FLOW OF NEWTONIAN FLUIDS THROUGH SINUSOID ALLY CONSTRICTED TUBES. NUMERICAL AND EXPERIMENTAL RESULTS

Numerical and experimental predictions of pressure drops in the flow of Newtonian fluids through sinusoidally constricted tubes (SCT) are carried out. The numerical evaluations analyzed in this work are obtained from the following methods: Geometric Iteration (GJM), Geometric Iteration with First and Second Upwinds (GIM1, GIM2), Successive Over Relaxation by Line (SORBLM), Global Galerkin Spectral Method (GGSM), Collocation (CM) and Dufort-Frankel (DFM). The GIM1, GIM2 and SORBLM are applied to SCT and explained in this work. The other methods have been previously reported in the literature with the same purpose. Experiments are accomplished for constrictions of approximately 40,60 and 80% of the average tube diameter and results compare well with numerical predictions of the steady flow. It is concluded that special attention should be given to evaluations of the friction factor ƒ for Reynolds numbers Re between the onsets of flow separation and turbulence due to flow instability. Finally, the SCT as a model for porous media (PM) is discussed.     

Intelligent assistance for teachers in collaborative e-learning environments

Collaborative learning environments provide a set of tools for students acting in groups to interact and accomplish an assigned task. In this kind of systems, students are free to express and communicate with each other, which usually lead to collaboration and communication problems that may require the intervention of a teacher. In this article, we introduce an intelligent agent approach to assist teachers through monitoring participations made by students within a collaborative distance learning environment, detecting conflictive situations in which a teacher’s intervention may be necessary. High precision rates achieved on conflict detection scenarios suggest great potential for the application of the proposed rule-based approach for providing personalized assistance to teachers during the development of group works.     

Correlation between instrumental and sensory ratings by evaluation of some texture reference scales

The objective of this study was to investigate instrumental–sensory relationships of some texture scales using argentine foods as references. Textural characteristics of these foods were instrumentally investigated by the texture profile analysis technique. Principal component analysis (PCA) was used to describe the main attributes of the food samples. High Pearson’s correlation coefficients were found between hardness and fracturability (r = 0.94; P < 0.0001), hardness and gumminess (r = 0.71; P < 0.0001) and springiness and cohesiveness (r = 0.85; P < 0.0001). PCA identified two significant principal components, which accounted for 81.2% of the variance in the instrumental data. Additionally, a trained panel described the texture characteristics of the food samples according to the standard reference scales. The correlation curves showed nonlinear relationships (R² between 85.6% and 99.9%) which were used to predict sensory attributes of other food samples. Some texture attributes like hardness and fracturability were accurately predicted by mechanical properties, while others like cohesiveness and adhesiveness were less representative.     

Modeling the vulcanization of rubbers in an oscillating bicone cell

This work studies the evolution of dynamic rheometric functions (dynamic viscosity η′(ω) and storage modulus G′(ω)) with the degree of vulcanization and temperature of well-characterized SBR and NBR compounds. Experiments are carried out in an oscillating bicone cell, and results are well fitted through a model that considers flow kinematics, heat transfer, curing kinetics, and rheological functions. It is possible to quantify the model parameters and to understand better the evolution of filled rubber compounds toward crosslinked networks or vulcanizates. Conclusions are also obtained on the interplay between rheological functions and molecular parameters (average molecular weight and polydispersity). Experimental evaluations of the ratios between oscillating torques, at different times of the curing process, correlate directly with changes in rheological properties of the samples studied. © 1995 John Wiley & Sons, Inc.     

Automatic recognition of handwritten numerical strings: a recognition and verification strategy

A modular system to recognize handwritten numerical strings is proposed. It uses a segmentation-based recognition approach and a recognition and verification strategy. The approach combines the outputs from different levels such as segmentation, recognition, and postprocessing in a probabilistic model. A new verification scheme which contains two verifiers to deal with the problems of oversegmentation and undersegmentation is presented. A new feature set is also introduced to feed the oversegmentation verifier. A postprocessor based on a deterministic automaton is used and the global decision module makes an accept/reject decision. Finally, experimental results on two databases are presented: numerical amounts on Brazilian bank checks and NIST SD19. The latter aims at validating the concept of modular system and showing the robustness of the system using a well-known database.     

