Polyurethane (PU) scaffolds prepared by solvent casting/particulate leaching (SCPL) combined with centrifugation

This article reports an enhanced solvent casting/particulate (salt) leaching (SCPL) method developed for preparing three-dimensional porous polyurethane (PU) scaffolds for cardiac tissue engineering. The solvent for the preparation of the PU scaffolds was a mixture of dimethylformamide (DFM) and tetrahydrofuran (THF). The enhanced method involved the combination of a conventional SCPL method and a step of centrifugation, with the centrifugation being employed to improve the pore uniformity and the pore interconnectivity of scaffolds. Highly porous three-dimensional scaffolds with a well interconnected porous structure could be achieved at the polymer solution concentration of up to 20% by air or vacuum drying to remove the solvent. When the salt particle sizes of 212-295, 295-425, or 425-531 µm and a 15% w/v polymer solution concentration were used, the porosity of the scaffolds was between 83-92% and the compression moduli of the scaffolds were between 13 kPa and 28 kPa. Type I collagen acidic solution was introduced into the pores of a PU scaffold to coat the collagen onto the pore walls throughout the whole PU scaffold. The human aortic endothelial cells (HAECs) cultured in the collagen-coated PU scaffold for 2 weeks were observed by scanning electron microscopy (SEM). It was shown that the enhanced SCPL method and the collagen coating resulted in a spatially uniform distribution of cells throughout the collagen-coated PU scaffold.

Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia

This paper reports the application of multicriteria decision making techniques, PROMETHEE and GAIA, and receptor models, PCA/APCS and PMF, to data from an air monitoring site located on the campus of Queensland University of Technology in Brisbane, Australia and operated by Queensland Environmental Protection Agency (QEPA). The data consisted of the concentrations of 21 chemical species and meteorological data collected between 1995 and 2003. PROMETHEE/GAIA separated the samples into those collected when leaded and unleaded petrol were used to power vehicles in the region. The number and source profiles of the factors obtained from PCA/APCS and PMF analyses were compared. There are noticeable differences in the outcomes possibly because of the non-negative constraints imposed on the PMF analysis. While PCA/APCS identified 6 sources, PMF reduced the data to 9 factors. Each factor had distinctive compositions that suggested that motor vehicle emissions, controlled burning of forests, secondary sulphate, sea salt and road dust/soil were the most important sources of fine particulate matter at the site. The most plausible locations of the sources were identified by combining the results obtained from the receptor models with meteorological data. The study demonstrated the potential benefits of combining results from multi-criteria decision making analysis with those from receptor models in order to gain insights into information that could enhance the development of air pollution control measures.

Characterisation of soluble components and PAH in PM10 atmospheric particulate matter in Brisbane

Fours sets of PM10 samples were collected in three sites in SEQ from December 2002 to August 2004. Three of these sets of samples were collected by QLD EPA as a part of their regular air monitoring program at Woolloongabba, Rocklea and Eagle Farm. Half of the samples were used in this study for the analysis of water-soluble ions, which are Na+, K+, Mg2+, Ca2+, NH4 +, Cl-, NO3 -, SO4 2-, F-, Br-, NO2 -, PO4 -3 and the other half was retained by QLD EPA. The fourth set of samples was collected at Rocklea, specifically for this study. A quarter of the samples obtained from this set of samples were used to analyse water-soluble ions; a quarter of the sample was used to analyse Pb, Cu, Al, Fe, Mn and Zn; and the rests were used to analyse US EPA 16 priority PAHs. The water-soluble ions were extracted ultrasonically with water and the major watersoluble anions as well as NH4 + were analysed using IC. Na+, K+, Mg2+, Ca2+ Pb, Cu, Al, Fe, Mn and Zn were analysed using ICP-AES while PAHs were extracted by acetonitrile and analysed using HPLC. Of the analysed water-soluble ions, Cl-, NO3 -, SO4 2-, Na+, K+, Mg2+ and Ca2+ were high in concentration and determined in all the samples. F-, Br-, NO2 -, PO4 -3 and NH4 + ions were lower in concentration and determined only in some samples. Na+ and Cl- were high in all samples indicating the importance of a marine source. Principal Component Analysis (PCA) was used to examine the temporal variations of the water-soluble ions at the three sites. The results indicated that there was no major difference between the three sites. However, comparing the average concentrations of ions and Cl-/Na+ it was concluded that Woolloongabba had more marine influence than the other sites. Al, Fe and Zn were detected in all samples. Al and Fe were high in all samples indicating the significance of a source of crustal matter. Cu, Mn and Pb were in low concentrations and were determined only in some samples. The lower Pb concentrations observed in the study than in previous studies indicate that the phasing-out of leaded petrol had an appreciable impact on Pb levels in SEQ. This study reports for the first time, simultaneous data on the water-soluble, metal ion and PAH levels of PM10 aerosols in Brisbane, and provides information on the most likely sources of these chemical species. Such information can be used alongside those that already exist to formulate PM10 pollution reduction strategies for SEQ in order to protect the community from the adverse effects of PM pollution.

