilities the identification delay of a set of tags is mainly caused by the random access nature of the reading protocol, yielding a random identification time of the set of tags. In this paper, the cumulative distribution function of the identification time is evaluated using a discrete time Markov chain for single-set time-constrained passive RFID systems, namely those ones where a single group of tags is assumed to be in the reading area and only for a bounded time (sojourn time) before leaving. In these scenarios some tags in a set may leave the reader coverage area unidentified. The probability of this event is obtained from the cumulative distribution function of the identification time as a function of the sojourn time. This result provides a suitable criterion to minimize the probability of losing tags. Besides, an identification strategy based on splitting the set of tags in smaller subsets is also considered. Results demonstrate that there are optimal splitting configurations that reduce the overall identification time while keeping the same probability of losing tags.

Exergy optimization of cooling tower for HGSHP and HVAC applications

International Nuclear Information System (INIS)

Singh, Kuljeet; Das, Ranjan

2017-01-01

Highlights: • Development of new correlations for outlet parameters with all inlet parameters. • Simultaneous achievement of required heat load and minimum exergy destruction. • Multiple combinations of parameters found for same heat load at minimized exergy. • Study useful for optimum control of cooling tower under varying ambient conditions. • Generalized optimization study can be implemented for any mechanical cooling tower. - Abstract: In the present work, a constrained inverse optimization method for building cooling applications is proposed to control the mechanical draft wet cooling tower by minimizing the exergy destruction and satisfying an imposed heat load under varying environmental conditions. The optimization problem is formulated considering the cooling dominated heating, ventilation and air conditioning (HVAC) and hybrid ground source heat pump (HGSHP). As per the requirement, new second degree correlations for the tower outlet parameters (water temperature, air dry and wet-bulb temperatures) with five inlet parameters (dry-bulb temperature, relative humidity, water inlet temperature, water and air mass flow rates) are developed. The Box–Behnken design response surface method is implemented for developing the correlations. Subsequently, the constrained optimization problem is solved using augmented Lagrangian genetic algorithm. This work further developed optimum inlet parameters operating curves for the HGSHP and the HVAC systems under varying environmental conditions aimed at minimizing the exergy destruction along with the fulfillment of the required heat load.

Statistical mechanics of budget-constrained auctions

OpenAIRE

Altarelli, F.; Braunstein, A.; Realpe-Gomez, J.; Zecchina, R.

2009-01-01

Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). Based on the cavity method of statistical mechanics, we introduce a message passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution,...

Experimental design approach to the process parameter optimization for laser welding of martensitic stainless steels in a constrained overlap configuration

Science.gov (United States)

Khan, M. M. A.; Romoli, L.; Fiaschi, M.; Dini, G.; Sarri, F.

2011-02-01

This paper presents an experimental design approach to process parameter optimization for the laser welding of martensitic AISI 416 and AISI 440FSe stainless steels in a constrained overlap configuration in which outer shell was 0.55 mm thick. To determine the optimal laser-welding parameters, a set of mathematical models were developed relating welding parameters to each of the weld characteristics. These were validated both statistically and experimentally. The quality criteria set for the weld to determine optimal parameters were the minimization of weld width and the maximization of weld penetration depth, resistance length and shearing force. Laser power and welding speed in the range 855-930 W and 4.50-4.65 m/min, respectively, with a fiber diameter of 300 μm were identified as the optimal set of process parameters. However, the laser power and welding speed can be reduced to 800-840 W and increased to 4.75-5.37 m/min, respectively, to obtain stronger and better welds.

Optimization of a High Temperature PEMFC micro-CHP System by Formulation and Application of a Process Integration Methodology

DEFF Research Database (Denmark)

Arsalis, Alexandros; Nielsen, Mads Pagh; Kær, Søren Knudsen

2013-01-01

A 1 kWe micro combined heat and power (CHP) system based on high temperature proton exchange membrane fuel cell (PEMFC) technology is modeled and optimized by formulation and application of a process integration methodology. The system can provide heat and electricity for a singlefamily household...

Machine learning paradigms in design optimization: Applications in turbine aerodynamic design

Science.gov (United States)

Goel, Sanjay

Mechanisms of incorporating machine learning paradigms in design optimization have been investigated in the current research. The primary focus of the work is on machine learning algorithms which use computational models that are analogous to the hypothesized principles of natural or biological learning. Examples from structural and aerodynamic optimization have been used to demonstrate the potential of the proposed schemes. The first strategy examined in the current work seeks to improve the convergence of optimization problems by pruning the search space of weak variables. Such variables are identified by learning from a database of existing designs using neural networks. By using clustering techniques, different sets of weak variables are identified in different regions of the design space. Parameter sensitivity information obtained in the process of identifying weak variables provides accurate heuristics for formulating design rules. The impact of this methodology on obtaining converged designs has been investigated for a turbine design problem. Optimization results from a three-stage power turbine and an aircraft engine turbine are presented in this thesis. The second scheme is an evolutionary design optimization technique which gets progressively 'smarter' during the optimization process by learning from computed domain knowledge. This technique employs adaptive learning mechanisms (classifiers) which recognize the influence of the design variables on the problem solution and then generalize them to dynamically create or change design rules during optimization. This technique, when applied to a constrained optimization problem, shows progressive improvement in convergence of search, as successive generations of rules evolve by learning from the environment. To investigate this methodology, a truss optimization problem is solved with an objective of minimizing the truss weight subject to stress constraints in the truss members. A distinct convergent trend is

Convex optimisation approach to constrained fuel optimal control of spacecraft in close relative motion

Science.gov (United States)

Massioni, Paolo; Massari, Mauro

2018-05-01

This paper describes an interesting and powerful approach to the constrained fuel-optimal control of spacecraft in close relative motion. The proposed approach is well suited for problems under linear dynamic equations, therefore perfectly fitting to the case of spacecraft flying in close relative motion. If the solution of the optimisation is approximated as a polynomial with respect to the time variable, then the problem can be approached with a technique developed in the control engineering community, known as "Sum Of Squares" (SOS), and the constraints can be reduced to bounds on the polynomials. Such a technique allows rewriting polynomial bounding problems in the form of convex optimisation problems, at the cost of a certain amount of conservatism. The principles of the techniques are explained and some application related to spacecraft flying in close relative motion are shown.

From diets to foods: using linear programming to formulate a nutritious, minimum-cost porridge mix for children aged 1 to 2 years.

Science.gov (United States)

De Carvalho, Irene Stuart Torrié; Granfeldt, Yvonne; Dejmek, Petr; Håkansson, Andreas

2015-03-01

Linear programming has been used extensively as a tool for nutritional recommendations. Extending the methodology to food formulation presents new challenges, since not all combinations of nutritious ingredients will produce an acceptable food. Furthermore, it would help in implementation and in ensuring the feasibility of the suggested recommendations. To extend the previously used linear programming methodology from diet optimization to food formulation using consistency constraints. In addition, to exemplify usability using the case of a porridge mix formulation for emergency situations in rural Mozambique. The linear programming method was extended with a consistency constraint based on previously published empirical studies on swelling of starch in soft porridges. The new method was exemplified using the formulation of a nutritious, minimum-cost porridge mix for children aged 1 to 2 years for use as a complete relief food, based primarily on local ingredients, in rural Mozambique. A nutritious porridge fulfilling the consistency constraints was found; however, the minimum cost was unfeasible with local ingredients only. This illustrates the challenges in formulating nutritious yet economically feasible foods from local ingredients. The high cost was caused by the high cost of mineral-rich foods. A nutritious, low-cost porridge that fulfills the consistency constraints was obtained by including supplements of zinc and calcium salts as ingredients. The optimizations were successful in fulfilling all constraints and provided a feasible porridge, showing that the extended constrained linear programming methodology provides a systematic tool for designing nutritious foods.

Joint Optimal Production Planning for Complex Supply Chains Constrained by Carbon Emission Abatement Policies

Directory of Open Access Journals (Sweden)

Longfei He

2014-01-01

Full Text Available We focus on the joint production planning of complex supply chains facing stochastic demands and being constrained by carbon emission reduction policies. We pick two typical carbon emission reduction policies to research how emission regulation influences the profit and carbon footprint of a typical supply chain. We use the input-output model to capture the interrelated demand link between an arbitrary pair of two nodes in scenarios without or with carbon emission constraints. We design optimization algorithm to obtain joint optimal production quantities combination for maximizing overall profit under regulatory policies, respectively. Furthermore, numerical studies by featuring exponentially distributed demand compare systemwide performances in various scenarios. We build the “carbon emission elasticity of profit (CEEP” index as a metric to evaluate the impact of regulatory policies on both chainwide emissions and profit. Our results manifest that by facilitating the mandatory emission cap in proper installation within the network one can balance well effective emission reduction and associated acceptable profit loss. The outcome that CEEP index when implementing Carbon emission tax is elastic implies that the scale of profit loss is greater than that of emission reduction, which shows that this policy is less effective than mandatory cap from industry standpoint at least.

Multibody motion in implicitly constrained director format with links via explicit constraints

DEFF Research Database (Denmark)

Nielsen, Martin Bjerre; Krenk, Steen

2013-01-01

A conservative time integration algorithm is developed for constrained mechanical systems of kinematically linked rigid bodies based on convected base vectors. The base vectors are represented in terms of their absolute coordinates, hence the formulation makes use of three translation components...

Efficient Constrained Local Model Fitting for Non-Rigid Face Alignment.

Science.gov (United States)

Lucey, Simon; Wang, Yang; Cox, Mark; Sridharan, Sridha; Cohn, Jeffery F

2009-11-01

Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The "simultaneous" algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2-3 fps). The "project-out" algorithm for fitting an AAM achieves faster than real time performance (> 200 fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for non-rigid face registration/tracking referred to as a constrained local model (CLM). Our proposed method is able to achieve superior performance to the "simultaneous" AAM algorithm along with real time fitting speeds (35 fps). We improve upon the canonical CLM formulation, to gain this performance, in a number of ways by employing: (i) linear SVMs as patch-experts, (ii) a simplified optimization criteria, and (iii) a composite rather than additive warp update step. Most notably, our simplified optimization criteria for fitting the CLM divides the problem of finding a single complex registration/warp displacement into that of finding N simple warp displacements. From these N simple warp displacements, a single complex warp displacement is estimated using a weighted least-squares constraint. Another major advantage of this simplified optimization lends from its ability to be parallelized, a step which we also theoretically explore in this paper. We refer to our approach for fitting the CLM as the "exhaustive local search" (ELS) algorithm. Experiments were conducted on the CMU Multi-PIE database.

Application of quality by design approach to optimize process and formulation parameters of rizatriptan loaded chitosan nanoparticles

Directory of Open Access Journals (Sweden)

Ajinath Eknath Shirsat

2015-01-01

Full Text Available The purpose of present study was to optimize rizatriptan (RZT chitosan (CS nanoparticles using ionic gelation method by application of quality by design (QbD approach. Based on risk assessment, effect of three variables, that is CS %, tripolyphosphate % and stirring speed were studied on critical quality attributes (CQAs; particle size and entrapment efficiency. Central composite design (CCD was implemented for design of experimentation with 20 runs. RZT CS nanoparticles were characterized for particle size, polydispersity index, entrapment efficiency, in-vitro release study, differential scanning calorimetric, X-ray diffraction, scanning electron microscopy (SEM. Based on QbD approach, design space (DS was optimized with a combination of selected variables with entrapment efficiency > 50% w/w and a particle size between 400 and 600 nm. Validation of model was performed with 3 representative formulations from DS for which standard error of − 0.70-3.29 was observed between experimental and predicted values. In-vitro drug release followed initial burst release 20.26 ± 2.34% in 3-4 h with sustained drug release of 98.43 ± 2.45% in 60 h. Lower magnitude of standard error for CQAs confirms the validation of selected CCD model for optimization of RZT CS nanoparticles. In-vitro drug release followed dual mechanism via, diffusion and polymer erosion. RZT CS nanoparticles were prepared successfully using QbD approach with the understanding of the high risk process and formulation parameters involved and optimized DS with a multifactorial combination of critical parameters to obtain predetermined RZT loaded CS nanoparticle specifications.

Spray-drying nanocapsules in presence of colloidal silica as drying auxiliary agent: formulation and process variables optimization using experimental designs.

Science.gov (United States)

Tewa-Tagne, Patrice; Degobert, Ghania; Briançon, Stéphanie; Bordes, Claire; Gauvrit, Jean-Yves; Lanteri, Pierre; Fessi, Hatem

2007-04-01

Spray-drying process was used for the development of dried polymeric nanocapsules. The purpose of this research was to investigate the effects of formulation and process variables on the resulting powder characteristics in order to optimize them. Experimental designs were used in order to estimate the influence of formulation parameters (nanocapsules and silica concentrations) and process variables (inlet temperature, spray-flow air, feed flow rate and drying air flow rate) on spray-dried nanocapsules when using silica as drying auxiliary agent. The interactions among the formulation parameters and process variables were also studied. Responses analyzed for computing these effects and interactions were outlet temperature, moisture content, operation yield, particles size, and particulate density. Additional qualitative responses (particles morphology, powder behavior) were also considered. Nanocapsules and silica concentrations were the main factors influencing the yield, particulate density and particle size. In addition, they were concerned for the only significant interactions occurring among two different variables. None of the studied variables had major effect on the moisture content while the interaction between nanocapsules and silica in the feed was of first interest and determinant for both the qualitative and quantitative responses. The particles morphology depended on the feed formulation but was unaffected by the process conditions. This study demonstrated that drying nanocapsules using silica as auxiliary agent by spray drying process enables the obtaining of dried micronic particle size. The optimization of the process and the formulation variables resulted in a considerable improvement of product yield while minimizing the moisture content.

SmartFix: Indoor Locating Optimization Algorithm for Energy-Constrained Wearable Devices

Directory of Open Access Journals (Sweden)

Xiaoliang Wang

2017-01-01

Full Text Available Indoor localization technology based on Wi-Fi has long been a hot research topic in the past decade. Despite numerous solutions, new challenges have arisen along with the trend of smart home and wearable computing. For example, power efficiency needs to be significantly improved for resource-constrained wearable devices, such as smart watch and wristband. For a Wi-Fi-based locating system, most of the energy consumption can be attributed to real-time radio scan; however, simply reducing radio data collection will cause a serious loss of locating accuracy because of unstable Wi-Fi signals. In this paper, we present SmartFix, an optimization algorithm for indoor locating based on Wi-Fi RSS. SmartFix utilizes user motion features, extracts characteristic value from history trajectory, and corrects deviation caused by unstable Wi-Fi signals. We implemented a prototype of SmartFix both on Moto 360 2nd-generation Smartwatch and on HTC One Smartphone. We conducted experiments both in a large open area and in an office hall. Experiment results demonstrate that average locating error is less than 2 meters for more than 80% cases, and energy consumption is only 30% of Wi-Fi fingerprinting method under the same experiment circumstances.

Optimization of edible coating formulations for improving postharvest quality and shelf life of pear fruit using response surface methodology.

Science.gov (United States)

Nandane, A S; Dave, Rudri K; Rao, T V Ramana

2017-01-01

The effect of composite edible films containing soy protein isolate (SPI) in combination with additives like hydroxypropyl methylcellulose (HPMC) and olive oil on 'Babughosha' pear ( Pyrus communis L.) stored at ambient temperature (28 ± 5 °C and 60 ± 10% RH) was evaluated using Response surface methodology (RSM). A total of 30 edible coating formulations comprising of SPI (2-6%, w/v), olive oil (0.7-1.1%, v/v), HPMC (0.1-0.5%, w/v) and potassium sorbate (0-0.4% w/v) were evaluated for optimizing the most suitable combination. Quality parameters like weight loss%, TSS, pH and titrable acidity of the stored pears were selected as response variables for optimization. The optimization procedure was carried out using RSM. It was observed that the response variables were mainly effected by concentration of SPI and olive oil in the formulation. Edible coating comprising of SPI 5%, HPMC 0.40%, olive oil 1% and potassium sorbate 0.22% was found to be most suitable combination for pear fruit with predicted values of response variables indicated as weight loss% 3.50, pH 3.41, TSS 11.13 and TA% 0.513.

Optimization of the formulation and technology of pearl millet based 'ready-to-reconstitute' kheer mix powder.

Science.gov (United States)

Bunkar, Durga Shankar; Jha, Alok; Mahajan, Ankur

2014-10-01

The objective of this study was to optimize the process of manufacturing instant kheer mix based on pearl millet instead of rice. Dairy whitener, pearl millet and powdered sugar were the responses studied by employing the 3-factor Central Composite Rotatable Design. The formulation with 15 g sugar, 30 g dairy whitener and 20 g pearl millet was found suitable for obtaining dry kheer mix. The analyses were based on scores of consistency, cohesiveness, viscosity and overall acceptability. The reconstituted product from the formulated kheer mix had an overall acceptability score of 7.66 and desirability index of 0.7663. The moisture, fat, protein, carbohydrate and ash contents of the dry mix product were 2.8, 4.38, 5.84, 85.88 and 1.1 %, respectively.

The goldstino brane, the constrained superfields and matter in N=1 supergravity

International Nuclear Information System (INIS)

Bandos, Igor; Heller, Markus; Kuzenko, Sergei M.; Martucci, Luca; Sorokin, Dmitri

2016-01-01

We show that different (brane and constrained superfield) descriptions for the Volkov-Akulov goldstino coupled to N=1, D=4 supergravity with matter produce similar wide classes of models with spontaneously broken local supersymmetry and discuss the relation between the different formulations. As with the formulations with irreducible constrained superfields, the geometric goldstino brane approach has the advantage of being manifestly off-shell supersymmetric without the need to introduce auxiliary fields. It provides an explicit solution of the nilpotent superfield constraints and avoids issues with non-Gaussian integration of auxiliary fields. We describe general couplings of the supersymmetry breaking sector, including the goldstino and other non-supersymmetric matter, to supergravity and matter supermultiplets. Among various examples, we discuss a goldstino brane contribution to the gravitino mass term and the supersymmetrization of the anti-D3-brane contribution to the effective theory of type IIB warped flux compactifications.

