A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

Optimal motion planning for parallel robots via convex optimization and receding horizon

In this paper, the problem of motion planning for parallel robots in the presence of static and dynamic obstacles has been investigated. The proposed algorithm can be regarded as a synergy of convex optimization with discrete optimization and receding horizon. This algorithm has several advantages, including absence of trapping in local optimums and a high computational speed. This problem has been fully analyzed for two three-DOF parallel robots, ie 3s-RPR parallel mechanism and the so-called Tripteron, while the shortest path is selected as the objective function. It should be noted that the first case study is a parallel mechanism with complex singularity loci expression from a convex optimization problem standpoint, while the second case is a parallel manipulator for which each limb has two links, an issue which increases the complexity of the optimization problem. Since some of the constraints are non-convex, two approaches are introduced in order to convexify them: (1) A McCormick-based relaxation merged with a branch-and-prune algorithm to prevent it from becoming too loose and (2) a first-order approximation which linearizes the non-convex quadratic constraints. The computational time for the approaches presented in this paper is considerably low, which will pave the way for online applications.     

Stochastic flow-shop scheduling with minimizing the expected number of tardy jobs

In this research, minimizing the expected number of tardy jobs in a dynamic m machine flow-shop scheduling problem, i.e., $ {F_m}\left| {{r_j}\left| {{\text{E}}\left[ {\sum {{U_j}} } \right]} \right.} \right. $ is investigated. It is assumed that the jobs with deterministic processing times and stochastic due dates arrive randomly to the flow-shop cell. The due date of each job is assumed to be normally distributed with known mean and variance. A dynamic method is proposed for this problem by which the m machine stochastic flow-shop problem is decomposed into m stochastic single-machine sub-problems. Then, each sub-problem is solved as an independent stochastic single-machine scheduling problem by a mathematical programming model. Comparison of the proposed method with the most effective rule of thumb for the proposed problem, i.e., shortest processing time first rule shows that the proposed method performs 23.9 % better than the SPT rule on average for industry-size scheduling problems.     

Generation of sodium hypochlorite (NaOCl) from sodium chloride solution using C/PbO_2 and Pb/PbO_2 electrodes

Two modified electrodes (Pb/PbO2 and C/PbO2) were prepared by electrodepositing a lead oxide layer on lead and carbon sub-strates. These modified electrodes were used as anodes for the generation of sodium hypochlorite (NaOCl) from sodium chloride solution. Different operating conditions and factors affecting the treatment process of NaOCl generation, including current density, pH values, con-ductive electrolytes, and electrolysis time, were studied and optimized. By comparison the C/PbO2 electrode shows a higher efficiency than the Pb/PbO2 electrode for the generation of NaOCl.     

A state-of the-art survey of TOPSIS applications

Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors’ nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.     

Determining number of clusters and prototype locations via multi-scale clustering

In clustering algorithms, it is usually assumed that the number of clusters is known or given. In the absence of such a priori information, a procedure is needed to find an appropriate number of clusters. This paper presents a clustering algorithm that incorporates a mechanism for finding the appropriate number of clusters as well as the locations of cluster prototypes. This algorithm, called multi-scale clustering, is based on scale-space theory by considering that any prominent data structure ought to survive over many scales. The number of clusters as well as the locations of cluster prototypes are found in an objective manner by defining and using lifetime and drift speed clustering criteria. The outcome of this algorithm does not depend on the initial prototype locations that affect the outcome of many clustering algorithms. As an application of this algorithm, it is used to enhance the Hough transform technique.     

Axial offset control of PWR nuclear reactor core using intelligent techniques

Improved load following capability is one of the main technical performances of advanced PWR (APWR). Controlling the nuclear reactor core during load following operation encounters some difficulties. These difficulties mainly arise from nuclear reactor core limitations in local power peaking, while the core is subject to large and sharp variation of local power density during transients. Axial offset (AO) is the parameter usually used to represent of core power peaking, in form of a practical parameter. This paper, proposes a new intelligent approach to AO control of PWR nuclear reactors core during load following operation. This method uses a neural network model of the core to predict the dynamic behavior of the core and a fuzzy critic based on the operator knowledge and experience for the purpose of decision-making during load following operations. Simulation results show that this method can use optimum control rod groups maneuver with variable overlapping and may improve the reactor load following capability.     

Preparation of polypropylene‐based nanocomposites using nanosized MCM‐41 as support and in situ polymerization

MCM‐41 nanoparticles were used for preparing nanocomposites through the in situ polymerization of propylene. The performance of the catalytic system and the final properties of the materials obtained are highly dependent on the methodology used for impregnation of the catalyst onto the support particles, and therefore an optimization study for the impregnation methodology of the catalyst (Me₂Si(Ind)₂ZrCl₂) was carried out. Two different methodologies were used; the results in terms of catalytic activity and polymer molecular masses indicated that the most promising one involved the pre‐activation of the catalyst with the cocatalyst, methylaluminoxane, followed by impregnation onto the MCM‐41 nanoparticles. Thus, an optimized route for the preparation of polypropylene nanocomposites achieving significant improvements in catalyst activity was developed. The nanocomposite materials were characterized by GPC, TGA and DSC. The dispersion state and the size of the nanoparticles incorporated in the polypropylene matrix were investigated by transmission electron microcopy. Additionally, this methodology allows simultaneous control of the desired amount of support and the concentration of catalyst to be used in the in situ polymerization. © 2015 Society of Chemical Industry     

Graphene network enabled high speed electrical actuation of shape memory nanocomposite based on poly(vinyl acetate)

Here strong electroactive shape memory nanocomposites were prepared by incorporating graphene nanoplatelets into poly(vinyl acetate) (PVAc ) through the simple solvent mixing method. TEM and XRD revealed that well exfoliated graphene nanoplatelets formed a continuous network throughout the matrix with a large amount of interconnectedness. Dynamic mechanical analysis showed that the inclusion of graphene significantly improves both glassy and rubbery moduli of the matrix. Furthermore, the prepared nanocomposites demonstrated a marked electrical conductivity up to 24.7 S m^?1 and thereby surprisingly rapid electrical actuation behaviour exhibiting a 100% recovery ratio in 2.5 s. Moreover, PVAc and its nanocomposites displayed scratch self‐healing capability. This work demonstrates that the PVAc /graphene nanocomposites with high modulus and excellent electroactive shape memory performance can be a promising material in many applications such as sensors and fast deployable and actuating devices. © 2016 Society of Chemical Industry