Background: Neonatal mortality and morbidity are the greatest challenges in the current health care scenario. Majority of newborns die because mothers fail to identify danger signs of illness, and inappropriate/delayed care seeking. The most common identified newborn danger signs are fever, lethargy, inability to feed, low temperature, fast breathing, persistent vomiting, convulsions, pus draining/bleeding from umbilical area, lack of consciousness, yellow palm/sole/eye and eye discharge/redness. Objective was to assess the awareness level and factors associated towards neonatal danger sign among mothers who attend public health institutions of Mekelle City, Tigray, Ethiopia, 2015.

Modelling and forecasting of commodity price volatility has important applications for asset management, portfolio analysis and risk assessment due to the simple fact that volatility has informational content and contains signals of the market information flow. This article models and forecasts the gold price volatility using the exponentially weighted moving average (EWMA) and the generalized autoregressive conditional heteroscedasticity (GARCH) models for the period from 1998 to 2014. The gold series shows the classical characteristics of financial time series, such as leptokurtic distributions, data dependence and strong serial correlation in squared returns. Hence, the series can be modelled using both EWMA and GARCH-type models. Among the GARCH-type models, GARCH-M(2,2) with Student’s t distribution for the residuals was found to be the best-fit model. Moreover, the manuscript finds that interest rates, exchange rates and crude oil prices have a significant impact on gold volatility. The risk premium effect is found to be positive and statistically significant, suggesting increased volatility is followed by a higher mean. Finally, a comparison is made between the GARCH and the EWMA models. Using the relative mean squared error and mean absolute error measures, the empirical result suggests that GARCH models with explanatory variables are superior for volatility forecasting.

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Purpose In the airline industry the term load factor is defined as the percentage of seats filled by revenue passengers. The load factor is a metric that measures the airline's capacity and demand management. This paper aimed to identify serial and periodic autocorrelation on the load factors of the Europe-Mid East and Europe-Far East airline flights. Identifying the autocorrelation structure is helpful to develop the best fitted forecasting model of the load factors. Methods The paper applies spectral density estimation to investigate the structure of serial and periodic autocorrelation on the load factors. Then the paper applied multivariate trend model to develop a forecasting model of the load factors of the regional flights. The multivariate trend model is fitted using the Prais-Winsten recursive autoregression methodology. Results The primary analysis of the study identified that the airlines have better a demand than capacity management system for both the Europe-Mid East and Europe-Far East flights. The spectral density estimates showed that the load factors have both periodic and serial correlations for both regional flights. Therefore, in order to control the periodic autocorrelation, we introduce transcendental time functions as predictors of the load factor in the multivariate trend model. Finally, we build realistic and robust forecasting model of the load factors of the Europe-Mid East and Europe-Far East flights. Conclusions The econometric estimation results confirm that the load factors of the Europe-Mid East and Europe-Far East flights are both seasonal and differ between flights. The analysis implies that the load factor is still far from stable and stabilizing policies by airlines has so far not been successful. The AEA may therefore continuously focus on the stabilization and the improvement of the load in the industry.

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