Although the tube theory is successful in describing entangled polymers qualitatively, a more quantitative description requires precise and consistent definitions of its parameters. Here we investigate the simplest model of entangled polymers, namely a single Rouse chain in a cubic lattice of line obstacles, and illustrate the typical problems and uncertainties of the tube theory. In particular we show that in general one needs 3 entanglement related parameters, but only 2 combinations of them are relevant for the long-time dynamics. Conversely, the plateau modulus can not be determined from these two parameters and requires a more detailed model of entanglements with explicit entanglement forces, such as the slipsprings model. It is shown that for the grid model the Rouse time within the tube is larger than the Rouse time of the free chain, in contrast to what the standard tube theory assumes.
It has been reported that patients admitted with acute decompensated heart failure (ADHF) face high risk of mortality where 30-day mortality rates are reaching 10%. Identifying patient with high and low risk of mortality could improve clinical outcomes and hospital resources allocation. This paper proposed the use of fuzzy neural network to predict mortality for the patient admitted with ADHF. Results show that fuzzy neural network can predict mortality for ADHF patient with good prediction accuracy with overall accuracy of 88.8% for partition 50 and 90.40% for partition 80.
The relation extraction of crime news can help the monitoring specialists to accelerate the crime investigation. However, constructing patterns or designing templates manually requires domain experts. Also the built patterns do not guarantee complete differentiation among different relation instances. The automatic detection of crime entities and relationship among entities can help the regulatory authorities to accelerate the crime investigation and decision support instead of being reliant on manual process. This study aims to increase the effectiveness of the extraction of crime entities and relationship among entities based on the determination of crime lingusitic pattern using Minimal Differentiator Expressions (MDEs) that represent the cases that will be used by the CBR classifier. The proposed extraction methods can help in compiling a highly accurate and machine-understandable crime knowledge bases which can support the regulatory authorities’ investigation. This paper conducted on our proposed MDEs algorithm for linguistic pattern reuse in CBR approaches.
Named Entity Recognition (NER) is an elementary tool for all application areas in Natural Language Processing (NLP) such as Automatic Summarization, Information Extraction, Information Retrieval, Text Mining, Machine Translation, Question Answering, and Genetics. NER is a task to discover and categorises the named entities (‘atomic elements’) in the text into predefined classes such as the names of persons, organizations, locations, terminologies of time, quantity and etc. Different languages may have different morphologies and thus involve dissimilar NER procedures. For example, an Arabic NER system cannot be practically used in processing Malay texts due to the different morphological features. The morphological features of every language are rich and complex and donates to the difficulties of implementing an actual method to develop the accurate NER system. In this paper, we review on three main techniques that commonly used to develop an NER system well-known as Rule-Based, Machine Learning, and Hybrid approach. This paper also highlights the features of each technique.
Heart Disease are among the leading cause of death worldwide. The application of artificial neural network as decision support tool for heart disease detection have been previously proposed. However, artificial neural network using conventional back propagation algorithm for error minimization and these algorithm tend to stuck at local minima. This paper proposed the use of flower pollination algorithm as a substitute to conventional back propagation algorithm for error minimization. Heart disease dataset obtain from UCI machine learning repository is used to evaluate the performance of the proposed framework. The results show that the proposed flower pollination neural network able to produce higher classification accuracy compared to the conventional back propagation neural network algorithm.
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