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.     

DNA-directed immobilization of horseradish peroxidase onto porous SiO2optical transducers

ABSTRACT: Multifunctional porous Si nanostructure is designed to optically monitor enzymatic activity of Horseradish Peroxidase. First, an oxidized PSi optical nanostructure, a Fabry-Perot thin film, is synthesized and is used as the optical transducer element. Immobilization of the enzyme onto the nanostructure is performed through DNA-Directed Immobilization. Preliminary studies demonstrate high enzymatic activity levels of the immobilized Horseradish Peroxidase, while maintaining its specificity. The catalytic activity of the enzymes immobilized within the porous nanostructure is monitored in real time by reflective interferometric Fourier transform spectroscopy. We show that we can easily regenerate the surface for consecutive biosensing analysis by mild de-hybridization conditions.     

Thieno[3,2-b]thiophene as π-bridge at different acceptor systems for electrochromic applications

Benzoselenadiazole, quinoxaline and thieno[3,2-b]thiophene are the units preferred in conducting polymers due to their electrochemical properties. There are no reports in the literature on polymers containing both moieties. In this study, novel benzoselenadiazole, quinoxaline and thieno[3,2-b]thiophene based monomers; 4-(3a,6a-dihydrothieno[3,2-b]thiophen-2-yl)-7-(thieno[3,2-b]thiophenyl)benzo[c][1,2,5]selenadiazole (BSeTT) and 2,3-bis(3,4-bis(decyloxy)phenyl)-5,8-dibromo-2,3-dihydroquinoxaline (QTT) were synthesized via Stille Coupling and polymerized electrochemically. These polymers were characterized in terms of their spectroelectrochemical and electrochemical properties by cyclic voltammetry and UV–Vis–NIR spectroscopy. Spectroelectrochemistry analysis of PBSeTT revealed an electronic transition at 525 nm corresponding to π–π* transition with a band gap of 0.93 eV whereas PQTT revealed electronic transitions at 440 and 600 nm corresponding to π–π* transitions with a band gap of 1.30 eV. Electrochromic investigations showed that PBSeTT has gray color PQTT switching between green and gray. Switching time of the polymers was evaluated by a kinetic study upon measuring the percent transmittance (%T) at the maximum contrast point.     

A fugacity based continuous and dynamic fate and transport model for river networks and its application to Altamaha River

In this paper, a continuous and dynamic fugacity-based contaminant fate and transport model is developed. The dynamic interactions among all phases in the physical domain are addressed through the use of the fugacity approach instead of the use of concentration as the unknown variable. The full form of Saint Venant equations is used in order to solve for the hydrodynamic conditions in the river network. Then a fugacity-based advection-dispersion equation is modeled to examine the fate and transport of contaminants in the river network for all phases.The fugacity-based, dynamic and continuous contaminant fate and transport model developed here is applied to Altamaha River in Georgia, USA to demonstrate its use in environmental exposure analysis. Altamaha River is the largest river system east of Mississippi which offers habitat for many species, including about 100 rare endangered species, along its 140 mile course. Polychlorinated biphenyls (PCBs), a highly hydrophobic and toxic chemical ubiquitous in nature, and atrazine, the most commonly-used agricultural pesticide are modeled as contaminants in this demonstration. Through this approach the concentration distribution of PCBs and atrazine in the water column of Altamaha River as well as the sediments can be obtained with relative ease, which is an improvement over concentration based analysis of phase distribution of contaminants.     

Modelling and optimization of the firing zone of a tunnel kiln to predict the optimal feed locations and mass fluxes of the fuel and secondary air

Optimization of the firing zone was done to minimize the fuel cost as being the objective function by finding the optimal operating variables. These variables are the initial admixed coal (AC) in the brick body, the mass flux and locations of pulverized coal (PC) and the secondary air required for complete combustion. The constraints are the carbon coming from the AC which increases the porosity of the bricks, and should be consumed completely. Two other constraints to avoid thermal shock are the temperature difference between bricks and air, and the maximum heating rate of the bricks. The results were compared to the plant data.     

A novel nonchemical approach to the expansion of halloysite nanotubes and their uses in chitosan composite hydrogels for broad‐spectrum dye adsorption capacity

A novel method based on cryoscopic expansion of halloysite nanotubes via frozen water molecules entrapped in their lumens and subsequent lyophilization was described. Detailed analyses confirmed that the inner and outer diameters as well as the surface area of the nanotubes could be efficiently increased without disturbing the inherent tubular structure. The benefits of cryo‐expanded nanotubes for the enhancement of chitosan hydrogel performances were discussed. The composite hydrogels, depending on their compositions and morphologies, exhibited significantly enhanced swelling and mechanical properties compared with neat chitosan hydrogel. This effect was even more pronounced in the hydrogels containing cryo‐expanded halloysite nanotubes. Although neat chitosan is a selectively good adsorbent for anionic dyes, in the presence of a small amount of cryo‐expanded halloysite, the resultant composite hydrogel can establish a relatively high adsorption capacity for anionic and cationic dyes as a broad‐spectrum dye adsorbent. POLYM. COMPOS., 37:2770–2781, 2016. © 2015 Society of Plastics Engineers     

High-Performance Green Building Design Process Modeling and Integrated Use of Visualization Tools

High-performance green buildings require close integration of building systems with a special focus on energy, daylighting, and material analysis during their design processes. Design process modeling and use of visualization tools can facilitate better communication and collaboration between team members; hence better integration in the design process. This paper presents a case study of the integrated design process of an Early Childhood Learning Center as performed by a team of university students and faculty. A process modeling approach of key decisions, required consultants, and virtual prototypes of the building was used during the design development stages of the case study. This case illustrates the use of process modeling and visualization tools to provide an accurate building information system for integrated teams. Through this experience, process modeling and visualization tools were found to be useful mechanisms to achieve high performance design goals and minimize design process waste.     

Spectroscopic and conductometric studies on the interactions of thionine with anionic and nonionic surfactants

In this study, the interaction of thionine, a cationic dye, with anionic [sodium dodecyl sulphate (SDS), lithium dodecyl sulphate (LiDS), and sodium dodecylbenzene sulphonate (SDBS)], nonionic (Tween 20 and Triton X‐100), and binary mixtures of anionic and nonionic surfactants was studied by conductometric and spectrophotometric measurements. The degree of ionisation, the counterion binding parameters, and the equilibrium constants in the premicellar region were obtained from conductivity data. Binding constants of thionine to anionic, nonionic, and mixtures of anionic and nonionic micelles were determined by spectrophotometric measurements. The binding tendency of thionine to anionic micelles followed the order SDBS > SDS > LiDS. The presence of nonionic surfactants increased significantly the binding affinity of thionine to anionic micelles, and the highest binding constant was calculated in the presence of Tween 20. The results obtained from conductometric studies correlated with those obtained from spectroscopic studies. Data concerning dye–surfactant interaction are important for a fundamental understanding of the performance of single and mixed surfactants and for their industrial application.