PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Hot spot identification is a very relevant problem in a wide variety of areas such as health care, energy or transportation. A hot spot is defined as a region of high likelihood of occurrence of a particular event. To identify hot spots, location data for those events is required, which is typically collected by telematics devices. These sensors are constantly gathering information, generating very large volumes of data. Current state-of-the-art solutions are capable of identifying hot spots from big static batches of data by means of variations of clustering or instance selection techniques that pre-process the original input data, providing the most relevant locations. However, these approaches neglect to address changes in hot spots over time. This paper presents a dynamic bio-inspired approach to detect hot spots in big data streams. This computational intelligence method is designed and applied to the transportation sector as a case study to identify incidents in the roads caused by heavy goods vehicles. We adapt an immune-based algorithm to account for the temporary aspect of hot spots inspired by the idea of pheromones, which is then subsequently implemented using Apache Spark Streaming. Experimental results on real datasets with up to 4.5 million data points—provided by a telematics company—show that the algorithm is capable of quickly processing large streaming batches of data, as well as successfully adapting over time to detect hot spots. The outcome of this method is twofold, both reducing data storage requirements and demonstrating resilience to sudden changes in the input data (concept drift)

PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams

http://dx.doi.org/10.5902/2236130814684http://dx.doi.org/10.5902/2236130814684O gerenciamento de resíduos municipais é um tema que vem se tornando cada vez mais importante no contexto das preocupações mundiais dos governos, e teve um considerável desenvolvimento nas últimas décadas. Tanto os países desenvolvidos como os “em desenvolvimento” emitiram normativas legais restritivas, visando otimizar seus planos de tratamento e destinação final destes resíduos. O objetivo principal do trabalho é investigar a real situação deste cenário no Brasil e nos países desenvolvidos, demonstrando os resultados obtidos e traçando um paralelo comparativo e critico. São transcritos e analisados os dados obtidos, em cada fase de uma Gestão Integrada de Resíduos Sólidos Urbanos – GIRSU. Conclusões importantes são relatadas, tais como, o alto nível de investimento dos países desenvolvidos em relação às campanhas de conscientização para implantação de uma efetiva GIRSU, assim como contrastes marcantes entre os índices de reciclagem no Brasil e neste bloco diferenciado de países, ou seja, 2% e 20%, respectivamente, no montante dos resíduos totais gerados. A avaliação final é de que o diferencial esta nas ações políticas de incentivo econômico destes países desenvolvidos, em termos de subsídios, se comparados com o caso brasileiro

A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence

Detecting changepoints in datasets with many variates is a data science challenge of increasing importance. Motivated by the problem of detecting changes in the incidence of terrorism from a global terrorism database, we propose a novel approach to multiple changepoint detection in multivariate time series. Our method, which we call SUBSET, is a model-based approach which uses a penalised likelihood to detect changes for a wide class of parametric settings. We provide theory that guides the choice of penalties to use for SUBSET, and that shows it has high power to detect changes regardless of whether only a few variates or many variates change. Empirical results show that SUBSET out-performs many existing approaches for detecting changes in mean in Gaussian data; additionally, unlike these alternative methods, it can be easily extended to non-Gaussian settings such as are appropriate for modelling counts of terrorist events

A Model for the Analysis of Caries Occurrence in Primary Molar Tooth Surfaces

Recently methods of caries quantification in the primary dentition have moved away from summary ‘whole mouth’ measures at the individual level to methods based on generalised linear modelling (GLM) approaches or survival analysis approaches. However, GLM approaches based on logistic transformation fail to take into account the time-dependent process of tooth/surface survival to caries. There may also be practical difficulties associated with casting parametric survival-based approaches in a complex multilevel hierarchy and the selection of an optimal survival distribution, while non-parametric survival methods are not generally suitable for the assessment of supplementary information recorded on study participants. In the current investigation, a hybrid semi-parametric approach comprising elements of survival-based and GLM methodologies suitable for modelling of caries occurrence within fixed time periods is assessed, using an illustrative multilevel data set of caries occurrence in primary molars from a cohort study, with clustering of data assumed to occur at surface and tooth levels. Inferences of parameter significance were found to be consistent with previous parametric survival-based analyses of the same data set, with gender, socio-economic status, fluoridation status, tooth location, surface type and fluoridation status-surface type interaction significantly associated with caries occurrence. The appropriateness of the hierarchical structure facilitated by the hybrid approach was also confirmed. Hence the hybrid approach is proposed as a more appropriate alternative to primary caries modelling than non-parametric survival methods or other GLM-based models, and as a practical alternative to more rigorous survival-based methods unlikely to be fully accessible to most researchers

Altered glycolysis triggers impaired mitochondrial metabolism and mTORC1 activation in diabetic β-cells

Chronic hyperglycaemia causes a dramatic decrease in mitochondrial metabolism and insulin content in pancreatic β-cells. This underlies the progressive decline in β-cell function in diabetes. However, the molecular mechanisms by which hyperglycaemia produces these effects remain unresolved. Using isolated islets and INS-1 cells, we show here that one or more glycolytic metabolites downstream of phosphofructokinase and upstream of GAPDH mediates the effects of chronic hyperglycemia. This metabolite stimulates marked upregulation of mTORC1 and concomitant downregulation of AMPK. Increased mTORC1 activity causes inhibition of pyruvate dehydrogenase which reduces pyruvate entry into the tricarboxylic acid cycle and partially accounts for the hyperglycaemia-induced reduction in oxidative phosphorylation and insulin secretion. In addition, hyperglycaemia (or diabetes) dramatically inhibits GAPDH activity, thereby impairing glucose metabolism. Our data also reveal that restricting glucose metabolism during hyperglycaemia prevents these changes and thus may be of therapeutic benefit. In summary, we have identified a pathway by which chronic hyperglycaemia reduces β-cell function

Magnetic properties of Ni2.18Mn0.82Ga Heusler alloys with a coupled magnetostructural transition

Polycrystalline Ni2.18Mn0.82Ga Heusler alloys with a coupled magnetostructural transition are studied by differential scanning calorimetry, magnetic and resistivity measurements. Coupling of the magnetic and structural subsystems results in unusual magnetic features of the alloy. These uncommon magnetic properties of Ni2.18Mn0.82Ga are attributed to the first-order structural transition from a tetragonal ferromagnetic to a cubic paramagnetic phase.Comment: 4 pages, 4 figures, revtex

The chicken talpid3 gene encodes a novel protein that is essential for hedgehog signaling

Talpid(3) is a classical chicken mutant with abnormal limb patterning and malformations in other regions of the embryo known to depend on Hedgehog signaling. We combined the ease of manipulating chicken embryos with emerging knowledge of the chicken genome to reveal directly the basis of defective Hedgehog signal transduction in talpid(3) embryos and to identify the talpid(3) gene. We show in several regions of the embryo that the talpid(3) phenotype is completely ligand independent and demonstrate for the first time that talpid(3) is absolutely required for the function of both Gli repressor and activator in the intracellular Hedgehog pathway. We map the talpid(3) locus to chromosome 5 and find a frameshift mutation in a KIAA0586 ortholog (ENSGALG00000012025), a gene not previously attributed with any known function. We show a direct causal link between KIAA0586 and the mutant phenotype by rescue experiments. KIAA0586 encodes a novel protein, apparently specific to vertebrates, that localizes to the cytoplasm. We show that Gli3 processing is abnormal in talpid(3) mutant cells but that Gli3 can still translocate to the nucleus. These results suggest that the talpid(3) protein operates in the cytoplasm to regulate the activity of both Gli repressor and activator proteins