Evaluation of amines for the selective catalytic reduction (SCR) of NOx from diesel engine exhaust

With a view to developing onboard generation of selective reductants for NO_x removal from diesel engine exhaust we compared the performance of a primary, secondary and tertiary amine to NH₃ using a typical mini core NH₃-SCR catalyst. Primary amines with short hydrocarbon chains, e.g. CH₃NH₂ (maximum NO_x conversion, 50%) approached the NO_x conversion obtained using NH₃ (maximum NO_x conversion, 70%). Increasing the amine to NO_x ratio greater than 1 results in NO_x conversions closer to those of NH₃ (maximum NO_x conversion increased to 60%). Secondary and tertiary amines had smaller NO_x conversions as a function of temperature and the drop in NO and NO_x conversion decreased with increasing amine hydrogen substitution. Also, the maximum NO_x conversion for each reductant tends to move to a lower temperature as the degree of substitution increases.Unlike NH₃, the amines can react in the gas phase at temperatures within the range of diesel engine exhaust. Due to this gas phase reactivity the NO_x conversions measured using the mini core SCR catalyst also contain a gas phase conversion component. Gas phase conversions were investigated by replacing the mini core SCR catalyst with an equivalent length of quartz beads. Subtraction of the two results highlighted the differences between the mini core catalytic and gas phase conversions measured in this manner over the temperature range investigated. These differential NO_x conversions for the three amines had maxima at about 375 °C.     

Alcohol consumption detection through behavioural analysis using intelligent systems

We describe in this paper a new methodology for blood alcohol content (BAC) estimation of a subject. Rather than using external devices to determine the BAC value of a subject, we perform a behaviour analysis of this subject using intelligent systems. We monitor the user’s actions in an ordinary task and label those data to various measured BAC values. The obtained data-set is then used to train learning systems to detect alcoholic consumption and perform BAC estimation. We obtain good results on a mono-user base, and lower results with multiple users. We improve the results by combining multiple classifiers and regression algorithms.     

SiO_2对低温液相法制备锐钛型TiO_2/SiO_2纳米复合膜性能的影响

采用浸渍提拉法在玻璃表面制得SiO2/TiO2纳米复合膜,筛选出SiO2∶TiO2优化摩尔比为0.1∶1。结果表明,加入SiO2可有效提高TiO2膜层均匀性,降低晶粒直径,纳米复合膜对甲基橙溶液光催化降解率为38.58%,光照30min后膜接触角为5(°),纳米膜与玻璃之间附着力达到1级,且对各类介质均具有良好的耐蚀性。     

A new core–shell type fluorinated acrylic and siliconated polyurethane hybrid emulsion

A novel core–shell type fluorinated acrylic and siliconated polyurethane (FSiPUA) hybrid emulsion was prepared by seeded emulsion polymerization using siliconated polyurethane (SiPU) as a seed and forming the structure with SiPU as a shell and the copolymer of butyl acrylate (BA) with 2,2,2-trifluoroethylmethacrylate (TFEMA) as a core. SiPU was synthesized using isophorone diisocyanate (IPDI), polytetramethylene ether glycol (PTMG), polypropylene glycol (PPG), dihydroxybutyl-terminated polydimethylsiloxane (PDMS), dimethylol propionic acid (DMPA), 1,6-hexanediol (HDO) and triethylamine (TEA). The contents of siloxane and fluorine were determined according to the feed ratio. Fourier transform infrared spectroscopy (FTIR) was used to identify the chain structure of SiPU and FSiPUA. Investigation of transmission electron microscopy (TEM) confirmed the core–shell structure of FSiPUA emulsion and gave the particle size at about 50 nm. The measurement results of water contact angles and the solvent absorptions in water and n-octane for cured films showed that the water and the oil repellency for FSiPUA had been improved significantly with a suitable content of fluorine and siloxane.     

PVA-based activated carbon fibers with lotus root-like axially porous structure

A novel method was developed for the fabrication of activated carbon fiber (ACF) with wet spinning polyvinyl alcohol (PVA) fibers as the precursors. Through a combination of preoxidation, dehydration, carbonization and activation under a certain tension, PVA-based ACFs (PVA-ACFs) with high yields and good mechanical properties were obtained. The surface and cross-section morphologies, pore structures, surface geometries, surface functional groups and crystal structures of the PVA-ACFs were characterized using field emission scanning electron microscopy, low temperature nitrogen adsorption, Fourier transformed infrared spectrophotometry, X-ray photoelectron spectroscopy and X-ray power diffraction. All the PVA-ACF samples prepared had lotus root-like axially meso- and macroporous structures with the domination of micropores. Fractal geometries of PVA-ACFs deduced from nitrogen adsorption isotherms indicates that capillary force dominated in the interactions between nitrogen and the PVA-ACFs. The surface functional groups of the PVA-ACFs depend on the activating agents. Graphitoidal crystal structures were observed for the PVA-ACFs. The small crystal size and short range ordering between the crystallites ensured a high specific surface area of the PVA-ACFs.     

An exploratory study of buyers’ participation intentions in reputation systems: The relationship quality perspective

Drawing upon a relationship quality framework, this study identifies how satisfaction, trust and different styles of handling conflict influence online buyers’ participation intentions in reputation systems associated with a C2C online shopping platform. Furthermore, we investigate how these effects are moderated by social conformity and the perceived value of knowledge. The results of a survey of 269 online buyers indicate that satisfaction, trust and an accommodating conflict handling style positively impact the intention to submit positive ratings, whereas an avoiding conflict handling style negatively affects the intention to submit positive ratings. Implications and suggestions for future research are provided.     

Photocatalytic decolourisation and degradation of Reactive Orange 4 by TiO2-UV process

The photocatalytic decolourisation and degradation of an azo dye Reactive Orange 4 (RO4) in aqueous solution with TiO₂-P25 (Degussa) as photocatalyst in slurry form has been carried out using UV-A light (365 nm). There is a significant difference in adsorption of dye on TiO₂ surface with the change in the solution pH. The effect of various parameters such as catalyst loading, pH and initial concentration of the dye on decolourisation and degradation have been determined. The dye is decolourised in 80 min and completely degraded in 180 min under optimum conditions. The degradation was strongly enhanced in the presence of electron acceptors such as H₂O₂, (NH₄)₂S₂O₈ and KBrO₃. The photodecolourisation and degradation kinetics are discussed in terms of Langmuir–Hinshelwood kinetic model. The degradation intermediates were analysed by GC–MS technique.     

Multi-scale patch-based sparse appearance model for robust object tracking

When objects undergo large pose change, illumination variation or partial occlusion, most existing visual tracking algorithms tend to drift away from targets and even fail to track them. To address the issue, in this paper we propose a multi-scale patch-based appearance model with sparse representation and provide an efficient scheme involving the collaboration between multi-scale patches encoded by sparse coefficients. The key idea of our method is to model the appearance of an object by different scale patches, which are represented by sparse coefficients with different scale dictionaries. The model exploits both partial and spatial information of targets based on multi-scale patches. Afterwards, a similarity score of one candidate target is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Additionally, to decrease the visual drift caused by frequently updating model, we present a novel two-step object tracking method which exploits both the ground truth information of the target labeled in the first frame and the target obtained online with the multi-scale patch information. Experiments on some publicly available benchmarks of video sequences showed that the similarity involving complementary information can locate targets more accurately and the proposed tracker is more robust and effective than others.     

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max–Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC’s ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.