Modeling and solving mixed-model assembly line balancing problem with setups. Part I: A mixed integer linear programming model

This paper is the first one of the two papers entitled “modeling and solving mixed-model assembly line balancing problem with setups”, which has the aim of developing the mathematical programming formulation of the problem and solving it with a hybrid meta-heuristic approach. In this current part, a mixed-integer linear mathematical programming (MILP) model for mixed-model assembly line balancing problem with setups is developed. The proposed MILP model considers some particular features of the real world problems such as parallel workstations, zoning constraints, and sequence dependent setup times between tasks, which is an actual framework in assembly line balancing problems. The main endeavor of Part-I is to formulate the sequence dependent setup times between tasks in type-I mixed-model assembly line balancing problem. The proposed model considers the setups between the tasks of the same model and the setups because of the model switches in any workstation. The capability of our MILP is tested through a set of computational experiments. Part-II tackles the problem with a multiple colony hybrid bees algorithm. A set of computational experiments is also carried out for the proposed approach in Part-II.     

Toward an efficient and scalable feature selection approach for internet traffic classification

There is significant interest in the network management and industrial security community about the need to identify the “best” and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps to identify the best features in order to improve the classification accuracy as well as to reduce the computational complexity related to the construction of the classifier. In practice, feature selection (FS) techniques can be used as a preprocessing step to eliminate irrelevant features and as a knowledge discovery tool to reveal the “best” features in many soft computing applications. In this paper, we investigate the advantages and disadvantages of such FS techniques with new proposed metrics (namely goodness, stability and similarity). We continue our efforts toward developing an integrated FS technique that is built on the key strengths of existing FS techniques. A novel way is proposed to identify efficiently and accurately the “best” features by first combining the results of some well-known FS techniques to find consistent features, and then use the proposed concept of support to select a smallest set of features and cover data optimality. The empirical study over ten high-dimensional network traffic data sets demonstrates significant gain in accuracy and improved run-time performance of a classifier compared to individual results produced by some well-known FS techniques.     

Rule extraction from artificial neural networks to discover causes of quality defects in fabric production

In this paper, a novel classification rule extraction algorithm which has been recently proposed by authors is employed to determine the causes of quality defects in a fabric production facility in terms of predetermined parameters like machine type, warp type etc. The proposed rule extraction algorithm works on the trained artificial neural networks in order to discover the hidden information which is available in the form of connection weights in them. The proposed algorithm is mainly based on a swarm intelligence metaheuristic which is known as Touring Ant Colony Optimization (TACO). The algorithm has a hierarchical structure with two levels. In the first level, a multilayer perceptron type neural network is trained and its weights are extracted. After obtaining the weights, in the second level, the TACO-based algorithm is applied to extract classification rules. The main purpose of the present work is to determine and analyze the most effective parameters on the quality defects in fabric production. The parameters and their levels which give the best quality results are tried to be discovered and evaluated by making use of the proposed algorithm. It is also aimed to compare the accuracy of proposed algorithm with several other rule-based algorithms in order to present its competitiveness.     

An Analytical Approach to the Concept of Counter-UA Operations (CUAOPS)

Unmanned Aircraft (UA) have become an integral part of the present-day joint air operations. UA have potential to be employed across the full spectrum of Air Force functions. On the other hand, UA technology could be a subject of an asymmetric use by state actors in high or low density conflict, and/or by non-state actors in many ways including terrorism, drug smuggling, and limited attack with unconventional payloads i.e. biological or chemical agents. Therefore countering the threat associated with the hostile UA use could be necessary in the future. First part of the research revealed that UA can be a threat in the future. In the second part of the research, the SWOT (Strengths-Weaknesses-Opportunities-Threats) analysis supplemented with the Tree analysis (SWOT+Tree) provided a broader look for investigating the factors related to hostile UA use analytically. In literature, there is little information about the concepts for CUAOPS. The final goal of the paper is to find possible solutions and means for a better understanding of the nature of CUAOPS.     

A changing range genetic algorithm

During the last decade various methods have been proposed to handle linear and non‐linear constraints by using genetic algorithms to solve problems of numerical optimization. The key to success lies in focusing the search space towards a feasible region where a global optimum is located. This study investigates an approach that adaptively shifts and shrinks the size of the search space to the feasible region; it uses two strategies for estimating a point of attraction. Several test cases demonstrate the ability of this approach to reach effectively and accurately the global optimum with a low resolution of the binary representation scheme and without additional computational efforts. Copyright © 2004 John Wiley & Sons, Ltd.     

