Development of high performance materials based on smart elastomer nanocomposites

 The present thesis explores the fabrication of technologically relevant nanocomposites out of a few elastomers and conducting fillers like carbon nanotubes, graphene and polyaniline. The developed materials have good applications in sensors, shape memory devices and capacitors. Different characterization methods reveal the influence of filler-elastomer interactions on the various properties of the obtained nanocomposites as well.

Correlates and motives of pre-drinking with intoxication and harm around licensed venues in two cities

INTRODUCTION AND AIMS: The study investigates the prevalence of pre-drinking culture in the night-time economy (NTE) and its impact upon intoxication and alcohol-related harm and violence experienced by patrons. DESIGN AND METHODS: Cross-sectional surveys were conducted in and around licensed venues in Newcastle (NSW) and Geelong (Victoria) during peak trading hours (typically 9pm-1am). Participants completed a five minute structured interview which targeted: demographics, past and planned movements on the survey night, safety/experience of harm, and patron intoxication. 3949 people agreed to be interviewed, a response rate of 90.7%. Around half (54.9%) of interviewees were male and mean age was 24.4 years (SD = 5.8). RESULTS: 66.8% of participants reported pre-drinking prior to attending licensed venues. On a 1-10 scale measuring self-rated intoxication, pre-drinkers scored significantly higher compared to non pre-drinkers (P < 0.001). Compared to non-pre-drinkers, patrons who had consumed 6-10 standard pre-drinks were 1.5 times more likely to be involved in a violent incident in the past 12 months (OR = 1.50, 95%CI 1.03-2.19, P = 0.037) increasing to 1.8 times more likely for patrons who had 11-15 drinks (OR = 1.80, 95%CI 1.04-3.11 P = .036). Pre-drinking was also associated with both self-rated and observer-rated intoxication, as well as increased probability of illicit drug use. Amongst pre-drinkers, price was the most commonly reported motive for pre-drinking (51.8%). DISCUSSION AND CONCLUSIONS: 'Pre-drinking' was normal behaviour in the current sample and contributes significantly to the burden of harm and intoxication in the NTE. Price disparity between packaged vs. venue liquor is a key motivator for pre-drinking.

A multi-agent approach for enhancing transient stability of smart grids

Transient stability, an important issue to avoid the loss of synchronous operation in power systems, can be achieved through proper coordination and operation of protective devices within the critical clearing time (CCT). In view of this, the development of an intelligent decision support system is useful for providing better protection relay coordination. This paper presents an intelligent distributed agent-based scheme to enhance the transient stability of smart grids in light of CCT where a multi-agent framework (MAF) is developed and the agents are represented in such a way that they are equipped with protection relays (PRs). In addition to this, an algorithm is developed which assists the agents to make autonomous decision for controlling circuit breakers (CBs) independently. The proposed agents are responsible for the coordination of protection devices which is done through the precise detection and isolation of faults within the CCT. The agents also perform the duty of reclosing CBs after the clearance of faults. The performance of the proposed approach is demonstrated on a standard IEEE 39-bus test system by considering short-circuit faults at different locations under various load conditions. To further validate the suitability of the proposed scheme a benchmark 16-machine 68-bus power system is also considered. Simulation results show that MAF exhibits full flexibility to adapt the changes in system configurations and increase the stability margin for both test systems.

Functionalized mesoporous silica nanoparticles for smart agrochemical delivery system

The agrochemical delivery system has been built up based on mesoporous silica nanoparticles as carriers in a controllable fashion. Several peer reviewed papers have been published with this research work. This delivery system will benefit for the future agricultural application.

Who wants safer cities? Perceptions of public safety and attitudes to migrants among China's urban population

Most extant research in the economics of crime literature has focused on explaining variations in crime rates. Public action to prevent crime, however, is often dependent on the level of concern about public safety that is expressed in public perceptions surveys. The economics of crime literature has largely overlooked responses to such surveys as data sources and therefore it has not accounted for the role that public opinion might have in mobilizing public action against crime. We use a unique survey administered in 2003 in 32 Chinese cities to examine the determinants of perceptions of public safety among China's urban population. One of our major findings is that individuals who have a negative perception of rural-urban migrants living in their city have a poor perception of public safety. We also find that the unemployment rate, the masculinity ratio and expenditure on armed police in the city in which the individual resides, whether the individual lives in the coastal region as opposed to the central or western region and average changes in housing prices and average changes in rental prices in the city in which the individual lives are important predictors of perceptions of public safety. © 2007 Elsevier Inc. All rights reserved.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

