Vortices in anisotropic type-II superconductors

We present a generalized variational model of an isolated vortex, valid in the anisotropic case when the external magnetic field is along one of the symmetry directions. We discuss the effects of the field anisotropy on the core and magnetic pinning of individual vortices.     

A propositional probabilistic logic with discrete linear time for reasoning about evidence

The aim of the paper is to present a sound, strongly complete and decidable probabilistic temporal logic that can model reasoning about evidence. The formal system developed here is actually a solution of a problem proposed by Halpern and Pucella (J Artif Intell Res 26:1–34, 2006).     

Automatic recognition of gait-related health problems in the elderly using machine learning

This paper proposes a system for the early automatic recognition of health problems that manifest themselves in distinctive form of gait. Purpose of the system is to prolong the autonomous living of the elderly at home. When the system identifies a health problem, it automatically notifies a physician and provides an explanation of the automatic diagnosis. The gait of the elderly user is captured using a motion-capture system, which consists of body-worn tags and wall-mounted sensors. The positions of the tags are acquired by the sensors and the resulting time series of position coordinates are analyzed with machine-learning algorithms in order to recognize a specific health problem. Novel semantic features based on medical knowledge for training a machine-learning classifier are proposed in this paper. The classifier classifies the user’s gait into: 1) normal, 2) with hemiplegia, 3) with Parkinson’s disease, 4) with pain in the back and 5) with pain in the leg. The studies of 1) the feasibility of automatic recognition and 2) the impact of tag placement and noise level on the accuracy of the recognition of health problems are presented. The experimental results of the first study (12 tags, no noise) showed that the k-nearest neighbors and neural network algorithms achieved classification accuracies of 100%. The experimental results of the second study showed that classification accuracy of over 99% is achievable using several machine-learning algorithms and 8 or more tags with up to 15 mm standard deviation of noise. The results show that the proposed approach achieves high classification accuracy and can be used as a guide for further studies in the increasingly important area of Ambient Assisted Living. Since the system uses semantic features and an artificial-intelligence approach to interpret the health state, provides a natural explanation of the hypothesis and is embedded in the domestic environment of the elderly person; it is an example of the semantic ambient media for Ambient Assisted Living.     

Solid-state NMR study of N labelled polyaniline upon reaction with DPPH

The quantitative investigation of the radical scavenging properties of polyaniline (PANI) upon reaction with excess of the stable DPPH radical (a 4:1 ratio of DPPH to aniline units in the polymer) was carried out using ¹⁵N and ¹³C solid state NMR spectroscopy. During the process the polyaniline was oxidised so that the imine content increased from 45 to 65%. The extent of oxidation measured by NMR was confirmed by N1s XPS analysis. However, within a 30 min reaction time, about 85% of the DPPH radicals were scavenged as monitored by the decay in its EPR signal. This is about 20 times greater than the fraction of DPPH required to oxidize PANI from an imine content of 45-65%. An identification of further redox processes is required to explain the high degree of radical scavenging. At the same time, there was no evidence of significant chemical binding or trapping of DPPH in the PANI structure.     

Structural analysis of Aleksinac oil shale kerogen by high-resolution solid-state C n.m.r. spectroscopy

A sample of kerogen from Aleksinac oil shale was examined by high-resolution solid-state ¹³C n.m.r. spectroscopy. The presence and relative proportions of kerogen structural units were estimated using a combination of NQS and T_1C methods with a peak-synthesis technique applied to the ¹³C CP—MAS spectrum. Relaxation parameters from these experiments were used to estimate differences in relative ‘mobility’ of various structural units. The kerogen was found to be highly aliphatic and to contain 79% long-chain aliphatic plus alicyclic structures, as well as 9% aromatic structures. These findings are in good agreement with the characterization of the same kerogen from its oxidation products.     

Kinetics of the hydroformylation of soybean oil by ligand-modified homogeneous rhodium catalysis

The kinetics and mechanism of the hydroformylation of soybean oil by homogeneous ligand-modified rhodium catalysts were investigated at 70–130°C and 4000–11,000 kPa. The effects of reaction rates on systematic variations in reaction parameters were evaluated in order to develop an industrial process to convert vegetable oils to polyaldehydes. The activation energies in the presence of triphenylphosphine (Ph₃P) (61.1±0.8 kJ/mol) (mean±SD) and triphenyl phosphite [(PhO)₃P] (77.4±5.0 kJ/mol) were determined. The catalyst was deactivated at temperatures higher than 100°C. An evaluation of the effects of the reaction parameters on initial rates yielded the rate laws for Ph₃P {rate=k [olefin][Rh(CO)₂Acac]^1.1 [Ph₃P]^−0.5 (pH₂+pCO)^1.4, where Rh(CO)₂Acac is (acetylacetonato)dicarbonylrhodium (I)} and (PhO)₃P {rate=[olefin] [Rh(CO)₂Acac]^1.2 [(PhO)₃P]^−0.8 (pH₂+pCO)^0.9 at total pressures lower than 7000 kPa, and rate =[olefin] [Rh(CO)₂Acac]^1.2 [(PhO)₃P]^−0.8(pH₂+pCO)^1.7 at total pressures higher than 7000 kPa}.     