Load–frequency control : a GA-based multi-agent reinforcement learning

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.

The relationship between particulate air pollution and emergency hospital visits for hypertension in Beijing, China

BACKGROUND: A number of epidemiological studies have examined the adverse effect of air pollution on mortality and morbidity. Also, several studies have investigated the associations between air pollution and specific-cause diseases including arrhythmia, myocardial infarction, and heart failure. However, little is known about the relationship between air pollution and the onset of hypertension. OBJECTIVE: To explore the risk effect of particulate matter air pollution on the emergency hospital visits (EHVs) for hypertension in Beijing, China. METHODS: We gathered data on daily EHVs for hypertension, fine particulate matter less than 2.5 microm in aerodynamic diameter (PM(2.5)), particulate matter less than 10 microm in aerodynamic diameter (PM(10)), sulfur dioxide, and nitrogen dioxide in Beijing, China during 2007. A time-stratified case-crossover design with distributed lag model was used to evaluate associations between ambient air pollutants and hypertension. Daily mean temperature and relative humidity were controlled in all models. RESULTS: There were 1,491 EHVs for hypertension during the study period. In single pollutant models, an increase in 10 microg/m(3) in PM(2.5) and PM(10) was associated with EHVs for hypertension with odds ratios (overall effect of five days) of 1.084 (95% confidence interval (CI): 1.028, 1.139) and 1.060% (95% CI: 1.020, 1.101), respectively. CONCLUSION: Elevated levels of ambient particulate matters are associated with an increase in EHVs for hypertension in Beijing, China.

Application of reinforcement learning in an open railway access market price negotiation

In an open railway access market price negotiation, it is feasible to achieve higher cost recovery by applying the principles of price discrimination. The price negotiation can be modeled as an optimization problem of revenue intake. In this paper, we present the pricing negotiation based on reinforcement learning model. A negotiated-price setting technique based on agent learning is introduced, and the feasible applications of the proposed method for open railway access market simulation are discussed.

The application of profluorescent nitroxides to detect reactive oxygen species derived from combustion-generated particulate matter: Cigarette smoke – a case study

Reactive oxygen species (ROS) and related free radicals are considered to be key factors underpinning the various adverse health effects associated with exposure to ambient particulate matter. Therefore, measurement of ROS is a crucial factor for assessing the potential toxicity of particles. In this work, a novel profluorescent nitroxide, BPEAnit, was investigated as a probe for detecting particle-derived ROS. BPEAnit has a very low fluorescence emission due to inherent quenching by the nitroxide group, but upon radical trapping or redox activity, a strong fluorescence is observed. BPEAnit was tested for detection of ROS present in mainstream and sidestream cigarette smoke. In the case of mainstream cigarette smoke, there was a linear increase in fluorescence intensity with an increasing number of cigarette puffs, equivalent to an average of 101 nmol ROS per cigarette based on the number of moles of the probe reacted. Sidestream cigarette smoke sampled from an environmental chamber exposed BPEAnit to much lower concentrations of particles, but still resulted in a clearly detectible increase in fluorescence intensity with sampling time. It was calculated that the amount of ROS was equivalent to 50 ± 2 nmol per mg of particulate matter; however, this value decreased with ageing of the particles in the chamber. Overall, BPEAnit was shown to provide a sensitive response related to the oxidative capacity of the particulate matter. These findings present a good basis for employing the new BPEAnit probe for the investigation of particle-related ROS generated from cigarette smoke as well as from other combustion sources.

The application of a profluorescent nitroxide probe to detect reactive oxygen species derived from combustion-generated particulate matter

