Abstract: Uterine and oviducal fluids are necessary for
capacitation of the spermatozoa and early embryonic development.
The aim of the present study was to determine the effects of estrous
cycle phases (follicular and luteal) on some biological parameters
(enzymes, electrolytes and total proteins) in uterine and oviducal
secretions of ewes. Oviducal and uterine fluids were collected,
diluted and centrifuged. According to our results, concentrations of
GPT, G6PDH, total proteins, K and Na were significantly (P
Abstract: Modeling and simulation of fixed bed three-phase
catalytic reactors are considered for wet air catalytic oxidation of
phenol to perform a comparative numerical analysis between tricklebed
and packed-bubble column reactors. The modeling involves
material balances both for the catalyst particle as well as for different
fluid phases. Catalyst deactivation is also considered in a transient
reactor model to investigate the effects of various parameters
including reactor temperature on catalyst deactivation. The
simulation results indicated that packed-bubble columns were
slightly superior in performance than trickle beds. It was also found
that reaction temperature was the most effective parameter in catalyst
deactivation.
Abstract: Considering non-ideal behavior of fluids and its effects on hydrodynamic and mass transfer in multiphase flow is very essential. Simulations were performed that takes into account the effects of mass transfer and mixture non-ideality on hydrodynamics reported by Irani et al. In this paper, by assuming the density of phases to be constant and Raullt-s law instead of using EOS and fugacity coefficient definition, respectively for both the liquid and gas phases, the importance of non-ideality effects on mass transfer and hydrodynamic behavior was studied. The results for a system of octane/propane (T=323 K, P =445 kpa) also indicated that the assumption of constant density in simulation had major role to diverse from experimental data. Furthermore, comparison between obtained results and the previous report indicated significant differences between experimental data and simulation results with more ideal assumptions.
Abstract: In modern distributed software systems, the issue of communication among composing parts represents a critical point, but the idea of extending conventional programming languages with general purpose communication constructs seems difficult to realize. As a consequence, there is a (growing) gap between the abstraction level required by distributed applications and the concepts provided by platforms that enable communication. This work intends to discuss how the Model Driven Software Development approach can be considered as a mature technology to generate in automatic way the schematic part of applications related to communication, by providing at the same time high level specialized languages useful in all the phases of software production. To achieve the goal, a stack of languages (meta-meta¬models) has been introduced in order to describe – at different levels of abstraction – the collaborative behavior of generic entities in terms of communication actions related to a taxonomy of messages. Finally, the generation of platforms for communication is viewed as a form of specification of language semantics, that provides executable models of applications together with model-checking supports and effective runtime environments.
Abstract: This paper presents the design related to the
electronic system design of the respiratory signal, including phases
for processing, followed by the transmission and reception of this
signal and finally display. The processing of this signal is added to
the ECG and temperature sign, put up last year. Under this scheme is
proposed that in future also be conditioned blood pressure signal
under the same final printed circuit and worked.
Abstract: In this paper we present an efficient system for
independent speaker speech recognition based on neural network
approach. The proposed architecture comprises two phases: a
preprocessing phase which consists in segmental normalization and
features extraction and a classification phase which uses neural
networks based on nonparametric density estimation namely the
general regression neural network (GRNN). The relative
performances of the proposed model are compared to the similar
recognition systems based on the Multilayer Perceptron (MLP), the
Recurrent Neural Network (RNN) and the well known Discrete
Hidden Markov Model (HMM-VQ) that we have achieved also.
Experimental results obtained with Arabic digits have shown that the
use of nonparametric density estimation with an appropriate
smoothing factor (spread) improves the generalization power of the
neural network. The word error rate (WER) is reduced significantly
over the baseline HMM method. GRNN computation is a successful
alternative to the other neural network and DHMM.