Spectrophotometric analysis of phenols, which involves a hemin-graphene hybrid nanoparticles with peroxidase-like activity

Phenols are well known noxious compounds, which are often found in various water sources. A novel analytical method has been researched and developed based on the properties of hemin–graphene hybrid nanosheets (H–GNs). These nanosheets were synthesized using a wet-chemical method, and they have peroxidase-like activity. Also, in the presence of H2O2, the nanosheets are efficient catalysts for the oxidation of the substrate, 4-aminoantipine (4-AP), and the phenols. The products of such an oxidation reaction are the colored quinone-imines (benzodiazepines). Importantly, these products enabled the differentiation of the three common phenols – pyrocatechol, resorcin and hydroquinone, with the use of a novel, spectroscopic method, which was developed for the simultaneous determination of the above three analytes. This spectroscopic method produced linear calibrations for the pyrocatechol (0.4–4.0 mg L−1), resorcin (0.2–2.0 mg L−1) and hydroquinone (0.8–8.0 mg L−1) analytes. In addition, kinetic and spectral data, obtained from the formation of the colored benzodiazepines, were used to establish multi-variate calibrations for the prediction of the three phenol analytes found in various kinds of water; partial least squares (PLS), principal component regression (PCR) and artificial neural network (ANN) models were used and the PLS model performed best.

Evidence for a simple linear relationship between MRSA rates and hand-washing compliance [Comment]

Is there a threshold above which hand-rub solution consumption is efficient for decreasing MRSA incidence? [J Hosp Infect. 2009] Association between an index of consumption of hand-rub solution and the incidence of acquired meticillin-resistant Staphylococcus aureus in an intensive care unit.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Adaptive online gradient descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.

Single voxel Magnetic Resonance Spectroscopic (1H-MRS) analysis of deep brain structures after traumatic prefrontal brain injury (TBI) in rat

Chronic difficulties arising from mild brain injury (TBI) are difficult to predict because the processes underlying changes after TBI are poorly understood. In mild brain injury the extent of neuropsychiatric and cognitive symptoms correspond poorly to overt tissue loss (Barth 1983; Liu 2010). Cellular, immune and hormonal cascades occurring after injury and continuing during the healing process may impact uninjured brain regions sensitive to the effects of physiological and emotional stress, which receive projections from the injury site. Changes in these most basic properties due to injury or disease have profound implications for virtually every aspect of brain function through disruption of neurotransmitter, neuroendocrine and metabolic systems. In order to screen for changes in transmitter and metabolic activity, in this study we developed Single voxel proton Magnetic Resonance Spectroscopy (1H-MRS) for use in both injured and control animals. We first evaluated if 1H-MRS could be used to evaluate in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus in both control and injured animals after controlled cortical impact injury to the rat prefrontal cortex. We found that metabolite measurements for Myo-Inositol, Choline, creatine, Glutamate+Glutamine, and N-acetyl-acetate are attainable in deep brain structures in vivo in injured and controls rats. We next seek to evaluate longitudinally, in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus during the first month after controlled cortical impact injury to the rat prefrontal cortex. These ongoing studies will provide data on the changes in transmitters and metabolites over time in injured and non-injured subjects. These studies address some of the fundamental questions about how mild brain injury has such diverse effects on overall brain health and function.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Optimizing preventative maintenance strategies for linear assets

Linear assets are engineering infrastructure, such as pipelines, railway lines, and electricity cables, which span long distances and can be divided into different segments. Optimal management of such assets is critical for asset owners as they normally involve significant capital investment. Currently, Time Based Preventive Maintenance (TBPM) strategies are commonly used in industry to improve the reliability of such assets, as they are easy to implement compared with reliability or risk-based preventive maintenance strategies. Linear assets are normally of large scale and thus their preventive maintenance is costly. Their owners and maintainers are always seeking to optimize their TBPM outcomes in terms of minimizing total expected costs over a long term involving multiple maintenance cycles. These costs include repair costs, preventive maintenance costs, and production losses. A TBPM strategy defines when Preventive Maintenance (PM) starts, how frequently the PM is conducted and which segments of a linear asset are operated on in each PM action. A number of factors such as required minimal mission time, customer satisfaction, human resources, and acceptable risk levels need to be considered when planning such a strategy. However, in current practice, TBPM decisions are often made based on decision makers’ expertise or industrial historical practice, and lack a systematic analysis of the effects of these factors. To address this issue, here we investigate the characteristics of TBPM of linear assets, and develop an effective multiple criteria decision making approach for determining an optimal TBPM strategy. We develop a recursive optimization equation which makes it possible to evaluate the effect of different maintenance options for linear assets, such as the best partitioning of the asset into segments and the maintenance cost per segment.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Matter and movement: Expression as topological continuity or a return to Butades' wall

