Effects of temperature, ammonium and glucose concentrations on yeast growth in a model wine system

In enology, alcoholic fermentation is a complex process involving several mechanisms. Slow and incomplete alcoholic fermentation is a chronic problem for the wine industry and factors leading to sluggish and stuck fermentations have been extensively studied and reviewed. The most studied cause of sluggish and stuck fermentation is the nitrogen content limitation. Nevertheless, other factors, such as temperature of fermentation and sugar concentration can affect the growth of yeasts. In this study we modelled the yeast growth‐cycle in wine model system as a function of temperature, sugar and ammonium concentrations; the individual effects and the interaction of these factors were analysed by means of a quadratic response surface methodology. Cell concentrations and weight loss were monitored in the whole wine fermentation process. The results of central composite design show that lower is the availability of nitrogen, higher is the cell growth rate; moreover, initial nitrogen concentration also influences survival time of Saccharomyces cerevisiae.     

Effects of benazepril on stress testing blood pressure in essential hypertension

The effects of different doses of the angiotensin-converting enzyme inhibitor benazepril on cardiovascular response to a set of standardized laboratory tasks were analyzed. Eighteen patients (15 men and 3 women) with mild-to-moderate essential hypertension were randomly allocated to receive 10 or 20 mg of benazepril, or placebo, each administered once daily for 2 weeks, according to a double-blind, 3-period design. At the end of each treatment period, patients were examined at resting baseline and while performing mental arithmetic, handgrip and cycle ergometry tests. In comparison with placebo, the average reductions in resting systolic blood pressure (BP) were 8.7 mm Hg (95% confidence intervals [CI] -15.2 to -2.1) with 10 mg of benazepril, and 7.8 mm Hg (95% CI -14.4 to -1.3) with 20 mg; the corresponding reductions in resting diastolic BP were 5.1 mm Hg (95% CI -8.7 to -1.4) and 6.8 mm Hg (95% CI -10.4 to -3.1) (all p < 0.05). During mental arithmetic, the reductions in systolic BP were 10.4 mm Hg (95% CI -17.4 to -3.4) with 10 mg of benazepril, and 13.8 mm Hg (95% CI -20.8 to -6.8) with 20 mg; diastolic BP was reduced by 4.5 mm Hg (95% CI -8.5 to -0.5) and 8.3 mm Hg (95% CI -13.2 to -4.3), respectively (all p < 0.05).(ABSTRACT TRUNCATED AT 250 WORDS)     

Two-Way Protocol with Imperfect Devices

The security of a deterministic quantum scheme for communication, namely the LM05 [1], is studied in presence of a lossy channel under the assumption of imperfect generation and detection of single photons. It is shown that the scheme allows for a rate of distillable secure bits higher than that pertaining to BB84 [2]. We report on a first implementation of LM05 with weak pulses.     

Techniques for the mechanical characterisation of civil structures

Broadly speaking, structural defects include all those changes affecting a structure so that its behaviour is no longer in keeping with its intended use characteristics or differs from the behaviour it was originally designed for. In recent years, damage recognition and location methods, relying on the measurement of parameters that are indicative of a structure's mechanical behaviour, have benefitted from the introduction of innovative approaches, which make it possible to process and correlate an unprecedented wealth of measurble data. The aim of this work is to illustrate the basic problems associated with the different methods used for structural identification (parametric and non-parametric, linear and non-linear, with known and unknown inputs, etc.) and to report on some interesting applications for civil structural diagnosis.     

