Dendritic Poly(ether imine) Based Gene Delivery Vector

The nonviral vector based gene delivery approach is attractive due to advantages associated with molecular-level modifications suitable for optimization of vector properties. In a new class of nonviral gene delivery systems, we herein report the potential of poly(ether Mine) (PETIM) dendrimers to mediate an effective gene delivery function. PETIM dendrimer, constituted with tertiary amine branch points, n-propyl ether linkers and primary amines at their peripheries, exhibits significantly reduced toxicities, over a broad concentration range. The dendrimer complexes pDNA effectively, protects DNA from endosomal damages, and delivers to the cell nucleus. Gene transfection studies, utilizing a reporter plasmid pEGFP-C1 and upon complexation with dendrimer, showed a robust expression of the encoded protein. The study shows that PETIM dendrimers are hitherto unknown novel gene delivery vectors, combining features of poly(ethylene imine)-based polymers and dendrimers, yet are relatively nontoxic and structurally precise.

Stochastic Approximation Algorithms for Constrained Optimization via Simulation

We develop four algorithms for simulation-based optimization under multiple inequality constraints. Both the cost and the constraint functions are considered to be long-run averages of certain state-dependent single-stage functions. We pose the problem in the simulation optimization framework by using the Lagrange multiplier method. Two of our algorithms estimate only the gradient of the Lagrangian, while the other two estimate both the gradient and the Hessian of it. In the process, we also develop various new estimators for the gradient and Hessian. All our algorithms use two simulations each. Two of these algorithms are based on the smoothed functional (SF) technique, while the other two are based on the simultaneous perturbation stochastic approximation (SPSA) method. We prove the convergence of our algorithms and show numerical experiments on a setting involving an open Jackson network. The Newton-based SF algorithm is seen to show the best overall performance.

Connectionist approach for global optimization

A connectionist approach for global optimization is proposed. The standard function set is tested. Results obtained, in the case of large scale problems, indicate excellent scalability of the proposed approach

Test Application Time Minimization for RAS using Basis Optimization of Column Decoder

Random Access Scan, which addresses individual flip-flops in a design using a memory array like row and column decoder architecture, has recently attracted widespread attention, due to its potential for lower test application time, test data volume and test power dissipation when compared to traditional Serial Scan. This is because typically only a very limited number of random ``care'' bits in a test response need be modified to create the next test vector. Unlike traditional scan, most flip-flops need not be updated. Test application efficiency can be further improved by organizing the access by word instead of by bit. In this paper we present a new decoder structure that takes advantage of basis vectors and linear algebra to further significantly optimize test application in RAS by performing the write operations on multiple bits consecutively. Simulations performed on benchmark circuits show an average of 2-3 times speed up in test write time compared to conventional RAS.

Chance constrained uncertain classification via robust optimization

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 to be intractable. The key novelty is in employing Bernstein bounding schemes to relax the CCP as a convex second order cone program whose solution is guaranteed to satisfy the probabilistic constraint. Prior to this work, only the Chebyshev based relaxations were exploited in learning algorithms. Bernstein bounds employ richer partial information and hence can be far less conservative than Chebyshev bounds. Due to this efficient modeling of uncertainty, the resulting classifiers achieve higher classification margins and hence better generalization. Methodologies for classifying uncertain test data points and error measures for evaluating classifiers robust to uncertain data are discussed. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle data uncertainty and outperform state-of-the-art in many cases.

Adaptive optimization of continuous bioreactor using neural network model

An adaptive optimization algorithm using backpropogation neural network model for dynamic identification is developed. The algorithm is applied to maximize the cellular productivity of a continuous culture of baker's yeast. The robustness of the algorithm is demonstrated in determining and maintaining the optimal dilution rate of the continuous bioreactor in presence of disturbances in environmental conditions and microbial culture characteristics. The simulation results show that a significant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditional dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is briefly discussed.

Constrained optimization of heat exchangers

Optimizing a shell and tube heat exchanger for a given duty is an important and relatively difficult task. There is a need for a simple, general and reliable method for realizing this task. The authors present here one such method for optimizing single phase shell-and-tube heat exchangers with given geometric and thermohydraulic constraints. They discuss the problem in detail. Then they introduce a basic algorithm for optimizing the exchanger. This algorithm is based on data from an earlier study of a large collection of feasible designs generated for different process specifications. The algorithm ensures a near-optimal design satisfying the given heat duty and geometric constraints. The authors also provide several sub-algorithms to satisfy imposed velocity limitations. They illustrate how useful these sub-algorithms are with several examples where the exchanger weight is minimized.

