Response surface modeling for optimization of formulation variables and physical stability assessment of walnut oil-in-water beverage emulsions

The effect of Arabic gum content (5-10% w/w) and walnut-oil concentration (3-6% w/w) on properties of prepared walnut oil/water emulsion, including turbidity loss rate, density, size index, particle size and stability, was investigated using response surface methodology (RSM). For each response, a second-order polynomial model with high coefficient of determination (R²) values ranging from 0.907 to 0.989 was developed using multiple linear regression analysis. The lack of significant difference between the experimental and predicted values proved the adequacy of response surface equations for describing the physical changes of emulsions. An increase of Arabic gum content in range and initial concentration of walnut oil were associated with high emulsion stability and minimum droplet size. It can be concluded that RSM can determine the most suitable formulation (3% w/w walnut oil and 9.62% w/w Arabic gum) to achieve the highest stability in a developed beverage emulsion based on walnut oil.     

Incorporating microgrids coupling with utilization of flexible switching to enhance self-healing ability of electric distribution systems

Electric distribution networks have to deal with issues caused by natural disasters. These problems possess unique characteristics, and their severity can make load restoration methods impotent. One solution that can help in alleviating the aftermath is the use of microgrids (MGs). Employing the cumulative capacity of the generation resources through MG coupling facilitates the self-healing capability and leads to better-coordinated energy management during the restoration period, while the switching capability of the system should also be considered. In this paper, to form and schedule dynamic MGs in distribution systems, a novel model based on mixed-integer linear programming (MILP) is proposed. This approach employs graph-related theories to formulate the optimal formation of the networked MGs and management of their proper participation in the load recovery process. In addition, the Benders decomposition technique is applied to alleviate computability issues of the optimization problem. The validity and applicability of the proposed model are evaluated by several simulation studies.     

Isogeometric level set topology optimization for elastoplastic plane stress problems

International Journal of Mechanics and Materials in Design - This article aims to utilize IsoGeometric analysis (IGA) and Level set method for topology optimization of elastoplastic plane stress...     

GTD-111高温合金瞬时液相连接过程中显微组织的变化

研究连接温度和时间对瞬时液相连接GTD-111高温合金显微组织的影响.连接过程采用BNi-3填料,在1080、1120和1160℃下,分别进行195、135和90 min的等温凝固,再进行均匀化热处理.结果表明,在冷却过程中,接头区域连续形成富Ni硼化物、Ni?B?Si三元化合物和共晶γ的金属间化合物和共晶化合物.随着...     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.     

Quantifying nonclassicality of correlations based on the concept of nondisruptive local state identification

A bipartite state is classical with respect to party A if and only if party A can perform nondisruptive local state identification (NDLID) by a projective measurement. Motivated by this we introduce a class of quantum correlation measures for an arbitrary bipartite state. The measures utilize the general Schatten p-norm to quantify the amount of departure from the necessary and sufficient condition of classicality of correlations provided by the concept of NDLID. We show that for the case of Hilbert–Schmidt norm, i.e., \(p=2\), a closed formula is available for an arbitrary bipartite state. The reliability of the proposed measures is checked from the information-theoretic perspective. Also, the monotonicity behavior of these measures under LOCC is exemplified. The results reveal that for the general pure bipartite states these measures have an upper bound which is an entanglement monotone in its own right. This enables us to introduce a new measure of entanglement, for a general bipartite state, by convex roof construction. Some examples and comparison with other quantum correlation measures are also provided.     

Stability Margin Determination of Linear Discrete‐Time Systems Described By Fuzzy Convex Sets Uncertainty

This paper proposes a new method for determining the stability margin of uncertain linear discrete‐time systems with uncertainty described by fuzzy convex sets. Nonsymmetrical triangular membership functions describing the uncertainty of characteristic polynomial coefficients are considered. This method is based on transformation of the uncertain discrete systems to the continuous time area and generalization of Kharitonov theorem for determining the uncommon minimum confidence level that guarantees stability of the system.     

