By means of a simple mathematical model developed by the authors, the apparent movement of the Sun can be studied for arbitrary latitudes. Using this model, it is easy to gain insight into various phenomena, such as the passage of the seasons, dependences of position and time of sunrise or sunset on a specific day of year, day duration for different latitudes and seasons, and time dependence of the solar altitude reckoned from the horizontal plane. We present simulations of the Sun path alongside animated data. We show that the model adopted can also be used to explain the principle of a horizontal sundial and to determine shadow areas. We give recommendations as regards how to build energy-effective houses and how to optimize solar cell positions.

By means of a simple mathematical model recently developed by the authors (2010 Phys. Educ. 45 641), the passage of the seasons on the Earth is simulated for arbitrary latitudes, taking into account sunlight attenuation in the atmosphere. The method developed can be used to predict a realistic value of the solar energy input (insolation) that can be absorbed by horizontal ground at sea level on any cloudless day of year. Some ideas for estimating daily insolation on to horizontal ground using the duration of daylight and the length of a shadow at the solar noon are discussed. S Online supplementary data available from stacks.

The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

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