Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels

This paper addresses the supervised learning in which the class memberships of training data are subject to ambiguity. This problem is tackled in the ensemble learning and the Dempster-Shafer theory of evidence frameworks. The initial labels of the training data are ignored and by utilizing the main classes’ prototypes, each training pattern is reassigned to one class or a subset of the main classes based on the level of ambiguity concerning its class label. Multilayer perceptron neural network is employed to learn the characteristics of the data with new labels and for a given test pattern its outputs are considered as basic belief assignment. Experiments with artificial and real data demonstrate that taking into account the ambiguity in labels of the learning data can provide better classification results than single and ensemble classifiers that solve the classification problem using data with initial imperfect labels.     

Dipyridamole recognition and controlled release by uniformly sized molecularly imprinted nanospheres

We used novel synthetic conditions of precipitation polymerization to obtain uniformly sized molecularly imprinted nanospheres of dipyridamole for application in the design of new drug delivery systems. In addition, the morphology, drug release, and binding properties of molecularly imprinted polymers (MIPs) were studied, and the effects of morphology on other properties were investigated. The MIPs prepared by acetonitrile/chloroform (19:1, v/v) were uniformly sized nanospheres with an average mean diameter of approximately 88 nm at a wetted state, 50 nm at a dry state, and a polydispersity index of 0.062. The imprinted nanospheres showed excellent binding properties and had 62.7% of template binding compared with 17.1% of its blank polymer. The imprinted nanospheres with 67.5 (mg template/of polymer) of binding capacity had better imprinting efficiency than the 50.5% of binding capacity shown by irregularly shaped MIP particles that were prepared by chloroform. The molecular binding abilities of imprinted nanospheres in human serum were evaluated by HPLC analysis (binding about 77% of dipyridamole). Results from release experiments of MIPs showed a very slow, controlled, and satisfactory release of dipyridamole. The loaded drug was released up to 99% in 17 days for nanospheres and 22 days for irregularly shaped particles.     

Synthesis and characterization of paclitaxel-imprinted nanoparticles for recognition and controlled release of an anticancer drug

Imprinted nanoparticles as drug delivery carriers have been considered because owing to their cross-linked network, they act as the drug reservoir for controlled release. In this study, selective MIPs nanoparticles of paclitaxel (PTX) were successfully developed for application in the biological molecular recognition and in the design of new anticancer drug delivery systems. The MIPs nanoparticles prepared by miniemulsion polymerization technique using methacrylic acid (MAA) and methyl methacrylate as non-covalent functional monomer, ethylene glycol dimethacrylate and trimethylolpropane trimethacrylate (TRIM) as cross-linker agent, azobisisobutyronitrile as initiator, and hexadecane as hydrophobic agent. In order to prepare of MIP nanoparticles, the synthesis conditions and effective parameters, such as: cross-linker agent, different molar ratios of template–functional monomer–cross-linker agent, were investigated. In addition, the effect of different molar ratios of template and monomers on polymers binding and morphology were characterized. Structure and thermal properties of MIPs were confirmed by FT-IR spectroscopy and thermogravimetric analysis. Imprinted nanoparticles showed significant drug loading and encapsulation efficiency, 17.8 and 100 %, respectively. The particle size of MIP nanoparticles varies between 187 and 726 nm, according the SEM images and laser light scattering data. The imprinted nanoparticles showed satisfactory affinity (84 %) to PTX with a binding of 12 times higher than non-imprinted nanoparticles in biological samples when MAA and TRIM were used as functional and cross-linker monomer, respectively. Results from release experiments of MIPs showed a very slow and controlled release of PTX which would be helpful for sustained drug delivery.     

