Nano-CF: a Coordination Framework for macro-programming in wireless sensor networks

Wireless Sensor Networks (WSN) are being used for a number of applications involving infrastructure monitoring, building energy monitoring and industrial sensing. The difficulty of programming individual sensor nodes and the associated overhead have encouraged researchers to design macro-programming systems which can help program the network as a whole or as a combination of subnets. Most of the current macro-programming schemes do not support multiple users seamlessly deploying diverse applications on the same shared sensor network. As WSNs are becoming more common, it is important to provide such support, since it enables higher-level optimizations such as code reuse, energy savings, and traffic reduction. In this paper, we propose a macro-programming framework called Nano-CF, which, in addition to supporting in-network programming, allows multiple applications written by different programmers to be executed simultaneously on a sensor networking infrastructure. This framework enables the use of a common sensing infrastructure for a number of applications without the users having to worrying about the applications already deployed on the network. The framework also supports timing constraints and resource reservations using the Nano-RK operating system. Nano- CF is efficient at improving WSN performance by (a) combining multiple user programs, (b) aggregating packets for data delivery, and (c) satisfying timing and energy specifications using Rate- Harmonized Scheduling. Using representative applications, we demonstrate that Nano-CF achieves 90% reduction in Source Lines-of-Code (SLoC) and 50% energy savings from aggregated data delivery.

Mining social individuals movements and friends based on mobile devices

Trabalho de Projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de Computadores

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Cellulose micro/nano fibers conformational effects probed by nematic liquid crystal droplets

Probing micro-/nano-sized surface conformations, which are ubiquitous in biological systems, by using liquid crystal droplets, which change their ordering and optical appearance in response to the presence of more than ten times smaller cellulose based micro/nano fibers, might find new uses in a range of biological environments and sensors. Previous studies indicate that electrospun micro/nano cellulosic fibers produced from liquid crystalline solutions could present a twisted form [1]. In this work, we study the structures of nematic liquid crystal droplets threaded by cellulose fibers prepared from liquid crystalline and isotropic solutions as well as droplets pierced by spider-made fibers [2]. Planar anchoring at the fibers and planar and homeotropic at the drop surfaces allowed probing cellulose fibers different helical structures as well as aligned filaments.

Text mining e twitter : o poder das redes sociais num mercado competitivo

Actualmente, com a massificação da utilização das redes sociais, as empresas passam a sua mensagem nos seus canais de comunicação, mas os consumidores dão a sua opinião sobre ela. Argumentam, opinam, criticam (Nardi, Schiano, Gumbrecht, & Swartz, 2004). Positiva ou negativamente. Neste contexto o Text Mining surge como uma abordagem interessante para a resposta à necessidade de obter conhecimento a partir dos dados existentes. Neste trabalho utilizámos um algoritmo de Clustering hierárquico com o objectivo de descobrir temas distintos num conjunto de tweets obtidos ao longo de um determinado período de tempo para as empresas Burger King e McDonald’s. Com o intuito de compreender o sentimento associado a estes temas foi feita uma análise de sentimentos a cada tema encontrado, utilizando um algoritmo Bag-of-Words. Concluiu-se que o algoritmo de Clustering foi capaz de encontrar temas através do tweets obtidos, essencialmente ligados a produtos e serviços comercializados pelas empresas. O algoritmo de Sentiment Analysis atribuiu um sentimento a esses temas, permitindo compreender de entre os produtos/serviços identificados quais os que obtiveram uma polaridade positiva ou negativa, e deste modo sinalizar potencias situações problemáticas na estratégia das empresas, e situações positivas passíveis de identificação de decisões operacionais bem-sucedidas.

