The relative performance of alternative oxygen carriers for liquid chemical looping combustion and gasification

The relative performance of different potential liquid oxygen carriers within a novel system that can be configured for either chemical looping gasification or combustion is assessed. The parameters considered here are the melting temperature, the Gibbs free energy, reaction enthalpy, exergy and energy flows, syngas quality and temperature difference between the two reactors. Results show that lead, copper and antimony oxides are meritorious candidates for the proposed systems. Antimony oxide was found to offer strong potential for high quality syngas production because it has a reasonable oxygen mass ratio for gasification. A sufficiently low operating temperature to be compatible with concentrated solar thermal energy and a propensity to generate methane. In contrast, copper and lead oxides offer greater potential for liquid chemical looping combustion because they have higher oxygen mass ratio and a higher operating temperature, which enables better efficiency from a power plant. For all three metal oxides, the production of methane via the undesirable methanation reaction is less than 2% of the product gasses for all operating temperatures and an order of magnitude lower for lead.     

Electric vehicle energy consumption modelling and estimation—A case study

Electric vehicles (EVs) have a limited driving range compared to conventional vehicles. Accurate estimation of EV's range is therefore a significant need to eliminate “range anxiety” that refers to drivers' fear of running out of energy while driving. However, the range estimators used in the currently available EVs are not sufficiently accurate. To overcome this issue, more accurate range estimation techniques are investigated. Nonetheless, an accurate power‐based EV energy consumption model is crucial to obtain a precise range estimation. This paper describes a study on EV energy consumption modelling. For this purpose, EV modelling is carried out using MATLAB/Simulink software based on a real EV in the market, the BMW i3. The EV model includes vehicle powertrain system and longitudinal vehicle dynamics. The powertrain is modelled using efficiency maps of the electric motor and the power electronics' data available for BMW i3. It also includes a transmission and a battery model (ie, Thevenin equivalent circuit model). A driver model is developed as well to control the vehicle's speed and to represent human driver's behaviour. In addition, a regenerative braking strategy, based on a series brake system, is developed to model the behaviour of a real braking controller. Auxiliary devices are also included in the EV model to improve energy consumption estimation accuracy as they can have a significant impact on that. The vehicle model is validated against published energy consumption values that demonstrates a satisfactory level of accuracy with 2% to 6% error between simulation and experimental results for Environmental Protection Agency and NEDC tests.     

Thiazolidinediones: An In–Depth Study of Their Synthesis and Application to Medicinal Chemistry in the Treatment of Diabetes Mellitus

2,4-Thiazolidinedione (TZD) is a privileged and highly utilised scaffold for the development of pharmaceutically active compounds. This sulfur-containing heterocycle is a versatile pharmacophore that confers a diverse range of pharmacological activities. TZD has been shown to exhibit biological action towards a vast range of targets interesting to medicinal chemists. In this review, we attempt to provide insight into both the historical conventional and the use of novel methodologies to synthesise the TZD core framework. Further to this, synthetic procedures utilised to substitute the TZD molecule at the activated methylene C₅ and N₃ position are reviewed. Finally, research into developing clinical agents, which act as modulators of peroxisome proliferator-activated receptors gamma (PPARγ), protein tyrosine phosphatase 1B (PTP1B) and aldose reductase 2 (ALR2), are discussed. These are the three most targeted receptors for the treatment of diabetes mellitus (DM).     

Vaporization of Protective Oxide Films into Different Gas Atmospheres

Oxidation of Metals - Vaporization often accompanies high-temperature oxidation and corrosion. In this review, vaporization under a vacuum as well as static gas and flowing gas conditions is...     

Data Mining for Vehicle Telemetry

This article presents a data mining methodology for driving-condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems, and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labeling problems: Road Type (A, B, C, and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, that is, signal selection, feature extraction, and feature selection. The selection methods used include principal components analysis (PCA) and mutual information (MI), which are used to determine the relevance and redundancy of extracted features and are performed in various combinations. Finally, because there is an inherent bias toward certain road and carriageway labelings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height.     

Macro- and microscale variables regulate stent haemodynamics,fibrin deposition and thrombomodulin expression

Drug eluting stents are associated with late stent thrombosis (LST), delayed healing and prolonged exposure of stent struts to blood flow. Using macroscale disturbed and undisturbed fluid flow waveforms, we numerically and experimentally determined the effects of microscale model strut geometries upon the generation of prothrombotic conditions that are mediated by flow perturbations. Rectangular cross-sectional stent strut geometries of varying heights and corresponding streamlined versions were studied in the presence of disturbed and undisturbed bulk fluid flow. Numerical simulations and particle flow visualization experiments demonstrated that the interaction of bulk fluid flow and stent struts regulated the generation, size and dynamics of the peristrut flow recirculation zones. In the absence of endothelial cells, deposition of thrombin-generated fibrin occurred primarily in the recirculation zones. When endothelium was present, peristrut expression of anticoagulant thrombomodulin (TM) was dependent on strut height and geometry. Thinner and streamlined strut geometries reduced peristrut flow recirculation zones decreasing prothrombotic fibrin deposition and increasing endothelial anticoagulant TM expression. The studies define physical and functional consequences of macro- and microscale variables that relate to thrombogenicity associated with the most current stent designs, and particularly to LST.     