The goldstino brane, the constrained superfields and matter in N=1 supergravity

Energy Technology Data Exchange (ETDEWEB)

Bandos, Igor [Department of Theoretical Physics, University of the Basque Country UPV/EHU,P.O. Box 644, 48080 Bilbao (Spain); IKERBASQUE, Basque Foundation for Science,48011, Bilbao (Spain); Heller, Markus [Dipartimento di Fisica e Astronomia “Galileo Galilei' , Università degli Studi di Padova,Via Marzolo 8, 35131 Padova (Italy); Institut für Theoretische Physik, Ruprecht-Karls-Universität,Philosophenweg 19, 69120 Heidelberg (Germany); Kuzenko, Sergei M. [School of Physics M013, The University of Western Australia35 Stirling Highway, Crawley W.A. 6009 (Australia); Martucci, Luca [Dipartimento di Fisica e Astronomia “Galileo Galilei' , Università degli Studi di Padova,Via Marzolo 8, 35131 Padova (Italy); INFN - Sezione di Padova,Via Marzolo 8, 35131 Padova (Italy); Sorokin, Dmitri [INFN - Sezione di Padova,Via Marzolo 8, 35131 Padova (Italy); Dipartimento di Fisica e Astronomia “Galileo Galilei' , Università degli Studi di Padova,Via Marzolo 8, 35131 Padova (Italy)

2016-11-21

We show that different (brane and constrained superfield) descriptions for the Volkov-Akulov goldstino coupled to N=1, D=4 supergravity with matter produce similar wide classes of models with spontaneously broken local supersymmetry and discuss the relation between the different formulations. As with the formulations with irreducible constrained superfields, the geometric goldstino brane approach has the advantage of being manifestly off-shell supersymmetric without the need to introduce auxiliary fields. It provides an explicit solution of the nilpotent superfield constraints and avoids issues with non-Gaussian integration of auxiliary fields. We describe general couplings of the supersymmetry breaking sector, including the goldstino and other non-supersymmetric matter, to supergravity and matter supermultiplets. Among various examples, we discuss a goldstino brane contribution to the gravitino mass term and the supersymmetrization of the anti-D3-brane contribution to the effective theory of type IIB warped flux compactifications.

A policy iteration approach to online optimal control of continuous-time constrained-input systems.

Science.gov (United States)

Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L

2013-09-01

This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach. Copyright © 2013 ISA. All rights reserved.

Solving discretely-constrained MPEC problems with applications in electric power markets

International Nuclear Information System (INIS)

Gabriel, Steven A.; Leuthold, Florian U.

2010-01-01

Many of the European energy markets are characterized by dominant players that own a large share of their respective countries' generation capacities. In addition to that, there is a significant lack of cross-border transmission capacity. Combining both facts justifies the assumption that these dominant players are able to influence the market outcome of an internal European energy market due to strategic behavior. In this paper, we present a mathematical formulation in order to solve a Stackelberg game for a network-constrained energy market using integer programming. The strategic player is the Stackelberg leader and the independent system operator (including the decisions of the competitive fringe firms) acts as follower. We assume that there is one strategic player which results in a mathematical program with equilibrium constraints (MPEC). This MPEC is reformulated as mixed-integer linear program (MILP) by using disjunctive constraints and linearization. The MILP formulation gives the opportunity to solve the problems reliably and paves the way to add discrete constraints to the original MPEC formulation which can be used in order to solve discretely-constrained mathematical programs with equilibrium constraints (DC-MPECs). We report computational results for a small illustrative network as well as a stylized Western European grid with realistic data. (author)

Solving discretely-constrained MPEC problems with applications in electric power markets

Energy Technology Data Exchange (ETDEWEB)

Gabriel, Steven A. [1143 Glenn L. Martin Hall, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742-3021 (United States); Leuthold, Florian U. [Chair of Energy Economics and Public Sector Management, Dresden University of Technology, 01069 Dresden (Germany)

2010-01-15

Many of the European energy markets are characterized by dominant players that own a large share of their respective countries' generation capacities. In addition to that, there is a significant lack of cross-border transmission capacity. Combining both facts justifies the assumption that these dominant players are able to influence the market outcome of an internal European energy market due to strategic behavior. In this paper, we present a mathematical formulation in order to solve a Stackelberg game for a network-constrained energy market using integer programming. The strategic player is the Stackelberg leader and the independent system operator (including the decisions of the competitive fringe firms) acts as follower. We assume that there is one strategic player which results in a mathematical program with equilibrium constraints (MPEC). This MPEC is reformulated as mixed-integer linear program (MILP) by using disjunctive constraints and linearization. The MILP formulation gives the opportunity to solve the problems reliably and paves the way to add discrete constraints to the original MPEC formulation which can be used in order to solve discretely-constrained mathematical programs with equilibrium constraints (DC-MPECs). We report computational results for a small illustrative network as well as a stylized Western European grid with realistic data. (author)

Problem formulation for risk assessment of combined exposures to chemicals and other stressors in humans.

Science.gov (United States)

Solomon, Keith R; Wilks, Martin F; Bachman, Ammie; Boobis, Alan; Moretto, Angelo; Pastoor, Timothy P; Phillips, Richard; Embry, Michelle R

2016-11-01

When the human health risk assessment/risk management paradigm was developed, it did not explicitly include a "problem formulation" phase. The concept of problem formulation was first introduced in the context of ecological risk assessment (ERA) for the pragmatic reason to constrain and focus ERAs on the key questions. However, this need also exists for human health risk assessment, particularly for cumulative risk assessment (CRA), because of its complexity. CRA encompasses the combined threats to health from exposure via all relevant routes to multiple stressors, including biological, chemical, physical and psychosocial stressors. As part of the HESI Risk Assessment in the 21st Century (RISK21) Project, a framework for CRA was developed in which problem formulation plays a critical role. The focus of this effort is primarily on a chemical CRA (i.e., two or more chemicals) with subsequent consideration of non-chemical stressors, defined as "modulating factors" (ModFs). Problem formulation is a systematic approach that identifies all factors critical to a specific risk assessment and considers the purpose of the assessment, scope and depth of the necessary analysis, analytical approach, available resources and outcomes, and overall risk management goal. There are numerous considerations that are specific to multiple stressors, and proper problem formulation can help to focus a CRA to the key factors in order to optimize resources. As part of the problem formulation, conceptual models for exposures and responses can be developed that address these factors, such as temporal relationships between stressors and consideration of the appropriate ModFs.

Bayesian Optimization Under Mixed Constraints with A Slack-Variable Augmented Lagrangian

Energy Technology Data Exchange (ETDEWEB)

Picheny, Victor; Gramacy, Robert B.; Wild, Stefan M.; Le Digabel, Sebastien

2016-12-05

An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e.g., unconstrained) problems, which are then usually solved with local solvers. Recently, surrogate-based Bayesian optimization (BO) sub-solvers have been successfully deployed in the AL framework for a more global search in the presence of inequality constraints; however, a drawback was that expected improvement (EI) evaluations relied on Monte Carlo. Here we introduce an alternative slack variable AL, and show that in this formulation the EI may be evaluated with library routines. The slack variables furthermore facilitate equality as well as inequality constraints, and mixtures thereof. We show our new slack “ALBO” compares favorably to the original. Its superiority over conventional alternatives is reinforced on several mixed constraint examples.

Reliability based topology optimization for continuum structures with local failure constraints

DEFF Research Database (Denmark)

Luo, Yangjun; Zhou, Mingdong; Wang, Michael Yu

2014-01-01

This paper presents an effective method for stress constrained topology optimization problems under load and material uncertainties. Based on the Performance Measure Approach (PMA), the optimization problem is formulated as to minimize the objective function under a large number of (stress......-related) target performance constraints. In order to overcome the stress singularity phenomenon caused by the combined stress and reliability constraints, a reduction strategy on target reliability index is proposed and utilized together with the ε-relaxation approach. Meanwhile, an enhanced aggregation method...... is employed to aggregate the selected active constraints using a general K–S function, which avoids expensive computational cost from the large-scale nature of local failure constraints. Several numerical examples are given to demonstrate the validity of the present method....

Optimization of chlorphenesin emulgel formulation.

Science.gov (United States)

Mohamed, Magdy I

2004-10-11

This study was conducted to develop an emulgel formulation of chlorphenesin (CHL) using 2 types of gelling agents: hydroxypropylmethyl cellulose (HPMC) and Carbopol 934. The influence of the type of the gelling agent and the concentration of both the oil phase and emulsifying agent on the drug release from the prepared emulgels was investigated using a 2(3) factorial design. The prepared emulgels were evaluated for their physical appearance, rheological behavior, drug release, antifungal activity, and stability. Commercially available CHL topical powder was used for comparison. All the prepared emulgels showed acceptable physical properties concerning color, homogeneity, consistency, spreadability, and pH value. They also exhibited higher drug release and antifungal activity than the CHL powder. It was found that the emulsifying agent concentration had the most pronounced effect on the drug release from the emulgels followed by the oil phase concentration and finally the type of the gelling agent. The drug release from all the emulgels was found to follow diffusion-controlled mechanism. Rheological studies revealed that the CHL emulgels exhibited a shear-thinning behavior with thixotropy. Stability studies showed that the physical appearance, rheological properties, drug release, and antifungal activity in all the prepared emulgels remained unchanged upon storage for 3 months. As a general conclusion, it was suggested that the CHL emulgel formulation prepared with HPMC with the oil phase concentration in its low level and emulsifying agent concentration in its high level was the formula of choice since it showed the highest drug release and antifungal activity.

Formulation development and optimization of sustained release matrix tablet of Itopride HCl by response surface methodology and its evaluation of release kinetics.

Science.gov (United States)

Bose, Anirbandeep; Wong, Tin Wui; Singh, Navjot

2013-04-01

The objective of this present investigation was to develop and formulate sustained release (SR) matrix tablets of Itopride HCl, by using different polymer combinations and fillers, to optimize by Central Composite Design response surface methodology for different drug release variables and to evaluate drug release pattern of the optimized product. Sustained release matrix tablets of various combinations were prepared with cellulose-based polymers: hydroxy propyl methyl cellulose (HPMC) and polyvinyl pyrolidine (pvp) and lactose as fillers. Study of pre-compression and post-compression parameters facilitated the screening of a formulation with best characteristics that underwent here optimization study by response surface methodology (Central Composite Design). The optimized tablet was further subjected to scanning electron microscopy to reveal its release pattern. The in vitro study revealed that combining of HPMC K100M (24.65 MG) with pvp(20 mg)and use of LACTOSE as filler sustained the action more than 12 h. The developed sustained release matrix tablet of improved efficacy can perform therapeutically better than a conventional tablet.

Optimized and validated flow-injection spectrophotometric analysis of topiramate, piracetam and levetiracetam in pharmaceutical formulations.

Science.gov (United States)

Hadad, Ghada M; Abdel-Salam, Randa A; Emara, Samy

2011-12-01

Application of a sensitive and rapid flow injection analysis (FIA) method for determination of topiramate, piracetam, and levetiracetam in pharmaceutical formulations has been investigated. The method is based on the reaction with ortho-phtalaldehyde and 2-mercaptoethanol in a basic buffer and measurement of absorbance at 295 nm under flow conditions. Variables affecting the determination such as sample injection volume, pH, ionic strength, reagent concentrations, flow rate of reagent and other FIA parameters were optimized to produce the most sensitive and reproducible results using a quarter-fraction factorial design, for five factors at two levels. Also, the method has been optimized and fully validated in terms of linearity and range, limit of detection and quantitation, precision, selectivity and accuracy. The method was successfully applied to the analysis of pharmaceutical preparations.

A constrained robust least squares approach for contaminant release history identification

Science.gov (United States)

Sun, Alexander Y.; Painter, Scott L.; Wittmeyer, Gordon W.

2006-04-01

Contaminant source identification is an important type of inverse problem in groundwater modeling and is subject to both data and model uncertainty. Model uncertainty was rarely considered in the previous studies. In this work, a robust framework for solving contaminant source recovery problems is introduced. The contaminant source identification problem is first cast into one of solving uncertain linear equations, where the response matrix is constructed using a superposition technique. The formulation presented here is general and is applicable to any porous media flow and transport solvers. The robust least squares (RLS) estimator, which originated in the field of robust identification, directly accounts for errors arising from model uncertainty and has been shown to significantly reduce the sensitivity of the optimal solution to perturbations in model and data. In this work, a new variant of RLS, the constrained robust least squares (CRLS), is formulated for solving uncertain linear equations. CRLS allows for additional constraints, such as nonnegativity, to be imposed. The performance of CRLS is demonstrated through one- and two-dimensional test problems. When the system is ill-conditioned and uncertain, it is found that CRLS gave much better performance than its classical counterpart, the nonnegative least squares. The source identification framework developed in this work thus constitutes a reliable tool for recovering source release histories in real applications.

Application of D-optimal experimental design method to optimize the formulation of O/W cosmetic emulsions.

Science.gov (United States)

Djuris, J; Vasiljevic, D; Jokic, S; Ibric, S

2014-02-01

This study investigates the application of D-optimal mixture experimental design in optimization of O/W cosmetic emulsions. Cetearyl glucoside was used as a natural, biodegradable non-ionic emulsifier in the relatively low concentration (1%), and the mixture of co-emulsifiers (stearic acid, cetyl alcohol, stearyl alcohol and glyceryl stearate) was used to stabilize the formulations. To determine the optimal composition of co-emulsifiers mixture, D-optimal mixture experimental design was used. Prepared emulsions were characterized with rheological measurements, centrifugation test, specific conductivity and pH value measurements. All prepared samples appeared as white and homogenous creams, except for one homogenous and viscous lotion co-stabilized by stearic acid alone. Centrifugation testing revealed some phase separation only in the case of sample co-stabilized using glyceryl stearate alone. The obtained pH values indicated that all samples expressed mild acid value acceptable for cosmetic preparations. Specific conductivity values are attributed to the multiple phases O/W emulsions with high percentages of fixed water. Results of the rheological measurements have shown that the investigated samples exhibited non-Newtonian thixotropic behaviour. To determine the influence of each of the co-emulsifiers on emulsions properties, the obtained results were evaluated by the means of statistical analysis (ANOVA test). On the basis of comparison of statistical parameters for each of the studied responses, mixture reduced quadratic model was selected over the linear model implying that interactions between co-emulsifiers play the significant role in overall influence of co-emulsifiers on emulsions properties. Glyceryl stearate was found to be the dominant co-emulsifier affecting emulsions properties. Interactions between the glyceryl stearate and other co-emulsifiers were also found to significantly influence emulsions properties. These findings are especially important

Material Distribution Optimization for the Shell Aircraft Composite Structure

Science.gov (United States)

Shevtsov, S.; Zhilyaev, I.; Oganesyan, P.; Axenov, V.

2016-09-01

One of the main goal in aircraft structures designing isweight decreasing and stiffness increasing. Composite structures recently became popular in aircraft because of their mechanical properties and wide range of optimization possibilities.Weight distribution and lay-up are keys to creating lightweight stiff strictures. In this paperwe discuss optimization of specific structure that undergoes the non-uniform air pressure at the different flight conditions and reduce a level of noise caused by the airflowinduced vibrations at the constrained weight of the part. Initial model was created with CAD tool Siemens NX, finite element analysis and post processing were performed with COMSOL Multiphysicsr and MATLABr. Numerical solutions of the Reynolds averaged Navier-Stokes (RANS) equations supplemented by k-w turbulence model provide the spatial distributions of air pressure applied to the shell surface. At the formulation of optimization problem the global strain energy calculated within the optimized shell was assumed as the objective. Wall thickness has been changed using parametric approach by an initiation of auxiliary sphere with varied radius and coordinates of the center, which were the design variables. To avoid a local stress concentration, wall thickness increment was defined as smooth function on the shell surface dependent of auxiliary sphere position and size. Our study consists of multiple steps: CAD/CAE transformation of the model, determining wind pressure for different flow angles, optimizing wall thickness distribution for specific flow angles, designing a lay-up for optimal material distribution. The studied structure was improved in terms of maximum and average strain energy at the constrained expense ofweight growth. Developed methods and tools can be applied to wide range of shell-like structures made of multilayered quasi-isotropic laminates.

Optimization of conditions for probiotic curd formulation by Enterococcus faecium MTCC 5695 with probiotic properties using response surface methodology.

Science.gov (United States)

Ramakrishnan, Vrinda; Goveas, Louella Concepta; Prakash, Maya; Halami, Prakash M; Narayan, Bhaskar

2014-11-01

Enterococcus faecium MTCC 5695 possessing potential probiotic properties as well as enterocin producing ability was used as starter culture. Effect of time (12-24 h) and inoculum level (3-7 % v/v) on cell growth, bacteriocin production, antioxidant property, titrable acidity and pH of curd was studied by response surface methodology (RSM). The optimized conditions were 26.48 h and 2.17%v/v inoculum and the second order model validated. Co cultivation studies revealed that the formulated product had the ability to prevent growth of foodborne pathogens that affect keeping quality of the product during storage. The results indicated that application of E. faecium MTCC 5695 along with usage of optimized conditions attributed to the formation of highly consistent well set curd with bioactive and bioprotective properties. Formulated curd with potential probiotic attributes can be used as therapeutic agent for the treatment of foodborne diseases like Traveler's diarrhea and gastroenteritis which thereby help in improvement of bowel health.

