Miscibility of polyamide-6 and polystyrene functionalized with sulfonic acid groups

Polyamide-6 (PA-6) and sulfonated polystyrene (SPS) interact strongly in solution. Thus, solution blends of PA-6 and SPS (sulfonation levels from 0 to 12.3 mol%) in m-cresol form clear, hazy, or gelled solutions–depending on the ratio of amide (A) to sulfonic acid (S) groups (A/S). NMR studies show that the S groups interact with the nitrogen of the amides, most probably through the transfer of the sulfonic acid protons. During melt blending, the addition of SPS to the PA-6 leads to a torque rise because of strong interactions. The glass transition temperatures of the components in the melt blended samples shift towards each other as the sulfonation level of the SPS increases, indicating enhanced miscibility. The analysis of the extracted PA-6 from thee blends, however, shows that it is degraded at processing temperatures (250°C), owing to some residual water and the catalytic activity of the sulfonic acid groups. The extent of the PA-6 degradation is inversely related to the A/S ratio. Each sulfonic acid group causes between one and two chain scissions of the PA-6. In blends with high A/S ratios, the degradation of the PA-6 is minimal; yet considerable dispersion of the SPS can be achieved, as evidenced by scanning electron micrographs of the fracture surfaces.     

Blends of polyurethane carboxylates with styrene vinylpyridinium copolymers

This is a continuation of an ongoing investigation of miscibility enhancement via ionic interactions in urethane-styrene blends. The present study deals with a polyurethane which contains in the hard segments pendant carboxylate groups neutralized by ammonium ions while the polystyrene contains pendant vinyl pyridinium methyl iodide groups. Miscibility of the styrene is seen with the hard segments of the urethane because of ion pair/ion pair interactions. Methanol extraction raises the T_g because it eliminates low molecular weight components, without removing the microions.     

An efficient phishing webpage detector

Phishing attack is growing significantly each year and is considered as one of the most dangerous threats in the Internet which may cause people to lose confidence in e-commerce. In this paper, we present a heuristic method to determine whether a webpage is a legitimate or a phishing page. This scheme could detect new phishing pages which black list based anti-phishing tools could not. We first convert a web page into 12 features which are well selected based on the existing normal and fishing pages. A training set of web pages including normal and fishing pages are then input for a support vector machine to do training. A testing set is finally fed into the trained model to do the testing. Compared to the existing methods, the experimental results show that the proposed phishing detector can achieve the high accuracy rate with relatively low false positive and low false negative rates.     

The influence of micronised particulates on the aerosolisation properties of pressurised metered dose inhalers

A fundamental study of the interactions between binary particulate formulations in HFA-based pressurised metered dose inhaler (pMDI) systems was undertaken. Differences in the surface energetics of lactose, mannitol and sibenadet hydrochloride were observed, which could be related to the degree of particle adhesion/cohesion, the sedimentation rate and the aerosolisation performance of formulations containing different mixtures of these components. In general, lactose was shown to be the most cohesive material and readily formed coarse aggregates with sibenadet hydrochloride which sedimented rapidly. This had the greatest effect on aerosolisation performance at high-lactose concentrations (and low-sibenadet hydrochloride concentrations), where heavily agglomerated particles were observed in collected samples of the emitted aerosol. A model, relating the adhesive and cohesive forces between the components in these systems has been proposed, which satisfactorily describes these phenomena. Furthermore, although the more energetic systems were shown to result in significantly decreased aerosol performance, the through-life performance was enhanced.     

Right ventricular strain and volume analyses through deep learning-based fully automatic segmentation based on radial long-axis reconstruction of short-axis cine magnetic resonance images

Magnetic Resonance Materials in Physics, Biology and Medicine - We propose a deep learning-based fully automatic right ventricle (RV) segmentation technique that targets radially reconstructed...     

A Data Classification Method for Inconsistency and Incompleteness Detection in Access Control Policy Sets

Access control policies may contain anomalies such as incompleteness and inconsistency, which can result in security vulnerabilities. Detecting such anomalies in large sets of complex policies automatically is a difficult and challenging problem. In this paper, we propose a novel method for detecting inconsistency and incompleteness in access control policies with the help of data classification tools well known in data mining. Our proposed method consists of three phases: firstly, we perform parsing on the policy data set; this includes ordering of attributes and normalization of Boolean expressions. Secondly, we generate decision trees with the help of our proposed algorithm, which is a modification of the well-known C4.5 algorithm. Thirdly, we execute our proposed anomaly detection algorithm on the resulting decision trees. The results of the anomaly detection algorithm are presented to the policy administrator who will take remediation measures. In contrast to other known policy validation methods, our method provides means for handling incompleteness, continuous values and complex Boolean expressions. In order to demonstrate the efficiency of our method in discovering inconsistencies, incompleteness and redundancies in access control policies, we also provide a proof-of-concept implementation.     

Synthesis of short-channeled amino-functionalized SBA-15 and its beneficial applications in base-catalyzed reactions

Amino-functionalized-SBA-15 catalysts with nanostructured hexagonal platelet morphologies were synthesized directly by using microwave synthesis from the hydrolysis and co-condensation of aminopropyltriethoxysilane (APTES) and sodium metasilicate under a strong acidic condition with the Pluronic P123 triblock copolymer as a structure-directing agent. The amino-functionalized-SBA-15 catalysts were proven by SEM and TEM techniques to have very short channels in the range of 200–300 nm perpendicular to the hexagonal platelet morphologies. These short-channeled catalysts were proven to be effective heterogeneous catalysts in liquid-phase reactions such as Knoevenagel and Claisen-Schmidt condensations as well as in Henry reaction. They exhibited greatly improved catalytic activities over the typical fibrous type SBA-15 catalyst having long channels. Obviously, the easy diffusion and rapid mass transfer of substrate into the short channel mesopores played important roles in the significant improvement in the catalytic activities.     

Improving class separability using extended pixel planes: a comparative study

In this work we explored class separability in feature spaces built on extended representations of pixel planes (EPP) produced using scale pyramid, subband pyramid, and image transforms. The image transforms included Chebyshev, Fourier, wavelets, gradient, and Laplacian; we also utilized transform combinations, including Fourier, Chebyshev, and wavelets of the gradient transform, as well as Fourier of the Laplacian transform. We demonstrate that all three types of EPP promote class separation. We also explored the effect of EPP on suboptimal feature libraries, using only textural features in one case and only Haralick features in another. The effect of EPP was especially clear for these suboptimal libraries, where the transform-based representations were found to increase separability to a greater extent than scale or subband pyramids. EPP can be particularly useful in new applications where optimal features have not yet been developed.     

Learning to classify with missing and corrupted features

A common assumption in supervised machine learning is that the training examples provided to the learning algorithm are statistically identical to the instances encountered later on, during the classification phase. This assumption is unrealistic in many real-world situations where machine learning techniques are used. We focus on the case where features of a binary classification problem, which were available during the training phase, are either deleted or become corrupted during the classification phase. We prepare for the worst by assuming that the subset of deleted and corrupted features is controlled by an adversary, and may vary from instance to instance. We design and analyze two novel learning algorithms that anticipate the actions of the adversary and account for them when training a classifier. Our first technique formulates the learning problem as a linear program. We discuss how the particular structure of this program can be exploited for computational efficiency and we prove statistical bounds on the risk of the resulting classifier. Our second technique addresses the robust learning problem by combining a modified version of the Perceptron algorithm with an online-to-batch conversion technique, and also comes with statistical generalization guarantees. We demonstrate the effectiveness of our approach with a set of experiments.