Chlamydia pneumoniae and Mycoplasma pneumoniae pneumonia: comparison of clinical, epidemiological characteristics and laboratory profiles

The purpose of our retrospective 3-year study was to analyse and compare clinical and epidemiological characteristics in hospitalized patients older than 6 years with community-acquired pneumonia (CAP) caused by Chlamydia pneumoniae (87 patients) and Mycoplasma pneumoniae (147 patients). C. pneumoniae and M. pneumoniae infection was confirmed by serology. C. pneumoniae patients were older (42·12 vs. 24·64 years), and were less likely to have a cough, rhinitis, and hoarseness (P<0·001). C. pneumoniae patients had higher levels of C-reactive protein (CRP), and aspartate aminotransferase (AST) than M. pneumoniae patients (P<0·001). Pleural effusion was recorded more frequently in patients with M. pneumoniae (8·84 vs. 3·37%). There were no characteristic epidemiological and clinical findings that would distinguish CAP caused by M. pneumoniae from C. pneumoniae. However, some factors are indicative for C. pneumoniae such as older age, lack of cough, rhinitis, hoarseness, and higher value of CRP, and AST

Bayesian CNN-BiLSTM and Vine-GMCM Based Probabilistic Forecasting of Hour-Ahead Wind Farm Power Outputs

The importance of the accurate forecasting of power outputs of wind-based generation systems is increasing, as their contributions to the total system generation are rising. However, wind energy resource exhibits strong and stochastic spatio-temporal variations, which further combine with the uncertainties in WF operating regimes, i.e., numbers of wind turbines in normal operation, under curtailment, or that are faulty/disconnected. This paper presents a novel approach for efficient dealing with uncertainties in hour-ahead forecasted WF power outputs. It first applies Bayesian convolutional neural network-bidirectional long short-term memory (Bayesian CNN-BiLSTM) method, which allows for a more accurate probabilistic forecasting of wind speed, air density and wind direction, using the nearby WFs as additional input data. The WF operating regimes are also predicted using the same Bayesian CNN-BiLSTM structure. Afterwards, a high-dimensional Vine-Gaussian mixture Copula model is combined with Bayesian CNN-BiLSTM model to evaluate uncertainties in the WF outputs based on a cross-correlational conditioning of the forecasted weather variables and operating regimes. The proposed combined model is applied and validated using the actual field measurements from two WF clusters in close locations in Croatia and is also benchmarked against several other models

Current status of endemic goiter in Croatia: The results of a nationwide study (1995)

In the beginning of the nineties, 40 years after introduction of iodine prophylaxis in Croatia, on a basis of a frequent reports coming from general practitioners about the presence of a rather high prevalence of goiter among schoolchildren, a nationwide study was initiated with the aim to determine the real prevalence of goiter in the country. A total of 2856 schoolchildren of both sexes, aged 7-15 years, were included into the study. Investigations were designed in a way to cover most of geographical regions in Croatia and subjects were randomly selected. The prevalence of goiter in schoolchildren was assessed by palpation and in part by ultrasonography of the neck. At the same time urinary iodine excretion was measured and iodine content in salt samples was determined. The results have revealed the persistence of mild endemic goiter in inland parts of Croatia with the prevalence of 6-29% in the age group 7-11 years and those of 10-43% among the age group 12-15 years. The overall goiter prevalence in schoolchildren in Croatia fluctuates from 8% to 35%. Such prevalence, most probably due to less than optimum iodine intake, is unlikely to change until iodine content of the salt is increased from its present level of 10 mg of Kl per kg of salt.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Bankruptcy prediction in Norway: a comparison study

In this article we develop statistical models for bankruptcy prediction of Norwegian firms in the limited liability sector using annual balance sheet information. We fit generalized linear, generalized linear mixed and Generalized Additive Models (GAM) in a discrete hazard setting. It is demonstrated that careful examination of the functional relationship between the explanatory variables and the probability of bankruptcy enhances the models' forecasting performance. Using information on the industry sector we model the unobserved heterogeneity between different sectors through an industry-specific random factor in the generalized linear mixed model. The models developed are shown to outperform the model with Altman's variables.