Performance Evaluation of Abrasive Grinding Wheel Formulated ...

African Journals Online (AJOL)

This paper presents a study on the formulation and manufacture of abrasive grinding wheel using locally formulated silicon carbide abrasive grains. Six local raw material substitutes were identified through pilot study and with the initial mix of the identified materials, a systematic search for an optimal formulation of silicon ...

LDRD Report: Topological Design Optimization of Convolutes in Next Generation Pulsed Power Devices.

Energy Technology Data Exchange (ETDEWEB)

Cyr, Eric C. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); von Winckel, Gregory John [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kouri, Drew Philip [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Gardiner, Thomas Anthony [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ridzal, Denis [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Shadid, John N. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Miller, Sean [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-09-01

This LDRD project was developed around the ambitious goal of applying PDE-constrained opti- mization approaches to design Z-machine components whose performance is governed by elec- tromagnetic and plasma models. This report documents the results of this LDRD project. Our differentiating approach was to use topology optimization methods developed for structural design and extend them for application to electromagnetic systems pertinent to the Z-machine. To achieve this objective a suite of optimization algorithms were implemented in the ROL library part of the Trilinos framework. These methods were applied to standalone demonstration problems and the Drekar multi-physics research application. Out of this exploration a new augmented Lagrangian approach to structural design problems was developed. We demonstrate that this approach has favorable mesh-independent performance. Both the final design and the algorithmic performance were independent of the size of the mesh. In addition, topology optimization formulations for the design of conducting networks were developed and demonstrated. Of note, this formulation was used to develop a design for the inner magnetically insulated transmission line on the Z-machine. The resulting electromagnetic device is compared with theoretically postulated designs.

Baseline LAW Glass Formulation Testing

International Nuclear Information System (INIS)

Kruger, Albert A.; Mooers, Cavin; Bazemore, Gina; Pegg, Ian L.; Hight, Kenneth; Lai, Shan Tao; Buechele, Andrew; Rielley, Elizabeth; Gan, Hao; Muller, Isabelle S.; Cecil, Richard

2013-01-01

The major objective of the baseline glass formulation work was to develop and select glass formulations that are compliant with contractual and processing requirements for each of the LAW waste streams. Other objectives of the work included preparation and characterization of glasses with respect to the properties of interest, optimization of sulfate loading in the glasses, evaluation of ability to achieve waste loading limits, testing to demonstrate compatibility of glass melts with melter materials of construction, development of glass formulations to support ILAW qualification activities, and identification of glass formulation issues with respect to contract specifications and processing requirements

Constraining neutron guide optimizations with phase-space considerations

Energy Technology Data Exchange (ETDEWEB)

Bertelsen, Mads, E-mail: mads.bertelsen@gmail.com; Lefmann, Kim

2016-09-11

We introduce a method named the Minimalist Principle that serves to reduce the parameter space for neutron guide optimization when the required beam divergence is limited. The reduced parameter space will restrict the optimization to guides with a minimal neutron intake that are still theoretically able to deliver the maximal possible performance. The geometrical constraints are derived using phase-space propagation from moderator to guide and from guide to sample, while assuming that the optimized guides will achieve perfect transport of the limited neutron intake. Guide systems optimized using these constraints are shown to provide performance close to guides optimized without any constraints, however the divergence received at the sample is limited to the desired interval, even when the neutron transport is not limited by the supermirrors used in the guide. As the constraints strongly limit the parameter space for the optimizer, two control parameters are introduced that can be used to adjust the selected subspace, effectively balancing between maximizing neutron transport and avoiding background from unnecessary neutrons. One parameter is needed to describe the expected focusing abilities of the guide to be optimized, going from perfectly focusing to no correlation between position and velocity. The second parameter controls neutron intake into the guide, so that one can select exactly how aggressively the background should be limited. We show examples of guides optimized using these constraints which demonstrates the higher signal to noise than conventional optimizations. Furthermore the parameter controlling neutron intake is explored which shows that the simulated optimal neutron intake is close to the analytically predicted, when assuming that the guide is dominated by multiple scattering events.

Formulating viscous hydrodynamics for large velocity gradients

International Nuclear Information System (INIS)

Pratt, Scott

2008-01-01

Viscous corrections to relativistic hydrodynamics, which are usually formulated for small velocity gradients, have recently been extended from Navier-Stokes formulations to a class of treatments based on Israel-Stewart equations. Israel-Stewart treatments, which treat the spatial components of the stress-energy tensor τ ij as dynamical objects, introduce new parameters, such as the relaxation times describing nonequilibrium behavior of the elements τ ij . By considering linear response theory and entropy constraints, we show how the additional parameters are related to fluctuations of τ ij . Furthermore, the Israel-Stewart parameters are analyzed for their ability to provide stable and physical solutions for sound waves. Finally, it is shown how these parameters, which are naturally described by correlation functions in real time, might be constrained by lattice calculations, which are based on path-integral formulations in imaginary time

Improved solution for ill-posed linear systems using a constrained optimization ruled by a penalty: evaluation in nuclear medicine tomography

International Nuclear Information System (INIS)

Walrand, Stephan; Jamar, François; Pauwels, Stanislas

2009-01-01

Ill-posed linear systems occur in many different fields. A class of regularization methods, called constrained optimization, aims to determine the extremum of a penalty function whilst constraining an objective function to a likely value. We propose here a novel heuristic way to screen the local extrema satisfying the discrepancy principle. A modified version of the Landweber algorithm is used for the iteration process. After finding a local extremum, a bound is performed to the 'farthest' estimate in the data space still satisfying the discrepancy principle. Afterwards, the modified Landweber algorithm is again applied to find a new local extremum. This bound-iteration process is repeated until a satisfying solution is reached. For evaluation in nuclear medicine tomography, a novel penalty function that preserves the edge steps in the reconstructed solution was evaluated on Monte Carlo simulations and using real SPECT acquisitions as well. Surprisingly, the first bound always provided a significantly better solution in a wide range of statistics

Disintegration mediated controlled release supersaturating solid dispersion formulation of an insoluble drug: design, development, optimization, and in vitro evaluation.

Science.gov (United States)

Verma, Sanjay; Rudraraju, Varma S

2015-02-01

The objective of this study was to develop a solid dispersion based controlled release system for drug substances that are poorly soluble in water. A wax-based disintegration mediated controlled release system was designed based on the fact that an amorphous drug can crystallize out from hydrophilic matrices. For this study, cilostazol (CIL) was selected as the model drug, as it exhibits poor aqueous solubility. An amorphous solid dispersion was prepared to assist the drug to attain a supersaturated state. Povidone was used as carrier for solid dispersion (spray drying technique), hydrogenated vegetable oil (HVO) as wax matrix former, and sodium carboxymethyl cellulose (NaCMC) as a disintegrant. The extreme vertices mixture design (EVMD) was applied to optimize the designed and developed composition. The optimized formulation provided a dissolution pattern which was equivalent to the predicted curve, ascertaining that the optimal formulation could be accomplished with EVMD. The release profile of CIL was described by the Higuchi's model better than zero-order, first-order, and Hixson-Crowell's model, which indicated that the supersaturation state of CIL dominated to allow drug release by diffusion rather than disintegration regulated release as is generally observed by Hixson-Crowell's model. The optimized composition was evaluated for disintegration, dissolution, XRD, and stability studies. It was found that the amorphous state as well as the dissolution profile of CIL was maintained under the accelerated conditions of 40°C/75% RH for 6 months.

A globally nonsingular quaternion-based formulation for all-electric satellite trajectory optimization

Science.gov (United States)

Libraro, Paola

The general electric propulsion orbit-raising maneuver of a spacecraft must contend with four main limiting factors: the longer time of flight, multiple eclipses prohibiting continuous thrusting, long exposure to radiation from the Van Allen belt and high power requirement of the electric engines. In order to optimize a low-thrust transfer with respect to these challenges, the choice of coordinates and corresponding equations of motion used to describe the kinematical and dynamical behavior of the satellite is of critical importance. This choice can potentially affect the numerical optimization process as well as limit the set of mission scenarios that can be investigated. To increase the ability to determine the feasible set of mission scenarios able to address the challenges of an all-electric orbit-raising, a set of equations free of any singularities is required to consider a completely arbitrary injection orbit. For this purpose a new quaternion-based formulation of a spacecraft translational dynamics that is globally nonsingular has been developed. The minimum-time low-thrust problem has been solved using the new set of equations of motion inside a direct optimization scheme in order to investigate optimal low-thrust trajectories over the full range of injection orbit inclinations between 0 and 90 degrees with particular focus on high-inclinations. The numerical results consider a specific mission scenario in order to analyze three key aspects of the problem: the effect of the initial guess on the shape and duration of the transfer, the effect of Earth oblateness on transfer time and the role played by, radiation damage and power degradation in all-electric minimum-time transfers. Finally trade-offs between mass and cost savings are introduced through a test case.

Depletion mapping and constrained optimization to support managing groundwater extraction

Science.gov (United States)

Fienen, Michael N.; Bradbury, Kenneth R.; Kniffin, Maribeth; Barlow, Paul M.

2018-01-01

Groundwater models often serve as management tools to evaluate competing water uses including ecosystems, irrigated agriculture, industry, municipal supply, and others. Depletion potential mapping—showing the model-calculated potential impacts that wells have on stream baseflow—can form the basis for multiple potential management approaches in an oversubscribed basin. Specific management approaches can include scenarios proposed by stakeholders, systematic changes in well pumping based on depletion potential, and formal constrained optimization, which can be used to quantify the tradeoff between water use and stream baseflow. Variables such as the maximum amount of reduction allowed in each well and various groupings of wells using, for example, K-means clustering considering spatial proximity and depletion potential are considered. These approaches provide a potential starting point and guidance for resource managers and stakeholders to make decisions about groundwater management in a basin, spreading responsibility in different ways. We illustrate these approaches in the Little Plover River basin in central Wisconsin, United States—home to a rich agricultural tradition, with farmland and urban areas both in close proximity to a groundwater-dependent trout stream. Groundwater withdrawals have reduced baseflow supplying the Little Plover River below a legally established minimum. The techniques in this work were developed in response to engaged stakeholders with various interests and goals for the basin. They sought to develop a collaborative management plan at a watershed scale that restores the flow rate in the river in a manner that incorporates principles of shared governance and results in effective and minimally disruptive changes in groundwater extraction practices.

A Novel Optimal Joint Resource Allocation Method in Cooperative Multicarrier Networks: Theory and Practice

Directory of Open Access Journals (Sweden)

Yuan Gao

2016-04-01

Full Text Available With the increasing demands for better transmission speed and robust quality of service (QoS, the capacity constrained backhaul gradually becomes a bottleneck in cooperative wireless networks, e.g., in the Internet of Things (IoT scenario in joint processing mode of LTE-Advanced Pro. This paper focuses on resource allocation within capacity constrained backhaul in uplink cooperative wireless networks, where two base stations (BSs equipped with single antennae serve multiple single-antennae users via multi-carrier transmission mode. In this work, we propose a novel cooperative transmission scheme based on compress-and-forward with user pairing to solve the joint mixed integer programming problem. To maximize the system capacity under the limited backhaul, we formulate the joint optimization problem of user sorting, subcarrier mapping and backhaul resource sharing among different pairs (subcarriers for users. A novel robust and efficient centralized algorithm based on alternating optimization strategy and perfect mapping is proposed. Simulations show that our novel method can improve the system capacity significantly under the constraint of the backhaul resource compared with the blind alternatives.

3Es System Optimization under Uncertainty Using Hybrid Intelligent Algorithm: A Fuzzy Chance-Constrained Programming Model

Directory of Open Access Journals (Sweden)

Jiekun Song

2016-01-01

Full Text Available Harmonious development of 3Es (economy-energy-environment system is the key to realize regional sustainable development. The structure and components of 3Es system are analyzed. Based on the analysis of causality diagram, GDP and industrial structure are selected as the target parameters of economy subsystem, energy consumption intensity is selected as the target parameter of energy subsystem, and the emissions of COD, ammonia nitrogen, SO2, and NOX and CO2 emission intensity are selected as the target parameters of environment system. Fixed assets investment of three industries, total energy consumption, and investment in environmental pollution control are selected as the decision variables. By regarding the parameters of 3Es system optimization as fuzzy numbers, a fuzzy chance-constrained goal programming (FCCGP model is constructed, and a hybrid intelligent algorithm including fuzzy simulation and genetic algorithm is proposed for solving it. The results of empirical analysis on Shandong province of China show that the FCCGP model can reflect the inherent relationship and evolution law of 3Es system and provide the effective decision-making support for 3Es system optimization.

Risk-constrained self-scheduling of a fuel and emission constrained power producer using rolling window procedure

International Nuclear Information System (INIS)

Kazempour, S. Jalal; Moghaddam, Mohsen Parsa

2011-01-01

This work addresses a relevant methodology for self-scheduling of a price-taker fuel and emission constrained power producer in day-ahead correlated energy, spinning reserve and fuel markets to achieve a trade-off between the expected profit and the risk versus different risk levels based on Markowitz's seminal work in the area of portfolio selection. Here, a set of uncertainties including price forecasting errors and available fuel uncertainty are considered. The latter uncertainty arises because of uncertainties in being called for reserve deployment in the spinning reserve market and availability of power plant. To tackle the price forecasting errors, variances of energy, spinning reserve and fuel prices along with their covariances which are due to markets correlation are taken into account using relevant historical data. In order to tackle available fuel uncertainty, a framework for self-scheduling referred to as rolling window is proposed. This risk-constrained self-scheduling framework is therefore formulated and solved as a mixed-integer non-linear programming problem. Furthermore, numerical results for a case study are discussed. (author)

3rd International Conference on Numerical Analysis and Optimization : Theory, Methods, Applications and Technology Transfer

CERN Document Server

Grandinetti, Lucio; Purnama, Anton

2015-01-01

Presenting the latest findings in the field of numerical analysis and optimization, this volume balances pure research with practical applications of the subject. Accompanied by detailed tables, figures, and examinations of useful software tools, this volume will equip the reader to perform detailed and layered analysis of complex datasets. Many real-world complex problems can be formulated as optimization tasks. Such problems can be characterized as large scale, unconstrained, constrained, non-convex, non-differentiable, and discontinuous, and therefore require adequate computational methods, algorithms, and software tools. These same tools are often employed by researchers working in current IT hot topics such as big data, optimization and other complex numerical algorithms on the cloud, devising special techniques for supercomputing systems. The list of topics covered include, but are not limited to: numerical analysis, numerical optimization, numerical linear algebra, numerical differential equations, opt...

OPTIMIZATION OF DIRECT COMPRESSION TABLET FORMULATIONS FOR USE IN TROPICAL COUNTRIES

NARCIS (Netherlands)

Bos, C. E.; BOLHUIS, G. K.; LERK, C. F.; de Boer, J. H.; DUINEVELD, C. A. A.; Smilde, A. K.; Doornbos, D. A.

1991-01-01

With the aid of a combined mixture- and factorial- design, 2 standard tablet formulations were selected suitable for use in tropical countries. The formulations were based on native ingredients or ingredients that are available worldwide. The selection of the standard formulations was based on both

Optimal path planning for single and multiple aircraft using a reduced order formulation

Science.gov (United States)

Twigg, Shannon S.

High-flying unmanned reconnaissance and surveillance systems are now being used extensively in the United States military. Current development programs are producing demonstrations of next-generation unmanned flight systems that are designed to perform combat missions. Their use in first-strike combat operations will dictate operations in densely cluttered environments that include unknown obstacles and threats, and will require the use of terrain for masking. The demand for autonomy of operations in such environments dictates the need for advanced trajectory optimization capabilities. In addition, the ability to coordinate the movements of more than one aircraft in the same area is an emerging challenge. This thesis examines using an analytical reduced order formulation for trajectory generation for minimum time and terrain masking cases. First, pseudo-3D constant velocity equations of motion are used for path planning for a single vehicle. In addition, the inclusion of winds, moving targets and moving threats is considered. Then, this formulation is increased to using 3D equations of motion, both with a constant velocity and with a simplified varying velocity model. Next, the constant velocity equations of motion are expanded to include the simultaneous path planning of an unspecified number of vehicles, for both aircraft avoidance situations and formation flight cases.

Optimal Pricing and Production Master Planning in a Multiperiod Horizon Considering Capacity and Inventory Constraints

Directory of Open Access Journals (Sweden)

Neale R. Smith

2009-01-01

Full Text Available We formulate and solve a single-item joint pricing and master planning optimization problem with capacity and inventory constrains. The objective is to maximize profits over a discrete-time multiperiod horizon. The solution process consists of two steps. First, we solve the single-period problem exactly. Second, using the exact solution of the single-period problem, we solve the multiperiod problem using a dynamic programming approach. The solution process and the importance of considering both capacity and inventory constraints are illustrated with numerical examples.

Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

International Nuclear Information System (INIS)

Qi Pei-Han; Zheng Shi-Lian; Yang Xiao-Niu; Zhao Zhi-Jin

2016-01-01

Adaptation is one of the key capabilities of cognitive radio, which focuses on how to adjust the radio parameters to optimize the system performance based on the knowledge of the radio environment and its capability and characteristics. In this paper, we consider the cognitive radio adaptation problem for power consumption minimization. The problem is formulated as a constrained power consumption minimization problem, and the biogeography-based optimization (BBO) is introduced to solve this optimization problem. A novel habitat suitability index (HSI) evaluation mechanism is proposed, in which both the power consumption minimization objective and the quality of services (QoS) constraints are taken into account. The results show that under different QoS requirement settings corresponding to different types of services, the algorithm can minimize power consumption while still maintaining the QoS requirements. Comparison with particle swarm optimization (PSO) and cat swarm optimization (CSO) reveals that BBO works better, especially at the early stage of the search, which means that the BBO is a better choice for real-time applications. (paper)

Optimal time points sampling in pathway modelling.