This paper aims to address the ways in which drawing can be understood as the becoming-expressive of materials, site, and body, over time. The discussion pivots around a series of studies that replace linear or causal relationships – in history, drawing and expression – with topological movement. My approach is largely through a speculative case study. In a rereading of the familiar Butades myth, I examine how a shadow tracing can variously be taken as the first mimetic art with its origins in the urge to “capture”, and, antithetically, as the originary expressive folding of matter, site and body. The paper is divided into five sections. The first presents the Butades myth, identifying the representational problem that lies at the roots of its traditional telling. The next three sections outline a series of topologies that facilitate a discussion of the Butades myth from historical, disciplinary, and expressive perspectives. The final section aims to show the relevance of this discussion to a contemporary drawing practice, using my own drawing research as a case study. The field of inquiry is that of representational critique. The fold, an image associated with a topological geometry, replaces the relational or signifying disjuncture of representational structures, and suggests a becoming- expressive of subject and object, form and matter.

Crystal structures and hydrogen-bonding in the co-crystalline adducts of 3,5-dinitrobenzoic acid with 4-aminosalicylic acid and 2-hydroxy-3-(1H-indol-3-yl)propenoic acid

The structures of the cocrystalline adducts of 3,5-dinitrobenzoic acid (3,5-DNBA) with 4-aminosalicylic acid (PASA), the 1:1 partial hydrate, C7H4N2O6 .C7H7NO3 . 2H2O, (I) and 2-hydroxy-3-(1H-indol-3-yl)propenoic acid (HIPA) and the 1:1:1 d6-dimethylsulfoxide solvate, C7H4N2O6 . C11H9NO3 . C2D6OS, (II) are reported. The crystal substructure of (I) comprises two centrosymmetric hydrogen-bonded R2/2(8) homodimers, one with 3,5-DNBA, the other with PASA, and an R2/2(8) 3,5-DNBA-PASA heterodimer. In the crystal, inter-unit amine N-H...O and water O-H...O hydrogen bonds generate a three-dimensional supramolecular structure. In (II), the asymmetric unit consists of the three constituent molecules which form an essentially planar cyclic hydrogen-bonded heterotrimer unit [graph set R2/3(17)] through carboxyl, hydroxy and amino groups. These units associate across a crystallographic inversion centre through the HIPA carboxylic acid group in an R2/2~(8) hydrogen-bonding association, giving a zero-dimensional structure lying parallel to (100). In both structures, pi--pi interactions are present [minimum ring centroid separations: 3.6471(18)A in (I) and 3.5819(10)A in (II)].

Crystal structures of the co-crystalline adduct 5-(4-bromophenyl)-1,3,4-thiadiazol-2-amine--4-nitrobenzoic acid (1/1) and the salt 2-amino-5-(4-bromophenyl)-1,3,4-thiadiazol-3-ium 2-carboxy-4,6-dinitrophenolate

