Study on quality characteristics of camel burger and evaluating its stability during frozen storage

This study investigated the influence of replacing One-humped camel meat instead of cow meat (0, 25, 50, 75 and 100 %) on quality characteristics and frozen storage stability in burgers. For this purpose thiobarbituric acid test (TBA-RS), cooking characteristics, color parameters, texture and sensory properties were studied during 3 months at ?18 °C. Significant differences (p < 0.05) were observed in the moisture retention, diameter reduction, sensory properties, lightness, yellowness and springiness. Cooking yield, TBA-RS, fat retention, cohesiveness, flavor and texture showed significant differences (p < 0.05) through storage. Other evaluated properties of burgers showed no significant differences (p > 0.05) in various levels of camel meat and over storage term. Moisture retention of burgers increased with increasing of camel meat content. The sensory panel scores for flavor, texture, juiciness and overall acceptability increased but color scores decreased with increasing the level of camel meat. Cooking yield, fat retention and cohesiveness decreased by increasing the storage period.     

Vehicle routing scheduling using an enhanced hybrid optimization approach

Cross docking play an indispensable role in streamlining the efficiency and effectiveness of any supply chain operations. Owing to the need to reduce transportation lead time and increase coordination between other supply chain activities such as just-in-time, make-to-order, or merge-in-transit strategies, shortening the total transfer time at cross docking is increasing important. Thus, in this paper we propose a new hybrid metaheuristic for vehicle routing scheduling in cross-docking systems. This new hybrid algorithm incorporates the elements from Particle Swam Optimization, Simulated Annealing and Variable Neighborhood Search to enhance its search capabilities. On view of the fact that the performance of metaheuristic algorithms are considerably influenced by the proper tuning of their parameters, we take advantage of Taguchi??s robust design method to come up with the best parameters of the before-mentioned algorithms. In order to measure the performance of our proposed algorithm, we compared it with the Tabu Search algorithm presented by Lee et?al. (Comput Ind Eng 51:247?C256, 2006). The computational evaluations clearly support the high performance of our proposed algorithm against other algorithm in the literature.     

A class of type-2 fuzzy neural networks for nonlinear dynamical system identification

This paper presents the ability of the interval type-2 Takagi–Sugeno–Kang fuzzy neural networks (IT2-TSK-FNN) for nonlinear dynamical system identification. The proposed IT2-TSK-FNN has seven layers. The first two layers consist of type-2 fuzzy neurons with uncertainty in the mean of Gaussian membership functions. Third layer is rule layer. Type-reduction is done in fourth layer. In the fifth, sixth, and seventh layers, consequent left–right firing points, two end points, and output are evaluated, respectively. In this paper, gradient descent with adaptive learning rate backpropagation is used in learning phase. IT2-TSK-FNN is used for the identification of three nonlinear systems, and then results are compared with adaptive-network-based fuzzy inference system (ANFIS).     

Application of artificial neural networks and genetic programming in vapor–liquid equilibrium of C1 to C7 alkane binary mixtures

In this study, the capacity of artificial neural networks (ANNs) and genetic programming (GP) in making possible, fast and reliable predictions of equilibrium compositions of alkane binary mixtures is investigated. A data set comprising 847 data points was gathered and used in both training the proposed ANN and generating the closed-form expressions of the GP procedure. The results obtained demonstrate the relative precision of the proposed ANN, while, on the other hand, exhibit that the GP model, although less precise, affords high CPU time efficiency and simplicity. Concisely, the proposed models can serve the purpose of being close first estimates for more thermodynamically rigorous vapor–liquid equilibrium calculation procedures and do obviate the necessity for the availability of a large set of experimental binary interaction coefficients. Mean absolute errors of 0.0100 and 0.0404 for liquid compositions and of 0.0054 and 0.0254 for vapor-phase mole fractions, for the proposed ANN and GP models, respectively, are a testament to the reliability of the proposed models.

     

Dielectrophoresis-based microfluidic platform to sort micro-particles in continuous flow

Non-invasive separation of particles with different sizes and sensitivities has been a challenge and interest for point-of-care diagnostics and personalized treatment. Dielectrophoresis is widely known as a powerful technique to sort the particles and (most importantly to) distinguish cells and monitor their state without the need for biochemical tags. In this paper, a dielectrophoresis-based microchannel design is proposed which allows for continuous particle sorting and separation under the applied AC field. It is also practical to implement the platform for monitoring cell behavior irregularities caused by certain diseases toward diagnosis and treatment. In this regard, the device employs dielectrophoretic (DEP) force exerted on the particles by only two electrodes with oblique arrangement in the channel. The electrodes are arranged with a bevel angle to the fluid flow direction but they are not parallel and therefore a gradually decreasing electric field is achieved along the channel’s width. As a result, the dielectrophoretic force, acting on the particles of different sizes, would also gradually decrease along channels width which renders the necessary distinguishing lateral displacements of particles for separation. Therefore, the particles with different sizes can be sorted in a continuous-flow regime and be received at multiple outlet reservoirs with no need to turn the electric field on/off. The presented device is fabricated and evaluated in the experiment to prove its feasibility. Afterward, using numerical simulations, we investigate the optimum design parameters in the presented device to enhance device efficiency for separating particles with different size ranges.

