In situ measurements of human cough aerosol hygroscopicity

The airborne dynamics of respiratory droplets, and the transmission routes of pathogens embedded within them, are governed primarily by the diameter of the particles. These particles are composed of the fluid which lines the respiratory tract, and is primarily mucins and salts, which will interact with the atmosphere and evaporate to reach an equilibrium diameter. Measuring organic volume fraction (OVF) of cough aerosol has proved challenging due to large variability and low material volume produced after coughing. Here, the diametric hygroscopic growth factors (GF) of the cough aerosol produced by healthy participants were measured in situ using a rotating aerosol suspension chamber and a humidification tandem differential mobility analyser. Using hygroscopicity models, it was estimated that the average OVF in the evaporated cough aerosol was 0.88 ± 0.07 and the average GF at 90% relative humidity (RH) was 1.31 ± 0.03. To reach equilibrium in dry air the droplets will reduce in diameter by a factor of approximately 2.8 with an evaporation factor of 0.36 ± 0.05. Hysteresis was observed in cough aerosol at RH = ∼35% and RH = ∼65% for efflorescence and deliquescence, respectively, and may depend on the OVF. The same behaviour and GF were observed in nebulized bovine bronchoalveolar lavage fluid.     

A fuzzy logic based-method for prognostic decision making in breast and prostate cancers

Accurate and reliable decision making in oncological prognosis can help in the planning of suitable surgery and therapy, and generally, improve patient management through the different stages of the disease. In recent years, several prognostic markers have been used as indicators of disease progression in oncology. However, the rapid increase in the discovery of novel prognostic markers resulting from the development in medical technology, has dictated the need for developing reliable methods for extracting clinically significant markers where complex and nonlinear interactions between these markers naturally exist. The aim of this paper is to investigate the fuzzy k-nearest neighbor (FK-NN) classifier as a fuzzy logic method that provides a certainty degree for prognostic decision and assessment of the markers, and to compare it with: 1) logistic regression as a statistical method and 2) multilayer feedforward backpropagation neural networks an artificial neural-network tool, the latter two techniques having been widely used for oncological prognosis. In order to achieve this aim, breast and prostate cancer data sets are considered as benchmarks for this analysis. The overall results obtained indicate that the FK-NN-based method yields the highest predictive accuracy, and that it has produced a more reliable prognostic marker model than both the statistical and artificial neural-network-based methods.     

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.     

Multicarrier modulation for narrowband PCS

Investigation of multicarrier modulation has demonstrated the feasibility of using this modulation format to deliver relatively high channel bit rates (24 kbps) to a low cost, pager-like personal communications service (PCS) terminal that enjoys long battery life (e.g., one month on a few pen-light cells). This is to be compared to the 1200 bps carried by typical current paging systems or the 6250 bps attained by the new ERMES paging system. While more complex modulation formats, such as PSK and QAM, might achieve higher bit rates in less (power, size, cost) constrained environments, amplitude keying of the component subcarriers allows simple noncoherent energy detection that is found to be robust in this short message delivery application     

Review Mechanical properties of ice and snow

The mechanical properties of ice and snow are reviewed. The tensile strength of ice varies from 0.7–3.1 MPa and the compressive strength varies from 5–25 MPa over the temperature range –10°C to –20°C. The ice compressive strength increases with decreasing temperature and increasing strain rate, but ice tensile strength is relatively insensitive to these variables. The tensile strength of ice decreases with increasing ice grain size. The strength of ice decreases with increasing volume, and the estimated Weibull modulus is 5. The fracture toughness of ice is in the range of 50–150 kPa m^1/2 and the fracture-initiating flaw size is similar to the grain size. Ice-soil composite mixtures are both stronger and tougher than ice alone. Snow is a open cellular form of ice. Both the strength and fracture toughness of snow are substantially lower than those of ice. Fracture-initiating flaw sizes in snow appear to correlate to the snow cell size.     

Iodide mediated electrolysis of acidic coke/coal suspension

Hydrogen is among the emerging energy vectors that are being developed to replace nonrenewable hydrocarbon energy sources. The preferred method to produce hydrogen without generating greenhouse gases is the electrolysis of water using renewable energy. The reduction of energy during the water electrolysis process is a desirable goal regardless of the source of electric power. Similar to ferrous/ferric mediation, iodide can be used as a mediator in the electrolysis of an acidic suspension of coke or coal. The iodide ion is oxidized at a far lower anodic potential than the alternative oxygen evolution reaction. This reduces the cell electrolysis voltage and, consequently, reduces electricity use. The iodide consumed at the anode is continuously regenerated by chemical reaction with the coke/coal in suspension.     

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.