Evolutionary Hot Spots Data Mining

1999 ◽  
pp. 184-193 ◽  
Author(s):  
Graham J. Williams
Keyword(s):  
Data Mining ◽  
Hot Spots

scholarly journals Caracterização de Cicatrizes de Queimadas nas Mesorregiões do Sertão e São Francisco Pernambucano a partir de dados do Sensor MODIS

2021 ◽  
Vol 14 (2) ◽  
pp. 881
Author(s):  
José Rafael Ferreira de Gouveia ◽  
Cristina Rodrigues Nascimento ◽  
José Galdino de Oliveira Júnior ◽  
Geber Barbosa de Albuquerque Moura ◽  
Pabrício Marcos Oliveira Lopes
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Hot Spots ◽  
Natural Regeneration ◽  
Vegetation Index ◽  
Knowledge Analysis ◽  
Vegetation Indexes ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

As mesorregiões do Sertão e São Francisco Pernambucano apresentam clima semiárido, que podem afetar a produção agrícola, em função do clima quente e seco, com temperaturas elevadas e regime pluviométrico irregular. O bioma predominante da região é a Caatinga, que vem sofrendo ao longo dos anos com várias ações antrópicas, incluindo além do desmatamento eventos de queimadas. O objetivo deste artigo foi mapear, caracterizar e quantificar a incidência de focos de calor nas mesorregiões acima relacionadas, bem como a capacidade de recuperação e/ou regeneração natural da vegetação por meio do sensoriamento remoto e técnicas de mineração de dados. Imagens do sensor Moderate Resolution Imaging Spectroradiometer (MODIS) a bordo da plataforma TERRA foram utilizadas para analisar o estado da vegetação nos períodos pré, durante e pós-queima. Para avaliar as condições necessárias para que ocorra a regeneração natural da superfície vegetal foi utilizado o software de mineração de dados Waikato Environment for Knowledge Analysis (WEKA) a partir do cruzamento dos dados do Índice de Vegetação da Diferença Normalizada (NDVI) e precipitação local. Os resultados demonstram um aumento na ocorrência dos focos no período analisado. Existe uma correlação de 91,76% entre o NDVI durante e 48 dias após o evento da queima. Além disso, os parâmetros NDVI 30 e 48 dias após a queima apresentaram um coeficiente de correlação de 83,96%. Portanto, as técnicas de sensoriamento remoto e mineração de dados permitiram avaliar as relações existentes entre o NDVI e a precipitação local para que ocorra a regeneração vegetal.   Characterization of Burning Scars in the Sertão and São Francisco Pernambucano Mesoregions from MODIS Sensor dataA B S T R A C T The Sertão and São Francisco Pernambucano mesoregions have a semi-arid climate, which can affect agricultural production, due to the hot and dry climate, with high temperatures and irregular rainfall. The predominant biome of the region is the Caatinga, which has been suffering over the years with several anthropic actions, including in addition to deforestation, burning events. The purpose of this article was to map, characterize and quantify the incidence of hot spots in the mesoregions listed above, as well as the capacity for recovery and / or natural regeneration of vegetation through remote sensing and data mining techniques. Images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the TERRA platform were used to analyze the state of vegetation in the pre, during and post-firing periods. To assess the conditions necessary for the natural regeneration of the plant surface to occur, the data mining software Waikato Environment for Knowledge Analysis (WEKA) was used, by crossing the data from the Normalized Ddifference Vegetation Index (NDVI) and precipitation. The results demostrate an increase in the occurrence of outbreaks in the analyzed period. There is a 91.76% correlation between NDVI during and 48 days after burning event. In addition, the NDVI parameters 30 and 48 days after burning presented a correlation coefficient of 83.96%. Therefore, the techniques of remote sensing and data mining allowed to evaluate the existing relationships between NDVI and local precipitation so that plant regeneration to occurs.Keywords: remote sensing, vegetation indexes, hot spots, data mining.

scholarly journals Data mining and socio-spatial patterns of COVID-19: geo-prevention keys for tackling the pandemic

2021 ◽  
Author(s):  
Olga De Cos Guerra ◽  
Valentín Castillo Salcines ◽  
David Cantarero Prieto
Keyword(s):  
Data Mining ◽  
Spatial Patterns ◽  
Hot Spots ◽  
Time Perspective ◽  
Space Time ◽  
Least Square ◽  
Regional Government ◽  
Potential Health ◽  
Trade Offs ◽  
Set Up

A geographic perspective is essential in tackling COVID-19. This research study is framed in the collaboration project set up by the University of Cantabria, the Valdecilla Hospital Research Institute (IDIVAL) and the Regional Government of Cantabria. The case study is the Santander functional urban area (FUA), which is considered from a multi-scale perspective. The main source is the daily records of micro-data on COVID-19 cases and the methodology is based on ESRI geo-technologies, and more specifically on a tool called SITAR (a Spanish acronym which stands for Fast-Action Territorial Information System). The main goal is to analyse and contribute to knowledge of the spatial patterns of COVID-19 at neighbourhood level from a space-time perspective. To that end the research is based on data mining methods (3D bins and emerging hot-spots) and exploratory geo-statistical analysis (Global Moran’s Index, Nearest Neighbourhood and Ordinary Least Square analyses, among others). The study identifies space-time patterns that show significant hot-spots and demonstrates a high presence of the virus at building level in neighbourhoods where residential and economic uses are mixed. Knowing the spatial behaviour of the virus is strategically important for proposing geo-prevention keys, reducing spread and balancing trade-offs between potential health gains and economic burdens resulting from interventions to deal with the pandemic.

A high-performance oil-free backing pump for electron microscopes

1978 ◽  
Vol 36 (1) ◽  
pp. 110-111
Author(s):  
G.K.W. Balkau ◽  
E. Bez ◽  
J.L. Farrant
Keyword(s):  
Molecular Weight ◽  
Electron Microscope ◽  
Hot Spots ◽  
High Performance ◽  
Carbonaceous Material ◽  
Contamination Rate ◽  
Low Molecular Weight ◽  
Principal Source ◽  
Rotary Pumps ◽  
Electron Microscopes

The earliest account of the contamination of electron microscope specimens by the deposition of carbonaceous material during electron irradiation was published in 1947 by Watson who was then working in Canada. It was soon established that this carbonaceous material is formed from organic vapours, and it is now recognized that the principal source is the oil-sealed rotary pumps which provide the backing vacuum. It has been shown that the organic vapours consist of low molecular weight fragments of oil molecules which have been degraded at hot spots produced by friction between the vanes and the surfaces on which they slide. As satisfactory oil-free pumps are unavailable, it is standard electron microscope practice to reduce the partial pressure of organic vapours in the microscope in the vicinity of the specimen by using liquid-nitrogen cooled anti-contamination devices. Traps of this type are sufficient to reduce the contamination rate to about 0.1 Å per min, which is tolerable for many investigations.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

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