Machine Learning for Mathematical Software

While there has been some discussion on how Symbolic Computation could be used for AI there is little literature on applications in the other direction. However, recent results for quantifier elimination suggest that, given enough example problems, there is scope for machine learning tools like Support Vector Machines to improve the performance of Computer Algebra Systems. We survey the authors own work and similar applications for other mathematical software. It may seem that the inherently probabilistic nature of machine learning tools would invalidate the exact results prized by mathematical software. However, algorithms and implementations often come with a range of choices which have no effect on the mathematical correctness of the end result but a great effect on the resources required to find it, and thus here, machine learning can have a significant impact.Comment: To appear in Proc. ICMS 201

Organically modified montmorillonite and chitosan-phosphotungstic acid complex nanocomposites as high performance membranes for fuel cell applications

Nanocomposite membranes based on polyelectrolyte complex (PEC) of chitosan/phosphotungstic acid (PWA) and different types of montmorillonite (MMT) were prepared as alternative membranes to Nafion for direct methanol fuel cell (DMFC) applications. Fourier transform infrared spectroscopy (FTIR) revealed an electrostatically fixed PWA within the PEC membranes, which avoids a decrease in proton conductivity at practical condition. Various amounts of pristine as well as organically modified MMT (OMMT) (MMT: Cloisite Na, OMMT: Cloisite 15A, and Cloisite 30B) were introduced to the PEC membranes to decrease in methanol permeability and, thus, enhance efficiency and power density of the cells. X-ray diffraction patterns of the nanocomposite membranes proved that MMT (or OMMT) layers were exfoliated in the membranes at loading weights of lower than 3 wt.%. Moreover, the proton conductivity and the methanol permeability as well as the water uptake behavior of the manufactured nanocomposite membranes were studied. According to the selectivity parameter, ratio of proton conductivity to methanol permeability, the PEC/2 wt.% MMT 30B was identified as the optimum composition. The DMFC performance tests were carried out at 70 A degrees C and 5 M methanol feed and the optimum membrane showed higher maximum power density as well as acceptable durability compared to Nafion 117. The obtained results indicated that owing to the relatively high selectivity and power density, the optimum nanocomposite membrane could be considered as a promising polyelectrolyte membrane (PEM) for DMFC applications

Prepration and Characterization of Novel Ionoic Polymers to beUsed as Artificial Muscles: Novel ionic polymers for artificial muscles

The muscle-like technology would be of enormous advantages for biomedical applications such as medical implants and human assist devices. Ionic polymer metal composites (IPMCs) are one kind of biomimetic actuators. An ionic polymer metal composite composed from an ionomer with high ion exchange capacity that packed between two thin metal layers. In the present study we focused on the preparation of a novel alternative polymeric ionomer to be used as artificial muscles. Sulfonated poly(ether ether ketone) (PEEK) have been synthesized as a new class of ionomeric membrane materials. PEEK was sulfonated at various degrees with sulfuric acid and N,N-Dimethylacetamide as a solvent. Fourier transfer infrared spectroscopy confirmed the quality of substitution reaction. Sulfonated samples showed O-H vibration at 3490 and S=O peaks at 1085 and 1100-1300 cm-1. By increasing degree of sulfonation to 80%, ion exchange capacity, water uptake and the number of water molecules per the fixed sulfone groups (λ) were increased to about 2.4 meq.g-1, 75% and 19, respectively. After calculating the optimum degree of sulfonation, the applications of these ionomers as actuators are studied. Rigid microstructure of PEEK backbone causes to slow displacement. However, this inflexible backbone showed the acceptable tip force during its actuation. These IPMC are easy to prepare and much less expensive than the commercial per-fluorinated membranes such as Nafion®. The results approve the utilization of sulfonated aromatic for artificial muscles applications as novel strong muscles with low flexibility

Comparison of Data Mining Techniques in the Cloud for Software Engineering

Mining software engineering data has recently become an important research topic to meet the goal of improving the software engineering processes, software productivity, and quality. On the other hand, mining software engineering data poses several challenges such as high computational cost, hardware limitations, and data management issues (i.e., the availability, reliability, and security of data). To address these problems, this chapter proposes the application of data mining techniques in cloud, the environment on software engineering data, due to cloud computing benefits such as increased computing speed, scalability, flexibility, availability, and cost efficiency. It compares the performances of five classification algorithms (decision forest, neural network, support vector machine, logistic regression, and Bayes point machine) in the cloud in terms of both accuracy and runtime efficiency. It presents experimental studies conducted on five different real-world software engineering data related to the various software engineering tasks, including software defect prediction, software quality evaluation, vulnerability analysis, issue lifetime estimation, and code readability prediction. Experimental results show that the cloud is a powerful platform to build data mining applications for software engineering