     

Quantum relative phase, <Emphasis Type="Italic">m</Emphasis>-tangle,and multi-local Lorentz-group invariant

In this paper we define a family of hermitian operators by which to extract what we call quantum-relative-phase properties of a pure or mixed multipartite quantum state, and we relate these properties to known measures of entanglement, namely the m-tangle and the invariant \({S_{(m)}^2}\) of the multi-local Lorentz-group \({SL(2, \mathbb{C})^{\otimes m}}\) . Our construction is based on the orthogonal complement of a positive operator valued measure on quantum phase.     

An improved fuzzy preference programming to evaluate entrepreneurship orientation

This paper describes an approach to measuring the entrepreneurship orientation (EO) of firms. EO is a widely accepted way to measure the degree in which a firm is entrepreneurial. The scale has three dimensions – innovativeness, risk-taking and proactiveness – each of which is assessed using multiple items. Measuring EO is important for entrepreneurial firms and for organizations like venture capitalists, business angels, investment banks and governments investing in these firms. Both the traditional statistical and the simple approach of assessing the overall level of EO (adding item scores) have their disadvantages. The aim of this article is to discuss these disadvantages and describe how some of them can be removed by using fuzzy analytic hierarchy process (AHP), which is a multi-criteria decision-making (MCDM) method that is particularly suited to tackle multi-dimensional, fuzzy, and perception-based constructs such as EO. We first improve a fuzzy AHP and then apply it using the pairwise comparisons of three experts to evaluate the EO of 59 small to medium-sized enterprises (SMEs) and rank the firms based on their EO score. The results indicate that proactiveness is by far the most important dimension, followed by innovativeness. Furthermore, there are considerable differences when it comes to the weights of the items.     

A stochastic optimization model for integrated forward/reverse logistics network design

In this paper, we develop a stochastic programming model for an integrated forward/reverse logistics network design under uncertainty. First, an efficient deterministic mixed integer linear programming model is developed for integrated logistics network design to avoid the sub-optimality caused by the separate design of the forward and reverse networks. Then the stochastic counterpart of the proposed MILP model is developed by using scenario-based stochastic approach. Numerical results show the power of the proposed stochastic model in handling data uncertainty.     

A new Wavelet Method for Variable‐Order Fractional Optimal Control Problems

In this paper, a new computational method based on the Legendre wavelets (LWs) is proposed for solving a class of variable‐order fractional optimal control problems (V‐FOCPs). To do this, a new operational matrix of variable‐order fractional integration (OMV‐FI) in the Riemann‐Liouville sense for the LWs is derived and used to obtain an approximate solution for the problem under study. Along the way the hat functions (HFs) are introduced and employed to derive a general procedure to compute this matrix. In the proposed method, the variable‐order fractional dynamical system is transformed to an equivalent variable‐order fractional integro‐differential dynamical system, at first. Then, the highest integer order of the derivative of the state variable and the control variable are expanded by the LWs with unknown coefficients. Next, the OMV‐FI in the the Riemann‐Liouville sense together with some properties of the LWs are employed to achieve a nonlinear algebraic equation in place of the performance index and a nonlinear system of algebraic equations in place of the dynamical system in terms of the unknown coefficients. Finally, the method of constrained extremum is applied which consists of adjoining the constraint equations derived from the given dynamical system to the performance index by a set of undetermined Lagrange multipliers. As a result, the necessary conditions of optimality are derived as a system of algebraic equations in the unknown coefficients of the state variable, control variable and Lagrange multipliers. Furthermore, the efficiency and accuracy of the proposed method are demonstrated for some concrete examples. The obtained results show that the proposed method is very efficient and accurate.     

Semantic routing of search queries in P2P networks

Peer-to-peer (P2P) networks are beginning to form the infrastructure of future applications. Heavy network traffic limits the scalability of P2P networks. Indexing is a method to reduce this traffic. But indexes tend to become large with the growth of the network. Also, limiting the size of these indexes causes loss of indexing information. In this paper we introduce a novel ontology based index (OI) which limits the size of the indexes without sacrificing indexing information. We show that the method can be employed by many P2P networks. The OI sits on top of routing and maintenance modules of a P2P network and enhances it. The OI prunes branches of search trees which have no chance to proceed to a response. Also the OI guarantees that an enhanced routing algorithm and its basic version have the same result set for a given search query. This means that the OI reduces traffic without reducing quality of service. To measure the performance of the OI we apply it on Chord (DHT based) and HyperCup (non-DHT based) P2P networks and show that it reduces the networks’ traffic significantly.