Spectrophotometric Determination of Ru(III) & Os(VIII) with Acetothioacetanilide

917-91

Rapid identification information and its influence on the perceived clues at a crime scene:an experimental study

Crime scenes can always be explained in multiple ways. Traces alone do not provide enough information to infer a whole series of events that has taken place; they only provide clues for these inferences. CSIs need additional information to be able to interpret observed traces. In the near future, a new source of information that could help to interpret a crime scene and testing hypotheses will become available with the advent of rapid identification techniques. A previous study with CSIs demonstrated that this information had an influence on the interpretation of the crime scene, yet it is still unknown what exact information was used for this interpretation and for the construction of their scenario. The present study builds on this study and gains more insight into (1) the exact investigative and forensic information that was used by CSIs to construct their scenario, (2) the inferences drawn from this information, and (3) the kind of evidence that was selected at the crime scene to (dis)prove this scenario. We asked 48 CSIs to investigate a potential murder crime scene on the computer and explicate what information they used to construct a scenario and to select traces for analysis. The results show that the introduction of rapid ID information at the start of an investigation contributes to the recognition of different clues at the crime scene, but also to different interpretations of identical information, depending on the kind of information available and the scenario one has in mind. Furthermore, not all relevant traces were recognized, showing that important information can be missed during the investigation. In this study, accurate crime scenarios where mainly build with forensic information, but we should be aware of the fact that crime scenes are always contaminated with unrelated traces and thus be cautious of the power of rapid ID at the crime scene

Rapid identification information and its influence on the perceived clues at a crime scene:an experimental study

Crime scenes can always be explained in multiple ways. Traces alone do not provide enough information to infer a whole series of events that has taken place; they only provide clues for these inferences. CSIs need additional information to be able to interpret observed traces. In the near future, a new source of information that could help to interpret a crime scene and testing hypotheses will become available with the advent of rapid identification techniques. A previous study with CSIs demonstrated that this information had an influence on the interpretation of the crime scene, yet it is still unknown what exact information was used for this interpretation and for the construction of their scenario. The present study builds on this study and gains more insight into (1) the exact investigative and forensic information that was used by CSIs to construct their scenario, (2) the inferences drawn from this information, and (3) the kind of evidence that was selected at the crime scene to (dis)prove this scenario. We asked 48 CSIs to investigate a potential murder crime scene on the computer and explicate what information they used to construct a scenario and to select traces for analysis. The results show that the introduction of rapid ID information at the start of an investigation contributes to the recognition of different clues at the crime scene, but also to different interpretations of identical information, depending on the kind of information available and the scenario one has in mind. Furthermore, not all relevant traces were recognized, showing that important information can be missed during the investigation. In this study, accurate crime scenarios where mainly build with forensic information, but we should be aware of the fact that crime scenes are always contaminated with unrelated traces and thus be cautious of the power of rapid ID at the crime scene

On the Benefit of Forward Error Correction at IEEE 802.11 Link Layer Level

This study examines the error distribution of aggregated MPDUs in 802.11n networks and whether or not forward error correction like raptor coding at the link layer would be useful in these networks. Several experiments with Qualcomm 4x4 802.11n hardware were performed. Two devices were used in a data link, while a third device sniffed all transmitted packets. The collected data was analyzed and used to calculate the packet error rate which would be obtained if FEC was used in order to determine whether FEC is useful at the link layer. It is shown that the error distribution of A-MPDUs does not follow the binomial distribution. Because of this, the performance of FEC in real networks is worse than for theoretical cases where a binomial distribution is assumed. Therefore, other ways to decrease the packet error rate have more impact than forward error correction

Kinetics & Mechanism of Substitution of Aquo Ligands from cis-Diaquo-bis(ethylenediamine)cobalt(III) Ion by Glycine in Water-Ethanol Mixture

