914 resultados para FINE PARTICLE SYSTEM
Resumo:
A method of producing particles having nano-sized grains comprises the steps of: (a) prepg. a soln. contg. one or more metal cations; (b) mixing the soln. from step (a) with one or more surfactants to form a surfactant/liq. mixt. and (c) heating the mixt. from step (b) above to form the particles. [on SciFinder(R)]
Resumo:
The interaction of Au particles with few layer graphene is of interest for the formation of the next generation of sensing devices(1). In this paper we investigate the coupling of single gold nanoparticles to a graphene sheet, and multiple gold nanoparticles with a graphene sheet using COMSOL Multiphysics. By using these simulations we are able to determine the electric field strength and associated hot-spots for various gold nanoparticle-graphene systems. The Au nanoparticles were modelled as 8 nm diameter spheres on 1.5 nm thick (5 layers) graphene, with properties of graphene obtained from the refractive index data of Weber(2) and the Au refractive index data from Palik(3). The field was incident along the plane of the sheet with polarisation tested for both s and p. The study showed strong localised interaction between the Au and graphene with limited spread; however the double particle case where the graphene sheet separated two Au nanoparticles showed distinct interaction between the particles and graphene. An offset was introduced (up to 4 nm) resulting in much reduced coupling between the opposed particles as the distance apart increased. Findings currently suggest that the graphene layer has limited interaction with incident fields with a single particle present whilst reducing the coupling region to a very fine area when opposing particles are involved. It is hoped that the results of this research will provide insight into graphene-plasmon interactions and spur the development of the next generation of sensing devices.
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This paper presents a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for daily Volt/Var control in distribution system including Distributed Generators (DGs). Due to the small X/R ratio and radial configuration of distribution systems, DGs have much impact on this problem. Since DGs are independent power producers or private ownership, a price based methodology is proposed as a proper signal to encourage owners of DGs in active power generation. Generally, the daily Volt/Var control is a nonlinear optimization problem. Therefore, an efficient hybrid evolutionary method based on Particle Swarm Optimization and Ant Colony Optimization (ACO), called HPSO, is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. The feasibility of the proposed algorithm is demonstrated and compared with methods based on the original PSO, ACO and GA algorithms on IEEE 34-bus distribution feeder.
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This research has developed an innovative road safety barrier system that will enhance roadside safety. In doing so, the research developed new knowledge in the field of road crash mitigation for high speed vehicle impact involving plastic road safety barriers. This road safety barrier system has the required feature to redirecting an errant vehicle with limited lateral displacement. Research was carried out using dynamic computer simulation technique support by experimental testing. Future road safety barrier designers may use the information in this research as a design guideline to improve the performance and redirectional capability of the road safety barrier system. This will lead to better safety conditions on the roadways and potentially save lives.
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In this study, an LPG fumigation system was fitted to a Euro III compression ignition (CI) engine to explore its impact on performance, and gaseous and particulate emissions. LPG was introduced to the intake air stream (as a secondary fuel) by using a low pressure fuel injector situated upstream of the turbocharger. LPG substitutions were test mode dependent, but varied in the range of 14-29% by energy. The engine was tested over a 5 point test cycle using ultra low sulphur diesel (ULSD), and a low and high LPG substitution at each test mode. The results show that LPG fumigation coerces the combustion into pre-mixed mode, as increases in the peak combustion pressure (and the rate of pressure rise) were observed in most tests. The emissions results show decreases in nitric oxide (NO) and particulate matter (PM2.5) emissions; however, very significant increases in carbon monoxide (CO) and hydrocarbon (HC) emissions were observed. A more detailed investigation of the particulate emissions showed that the number of particles emitted was reduced with LPG fumigation at all test settings – apart from mode 6 of the ECE R49 test cycle. Furthermore, the particles emitted generally had a slightly larger median diameter with LPG fumigation, and had a smaller semi-volatile fraction relative to ULSD. Overall, the results show that with some modifications, LPG fumigation systems could be used to extend ULSD supplies without adversely impacting on engine performance and emissions.
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A numerical time-dependent model of an active magnetic regenerator (AMR) was developed for cooling in the kilowatt range. Earlier numerical models have been mostly developed for cooling power in the 0.4 kW range. In contrast, this paper reports the applicability of magnetic refrigeration to the 50 kW range. A packed bed active magnetic regenerator was modelled and the influence of parameters such as geometry and operating parameters were studied for different geometries. The pressure drop for AMR bed length and particle diameter was also studied. High cooling power and coefficient of performance (COP) were achieved by optimization of the diameter of the magnetocaloric powder particles and operating frequency. The optimum operating conditions of the AMR for a cooling capacity of 50 kW was determined for a temperature span of 15 K. The predicted coefficient of performance (COP) was found to be ∼6, making it an attractive alternative to vapour compression systems.