Science.gov (United States)

Hu, Shiyan

2004-01-01

Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

High-Level Waste Glass Formulation Model Sensitivity Study 2009 Glass Formulation Model Versus 1996 Glass Formulation Model

International Nuclear Information System (INIS)

Belsher, J.D.; Meinert, F.L.

2009-01-01

This document presents the differences between two HLW glass formulation models (GFM): The 1996 GFM and 2009 GFM. A glass formulation model is a collection of glass property correlations and associated limits, as well as model validity and solubility constraints; it uses the pretreated HLW feed composition to predict the amount and composition of glass forming additives necessary to produce acceptable HLW glass. The 2009 GFM presented in this report was constructed as a nonlinear optimization calculation based on updated glass property data and solubility limits described in PNNL-18501 (2009). Key mission drivers such as the total mass of HLW glass and waste oxide loading are compared between the two glass formulation models. In addition, a sensitivity study was performed within the 2009 GFM to determine the effect of relaxing various constraints on the predicted mass of the HLW glass.

Pharmacokinetics of IDX184, a liver-targeted oral prodrug of 2'-methylguanosine-5'-monophosphate, in the monkey and formulation optimization for human exposure.

Science.gov (United States)

Pan-Zhou, Xin-Ru; Mayes, Benjamin A; Rashidzadeh, Hassan; Gasparac, Rahela; Smith, Steven; Bhadresa, Sanjeev; Gupta, Kusum; Cohen, Marita Larsson; Bu, Charlie; Good, Steven S; Moussa, Adel; Rush, Roger

2016-10-01

IDX184 is a phosphoramidate prodrug of 2'-methylguanosine-5'-monophosphate, developed to treat patients infected with hepatitis C virus. A mass balance study of radiolabeled IDX184 and pharmacokinetic studies of IDX184 in portal vein-cannulated monkeys revealed relatively low IDX184 absorption but higher exposure of IDX184 in the portal vein than in the systemic circulation, indicating >90 % of the absorbed dose was subject to hepatic extraction. Systemic exposures to the main metabolite, 2'-methylguanosine (2'-MeG), were used as a surrogate for liver levels of the pharmacologically active entity 2'-MeG triphosphate, and accordingly, systemic levels of 2'-MeG in the monkey were used to optimize formulations for further clinical development of IDX184. Capsule formulations of IDX184 delivered acceptable levels of 2'-MeG in humans; however, the encapsulation process introduced low levels of the genotoxic impurity ethylene sulfide (ES), which necessitated formulation optimization. Animal pharmacokinetic data guided the development of a tablet with trace levels of ES and pharmacokinetic performance equal to that of the clinical capsule in the monkey. Under fed conditions in humans, the new tablet formulation showed similar exposure to the capsule used in prior clinical trials.

The use of D-optimal mixture design in optimising okara soap formulation for stratum corneum application.

Science.gov (United States)

Borhan, Farrah Payyadhah; Abd Gani, Siti Salwa; Shamsuddin, Rosnah

2014-01-01

Okara, soybean waste from tofu and soymilk production, was utilised as a natural antioxidant in soap formulation for stratum corneum application. D-optimal mixture design was employed to investigate the influence of the main compositions of okara soap containing different fatty acid and oils (virgin coconut oil A (24-28% w/w), olive oil B (15-20% w/w), palm oil C (6-10% w/w), castor oil D (15-20% w/w), cocoa butter E (6-10% w/w), and okara F (2-7% w/w)) by saponification process on the response hardness of the soap. The experimental data were utilized to carry out analysis of variance (ANOVA) and to develop a polynomial regression model for okara soap hardness in terms of the six design factors considered in this study. Results revealed that the best mixture was the formulation that included 26.537% A, 19.999% B, 9.998% C, 16.241% D, 7.633% E, and 7.000% F. The results proved that the difference in the level of fatty acid and oils in the formulation significantly affects the hardness of soap. Depending on the desirable level of those six variables, creation of okara based soap with desirable properties better than those of commercial ones is possible.

Optimization of the production process of a lyophilized formulation for radiopharmaceutical obtaining 99mTc-EDDA/HYNIC-E-[c(RGDfK)]2

International Nuclear Information System (INIS)

Sanchez R, S.

2013-01-01

In this work was optimized the production process of a lyophilized pharmaceutical formulation for the preparation of radiopharmaceutical 99m Tc-EDDA/HYNIC-E-[c(RGDfK)] 2 , the union specifies to the integrin s α v β 3 was demonstrated to be used in the nuclear medicine cabinets in the obtaining of scan images for the opportune detection of breast cancer. The good lyophilized pharmaceutical formulation for the preparation of radiopharmaceutical 99m Tc-EDDA/HYNIC-E-[c(RGDfK)] 2 was established like: HYNIC-E-[c(RGDfK)] 2 - 25 μg; Stannous chloride (SnCl 2 ) 20 μg; Ethylenediamine diacetic acid (EDDA) 10 mg; N-tris(hydroxymethyl)methyl glycin (Tricine) 20 mg; Mannitol 50 mg. The results of radiochemical purity of the sterile formulation and free of bacterial endotoxins for the three validation lots prepared under protocols of good manufacturing practices were 97.62 ± 1.48%, 96.54 ± 1.89%, and 97.66 ± 0.57%, for what the production procedure complies the predefined specifications. The radiopharmaceutical 99m Tc-EDDA/HYNIC-E-[c(RGDfK)]2 prepared from the lyophilized pharmaceutical formulation showed to be stable during a period 24 hours, for what can be used in the centers of molecular nuclear medicine. Images in vivo were obtained of the integrin s over-expression α v β 3 from the radiopharmaceutical 99m Tc-EDDA/HYNIC-E-[c(RGDfK)]2 obtained of the lyophilized and optimized pharmaceutical formulation. The lyophilized pharmaceutical formulation (HYNIC-RGD-Sn) showed stability during 12 months, due to this factor, is requested before the COFEPRIS the radiopharmaceutical expiration for this same period (accession number 123300401A0155). (Author)

Exact methods for time constrained routing and related scheduling problems

DEFF Research Database (Denmark)

Kohl, Niklas

1995-01-01

of customers. In the VRPTW customers must be serviced within a given time period - a so called time window. The objective can be to minimize operating costs (e.g. distance travelled), fixed costs (e.g. the number of vehicles needed) or a combination of these component costs. During the last decade optimization......This dissertation presents a number of optimization methods for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW is a generalization of the well known capacity constrained Vehicle Routing Problem (VRP), where a fleet of vehicles based at a central depot must service a set...... of J?rnsten, Madsen and S?rensen (1986), which has been tested computationally by Halse (1992). Both methods decompose the problem into a series of time and capacity constrained shotest path problems. This yields a tight lower bound on the optimal objective, and the dual gap can often be closed...

Sequential unconstrained minimization algorithms for constrained optimization

International Nuclear Information System (INIS)

Byrne, Charles

2008-01-01

The problem of minimizing a function f(x):R J → R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G k (x)=f(x)+g k (x), to obtain x k . The auxiliary functions g k (x):D subset of R J → R + are nonnegative on the set D, each x k is assumed to lie within D, and the objective is to minimize the continuous function f:R J → R over x in the set C = D-bar, the closure of D. We assume that such minimizers exist, and denote one such by x-circumflex. We assume that the functions g k (x) satisfy the inequalities 0≤g k (x)≤G k-1 (x)-G k-1 (x k-1 ), for k = 2, 3, .... Using this assumption, we show that the sequence {(x k )} is decreasing and converges to f(x-circumflex). If the restriction of f(x) to D has bounded level sets, which happens if x-circumflex is unique and f(x) is closed, proper and convex, then the sequence {x k } is bounded, and f(x*)=f(x-circumflex), for any cluster point x*. Therefore, if x-circumflex is unique, x* = x-circumflex and {x k } → x-circumflex. When x-circumflex is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton–Raphson method. The proof techniques used for SUMMA can be extended to obtain related results

Formulation strategies for optimizing the morphology of polymeric bulk heterojunction organic solar cells: a brief review

Science.gov (United States)

Vongsaysy, Uyxing; Bassani, Dario M.; Servant, Laurent; Pavageau, Bertrand; Wantz, Guillaume; Aziz, Hany

2014-01-01

Polymeric bulk heterojunction (BHJ) organic solar cells represent one of the most promising technologies for renewable energy with a low fabrication cost. Control over BHJ morphology is one of the key factors in obtaining high-efficiency devices. This review focuses on formulation strategies for optimizing the BHJ morphology. We address how solvent choice and the introduction of processing additives affect the morphology. We also review a number of recent studies concerning prediction methods that utilize the Hansen solubility parameters to develop efficient solvent systems.

Cohesive phase-field fracture and a PDE constrained optimization approach to fracture inverse problems

Energy Technology Data Exchange (ETDEWEB)

Tupek, Michael R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2016-06-30

In recent years there has been a proliferation of modeling techniques for forward predictions of crack propagation in brittle materials, including: phase-field/gradient damage models, peridynamics, cohesive-zone models, and G/XFEM enrichment techniques. However, progress on the corresponding inverse problems has been relatively lacking. Taking advantage of key features of existing modeling approaches, we propose a parabolic regularization of Barenblatt cohesive models which borrows extensively from previous phase-field and gradient damage formulations. An efficient explicit time integration strategy for this type of nonlocal fracture model is then proposed and justified. In addition, we present a C++ computational framework for computing in- put parameter sensitivities efficiently for explicit dynamic problems using the adjoint method. This capability allows for solving inverse problems involving crack propagation to answer interesting engineering questions such as: 1) what is the optimal design topology and material placement for a heterogeneous structure to maximize fracture resistance, 2) what loads must have been applied to a structure for it to have failed in an observed way, 3) what are the existing cracks in a structure given various experimental observations, etc. In this work, we focus on the first of these engineering questions and demonstrate a capability to automatically and efficiently compute optimal designs intended to minimize crack propagation in structures.

OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD

Directory of Open Access Journals (Sweden)

Dhananjay Kumar

2016-01-01

Full Text Available Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider’s resources aren’t enough to satisfy the customer’s demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO. These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.

Formulation and Pharmacokinetic Evaluation of Controlled-Release ...

African Journals Online (AJOL)

The effect of several formulation variables on in ... The in vivo pharmacokinetics of the optimized formulation was compared ... Results: The core tablets exhibited extended release consisting of drug release from the embedded ... important factor in medical treatment with respect ... The solvents for high-performance liquid.

Application of a Double-Sided Chance-Constrained Integer Linear Program for Optimization of the Incremental Value of Ecosystem Services in Jilin Province, China

Directory of Open Access Journals (Sweden)

Baofeng Cai

2017-08-01

Full Text Available The Interconnected River System Network Project (IRSNP is a significant water supply engineering project, which is capable of effectively utilizing flood resources to generate ecological value, by connecting 198 lakes and ponds in western Jilin, northeast China. In this article, an optimization research approach has been proposed to maximize the incremental value of IRSNP ecosystem services. A double-sided chance-constrained integer linear program (DCCILP method has been proposed to support the optimization, which can deal with uncertainties presented as integers or random parameters that appear on both sides of the decision variable at the same time. The optimal scheme indicates that after rational optimization, the total incremental value of ecosystem services from the interconnected river system network project increased 22.25%, providing an increase in benefits of 3.26 × 109 ¥ compared to the original scheme. Most of the functional area is swamp wetland, which provides the greatest ecological benefits. Adjustment services increased obviously, implying that the optimization scheme prioritizes ecological benefits rather than supply and production services.

Multivariable controller for discrete stochastic amplitude-constrained systems

Directory of Open Access Journals (Sweden)

Hannu T. Toivonen

1983-04-01

Full Text Available A sub-optimal multivariable controller for discrete stochastic amplitude-constrained systems is presented. In the approach the regulator structure is restricted to the class of linear saturated feedback laws. The stationary covariances of the controlled system are evaluated by approximating the stationary probability distribution of the state by a gaussian distribution. An algorithm for minimizing a quadratic loss function is given, and examples are presented to illustrate the performance of the sub-optimal controller.

A buffer material optimal design in the radioactive wastes geological disposal using the satisficing trade-off method and the self-organizing map

International Nuclear Information System (INIS)

Okamoto, Takashi; Hanaoka, Yuya; Aiyoshi, Eitaro; Kobayashi, Yoko

2012-01-01

In this paper, we consider a multi-objective optimization method in order to obtain a preferred solution for the buffer material optimal design problem in the high-level radioactive wastes geological disposal. The buffer material optimal design problem is formulated as a constrained multi-objective optimization problem. Its Pareto optimal solutions are distributed evenly on whole bounds of the feasible region. Hence, we develop a search method to find a preferred solution easily for a decision maker from the Pareto optimal solutions which are distributed evenly and vastly. In the preferred solution search method, the visualization technique of a Pareto optimal solution set using the self-organizing map is introduced into the satisficing trade-off method which is the interactive method to obtain a Pareto optimal solution that satisfies a decision maker. We confirm the effectiveness of the preferred solution search method in the buffer material optimal design problem. (author)

Binary classification posed as a quadratically constrained quadratic ...

Indian Academy of Sciences (India)

Binary classification is posed as a quadratically constrained quadratic problem and solved using the proposed method. Each class in the binary classification problem is modeled as a multidimensional ellipsoid to forma quadratic constraint in the problem. Particle swarms help in determining the optimal hyperplane or ...

Optimal Design and Tuning of PID-Type Interval Type-2 Fuzzy Logic Controllers for Delta Parallel Robots

Directory of Open Access Journals (Sweden)

Xingguo Lu

2016-05-01

Full Text Available In this work, we propose a new method for the optimal design and tuning of a Proportional-Integral-Derivative type (PID-type interval type-2 fuzzy logic controller (IT2 FLC for Delta parallel robot trajectory tracking control. The presented methodology starts with an optimal design problem of IT2 FLC. A group of IT2 FLCs are obtained by blurring the membership functions using a variable called blurring degree. By comparing the performance of the controllers, the optimal structure of IT2 FLC is obtained. Then, a multi-objective optimization problem is formulated to tune the scaling factors of the PID-type IT2 FLC. The Non-dominated Sorting Genetic Algorithm (NSGA-II is adopted to solve the constrained nonlinear multi-objective optimization problem. Simulation results of the optimized controller are presented and discussed regarding application in the Delta parallel robot. The proposed method provides an effective way to design and tune the PID-type IT2 FLC with a desired control performance.

Enhancement of dissolution and oral bioavailability of lacidipine via pluronic P123/F127 mixed polymeric micelles: formulation, optimization using central composite design and in vivo bioavailability study.

Science.gov (United States)

Fares, Ahmed R; ElMeshad, Aliaa N; Kassem, Mohamed A A

2018-11-01

This study aims at preparing and optimizing lacidipine (LCDP) polymeric micelles using thin film hydration technique in order to overcome LCDP solubility-limited oral bioavailability. A two-factor three-level central composite face-centered design (CCFD) was employed to optimize the formulation variables to obtain LCDP polymeric micelles of high entrapment efficiency and small and uniform particle size (PS). Formulation variables were: Pluronic to drug ratio (A) and Pluronic P123 percentage (B). LCDP polymeric micelles were assessed for entrapment efficiency (EE%), PS and polydispersity index (PDI). The formula with the highest desirability (0.959) was chosen as the optimized formula. The values of the formulation variables (A and B) in the optimized polymeric micelles formula were 45% and 80%, respectively. Optimum LCDP polymeric micelles had entrapment efficiency of 99.23%, PS of 21.08 nm and PDI of 0.11. Optimum LCDP polymeric micelles formula was physically characterized using transmission electron microscopy. LCDP polymeric micelles showed saturation solubility approximately 450 times that of raw LCDP in addition to significantly enhanced dissolution rate. Bioavailability study of optimum LCDP polymeric micelles formula in rabbits revealed a 6.85-fold increase in LCDP bioavailability compared to LCDP oral suspension.

Robust Layout Synthesis of a MEM Crab-Leg Resonator Using a Constrained Genetic Algorithm

DEFF Research Database (Denmark)

Fan, Zhun; Achiche, Sofiane

2007-01-01

The research work carried out in this paper introduces a robust design method for layout synthesis of MEM resonator subject to inherent geometric uncertainties such as the fabrication error on the sidewall of the structure. The robust design problem is formulated as a multi-objective constrained...

SN-38 loading capacity of hydrophobic polymer blend nanoparticles: formulation, optimization and efficacy evaluation.

Science.gov (United States)

Dimchevska, Simona; Geskovski, Nikola; Petruševski, Gjorgji; Chacorovska, Marina; Popeski-Dimovski, Riste; Ugarkovic, Sonja; Goracinova, Katerina

2017-03-01

One of the most important problems in nanoencapsulation of extremely hydrophobic drugs is poor drug loading due to rapid drug crystallization outside the polymer core. The effort to use nanoprecipitation, as a simple one-step procedure with good reproducibility and FDA approved polymers like Poly(lactic-co-glycolic acid) (PLGA) and Polycaprolactone (PCL), will only potentiate this issue. Considering that drug loading is one of the key defining characteristics, in this study we attempted to examine whether the nanoparticle (NP) core composed of two hydrophobic polymers will provide increased drug loading for 7-Ethyl-10-hydroxy-camptothecin (SN-38), relative to NPs prepared using individual polymers. D-optimal design was applied to optimize PLGA/PCL ratio in the polymer blend and the mode of addition of the amphiphilic copolymer Lutrol ® F127 in order to maximize SN-38 loading and obtain NPs with acceptable size for passive tumor targeting. Drug/polymer and polymer/polymer interaction analysis pointed to high degree of compatibility and miscibility among both hydrophobic polymers, providing core configuration with higher drug loading capacity. Toxicity studies outlined the biocompatibility of the blank NPs. Increased in vitro efficacy of drug-loaded NPs compared to the free drug was confirmed by growth inhibition studies using SW-480 cell line. Additionally, the optimized NP formulation showed very promising blood circulation profile with elimination half-time of 7.4 h.