The structures of the 1:1 co-crystalline adduct C8H6BrN3S . C7H5NO4 (I) and the salt C8H7BrN3S+ C7H3N2O7- (II) from the interaction of 5-(4-bromophenyl)-1,3,4-thiadiazol-2-amine with 4-nitrobenzoic acid and 3,5-dinitrosalicylic acid, respectively, have been determined. The primary inter-species association in both (I) and (II) is through duplex R2/2(8) (N-H...O/O-H...O) or (N-H...O/N-H...O) hydrogen bonds, respectively, giving heterodimers. In (II), these are close to planar [dihedral angles between the thiadiazole ring and the two phenyl rings are 2.1(3)deg. (intra) and 9.8(2)deg. (inter)], while in (I) these angles are 22.11(15) and 26.08(18)deg., respectively. In the crystal of (I), the heterodimers are extended into a one-dimensional chain along b through an amine N-...N(thiadiazole) hydrogen bond but in (II), a centrosymmetric cyclic heterotetramer structure is generated through N-H...O hydrogen bonds to phenol and nitro O-atom acceptors and features, together with the primary R2/2(8) interaction, conjoined R4/6(12), R2/1(6) and S(6) ring motifs. Also present in (I) are pi--pi interactions between thiadiazole rings [minimum ring centroid separation, 3.4624(16)deg.] as well as short Br...O(nitro) interactions in both (I) and (II) [3.296(3)A and 3.104(3)A, respectively].

Crystal structures and hydrogen bonding in the proton transfer salts of nicotine with 3,5-dinitrosalicylic acid and 5-sulfosalicylic acid

The structures of the 1:1 anhydrous salts of nicotine (NIC) with 3,5-dinitrosalicylic acid (DNSA) and 5-sulfosalicylic acid (5-SSA), namely (1R,2S)-1-methyl-2-(3-pyridyl)-1H-pyrrolidin-1-ium 2-carboxy-4,6-dinitrophenolate, C10H15N2+ C7H3N2O7-, (I) and (1R,2S)-1-methyl-2-(3-pyridyl)-1H-pyrrolidin-1-ium 3-carboxy-4-hydroxybenzenesulfonate, C10H15N2+ C7H5O6S-, (II) are reported. The asymmetric units of both (I) and (II) comprise two independent nicotinium cations (C and D) and either two DNSA or two 5-SSA anions (A and B), respectively. One of the DNSA anions shows a 25% rotational disorder in the benzene ring system. In the crystal of (I), inter-unit pyrrolidinium N-H...N(pyridine) hydrogen bonds generate zigzag NIC cation chains which extend along a while the DNSA anions are not involved in any formal inter-species hydrogen bonding but instead form pi--pi associated stacks which parallel the NIC chains along a [ring centroid separation, 3.857(2)A]. Weak C-H...O interactions between chain substructures give an overall three-dimensional structure. With (II), A and B anions form independent zigzag chains with C and D cations, respectively, through carboxylic acid O-H...N(pyridine) hydrogen bonds. These chains, which extend along b are pseudo-centrosymmetrically related and give pi--pi interactions between the benzene rings of anions A and B and the pyridine rings of the NIC cations C and D, respectively [ring centroid separations, 3.6422(19) and 3.7117(19)A]. Present also are weak intermolecular C-H...O hydrogen-bonding interactions between the chains, giving an overall three-dimensional structure.

Two-dimensional hydrogen-bonded polymers in the crystal structures of the ammonium salts of phenoxyacetic acid, (4-fluorophenoxy)acetic acid and (4-chloro-2-methylphenoxy)acetic acid

The structures of the ammonium salts of phenoxyacetic acid, NH4+ C8H6O3- (I), (4-fluorophenoxy)acetic acid NH4+ C8H5FO3- (II) and the herbicidally active (4-chloro-2-methylphenoxy)acetic acid (MCPA), NH4+ C9H8ClO3-. 0.5(H2O) (III) have been determined. All have two-dimensional layered structures based on inter-species ammonium N-H...O hydrogen-bonding associations which give core substructures consisting primarily of conjoined cyclic motifs. Crystals of (I) and (II) are isomorphous with the core comprising R2/1(5), R2/1(4) and centrosymmetric R2/4(8) ring motifs, giving two-dimensional layers lying parallel to (100). In (III), the water molecule of solvation lies on a crystallographic twofold rotation axis and bridges two carboxyl O-atoms in an R4/4(12) hydrogen-bonded motif, creating two R3/4(10) rings which together with a conjoined centrosymmetric R2/4(8) ring incorporating both ammonium cations, generate two-dimensional layers lying parallel to (100). No pi-pi ring associations are present in any of the structures.