Functional Process Capability Indices for Circular Profile

Profile is a relation between one response variable and one or more explanatory variables that represent quality of a product or performance of a process. On the other hand, process capability indices are measures to help practitioners in improving the processes to satisfy the customer's expectations. Few researches are done to account for the process capability index in the areas of profile monitoring. All of these researches are focused on process capability index in simple linear profile. In all of these methods, response variables in different levels of explanatory variable are considered, and the relationship in all range of explanatory variable is neglected. In this paper, a functional method is proposed to measure process capability index of circular profiles in all range of explanatory variable. The proposed method follows the traditional definition of process capability indices. The functional method uses reference profile, functional specification limits and functional natural tolerance limits to present a functional form of process capability indices. This functional form results in measuring the process capability in each level of explanatory variable in circular profile as well as a unique value of process capability index for circular profile. The application of the proposed method is illustrated through a real case in automotive industry. Copyright © 2013 John Wiley & Sons, Ltd.     

Identifying the period of a step change in high‐yield processes

Quality control charts have proven to be very effective in detecting out‐of‐control states. When a signal is detected a search begins to identify and eliminate the source(s) of the signal. A critical issue that keeps the mind of the process engineer busy at this point is determining the time when the process first changed. Knowing when the process first changed can assist process engineers to focus efforts effectively on eliminating the source(s) of the signal. The time when a change in the process takes place is referred to as the change point. This paper provides an estimator for a period of time in which a step change in the process non‐conformity proportion in high‐yield processes occurs. In such processes, the number of items until the occurrence of the first non‐conforming item can be modeled by a geometric distribution. The performance of the proposed model is investigated through several numerical examples. The results indicate that the proposed estimator provides a reasonable estimate for the period when the step change occurred at the process non‐conformity level. Copyright © 2009 John Wiley & Sons, Ltd.     

An integrated supervised learning solution for monitoring process mean vector

When an out-of-control condition is detected by a control chart, a search begins to identify and eliminate the source(s) of the signal. Identification of the time when a process first changed is an important step in root cause analysis which helps a process engineer to eliminate the source(s) of assignable cause effectively. The time when a change takes place in the process is referred to as the change point. In multivariate environment, since there is more than one variable involved, then root cause analysis is relatively harder compared to the case of univariate because it is not clear exactly which variable has contributed to the out-of-control condition and in what direction its mean has shifted. Hence, a procedure that identifies the change point, performs diagnostic analysis, and specifies the direction of the shift in the mean of the contributing variable(s) all simultaneously could help to conduct root cause analysis effectively. Although different multivariate methods exist in the literature that allow to either estimate change point in the process mean vector or identify the contributing variables leading to the out-of-control condition, but in this research, an integrated supervised learning solution is proposed, which helps to (1) detect of an out-of-control condition, (2) identify the change point leading to shift in the mean vector, (3) specify the variable(s) contributing to the out-of-condition, and (4) identify the direction of the shift in the mean of each contributing variable simultaneously. A real case study is used to evaluate and compare the performance of the proposed integrated approach to existing methods in the literature.     

Functionally Antagonistic Hybrid Electrode with Hollow Tubular Graphene Mesh and Nitrogen‐Doped Crumpled Graphene for High‐Performance Ionic Soft Actuators

Ionic soft actuators, which exhibit large mechanical deformations under low electrical stimuli, are attracting attention in recent years with the advent of soft and wearable electronics. However, a key challenge for making high‐performance ionic soft actuators with large bending deformation and fast actuation speed is to develop a stretchable and flexible electrode having high electrical conductivity and electrochemical capacitance. Here, a functionally antagonistic hybrid electrode with hollow tubular graphene meshes and nitrogen‐doped crumpled graphene is newly reported for superior ionic soft actuators. Three‐dimensional network of hollow tubular graphene mesh provides high electrical conductivity and mechanically resilient functionality on whole electrode domain. On the contrary, nitrogen‐doped wrinkled graphene supplies ultrahigh capacitance and stretchability, which are indispensably required for improving electrochemical activity in ionic soft actuators. Present results show that the functionally antagonistic hybrid electrode greatly enhances the actuation performances of ionic soft actuators, resulting in much larger bending deformation up to 620%, ten times faster rise time and much lower phase delay in a broad range of input frequencies. This outstanding enhancement mostly attributes to exceptional properties and synergistic effects between hollow tubular graphene mesh and nitrogen‐doped crumpled graphene, which have functionally antagonistic roles in charge transfer and charge injection, respectively.