Candida molischianaβ-Glucosidase Production by Saccharomyces cerevisiae and its Application in Winemaking

ABSTRACT: A recombinant Saccharomyces cerevisiae wine yeast strain expressing the Candida molischiana bgln gene encoding a β-glucosidase (BGLN) has been used to produce this enzyme. Shaking rate, pH, and aeration rate conditions have been optimized to obtain maximum activity to facilitate enzyme purification. The ability of the heterologous enzyme to efficiently release terpenols and alcohols from a Muscat wine glycoside extract and also directly from wine has been demonstrated. Terpenol glycoside content decreased by 50% after 1 mo of wine storage in agreement with results reported for the β-glucosidase produced by C. molischiana.     

Wake and aerodynamics loads in multiple bodies—application to turbomachinery blade rows

Two-dimensional, unsteady flow around bodies of complex geometry (or multiple bodies) at high Reynolds number is simulated using the vortex method. This method is modified to take into account the sub-grid scale phenomena through a second order velocity structure function model adapted to the Lagrangian scheme. The dynamics of the body wake is computed using the convection-diffusion splitting algorithm; the convection process is carried out with a Lagrangian Adams-Bashforth time-marching scheme and the diffusion process is simulated using the random walk method. The pressure distribution is obtained using an integral equation derived from the pressure Poisson equation, which was first developed for a single body. Results for the numerical simulation around a linear cascade of airfoils are presented. As the flow is periodic in the y direction, the discrete vortex shedding need only be considered for a reference airfoil. The flow characteristics around the NACA 65-410 series airfoils are calculated and comparisons are made with results available in the literature.     

Long-term energy scenarios for Mexico: Policy options for carbon savings and main barriers

This paper shows that possibilities exist for maintaining current energy-related carbon emissions per capita in Mexico at almost constant levels in the long term. It is argued, however, that the identified carbon emissions reductions will not be achieved easily or rapidly, as they require a restructuring of Mexico's current technological base and an unusual degree of coordination among the government, lending institutions, equipment manufacturers, and final users. Such changes also will entail surmounting major institutional and financial barriers and creating a better international environment for technology transfer and capital lending.     

Applying black-box testing to UML/OCL database models

Most Unified Modeling Language (UML) computer-aided software engineering tools have been insufficient in the development process because they provide little support for conceptual model testing. Model testing aims to ensure the correctness of a UML/OCL class diagram, or, in other words, that a given class diagram can perfectly meet the user’s requirements. This study proposes the validation of class diagrams with black-box testing, a technique used to test software without focusing on the software’s implementation or structure. An approach is proposed for the automatic transformation of the constraints of a UML/OCL class diagram into test cases. Following the creation of the test cases, they are executed with JUnit and the results produced are shown to the tester. To demonstrate the applicability of this approach, an effectiveness evaluation and an efficiency evaluation are performed here. Evaluation studies show that all faults included in a class diagram have been detected within an efficient time.     

Predictors of user perceptions of web recommender systems: How the basis for generating experience and search product recommendations affects user responses

One critical question suggested by Web 2.0 is as follows: When is it better to leverage the knowledge of other users vs. rely on the product characteristic-based metrics for online product recommenders? Three recent and notable changes of recommender systems have been as follows: (1) a shift from characteristic-based recommendation algorithms to social-based recommendation algorithms; (2) an increase in the number of dimensions on which algorithms are based; and (3) availability of products that cannot be examined for quality before purchase. The combination of these elements is affecting users’ perceptions and attitudes regarding recommender systems and the products recommended by them, but the psychological effects of these trends remain unexplored. The current study empirically examines the effects of these elements, using a 2 (recommendation approach: content-based vs. collaborative-based, within)×2 (dimensions used to generate recommendations: 6 vs. 30, between)×2 (product type: experience products (fragrances) vs. search products (rugs), between) Web-based study (N=80). Participants were told that they would use two recommender systems distinguished by recommendation approach (in fact, the recommendations were identical). There were no substantive main effects, but all three variables exhibited two-way interactions, indicating that design strategies must be grounded in a multi-dimensional understanding of these variables. The implications of this research for the psychology and design of recommender systems are presented.     

MOE prediction in Abies pinsapo Boiss. timber: Application of an artificial neural network using non-destructive testing

Determining the modulus of elasticity of wood by applying an artificial neural network using the physical properties and non-destructive testing can be a useful method in assessments of the timber structure in old constructions. The modulus of elasticity of Abies pinsapo Boiss. timber was predicted in this study through the parameters of density, width, thickness, moisture content, ultrasonic wave propagation velocity and visual grading of the test pieces. A feedforward multilayer perceptron network was designed for this purpose, achieving 75.0% success in the testing or unknown group.     

Reinforcement learning of pedagogical policies in adaptive and intelligent educational systems

In an adaptive and intelligent educational system (AIES), the process of learning pedagogical policies according the students needs fits as a Reinforcement Learning (RL) problem. Previous works have demonstrated that a great amount of experience is needed in order for the system to learn to teach properly, so applying RL to the AIES from scratch is unfeasible. Other works have previously demonstrated in a theoretical way that seeding the AIES with an initial value function learned with simulated students reduce the experience required to learn an accurate pedagogical policy. In this paper we present empirical results demonstrating that a value function learned with simulated students can provide the AIES with a very accurate initial pedagogical policy. The evaluation is based on the interaction of more than 70 Computer Science undergraduate students, and demonstrates that an efficient and useful guide through the contents of the educational system is obtained.