In this paper we suggest a lightning detection system capable of warning a local populous of incoming lightning weather using a combination of the AS3935 sensor and the one-board-computer Raspberry Pi, in an attempt to design a product that is cheap, mobile and easy to use. The product is composed of a sensor net that registers and reports lightnings on a webserver. The server is reachable as a normal website based on the LAMP method. The project reached a stadium which should satisfy a “proof of concept”, however, the product is far from complete, as it lacks the necessary algorithms needed for proper data computation. The current system only uses Raspberry pi, but it is theoretically possible to introduce other hardware to the system, but the current developed product does not support such functionality. / I den här rapporten föreslår vi ett blixtlokaliseringssystem kapabelt att varna lokal befolkningen om inkommande blixtväder med hjälp av en kombination av sensorn AS3935 och enchipsdatorn Raspberry Pi, i ett försök att skapa en produkt som är billig, mobil och användarvänlig. Systemet består av ett nät av sensorer som registrerar och rapporterar blixtaktivitet på en webbserver. Servern är nåbar som en vanlig hemsida som är byggd enligt LAMP metoden. Projektet har nått ett stadium som borde uppnå kraven för ett ”bevis av ett koncept”, men produkten är långt ifrån färdig, då den saknar de nödvändiga algoritmerna som krävs för ordentlig databehandling. Det nuvarande systemet använder sig enbart av Rasberry Pi:s, men i teorin vore det fullt möjligt att introducera ny hårdvara i nätet, men den nuvarande produkten stöder inte sådan funktionalitet.

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Human/wildlife interactions at the Bosque del Apache National Wildlife Refuge were investigated in this research. Personal interviews and a mapping exercise were used to examine a variety of questions about recreational use. These included visitor backgrounds (their recreational histories, socioeconomic profiles, and past refuge visits), their perceptions (of current and proposed management policies, and visitor effects on wildlife), and their experiences of the refuge (their activities, most memorable experiences and exploration of the environment). Most visitors questioned were middle-aged individuals who resided in New Mexico. The refuge's unique character--its diversity of wildlife and landscape features--was perceived as its greatest asset. Visitors saw the refuge as a wildlife sanctuary, protected and controlled by officials who prevented visitors from having damaging impacts on the resource. While visitors differed by season of visit and purpose of visit, most did agree that the refuge should be managed for the sake of wildlife first. After that, visitors felt that it should be managed for the sake of recreation. Snow Geese responses to certain visitor behaviors were also examined. Simulations of visitor behaviors were made in the presence of small groups of geese. Behavioral observations of geese were made prior to, during, and after simulations. Environmental factors (weather, habitat, and temporal) were also examined. This analysis revealed a general tendency among geese to respond to visitor simulations in characteristic ways. However, variations in geese responses were also observed. Several factors may have most strongly influenced geese behavior. The predictability of visitor behaviors as perceived by geese influenced their patterns of response. Then too, certain environmental factors (habitat and temporal) were important, as well as the particular nature of the animal behaviors themselves. This research indicates that significant transactions occur between people, animals, and the environment. Understanding them is crucial in managing natural resources for ecological as well as recreational values.

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

El documento digital no refiere asesor / Publicación a texto completo no autorizada por el autor / Valora los scores REMS y APACHE II, como predictores de mortalidad en pacientes que ingresan a Emergencia del Hospital Nacional Dos de Mayo. Se realizó un estudio observacional, descriptivo, retrospectivo y transversal. Se revisaron 29 historias clínicas de pacientes atendidos en Emergencia en quiénes se aplicó los scores REMS y APACHE; en el periodo que corresponde al estudio. Del 51.7% de los pacientes fueron varones y el 48.3% fueron mujeres. El promedio de la media global de 63.8+/ -23.6 años, con una mínima a de 18 años y una máxima de 90 años. Los pacientes que fallecieron tuvieron una presión sistólica de 106.6+/ - 41.6 mmHg, presión diastólica (56.6+/ -25.1) , frecuencia respiratoria (36+/ -5.2) , pulso (111.6+/ -2.8) , y saturación de oxigeno (79.6+/ -9.0) ; mientras que los pacientes que sobrevivieron tuvieron una presión sistólica de 93+/ -33.7mmHg, presión diastólica (53.6+/ -23.2) , frecuencia respiratoria (27.6+/ -6.9) , pulso (92.1+/ -20.6) , saturación de oxigeno (88+/ -7.2) . El diagnóstico de ingreso de mayor frecuencia fue la neumonía adquirida en la comunidad (17.2%), seguido de insuficiencia cardiaca (10.3%). Se concluye que los scores REMS y APACHE II, son buenos predictores de mortalidad después del ingreso en pacientes que ingresan a Emergencia del Hospital Nacional Dos de Mayo (P<0.05). La mayoría de pacientes son mayores de 60 años. No hubo diferencias estadísticamente significativas entre las valoraciones de las funciones vitales, y de la saturación de oxigeno según mortalidad (P>0.05). / Trabajo académico