Lagrangian formulation of classical BMT-theory

International Nuclear Information System (INIS)

Pupasov-Maksimov, Andrey; Deriglazov, Alexei; Guzman, Walberto

2013-01-01

Full text: The most popular classical theory of electron has been formulated by Bargmann, Michel and Telegdi (BMT) in 1959. The BMT equations give classical relativistic description of a charged particle with spin and anomalous magnetic momentum moving in homogeneous electro-magnetic field. This allows to study spin dynamics of polarized beams in uniform fields. In particular, first experimental measurements of muon anomalous magnetic momentum were done using changing of helicity predicted by BMT equations. Surprisingly enough, a systematic formulation and the analysis of the BMT theory are absent in literature. In the present work we particularly fill this gap by deducing Lagrangian formulation (variational problem) for BMT equations. Various equivalent forms of Lagrangian will be discussed in details. An advantage of the obtained classical model is that the Lagrangian action describes a relativistic spinning particle without Grassmann variables, for both free and interacting cases. This implies also the possibility of canonical quantization. In the interacting case, an arbitrary electromagnetic background may be considered, which generalizes the BMT theory formulated to the case of homogeneous fields. The classical model has two local symmetries, which gives an interesting example of constrained classical dynamics. It is surprising, that the case of vanishing anomalous part of the magnetic momentum is naturally highlighted in our construction. (author)

Optimally Joint Subcarrier Matching and Power Allocation in OFDM Multihop System

Directory of Open Access Journals (Sweden)

Shuyuan Yang

2008-04-01

Full Text Available Orthogonal frequency division multiplexing (OFDM multihop system is a promising way to increase capacity and coverage. In this paper, we propose an optimally joint subcarrier matching and power allocation scheme to further maximize the total channel capacity with the constrained total system power. First, the problem is formulated as a mixed binary integer programming problem, which is prohibitive to find the global optimum in terms of complexity. Second, by making use of the equivalent channel power gain for any matched subcarrier pair, a low complexity scheme is proposed. The optimal subcarrier matching is to match subcarriers by the order of the channel power gains. The optimal power allocation among the matched subcarrier pairs is water-filling. An analytical argument is given to prove that the two steps achieve the optimally joint subcarrier matching and power allocation. The simulation results show that the proposed scheme achieves the largest total channel capacity as compared to the other schemes, where there is no subcarrier matching or no power allocation.

Optimally Joint Subcarrier Matching and Power Allocation in OFDM Multihop System

Directory of Open Access Journals (Sweden)

Wang Wenyi

2008-01-01

Full Text Available Orthogonal frequency division multiplexing (OFDM multihop system is a promising way to increase capacity and coverage. In this paper, we propose an optimally joint subcarrier matching and power allocation scheme to further maximize the total channel capacity with the constrained total system power. First, the problem is formulated as a mixed binary integer programming problem, which is prohibitive to find the global optimum in terms of complexity. Second, by making use of the equivalent channel power gain for any matched subcarrier pair, a low complexity scheme is proposed. The optimal subcarrier matching is to match subcarriers by the order of the channel power gains. The optimal power allocation among the matched subcarrier pairs is water-filling. An analytical argument is given to prove that the two steps achieve the optimally joint subcarrier matching and power allocation. The simulation results show that the proposed scheme achieves the largest total channel capacity as compared to the other schemes, where there is no subcarrier matching or no power allocation.

The Use of D-Optimal Mixture Design in Optimising Okara Soap Formulation for Stratum Corneum Application

Science.gov (United States)

Borhan, Farrah Payyadhah; Abd Gani, Siti Salwa; Shamsuddin, Rosnah

2014-01-01

Okara, soybean waste from tofu and soymilk production, was utilised as a natural antioxidant in soap formulation for stratum corneum application. D-optimal mixture design was employed to investigate the influence of the main compositions of okara soap containing different fatty acid and oils (virgin coconut oil A (24–28% w/w), olive oil B (15–20% w/w), palm oil C (6–10% w/w), castor oil D (15–20% w/w), cocoa butter E (6–10% w/w), and okara F (2–7% w/w)) by saponification process on the response hardness of the soap. The experimental data were utilized to carry out analysis of variance (ANOVA) and to develop a polynomial regression model for okara soap hardness in terms of the six design factors considered in this study. Results revealed that the best mixture was the formulation that included 26.537% A, 19.999% B, 9.998% C, 16.241% D, 7.633% E, and 7.000% F. The results proved that the difference in the level of fatty acid and oils in the formulation significantly affects the hardness of soap. Depending on the desirable level of those six variables, creation of okara based soap with desirable properties better than those of commercial ones is possible. PMID:25548777

The Use of D-Optimal Mixture Design in Optimising Okara Soap Formulation for Stratum Corneum Application

Directory of Open Access Journals (Sweden)

Farrah Payyadhah Borhan

2014-01-01

Full Text Available Okara, soybean waste from tofu and soymilk production, was utilised as a natural antioxidant in soap formulation for stratum corneum application. D-optimal mixture design was employed to investigate the influence of the main compositions of okara soap containing different fatty acid and oils (virgin coconut oil A (24–28% w/w, olive oil B (15–20% w/w, palm oil C (6–10% w/w, castor oil D (15–20% w/w, cocoa butter E (6–10% w/w, and okara F (2–7% w/w by saponification process on the response hardness of the soap. The experimental data were utilized to carry out analysis of variance (ANOVA and to develop a polynomial regression model for okara soap hardness in terms of the six design factors considered in this study. Results revealed that the best mixture was the formulation that included 26.537% A, 19.999% B, 9.998% C, 16.241% D, 7.633% E, and 7.000% F. The results proved that the difference in the level of fatty acid and oils in the formulation significantly affects the hardness of soap. Depending on the desirable level of those six variables, creation of okara based soap with desirable properties better than those of commercial ones is possible.

Mixed finite-element formulations in piezoelectricity and flexoelectricity.

Science.gov (United States)

Mao, Sheng; Purohit, Prashant K; Aravas, Nikolaos

2016-06-01

Flexoelectricity, the linear coupling of strain gradient and electric polarization, is inherently a size-dependent phenomenon. The energy storage function for a flexoelectric material depends not only on polarization and strain, but also strain-gradient. Thus, conventional finite-element methods formulated solely on displacement are inadequate to treat flexoelectric solids since gradients raise the order of the governing differential equations. Here, we introduce a computational framework based on a mixed formulation developed previously by one of the present authors and a colleague. This formulation uses displacement and displacement-gradient as separate variables which are constrained in a 'weighted integral sense' to enforce their known relation. We derive a variational formulation for boundary-value problems for piezo- and/or flexoelectric solids. We validate this computational framework against available exact solutions. Our new computational method is applied to more complex problems, including a plate with an elliptical hole, stationary cracks, as well as tension and shear of solids with a repeating unit cell. Our results address several issues of theoretical interest, generate predictions of experimental merit and reveal interesting flexoelectric phenomena with potential for application.

Formulation, optimization, and pharmacodynamic evaluation of chitosan/phospholipid/β-cyclodextrin microspheres

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Shan L

2016-01-01

Full Text Available Lu Shan,1 En-Xue Tao,2 Qing-Hui Meng,3 Wen-Xia Hou,3 Kang Liu,1 Hong-Cai Shang,4 Jin-Bao Tang,1 Wei-Fen Zhang1,4 1School of Pharmacy, Weifang Medical University, 2The Affiliated Hospital of Weifang Medical University, 3School of Nursing, Weifang Medical University, Weifang, 4Key Laboratory of Chinese Internal Medicine of Ministry of Education and Beijing, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, People’s Republic of China Abstract: Cholinergic neurotransmission loss is the main cause of cognitive impairment in patients with Alzheimer’s disease. Phospholipids (PLs play an essential role in memory and learning abilities. Moreover, PLs act as a source of choline in acetylcholine synthesis. This study aimed to prepare and optimize the formulation of chitosan/phospholipid/β-cyclodextrin (CTS/PL/β-CD microspheres that can improve cognitive impairment. The CTS/PL/β-CD microspheres were prepared by spray drying, and optimized with an orthogonal design. These microspheres were also characterized in terms of morphology, structure, thermostability, drug loading, and encapsulation efficiency. The spatial learning and memory of rats were evaluated using the Morris water maze test, and the neuroprotective effects of the CTS/PL/β-CD microspheres were investigated by immunohistochemistry. Scanning electron microscopic images showed that the CTS/PL/β-CD microspheres were spherical with slightly wrinkled surfaces. Fourier transform infrared spectroscopy and differential scanning calorimetry proved that PLs formed hydrogen bonds with the amide group of CTS and the hydroxyl group of β-CD. The learning and memory abilities of rats in the treated group significantly improved compared with those in the model group. Immunohistochemical analysis revealed that treatment with the CTS/PL/β-CD microspheres attenuated the expression of protein kinase C-δ and inhibited the activation of microglias. These results suggest that the

Formulating an optimal long-term energy supply strategy for Syria using MESSAGE model

International Nuclear Information System (INIS)

Hainoun, A.; Seif Aldin, M.; Almoustafa, S.

2010-01-01

An optimal long-term energy supply strategy has been formulated based on minimizing the total system costs for the entire study period 2003-2030. The national energy chain was modelled covering all energy levels and conversion technologies. The results indicate that the primary energy will grow at annual average rate of 4.8% arriving 68 Mtoe in 2030. The total installed electric capacity will be optimally expanded from 6885 to 19500 MW in 2030. Furthermore, to ensure supply security the future national energy system will rely mainly upon oil and natural gas (NG) with limited contribution of renewables and nuclear to the end of study period. The share of NG will increase gradually up to 2020 and then retreat. Owing to the continuous decrease of oil production, oil export is expected to vanish in 2012 and the country will import about 63% of its primary energy demand in 2030. Thus, the expected long-term development of national energy sector indicates a hard challenge for the future national economy. The employing of sensitivity analysis clarifies the importance of wind turbines operation time and discount rate. The analysis proves that nuclear option is insensitive to overnight cost increase up to 85% of the reference case value.

Aerosol formulation and clinical efficacy of bronchodilators

NARCIS (Netherlands)

Zanen, Pieter

1998-01-01

This thesis subject is the improvement of the formulation of inhaled aerosols. It is well known that the formulation of inhaled drugs is not optimal: the major part of the mass delivered does not reach the lower airways. This phenomenon is due to the particle size of the inhaled particles, which

Transient thermal stresses in a circular cylinder with constrained ends

International Nuclear Information System (INIS)

Goshima, Takahito; Miyao, Kaju

1986-01-01

This paker deals with the transient thermal stresses in a finite circular cylinder constrained at both end surfaces and subjected to axisymmetric temperature distribution on the lateral surface. The thermoelastic problem is formulated in terms of a thermoelastic displacement potential and three harmonic stress functions. Numerical calculations are carried out for the case of the uniform temperature distribution on the lateral surface. The stress distributions on the constrained end and the free suface are shown graphically, and the singularity in stresses appearing at the circumferencial edge is considered. Moreover, the approximate solution based upon the plane strain theory is introduced in order to compare the rigorous one, and it is considered how the length of the cylinder and the time proceeds affect on the accuracy of the approximation. (author)

OPTIMIZATION OF DEMULSIFIER FORMULATION FOR SEPARATION OF WATER FROM CRUDE OIL EMULSIONS

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P. Hajivand

2015-03-01

Full Text Available Abstract In this study, various water-soluble and oil-soluble demulsifiers were selected for separation of water from crude oil emulsions and their productivity measured using the Bottle-test method at 70 °C and 10 ppm concentration. The best ones among 23 demulsifiers examined through the screening process were fatty alcohol ethoxylate, triethanol amine and urea from the water-soluble group and Basororol E2032, Basorol PDB 9935 and TOMAC from the oil-soluble category. Furthermore, the present study investigated the factors effective for demulsification such as temperature, concentration, pH, salinity and modifiers. It was found that the separation improves with increasing demulsifier concentration, increasing salt content, increasing temperature up to 80 °C, keeping the pH values between 5-9. Adding solvent modifiers proved unnecessary. Two formulations were prepared based on suggested optimal concentrations of demulsifier content by experimental design using Qualitec 4 and these proved to be highly effective in treating real and synthetic emulsions.

An exact approach for aggregated formulations

DEFF Research Database (Denmark)

Gamst, Mette; Spoorendonk, Simon; Røpke, Stefan

Aggregating formulations is a powerful approach for problems to take on tractable forms. Aggregation may lead to loss of information, i.e. the aggregated formulation may be an approximation of the original problem. In branch-and-bound context, aggregation can also complicate branching, e.g. when...... optimality cannot be guaranteed by branching on aggregated variables. We present a generic exact solution method to remedy the drawbacks of aggregation. It combines the original and aggregated formulations and applies Benders' decomposition. We apply the method to the Split Delivery Vehicle Routing Problem....

Balancing Long Lifetime and Satisfying Fairness in WBAN Using a Constrained Markov Decision Process

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Yingqi Yin

2015-01-01

Full Text Available As an important part of the Internet of Things (IOT and the special case of device-to-device (D2D communication, wireless body area network (WBAN gradually becomes the focus of attention. Since WBAN is a body-centered network, the energy of sensor nodes is strictly restrained since they are supplied by battery with limited power. In each data collection, only one sensor node is scheduled to transmit its measurements directly to the access point (AP through the fading channel. We formulate the problem of dynamically choosing which sensor should communicate with the AP to maximize network lifetime under the constraint of fairness as a constrained markov decision process (CMDP. The optimal lifetime and optimal policy are obtained by Bellman equation in dynamic programming. The proposed algorithm defines the limiting performance in WBAN lifetime under different degrees of fairness constraints. Due to the defect of large implementation overhead in acquiring global channel state information (CSI, we put forward a distributed scheduling algorithm that adopts local CSI, which saves the network overhead and simplifies the algorithm. It was demonstrated via simulation that this scheduling algorithm can allocate time slot reasonably under different channel conditions to balance the performances of network lifetime and fairness.

The Use of D-Optimal Mixture Design in Optimizing Development of Okara Tablet Formulation as a Dietary Supplement

Science.gov (United States)

Mohamad Zen, Nur Izzati; Shamsudin, Rosnah

2015-01-01

The usage of soy is increasing year by year. It increases the problem of financial crisis due to the limited sources of soybeans. Therefore, production of oral tablets containing the nutritious leftover of soymilk production, called okara, as the main ingredient was investigated. The okara tablets were produced using the direct compression method. The percentage of okara, guar gum, microcrystalline cellulose (Avicel PH-101), and maltodextrin influenced tablets' hardness and friability which are analyzed using a D-optimal mixture design. Composition of Avicel PH-101 had positive effects for both hardness and friability tests of the tablets. Maltodextrin and okara composition had a significant positive effect on tablets' hardness, but not on percentage of friability of tablets. However, guar gum had a negative effect on both physical tests. The optimum tablet formulation was obtained: 47.0% of okara, 2.0% of guar gum, 35.0% of Avicel PH-101, and 14.0% of maltodextrin. PMID:26171418

The Use of D-Optimal Mixture Design in Optimizing Development of Okara Tablet Formulation as a Dietary Supplement

Directory of Open Access Journals (Sweden)

Nur Izzati Mohamad Zen

2015-01-01

Full Text Available The usage of soy is increasing year by year. It increases the problem of financial crisis due to the limited sources of soybeans. Therefore, production of oral tablets containing the nutritious leftover of soymilk production, called okara, as the main ingredient was investigated. The okara tablets were produced using the direct compression method. The percentage of okara, guar gum, microcrystalline cellulose (Avicel PH-101, and maltodextrin influenced tablets’ hardness and friability which are analyzed using a D-optimal mixture design. Composition of Avicel PH-101 had positive effects for both hardness and friability tests of the tablets. Maltodextrin and okara composition had a significant positive effect on tablets’ hardness, but not on percentage of friability of tablets. However, guar gum had a negative effect on both physical tests. The optimum tablet formulation was obtained: 47.0% of okara, 2.0% of guar gum, 35.0% of Avicel PH-101, and 14.0% of maltodextrin.

Optimization of formulation of soy-cakes baked in infrared-microwave combination oven by response surface methodology.

Science.gov (United States)

Şakıyan, Özge

2015-05-01

The aim of present work is to optimize the formulation of a functional cake (soy-cake) to be baked in infrared-microwave combination oven. For this optimization process response surface methodology was utilized. It was also aimed to optimize the processing conditions of the combination baking. The independent variables were the baking time (8, 9, 10 min), the soy flour concentration (30, 40, 50 %) and the DATEM (diacetyltartaric acid esters of monoglycerides) concentration (0.4, 0.6 and 0.8 %). The quality parameters that were examined in the study were specific volume, weight loss, total color change and firmness of the cake samples. The results were analyzed by multiple regression; and the significant linear, quadratic, and interaction terms were used in the second order mathematical model. The optimum baking time, soy-flour concentration and DATEM concentration were found as 9.5 min, 30 and 0.72 %, respectively. The corresponding responses of the optimum points were almost comparable with those of conventionally baked soy-cakes. So it may be declared that it is possible to produce high quality soy cakes in a very short time by using infrared-microwave combination oven.

Novel microemulsion-based gel formulation of tazarotene for therapy of acne.