A decentralised multi-agent approach to enhance the stability of smart microgrids with renewable energy

This paper presents the impact of large penetration of wind power on the transient stability through a dynamic evaluation of the critical clearing times (CCTs) by using intelligent agent-based approach. A decentralised multi-agent-based framework is developed, where agents represent a number of physical device models to form a complex infrastructure for computation and communication. They enable the dynamic flow of information and energy for the interaction between the physical processes and their activities. These agents dynamically adapt online measurements and use the CCT information for relay coordination to improve the transient stability of power systems. Simulations are carried out on a smart microgrid system for faults at increasing wind power penetration levels and the improvement in transient stability using the proposed agent-based framework is demonstrated.

A secure cloud computing based framework for big data information management of smart grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call 'Smart-Frame.' The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.

Predictive data mining for converged internet of things: a mobile health perspective

Mobile Health (mHealth) is now emerging with Internet of Things (IoT), Cloud and big data along with the prevalence of smart wearable devices and sensors. There is also the emergence of smart environments such as smart homes, cars, highways, cities, factories and grids. Presently, it is difficult to quickly forecast or prevent urgent health situations in real-time as health data are analyzed offline by a physician. Sensors are expected to be overloaded by demands of providing health data from IoT networks and smart environments. This paper proposes to resolve the problems by introducing an inference system so that life-threatening situations can be prevented in advance based on a short and long term health status prediction. This prediction is inferred from personal health information that is built by big data in Cloud. The inference system can also resolve the problem of data overload in sensor nodes by reducing data volume and frequency to reduce workload in sensor nodes. This paper presents a novel idea of tracking down and predicting a personal health status as well as intelligent functionality of inference in sensor nodes to interface IoT networks

Optimal coordination of G2V and V2G to support power grids with high penetration of renewable energy

Electric vehicles (EVs) have recently gained much popularity as a green alternative to fossil-fuel cars and a feasible solution to reduce air pollution in big cities. The use of EVs can also be extended as a demand response tool to support high penetration of renewable energy (RE) sources in future smart grid. Based on the certainty equivalent adaptive control (CECA) principle and a customer participation program, this paper presents a novel control strategy using optimization technique to coordinate not only the charging but also the discharging of EV batteries to deal with the intermittency in RE production. In addition, customer charging requirements and schedules are incorporated into the optimization algorithm to ensure customer satisfaction, and further improve the control performance. The merits of this scheme are its simplicity, efficiency, robustness and readiness for practical applications. The effectiveness of the proposed control algorithm is demonstrated by computer simulations of a power system with high level of wind energy integration.

Monitoramento do impacto de dejetos industriais em amostras de água do Rio Caí através do teste SMART em Dorsophila melanogaster

O presente estudo teve como objetivos (i) avaliar a validade do emprego do teste SMART, em Drosophila melanogaster, como indicador da contaminação de amostras de água superficial associada a misturas complexas, (ii) detectar a atividade tóxico-genética de dejetos industriais, lançados no rio Caí, empregando o cruzamento aprimorado. Dentro desta perspectiva, pretendeu também (iii) comparar os dados obtidos para as amostras sob influência de despejos industriais com aqueles previamente observados para amostras sob influência de dejetos de origem urbana, provenientes das cidades de Montenegro e São Sebastião do Caí (Silva., 1999). Na tentativa de avaliar a genotoxicidade, associada ao curso final do rio Caí, foram selecionados os seguintes pontos de coleta de despejos industriais: Km 18,6 - situado na foz do arroio Bom Jardim, próximo à área de disposição do efluente final líquido e da drenagem das áreas de disposição dos resíduos sólidos do complexo industrial – e Km 13,6 - no canal da bacia de acumulação e segurança 7 do pólo industrial Neste ensaio genético, cada amostra industrial foi administrada às larvas de terceiro estágio em duas diluições (25% e 50%), bem como na sua forma crua (100%) - sendo avaliados um total de 40 indivíduos por amostra por concentração, totalizando a análise de 11.712.000 células por amostra. Foram utilizados dois controles negativos, o controle de campo – representado pela nascente de um riacho localizada em uma área conservada com fraca ação antrópica e próxima aos pontos do rio – assim como o diluente água destilada. Uma vez que as freqüências das diferentes categorias de manchas não foram significantemente superiores àquelas observadas nos controles negativos (água destilada), os pontos Km 18,6 e Km 13,6 foram caracterizados como destituídos de ação genotóxica nos três meses de coleta : março, junho e setembro. Estes achados sugerem que, nas condições experimentais empregadas, os dejetos de origem industrial não foram capazes de induzir lesões do tipo mutação gênica, cromossômica, assim como eventos relacionados com recombinação mitótica. Por outro lado, a comparação dos dados obtidos no presente estudo com os observados por Silva (1999) para dejetos urbanos, revelou a validade do emprego do teste SMART como uma ferramenta para detecção de contaminação ambiental. De fato, as amostras urbanas referentes aos meses de março (Km 52, 78 e 80) e setembro (Km 52) – coletadas concomitantemente com as de origem industrial – foram diagnosticadas como indutoras de aneuploidias e/ou de grandes deleções cromossômicas. As potências genotóxicas médias estimadas mostraram que o Km 80 foi o local com o maior grau de genotoxicidade – seguido pelos Km 78 e 52 – que apresentaram potências semelhantes Considerando os resultados obtidos, em cinco pontos situados ao longo do curso final do rio Caí, conclui-se que os prejuízos causados pelos dejetos urbanos podem ser tão ou mais nocivos que os impostos pelos de origem industrial – especialmente em função de seu grande volume de lançamento.

