Synthesis of N,N-diethyl-N-{4-[(E)-(4-nitrophenyl)diazenyl]phenyl}amine via in situ diazotisation and coupling in supercritical carbon dioxide

The synthesis of azo dyes via a conventional aqueous-based diazotisation and coupling reaction requires the use of relatively high concentrations of mineral acids, which leads to high electrolyte concentrations in wastewater. Reported in this paper is an environmentally benign one-pot method for the synthesis of a nonionic azo dye, N,N -diethyl- N -{4-[( E )-(4-nitrophenyl)diazenyl]phenyl}amine, in supercritical carbon dioxide without using a mineral acid. The product yield increased significantly with temperature, with 91% theoretical yield afforded at 80 °C. The pressure of the system had little influence on product yield.     

Enzyme‐catalyzed hydrolysis of poly(ethylene terephthalate) cyclic trimer

Oligoesters present in poly(ethylene terephthalate) fibers are commonly extracted from the fiber during processing, such as aqueous‐based dyeing. Aqueous, insoluble oligoesters, particularly cyclic trimers that precipitate on processing machinery and on the fiber surface are difficult to remove under benign conditions. Reported is a new method for the efficient removal of cyclic trimer by enzyme‐catalyzed hydrolysis. Almost complete hydrolysis of trimer was accomplished at pH 8 and 60°C. Effects of time, agitation, surfactant, and enzyme and trimer concentrations on the efficiency of hydrolysis are reported. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 2545–2552, 2003     

A comparison of vapour pressure measurements of quinizarin and leuco-quinizarin via transpiration and thermogravimetry

The vapour pressure of solids can be obtained using a number of methods, including the Knudsen effusion method, the Knudsen torque-effusion method and a transpiration method. Each method has benefits and disadvantages. Reported is a comparison of vapour pressure data for two compounds, quinizarin and leuco -quinizarin, using a transpiration method and a recently developed method based on thermogravimetry. Thermogravimetry provided vapour pressure–temperature dependence data for each compound with expediency and in agreement with the transpiration method.     

The stability and unfolding of an IgG binding protein based upon the B domain of protein A from Staphylococcus aureus probed by tryptophan substitution and fluorescence spectroscopy

The stability and unfolding of an immunoglobulin (Ig) G bindingprotein based upon the B domain of protein A (SpAB) from Staphylococcusaureus were studied by substituting tryptophan residues at strategiclocations within each of the three a-helical regions (al-a3)of the domain. The role of the C-terminal helix, a3, was investigatedby generating two protein constructs, one corresponding to thecomplete SpAB, the other lacking a part of ct3; the Trp substitutionswere made in both one-and two-domain versions of each of theseconstructs. The fluorescence properties of each of the single-tryptophanmutants were studied in the native state and as a function ofguanidine-HCl-mediated unfolding, and their IgG binding activitieswere determined by a competitive enzyme-linked immunosorbentassay. The free energies of folding and of binding to IgG foreach mutant were compared with those for the native domains.The effect of each substitution upon the overall structure andupon the IgG binding interface was modelled by molecular graphicsand energy minimization. These studies indicate that (i) 3 contributesto the overall stability of the domain and to the formationof the IgG binding site in l and 2, and (ii) al unfolds first,followed by 2 and 3 together.     

Robustness of ISS systems to inputs with limited moving average: Application to spacecraft formations

We provide a theoretical framework that fits realistic challenges related to spacecraft formation with disturbances. We show that the input‐to‐state stability of such systems guarantees some robustness with respect to a class of signals with bounded average‐energy, which encompasses the typical disturbances acting on spacecraft formations. Solutions are shown to converge to the desired formation, up to an offset, which is somewhat proportional to the considered moving average of disturbances. In the presence of fast peaking perturbations, the approach provides a tighter evaluation of the disturbances' influence, which allows for the use of more parsimonious control gains. Copyright © 2015 John Wiley & Sons, Ltd.     

Learning traversability models for autonomous mobile vehicles

Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is presented.

A new document representation using term frequency and vectorized graph connectionists with application to document retrieval

This paper presents a new document representation with vectorized multiple features including term frequency and term-connection-frequency. A document is represented by undirected and directed graph, respectively. Then terms and vectorized graph connectionists are extracted from the graphs by employing several feature extraction methods. This hybrid document feature representation more accurately reflects the underlying semantics that are difficult to achieve from the currently used term histograms, and it facilitates the matching of complex graph. In application level, we develop a document retrieval system based on self-organizing map (SOM) to speed up the retrieval process. We perform extensive experimental verification, and the results suggest that the proposed method is computationally efficient and accurate for document retrieval.     

Prediction of the binding mode of biarylpropylsulfonamide allosteric AMPA receptor modulators based on docking, GRID molecular interaction fields and 3D-QSAR analysis

A novel approach of combining flexible molecular docking, GRID molecular interaction fields, analysis of ligand-protein hydrogen bond interactions, conformational energy penalties and 3D-QSAR analysis was used to propose a binding mode in the dimer interface of the iGluR2 receptor for the biarylpropylsulfonamide class of positive allosteric AMPA modulators. Possible binding poses were generated by flexible molecular docking. GRID molecular interaction fields of the binding site, ligand-protein hydrogen bonding interactions and conformational energy penalties were used to select the most likely binding mode. The selected binding poses were subjected to a 3D-QSAR analysis using previously published activity data. The resulting model (2 LVs, R2=0.89, q2=0.61) predicted the activities of the compounds in the test set with a standard deviation on error of prediction of 0.17. The proposed binding mode was validated by interpretation of the PLS-coefficient regions from the 3D-QSAR analysis in terms of interactions between the receptor and the modulators.     

Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction

Dealing with high-dimensional data has always been a major problem in many pattern recognition and machine learning applications. Trace ratio criterion is a criterion that can be applicable to many dimensionality reduction methods as it directly reflects Euclidean distance between data points of within or between classes. In this paper, we analyze the trace ratio problem and propose a new efficient algorithm to find the optimal solution. Based on the proposed algorithm, we are able to derive an orthogonal constrained semi-supervised learning framework. The new algorithm incorporates unlabeled data into training procedure so that it is able to preserve the discriminative structure as well as geometrical structure embedded in the original dataset. Under such a framework, many existing semi-supervised dimensionality reduction methods such as SDA, Lap-LDA, SSDR, SSMMC, can be improved using our proposed framework, which can also be used to formulate a corresponding kernel framework for handling nonlinear problems. Theoretical analysis indicates that there are certain relationships between linear and nonlinear methods. Finally, extensive simulations on synthetic dataset and real world dataset are presented to show the effectiveness of our algorithms. The results demonstrate that our proposed algorithm can achieve great superiority to other state-of-art algorithms.