Particulate pollution has been widely recognised as an important risk factor to human health. In addition to increases in respiratory and cardiovascular morbidity associated with exposure to particulate matter (PM), WHO estimates that urban PM causes 0.8 million premature deaths globally and that 1.5 million people die prematurely from exposure to indoor smoke generated from the combustion of solid fuels. Despite the availability of a huge body of research, the underlying toxicological mechanisms by which particles induce adverse health effects are not yet entirely understood. Oxidative stress caused by generation of free radicals and related reactive oxygen species (ROS) at the sites of deposition has been proposed as a mechanism for many of the adverse health outcomes associated with exposure to PM. In addition to particle-induced generation of ROS in lung tissue cells, several recent studies have shown that particles may also contain ROS. As such, they present a direct cause of oxidative stress and related adverse health effects. Cellular responses to oxidative stress have been widely investigated using various cell exposure assays. However, for a rapid screening of the oxidative potential of PM, less time-consuming and less expensive, cell-free assays are needed. The main aim of this research project was to investigate the application of a novel profluorescent nitroxide probe, synthesised at QUT, as a rapid screening assay in assessing the oxidative potential of PM. Considering that this was the first time that a profluorescent nitroxide probe was applied in investigating the oxidative stress potential of PM, the proof of concept regarding the detection of PM–derived ROS by using such probes needed to be demonstrated and a sampling methodology needed to be developed. Sampling through an impinger containing profluorescent nitroxide solution was chosen as a means of particle collection as it allowed particles to react with the profluorescent nitroxide probe during sampling, avoiding in that way any possible chemical changes resulting from delays between the sampling and the analysis of the PM. Among several profluorescent nitroxide probes available at QUT, bis(phenylethynyl)anthracene-nitroxide (BPEAnit) was found to be the most suitable probe, mainly due to relatively long excitation and emission wavelengths (λex= 430 nm; λem= 485 and 513 nm). These wavelengths are long enough to avoid overlap with the background fluorescence coming from light absorbing compounds which may be present in PM (e.g. polycyclic aromatic hydrocarbons and their derivatives). Given that combustion, in general, is one of the major sources of ambient PM, this project aimed at getting an insight into the oxidative stress potential of combustion-generated PM, namely cigarette smoke, diesel exhaust and wood smoke PM. During the course of this research project, it was demonstrated that the BPEAnit probe based assay is sufficiently sensitive and robust enough to be applied as a rapid screening test for PM-derived ROS detection. Considering that for all three aerosol sources (i.e. cigarette smoke, diesel exhaust and wood smoke) the same assay was applied, the results presented in this thesis allow direct comparison of the oxidative potential measured for all three sources of PM. In summary, it was found that there was a substantial difference between the amounts of ROS per unit of PM mass (ROS concentration) for particles emitted by different combustion sources. For example, particles from cigarette smoke were found to have up to 80 times less ROS per unit of mass than particles produced during logwood combustion. For both diesel and wood combustion it has been demonstrated that the type of fuel significantly affects the oxidative potential of the particles emitted. Similarly, the operating conditions of the combustion source were also found to affect the oxidative potential of particulate emissions. Moreover, this project has demonstrated a strong link between semivolatile (i.e. organic) species and ROS and therefore, clearly highlights the importance of semivolatile species in particle-induced toxicity.

Effect of spacing of reinforcement on the behaviour of partially grouted masonry shear walls

Partially Grouted Reinforced Masonry (PGRM) shear walls perform well in places where the cyclonic wind pressure dominates the design. Their out-of-plane flexural performance is better understood than their inplane shear behaviour; in particular, it is not clear whether the PGRM shear walls act as unreinforced masonry (URM) walls embedded with discrete reinforced grouted cores or as integral systems of reinforced masonry (RM) with wider spacing of reinforcement. With a view to understanding the inplane response of PGRM shear walls, ten full scale single leaf, clay block walls were constructed and tested under monotonic and cyclic inplane loading cases. It has been shown that where the spacing of the vertical reinforcement is less than 2000mm, the walls behave as an integral system of RM; for spacing greater than 2000mm, the walls behave similar to URM with no significant benefit from the reinforced cores based on the displacement ductility and stiffness degradation factors derived from the complete lateral load – lateral displacement curves.

Air quality and its impact on health: focus on particulate matter

Inadequate air quality and the inhalation of airborne pollutants pose many risks to human health and wellbeing, and are listed among the top environmental risks worldwide. The importance of outdoor air quality was recognised in the 1950s and indoor air quality emerged as an issue some time later and was soon recognised as having an equal, if not greater importance than outdoor air quality. Identification of ambient air pollution as a health hazard was followed by steps, undertaken by a broad range of national and international professional and government organisations, aimed at reduction or elimination of the hazard. However, the process of achieving better air quality is still in progress. The last 10 years or so have seen an unprecedented increase in the interest in, and attention to, airborne particles, with a special focus on their finer size fractions, including ultrafine (< 0.1 m) and their subset, nano particles (< 0.05 m). This paper discusses the current status of scientific knowledge on the links between air quality and health, with a particular focus on airborne particulate matter, and the directions taken by national and international bodies to improve air quality.

REGAL : a regularization based algorithm for reinforcement learning in weakly communicating MDPs

We provide an algorithm that achieves the optimal regret rate in an unknown weakly communicating Markov Decision Process (MDP). The algorithm proceeds in episodes where, in each episode, it picks a policy using regularization based on the span of the optimal bias vector. For an MDP with S states and A actions whose optimal bias vector has span bounded by H, we show a regret bound of ~ O(HS p AT ). We also relate the span to various diameter-like quantities associated with the MDP, demonstrating how our results improve on previous regret bounds.