APPLICATION OF A COMMERCIAL ENZYME PREPARATION IN THE BARLEY MALTING PROCESS

This paper presents data on barley micromalting with addition of the CELLUCLAST enzyme complex. This is a commercial, multicarbohydrase with distinct β-glucanase and proteinase activities. The enzyme was added to steeping and germination phases, in different quantities (0.05%; 0.75% and 0.1% of initial barley). The enzyme was added to different malting phases: to the 2nd and 3rd steep water, at the beginning of germination on the 1st day by spraying, on the 2nd day of germination and in combination of addition to 3rd steeping water and in germination start (50% of total quantity of each). CELLUCLAST enzyme had a significant effect on reduction of wort viscosity, extract difference, wort filterability and protein breakdown, depending on the quantity of added enzyme and the malting phase to which it was added. There was no negative effect on other malt quality parameters. The best values of cytolytic breakdown parameters (viscosity, extract difference, filtration rate) were obtained with addition of 0.075% of CELLUCLAST, on the first day of germination.     

Application of Unconventional Raw Materials and Procedures in Wort Production*

This paper deals with investigations of wort production by the application of unmalted raw material — native barley as a partial substitute for malt in grist. This adjunct was differently treated: (1) thermal decomposition, (2) enzymic decomposition by the addition of commercial enzyme “Termamyl”, and (3) hydrothermic decomposition i.e. extruding. Native barley prepared by these methods was added in proportions up to 70% of the grist, without the addition of exogenous enzymes. Experiments were carried out on a laboratory scale by using an infusion mashing procedure for wort production. The results obtained indicated that the application of native barley as the adjunct, gelatinised by heat treatment gave good results up to 10% as malt substitute. Larger amounts of barley gelatinised by heat treatment decreased extract yields. Malt substitution with pretreatment of barley with enzyme did not produce satisfactory results. Worts had reduced levels of extract and soluble nitrogen, which were insufficient for the production of high quality beer. Application of barley flakes in quantities up to 50% produced wort which had good analytical quality parameters.     

Correlations between submission and acceptance of papers in peer review journals

Scientometrics - This paper provides a comparative study about seasonal influence on editorial decisions for papers submitted to two peer review journals. We distinguish a specialized one, the...     

Optimization of a microfluidic mixer for studying protein folding kinetics

We have applied an optimization method in conjunction with numerical simulations to minimize the mixing time of a microfluidic mixer developed for protein folding studies. The optimization method uses a semideterministic algorithm to find the global minimum of the mixing time by varying the mixer geometry and flow conditions. We describe the minimization problem and constraints and give a brief overview of the optimization algorithm. We present results of the optimization, including the optimized geometry and parameter sensitivities, and we demonstrate the improvement in mixing performance with experiments using microfabricated mixers. The dye-quenching experiments of the original and optimized mixer designs show respective mixing times of 7 and 4 mus, a 40% reduction. The new design also provides more uniform mixing across streamlines that enter the mixer. The optimized mixer is the fastest reported continuous flow mixer for protein folding.     

Least Squares Policy Evaluation Algorithms with Linear Function Approximation

We consider policy evaluation algorithms within the context of infinite-horizon dynamic programming problems with discounted cost. We focus on discrete-time dynamic systems with a large number of states, and we discuss two methods, which use simulation, temporal differences, and linear cost function approximation. The first method is a new gradient-like algorithm involving least-squares subproblems and a diminishing stepsize, which is based on the -policy iteration method of Bertsekas and Ioffe. The second method is the LSTD() algorithm recently proposed by Boyan, which for =0 coincides with the linear least-squares temporal-difference algorithm of Bradtke and Barto. At present, there is only a convergence result by Bradtke and Barto for the LSTD(0) algorithm. Here, we strengthen this result by showing the convergence of LSTD(), with probability 1, for every [0, 1].     

On stochastic gradient and subgradient methods with adaptive steplength sequences

Traditionally, stochastic approximation (SA) schemes have been popular choices for solving stochastic optimization problems. However, the performance of standard SA implementations can vary significantly based on the choice of the steplength sequence, and in general, little guidance is provided about good choices. Motivated by this gap, we present two adaptive steplength schemes for strongly convex differentiable stochastic optimization problems, equipped with convergence theory, that aim to overcome some of the reliance on user-specific parameters. The first scheme, referred to as a recursive steplength stochastic approximation (RSA) scheme, optimizes the error bounds to derive a rule that expresses the steplength at a given iteration as a simple function of the steplength at the previous iteration and certain problem parameters. The second scheme, termed as a cascading steplength stochastic approximation (CSA) scheme, maintains the steplength sequence as a piecewise-constant decreasing function with the reduction in the steplength occurring when a suitable error threshold is met. Then, we allow for nondifferentiable objectives but with bounded subgradients over a certain domain. In such a regime, we propose a local smoothing technique, based on random local perturbations of the objective function, that leads to a differentiable approximation of the function. Assuming a uniform distribution on the local randomness, we establish a Lipschitzian property for the gradient of the approximation and prove that the obtained Lipschitz bound grows at a modest rate with problem size. This facilitates the development of an adaptive steplength stochastic approximation framework, which now requires sampling in the product space of the original measure and the artificially introduced distribution.