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.     

A unified method for optimal arbitrary pole placement

We consider the classic problem of pole placement by state feedback. We offer an eigenstructure assignment algorithm to obtain a novel parametric form for the pole-placing feedback matrix that can deliver any set of desired closed-loop eigenvalues, with any desired multiplicities. This parametric formula is then exploited to introduce an unconstrained nonlinear optimisation algorithm to obtain a feedback matrix that delivers the desired pole placement with optimal robustness and minimum gain. Lastly we compare the performance of our method against several others from the recent literature.     

Use of laser scanning confocal microscopy for characterizing changes in film thickness and local surface morphology of UV-exposed polymer coatings

Laser scanning confocal microscopy (LSCM) has been used to characterize the changes in film thickness and local surface morphology of polymer coatings during the UV degradation process. With the noninvasive feature of LSCM, one can obtain thickness information directly and nondestructively at various exposure times without destroying the specimens or deriving the thickness values from IR measurement by assuming uniform film ablation. Two acrylic polymer coatings were chosen for the study, and the physical and chemical changes of the two systems at various exposure times were measured and analyzed. Those measurable physical changes caused by UV exposure include film ablation, formation of pits and other surface defects, and increases in surface roughness. It was found in both coatings that changes in measured film thickness by LSCM were not correlated linearly to the predicted thickness loss using the changes in the CH band obtained by the Fourier Transform Infrared (FTIR) spectroscopy measurements in the later degradation stages. This result suggested it was not a uniform film ablation process during the UV degradation. At later stages, where surface deformation became severe, surface roughness and profile information using LSCM were also proven to be useful for analyzing the surface degradation process Presented at the 81st Annual Meeting of the Federation of Societies for Coatings Technology, November 13–14, 2004 in Philadelphia, PA.     

Process conditions affect pigment quality and yield in extracts of purple sunflower hulls

Extract of purple sunflower hulls is a potential red food colorant; however, suitable process conditions must still be identified. Selected process variables were studied using bench-scale units to prepare, clarify, concentrate and spray dry extracts. Concentration by evaporation at 32°C and addition of maltodextrin to 15% (wt/dry wt) prior to drying largely eliminated pigment degradation during those steps. Relative to water extracts, extracts prepared with 5 to 15% ethanol in water generally yielded more pigment with similar levels of degradation and loss during subsequent processing. Use of 5% ethanol/2% citric acid reduced yield by about half relative to water, but gave a powder with a lower degradation index and wetting time. Percentage recovery of pigment during concentration and drying was not greatly affected by solvent type or extraction temperature.     

Peer-to-peer bichromatic reverse nearest neighbours in mobile ad-hoc networks

The increasing use of mobile communications has raised many issues of decision support and resource allocation. A crucial problem is how to solve queries of Reverse Nearest Neighbour (RNN). An RNN query returns all objects that consider the query object as their nearest neighbour. Existing methods mostly rely on a centralised base station. However, mobile P2P systems offer many benefits, including self-organisation, fault-tolerance and load-balancing. In this study, we propose and evaluate 3 distinct P2P algorithms focusing on bichromatic RNN queries, in which mobile query peers and static objects of interest are of two different categories, based on a time-out mechanism and a boundary polygon around the mobile query peers. The Brute-Force Search Algorithm provides a naive approach to exploit shared information among peers whereas two other Boundary Search Algorithms filter a number of peers involved in query processing. The algorithms are evaluated in the MiXiM simulation framework with both real and synthetic datasets. The results show the practical feasibility of the P2P approach for solving bichromatic RNN queries for mobile networks.     

Improved oxidative stability of sunflower oil in the presence of an oxygen-scavenging film

The oxidative stability of sunflower oil (SFO) was measured during storage at 23 and 37°C in the presence of a novel oxygen-scavenging film that contained polyfuryloxirane (PFO). Commercially refined and deodorized SFO was stored in a lighted room in sealed transparent packages containing either PFO film or an antioxidant, 0.02% butylated hydroxytoluene (BHT). Oxidative stability was evaluated by determination of peroxide values and gas-chromatographic measurement of headspace hexanal. SFO stored in the presence of the oxygen-scavenging film was more stable than oil stored without the film, or than film stored with 0.02% BHT. The PFO film scavenges oxygen through energy-transfer sensitization of singlet oxygen. The film is doped with eosin and the naturally-occurring dye, curcumin, which absorb over a wide range of visible wavelengths. Curcumin transfers its absorbed energy to eosin, which sensitizes the production of singlet oxygen. The singlet oxygen is scavenged by PFO. The use of two dyes increases the efficiency of the sensitization process, reducing the illumination time and intensity required for effective oxygen scavenging.     

Mood sensing from social media texts and its applications

We present a large-scale mood analysis in social media texts. We organise the paper in three parts: (1) addressing the problem of feature selection and classification of mood in blogosphere, (2) we extract global mood patterns at different level of aggregation from a large-scale data set of approximately 18 millions documents (3) and finally, we extract mood trajectory for an egocentric user and study how it can be used to detect subtle emotion signals in a user-centric manner, supporting discovery of hyper-groups of communities based on sentiment information. For mood classification, two feature sets proposed in psychology are used, showing that these features are efficient, do not require a training phase and yield classification results comparable to state of the art, supervised feature selection schemes; on mood patterns, empirical results for mood organisation in the blogosphere are provided, analogous to the structure of human emotion proposed independently in the psychology literature; and on community structure discovery, sentiment-based approach can yield useful insights into community formation.     

Closed motifs for streaming time series classification

A streaming time series is a continuous and unbounded group of chronological observations that are found in many scientific and business applications. Motifs that are frequent subsequences are highly representative for the time series and play an important role in time series mining. Discovering motifs in time series has received much attention during recent years, and several algorithms have been proposed to solve this problem. However, these algorithms can only find motifs with a predefined length, which greatly affects their performance and practicality. Recent algorithms can discover motifs with different lengths, but require multiple scanning of the time series and are thus not applicable to streaming time series. In addition, it is difficult to determine the optimal length of interesting motifs; a suboptimal choice results in missing the key motifs or having too many redundant motifs. To overcome this challenge, we introduce the notion of a \(closed\) motif; a motif is \(closed\) if there is no motif with a longer length having the same number of occurrences. We propose a novel algorithm \(closedMotif\) to discover closed motifs in a single scan for streaming time series. We also use the nearest neighbor classifier with the most distinctive closed motifs to validate their potential in time series classification. Extensive experiments show that our approach can efficiently discover motifs with different lengths. In addition, our closed-motif-based classifier is shown to be more accurate than \(Logical\text{- }Shapelet\) , a state-of-the-art time series classifier. Finally, we demonstrate the scalability of \(closedMotif\) on several large datasets in diverse domains like video surveillance, sensor networks, and biometrics.     

Personalized identification of abdominal wall hernia meshes on computed tomography

An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.     

MET𝔸P: revisiting Privacy-Preserving Data Publishing using secure devices

The goal of Privacy-Preserving Data Publishing (PPDP) is to generate a sanitized (i.e. harmless) view of sensitive personal data (e.g. a health survey), to be released to some agencies or simply the public. However, traditional PPDP practices all make the assumption that the process is run on a trusted central server. In this article, we argue that the trust assumption on the central server is far too strong. We propose Met _??P, a generic fully distributed protocol, to execute various forms of PPDP algorithms on an asymmetric architecture composed of low power secure devices and a powerful but untrusted infrastructure. We show that this protocol is both correct and secure against honest-but-curious or malicious adversaries. Finally, we provide an experimental validation showing that this protocol can support PPDP processes scaling up to nation-wide surveys.