Smart visible sets para ambientes de rede

A visualização em tempo real de cenas complexas através de ambientes de rede é um dos desafios na computação gráfica. O uso da visibilidade pré-computada associada a regiões do espaço, tal como a abordagem dos Potentially Visible Sets (PVS), pode reduzir a quantidade de dados enviados através da rede. Entretanto, o PVS para algumas regiões pode ainda ser bastante complexo, e portanto uma estratégia diferente para diminuir a quantidade de informações é necessária. Neste trabalho é introduzido o conceito de Smart Visible Set (SVS), que corresponde a uma partição das informações contidas no PVS segundo o ângulo de visão do observador e as distâncias entre as regiões. Dessa forma, o conceito de “visível” ou de “não-visível” encontrado nos PVS é estendido. A informação referente ao conjunto “visível” é ampliada para “dentro do campo de visão” ou “fora do campo de visão” e “longe” ou “perto”. Desta forma a informação referente ao conjunto “visível” é subdividida, permitindo um maior controle sobre cortes ou ajustes nos dados que devem ser feitos para adequar a quantidade de dados a ser transmitida aos limites impostos pela rede. O armazenamento dos SVS como matrizes de bits permite ainda uma interação entre diferentes SVS. Outros SVS podem ser adicionados ou subtraídos entre si com um custo computacional muito pequeno permitindo uma rápida alteração no resultado final. Transmitir apenas a informação dentro de campo de visão do usuário ou não transmitir a informação muito distante são exemplos dos tipos de ajustes que podem ser realizados para se diminuir a quantidade de informações enviadas. Como o cálculo do SVS depende da existência de informação de visibilidade entre regiões foi implementado o algoritmo conhecido como “Dual Ray Space”, que por sua vez depende do particionamento da cena em regiões. Para o particionamento da cena em uma BSP-Tree, foi modificada a aplicação QBSP3. Depois de calculada, a visibilidade é particionada em diferentes conjuntos através da aplicação SVS. Finalmente, diferentes tipos de SVS puderam ser testados em uma aplicação de navegação por um cenário 3D chamada BSPViewer. Essa aplicação também permite comparações entre diferentes tipos de SVS e PVS. Os resultados obtidos apontam o SVS como uma forma de redução da quantidade de polígonos que devem ser renderizados em uma cena, diminuindo a quantidade de informação que deve ser enviada aos usuários. O SVS particionado pela distância entre as regiões permite um corte rápido na informação muito distante do usuário. Outra vantagem do uso dos SVS é que pode ser realizado um ordenamento das informações segundo sua importância para o usuário, desde que uma métrica de importância visual tenha sido definida previamente.

O efeito smart money na indústria de fundos brasileira

O presente trabalho estuda o efeito Smart Money, inicialmente identificado por GRUBER(1996) e ZHENG (1999), na indústria de fundos brasileira no período de 2001 a 2005. Buscou-se identificar se os fundos que apresentaram maior captação líquida em seguida performam melhor do que os fundos de menor captação líquida. O efeito Smart Money foi identificado nos fundos de ações mesmo após ter sido controlado pelo efeito momentum. Nos fundos multimercados com renda variável e nos fundos de renda fixa não foi possível identificar tal fenômeno.