Optimization of cold and warm workability in 304 stainless steel using instability maps

The deformation characteristics of stainless steel type AISI 3O4 under compression in the temperature range 20 degrees C to 600 degrees C and strain-rate range 0.001 to 100 s(-1) have been studied with a view to characterizing the flow instabilities occurring in the microstructure. At strain rates less than 5 s(-1), 304 stainless steel exhibits flow localization, whereas dynamic strain aging occurs at intermediate temperatures and below 0.5 s(-1). At room temperatures and strain rates less than 10 s(-1), martensite formation is observed. To avoid the preceding microstructural instabilities, cold and warm working should be carried out at strain rates greater than 5 s(-1). The continuum criterion, developed on the basis of the principles of maximum rate of entropy production and separability of the dissipation function, predicts accurately all the preceding instability features.

Optimization of Helicopter Rotor Using Polynomial and Neural Network Metamodels

This study aims to determine optimal locations of dual trailing-edge flaps and blade stiffness to achieve minimum hub vibration levels in a helicopter, with low penalty in terms of required trailing-edge flap control power. An aeroelastic analysis based on finite elements in space and time is used in conjunction with an optimal control algorithm to determine the flap time history for vibration minimization. Using the aeroelastic analysis, it is found that the objective functions are highly nonlinear and polynomial response surface approximations cannot describe the objectives adequately. A neural network is then used for approximating the objective functions for optimization. Pareto-optimal points minimizing both helicopter vibration and flap power ale obtained using the response surface and neural network metamodels. The two metamodels give useful improved designs resulting in about 27% reduction in hub vibration and about 45% reduction in flap power. However, the design obtained using response surface is less sensitive to small perturbations in the design variables.

Optimization of cold and warm workability in stainless steel type AISI 316L using instability maps

The deformation characteristics of stainless steel type AISI 316L under compression in the temperature range 20 to 600 degrees C and strain rate range 0.001 to 100 s(-1) have been studied with a view to characterizing the flow instabilities occurring in the microstructure. At temperatures lower than 100 degrees C and strain rates higher than 0.1 s(-1), 316L stainless steel exhibits flow localization whereas dynamic strain aging (DSA) occurs at intermediate temperatures and below 1 s(-1). To avoid the above flow instabilities, cold working should be carried out at strain rates less than 0.1 s(-1). Warm working of stainless steel type AISI 316L may be done in the temperature and strain rate regime of: 300 to 400 degrees C and 0.001 s(-1) 300 to 450 degrees C and 0.01 s(-1): 450 to 600 degrees C and 0.1 s(-1); 500 degrees C and 1 s(-1) since these regions are free from flow instabilities like DSA and flow localization. The continuum criterion, developed on the basis of the principles of maximum rate of entropy production and separability of the dissipation function, predicts accurately all the above instability features.

Optimization of hot workability of an Al-Mg-Si alloy using processing maps

The hot workability of an Al-Mg-Si alloy has been studied by conducting constant strain-rate compression tests. The temperature range and strain-rate regime selected for the present study were 300-550 degrees C and 0.001-1 s(-1), respectively. On the basis of true stress data, the strain-rate sensitivity values were calculated and used for establishing processing maps following the dynamic materials model. These maps delineate characteristic domains of different dissipative mechanisms. Two domains of dynamic recrystallization (DRX) have been identified which are associated with the peak efficiency of power dissipation (34%) and complete reconstitution of as-cast microstructure. As a result, optimum hot ductility is achieved in the DRX domains. The strain rates at which DRX domains occur are determined by the second-phase particles such as Mg2Si precipitates and intermetallic compounds. The alloy also exhibits microstructural instability in the form of localized plastic deformation in the temperature range 300-350 degrees C and at strain rate 1 s(-1).

Optimization of Lime Content for Fly Ash

Abundant quantities of fly ash have been produced by thermal power plants situated ail over the world. Many applications of fly ash depend upon its pozzolanic reactivity. This reactivity depends upon many factors, including lime content. Many fly ashes show marked improvement with the addition of lime. However, for every fly ash, there is an optimum lime content for its maximum reactivity. There is no well-established simple test to determine the optimum lime content. In this paper an attempt is made to use a simple physical and physico chemical test to determine the optimum lime content. The principle behind the use of a pH test, liquid limit test, and free swell index test to determine the optimum lime content has been explained. All the methods predict nearly the same optimum lime content and correlate well with that determined by the strength test.

Lazy structure sharing for query optimization

We study lazy structure sharing as a tool for optimizing equivalence testing on complex data types, We investigate a number of strategies for implementing lazy structure sharing and provide upper and lower bounds on their performance (how quickly they effect ideal configurations of our data structure). In most cases when the strategies are applied to a restricted case of the problem, the bounds provide nontrivial improvements over the naive linear-time equivalence-testing strategy that employs no optimization. Only one strategy, however, which employs path compression, seems promising for the most general case of the problem.

A two timescale stochastic approximation scheme for simulation-based parametric optimization

A two timescale stochastic approximation scheme which uses coupled iterations is used for simulation-based parametric optimization as an alternative to traditional "infinitesimal perturbation analysis" schemes, It avoids the aggregation of data present in many other schemes. Its convergence is analyzed, and a queueing example is presented.