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An advanced three-dimensional printing process for producing the silver clusters/ Silver(I) chloride/titanium dioxide-coupled photocatalyst was developed and tested for its stability and degradability in relation to azo dye (Orange II) and bacteria (Escherichia coli). The titanium dioxide structure is produced through fused filament fabrication (FFF) with filaments of thermoplastic material, which is composed of titanium dioxide anatase nanoparticles, high density polyethylene (HDPE), stearic acid, wax, and plasticizer. The TiO2 structure is then solvent degreased, thermal degreased, and sintered to become a fundamental structure to couple the AgCl particles through an ion exchange process. Following the photoreduction of UV radiation, a silver clusters/Silver(I) chloride/titanium dioxide-coupled photocatalyst is formed. In our experiments, the degradation of Orange II dye and E. coli was performed under visible and ultraviolet light irradiation. The degradation kinetics of Orange II dye was a first-order reaction, with the degradability (94%) persisting for five cycles. The sterilization of E. coli was accomplished within 120 min, and the degradation kinetics were characteristic of a hyperbolic reaction. The photocatalytic module prepared through FFF not only exhibited the ability to degrade contaminants in water but also exhibited durability and reliability after repeated use. [ABSTRACT FROM AUTHOR]

This study used a three-dimensional (3D) printing process to develop the Ag/AgCl/TiO2 coupled photocatalyst with a specific surface area of 748 mm2/g. We examined the catalytic capability of this photocatalyst in degrading methyl blue (MB) dye and sterilizing Escherichia coli as well as the durability and reliability of its continuous use. A TiO2 module was constructed through fused filament fabrication (FFF), and the adopted 3D printing filament was composed of anatase TiO2 nanoparticles, stearic acid, wax, and a plasticizer. The green compact of the TiO2 module was subjected to solvent debinding, thermal debinding, and sintering to obtain a fundamental structure that was subsequently coupled with AgCl through a precipitation reaction. Ultraviolet radiation was used for the photoreduction to obtain the Ag/AgCl/TiO2 coupled photocatalyst coupling module. This photocatalyst can effectively degrade MB dye and disinfect E. coli. The degradation of MB dye and sterilization of E. coli were conducted under visible and ultraviolet light. The degradation of MB dye by the Ag/AgCl/TiO2 coupled photocatalyst was a first-order reaction. In addition, this coupled photocatalyst could retain its MB dye degradation rate (95%) for five cycles. E. coli was sterilized using the prepared photocatalytic module in a 120-min test, and this sterilization phenomenon could be presented as a hyperbolic reaction. The photocatalytic module manufactured in this study through FFF could efficiently degrade pollutants in water, and its durability and reliability after repeated use have been approved. [ABSTRACT FROM AUTHOR]

This paper discusses how to predict hepatitis C virus protease cleavage sites in proteins using generalized linear indicator regression models. The mutual information is used for model-size optimization. Two simulation strategies are adopted, i.e., building a model based on published peptides and building a model based on the published peptides plus newly collected sequences. It is found that the latter outperforms the former significantly. The simulation also shows that the generalized linear indicator regression model far outperforms the multilayer perceptron model. Index Terms--Cleavage site prediction, generalized linear indicator regression models, hepatitis C virus.

A novel radial basis function neural network for discriminant analysis is presented in this paper. In contrast to many other researches, this work focuses on the exploitation of the weight structure of radial basis function neural networks using the Bayesian method. It is expected that the performance of a radial basis function neural network with a well-explored weight structure can be improved. As the weight structure of a radial basis function neural network is commonly unknown, the Bayesian method is, therefore, used in this paper to study this a priori structure. Two weight structures are investigated in this study, i.e., a single-Gaussian structure and a two-Gaussian structure. An expectation-maximization learning algorithm is used to estimate the weights. The simulation results showed that the proposed radial basis function neural network with a weight structure of two Gaussians outperformed the other algorithms. Index Terms--Bayesian method, discriminant analysis, radial basis function neural networks (RBFNNs).

A novel pattern recognition algorithm called all orthogonal kernel machine (OKM) is presented for the prediction of functional sites in proteins. Two novelties in OKM are that the kernel function is specially designed for measuring the similarity between a pair of protein sequences and the kernels are selected using the orthogonal method. Based on a set of well-recognized orthogonal kernels, this algorithm demonstrates its superior performance compared with other methods. An application of this algorithm to a real problem is presented. Index Terms--Bioinformatics, kernel-based approach and orthogonal method, pattern recognition.

The prediction of protease cleavage sites in proteins is critical to effective drug design. One of the important issues in constructing an accurate and efficient predictor is how to present nonnumerical amino acids to a model effectively. As this issue has not yet been paid full attention and is closely related to model efficiency and accuracy, we present a novel neural learning algorithm aimed at improving the prediction accuracy and reducing the time involved in training. The algorithm is developed based on the conventional radial basis function neural networks (RBFNNs) and is referred to as a bio-basis function neural network (BBFNN). The basic principle is to replace the radial basis function used in RBFNNs by a novel bio-basis function. Each bio-basis is a feature dimension in a numerical feature space, to which a nonnumerical sequence space is mapped for analysis. The bio-basis function is designed using an amino acid mutation matrix verified in biology. Thus, the biological content in protein sequences can be maximally utilized for accurate modeling. Mutual information (MI) is used to select the most informative bio-bases and an ensemble method is used to enhance a decision-making process, hence, improving the prediction accuracy further. The algorithm has been successfully verified in two case studies, namely the prediction of Human Immunodeficiency Virus (HIV) protease cleavage sites and trypsin cleavage sites in proteins. Index Terms--Amino acid mutation matrix, bio-basis function neural network (BBFNN), pattern recognition, protease cleavage site prediction.

An unsupervised probabilistic net (UPN) is introduced to identify health inequalities among countries according to their health status measured by the collected health indicators. By estimating the underlying probability density function of the health indicators using UPN, countries, which have similar health status, will be categorized into the same cluster. From this, the intercluster health inequalities are identified by the Mahalanobis distance, and the intracluster health inequalities are identified by the diversity within the clusters. To extract the typical health status, the concept of virtual objects is used in this study. Each virtual object in this study, therefore, represents a hypothetical country, which does not exist in a data set but can be found through learning. The identified virtual objects represent the hidden knowledge in a data set and can be valuable to social scientists in health promotion planning. Moreover, the investigation of the behavior of the virtual objects can help us to find the realistic and reasonable health promotion target for a country with a poor health status. Index Terms--Clustering, health inequalities, health promotion, probabilistic net, virtual objects.