Short time-scale optical variability of the dwarf Seyfert nucleus in NGC 4395

We present optical spectroscopic observations of the least-luminous known Seyfert 1 galaxy, NGC 4395, which was monitored every half-hour over the course of 3 nights. The continuum emission varied by ~35 per cent over the course of 3 nights, and we find marginal evidence for greater variability in the blue continuum than the red. A number of diagnostic checks were performed on the data in order to constrain any systematic or aperture effects. No correlations were found that adequately explained the observed variability, hence we conclude that we have observed real intrinsic variability of the nuclear source. No simultaneous variability was measured in the broad H-beta line, although given the difficulty in deblending the broad and narrow components it is difficult to comment on the significance of this result. The observed short time-scale continuum variability is consistent with NGC 4395 having an intermediate-mass (~10^5 solar masses) central supermassive black hole, rather than a very low accretion rate. Comparison with the Seyfert 1 galaxy NGC 5548 shows that the observed variability seems to scale with black hole mass in roughly the manner expected in accretion models. However the absolute time-scale of variability differs by several orders of magnitude from that expected in simple accretion disc models in both cases.Comment: 16 pages, 14 figures, 5 tables, accepted for publication in MNRA

Café Conilon: alternativa para a agricultura de base familiar na região de Coruripe, AL.

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Comportamento de linhagens e cultivares de algodoeiro no Cerrado do Mato Grosso: resultados da safra 2003/2004.

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Distribuição diamétrica de andirobeiras (Carapa sp.) na floresta de várzea da APA da Fazendinha, Macapá - AP.

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Produção de sementes de Carapa sp. na APA da Fazendinha, Macapá-AP, nos anos de 2008 a 2010.

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Ocorrência natural de fungos entomopatogênicos associados à broca-do-mogno, Hypsipyla grandella (Lepidoptera: Pyralidae).

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Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption predictions based on artificial neural networks were directly compared to XGBoost regressor adaptation to perform this task as an alternative with lower computational cost. AR and ARIMA linear models were applied as a benchmark, and the PSO optimizer was used as an alternative during model adjustment. In summary, it was possible to observe that AR and ARIMA-PSO had similar performances in operations and lower error distributions during full-load power output with normal error frequency distribution of −0.03 ± 3.55 and 0.03 ± 3.78 kg/h, respectively. Despite their similarities, ARIMA-PSO achieved better adherence in capturing load adjustment periods. On the other hand, the nonlinear approaches NAR and XGBoost showed significantly better performance, achieving mean absolute error reductions of 42.37% and 30.30%, respectively, when compared with the best linear model. XGBoost modeling was 8.7 times computationally faster than NAR during training. The nonlinear models were better at capturing disturbances related to fuel consumption ramp, shut-down, and sudden fluctuations steps, despite being inferior in forecasting at full-load, especially XGBoost due to its high sensitivity with slight fuel consumption variations

The Reddening-Free Decline Rate Versus Luminosity Relationship for Type Ia Supernovae

We develop a method for estimating the host galaxy dust extinction for type Ia supernovae based on an observational coincidence first noted by Lira (1995), who found that the B-V evolution during the period from 30-90 days after V maximum is remarkably similar for all events, regardless of light curve shape. This fact is used to calibrate the dependence of the B(max)-V(max) and V(max)-I(max) colors on the light curve decline rate parameter delta-m15, which can, in turn, be used to separately estimate the host galaxy extinction. Using these methods to eliminate the effects of reddening, we reexamine the functional form of the decline rate versus luminosity relationship and provide an updated estimate of the Hubble constant of Ho = 63.3 +- 2.2(internal) +- 3.5(external) km/s/Mpc.Comment: 32 pages, 10 figures, AJ 1999 in pres