The mechanism and kinetics of the electrochemical cleavage of azo bond of 2-hydroxy-5-sulfophenyl-azo-benzoic acids

The electrochemical reduction of 2-hydroxy-5-[(4-sulfophenyl)azo]benzoic acid, 2-hydroxy-5-[(3-sulfophenyl)azo]benzoic acid, 2-hydroxy-5-[(2-sulfophenyl)azo]benzoic acid and 2-hydroxy-5-azo-benzoic acid has been carried out in aqueous solutions at glassy carbon electrode using cyclic voltammetry and chronoamperometry. The position of sulfo substituent relative to azo bridge as well as pH of the solution have significant impact on the electrochemical behavior of these compounds. It has been proposed that these compounds are reduced predominantly as hydrazone tautomers resulting in corresponding hydrazo compounds. The overall electrochemical reduction follows DISP2 mechanism, ultimately leading to the 5-amino salicylic acid and sulfanilic acid. The rate determining step is the homogenous redox reaction between intermediate hydrazo compound and 5-amino salicylic acid quinoneimine. The mechanism is proposed in which activated complex of 5-amino salicylic acid quinoneimine and intermediate hydrazo compound is formed with the simultaneous loss of one proton.     

Novel dielectrics from IPNs derived from castor oil based polyurethanes

Three series of interpenetrating polymer networks (IPNs) based on a polyurethane (castor oil + toluene diisocyanate) with polystyrene, poly(methyl methacrylate), and poly(n-butyl methacrylate) were synthesized and characterized. Dielectric relaxation studies of these IPNs were carried out from ?150 to 100°C in the 100 Hz to 100 kHz range. The effects of structural variables such as composition, type of vinyl monomer, as well as the effect of interaction of the phases on the dielectric properties were studied. A certain degree of phase mixing was observed to exist in all series as detected by the variation of the glass-transition temperatures of the IPNs. Maxwell–Wagner–Sillars polarization at the interface of the two phases was observed. © 1994 John Wiley & Sons, Inc.     

Remote sensing of photosynthetic light-use efficiency across two forested biomes: Spatial scaling

Eddy covariance (EC) measurements have greatly advanced our knowledge of carbon exchange in terrestrial ecosystems. However, appropriate techniques are required to upscale these spatially discrete findings globally. Satellite remote sensing provides unique opportunities in this respect, but remote sensing of the photosynthetic light-use efficiency (ε), one of the key components of Gross Primary Production, is challenging. Some progress has been made in recent years using the photochemical reflectance index, a narrow waveband index centered at 531 and 570 nm. The high sensitivity of this index to various extraneous effects such as canopy structure, and the view observer geometry has so far prevented its use at landscape and global scales. One critical aspect of upscaling PRI is the development of generic algorithms to account for structural differences in vegetation. Building on previous work, this study compares the differences in the PRI: ? relationship between a coastal Douglas-fir forest located on Vancouver Island, British Columbia, and a mature Aspen stand located in central Saskatchewan, Canada. Using continuous, tower-based observations acquired from an automated multi-angular spectro-radiometer (AMSPEC II) installed at each site, we demonstrate that PRI can be used to measure ? throughout the vegetation season at the DF-49 stand (r² = 0.91, p < 0.00) as well as the deciduous site (r² = 0.88, p < 0.00). It is further shown that this PRI signal can be also observed from space at both sites using daily observations from the Moderate Resolution Imaging Spectro-radiometer (MODIS) and a multi-angular implementation of atmospheric correction (MAIAC) (r² = 0.54 DF-49; r² = 0.63 SOA; p < 0.00). By implementing a simple hillshade model derived from airborne light detection and ranging (LiDAR) to approximate canopy shadow fractions (α_s), it is further demonstrated that the differences observed in the relationship between PRI and ε at DF-49 and SOA can be attributed largely to differences in α_s. The findings of this study suggest that algorithms used to separate physiological from extraneous effects in PRI reflectance may be more broadly applicable and portable across these two climatically and structurally different biome types, when the differences in canopy structure are known.     

Explanation and reliability of prediction models: the case of breast cancer recurrence

In this paper, we describe the first practical application of two methods, which bridge the gap between the non-expert user and machine learning models. The first is a method for explaining classifiers’ predictions, which provides the user with additional information about the decision-making process of a classifier. The second is a reliability estimation methodology for regression predictions, which helps the users to decide to what extent to trust a particular prediction. Both methods are successfully applied to a novel breast cancer recurrence prediction data set and the results are evaluated by expert oncologists.