Quantitative methods for correlating dispersion and electrical conductivity in conductor–polymer nanostrand composites

The development of electrically conductive polymer composite systems continues to be an area of wide interest. In this work, a method is presented for quantifying the macro-scale dispersion characteristics of electrically conductive nickel nanostrands in polymer systems. A single polymer system is considered at multiple volume fractions of nanostrands, with variations in dispersion processing practices. Image analysis methods based on statistical homology parameters are developed to characterize samples of the dispersed nanostructure, including nearest neighbor distance, mean cluster size, area/perimeter ratios, and topological Betti number metrics. It is found that the Betti numbers are particularly well suited for monitoring nanostrands dispersed in polymer systems. Correlation of the dispersed structure to the electrical conductivity properties of the nanocomposite system is demonstrated. The method is well suited as a batch sampling technique for monitoring nanostructures during dispersion processes, and is also analogous for any dispersed system that involves highly structured materials.     

Development of a Management Tool to Support the Beneficial Use of Treated Coal Seam Gas Water for Irrigation in Eastern Australia

An infiltration and salinity transport model (ISTM) based on the HYDRUS modeling platform was developed to support the beneficial use of treated coal seam gas (CSG) water to irrigate perennial forage plots in eastern Australia. The ISTM was used to support the expansion of irrigated areas under the CSG industry’s first specific irrigation beneficial use approval issued by the Queensland environmental regulatory administering authorities in March 2009. To support meeting regulatory requirements for expansion beyond the original irrigation area, forward modelling within the vadose zone was used to evaluate the fate and transport of the irrigation water as it migrates through the shallow soils and unsaturated bedrock toward the saturated zone; simulations beneath three agricultural systems with contrasting infiltration capacities were assessed. The model simulation results were used to guide irrigation activities to maximize water use in the readily available soil water capacity range for these agroecosystems, without detrimental impacts to plant growth or surface water/groundwater and soil resources. In this paper, a practical irrigation and rainfall systems-driven hydrogeochemical conceptual model is presented to identify processes that are likely to govern infiltration from the land surface, root water uptake, and solute transport into, and potentially through, the unsaturated zone. A numerical model is then parameterized to represent the primary processes hypothesized to affect water and salinity movement in the vadose zone. Finally, a series of simulations, conducted to identify the key parameters and processes governing the potential movement of water and salt through the root zone and into the bedrock vadose zone, are discussed. The simulations are used to support continuous improvement in modeling approaches for sustainably managing treated CSG extraction water allocated to irrigation.     

The impact of childhood symptoms of conduct disorder on driver aggression in adulthood

Background

Despite limited empirical investigation, existing scientific literature suggests that individuals with a history or current diagnosis of conduct disorder (CD) may be more likely to demonstrate reckless and aggressive driving. Much of the limited research in this field examines the impact of childhood CD on driver behaviour and collision risk in young adults. Few if any, studies assess the impact of this disorder on driver behaviour beyond age 21 years. The current research is a population-based study of the impact of CD symptoms during childhood on the risk of engaging in driver aggression during adulthood.

Methods

Data are based on telephone interviews with 5230 respondents who reported having driven in the past year. Data are derived from the 2011–2013 cycles of the CAMH Monitor, an ongoing cross-sectional survey of adults in Ontario, Canada aged 18 years and older. A binary logistic regression analysis of self-reported driver aggression in the previous 12 months was conducted, consisting of measures of demographic characteristics, driving exposure, problem substance use, alcohol- and drug-impaired driving, symptoms of attention deficit hyperactivity disorder, and childhood (before age 15) symptoms of CD.

Results

When entered with demographic characteristics, driving exposure, and other potential confounders, childhood symptoms of CD increased the odds of reporting driver aggression more than two-fold (adjusted OR = 2.12). Exploratory analyses of the interaction between childhood symptoms of CD and age was not a significant predictor of driver aggression.

Conclusions

Results suggest that symptoms of CD during childhood are associated with significantly increased odds of self-reported driver aggression during adulthood. Limitations and future directions of the research are discussed.