Interacting kinetics of neutral and ionic species in an atmospheric-pressure helium-oxygen plasma with humid air impurities

We unravel the complex chemistry in both the neutral and ionic systems of a radio-frequency-driven atmospheric-pressure plasma in a helium-oxygen mixture (He-0.5% O) with air impurity levels from 0 to 500 ppm of relative humidity from 0% to 100% using a zero-dimensional, time-dependent global model. Effects of humid air impurity on absolute densities and the dominant production and destruction pathways of biologically relevant reactive neutral species are clarified. A few hundred ppm of air impurity crucially changes the plasma from a simple oxygen-dependent plasma to a complex oxygen-nitrogen-hydrogen plasma. The density of reactive oxygen species decreases from 10 to 10 cm, which in turn results in a decrease in the overall chemical reactivity. Reactive nitrogen species (10 cm ), atomic hydrogen and hydroxyl radicals (10-10 cm) are generated in the plasma. With 500 ppm of humid air impurity, the densities of positively charged ions and negatively charged ions slightly increase and the electron density slightly decreases (to the order of 10 cm). The electronegativity increases up to 2.3 compared with 1.5 without air admixture. Atomic hydrogen, hydroxyl radicals and oxygen ions significantly contribute to the production and destruction of reactive oxygen and reactive nitrogen species. © 2013 IOP Publishing Ltd.

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Mechanism of Alizarin Red S and Methylene Blue Biosorption onto Olive Stone: Isotherm study in Single and Binary Systems

The biosorption process of anionic dye Alizarin Red S (ARS) and cationic dye methylene blue (MB) as a function of contact time, initial concentration and solution pH onto olive stone (OS) biomass has been investigated. Equilibrium biosorption isotherms in single and binary systems and kinetics in batch mode were also examined. The kinetic data of the two dyes were better described by the pseudo second-order model. At low concentration, ARS dye appeared to follow a two-step diffusion process, while MB dye followed a three-step diffusion process. The biosorption experimental data for ARS and MB dyes were well suited to the Redlich-Peterson isotherm. The maximum biosorption of ARS dye, qmax = 16.10 mg/g, was obtained at pH 3.28 and the maximum biosorption of MB dye, qmax = 13.20 mg/g, was observed at basic pH values. In the binary system, it was indicated that the MB dye diffuses firstly inside the biosorbent particle and occupies the biosorption sites forming a monodentate complex and then the ARS dye enters and can only bind to untaken sites; forms a tridentate complex with OS active sites.

Generation of cluster states in optomechanical quantum systems

We consider an optomechanical quantum system composed of a single cavity mode interacting with N mechanical resonators. We propose a scheme for generating continuous-variable graph states of arbitrary size and shape, including the so-called cluster states for universal quantum computation. The main feature of this scheme is that, differently from previous approaches, the graph states are hosted in the mechanical degrees of freedom rather than in the radiative ones. Specifically, via a 2N-tone drive, we engineer a linear Hamiltonian which is instrumental to dissipatively drive the system to the desired target state. The robustness of this scheme is assessed against finite interaction times and mechanical noise, confirming it as a valuable approach towards quantum state engineering for continuous-variable computation in a solid-state platform.

Wearable antennas for medical monitoring systems

A wearable silver nano particle inkjet printed antenna suitable for wireless biomedical sensing is presented. The performance is evaluated on a synthetic variable layered phantom test-bed, representative of human tissue for operation in the 868/915 MHz, and 2400 MHz industrial, scientific and medical frequency bands. Antenna radiation efficiency measurements on the phantom were compared with antennas prototyped with copper. Total radiation efficiencies up to ???6.5 dB are reported, with less than 0.5 dB difference in performance between copper and silver nano particle variants, showing promising application for low-cost disposable wireless sensing.

A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements.

In this brief, a hybrid filter algorithm is developed to deal with the state estimation (SE) problem for power systems by taking into account the impact from the phasor measurement units (PMUs). Our aim is to include PMU measurements when designing the dynamic state estimators for power systems with traditional measurements. Also, as data dropouts inevitably occur in the transmission channels of traditional measurements from the meters to the control center, the missing measurement phenomenon is also tackled in the state estimator design. In the framework of extended Kalman filter (EKF) algorithm, the PMU measurements are treated as inequality constraints on the states with the aid of the statistical criterion, and then the addressed SE problem becomes a constrained optimization one based on the probability-maximization method. The resulting constrained optimization problem is then solved using the particle swarm optimization algorithm together with the penalty function approach. The proposed algorithm is applied to estimate the states of the power systems with both traditional and PMU measurements in the presence of probabilistic data missing phenomenon. Extensive simulations are carried out on the IEEE 14-bus test system and it is shown that the proposed algorithm gives much improved estimation performances over the traditional EKF method.

Interacting agents on complex networks and stochastic processes in them

Nas últimas décadas, um grande número de processos têm sido descritos em termos de redes complexas. A teoria de redes complexas vem sendo utilizada com sucesso para descrever, modelar e caracterizar sistemas naturais, artificias e sociais, tais como ecossistemas, interações entre proteínas, a Internet, WWW, até mesmo as relações interpessoais na sociedade. Nesta tese de doutoramento apresentamos alguns modelos de agentes interagentes em redes complexas. Inicialmente, apresentamos uma breve introdução histórica (Capítulo 1), seguida de algumas noções básicas sobre redes complexas (Capítulo 2) e de alguns trabalhos e modelos mais relevantes a esta tese de doutoramento (Capítulo 3). Apresentamos, no Capítulo 4, o estudo de um modelo de dinâmica de opiniões, onde busca-se o consenso entre os agentes em uma população, seguido do estudo da evolução de agentes interagentes em um processo de ramificação espacialmente definido (Capítulo 5). No Capítulo 6 apresentamos um modelo de otimização de fluxos em rede e um estudo do surgimento de redes livres de escala a partir de um processo de otimização . Finalmente, no Capítulo 7, apresentamos nossas conclusões e perspectivas futuras.

Metal speciation dynamics in colloidal ligand dispersions. Part 3: Lability features of steady-state systems

A lability criterion is developed for dynamic metal binding by colloidal ligands with convective diffusion as the dominant mode of mass transport. Scanned stripping chronopotentiometric measurements of Pb(II) and Cd(II) binding by carboxylated latex core-shell particles were in good agreement with the predicted values. The dynamic features of metal ion binding by these particles illustrate that the conventional approach of assuming a smeared-out homogeneous ligand distribution overestimates the lability of a colloidal ligand system. Due to the nature of the spatial distribution of the binding sites, the change in lability of a metal species with changing ligand concentration depends on whether the ligand concentration is varied via manipulation of the pH (degree of protonation) or via the particle concentration. In the former case the local ligand density varies, whereas in the latter case it is constant. This feature provides a useful diagnostic tool for the presence of geometrically constrained binding sites.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

Fractional order modelling of zero length column desorption response for adsorbents with variable particle sizes

This manuscript analyses the data generated by a Zero Length Column (ZLC) diffusion experimental set-up, for 1,3 Di-isopropyl benzene in a 100% alumina matrix with variable particle size. The time evolution of the phenomena resembles those of fractional order systems, namely those with a fast initial transient followed by long and slow tails. The experimental measurements are best fitted with the Harris model revealing a power law behavior.

Entropy analysis of integer and fractional dynamical systems

This paper investigates the adoption of entropy for analyzing the dynamics of a multiple independent particles system. Several entropy definitions and types of particle dynamics with integer and fractional behavior are studied. The results reveal the adequacy of the entropy concept in the analysis of complex dynamical systems.