An Efficient Procedure for Identifying the Prediction Model Between Residual Stress and Barkhausen Noise

Residual stress of case-hardened steel samples is predicted in this paper with the linear multivariable regression model. The development of the prediction model is based on the huge set of features calculated from the Barkhausen noise measurement signal among which the most suitable ones are chosen. The selection uses a genetic algorithm with leave-multiple-out cross-validation in the objective function. The original feature set contains collinear features that make the selection task even more complex. Thus a feature elimination procedure based on the successive projections algorithm is studied in this paper. Also the standard genetic algorithm is slightly modified to better serve the feature selection task. The obtained results are good showing that the proposed procedures suit well for residual stress predictions. Also the applied feature elimination procedure is applicable and can be safely used to reduce the dimensionality of the selection problem.     

Purification and characterization of sea squirt alpha-N-acetylgalactosaminidase

Sea squirt alpha-N-acetylgalactosaminidase was purified to homogeneity. Its molecular weight was estimated to be approximately 160,000 by gel filtration and 40,000 by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) under reducing condition. The chromatographic and electrophoretic behaviors indicated that the enzyme was composed of four subunits. The optimum pH of the enzyme reaction was about 4.0 at 37 degrees C, while the enzyme was stable in the range of pH 5.0 to 6.0 during 4 h preincubation at 37 degrees C. Although the enzyme (0.1 unit) was stable at 0 degrees C for 30 min in the presence of 7.5 mM metal ions (Al3+, Ba2+, Ca2+, K+, Mn2+, Pb2+, Sr2+, and Zn2+), almost 40% of the enzyme activity was lost in the presence of Cu2+, Hg2+, monoiodoacetic acid, and EDTA. The enzyme hydrolyzed aryl N-acetyl-alpha-D-galactosaminide as well as GalNAcalpha1(-->4GalNAcalpha1-->)n 4GalNAc-p-aminobenzoic acid ethyl ester (ABEE) (n = 1-4), but GalNAcalpha1-->4GalNAc-ABEE only scarcely. Furthermore, an allergenic pentasaccharitol ABEE derivative, GalNAcalpha1-->2Fucalpha1-->3(GalNAcbeta1-->4) GlcNAcbeta1-->2(3-acetoamido-3-deoxy)L-threose-ABEE, the minimum structural unit for the sea squirt allergenicity was hydrolyzed to 95 mol% for 72 h incubation with the enzyme. The enzyme could be utilized as a powerful tool for the structural analyses of the carbohydrate epitopes of the sea squirt allergen molecules.     

Impact of coagulation and adsorption on DOC fractions of secondary effluent and resulting fouling behaviour in ultrafiltration

Membrane fouling by macromolecular dissolved organic compounds is still a fundamental drawback in low-pressure membrane filtration of secondary effluent. In this study, pre-treatment of secondary effluent by coagulation and/or adsorption was investigated in terms of removal of different dissolved organic carbon (DOC) fractions, especially macromolecular substances. DOC fractionation has been characterised by size exclusion chromatography. Adsorption tests using four commercially available activated carbons yielded a removal of small as well as larger organic compounds, revealing differences in the affinity towards macromolecules depending on the type of applied activated carbon. By contrast, coagulation removed predominantly larger molecules, i.e., biopolymers and humic substances. In terms of DOC reduction, the coagulant ferric chloride was superior to aluminium chloride. A combination of coagulation and adsorption resulted in the addition of individual removal efficiencies, suggesting that different fractions of organic compounds were involved in each of the processes. After removal of macromolecular organic compounds either by coagulation or by adsorption, a significant reduction of membrane fouling was observed in tests using two different types of ultrafiltration flat-sheet membranes in 20-h cross-flow filtration tests.     

