Kinetic Model for Invertase‐Induced Sucrose Hydrolysis: Initial Time Lag

The kinetics of sucrose hydrolysis by invertase was studied in order to find a comprehensive model for the reaction pathway and mechanism. First, three common models of Michaelis‐Menten (MM), substrate inhibition (S²), and substrate clusters' inhibition (S³(I)) were investigated. The third model was found to better predict the initial sucrose concentration. Then, the S³(I) model was modified to cover the remaining pathway (S³(II)). Finally, a new comprehensive model (S³(III)) was evaluated, which in addition to what is considered in the two previously mentioned models (S³(I) and S³(II)) also involved the initial time lag. The model predictions showed an excellent agreement with the experimental data. The mean absolute error for the MM model is significantly reduced for the S³(III) model.     

Mesoscale finite element prediction of concrete failure

The present paper studies the failure of concrete from the mesoscopic point of view. Biphasic cubic concrete samples containing spherical aggregates embedded in a homogenized mortar have been simulated using standard finite element method. Linear elasticity and damage-plasticity hypotheses are considered for the aggregates and mortar, respectively. Various triaxial loading conditions are assumed for each sample to generate adequate discrete failure points within the stress space. In the next step, the approximated failure surfaces of specimens are constructed using the Delaunay triangulation technique. The effects of mesostructural features such as aggregate grading curve, aggregate volumetric share, and more importantly the controlling parameters of mortar’s damage-plasticity constitutive model have been investigated. Finally, the failure modes of some selected samples have been reported and discussed.     

Performance assessment of decision-making units using an adaptive neural network algorithm: one period case

This study proposes a nonparametric method based on adaptive neural network (ANN) technique for measuring efficiency of decision making units (DMUs) in one period case as a complementary tool for the ANN-based efficiency methods in the previous studies. In previous studies, there are needed to have large volume of data, and so the proposed method in this study is more applicable because it can be used for the cases which have no historical data. In fact, a limitative weakness of the ANN-based efficiency methods about applying them for these cases is removed. So, it can be a competitive method to the other common tools for measuring efficiency. By noting the importance of flexible manufacturing system, this study presents a decision-making model for optimization of operators’ allocation in cellular manufacturing system by computer simulation. The methodology is illustrated through its application on a previously reported dataset. It was found out that ANN provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored.     

Predictions of micelle-water partition coefficients and retention in micellar electrokinetic chromatography from solute structure. 2. Fragmental constant approach

The fragmental constant approach (FCA) was used to calculate water-sodium dodecyl sulfate (SDS) micelle partition coefficients, K(mw), for uncharged solutes from their structure. Subsequently, the availability of K(mw) values allows prediction of retention factor, k, in micellar electrokinetic chromatography (MEKC) using the simple relationship k = K(mw)phi, where phi is the phase ratio. The FCA model describes a micelle-water partition coefficient as the sum of the partition coefficients of the constituent atomic/molecular fragments, measured by fragmental constant values, f (i), as well as correction factors to account for various "intramolecular effects" that cause deviations from the predicted partition coefficients as, log K(mw) = sum(n)(i=1)aif i+sum(m)(i=1)kiCm. The fragmental constants for a set of 41 fragments were determined using a training set of 229 aromatic solutes and 198 aliphatic compounds. The K(mw) of the aromatic compounds in the training set were determined by MEKC, while the K(mw) of the aliphatic solutes were estimated using the linear solvation energy relationship (LSER) for the SDS micelles. The fragments consisted of both aromatic fragments (i.e., directly attached to an aromatic ring) and aliphatic fragments. The FCA predictions agree nicely with the observed and LSER partition coefficient values, even for complex molecular structures such as beta-blocker drugs. The results show the great potential of the FCA for a priori prediction of retention behavior in MEKC from solute structure.     

