Constitutive modeling of Leighton Buzzard Sands using genetic programming

This paper investigates the results of laboratory experiments and numerical simulations conducted to examine the behavior of mixtures composed of coarse (i.e. Leighton Buzzard Sand fraction B) and fine (i.e. Leighton Buzzard Sand fraction E) sand particles. Emphasis was placed on assessing the role of fines content in mixture and strain level on the deviatoric stress and pore water pressure generation using experimental (i.e. Triaxial testing) and numerical approaches (i.e. genetic programming, GP). The experimental database used for GP modeling is based on a laboratory study of the properties of saturated coarse and fine sand mixtures with various mix ratios under a 100 kPa effective stresses in a 100 mm diameter conventional triaxial testing apparatus. Experimental results show that coarse–fine sand mixtures exhibit clay-like behavior due to particle–particle effects with the increase in fines content. It is shown that GP modeling of coarse–fine sand mixtures is observed to be quite satisfactory. The results have implications in the design of compressible particulate systems and in the development of prediction tools for the field performance coarse–fine sands.     

CFRP Effect on the Buckling Behavior of Dented Cylindrical Shells

Considering that the use of thin-walled shells is expanding every day, it is important to examine the problem of instability in this form of structure. Many steel structures such as high-water tanks, water and oil reservoirs, marine structures, and pressure vessels, including shell elements, are under stress tension. In addition, shell elements are subject to instability owing to the loads applied. Ten thin-walled cylindrical shell specimens in two groups with different dent depths of t_c and 2t_c, and the different dent number subject to uniform external pressure were tested in the present research (t_c is the thickness of cylindrical shell). The samples were modified to include either one or two dent line with amplitudes of h/3 in height (h the height of cylinder shell). Moreover, CFRP Strips on the dent depth was used in one of the groups. The results of testing under different theories and codes are compared.

     

Hyperbranched homo and thermoresponsive graft copolymers by using ATRP‐macromonomer initiators

Macromonomer initiators behave as macro cross‐linkers, macro initiators, and macromonomers to obtain branched and cross‐linked block/graft copolymers. A series of new macromonomer initiators for atom transfer radical polymerization (MIM‐ATRP) based on polyethylene glycol (M_n = 495D, 2203D, and 4203D) (PEG) were synthesized by the reaction of the hydroxyl end of mono‐methacryloyl polyethylene glycol with 2‐bromo propanoyl chloride, leading to methacryloyl polyethylene glycol 2‐bromo propanoyl ester. Poly (ethylene glycol) functionalized with methacrylate at one end was reacted with 2‐bromopropionyl chloride to form a macromonomeric initiator for ATRP. ATRP was found to be a more controllable polymerization method than conventional free radical polymerization in view of fewer cross‐linked polymers and highly branched polymers produced from macromonomer initiators as well. In another scenario, ATRP of N‐isopropylacrylamide (NIPAM) was initiated by MIM‐ATRP to obtain PEG‐b‐PNIPAM branched block/graft copolymers. Thermal analysis, FTIR, ¹H NMR, TEM, and SEM techniques were used in the characterization of the products. They had a thermo‐responsive character and exhibited volume phase transition at ～ 36°C. A plasticizer effect of PEG in graft copolymers was also observed, indicating a lower glass transition temperature than that of pure PNIPAM. Homo and copolymerization kinetics were also evaluated. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Synthesis and characterization of cryogels of p(HEMA-N-vinylformamide) and p(HEMA-N-Vinylpyrrolidone) for chemical release behaviour

Journal of Porous Materials - Macroporous polymeric gels has received great attention in many fields focused on biotechnological applications. In this study, two new HEMA-based cryogel columns were...     

A robust technique based on invariant moments – ANFIS for recognition of human parasite eggs in microscopic images

In this study, we propose a robust technique based on invariant moments – adaptive network based fuzzy inference system (IM-ANFIS). In this technique, some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of IM-ANFIS approach used in this study. Recently, the pattern recognition principles have come into prominence. The pattern recognition includes operation and design of systems that recognize patterns in data sets. Important application areas of pattern recognition techniques are character recognition, speech analysis, image segmentation, man and machine diagnostics and industrial inspection. The technique presented in this study enables to classify 16 different parasite eggs from their microscopic images. This proposed recognition method includes three stages. In first stage, a preprocessing subsystem is realized for obtaining unique features from the same group of patterns. In second stage, a feature extraction mechanism which is based on the invariant moments is used. In third stage, an adaptive network based fuzzy inference system (ANFIS) classifier is used for recognition process. We conduct computer simulations on MATLAB environment. The overall success rate is almost 95%.     

Effective diagnosis of heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease. A neural networks ensemble method is in the centre of the proposed system. This ensemble based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with the proposed tool. We obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease database. We also obtained 80.95% and 95.91% sensitivity and specificity values, respectively, in heart disease diagnosis.