350-35

Fast marginal likelihood estimation of penalties for group-adaptive elastic net

Nowadays, clinical research routinely uses omics data, such as gene expression, for predicting clinical outcomes or selecting markers. Additionally, so-called co-data are often available, providing complementary information on the covariates, like p-values from previously published studies or groups of genes corresponding to pathways. Elastic net penalisation is widely used for prediction and covariate selection. Group-adaptive elastic net penalisation learns from co-data to improve the prediction and covariate selection, by penalising important groups of covariates less than other groups. Existing methods are, however, computationally expensive. Here we present a fast method for marginal likelihood estimation of group-adaptive elastic net penalties for generalised linear models. We first derive a low-dimensional representation of the Taylor approximation of the marginal likelihood and its first derivative for group-adaptive ridge penalties, to efficiently estimate these penalties. Then we show by using asymptotic normality of the linear predictors that the marginal likelihood for elastic net models may be approximated well by the marginal likelihood for ridge models. The ridge group penalties are then transformed to elastic net group penalties by using the variance function. The method allows for overlapping groups and unpenalised variables. We demonstrate the method in a model-based simulation study and an application to cancer genomics. The method substantially decreases computation time and outperforms or matches other methods by learning from co-data.Comment: 16 pages, 6 figures, 1 tabl

Access to out-of-hospital emergency care in Africa : consensus conference recommendations

Abstract: Out-of-hospital emergency care (OHEC) should be accessible to all who require it. However available data suggests that there are a number of barriers to such access in Africa, mainly centred around challenges in public knowledge, perception and appropriate utilisation of OHEC. Having reached consensus in 2013 on a two-tier system of African OHEC, the African Federation for Emergency Medicine (AFEM) OHEC Group sought to gain further consensus on the narrower subject of access to OHEC in Africa. The objective of this paper is to report the outputs and statements arising from the AFEM OHEC access consensus meeting, held in Cape Town, South Africa in April 2015. The discussion was structured around six dimensions of access to care (awareness, availability, accessibility, accommodation, affordability and acceptability) and tackled both Tier-1 (community first responder) and Tier-2 (formal prehospital services and Emergency Medical Services) OHEC systems. In Tier-1 systems, the role of community involvement and support was emphasised, along with the importance of a first responder system acceptable to the community in which it is embedded in order to optimise access. In Tier-2 systems, the consensus group highlighted the primacy of a single toll-free emergency number , matching of Emergency Medical Services resource demand and availability through appropriate planning and the cost-free nature of Tier-2 emergency care, among other factors that impact accessibility. Much work is still needed in prioritising the steps and clarifying the tools and metrics that would enable the ideal of optimal access to OHEC in Africa

Third-order density-functional perturbation theory: a practical implementation with applications to anharmonic couplings in Si

We present a formulation of third-order density-functional perturbation theory which is manifestly invariant with respect to unitary transfomations within the occupied-states manifold and is particularly suitable for a practical implementation of the so called `2n+1' theorem. Our implementation is demonstrated with the calculation of the third-order anharmonic coupling coefficients for some high-simmetry phonons in Silicon.Comment: 6 pages, Plane Tex, SISSA Ref. 78/94/CM/SC (June 94

Fast cross-validation for multi-penalty ridge regression

High-dimensional prediction with multiple data types needs to account for potentially strong differences in predictive signal. Ridge regression is a simple model for high-dimensional data that has challenged the predictive performance of many more complex models and learners, and that allows inclusion of data type specific penalties. The largest challenge for multi-penalty ridge is to optimize these penalties efficiently in a cross-validation (CV) setting, in particular for GLM and Cox ridge regression, which require an additional estimation loop by iterative weighted least squares (IWLS). Our main contribution is a computationally very efficient formula for the multi-penalty, sample-weighted hat-matrix, as used in the IWLS algorithm. As a result, nearly all computations are in low-dimensional space, rendering a speed-up of several orders of magnitude. We developed a flexible framework that facilitates multiple types of response, unpenalized covariates, several performance criteria and repeated CV. Extensions to paired and preferential data types are included and illustrated on several cancer genomics survival prediction problems. Moreover, we present similar computational shortcuts for maximum marginal likelihood and Bayesian probit regression. The corresponding R-package, multiridge, serves as a versatile standalone tool, but also as a fast benchmark for other more complex models and multi-view learners