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes the instances across multiple nodes, and an approximate local sensitive hashing method that builds multiple hash tables to index the data. The results indicated that the predictions of the tree-based method are on par with those of an exact method while reducing the execution times in all the scenarios. The aforementioned method is then used to evaluate the quality of a selected feature subset. The optimal adaptation for a multi-label feature selection criterion is discussed and two distributed feature selection methods for multi-label problems are proposed: a method that selects the feature subset that maximizes the Euclidean norm of individual information measures, and a method that selects the subset of features maximizing the geometric mean. The results indicate that each method excels in different scenarios depending on type of features and the number of labels. Rigorous experimental studies and statistical analyses over many multi-label metrics and datasets confirm that the proposals achieve better performances and provide better scalability to bigger data than the methods compared in the state of the art.

Thesis (Ph. D.)--University of California, San Diego, 2002. / Vita. Includes bibliographical references.

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Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-09-06T18:32:40Z No. of bitstreams: 1 DissMRC.pdf: 1562148 bytes, checksum: 9921840ad67ef82d956e399ab96dd78c (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-09-25T16:56:27Z (GMT) No. of bitstreams: 1 DissMRC.pdf: 1562148 bytes, checksum: 9921840ad67ef82d956e399ab96dd78c (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-09-25T16:56:34Z (GMT) No. of bitstreams: 1 DissMRC.pdf: 1562148 bytes, checksum: 9921840ad67ef82d956e399ab96dd78c (MD5) / Made available in DSpace on 2017-09-25T17:05:03Z (GMT). No. of bitstreams: 1 DissMRC.pdf: 1562148 bytes, checksum: 9921840ad67ef82d956e399ab96dd78c (MD5) Previous issue date: 2017-02-13 / Outra / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) / With the evolution of next generation sequencing devices, the cost for obtaining genomic data has significantly reduced. With reduced costs for sequencing, the amount of genomic data to be processed has increased exponentially. Such data growth supersedes the rate at which computing power can be increased year after year by the hardware and software evolution. Thus, the higher rate of data growth in bioinformatics raises the need for exploiting more efficient and scalable techniques based on parallel and distributed processing, including platforms like Clusters, and Cloud Computing. BLAST is a widely used tool for genomic sequences alignment, which has native support for multicore-based parallel processing. However, its scalability is limited to a single machine. On the other hand, Cloud computing has emerged as an important technology for supporting rapid and elastic provisioning of large amounts of resources. Current frameworks like Apache Hadoop and Apache Spark provide support for the execution of distributed applications. Such environments provide mechanisms for embedding external applications in order to compose large distributed jobs which can be executed on clusters and cloud platforms. In this work, we used Spark to support the high scalable and efficient parallelization of BLAST (Basic Local Alingment Search Tool) to execute on dozens to hundreds of processing cores on a cloud platform. As result, our prototype has demonstrated better performance and scalability then CloudBLAST, a Hadoop based parallelization of BLAST. / Com a redução dos custos e evolução dos mecanismos que efetuam o sequenciamento genômico, tem havido um grande aumento na quantidade de dados referentes aos estudos da genomica. O crescimento desses dados tem ocorrido a taxas mais elevadas do que a industria tem conseguido aumentar o poder dos computadores a cada ano. Para melhor atender a necessidade de processamento e analise de dados em bioinformatica faz-se o uso de sistemas paralelos e distribuídos, como por exemplo: Clusters, Grids e Nuvens Computacionais. Contudo, muitas ferramentas, como o BLAST, que fazem o alinhamento entre sequencias e banco de dados, nao foram desenvolvidas para serem processadas de forma distribuída e escalavel. Os atuais frameworks Apache Hadoop e Apache Spark permitem a execucao de aplicacoes de forma distribuída e paralela, desde que as aplicacoes possam ser devidamente adaptadas e paralelizadas. Estudos que permitam melhorar desempenho de aplicacoes em bioinformatica tem se tornado um esforço contínuo. O Spark tem se mostrado uma ferramenta robusta para processamento massivo de dados. Nesta pesquisa de mestrado a ferramenta Apache Spark foi utilizada para dar suporte ao paralelismo da ferramenta BLAST (Basic Local Alingment Search Tool). Experimentos realizados na nuvem Google Cloud e Microsoft Azure demonstram desempenho (speedup) obtido foi similar ou melhor que trabalhos semelhantes ja desenvolvidos em Hadoop.