Science.gov (United States)

Patel, Mrunali Rashmin; Patel, Rashmin Bharatbhai; Parikh, Jolly R; Patel, Bharat G

2016-12-01

The objective of this study was to develop and evaluate a novel microemulsion based gel formulation containing tazarotene for targeted topical therapy of acne. Psudoternary phase diagrams were constructed to obtain the concentration range of oil, surfactant, and co-surfactant for microemulsion formation. The optimized microemulsion formulation containing 0.05% tazarotene was formulated by spontaneous microemulsification method consisting of 10% Labrafac CC, mixed emulsifiers 15% Labrasol-Cremophor-RH 40 (1:1), 15% Capmul MCM, and 60% distilled water (w/w) as an external phase. All plain and tazarotene-loaded microemulsions were clear and showed physicochemical parameters for desired topical delivery and stability. The permeation profiles of tazarotene through rat skin from optimized microemulsion formulation followed the Higuchi model for controlled permeation. Microemulsion-based gel was prepared by incorporating Carbopol®971P NF in optimized microemulsion formulation having suitable skin permeation rate and skin uptake. Microemulsion-based gel showed desired physicochemical parameters and demonstrated advantage over marketed formulation in improving the skin tolerability of tazarotene indicating its potential in improving its topical delivery. The developed microemulsion-based gel may be a potential drug delivery vehicle for targeted topical delivery of tazarotene in the treatment of acne.

Extended Lagrangian formulation of charge-constrained tight-binding molecular dynamics.

Science.gov (United States)

Cawkwell, M J; Coe, J D; Yadav, S K; Liu, X-Y; Niklasson, A M N

2015-06-09

The extended Lagrangian Born-Oppenheimer molecular dynamics formalism [Niklasson, Phys. Rev. Lett., 2008, 100, 123004] has been applied to a tight-binding model under the constraint of local charge neutrality to yield microcanonical trajectories with both precise, long-term energy conservation and a reduced number of self-consistent field optimizations at each time step. The extended Lagrangian molecular dynamics formalism restores time reversal symmetry in the propagation of the electronic degrees of freedom, and it enables the efficient and accurate self-consistent optimization of the chemical potential and atomwise potential energy shifts in the on-site elements of the tight-binding Hamiltonian that are required when enforcing local charge neutrality. These capabilities are illustrated with microcanonical molecular dynamics simulations of a small metallic cluster using an sd-valent tight-binding model for titanium. The effects of weak dissipation on the propagation of the auxiliary degrees of freedom for the chemical potential and on-site Hamiltonian matrix elements that is used to counteract the accumulation of numerical noise during trajectories was also investigated.

Formulation of Fast-Dissolving Tablets of Promethazine Theoclate ...

African Journals Online (AJOL)

Purpose: To optimize and formulate promethazine theoclate fast-dissolving tablets that offer a suitable approach to the treatment of nausea and vomiting. Method: The solubility of promethazine theoclate was increased by formulating it as a fast-dissolving tablet containing β-cyclodextrin, crospovidone, and camphor, using ...

Optimal Policy of Cross-Layer Design for Channel Access and Transmission Rate Adaptation in Cognitive Radio Networks

Science.gov (United States)

He, Hao; Wang, Jun; Zhu, Jiang; Li, Shaoqian

2010-12-01

In this paper, we investigate the cross-layer design of joint channel access and transmission rate adaptation in CR networks with multiple channels for both centralized and decentralized cases. Our target is to maximize the throughput of CR network under transmission power constraint by taking spectrum sensing errors into account. In centralized case, this problem is formulated as a special constrained Markov decision process (CMDP), which can be solved by standard linear programming (LP) method. As the complexity of finding the optimal policy by LP increases exponentially with the size of action space and state space, we further apply action set reduction and state aggregation to reduce the complexity without loss of optimality. Meanwhile, for the convenience of implementation, we also consider the pure policy design and analyze the corresponding characteristics. In decentralized case, where only local information is available and there is no coordination among the CR users, we prove the existence of the constrained Nash equilibrium and obtain the optimal decentralized policy. Finally, in the case that the traffic load parameters of the licensed users are unknown for the CR users, we propose two methods to estimate the parameters for two different cases. Numerical results validate the theoretic analysis.

Optimal Policy of Cross-Layer Design for Channel Access and Transmission Rate Adaptation in Cognitive Radio Networks

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Jiang Zhu

2010-01-01

Full Text Available In this paper, we investigate the cross-layer design of joint channel access and transmission rate adaptation in CR networks with multiple channels for both centralized and decentralized cases. Our target is to maximize the throughput of CR network under transmission power constraint by taking spectrum sensing errors into account. In centralized case, this problem is formulated as a special constrained Markov decision process (CMDP, which can be solved by standard linear programming (LP method. As the complexity of finding the optimal policy by LP increases exponentially with the size of action space and state space, we further apply action set reduction and state aggregation to reduce the complexity without loss of optimality. Meanwhile, for the convenience of implementation, we also consider the pure policy design and analyze the corresponding characteristics. In decentralized case, where only local information is available and there is no coordination among the CR users, we prove the existence of the constrained Nash equilibrium and obtain the optimal decentralized policy. Finally, in the case that the traffic load parameters of the licensed users are unknown for the CR users, we propose two methods to estimate the parameters for two different cases. Numerical results validate the theoretic analysis.

Statistical mechanics of budget-constrained auctions

International Nuclear Information System (INIS)

Altarelli, F; Braunstein, A; Realpe-Gomez, J; Zecchina, R

2009-01-01

Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being in the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). On the basis of the cavity method of statistical mechanics, we introduce a message-passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution, and we derive from its properties the phase diagram of the problem. As the control parameter (average value of the budgets) is varied, we find two phase transitions delimiting a region in which long-range correlations arise

Statistical mechanics of budget-constrained auctions

Science.gov (United States)

Altarelli, F.; Braunstein, A.; Realpe-Gomez, J.; Zecchina, R.

2009-07-01

Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being in the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). On the basis of the cavity method of statistical mechanics, we introduce a message-passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution, and we derive from its properties the phase diagram of the problem. As the control parameter (average value of the budgets) is varied, we find two phase transitions delimiting a region in which long-range correlations arise.

Constrained variational calculus for higher order classical field theories

Energy Technology Data Exchange (ETDEWEB)

Campos, Cedric M; De Leon, Manuel; De Diego, David MartIn, E-mail: cedricmc@icmat.e, E-mail: mdeleon@icmat.e, E-mail: david.martin@icmat.e [Instituto de Ciencias Matematicas, CSIC-UAM-UC3M-UCM, Serrano 123, 28006 Madrid (Spain)

2010-11-12

We develop an intrinsic geometrical setting for higher order constrained field theories. As a main tool we use an appropriate generalization of the classical Skinner-Rusk formalism. Some examples of applications are studied, in particular to the geometrical description of optimal control theory for partial differential equations.

Constrained variational calculus for higher order classical field theories

International Nuclear Information System (INIS)

Campos, Cedric M; De Leon, Manuel; De Diego, David MartIn

2010-01-01

We develop an intrinsic geometrical setting for higher order constrained field theories. As a main tool we use an appropriate generalization of the classical Skinner-Rusk formalism. Some examples of applications are studied, in particular to the geometrical description of optimal control theory for partial differential equations.

Optimization of Gluten-Free Tulumba Dessert Formulation Including Corn Flour: Response Surface Methodology Approach

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Yildiz Önder

2017-03-01

Full Text Available Tulumba dessert is widely preferred in Turkey; however, it cannot be consumed by celiac patients because it includes gluten. The diversity of gluten-free products should be expanded so that celiac patients may meet their daily needs regularly. In this study, corn flour (CF / potato starch (PS blend to be used in the gluten-free tulumba dessert formulation was optimized using the Response Surface Methodology (RSM. Increasing ratio of PS in the CF-PS led to a decrease in hardness of the dessert and to an increase in expansion, viscosity, adhesiveness, yield of dessert both with and without syrup (P0.05, additionally these desserts had a much higher sensory score compared to the control sample in terms of the overall quality and pore structure (P<0.05.

[Orthogonal experiments for optimizing the formulation and preparation conditions of temozolomide solid lipid nanoparticles].

Science.gov (United States)

Dou, Mingjin; Huang, Guihua; Xi, Yanwei; Zhang, Na

2008-10-01

TMZ-SLN were prepared by emulsification-low temperature solidification method with stearic acid. The formulation and the preparation conditions were optimized by orthogonal experiments using entrapment efficiency as the evaluation index. The morphology was detected by transmission electron microscope. The Zeta potentials and the particle size distribution were evaluated by Laser Doppler Anemometry. The entrapment efficiencies and the drug release characteristics in vitro were assessed. The result showed that TMZ-SLN were concinnous and spherical in shape. The mean diameter (d(av) ) was 65.0 +/- 6.2 nm and the Zeta potential was -37.2 mV. The average entrapment efficiency was 58.9% +/- 1.21 %. The drug release behavior in vitro conformed to Higuchi Equation. The formation of a new material phase was testified by analysis of differential scanning calorimetry.

Optimization and Formulation of Orodispersible Tablets of Meloxicam

African Journals Online (AJOL)

... 98.5% and fast drug release rate of 99.5% within 30 min, as compared with the conventional tablet (49.5%) . Conclusion: It is feasible to formulate orodispersible tablets of meloxican with acceptable disintegration time, rapid drug release and good hardness, which could be amenable to replication on an industrial scale.

Path-Constrained Motion Planning for Robotics Based on Kinematic Constraints

NARCIS (Netherlands)

Dijk, van N.J.M.; Wouw, van de N.; Pancras, W.C.M.; Nijmeijer, H.

2007-01-01

Common robotic tracking tasks consist of motions along predefined paths. The design of time-optimal path-constrained trajectories for robotic applications is discussed in this paper. To increase industrial applicability, the proposed method accounts for robot kinematics together with actuator

Current advances on polynomial resultant formulations

Science.gov (United States)

Sulaiman, Surajo; Aris, Nor'aini; Ahmad, Shamsatun Nahar

2017-08-01

Availability of computer algebra systems (CAS) lead to the resurrection of the resultant method for eliminating one or more variables from the polynomials system. The resultant matrix method has advantages over the Groebner basis and Ritt-Wu method due to their high complexity and storage requirement. This paper focuses on the current resultant matrix formulations and investigates their ability or otherwise towards producing optimal resultant matrices. A determinantal formula that gives exact resultant or a formulation that can minimize the presence of extraneous factors in the resultant formulation is often sought for when certain conditions that it exists can be determined. We present some applications of elimination theory via resultant formulations and examples are given to explain each of the presented settings.

On Efficient Deployment of Wireless Sensors for Coverage and Connectivity in Constrained 3D Space.

Science.gov (United States)

Wu, Chase Q; Wang, Li

2017-10-10

Sensor networks have been used in a rapidly increasing number of applications in many fields. This work generalizes a sensor deployment problem to place a minimum set of wireless sensors at candidate locations in constrained 3D space to k -cover a given set of target objects. By exhausting the combinations of discreteness/continuousness constraints on either sensor locations or target objects, we formulate four classes of sensor deployment problems in 3D space: deploy sensors at Discrete/Continuous Locations (D/CL) to cover Discrete/Continuous Targets (D/CT). We begin with the design of an approximate algorithm for DLDT and then reduce DLCT, CLDT, and CLCT to DLDT by discretizing continuous sensor locations or target objects into a set of divisions without sacrificing sensing precision. Furthermore, we consider a connected version of each problem where the deployed sensors must form a connected network, and design an approximation algorithm to minimize the number of deployed sensors with connectivity guarantee. For performance comparison, we design and implement an optimal solution and a genetic algorithm (GA)-based approach. Extensive simulation results show that the proposed deployment algorithms consistently outperform the GA-based heuristic and achieve a close-to-optimal performance in small-scale problem instances and a significantly superior overall performance than the theoretical upper bound.

Structural Optimization with Reliability Constraints

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Thoft-Christensen, Palle

1986-01-01

During the last 25 years considerable progress has been made in the fields of structural optimization and structural reliability theory. In classical deterministic structural optimization all variables are assumed to be deterministic. Due to the unpredictability of loads and strengths of actual......]. In this paper we consider only structures which can be modelled as systems of elasto-plastic elements, e.g. frame and truss structures. In section 2 a method to evaluate the reliability of such structural systems is presented. Based on a probabilistic point of view a modern structural optimization problem...... is formulated in section 3. The formulation is a natural extension of the commonly used formulations in determinstic structural optimization. The mathematical form of the optimization problem is briefly discussed. In section 4 two new optimization procedures especially designed for the reliability...

Artificial intelligence in pharmaceutical product formulation: neural computing

OpenAIRE

Svetlana Ibrić; Jelena Petrović; Jelena Parojčić; Zorica Djurić

2009-01-01

The properties of a formulation are determined not only by the ratios in which the ingredients are combined but also by the processing conditions. Although the relationships between the ingredient levels, processing conditions, and product performance may be known anecdotally, they can rarely be quantified. In the past, formulators tended to use statistical techniques to model their formulations, relying on response surfaces to provide a mechanism for optimazation. However, the optimization b...

Joint Chance-Constrained Dynamic Programming

Science.gov (United States)

Ono, Masahiro; Kuwata, Yoshiaki; Balaram, J. Bob

2012-01-01

This paper presents a novel dynamic programming algorithm with a joint chance constraint, which explicitly bounds the risk of failure in order to maintain the state within a specified feasible region. A joint chance constraint cannot be handled by existing constrained dynamic programming approaches since their application is limited to constraints in the same form as the cost function, that is, an expectation over a sum of one-stage costs. We overcome this challenge by reformulating the joint chance constraint into a constraint on an expectation over a sum of indicator functions, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the primal variables can be optimized by a standard dynamic programming, while the dual variable is optimized by a root-finding algorithm that converges exponentially. Error bounds on the primal and dual objective values are rigorously derived. We demonstrate the algorithm on a path planning problem, as well as an optimal control problem for Mars entry, descent and landing. The simulations are conducted using a real terrain data of Mars, with four million discrete states at each time step.

Resource Constrained Project Scheduling Subject to Due Dates: Preemption Permitted with Penalty

Directory of Open Access Journals (Sweden)

Behrouz Afshar-Nadjafi

2014-01-01

Full Text Available Extensive research works have been carried out in resource constrained project scheduling problem. However, scarce researches have studied the problems in which a setup cost must be incurred if activities are preempted. In this research, we investigate the resource constrained project scheduling problem to minimize the total project cost, considering earliness-tardiness and preemption penalties. A mixed integer programming formulation is proposed for the problem. The resulting problem is NP-hard. So, we try to obtain a satisfying solution using simulated annealing (SA algorithm. The efficiency of the proposed algorithm is tested based on 150 randomly produced examples. Statistical comparison in terms of the computational times and objective function indicates that the proposed algorithm is efficient and effective.

Numerically Optimized Uniformly Most Powerful Alphabets for Hierarchical-Decode-and-Forward Two-Way Relaying

Directory of Open Access Journals (Sweden)

M. Hekrdla

2011-01-01

Full Text Available We address the issue of the parametric performance of the Hierarchical-Decode-and-Forward (HDF strategy in a wireless 2-way relay channel. Promising HDF, representing the concept of wireless network coding, performs well with a pre-coding strategy that requires Channel State Information (CSI on the transceiver side. Assuming a practical case when CSI is available only on the receiver side and the channel conditions do not allow adaptive strategies, the parametrization causes significant HDF performance degradation for some modulation alphabets. Alphabets that are robust to the parametrization (denoted Uniformly Most Powerful (UMP have already been proposed restricting on the class of non-linear multi-dimensional frequency modulations. In this work, we focus on the general design of unrestricted UMP alphabets. We formulate an optimization problem which is solved by standard non-linear convex constrained optimization algorithms, particularly by Nelder-Mead global optimization search, which is further refined by the local interior-pointsmethod.

System modelling and online optimal management of MicroGrid using Mesh Adaptive Direct Search

Energy Technology Data Exchange (ETDEWEB)

Mohamed, Faisal A. [Department of Electrical Engineering, Omar Al-Mukhtar University, P.O. Box 919, El-Bieda (Libya); Koivo, Heikki N. [Department of Automation and Systems Technology, Helsinki University of Technology, P.O. Box 5500, FIN-02015 HUT (Finland)

2010-06-15

This paper presents a generalized formulation to determine the optimal operating strategy and cost optimization scheme for a MicroGrid. Prior to the optimization of the MicroGrid itself, models for the system components are determined using real data. The proposed cost function takes into consideration the costs of the emissions, NO{sub x}, SO{sub 2}, and CO{sub 2}, start-up costs, as well as the operation and maintenance costs. A daily income and outgo from sold or purchased power is also added. The MicroGrid considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array, a fuel cell, and a battery storage. In this work, the Mesh Adaptive Direct Search (MADS) algorithm is used to minimize the cost function of the system while constraining it to meet the customer demand and safety of the system. In comparison with previously proposed techniques, a significant reduction is obtained. (author)

Optimal Attack Strategies Subject to Detection Constraints Against Cyber-Physical Systems

International Nuclear Information System (INIS)

Chen, Yuan; Kar, Soummya; Moura, Jose M. F.

2017-01-01

This paper studies an attacker against a cyberphysical system (CPS) whose goal is to move the state of a CPS to a target state while ensuring that his or her probability of being detected does not exceed a given bound. The attacker’s probability of being detected is related to the nonnegative bias induced by his or her attack on the CPS’s detection statistic. We formulate a linear quadratic cost function that captures the attacker’s control goal and establish constraints on the induced bias that reflect the attacker’s detection-avoidance objectives. When the attacker is constrained to be detected at the false-alarm rate of the detector, we show that the optimal attack strategy reduces to a linear feedback of the attacker’s state estimate. In the case that the attacker’s bias is upper bounded by a positive constant, we provide two algorithms – an optimal algorithm and a sub-optimal, less computationally intensive algorithm – to find suitable attack sequences. Lastly, we illustrate our attack strategies in numerical examples based on a remotely-controlled helicopter under attack.