Framework for developing image-based dirt particle classifiers for dry pulp sheets

One important aspect of assessing the quality in pulp and papermaking is dirt particle counting and classification. Knowing the number and types of dirt particles present in pulp is useful for detecting problems in the production process as early as possible and for fixing them. Since manual quality control is a time-consuming and laborious task, the problem calls for an automated solution using machine vision techniques. However, the ground truth required to train an automated system is difficult to ascertain, since all of the dirt particles should be manually segmented and classified based on image information. This paper proposes a framework for developing and tuning dirt particle detection and classification systems. To avoid manual annotation, dry pulp sheets with a single dirt type in each were exploited to generate semisynthetic images with the ground truth information. To classify the dirt particles, a set of features were computed for each image segment. Sequential feature selection was employed to determine a close-to-optimal set of features to be used in classification. The framework was tested both with semisynthetically generated images based on real pulp sheets and with independent original real pulp sheets without any generation. The results of the experiments show that the semisynthetic procedure does not significantly change the properties of images and has little effect on the particle segmentation. The feature selection proved to be important when the number of dirt classes changes since it allows to improve the classification results. Using the standard classification methods, it is possible to obtain satisfactory results, although the methods modeling the data, such as the Bayesian classifier using the Gaussian Mixture Model, show better performance.     

Loss of H19 imprinting and up-regulation of H19 and SNRPN in a case with malignant mixed Müllerian tumor of the uterus

An unusual clinical presentation of a patient with neuronal intestinal dysplasia is presented. A 46-year-old male noted a palpable mass in the right lower quadrant of his abdomen for two months. A computed axial tomographic scan showed a thickened wall of the cecum with a tumor-like appearance. The excised specimen consisted of a mass caused by the thickened, edematous wall of the dilated cecum and appendix. The wall of the cecum and appendix measured up to 2.5 and 0.8 cm, respectively, in thickness. Microscopic studies showed extensive hyperplasia and hypertrophy of the ganglia and nerve plexuses and hypertrophy of the muscularis propria, consistent with neuronal intestinal dysplasia.     

Fracture mechanics of diamond-like carbon (DLC) films coated on flexible polymer substrates

Diamond-like carbon (DLC) films have been widely used for many industrial applications due to their outstanding physical properties such as high hardness, wear resistance and biological compatibility. The DLC films coated on polymer substrates have also been extensively used and investigated because recently, quite a few applications for the use of these polymer-DLC composites have been proposed and actively discussed. The applications range from DLC-coated Polyethylene Terephthalate film (DLC-PET), through DLC-coated Polycarbonate (DLC-PC) to other DLC-coated rubbers. In this work, thin DLC films coated on several polymer substrates possessing different chemical structures and Young's moduli were introduced. The DLC-polymer films were stretched to different strains and the extended surface was investigated by optical microscopy and scanning electron microscopy (SEM) to study the fracture mechanics of the DLC-coated polymer films. Horizontally and vertically aligned micro-cracks and micro-buckling were observed, constructing periodic lattice-like fracture patterns on the surface of the extended DLC-polymer films. It was found that the lattice patterns were significantly influenced by Young's moduli of polymer substrates and DLC films, and that the patterns were also dependent on the adhesion between the DLC films and the polymers.     

Prediction of Residual Stresses Using Partial Least Squares Regression on Barkhausen Noise Signals

Residual stresses are quantitatively predicted based on the Barkhausen noise measurement with a partial least squares regression model. The measurements are taken from two sets of case-hardened samples. The benefits of using certain feature elimination strategies prior model identification are also studied. The elimination methods applied are correlation-based elimination, uninformative variable elimination and successive projections algorithm. The results show that the best predictions are usually obtained when the successive projections algorithm is applied. The prediction accuracy of the best models found shows that partial least squares models can be successfully used for prediction of material properties based on the Barkhausen noise measurement.     

Analysis of energy service systems in urban areas and their CO2 mitigations and economic impacts

Three kinds of energy service system were examined as alternative energy systems in urban areas. A comparison of these energy systems was performed by finding Pareto optimum solutions for a multi-objective model. The model had two objective functions: CO₂ emission and cost to consumer. Various energy pricings were provided in the model as variables.