Saúde em Portugal : estudo das urgências hospitalares através do Data Mining

Qualquer assunto relacionado com a saúde é sempre um tema sensível, pela importância que tem junto da população, já que interage diretamente com o bem-estar das pessoas e, essencialmente, com a sensação de segurança que as estas pretendem ter na prestação dos cuidados básicos de saúde. Dados estatísticos mostram que a população está cada vez mais envelhecida, reforçando a importância da existência de bons centros hospitalares e de um bom Sistema Nacional de Saúde (SNS) (Plano Nacional de Saúde, 2010). Em Portugal, caso os pacientes necessitem de cuidados mais urgentes, podem recorrer ao Serviço de Urgências disponibilizado para toda a população através do SNS. No entanto, a gestão e planeamento deste serviço é complexa, dado este serviço ser frequentemente utilizado por pacientes que não necessitam de cuidados urgentes, levando a que os hospitais deixem de conseguir dar a resposta esperada, implicando a prestação por vezes um serviço de menor qualidade. Neste sentido, analisaram-se dados de um hospital do norte do país com o intuito de perceber o ponto de situação das urgências, de forma a encontrar padrões relevantes através da análise de clusters e de regras de associação. Começando pela análise de clusters, utilizaram-se apenas as variáveis que foram consideradas importantes para o problema, resultando da análise final 3 clusters. O primeiro cluster é constituído por elementos do sexo masculino de todas as idades, o segundo cluster por elementos do sexo masculino mais jovens e por elementos do sexo feminino até aos 60 anos e o terceiro cluster apenas por elementos do sexo feminino a partir dos 40 anos. No final verificaram-se muitas semelhanças entre os clusters 1 e 3, pois ambos continham os pacientes mais idosos, havendo um padrão comum no seu comportamento. No ano 2012 não houve registo de nenhuma epidemia, não havendo por isso nenhuma doença que se destacasse comparativamente às restantes. Concluiu-se também que na maior parte dos casos houve a necessidade de uma intervenção urgente (pulseira de cor Amarela), no entanto a maioria dos pacientes observados conseguiu regressar às suas habitações após as consultas nas Urgências Hospitalares, sem intervenções médicas adicionais. Relativamente às regras de associação, houve a necessidade de transformar e eliminar algumas variáveis que enviesassem o estudo. Após o processo da criação das regras de associação, percebeu-se que as regras eram muito similares entre si, apresentando uma maior confiança nas variáveis que apareceram em maior número (“Pacientes com pulseira de cor Amarela”, “distrito do Porto” ou “Alta Médica para a Residência”).

Real-time monitoring of protein conformational changes using a nano-mechanical sensor.

Proteins can switch between different conformations in response to stimuli, such as pH or temperature variations, or to the binding of ligands. Such plasticity and its kinetics can have a crucial functional role, and their characterization has taken center stage in protein research. As an example, Topoisomerases are particularly interesting enzymes capable of managing tangled and supercoiled double-stranded DNA, thus facilitating many physiological processes. In this work, we describe the use of a cantilever-based nanomotion sensor to characterize the dynamics of human topoisomerase II (Topo II) enzymes and their response to different kinds of ligands, such as ATP, which enhance the conformational dynamics. The sensitivity and time resolution of this sensor allow determining quantitatively the correlation between the ATP concentration and the rate of Topo II conformational changes. Furthermore, we show how to rationalize the experimental results in a comprehensive model that takes into account both the physics of the cantilever and the dynamics of the ATPase cycle of the enzyme, shedding light on the kinetics of the process. Finally, we study the effect of aclarubicin, an anticancer drug, demonstrating that it affects directly the Topo II molecule inhibiting its conformational changes. These results pave the way to a new way of studying the intrinsic dynamics of proteins and of protein complexes allowing new applications ranging from fundamental proteomics to drug discovery and development and possibly to clinical practice.