Vitamin E TPGS emulsified vinorelbine bitartrate loaded solid lipid nanoparticles (SLN): Formulation development, optimization and in vitro characterization.

Science.gov (United States)

Maurya, Lakshmi; Rajamanickam, Vijayakumar Mahalingam; Narayan, Gopeshwar; Singh, Sanjay

2018-04-08

Vinorelbine bitartrate (VRL), a semi synthetic vinca alkaloid approved for breast cancer, has been proved to beneficial as first line and subsequent therapies. However, it's hydrophilic and thermo labile nature provides hindrance to oral clinical translation. The current work focused on the application of DOE a modern statistical optimization tool for the development and optimization of a solid lipid nanoparticle (SLN) formulation that can encapsulate hydrophilic and thermolabile Vinorelbine bitartrate (VRL) to a maximum extent without compromising integrity and anticancer activity of the drug. SLNs were prepared by solvent diffusion technique employing Taguchi orthogonal array design with optimized formulation and process variables. The emulsifying nature and low melting point of glyceryl mono-oleate (GMO) were exploited to enhance entrapment and minimizing temperature associated degradation, respectively. Moreover, two types of surfactants, Vitamin E TPGS (TPGS) and Poloxamer-188 were utilized to obtain TPGS-VRL-SLNs and PL-VRL-SLNs, respectively. The SLNs were characterized for various physicochemical properties, in-vitro drug release kinetics and anticancer activity by MTT assay on MCF-7 cancer cell lines. The SLNs were found to be spherical in shape with entrapment efficiency (EE) up to 58 %. In-vitro release studies showed biphasic release pattern following Korsemeyer peppas model with fickian release kinetics. Results of MTT assay revealed that TPGS-VRL-SLNs and PL-VRL-SLNs were 39.5 and 18.5 fold more effective, respectively, compared to the pristine VRL. DOE approach was successfully applied for the development of VRL-SLNs. Enhanced entrapment and anticancer efficacy of TPGS-VRL-SLN can be attributed to emulsifying nature of GMO and inherent cytotoxic nature of TPGS, respectively, which synergizes with VRL. Therefore, TPGS associated SLNs may be potential carrier in cancer chemotherapeutics. Copyright© Bentham Science Publishers; For any queries, please

Real-Time Optimization for use in a Control Allocation System to Recover from Pilot Induced Oscillations

Science.gov (United States)

Leonard, Michael W.

2013-01-01

Integration of the Control Allocation technique to recover from Pilot Induced Oscillations (CAPIO) System into the control system of a Short Takeoff and Landing Mobility Concept Vehicle simulation presents a challenge because the CAPIO formulation requires that constrained optimization problems be solved at the controller operating frequency. We present a solution that utilizes a modified version of the well-known L-BFGS-B solver. Despite the iterative nature of the solver, the method is seen to converge in real time with sufficient reliability to support three weeks of piloted runs at the NASA Ames Vertical Motion Simulator (VMS) facility. The results of the optimization are seen to be excellent in the vast majority of real-time frames. Deficiencies in the quality of the results in some frames are shown to be improvable with simple termination criteria adjustments, though more real-time optimization iterations would be required.

A targeted liposome delivery system for combretastatin A4: formulation optimization through drug loading and in vitro release studies.

Science.gov (United States)

Nallamothu, Ramakrishna; Wood, George C; Kiani, Mohammad F; Moore, Bob M; Horton, Frank P; Thoma, Laura A

2006-01-01

Efficient liposomal therapeutics require high drug loading and low leakage. The objective of this study is to develop a targeted liposome delivery system for combretastatin A4 (CA4), a novel antivascular agent, with high loading and stable drug encapsulation. Liposomes composed of hydrogenated soybean phosphatidylcholine (HSPC), cholesterol, and distearoyl phosphoethanolamine-PEG-2000 conjugate (DSPE-PEG) were prepared by the lipid film hydration and extrusion process. Cyclic arginine-glycine-aspartic acid (RGD) peptides with affinity for alphav beta3-integrins overexpressed on tumor vascular endothelial cells were coupled to the distal end of polyethylene glycol (PEG) on the liposomes sterically stabilized with PEG (non-targeted liposomes; LCLs). Effect of lipid concentration, drug-to-lipid ratio, cholesterol, and DSPE-PEG content in the formulation on CA4 loading and its release from the liposomes was studied. Total liposomal CA4 levels obtained increased with increasing lipid concentration in the formulation. As the drug-to-lipid ratio increased from 10:100 to 20:100, total drug in the liposome formulation increased from 1.05+/-0.11 mg/mL to 1.55+/-0.13 mg/mL, respectively. When the drug-to-lipid ratio was further raised to 40:100, the total drug in liposome formulation did not increase, but the amount of free drug increased significantly, thereby decreasing the percent of entrapped drug. Increasing cholesterol content in the formulation decreased drug loading. In vitro drug leakage from the liposomes increased with increase in drug-to-lipid ratio or DSPE-PEG content in the formulation; whereas increasing cholesterol content of the formulation up to 30 mol-percent, decreased CA4 leakage from the liposomes. Ligand coupling to the liposome surface increased drug leakage as a function of ligand density. Optimized liposome formulation with 100 mM lipid concentration, 20:100 drug-to-lipid ratio, 30 mol-percent cholesterol, 4 mol-percent DSPE-PEG, and 1 mol

Animal Diet Formulation with Floating Price

Directory of Open Access Journals (Sweden)

S.H Nasseri

2016-12-01

Full Text Available In the process of milk production, the highest cost relates to animal feed. Based on reports provided by the experts, around seventy percent of dairy livestock costs included feed costs. In order to minimize the total price of livestock feed, according to the limits of feed sources in each region or season, and also the transportation and maintenance costs and ultimately milk price reduction, optimization of the livestock nutrition program is an essential issue. Because of the uncertainty and lack of precision in the optimal food ration done with existing methods based on linear programming, there is a need to use appropriate methods to meet this purpose. Therefore, in this study formulation of completely mixed nutrient diets of dairy cows is done by using a fuzzy linear programming in early lactation. Application of fuzzy optimization method and floating price make it possible to formulate and change the completely mixed diets with adequate safety margins. Therefore, applications of fuzzy methods in feed rations of dairy cattle are recommended to optimize the diets. Obviously, it would be useful to design suitable software, which provides the possibility of using floating prices to set feed rations by the use of fuzzy optimization method.

Optimization of the production process of hybrid and multivalent formulation Bombesin/RGD for the opportune detection of breast cancer

International Nuclear Information System (INIS)

Robles M, M.

2013-01-01

The radiopharmaceuticals of third generation are used in nuclear medicine to obtain images of specific molecular targets, and they are unique in their capacity to detect in vivo specific biochemical sites as receptors that are over-expressed in diverse illness. In cancer cells several types of receptors are over-expressed, as the integrin s α(v)β(3) and α(v)β(5) that specifically recognize the sequence RGD (Arginine-Glycin-Ac. Aspartic) and gastrin-releasing peptide that recognizes specifically to the peptide Lys 3 -Bombesin. The integrin s α(v)β(3) and α(v)β(5) are involved in the tumor angio genesis processes and the gastrin-releasing peptide is over-expressed in breast and prostate cancer. The molecular recognition of the specific receptors is the basis to be utilized as targets of the radiopharmaceuticals 99m Tc-HYNIC-Bombesin and 99m Tc-HYNIC-RGD. In this work was developed a lyophilized pharmaceutical formulation effective, stable and safe for the simultaneous obtaining of the radiopharmaceuticals 99m Tc-HYNIC-Bombesin ( 99m Tc-EDDA/HYNIC-Lys 3 -Bombesin) and 99m Tc-HYNIC-RGD ( 99m Tc EDDA/HYNIC-E-[c(RGDfK)] 2 ). Later on the production process of the product HYNIC-Bombesin/RGD-Sn was optimized using a factorial design and the formulation was transferred to the production plant of radiopharmaceuticals of the Instituto Nacional de Investigaciones Nucleares (ININ). The optimized formulation is described in the following chart: HYNIC-[Lys 3 ]-Bombesin - 12.5 μg; HYNIC-E-c[RGDfK] 2 - 12.5 μg; Stannous chloride (SnCl 2 ) - 20 μg; Ethylenediamine diacetic acid (EDDA) - 10 mg; N-tris(hydroxymethyl)methyl glycin (Tricine) - 20 mg; Mannitol - 50 mg. The production process was validated and were carried out the stability studies under refrigeration conditions. (Author)

Optimization of Regional Geodynamic Models for Mantle Dynamics

Science.gov (United States)

Knepley, M.; Isaac, T.; Jadamec, M. A.

2016-12-01

The SubductionGenerator program is used to construct high resolution, 3D regional thermal structures for mantle convection simulations using a variety of data sources, including sea floor ages and geographically referenced 3D slab locations based on seismic observations. The initial bulk temperature field is constructed using a half-space cooling model or plate cooling model, and related smoothing functions based on a diffusion length-scale analysis. In this work, we seek to improve the 3D thermal model and test different model geometries and dynamically driven flow fields using constraints from observed seismic velocities and plate motions. Through a formal adjoint analysis, we construct the primal-dual version of the multi-objective PDE-constrained optimization problem for the plate motions and seismic misfit. We have efficient, scalable preconditioners for both the forward and adjoint problems based upon a block preconditioning strategy, and a simple gradient update is used to improve the control residual. The full optimal control problem is formulated on a nested hierarchy of grids, allowing a nonlinear multigrid method to accelerate the solution.

Thermal analysis and performance optimization of a solar water heater flat plate collector: Application to Tetouan (Morocco)

International Nuclear Information System (INIS)

Dagdougui, Hanane; Ouammi, Ahmed; Robba, Michela; Sacile, Roberto

2011-01-01

The development of sustainable energy services like the supply of heating water may face a trade-off with a comfortable quality of life, especially in the winter season where suitable strategies to deliver an effective service are required. This paper investigates the heat transfer process as well as the thermal behavior of a flat plate collector evaluating different cover configurations. This investigation is performed according to a two-folded approach. Firstly, a complete model is formulated and implemented taking into account various modes of heat transfer in the collector. The goal is to investigate the impact of the number and types of covers on the top heat loss and the related thermal performance in order to support decision makers about the most cost-effective design. The proposed model can also be used to investigate the effect of the different parameters which may affect the performance of the collector. Secondly, a two objective constrained optimization model has been formulated and implemented to evaluate the optimality of different design approaches. The goal is to support decision makers in the definition of the optimal water flow and of the optimal collector flat area in order to give a good compromise between the collector efficiency and the output water temperature. The overall methodology has been tested on environmental data (temperature and irradiation) which are characteristic of Tetouan (Morocco). (author)

Formulation and optimization of doxorubicin loaded polymeric nanoparticles using Box-Behnken design: ex-vivo stability and in-vitro activity.

Science.gov (United States)

Shaikh, Muhammad Vaseem; Kala, Manika; Nivsarkar, Manish

2017-03-30

Biodegradable nanoparticles (NPs) have gained tremendous interest for targeting chemotherapeutic drugs to the tumor environment. Inspite of several advances sufficient encapsulation along with the controlled release and desired size range have remained as considerable challenges. Hence, the present study examines the formulation optimization of doxorubicin loaded PLGA NPs (DOX-PLGA-NPs), prepared by single emulsion method for cancer targeting. Critical process parameters (CPP) were selected by initial screening. Later, Box-Behnken design (BBD) was used for analyzing the effect of the selected CPP on critical quality attributes (CQA) and to generate a design space. The optimized formulation was stabilized by lyophilization and was used for in-vitro drug release and in-vitro activity on A549 cell line. Moreover, colloidal stability of the NPs in the biological milieu was assessed. Amount of PLGA and PVA, oil:water ratio and sonication time were the selected independent factors for BBD. The statistical data showed that a quadratic model was fitted to the data obtained. Additionally, the lack of fit values for the models was not significant. The delivery system showed sustained release behavior over a period of 120h and was governed by Fickian diffusion. The multipoint analysis at 24, 48 and 72h showed gradual reduction in IC50 value of DOX-PLGA-NPs (p<0.05, Fig. 9). DOX-PLGA-NPs were found to be stable in the biological fluids indicating their in-vivo applicability. In conclusion, optimization of the DOX-PLGA-NPs by BBD yielded in a promising drug carrier for doxorubicin that could provide a novel treatment modality for cancer. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimization with PDE constraints ESF networking program 'OPTPDE'

CERN Document Server

2014-01-01

This book on PDE Constrained Optimization contains contributions on the mathematical analysis and numerical solution of constrained optimal control and optimization problems where a partial differential equation (PDE) or a system of PDEs appears as an essential part of the constraints. The appropriate treatment of such problems requires a fundamental understanding of the subtle interplay between optimization in function spaces and numerical discretization techniques and relies on advanced methodologies from the theory of PDEs and numerical analysis as well as scientific computing. The contributions reflect the work of the European Science Foundation Networking Programme ’Optimization with PDEs’ (OPTPDE).

Model-based dynamic control and optimization of gas networks

Energy Technology Data Exchange (ETDEWEB)

Hofsten, Kai

2001-07-01

This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum

Optimization and physicochemical characterization of a cationic lipid-phosphatidylcholine mixed emulsion formulated as a highly efficient vehicle that facilitates adenoviral gene transfer

Directory of Open Access Journals (Sweden)

Kim SY

2017-10-01

Full Text Available Soo-Yeon Kim,1,2 Sang-Jin Lee,2 Jin-Ki Kim,3 Han-Gon Choi,3 Soo-Jeong Lim1 1Department of Bioscience and Bioengineering, Sejong University, Seoul, Kwangjin-gu, Seoul, 2Immunotherapeutics Branch, Research Institute, National Cancer Center, Ilsandong-gu, Goyang-si, Gyeonggi-do, 3College of Pharmacy & Institute of Pharmaceutical Science and Technology, Hanyang University, Sangnok-gu, Ansan, Republic of Korea Abstract: Cationic lipid-based nanoparticles enhance viral gene transfer by forming electrostatic complexes with adenoviral vectors. We recently demonstrated the superior complexation capabilities of 1,2-dioleoyl-3-trimethylammonium propane (DOTAP emulsion compared with a liposomal counterpart but the cytotoxicity of DOTAP emulsions remained a challenge. The present study is aimed at formulating an emulsion capable of acting as a highly effective viral gene transfer vehicle with reduced cytotoxicity and to physicochemically characterize the structures of virus-emulsion complexes in comparison with virus–liposome complexes when the only difference between emulsions and liposomes was the presence or absence of inner oil core. The emulsion formulation was performed by 1 reducing the content of DOTAP while increasing the content of zwitterionic lipid 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC, and 2 optimizing the oil content. The complexation capability of formulated DOTAP:DMPC mixed emulsions was similar to those of emulsions containing DOTAP alone while displaying significantly lower cytotoxicity. The complexation capabilities of the DOTAP:DMPC mixed emulsion were serum-compatible and were monitored in a variety of cell types, whereas its liposomal counterpart was totally ineffective. Characterization by scanning electron microscopy, transmission electron microscopy, atomic force microscopy, and dynamic light scattering studies indicated that the optimized emulsions spontaneously surrounded the virus particles to generate emulsions that

Using information Theory in Optimal Test Point Selection for Health Management in NASA's Exploration Vehicles

Science.gov (United States)

Mehr, Ali Farhang; Tumer, Irem

2005-01-01

In this paper, we will present a new methodology that measures the "worth" of deploying an additional testing instrument (sensor) in terms of the amount of information that can be retrieved from such measurement. This quantity is obtained using a probabilistic model of RLV's that has been partially developed in the NASA Ames Research Center. A number of correlated attributes are identified and used to obtain the worth of deploying a sensor in a given test point from an information-theoretic viewpoint. Once the information-theoretic worth of sensors is formulated and incorporated into our general model for IHM performance, the problem can be formulated as a constrained optimization problem where reliability and operational safety of the system as a whole is considered. Although this research is conducted specifically for RLV's, the proposed methodology in its generic form can be easily extended to other domains of systems health monitoring.

Formulation and Optimization of Oral Mucoadhesive Patches of Myrtus Communis by Box Behnken Design.

Science.gov (United States)

Hashemi, Mahbubeh; Ramezani, Vahid; Seyedabadi, Mohammad; Ranjbar, Ali Mohamad; Jafari, Hossein; Honarvar, Mina; Fanaei, Hamed

2017-09-01

Purpose: Recurrent aphthous stomatitis (RAS) is the most common painful ulcerative disease of oral mucosa happening in ~20% of people. Aimed to develop Myrtus communis L. (Myrtle) containing oral patches, we applied box-behnken design to evaluate the effect of polymers such as Polyvinyl pyrrolidone (PVP), Gelatin, Methylcellulose (MC) and Pectin. Methods: The patches properties such as tensile strength, folding endurance, swelling index, thickness, mucoadhesive strength and the pattern of myrtle release were evaluated as dependent variables. Then, the model was adjusted according to the best fitted equation with box behnken design. Results: The results indicated that preparation of myrtle patch with hydrophilic polymers showed the disintegration time up to 24h and more. Using of polyvinyl pyrrolidone as a water soluble polymer and a pore-former polymer led to faster release of soluble materials from the patch to 29 (min -1 ). Also it decreases swelling index by increasing the patch disintegration. Gelatin and Pectin, with rigid matrix and water interaction properties, decreased the swelling ratio. Pectin increased the tensile strength, but gelatin produced an opposite effect. Thinner Myrtle patch (about 28µm) was obtained by formulation of methyl cellulose with equal ratio with polyvinyl pyrrolidone or gelatin. Conclusion: Altogether, the analysis showed that the optimal formulation was achieved with of 35.04 mg of Gelatin, 7.22 mg of Pectin, 7.20 mg of polyvinyl pyrrolidone, 50.52 mg of methyl cellulose and 20 mg of Myrtle extract.

