Phase Diagram,Equilibrium Data,and Correlation for Aqueous Two-Phase System Formed by Tetrahydrofuran and Sorbitol

Theoretical Foundations of Chemical Engineering - The importance of aqueous two-phase systems has been increased recently as a method with greater performance capability in separation and...     

Study on single and binary catalytic systems of pyridine-imine catalysts based on nickel and iron in synthesis of reactor blends and low-density polyethylene nanocomposites

In the presence of modified methylaluminoxane as cocatalyst, the behavior of a binary catalytic system based on pyridine-imine nickel ( N ) and iron ( F ) catalysts was evaluated in order to reach a proper mixture of polyethylene (PE). A computational study along with kinetic profile suggested that the catalyst F with higher electron affinity (A) and electrophilicity (ω) in the methyl cationic active center and stronger interaction with the monomer led to high integrated monomer consumption and higher activity. In addition, the samples produced by the mixture of catalysts showed a higher value of [19.4 × 10⁴ g (PE) mol (Fe+Ni)⁻¹ h⁻¹)], melting point (127.8 °C), and crystallinity extent (41.29%) than the samples produced by the single catalysts. The addition of multiwalled carbon nanotubes (MWCNT) into the polymerization media reduced the activity of catalysts [from 7.50 × 10⁴ to 0.66 × 10⁴ g (PE) mol (Fe+Ni)⁻¹ h⁻¹] and the thermal properties of the low-density polyethylene nanocomposite samples. However, the sample containing 2.33% MWCNT_20-30 improved the total thermal stability of the neat polyethylene blend up to 400 °C. Scanning electron microscope images of the samples demonstrated irregular to virtually uniform morphologies were obtained through the in situ and solution-mixing techniques. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47376.     

A Full Connectable and High Scalable Key Pre-distribution Scheme Based on Combinatorial Designs for Resource-Constrained Devices in IoT Network

Considering the internet of things (IoT), end nodes such as wireless sensor network, RFID and embedded systems are used in many applications. These end nodes are known as resource-constrained devices in the IoT network. These devices have limitations such as computing and communication power, memory capacity and power. Key pre-distribution schemes (KPSs) have been introduced as a lightweight solution to key distribution in these devices. Key pre-distribution is a special type of key agreement that aims to select keys called session keys in order to establish secure communication between devices. One of these design types is the using of combinatorial designs in key pre-distribution, which is a deterministic scheme in key pre-distribution and has been considered in recent years. In this paper, by introducing a key pre-distribution scheme of this type, we stated that the model introduced in the two benchmarks of KPSs comparability had full connectivity and scalability among the designs introduced in recent years. Also, in recent years, among the combinatorial design-based key pre-distribution schemes, in order to increase resiliency as another criterion for comparing KPSs, attempts were made to include changes in combinatorial designs or they combine them with random key pre-distribution schemes and hybrid schemes were introduced that would significantly reduce the design connectivity. In this paper, using theoretical analysis and maintaining full connectivity, we showed that the strength of the proposed design was better than the similar designs while maintaining higher scalability.

     

An extended minimax disparity to determine the OWA operator weights

This paper contributes to extend the minimax disparity to determine the ordered weighted averaging (OWA) model based on linear programming. It introduces the minimax disparity approach between any distinct pairs of the weights and uses the duality of linear programming to prove the feasibility of the extended OWA operator weights model. The paper finishes with an open problem.     

Opposition-based learning in the shuffled differential evolution algorithm

This paper proposes using the opposition-based learning (OBL) strategy in the shuffled differential evolution (SDE). In the SDE, population is divided into several memeplexes and each memeplex is improved by the differential evolution (DE) algorithm. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the DE which, on one hand, use the partitioning and shuffling concepts of SDE to compensate for the limited amount of search moves of the original DE and, on the other hand, employ the OBL to accelerate the DE without making premature convergence. Four versions of DE algorithm are proposed based on the OBL and SDE strategies. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments on 25 benchmark functions designed for the special session on real-parameter optimization of CEC2005 and non-parametric analysis of obtained results demonstrate that the performances of the proposed algorithms are better than the SDE. The fourth version of proposed algorithm has a significant difference compared to the SDE in terms of all considered aspects. The emphasis of comparison results is to obtain some successful performances on unsolved functions for the first time, which so far have not been reported any successful runs on them. In a later part of the comparative experiments, performance comparisons of the proposed algorithm with some modern DE algorithms reported in the literature confirm a significantly better performance of our proposed algorithm, especially on high-dimensional functions.     

Direct adaptive fuzzy control with membership function tuning

In this paper, a direct self‐structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Crystallization of poly(3‐hydroxybutyrate) by exfoliated graphite nanoplatelets

Poly(3‐hydroxybutyrate) (PHB) has been shown to be efficiently nucleated by exfoliated graphite nanoplatelets (xGnP). The nucleating effect of xGnP was investigated using differential scanning calorimetry, optical microscopy and atomic force microscopy. Nonisothermal crystallization of PHB from the melt required lower activation energies for PHB containing 1 wt % and 3 wt % xGnP (?214 and ?102 kJ/mol respectively) than for pure PHB (?60 kJ/mol). A kinetic study of the PHB/xGnP crystallization employing a modified form of the Avrami equation revealed that the presence of xGnP increased the PHB crystallization temperature, as well as the crystallization rates, and generated smaller and more numerous spherulites. Optical microscopy and atomic force microscopy confirmed the incorporation of xGnP into the lamellar structure of the PHB spherulites and provided insight into the influence of xGnP on spherulite size and lamellae thickness. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007     

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.     

Combination of data replication and scheduling algorithm for improving data availability in Data Grids

Data Grid is a geographically distributed environment that deals with large-scale data-intensive applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Data replication is another key optimization technique for reducing access latency and managing large data by storing data in a wisely manner. In this paper two algorithms are proposed, first a novel job scheduling algorithm called Combined Scheduling Strategy (CSS) that uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers the number of jobs waiting in queue, the location of required data for the job and the computing capacity of sites. Second a dynamic data replication strategy, called the Modified Dynamic Hierarchical Replication Algorithm (MDHRA) that improves file access time. This strategy is an enhanced version of Dynamic Hierarchical Replication (DHR) strategy. Data replication should be used wisely because the storage capacity of each Grid site is limited. Thus, it is important to design an effective strategy for the replication replacement. MDHRA replaces replicas based on the last time the replica was requested, number of access, and size of replica. It selects the best replica location from among the many replicas based on response time that can be determined by considering the data transfer time, the storage access latency, the replica requests that waiting in the storage queue and the distance between nodes. The simulation results demonstrate the proposed replication and scheduling strategies give better performance compared to the other algorithms.     

Optimizing search engines results using linear programming

When a query is passed to multiple search engines, each search engine returns a ranked list of documents. Researchers have demonstrated that combining results, in the form of a “metasearch engine”, produces a significant improvement in coverage and search effectiveness. This paper proposes a linear programming mathematical model for optimizing the ranked list result of a given group of Web search engines for an issued query. An application with a numerical illustration shows the advantages of the proposed method.