Investigating the potential of thecamoebians (testate amoebae) as bio-indicators of impact of oil sands mining operations on freshwater environments in Northeastern Alberta, Canada

Thecamoebian (testate amoeba) species diversity and assemblages in reclamation wetlands and lakes in northeastern Alberta respond to chemical and physical parameters associated with oil sands extraction. Ecosystems more impacted by OSPM (oil sands process-affected material) contain sparse, low-diversity populations dominated by centropyxid taxa and Arcella vulgaris. More abundant and diverse thecamoebian populations rich in difflugiid species characterize environments with lower OSPM concentrations. These shelled protists respond quickly to environmental change, allowing year-to-year variations in OSPM impact to be recorded. Their fossil record thus provides corporations with interests in the Athabasca Oil Sands with a potential means of measuring the progression of highlyimpacted aquatic environments to more natural wetlands. Development of this metric required investigation of controls on their fossil assemblage (e.g. seasonal variability, fossilization potential) and their biogeographic distribution, not only in the constructed lakes and wetlands on the oil sands leases, but also in natural environments across Alberta.

Glacial stratigraphy and micromorphology of the Pine Point Pb-­‐Zn mining district, Northwest Territories

This investigation aims to gain a better understanding of the glacial history of the Pine Point Mining district, Northwest Territories, by examining the sedimentological properties of the glacial sediments including, geochemical analysis, heavy mineral concentrate analysis, clast macro-­‐fabrics, pebble lithologies, and micromorphological investigation. Four till units were identified, and three were associated with identified erosional bedrock features and streamlined landforms in the area, indicating a minimum of three ice flow directions. Sedimentological properties suggest that these units were all Type-­B tectomict/mélange till, emplaced as part of a soft subglacial deformable bed. The lack of ice-­‐marginal advance and retreat sequences within the studied till, suggests the Middle Wisconsinan Laurentide Ice margin was likely north and west of the Pine Point area, as opposed to along the margin of the Canadian Shield and Western Sedimentary Basin where it has been suggested to have existed.

Structural, Magnetic and Thermal Studies of Ce1-xEuxCrO3 Nano-Powders

A new series of nano-sized Ce1-xEuxCrO3 (x = 0.0 to 1.0) with an average particle size of 50 - 80 nm were synthesized using a solution combustion method. Nano-powders Ce1-xEuxCrO3 with the canted antiferromagnetic property exhibited interesting magnetic behaviours including the reversal magnetization and the exchange bias effect. The effect of europium doping as the ion with the smaller radius size and different electron con figuration on structural, magnetic and thermal properties of Ce1-xEuxCrO3 were investigated using various experimental techniques, i.e. DC/AC magnetic susceptibility, heat capacity, thermal expansion, Raman scattering, X-ray photoemission spectroscopy, transmission/scanning electron microscopy, X-ray powder diffraction and neutron scattering. An exchange bias effect, magnetization irreversibility and AC susceptibility dispersion in these samples confirmed the existence of the spin disorder magnetic phase in Ce1-xEuxCrO3 compounds. The exchange bias phenomenon, which is assigned to the exchange coupling between glassy-like shell and canted antiferromagnetic core, showed the opposite sign in CeCrO3 and EuCrO3 at low temperatures, suggesting different exchange interactions at the interfaces in these compounds. The energy level excitation of samples were examined by an inelastic neutron scattering which was in good agreement with the heat capacity data. Neutron scattering analysis of EuCrO3 was challenging due to the large neutron absorption cross-section of europium. All diffraction patterns of Ce1-xEuxCrO3 showed the magnetic peak attributed to the antiferromagnetic Cr3+ spins while none of the diffraction patterns could detect the magnetic ordering of the rare-earth ions in these samples.

Union Pacific Railway from Kansas City , Omaha, St. Joseph to Denver, San Francisco, Portland, Helena, Butte, Boise, Leadville, Durango, Deadwood and all cities and mining camps in the west schedule

Union Pacific Railway from Kansas City , Omaha, St. Joseph to Denver, San Francisco, Portland, Helena, Butte, Boise, Leadville, Durango, Deadwood and all cities and mining camps in the west schedule, Jan. 15, 1882.

Mejoramiento del efecto barrera en materiales endurecidos de cemento portland mediante una aplicación innovadora de nano-partículas de silicio.

Tesis (Maestro en Ciencias con orientación en Materiales de Construcción) UANL, 2014.