Predicting potential deadlocks in multithreaded programs

In a multithreaded program, competition of threads for shared resources raises the deadlock possibility, which narrows the system liveness. Because such errors appear in specific schedules of concurrent executions of threads, runtime verification of threads behavior is a significant concern. In this study, we extended our previous approach for prediction of runtime behavior of threads may lead to an impasse. Such a prediction is of importance because of the nondeterministic manner of competing threads. The prediction process tries to forecast future behavior of threads based on their observed behavior. To this end, we map observed behavior of threads into time‐series data sets and use statistical and artificial intelligence methods for forecasting subsequent members of the sets as future behavior of the threads. The deadlock prediction is carried out based on probing the allocation graph obtained from actual and predicted allocation of resources to threads. In our approach, we use an artificial neural network (ANN) because ANNs enjoy the applicable performance and flexibility in predicting complex behavior. Using three case studies, we contrasted results of the current and our previous approaches to demonstrate results. Copyright © 2015 John Wiley & Sons, Ltd.     

A method for the optimum selection of datacenters in geographically distributed clouds

The optimal selection of a datacenter is one of the most important challenges in the structure of a network for the wide distribution of resources in the environment of a geographically distributed cloud. This is due to the variety of datacenters with different quality-of-service (QoS) attributes. The user’s requests and the conditions of the service-level agreements (SLAs) should be considered in the selection of datacenters. In terms of the frequency of datacenters and the range of QoS attributes, the selection of the optimal datacenter is an NP-hard problem. A method is therefore required that can suggest the best datacenter, based on the user’s request and SLAs. Various attributes are considered in the SLA; in the current research, the focus is on the four important attributes of cost, response time, availability, and reliability. In a geo-distributed cloud environment, the nearest datacenter should be suggested after receiving the user’s request, and according to its conditions, SLA violations can be minimized. In the approach proposed here, datacenters are clustered according to these four important attributes, so that the user can access these quickly based on specific need. In addition, in this method, cost and response time are taken as negative criteria, while accessibility and reliability are taken as positive, and the multi-objective NSGA-II algorithm is used for the selection of the optimal datacenter according to these positive and negative attributes. In this paper, the proposed method, known as NSGAII_Cluster, is implemented with the Random, Greedy and MOPSO algorithms; the extent of SLA violation of each of the above-mentioned attributes are compared using four methods. The simulation results indicate that compared to the Random, Greedy and MOPSO methods, the proposed approach has fewer SLA violations in terms of the cost, response time, availability, and reliability of the selected datacenters.     

Effectiveness of Zataria multiflora Boiss essential oil and grape seed extract impregnated chitosan film on ready-to-eat mortadella-type sausages during refrigerated storage

BACKGROUND: The effectiveness of chitosan films containing Zataria multiflora Boiss essential oil (ZEO) (5 and 10 g kg^?1) and grape seed extract (GSE) (10 g kg^?1) on lipid oxidation and microbial (lactic acid bacteria, aerobic mesophiles and inoculated Listeria monocytogenes) characteristics of mortadella sausage at 4 °C for 21 days was evaluated. The release of total phenolics (TPs) into sausage was also assessed. RESULTS: All films exhibited antibacterial activity against L. monocytogenes on agar culture media. Chitosan films containing ZEO were the most effective on the growth of bacteria. The growth of L. monocytogenes was significantly inhibited by ZEO‐GSE containing films especially during storage of the sausages for 6 days. Aerobic mesophiles and lactic acid bacteria were the most sensitive and resistant groups to films by 0.1–1.1 and 0.1–0.7 log cycles reduction, respectively. Sausages wrapped by 10 g kg^?1 GSE + 10 g kg^?1 ZEO films had the lowest degrees of lipid oxidation, which was 23% lower than the control. The TPs of ZEO films decreased to zero after 6 days, whereas TPs of GSE films followed a slight decrease during the storage. CONCLUSION: Antimicrobial/antioxidant chitosan film could be developed by incorporating GSE and ZEO for extending the shelf life of mortadella sausage. Copyright © 2011 Society of Chemical Industry