ABSTRACT
In arson cases, evidence such as DNA or fingerprints is often destroyed. One of the most important evidence modalities left is relating fire accelerants to a suspect. When gasoline is used as accelerant, the aim is to find a strong indication that a gasoline sample from a fire scene is related to a sample of a suspect. Gasoline samples from a fire scene are weathered, which prohibits a straightforward comparison. We combine machine learning, thermodynamic modeling, and quantum mechanics to predict the composition of unweathered gasoline samples starting from weathered ones. Our approach predicts the initial (unweathered) composition of the sixty main components in a weathered gasoline sample, with error bars of ca. 4% when weathered up to 80% w/w. This shows that machine learning is a valuable tool for predicting the initial composition of a weathered gasoline, and thereby relating samples to suspects.
ABSTRACT
The characteristic that discriminates gasoline from other ignitable liquids is that it contains high-octane blending components. This study elaborates on the idea that the presence of gasoline in fire debris samples should be based on the detection of known high-octane blending components. The potential of the high-octane blending component alkylate as a characteristic feature for gasoline detection and identification in fire debris samples is explored. We have devised characteristic features for the detection of alkylate and verified the presence of alkylate in a large collection of gasoline samples from petrol stations in the Netherlands. Alkylate was detected in the vast majority of the samples. It is demonstrated that alkylate can be detected in fire debris samples that contain traces of gasoline by means of routine GC-MS methods. Detection of alkylate, alongside other gasoline blend components, results in a more solid foundation for gasoline detection and identification in fire debris samples.
ABSTRACT
BACKGROUND: Education is important in improving economies and creating literate, self-reliant and healthy societies. However, hunger is a barrier to basic education in Malawi. Hunger is also associated with a number of health risk behaviours, such as bullying, suicide ideation and unhygienic behaviours that may jeopardize the future of children. There are, however, limited data on the prevalence and associated factors of hunger among school children in Malawi. METHODS: The study used data from the Malawi Global School-Based Health Survey conducted in 2009 to estimate the prevalence of self-reported hunger within the last 30 days among primary and secondary school age group. It also assessed the association between self-reported hunger and some selected list of independent variables using frequency distribution, chi-squared test and logistic regression. RESULTS: A total of 2359 students were available for analysis. The overall self-reported prevalence of hunger within the last 30 days was 12.5% (18.9% (172) in the rural and 8.3% (115) in urban areas; and 11.9%(123) for male and 12.5(148) for female children). In the final analysis, geographical location, eating fruits, having been bullied, suicide ideation, and washing hands with soap were significantly associated with hunger. CONCLUSION: Hunger in both primary and secondary school children in Malawi is a major social problem. The design of school feeding programmes aimed to reduce hunger should incorporate the factors identified as associated with hunger.