Oriented immobilized anti-LDL antibody carrying poly(hydroxyethyl methacrylate) cryogel for cholesterol removal from human plasma

Low density lipoprotein (LDL) cholesterol is a major ingredient of the plaque that collects in the coronary arteries and causes coronary heart diseases. Among the methods used for the extracorporeal elimination of LDL from intravasal volume, immunoaffinity technique using anti-LDL antibody as a ligand offers superior selectivity and specificity. Proper orientation of the immobilized antibody is the main issue in immunoaffinity techniques. In this study, anti-human β-lipoprotein antibody (anti-LDL antibody) molecules were immobilized and oriented through protein A onto poly(2-hydroxyethyl methacrylate) (PHEMA) cryogel in order to remove LDL from hypercholesterolemic human plasma. PHEMA cryogel was prepared by free radical polymerization initiated with N,N,N′,N′-tetramethylene diamine (TEMED). PHEMA cryogel with a swelling degree of 8.89 g H₂O/g and 67% macro-porosity was characterized by swelling studies, scanning electron microscope (SEM) and blood compatibility tests. All the clotting times were increased when compared with control plasma. The maximum immobilized anti-LDL antibody amount was 63.2 mg/g in the case of random antibody immobilization and 19.6 mg/g in the case of oriented antibody immobilization (protein A loading was 57.0 mg/g). Random and oriented anti-LDL antibody immobilized PHEMA cryogels adsorbed 111 and 129 mg LDL/g cryogel from hypercholesterolemic human plasma, respectively. Up to 80% of the adsorbed LDL was desorbed. The adsorption–desorption cycle was repeated 6 times using the same cryogel. There was no significant loss of LDL adsorption capacity.     

A Novel Sprague-Dawley Rat Model Presents Improved NASH/NAFLD Symptoms with PEG Coated Vitexin Liposomes

Chronic liver disease (CLD) is a global threat to the human population, with manifestations resulting from alcohol-related liver disease (ALD) and non-alcohol fatty liver disease (NAFLD). NAFLD, if not treated, may progress to non-alcoholic steatohepatitis (NASH). Furthermore, inflammation leads to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Vitexin, a natural flavonoid, has been recently reported for inhibiting NAFLD. It is a lipogenesis inhibitor and activates lipolysis and fatty acid oxidation. In addition, owing to its antioxidant properties, it appeared as a hepatoprotective candidate. However, it exhibits low bioavailability and low efficacy due to its hydrophobic nature. A novel rat model for liver cirrhosis was developed by CCL4/Urethane co-administration. Vitexin encapsulated liposomes were synthesized by the ‘thin-film hydration’ method. Polyethylene glycol (PEG) was coated on liposomes to enhance stability and stealth effect. The diseased rats were then treated with vitexin and PEGylated vitexin liposomes, administered intravenously and orally. Results ascertained the liposomal encapsulation of vitexin and subsequent PEG coating to be a substantial strategy for treating liver cirrhosis through oral drug delivery.     

Experimental study of thermal performance improvement in a cross-flow heat exchanger by using copper foam

In this study, copper foam was used as a porous medium in place of traditional aluminum fins. A comparison between the two heat exchangers—one with fins and the other with copper foam—was conducted under various conditions. The air inlet velocity ranged from 0.9 to 9.3 m/s, and the water inlet temperature ranged from 10°C to 18°C. Different water flow rates were tested. A comparison was made between the performance of copper foam and aluminum fins by calculating several parameters, including thermal resistance, heat exchanger effectiveness, Colburn factor, Nusselt number, friction factor, and area goodness factor (AG). The experimental results showed that at low air velocities, the heat transfer coefficient for both types of heat exchangers was almost equal. However, at high air velocities, the copper foam exhibited a higher heat transfer coefficient. The Colburn factor was higher for the heat exchanger with copper foam than in the conventional fins, where it was equal to 0.1959 for the copper foam and 0.1186 for the fins. On the other hand, the AG was higher in the case of fins than in the heat exchanger with copper foam.     

Ferromagnetic nano model study for the peristaltic flow in a plumb duct with permeable walls

Microsystem Technologies - The purpose of the present enquiry is to analyse the mechanics of an incompressible fluid, with water as base fluid, through a radially symmetric plumb duct with...