ELECTROCHEMICAL REDUCTION PROCESS OF BILIRUBIN IN DIMETHYLFORMAMIDE

The electrochemical reduction behavior of bilirubin (BR) at platinum electrode in DMF was investigated by cyclic voltammetry, in situ electron spin resonance spectroscopy and in situ rapid scanning thin layer spectroelectrochemistry. Experimental results revealed that the reduction of BR firstly undergoes an ECE process: GRAPHICS The generated (BR)(2)(3-). can be re-oxidized to BR and then to purpurin (Pu) by a series of oxidation processes: GRAPHICS However, the re-reduction reactions of Pu are not the reverse processes. The different reduction mechanisms are discussed in detail.

Fuzzy-Rough Attribute Reduction with Application to Web Categorization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Attribute Reduction with Application to Web Categorization,' Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469-485, 2004.

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Influence of processing parameters on nanomaterials synthesis efficiency by a carbothermal reduction process

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Synthesis of SnO2 nanoribbons by a carbothermal reduction process

This communication describes, for the first time, the growth of SnO2 nanoribbons by a controlled carbothermal reduction process. An analysis of the transmission electron microscopy image revealed that these nanoribbons have a well-defined shape, with a typical width in the range of 70-300 nm. In general, the nanostructured ribbons were more than 100 mum in length. The results reported here support the hypothesis that this ribbon-like nanostructured material grows by a vapor-solid process. This study introduces two hypotheses to explain the SnO2 nanoribbon growth process.

A Distance-Based Method for Attribute Reduction in Incomplete Decision Systems

There are limitations in recent research undertaken on attribute reduction in incomplete decision systems. In this paper, we propose a distance-based method for attribute reduction in an incomplete decision system. In addition, we prove theoretically that our method is more effective than some other methods.

Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems

A rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most attribute reduction methods are performed on a complete decision system table. In this paper, we propose methods for attribute reduction in static incomplete decision systems and dynamic incomplete decision systems with dynamically-increasing and decreasing conditional attributes. Our methods use generalized discernibility matrix and function in tolerance-based rough sets.

Improving the Scalability of Reduct Determination in Rough Sets

Rough Set Data Analysis (RSDA) is a non-invasive data analysis approach that solely relies on the data to find patterns and decision rules. Despite its noninvasive approach and ability to generate human readable rules, classical RSDA has not been successfully used in commercial data mining and rule generating engines. The reason is its scalability. Classical RSDA slows down a great deal with the larger data sets and takes much longer times to generate the rules. This research is aimed to address the issue of scalability in rough sets by improving the performance of the attribute reduction step of the classical RSDA - which is the root cause of its slow performance. We propose to move the entire attribute reduction process into the database. We defined a new schema to store the initial data set. We then defined SOL queries on this new schema to find the attribute reducts correctly and faster than the traditional RSDA approach. We tested our technique on two typical data sets and compared our results with the traditional RSDA approach for attribute reduction. In the end we also highlighted some of the issues with our proposed approach which could lead to future research.