Transport Properties of Steam Curing Concrete Using Mineral Admixtures

Steam curing has been used widely in the precast concrete industry. However, investigations on steam curing concrete focused mainly on their strength development. Research on durability of steam curing concrete, particularly on mixes using mineral admixtures, is somewhat limited. In this study, transport properties and mechanical properties of concretes using mineral admixtures under different steam curing conditions were measured. Normal Portland cement, high early strength Portland cement, ground granulated blast-furnace slag and fly ash were used. Steam curing conditions were varied by changing the preliminary curing time, temperature drop rate and curing method after steam curing. Transport properties, such as oxygen gas diffusion coefficient and water diffusion coefficient were measured. Moreover, mechanical properties, such as compressive strength, static elastic modulus and dynamic elastic modulus were measured. From the experimental result, the degree of the influence that the differences in steam curing conditions give to transport properties varies according to the types of binder. It was confirmed that transport properties might decrease remarkably in comparison with the case of a standard steam curing condition

A low-bit learning algorithm for digital multilayer neural networks applied to pattern recognition

A new low-bit learning algorithm for digital multilayer neural networks applied to pattern recognition is proposed. The training can be carried out with a small number of bits. To make the neural network insensitive to noisy patterns, conditions to be satisfied by hidden layer outputs are discussed. Based on this, optimum targets are assigned to the hidden layers. Computer simulation demonstrated the efficiency of the proposed method

Quantization level increase in human face images using multilayer neural network

In this paper, quantization level increase in human face images using a multilayer neural network (NN) is investigated. Basically speaking, it is impossible to increase quality without any other information. However, when images are limited to some category, image restoration could be possible, based on the common properties in this category. The multilayer NN is trained using human face images of 32ﾃ・2 pixels with 8-levels as the input data, and 256-level images as the targets. The standard back-propagation (BP) algorithm is employed. 20, 40 and 100 training data are examined. By increasing the training data, a general function of regenerating missing information can be achieved. The internal structure of the trained NN is analyzed using some special input images. As a result, it has been confirmed that the NN regards the input image as the human face, and extracts features of the face. The input image is transformed using these features and the common properties of the training data, extracted and held on the connection weights, to the human face image

Memory capacity bound and threshold optimization in recurrent neural network with variable hysteresis threshold

Authors have proposed an asymmetrical associative neural network (NN) using variable hysteresis threshold and its learning and association algorithms. It can drastically improve noise performance, that is insensitivity to noise. In this paper, memory capacity bound and threshold optimization in this associative NN are further discussed. Binary random patterns are considered. First, relation between the number of patterns and the number of iterations is investigated. The latter gradually increases until some number of patterns. After that, it suddenly increases. This is a very peculiar phenomenon. This turning point gives the memory capacity bound, that is about 1.56N, where N is the number of units. Next, threshold optimization is discussed. Relation between threshold and noise performance, and effects of connection weight distribution on noise performance are theoretically discussed. Based on these results, a ratio of step-size and the threshold is optimized to be 0.5/(NP-1), where NP is the number of units on the pattern. Numerically statistical simulation demonstrates efficiency of the proposed methods

Generation of a Mutant Mucor hiemalis Endoglycosidase That Acts on Core-fucosylated N-Glycans

Endo-β-N-acetylglucosaminidase M (Endo-M), an endoglycosidase from the fungus Mucor hiemalis, is a useful tool for chemoenzymatic synthesis of glycoconjugates, including glycoprotein-based therapeutics having a precisely defined glycoform, by virtue of its transglycosylation activity. Although Endo-M has been known to act on various N-glycans, it does not act on core-fucosylated N-glycans, which exist widely in mammalian glycoproteins, thus limiting its application. Therefore, we performed site-directed mutagenesis on Endo-M to isolate mutant enzymes that are able to act on mammalian-type core-α1,6-fucosylated glycans. Among the Endo-M mutant enzymes generated, those in which the tryptophan at position 251 was substituted with alanine or asparagine showed altered substrate specificities. Such mutant enzymes exhibited increased hydrolysis of a synthetic α1,6-fucosylated trimannosyl core structure, whereas their activity on the afucosylated form decreased. In addition, among the Trp-251 mutants, the W251N mutant was most efficient in hydrolyzing the core-fucosylated substrate. W251N mutants could act on the immunoglobulin G-derived core-fucosylated glycopeptides and human lactoferrin glycoproteins. This mutant was also capable of transferring the sialyl glycan from an activated substrate intermediate (sialyl glyco-oxazoline) onto an α1,6-fucosyl-N-acetylglucosaminyl biotin. Furthermore, the W251N mutant gained a glycosynthase-like activity when a N175Q substitution was introduced and it caused accumulation of the transglycosylation products. These findings not only give insights into the substrate recognition mechanism of glycoside hydrolase family 85 enzymes but also widen their scope of application in preparing homogeneous glycoforms of core-fucosylated glycoproteins for the production of potent glycoprotein-based therapeutics