Ring-opening copolymerization (ROCOP): synthesis and properties of polyesters and polycarbonates

Controlled routes to prepare polyesters and polycarbonates are of interest due to the widespread application of these materials and the opportunities provided to prepare new copolymers. Furthermore, ring-opening copolymerization may enable new poly(ester–carbonate) materials to be prepared which are inaccessible using alternative polymerizations. This review highlights recent advances in the ring-opening copolymerization catalysis, using epoxides coupled with anhydrides or CO2, to produce polyesters and polycarbonates. In particular, the structures and performances of various homogeneous catalysts are presented for the epoxide–anhydride copolymerization. The properties of the resultant polyesters and polycarbonates are presented and future opportunities highlighted for developments of both the materials and catalysts

Mitigation of low-frequency current ripple in fuel-cell inverter systems through waveform control

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A cooperative feature gene extraction algorithm that combines classification and clustering

In feature gene selection, filtering model concerns classification accuracy while ignoring gene redundancy problem. On the other hand, gene clustering finds correlated genes without considering their predictive abilities. It is valuable to enhance their performances by the help of each other. We report a new feature gene extraction algorithm, namely Double-thresholding Extraction of Feature Gene (DEFG), that combines gene filtering and gene clustering. It firstly pre-select feature gene set from the original dataset. A modified gene clustering is then applied to refine this set. In the gene clustering, specific designs are employed to balance the predictive abilities and the redundancies of the extracted feature gene. We have tested DEFG on a microarray dataset and compared its performance with that of two benchmark algorithms. The experimental results show that DEFG is superior to them in terms of internal validation accuracy and external validation accuracy. Also, DEFG can generalize the pattern structure by a small number of training samples. ©2009 IEEE.published_or_final_versio

Mining Order-Preserving Submatrices from Data with Repeated Measurements

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Wild-type p53-dependent upregulation of c-myc mRNA is associated with indomethacin induced apoptosis in human gastric cancer cells

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Chemoselective polymerizations from mixtures of epoxide, lactone, anhydride, and carbon dioxide

Controlling polymer composition starting from mixtures of monomers is an important, but rarely achieved, target. Here a single switchable catalyst for both ring-opening polymeri-zation (ROP) of lactones and ring-opening copolymerization (ROCOP) of epoxides, anhydrides and CO2 is investigated, using both experimental and theoretical methods. Different combinations of four model monomers: -caprolactone, cyclohexene oxide, phthalic anhydride and carbon dioxide are investigated using a single dizinc catalyst. The catalyst switches between the distinct polymerization cycles and shows high monomer selectivity resulting in block sequence control and predictable compositions (esters and car-bonates) in the polymer chain. The understanding gained of the orthogonal reactivity of monomers, specifically con-trolled by the nature of the metal-chain end group, opens the way to engineer polymer block sequences

Bayesian regression filter and the issue of priors

We propose a Bayesian framework for regression problems, which covers areas which are usually dealt with by function approximation. An online learning algorithm is derived which solves regression problems with a Kalman filter. Its solution always improves with increasing model complexity, without the risk of over-fitting. In the infinite dimension limit it approaches the true Bayesian posterior. The issues of prior selection and over-fitting are also discussed, showing that some of the commonly held beliefs are misleading. The practical implementation is summarised. Simulations using 13 popular publicly available data sets are used to demonstrate the method and highlight important issues concerning the choice of priors

Anodization of nanoporous alumina on impurity-induced hemisphere curved surface of aluminum at room temperature

Nanoporous alumina which was produced by a conventional direct current anodization [DCA] process at low temperatures has received much attention in various applications such as nanomaterial synthesis, sensors, and photonics. In this article, we employed a newly developed hybrid pulse anodization [HPA] method to fabricate the nanoporous alumina on a flat and curved surface of an aluminum [Al] foil at room temperature [RT]. We fabricate the nanopores to grow on a hemisphere curved surface and characterize their behavior along the normal vectors of the hemisphere curve. In a conventional DCA approach, the structures of branched nanopores were grown on a photolithography-and-etched low-curvature curved surface with large interpore distances. However, a high-curvature hemisphere curved surface can be obtained by the HPA technique. Such a curved surface by HPA is intrinsically induced by the high-resistivity impurities in the aluminum foil and leads to branching and bending of nanopore growth via the electric field mechanism rather than the interpore distance in conventional approaches. It is noted that by the HPA technique, the Joule heat during the RT process has been significantly suppressed globally on the material, and nanopores have been grown along the normal vectors of a hemisphere curve. The curvature is much larger than that in other literatures due to different fabrication methods. In theory, the number of nanopores on the hemisphere surface is two times of the conventional flat plane, which is potentially useful for photocatalyst or other applications

Validation of a digital photographic method for assessment of dietary quality of school lunch sandwiches brought from home.

Background: It is a challenge to assess children's dietary intake. The digital photographic method (DPM) may be an objective method that can overcome some of these challenges. Objective: The aim of this study was to evaluate the validity and reliability of a DPM to assess the quality of dietary intake from school lunch sandwiches brought from home among children aged 7–13 years. Design: School lunch sandwiches (n=191) were prepared to represent randomly selected school lunch sandwiches from a large database. All components were weighed to provide an objective measure of the composition. The lunches were photographed using a standardised DPM. From the digital images, the dietary components were estimated by a trained image analyst using weights or household measures and the dietary quality was assessed using a validated Meal Index of Dietary Quality (Meal IQ). The dietary components and the Meal IQ obtained from the digital images were validated against the objective weighed foods of the school lunch sandwiches. To determine interrater reliability, the digital images were evaluated by a second image analyst. Results: Correlation coefficients between the DPM and the weighed foods ranged from 0.89 to 0.97. The proportion of meals classified in the same or an adjacent quartile ranged from 98% (starch) to 100% (fruits, vegetables, fish, whole grain, and Meal IQ). There was no statistical difference between fish, fat, starch, whole grains, and Meal IQ using the two methods. Differences were found for fruits and vegetables; Bland–Altman analyses showed a tendency to underestimate high amounts of these variables using the DPM. For interrater reliability, kappa statistics ranged from 0.59 to 0.82 across the dietary components and Meal IQ. Conclusions: The standardised DPM is a valid and reliable method for assessing the dietary quality of school lunch sandwiches brought from home