THE STUDY ON THE EFFECT OF FORMULATION VARIABLES ON IN VITRO FLOATING TIME AND THE RELEASE PROPERTIES OF A FLOATING DRUG DELIVERY SYSTEM BY A STATISTICAL OPTIMIZATION TECHNIQUE

Directory of Open Access Journals (Sweden)

C. NARENDRA

2008-03-01

Full Text Available The present investigation concerns the evaluation of the effect of formulation variables on in vitro floating time and the release properties in developing a floating drug delivery system (FDDS containing a highly water soluble drug metoprolol tartrate (MT in the presence of a gas generating agent. A 32 full factorial design was employed in formulating the FDDS containing hydroxyl propylmethylcellulose (HPMC K4M and sodium carboxymethylcellulose (NaCMC as swellable polymers. Drug-to-polymer ratio and polymer-to-polymer ratio were included as independent variables. The main effect and the interaction terms were quantitatively evaluated by a quadratic model to predict formulations with the floating time desired, and the release properties. It was found that only drug-to-polymer ratio and its quadratic term were found to be significantly affective for all the response variables. Non-Fickian transport was confirmed as a release mechanism from the optimized formulations. The desirability function was used to optimize the response variables, each having a different target, and the observed responses were highly agreed with experimental values. The results demonstrate the feasibility of the model in the development of FDDS containing a highly water-soluble drug MT.

Optimal Control of Partially Miscible Two-Phase Flow with Applications to Subsurface CO2 Sequestration

KAUST Repository

Simon, Moritz; Ulbrich, Michael

2013-01-01

Motivated by applications in subsurface CO2 sequestration, we investigate constrained optimal control problems with partially miscible two-phase flow in porous media. The objective is, e.g., to maximize the amount of trapped CO2 in an underground reservoir after a fixed period of CO2 injection, where the time-dependent injection rates in multiple wells are used as control parameters. We describe the governing two-phase two-component Darcy flow PDE system and formulate the optimal control problem. For the discretization we use a variant of the BOX method, a locally conservative control-volume FE method. The timestep-wise Lagrangian of the control problem is implemented as a functional in the PDE toolbox Sundance, which is part of the HPC software Trilinos. The resulting MPI parallelized Sundance state and adjoint solvers are linked to the interior point optimization package IPOPT. Finally, we present some numerical results in a heterogeneous model reservoir.

QUADRO: A SUPERVISED DIMENSION REDUCTION METHOD VIA RAYLEIGH QUOTIENT OPTIMIZATION.

Science.gov (United States)

Fan, Jianqing; Ke, Zheng Tracy; Liu, Han; Xia, Lucy

We propose a novel Rayleigh quotient based sparse quadratic dimension reduction method-named QUADRO (Quadratic Dimension Reduction via Rayleigh Optimization)-for analyzing high-dimensional data. Unlike in the linear setting where Rayleigh quotient optimization coincides with classification, these two problems are very different under nonlinear settings. In this paper, we clarify this difference and show that Rayleigh quotient optimization may be of independent scientific interests. One major challenge of Rayleigh quotient optimization is that the variance of quadratic statistics involves all fourth cross-moments of predictors, which are infeasible to compute for high-dimensional applications and may accumulate too many stochastic errors. This issue is resolved by considering a family of elliptical models. Moreover, for heavy-tail distributions, robust estimates of mean vectors and covariance matrices are employed to guarantee uniform convergence in estimating non-polynomially many parameters, even though only the fourth moments are assumed. Methodologically, QUADRO is based on elliptical models which allow us to formulate the Rayleigh quotient maximization as a convex optimization problem. Computationally, we propose an efficient linearized augmented Lagrangian method to solve the constrained optimization problem. Theoretically, we provide explicit rates of convergence in terms of Rayleigh quotient under both Gaussian and general elliptical models. Thorough numerical results on both synthetic and real datasets are also provided to back up our theoretical results.

Optimal dispatch strategy for the agile virtual power plant

DEFF Research Database (Denmark)

Petersen, Mette Højgaard; Bendtsen, Jan Dimon; Stoustrup, Jakob

2012-01-01

The introduction of large ratios of renewable energy into the existing power system is complicated by the inherent variability of production technologies, which harvest energy from wind, sun and waves. Fluctuations of renewable power production can be predicted to some extent, but the assumption...... of perfect prediction is unrealistic. This paper therefore introduces the Agile Virtual Power Plant. The Agile Virtual Power Plant assumes that the base load production planning based on best available knowledge is already given, so imbalances cannot be predicted. Consequently the Agile Virtual Power Plant...... attempts to preserve maneuverability (stay agile) rather than optimize performance according to predictions. In this paper the imbalance compensation problem for an Agile Virtual Power Plant is formulated. It is proved formally, that when local units are power and energy constrained integrators a dispatch...

Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs

International Nuclear Information System (INIS)

Martorell, S.; Sanchez, A.; Carlos, S.; Serradell, V.

2002-01-01

proved their capability to solve these kinds of problems, although GAs are essentially unconstrained optimization techniques that require adaptation for the intended constrained optimization, where TS and M-related parameters act as the decision variables. This paper encompasses, in , the problem formulation where the objective function is derived and constraints that apply in the simultaneous and multi-criteria optimization of TS and M activities based on risk and cost functions at system level. Fundamentals of a steady-state GA (SSGA) as an optimization method is given in , which satisfies the above requirements, paying special attention to its use in constrained optimization problems. A simple case of application is provided in , focussing on TS and M-related parameters optimization for a stand-by safety-related system, which demonstrates how the SSGA-based optimization approach works at the system level, providing practical and complete alternatives beyond only mathematical solutions to a particular parameter. Finally, presents our conclusions

Shield Optimization and Formulation of Regression Equations for Split-Ring Resonator

Directory of Open Access Journals (Sweden)

Tahir Ejaz

2016-01-01

Full Text Available Microwave resonators are widely used for numerous applications including communication, biomedical and chemical applications, material testing, and food grading. Split-ring resonators in both planar and nonplanar forms are a simple structure which has been in use for several decades. This type of resonator is characterized with low cost, ease of fabrication, moderate quality factor, low external noise interference, high stability, and so forth. Due to these attractive features and ease in handling, nonplanar form of structure has been utilized for material characterization in 1–5 GHz range. Resonant frequency and quality factor are two important parameters for determination of material properties utilizing perturbation theory. Shield made of conducting material is utilized to enclose split-ring resonator which enhances quality factor. This work presents a novel technique to develop shield around a predesigned nonplanar split-ring resonator to yield optimized quality factor. Based on this technique and statistical analysis regression equations have also been formulated for resonant frequency and quality factor which is a major outcome of this work. These equations quantify dependence of output parameters on various factors of shield made of different materials. Such analysis is instrumental in development of devices/designs where improved/optimum result is required.

Stepwise multi-criteria optimization for robotic radiosurgery

International Nuclear Information System (INIS)

Schlaefer, A.; Schweikard, A.

2008-01-01

Achieving good conformality and a steep dose gradient around the target volume remains a key aspect of radiosurgery. Clearly, this involves a trade-off between target coverage, conformality of the dose distribution, and sparing of critical structures. Yet, image guidance and robotic beam placement have extended highly conformal dose delivery to extracranial and moving targets. Therefore, the multi-criteria nature of the optimization problem becomes even more apparent, as multiple conflicting clinical goals need to be considered coordinate to obtain an optimal treatment plan. Typically, planning for robotic radiosurgery is based on constrained optimization, namely linear programming. An extension of that approach is presented, such that each of the clinical goals can be addressed separately and in any sequential order. For a set of common clinical goals the mapping to a mathematical objective and a corresponding constraint is defined. The trade-off among the clinical goals is explored by modifying the constraints and optimizing a simple objective, while retaining feasibility of the solution. Moreover, it becomes immediately obvious whether a desired goal can be achieved and where a trade-off is possible. No importance factors or predefined prioritizations of clinical goals are necessary. The presented framework forms the basis for interactive and automated planning procedures. It is demonstrated for a sample case that the linear programming formulation is suitable to search for a clinically optimal treatment, and that the optimization steps can be performed quickly to establish that a Pareto-efficient solution has been found. Furthermore, it is demonstrated how the stepwise approach is preferable compared to modifying importance factors

Continuous dynamic assimilation of the inner region data in hydrodynamics modelling: optimization approach

Directory of Open Access Journals (Sweden)

F. I. Pisnitchenko

2008-11-01

Full Text Available In meteorological and oceanological studies the classical approach for finding the numerical solution of the regional model consists in formulating and solving a Cauchy-Dirichlet problem. The boundary conditions are obtained by linear interpolation of coarse-grid data provided by a global model. Errors in boundary conditions due to interpolation may cause large deviations from the correct regional solution. The methods developed to reduce these errors deal with continuous dynamic assimilation of known global data available inside the regional domain. One of the approaches of this assimilation procedure performs a nudging of large-scale components of regional model solution to large-scale global data components by introducing relaxation forcing terms into the regional model equations. As a result, the obtained solution is not a valid numerical solution to the original regional model. Another approach is the use a four-dimensional variational data assimilation procedure which is free from the above-mentioned shortcoming. In this work we formulate the joint problem of finding the regional model solution and data assimilation as a PDE-constrained optimization problem. Three simple model examples (ODE Burgers equation, Rossby-Oboukhov equation, Korteweg-de Vries equation are considered in this paper. Numerical experiments indicate that the optimization approach can significantly improve the precision of the regional solution.

Reliability-Based Optimization in Structural Engineering

DEFF Research Database (Denmark)

Enevoldsen, I.; Sørensen, John Dalsgaard

1994-01-01

In this paper reliability-based optimization problems in structural engineering are formulated on the basis of the classical decision theory. Several formulations are presented: Reliability-based optimal design of structural systems with component or systems reliability constraints, reliability...

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

Science.gov (United States)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

Nanoemulsion-based gel formulation of diclofenac diethylamine: design, optimization, rheological behavior and in vitro diffusion studies.

Science.gov (United States)

Hamed, Rania; Basil, Marwa; AlBaraghthi, Tamadur; Sunoqrot, Suhair; Tarawneh, Ola

2016-12-01

Chronic oral administration of the non-steroidal anti-inflammatory drug, diclofenac diethylamine (DDEA), is often associated with gastrointestinal ulcers and bleeding. As an alternative to oral administration, a nanoemulsion-based gel (NE gel) formulation of DDEA was developed for topical administration. An optimized formulation for the o/w nanoemulsion of oil, surfactant and cosurfactant was selected based on nanoemulsion mean droplet size, clarity, stability, and flowability, and incorporated into the gelling agent Carbopol® 971P. Rheological studies of the DDEA NE gel were conducted and compared to those of conventional DDEA gel and emulgel. The three gels exhibited an elastic behavior, where G' dominated G″ at all frequencies, indicating the formation of strong gels. NE gel exhibited higher G' values than conventional gel and emulgel, which indicated the formation of a stronger gel network. Strat-M® membrane, a synthetic membrane with diffusion characteristics that are well correlated to human skin, was used for the in vitro diffusion studies. The release of DDEA from conventional gel, emulgel and NE gel showed a controlled release pattern over 12 h, which was consistent with the rheological properties of the gels. DDEA release kinetics from the three gels followed super case II transport as fitted by Korsmeyer-Peppas model.

First order sensitivity analysis of flexible multibody systems using absolute nodal coordinate formulation

International Nuclear Information System (INIS)

Pi Ting; Zhang Yunqing; Chen Liping

2012-01-01

Design sensitivity analysis of flexible multibody systems is important in optimizing the performance of mechanical systems. The choice of coordinates to describe the motion of multibody systems has a great influence on the efficiency and accuracy of both the dynamic and sensitivity analysis. In the flexible multibody system dynamics, both the floating frame of reference formulation (FFRF) and absolute nodal coordinate formulation (ANCF) are frequently utilized to describe flexibility, however, only the former has been used in design sensitivity analysis. In this article, ANCF, which has been recently developed and focuses on modeling of beams and plates in large deformation problems, is extended into design sensitivity analysis of flexible multibody systems. The Motion equations of a constrained flexible multibody system are expressed as a set of index-3 differential algebraic equations (DAEs), in which the element elastic forces are defined using nonlinear strain-displacement relations. Both the direct differentiation method and adjoint variable method are performed to do sensitivity analysis and the related dynamic and sensitivity equations are integrated with HHT-I3 algorithm. In this paper, a new method to deduce system sensitivity equations is proposed. With this approach, the system sensitivity equations are constructed by assembling the element sensitivity equations with the help of invariant matrices, which results in the advantage that the complex symbolic differentiation of the dynamic equations is avoided when the flexible multibody system model is changed. Besides that, the dynamic and sensitivity equations formed with the proposed method can be efficiently integrated using HHT-I3 method, which makes the efficiency of the direct differentiation method comparable to that of the adjoint variable method when the number of design variables is not extremely large. All these improvements greatly enhance the application value of the direct differentiation

PNL vitrification technology development project glass formulation strategy for LLW vitrification

International Nuclear Information System (INIS)

Kim, D.; Hrma, P.R.; Westsik, J.H. Jr.

1996-03-01

This Glass Formulation Strategy describes development approaches to optimize glass compositions for Hanford's low-level waste vitrification between now and the projected low-level waste facility start-up in 2005. The objectives of the glass formulation task are to develop optimized glass compositions with satisfactory long-term durability, acceptable processing characteristics, adequate flexibility to handle waste variations, maximize waste loading to practical limits, and to develop methodology to respond to further waste variations

Design and optimization of color lookup tables on a simplex topology.

Science.gov (United States)

Monga, Vishal; Bala, Raja; Mo, Xuan

2012-04-01

An important computational problem in color imaging is the design of color transforms that map color between devices or from a device-dependent space (e.g., RGB/CMYK) to a device-independent space (e.g., CIELAB) and vice versa. Real-time processing constraints entail that such nonlinear color transforms be implemented using multidimensional lookup tables (LUTs). Furthermore, relatively sparse LUTs (with efficient interpolation) are employed in practice because of storage and memory constraints. This paper presents a principled design methodology rooted in constrained convex optimization to design color LUTs on a simplex topology. The use of n simplexes, i.e., simplexes in n dimensions, as opposed to traditional lattices, recently has been of great interest in color LUT design for simplex topologies that allow both more analytically tractable formulations and greater efficiency in the LUT. In this framework of n-simplex interpolation, our central contribution is to develop an elegant iterative algorithm that jointly optimizes the placement of nodes of the color LUT and the output values at those nodes to minimize interpolation error in an expected sense. This is in contrast to existing work, which exclusively designs either node locations or the output values. We also develop new analytical results for the problem of node location optimization, which reduces to constrained optimization of a large but sparse interpolation matrix in our framework. We evaluate our n -simplex color LUTs against the state-of-the-art lattice (e.g., International Color Consortium profiles) and simplex-based techniques for approximating two representative multidimensional color transforms that characterize a CMYK xerographic printer and an RGB scanner, respectively. The results show that color LUTs designed on simplexes offer very significant benefits over traditional lattice-based alternatives in improving color transform accuracy even with a much smaller number of nodes.

Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

Science.gov (United States)

Chen, Wei

2015-07-01

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

A brief introduction to continuous evolutionary optimization

CERN Document Server

Kramer, Oliver

2014-01-01

Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel ...

The bounds of feasible space on constrained nonconvex quadratic programming

Science.gov (United States)

Zhu, Jinghao

2008-03-01

This paper presents a method to estimate the bounds of the radius of the feasible space for a class of constrained nonconvex quadratic programmingsE Results show that one may compute a bound of the radius of the feasible space by a linear programming which is known to be a P-problem [N. Karmarkar, A new polynomial-time algorithm for linear programming, Combinatorica 4 (1984) 373-395]. It is proposed that one applies this method for using the canonical dual transformation [D.Y. Gao, Canonical duality theory and solutions to constrained nonconvex quadratic programming, J. Global Optimization 29 (2004) 377-399] for solving a standard quadratic programming problem.

Time-oriented experimental design method to optimize hydrophilic matrix formulations with gelation kinetics and drug release profiles.

Science.gov (United States)

Shin, Sangmun; Choi, Du Hyung; Truong, Nguyen Khoa Viet; Kim, Nam Ah; Chu, Kyung Rok; Jeong, Seong Hoon

2011-04-04

A new experimental design methodology was developed by integrating the response surface methodology and the time series modeling. The major purposes were to identify significant factors in determining swelling and release rate from matrix tablets and their relative factor levels for optimizing the experimental responses. Properties of tablet swelling and drug release were assessed with ten factors and two default factors, a hydrophilic model drug (terazosin) and magnesium stearate, and compared with target values. The selected input control factors were arranged in a mixture simplex lattice design with 21 experimental runs. The obtained optimal settings for gelation were PEO, LH-11, Syloid, and Pharmacoat with weight ratios of 215.33 (88.50%), 5.68 (2.33%), 19.27 (7.92%), and 3.04 (1.25%), respectively. The optimal settings for drug release were PEO and citric acid with weight ratios of 191.99 (78.91%) and 51.32 (21.09%), respectively. Based on the results of matrix swelling and drug release, the optimal solutions, target values, and validation experiment results over time were similar and showed consistent patterns with very small biases. The experimental design methodology could be a very promising experimental design method to obtain maximum information with limited time and resources. It could also be very useful in formulation studies by providing a systematic and reliable screening method to characterize significant factors in the sustained release matrix tablet. Copyright © 2011 Elsevier B.V. All rights reserved.