Effects of thermal spot configurations on the flow through porous media driven by natural and forced convection

A study concerning the flow of a Newtonian fluid through a porous medium for the particular case natural convection is produced by hot and cold spots placed in the solid phase is presented. Results involving the interaction of forced convection with thermal spots are reported to visualize the mechanisms associated with the generation of complex flow patterns in the porous medium. For this purpose the computation of a two-field model is carried out. Two systems are studied: one is a rectangular porous cavity (RPC) of square cross section and the other is an annular porous cavity (APC) comprised by two concentric vertical cylindrical walls. It is shown, in general, that the flow patterns associated with each configuration and intensities of the thermal spots may be qualitatively inferred by following rules that are established through a basic study of mixed convection in the RPC.     

Simultaneous optimization of production planning and inventory management of polyurethane foam plant

In this work, the management of a polyurethane foam plant is tackled through a mixed integer linear programming model that simultaneously solves production and inventory planning problems. The production process considers the foaming stage where large polyurethane blocks are produced as well as the curing step where the blocks are dried. The proposed formulation takes into account several tradeoffs involved in the overall production process. The daily production planning is tightly related to production requirements, available space for the curing and stored elements. Moreover, the required time to dry blocks introduces a delay that must be appropriately considered in order to allow an adequate operation of downstream operations. Thus, an integrated approach where all these problems are jointly addressed is proposed using a mathematical programming model. Several study cases provided by a local company are tested to demonstrate the model performance.     

eTeacher: Providing personalized assistance to e-learning students

In this paper we present eTeacher, an intelligent agent that provides personalized assistance to e-learning students. eTeacher observes a student’s behavior while he/she is taking online courses and automatically builds the student’s profile. This profile comprises the student’s learning style and information about the student’s performance, such as exercises done, topics studied, exam results. In our approach, a student’s learning style is automatically detected from the student’s actions in an e-learning system using Bayesian networks. Then, eTeacher uses the information contained in the student profile to proactively assist the student by suggesting him/her personalized courses of action that will help him/her during the learning process. eTeacher has been evaluated when assisting System Engineering students and the results obtained thus far are promising.     

The recognition of handwritten numeral strings using a two-stage HMM-based method

In this paper, a two-stage HMM-based recognition method allows us to compensate for the possible loss in terms of recognition performance caused by the necessary trade-off between segmentation and recognition in an implicit segmentation-based strategy. The first stage consists of an implicit segmentation process that takes into account some contextual information to provide multiple segmentation-recognition hypotheses for a given preprocessed string. These hypotheses are verified and re-ranked in a second stage by using an isolated digit classifier. This method enables the use of two sets of features and numeral models: one taking into account both the segmentation and recognition aspects in an implicit segmentation-based strategy, and the other considering just the recognition aspects of isolated digits. These two stages have been shown to be complementary, in the sense that the verification stage compensates for the loss in terms of recognition performance brought about by the necessary tradeoff between segmentation and recognition carried out in the first stage. The experiments on 12,802 handwritten numeral strings of different lengths have shown that the use of a two-stage recognition strategy is a promising idea. The verification stage brought about an average improvement of 9.9% on the string recognition rates. On touching digit pairs, the method achieved a recognition rate of 89.6%. Received June 28, 2002 / Revised July 03, 2002     

Segmentation and recognition of handwritten dates: an HMM-MLP hybrid approach

This paper presents an HMM-MLP hybrid system for segmenting and recognizing complex date images written on Brazilian bank checks. Through the recognition process, the system makes use of an HMM-based approach to segment a date image into subfields. Then the three obligatory date subfields (day, month, and year) are processed. A neural approach has been adopted to decipher strings of digits (day and year) and a Markovian strategy to recognize and verify words (month). The final decision module makes an accept/reject decision. We also introduce the concept of metaclasses of digits to reduce the lexicon size of the day and year and improve the precision of their segmentation and recognition. Experiments show interesting results on date recognition.Received: 17 December 2002, Accepted: 16 July 2003, Published online: 17 November 2003Correspondence to: Marisa Morita