Study of the hinge thickness deviation for a 316L parallelogram flexure mechanism fabricated via selective laser melting

3D printing offers great potential for developing complex flexure mechanisms. Recently, thickness-correction factors (TCFs) were introduced to correct the thickness and stiffness deviations of powder-based metal 3D printed flexure hinges during design and analysis. However, the reasons for the different TCFs obtained in each study are not clear, resulting in a limited value of these TCFs for future design and fabrication. Herein, the influence of the porous layer of 3D printed flexure hinges on the hinge thickness is investigated. Samples of parallelogram flexure mechanisms (PFMs) were 3D printed using selective laser melting (SLM) and 316L stainless steel powder. A 3D manufacturing error analysis was completed for each PFM sample via 3D scanning, surface roughness measurement and morphological observation. The thickness of the porous layer of the flexure hinge was independent of the designed hinge thickness and remained close to the average powder particle diameter. The effective hinge thickness could be estimated by subtracting twice the value of the porous layer thickness from the designed value. Guidelines based on finite element analysis and stiffness experiments are proposed. The limitations of the presented method for evaluating the effective hinge thickness of flexure hinges 3D printed via SLM are also discussed.

     

On the multi‐input second‐order sliding mode control of nonlinear uncertain systems

This note addresses the multi‐input second‐order sliding mode control design for a class of nonlinear multivariable uncertain dynamics. Among the most important peculiarities of the considered control problem, the considered sliding vector variable has a uniform vector relative degree [2,2, … ,2] with respect to the vector control variable, and only the sign of the sliding vector and of its derivative are available for feedback. Additionally, the symmetric part of the state‐dependent control matrix is supposed to be positive definite. Under some further mild restrictions on the uncertain system's dynamics, a control algorithm that realizes a multi‐input version of the ‘twisting’ second‐order sliding mode control algorithm is suggested. Simple controller tuning conditions are derived by means of a constructive Lyapunov analysis, which demonstrates that the suggested control algorithm guarantees the semiglobal asymptotic convergence to the sliding manifold. Simulation results, which confirm the good performance of the proposed scheme and investigate the actual accuracy obtained under the discrete‐time implementation effects, are given. Copyright © 2011 John Wiley & Sons, Ltd.     

Fast support vector machines for convolution tree kernels

Feature engineering is one of the most complex aspects of system design in machine learning. Fortunately, kernel methods provide the designer with formidable tools to tackle such complexity. Among others, tree kernels (TKs) have been successfully applied for representing structured data in diverse domains, ranging from bioinformatics and data mining to natural language processing. One drawback of such methods is that learning with them typically requires a large number of kernel computations (quadratic in the number of training examples) between training examples. However, in practice substructures often repeat in the data which makes it possible to avoid a large number of redundant kernel evaluations. In this paper, we propose the use of Directed Acyclic Graphs (DAGs) to compactly represent trees in the training algorithm of Support Vector Machines. In particular, we use DAGs for each iteration of the cutting plane algorithm (CPA) to encode the model composed by a set of trees. This enables DAG kernels to efficiently evaluate TKs between the current model and a given training tree. Consequently, the amount of total computation is reduced by avoiding redundant evaluations over shared substructures. We provide theory and algorithms to formally characterize the above idea, which we tested on several datasets. The empirical results confirm the benefits of the approach in terms of significant speedups over previous state-of-the-art methods. In addition, we propose an alternative sampling strategy within the CPA to address the class-imbalance problem, which coupled with fast learning methods provides a viable TK learning framework for a large class of real-world applications.     

A dynamic programming approach to sequential pattern recognition

This paper presents the dynamic programming approach to the design of optimal pattern recognition systems when the costs of feature measurements describing the pattern samples are of considerable importance. A multistage or sequential pattern classifier which requires, on the average, a substantially smaller number of feature measurements than that required by an equally reliable nonsequential classifier is defined and constructed through the method of recursive optimization. Two methods of reducing the dimensionality in computation are presented for the cases where the observed feature measurements are 1) statistically independent, and 2) Markov dependent. Both models, in general, provide a ready solution to the optimal sequential classification problem. A generalization in the design of optimal classifiers capable of selecting a best sequence of feature measurements is also discussed. Computer simulated experiments in character recognition are shown to illustrate the feasibility of this approach.