On the relation between the IR continuum and the active galactic nucleus in Seyfert galaxies

A sample of the brightest known Seyfert galaxies from the CfA sample is analyzed on the basis of ISO photometric and spectroscopic data. Regardless of the Seyfert type, the mid-IR continuum emission from these galaxies is found to be correlated with the coronal line emission arising in the nuclear active region. Conversely, the correlation degrades progressively when moving from the mid- to the far-IR emission, where it ends to vanish. It is concluded that the mid-IR emission is largely dominated by dust heated by processes associated with the active nucleus whereas the far-IR is a different component most probably unrelated with the active region. We suggest that the far-IR component is due to dust heated by the stellar population in the disks of these galaxies.Comment: 6 pages, 3 figures To be published in Astronomy and Astrophysic

Actitudes hacia la ciencia y asignaturas pendientes : dos factores que afectan al rendimiento en ciencias

This paper analyses the influence of the attitude towarda Science with which some students attain secondary education (BUP) and the role of outstanding subjecrs in their school record

Actitudes hacia la ciencia en estudiantes universitarios de ciencias

We have performed a longitudinal study among university students, from eighteen to twenty-three years old, and their attitudes toward Science. This attitude does not become neither linear nor more negative through successive years. In contrast, we have observed a «saw tooth~pr ofile, which is clearer when the number of courses analysed increases and the sex variable is taken into account. In general, the maximums of one sex are coincidental with the minimums of the other

Actitudes hacia la ciencia a lo largo de BUP y COU : un estudio longitudinal

We have performed a longitudinal study among BUP and COU students, from fourteen to eighteen years old, and their attitudes to Science. This attitude becomes neither linear nor more negative through successive years. On the contrary, we have observed a «saw tooth» profile

La medida de las actitudes usando las técnicas de Likert y de diferencial semántico

We have established a comparison between two popular methods to assess the attitude toward experimental sciences: The summated rating method, generally known as the Likert scale; and the semantic differential method. This study is carried out with university students of chemistry and high school students, finding that both attitude measuring techniques are equally suitable in our educative system. Moreover, the sex variable is not significant

The method of Gaussian weighted trajectories. V. On the 1GB procedure for polyatomic processes

In recent years, many chemical reactions have been studied by means of the quasi-classical trajectory (QCT) method within the Gaussian binning (GB) procedure. The latter consists in "quantizing" the final vibrational actions in Bohr spirit by putting strong emphasis on the trajectories reaching the products with vibrational actions close to integer values. A major drawback of this procedure is that if N is the number of product vibrational modes, the amount of trajectories necessary to converge the calculations is ~ 10^N larger than with the standard QCT method. Applying it to polyatomic processes is thus problematic. In a recent paper, however, Czako and Bowman propose to quantize the total vibrational energy instead of the vibrational actions [G. Czako and J. M. Bowman, J. Chem. Phys., 131, 244302 (2009)], a procedure called 1GB here. The calculations are then only ~ 10 times more time-consuming than with the standard QCT method, allowing thereby for considerable numerical saving. In this paper, we propose some theoretical arguments supporting the 1GB procedure and check its validity on model test cases as well as the prototype four-atom reaction OH+D_2 -> HOD+D

Competing risk models in early warning systems for in-hospital deterioration: the role of missing data imputation

Early Warning Systems (EWS) are useful and very important tools for evaluating the health deteriorating of hospitalised patients, using vital signs (such as heart rate, temperature, etc.) as the main input, based on electronic health records (EHR) which most of the time result in sparse data sets with high rates of missing data. In this work, we aim to study the effect of different imputation techniques on time-to-event (survival) models. For each case we have patient's sex and age, as well as longitudinal data along the hospitalisation for 7 vital signs (temperature, systolic and diastolic pressure, heart and respiratory rates, oxygen saturation and neurological state). We summarise these longitudinal data with the following central tendency, order and dispersion statistics: maximum, minimum, first observation, last observation, mean, standard deviation, average variance percentage and average derivative, transforming the original variables into a cross-sectional higher dimensional space, that still having missing data problems. Each hospitalisation has two possible final states: clinical deterioration or favourable discharge. Here, we model the time-to-event with competitive risk models taking into account the covariates. In the Galdakao-Usansolo University Hospital (Basque Country, Spain), a total of 19.602 hospitalisations (lengths of stay at least 24 hours) were collected during the year 2019, of which 852 (4.35\%) resulted in deterioration. These data correspond to 55.8\% of males and 44.2\% of females. We are using a set of imputation methods, such as central tendency statistics (mean and mode), Multiple Imputation by Chained Equations (MICE), Non-Linear Principal Components Analysis (NLPCA) and Random Forest. We evaluate the performances of the imputation methods described before, via root mean square error and conclude the pros and cons of using each one in medical practice. Then, we use Fine and Gray's competitive risk models and the cause-specific Cox proportional hazard regression to model the time-to-event as a function of imputed summarised data. Finally, we evaluate these models employing the traditional and time-dependent area under the ROC curve, for horizon times of 24, 48, 72, 96 and 120 hospitalisation hours

Competing risk modelling for in-hospital length of stay

In this study, we propose a framework for analysing in-hospital patient data from electronic health records. We transform longitudinal sparse vital signs measurements into cross-sectional data via descriptive statistics, imputing missing values, and evaluating variables strongly associated with time to mutually exclusive events (favourable medical discharge or deterioration). We employ competing risk and random survival forest techniques to predict patients’ length of stay and evaluate models’ performance via Brier score