The effect of emulsifiers,sugar, shortening and soya flour on the staling of barbari flat bread

A standard method has been developed to bake barbari flat-type bread. Instron was used to measure the hardness of the crumb of bread samples as a criterion of staling. Different combinations of emulsifiers, sugar, shortening and soya flour were added to the formula of barbari bread to find their effect on the retardation of hardness. Addition of soya flour increased the loaf weight but decreased its total organoleptic score. Added amounts of different additives tried in this experiment improved the hardness approximately to the same level. The best result was obtained by the addition of sodium stearoyl-2-lactylate and shortening.     

Improving inter-node communications in multi-core clusters using a contention-free process mapping algorithm

High performance clusters, which are established by connecting many computing nodes together, are known as one of main architectures to obtain extremely high performance. Currently, these systems are moving from multi-core architectures to many-core architectures to enhance their computational capabilities. This trend would eventually cause network interfaces to be a performance bottleneck because these interfaces are few in number and cannot handle multiple network requests at a time. The consequence of such issue would be higher waiting time at the network interface queue and lower performance. In this paper, we tackle this problem by introducing a process mapping algorithm, which attempts to improve inter-node communications in multi-core clusters. Our mapping strategy reduces accesses to the network interface by distributing communication-intensive processes among computing nodes, which leads to lower waiting time at the network interface queue. Performance results for synthetic and real workloads reveal that the proposed strategy improves the performance from 8 % up to 90 % in tested cases compared to other methods.     

Neural Network Identification Based Multivariable Feedback Linearization Robust Control for a Two-Link Manipulator

Regarding to the variations of the load and unmodeled dynamic, robot manipulators are known as a nonlinear dynamic system. Overcoming such problems like uncertainties and nonlinear characteristics in the model of two-link manipulator is the principal goal of this paper. To approach to this aim, a neural network is combined with a linear robust control in which the result has the advantages of, the first, approximated nonlinear elements and the second, the guaranteed robustness. To design the proposed controller, at first, multivariable feedback linearization is employed to convert the nonlinear model to linear one. Second, the unknown parameters of the system are identified by neural network based on a new proposed learning rule. Third, Mixed linear feedback-H_?∞? robust control method is proposed to stabilize the closed loop system. The closed loop system based on the proposed controller is analyzed and some numerical simulations are performed. Results show suitable responses of the closed loop system.     

An integrated robust probing motion planning and control scheme: A tube-based MPC approach

‘This paper introduces the integration of a probing scheme into a robust MPC-based robot motion planning and control algorithm. The proposed solution tackles the output-feedback tube-based MPC problem using the partially-closed loop strategy to incorporate future measurements in a computationally efficient manner. This combination will provide not only a robust controller but also avoids overly conservative planning which is a drawback of the original implementation of the output-feedback tube-based MPC. The proposed solution is composed of two controllers: (i) a nominal MPC controller with probing feature to plan a globally convergent trajectory in conjunction with active localization, and (ii) an ancillary MPC controller to stabilize the robot motion around the planned trajectory. The performance and real-time implementation of the proposed planning and control algorithms have been verified through both extensive numerical simulations and experiments with a mobile robot.     

Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models

Producing oil from gas-lift wells are often faced with severe producing oscillatory flow regimes. A major source of the oscillations is recognized as casing–heading instability which is caused by dynamic interaction between injection gas and multiphase fluid. This phenomenon poses strict production-related challenges in terms of lower average production and strain on downstream equipment. In this paper, an effective solution is proposed based on integration of an online interpretation dynamic model and a nonlinear model predictive control (NMPC) scheme. The paper uses adaptive growing and pruning radial basis function (GAP-RBF) neural networks (NNs) to recursively capture the essential dynamics of casing–heading instability in a nonlinear model structure. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are comparatively investigated to adaptively train modified GAP-RBF NNs. NMPC methodology is developed on the basis of the identified nonlinear NN model for real-time stabilization of casing–heading instability in an oil reservoir equipped with a gas-lift production well. A set of test studies has been conducted to explore the superior performance of the proposed adaptive NMPC controller under different scenarios for an oil reservoir simulated in ECLIPSE and linked to a complementary gas-lifted oil well simulated in programming environment.