Resumo:
Infectious diseases such as SARS, influenza and bird flu may spread exponentially throughout communities. In fact, most infectious diseases remain major health risks due to the lack of vaccine or the lack of facilities to deliver the vaccines. Conventional vaccinations are based on damaged pathogens, live attenuated viruses and viral vectors. If the damage was not complete, the vaccination itself may cause adverse effects. Therefore, researchers have been prompted to prepare viable replacements for the attenuated vaccines that would be more effective and safer to use. DNA vaccines are generally composed of a double stranded plasmid that includes a gene encoding the target antigen under the transcriptional directory and control of a promoter region which is active in cells. Plasmid DNA (pDNA) vaccines allow the foreign genes to be expressed transiently in cells, mimicking intracellular pathogenic infection and inducing both humoral and cellular immune responses. Currently, because of their highly evolved and specialized components, viral systems are the most effective means for DNA delivery, and they achieve high efficiencies (generally >90%), for both DNA delivery and expression. As yet, viral-mediated deliveries have several limitations, including toxicity, limited DNA carrying capacity, restricted target to specific cell types, production and packing problems, and high cost. Thus, nonviral systems, particularly a synthetic DNA delivery system, are highly desirable in both research and clinical applications.
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Currently, there is a limited understanding of the sources of ambient fine particles that contribute to the exposure of children at urban schools. Since the size and chemical composition of airborne particle are key parameters for determining the source as well as toxicity, PM1 particles (mass concentration of particles with an aerodynamic diameter less than 1 µm) were collected at 24 urban schools in Brisbane, Australia and their elemental composition determined. Based on the elemental composition four main sources were identified; secondary sulphates, biomass burning, vehicle and industrial emissions. The largest contributing source was industrial emissions and this was considered as the main source of trace elements in the PM1 that children were exposed to at school. PM1 concentrations at the schools were compared to the elemental composition of the PM2.5 particles (mass concentration of particles with an aerodynamic diameter less than 2.5 µm) from a previous study conducted at a suburban and roadside site in Brisbane. This comparison revealed that the more toxic heavy metals (V, Cr, Ni, Cu, Zn and Pb), mostly from vehicle and industrial emissions, were predominantly in the PM1 fraction. Thus, the results from this study points to PM1 as a potentially better particle size fraction for investigating the health effects of airborne particles.
Resumo:
Important changes in the legal regulation of the fine culminated in the implementation of the day‐fine system in many European countries during the twentieth century. These changes resulted from various late nineteenth century rationalities that considered the fine a justifiable punishment. Therefore, they supported extending its application by making it affordable for people on low incomes, which meant imprisonment for fine default could mostly be avoided without undermining the end of punishment. In this paper I investigate the historical development of the penal fine as well as the changing forms of this penalty in Western European criminal systems from the end of the eighteenth century until the late nineteenth century.
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We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted from a convolutional neural network previously used to perform general object classification. We examine the effectiveness of these features to perform plant classification when used in combination with an extremely randomised forest. Using this system, with minimal tuning, we obtained relatively good results with a score of 0:249 on the test set of LifeCLEF 2014.
Resumo:
The main aim of the present study was to estimate size segregated doses from e-cigarette aerosols as a function of the airway generation number in lung lobes.. After a 2-second puff, 7.7×1010 particles (DTot) with a surface area of 3.6×103 mm2 (STot), and 3.3×1010 particles with a surface area of 4.2×103 mm2 were deposited in the respiratory system for the electronic and conventional cigarettes, respectively. Alveolar and tracheobronchial deposited doses were compared to the ones received by non-smoking individuals in Western countries, showing a similar order of magnitude. Total regional doses (DR), in head and lobar tracheobronchial and alveolar regions, ranged from 2.7×109 to 1.3×1010 particles and 1.1×109 to 5.3×1010 particles, for the electronic and conventional cigarettes, respectively. DR in the right-upper lung lobe was about twice that found in left-upper lobe and 20% greater in right-lower lobe than the left-lower lobe.
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The Air Pollution Model and Chemical Transport Model (TAPM-CTM) framework has been tested and applied originally in Sydney to quantify particle and gaseous concentration (Cope et al, 2014). However, the model performance had not been tested in the south-eastern Queensland region (SEQR), Australia.
Resumo:
Particle swarm optimization (PSO), a new population based algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area.Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time.This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star.To validate the effectiveness and usefulness of algorithms,a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.
Resumo:
In this study we present a combinatorial optimization method based on particle swarm optimization and local search algorithm on the multi-robot search system. Under this method, in order to create a balance between exploration and exploitation and guarantee the global convergence, at each iteration step if the distance between target and the robot become less than specific measure then a local search algorithm is performed. The local search encourages the particle to explore the local region beyond to reach the target in lesser search time. Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems.