Evaluation of corrosion behavior of Al-Mg-Li alloys in seawater

Weldalite 050, a high-strength Al-Mg-Li alloy, was evaluated for its corrosion resistance in deaerated and air saturated Arabian Gulf water to determine its suitability for marine applications. Weight loss and electrochemical studies showed that the alloy had minimum corrosion rates of 1.82 and 4.82 mpy (mils per year), respectively, in deaerated and air saturated Arabian Gulf water with very high total dissolved solids (TDS) content. Weldalite 050 exhibited good resistance to corrosion at velocities up to 3.9 m/s. The formation of Al₂MgLi, Al-Li, Al₁₂Mg₁₇, and Al-Li precipitates has a pronounced effect on its corrosion resistance. The corrosion resistance of Weldalite 050 compares favorably with that of alloys 5052 and 5054, wrought alloys 6061 and 6013, and silicon carbide (SiC) reinforced alloys 6061 and 6013.     

Microstructure and morphology of amine-cured epoxy coatings before and after outdoor exposures—An AFM study

Atomic force microscopy (AFM) has been used to study the morphology and microstructure of an amine-cured epoxy before and after outdoor exposure. Measurements were made from samples prepared in an essentially CO₂-free, H₂O-free glove box and from samples prepared in ambient conditions. For those prepared in a CO₂-free glove box, AFM imaging was conducted on (1) an unexposed air/coating surface, (2) an unexposed coating bulk, (3) an unexposed coating/substrate interface, and (4) a field exposed air/coating surface. For samples prepared in ambient conditions, only the unexposed air/coating surface was investigated. The same regions of the exposed samples were scanned periodically by the AFM to monitor changes in the surface morphology of the coating as UV exposure progressed. Small angle neutron scattering and Fourier transform infrared spectroscopy (FTIR) studies were performed to verify the microstructure and to follow chemical changes during outdoor exposure, respectively. The results have shown that amine blushing, which occurs only under ambient conditions, had a significant effect on the surface morphology and microstructure of the epoxy. The surface morphology of the samples prepared under CO₂-free, dry conditions was generally smooth and homogeneous. However, the interface and the bulk samples clearly revealed a two-phase structure consisting of bright nodular domains and dark interstitial regions, indicating an inhomogeneous microstructure. Such heterogeneous structure of the bulk was in good agreement with results obtained by small angle neutron scattering of unexposed samples and by AFM phase imaging of the degraded sample surface. The relationship between submicrometer physical changes and molecular chemical degradation is discussed. Presented at the 82nd Annual Meeting of the Federation of Societies for Coatings Technology, October 27–29, 2004, in Chicago, IL.     

Orthogonal and fine lithographic structures attained from the next generation proton beam writing facility

A second generation proton beam writing (PBW) system has been built at the Centre for Ion Beam Applications at the National University of Singapore for fabrication of high aspect ratio 3D nano lithographic structures. System improvements and a few lithographic structures obtained with this facility are presented in this paper. Through accurate alignment of the magnetic quadrupole lenses and the electrostatic scanning system, orthogonal beam scanning has been achieved. The earlier constrain of limited beam scan area has been overcome by adopting a combination of beam and stage scanning as well as stitching. With these improvements smallest ever Ni structure of 65 nm in width has been fabricated using nickel electroplating on a proton beam written PMMA sample in the second generation PBW facility. Using this improved PBW facility, we have also demonstrated the fabrication of fine lithographic patterns with 19 nm line width and 60 nm spacing in 100 nm thick negative high resolution hydrogen silsesquioxane resist. Future possible system improvements leading to finer resolution will be discussed briefly.     

An SDP Primal-Dual Algorithm for Approximating the Lovász-Theta Function

The Lovász ?-function (Lovász in IEEE Trans. Inf. Theory, 25:1–7, 1979) of a graph G=(V,E) can be defined as the maximum of the sum of the entries of a positive semidefinite matrix X, whose trace Tr(X) equals 1, and X _ij =0 whenever {i,j}∈E. This function appears as a subroutine for many algorithms for graph problems such as maximum independent set and maximum clique. We apply Arora and Kale’s primal-dual method for SDP to design an algorithm to approximate the ?-function within an additive error of δ>0, which runs in time $O(\frac{\vartheta ^{2} n^{2}}{\delta^{2}} \log n \cdot M_{e})$ , where ?=?(G) and M _e =O(n ³) is the time for a matrix exponentiation operation. It follows that for perfect graphs G, our primal-dual method computes ?(G) exactly in time O(? ² n ⁵logn). Moreover, our techniques generalize to the weighted Lovász ?-function, and both the maximum independent set weight and the maximum clique weight for vertex weighted perfect graphs can be approximated within a factor of (1+?) in time O(? ^?2 n ⁵logn).     

An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation

This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a 6 Degree Of Freedom (DOF) serial robotic arm. The kinematic and dynamic models of the robot have been derived to facilitate controller design. An on-board camera to scan the arm workspace permits autonomous applications development. The sensory system consists of position feedback from each joint of the robot and a force sensor mounted at the arm gripper. External devices can be interfaced with the platform through digital and analog I/O ports of the robot controller. To enhance the learning outcome for beginners, higher level commands have been provided. Advanced users can tailor the platform by exploiting the open-source custom-developed hardware and software architectures. The efficacy of the proposed platform has been demonstrated by implementing two experiments; autonomous sorting of objects and controller design. The proposed platform finds its potential to teach technical courses (like Robotics, Control, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. It can also be employed in an industrial environment to test various strategies prior to their execution on actual manipulators.     

Temperature-dependent diffusion coefficient of soluble substances during ethanol extraction of clove

The temperature dependence of the diffusion coefficient of ethanol-soluble substances from ground cloves (particle size 250 μm) during extraction was estimated by fitting batch extraction data at several temperatures (27.8, 40, 50, and 60°C) to a previously developed mass transfer model. The model was based on spherical geometry of particles. Nonlinear regression analysis was used to develop an equation that describes the diffusivity as a function of temperature. The temperature dependence ofD _A was of the Arrhenius type.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

Extracting lexical and phrasal paraphrases: a review of the literature

Recent advances in natural language processing have increased the popularity of paraphrase extraction. Most of the attention, however, has been focused on the extraction methods only without taking the resource factor into the consideration. Unknowingly, there is a strong relationship between them and the resource factor also plays an equally important role in paraphrase extraction. In addition, almost all of the previous studies have been focused on corpus-based methods that extract paraphrases from corpora based solely on syntactic similarity. Despite the popularity of corpus-based methods, a considerable amount of research has consistently shown that these methods are vulnerable to several types of erroneous paraphrases. For these reasons, it is necessary to evaluate whether the trend is moving in a positive direction. This paper reviews the major research on paraphrase extraction methods in detail. It begins by exploring the definition of paraphrase from different perspectives to provide a better understanding of the concept of paraphrase extraction. It then studies the characteristics and potential uses of different types of paraphrase resources. After that, it divides paraphrase extraction methods into four main categories: heuristic-based, knowledge-based, corpus-based and hybrid-based and summarizes their strengths and weaknesses. This paper concludes with some potential open research issues for future directions.     

Feature extraction and ML techniques for static gesture recognition

The main objective of this study is to explore the utility of a neural network-based approach in hand gesture recognition. The proposed system presents two recognition algorithms to recognize a set of six specific static hand gestures, namely open, close, cut, paste, maximize, and minimize. The hand gesture image is passed through three stages: preprocessing, feature extraction, and classification. In the first method, the hand contour is used as a feature that treats scaling and translation of problems (in some cases). However, the complex moment algorithm is used to describe the hand gesture and to treat the rotation problem in addition to scaling and translation. The back-propagation learning algorithm is employed in the multilayer neural network classifier. The second method proposed in this article achieves better recognition rate than the first method.