Uncertainty in Widmark calculations:ABV variation in packaged versions of the most popular beers in the UK

Forensic practitioners regularly use the Widmark equation to determine theoretical blood alcohol concentrations for use in cases involving alcohol. It is important in these calculations to determine the uncertainty associated with any result. Previous work has investigated the uncertainty in %ABV from beers produced by small independent breweries in the UK but did not study the top selling beers. The top selling lagers and ales/bitters in the UK were identified by sales volume and the alcohol by volume determined. This data was then used to determine the percent coefficient of variation (%CV) that should be used by forensic practitioners when constructing alcohol technical defence reports for use in forensic cases. These samples, from what may be described as ‘big’ brewers, were determined to have a smaller root mean square error (RMSE) (±0.1%v/v, n = 35), and %CV than those previously reported for beers produced by small, independent breweries in the UK. The results from this study shows that different RMSE's should be used for %ABV when determining the uncertainty of results from Widmark calculations when drinks have been consumed from either ‘big’ brewers or small, independent breweries

Improving uncertainty in Widmark equation calculations:alcohol volume, strength and density

The Widmark equation is probably the most commonly used calculation for medicolegal purposes. Recently the National Research Council (USA) and the Forensic Science Regulator (UK) have called for the uncertainty of all results to be given with all forensic measurements and calculations. To improve the uncertainty of measurement of results from Widmark calculations we have concentrated on the uncertainties of measurement involved in the calculation of alcohol, that of the volume of alcohol, the concentration of alcohol and the density of alcohol as previous studies have investigated some of the other factors involved . Using experimental studies, the scientific literature and legal statutes, we have determined revised and improved uncertainties of the concentration of ethanol for Widmark calculations for both the USA and UK. Based on the calculations that we have performed we recommend the use of Monte Carlo Simulation for the determination of uncertainty of measurement for Widmark Calculations

Indicating Asynchronous Array Multipliers

Multiplication is an important arithmetic operation that is frequently encountered in microprocessing and digital signal processing applications, and multiplication is physically realized using a multiplier. This paper discusses the physical implementation of many indicating asynchronous array multipliers, which are inherently elastic and modular and are robust to timing, process and parametric variations. We consider the physical realization of many indicating asynchronous array multipliers using a 32/28nm CMOS technology. The weak-indication array multipliers comprise strong-indication or weak-indication full adders, and strong-indication 2-input AND functions to realize the partial products. The multipliers were synthesized in a semi-custom ASIC design style using standard library cells including a custom-designed 2-input C-element. 4x4 and 8x8 multiplication operations were considered for the physical implementations. The 4-phase return-to-zero (RTZ) and the 4-phase return-to-one (RTO) handshake protocols were utilized for data communication, and the delay-insensitive dual-rail code was used for data encoding. Among several weak-indication array multipliers, a weak-indication array multiplier utilizing a biased weak-indication full adder and the strong-indication 2-input AND function is found to have reduced cycle time and power-cycle time product with respect to RTZ and RTO handshaking for 4x4 and 8x8 multiplications. Further, the 4-phase RTO handshaking is found to be preferable to the 4-phase RTZ handshaking for achieving enhanced optimizations of the design metrics.Comment: arXiv admin note: text overlap with arXiv:1903.0943

The influence of alcohol content variation in UK packaged beers on the uncertainty of calculations using the Widmark equation

It is common for forensic practitioners to calculate an individual's likely blood alcohol concentration following the consumption of alcoholic beverage(s) for legal purposes, such as in driving under the influence (DUI) cases. It is important in these cases to be able to give the uncertainty of measurement on any calculated result, for this reason uncertainty data for the variables used for any calculation are required. In order to determine the uncertainty associated with the alcohol concentration of beer in the UK the alcohol concentration (%v/v) of 218 packaged beers (112 with an alcohol concentration of ≤5.5%v/v and 106 with an alcohol concentration of >5.5%v/v) were tested using an industry standard near infra-red (NIR) analyser. The range of labelled beer alcohol by volume (ABV's) tested was 3.4%v/v – 14%v/v. The beers were obtained from a range of outlets throughout the UK over a period of 12 months. The root mean square error (RMSE) was found to be ±0.43%v/v (beers with declared %ABV of ≤5.5%v/v) and ±0.53%v/v (beers with declared %ABV of >5.5%v/v) the RMSE for all beers was ±0.48%v/v. The standard deviation from the declared %ABV is larger than those previously utilised for uncertainty calculations and illustrates the importance of appropriate experimental data for use in the determination of uncertainty in forensic calculations

A psychology-driven computational analysis of political interviews

Can an interviewer influence the cooperativeness of an interviewee? The role of an interviewer in actualising a successful interview is an active field of social psychological research. A large-scale analysis of interviews, however, typically involves time-exorbitant manual tasks and considerable human effort. Despite recent advances in computational fields, many automated methods continue to rely on manually labelled training data to establish ground-truth. This reliance obscures explainability and hinders the mobility of analysis between applications. In this work, we introduce a cross-disciplinary approach to analysing interviewer efficacy. We suggest computational success measures as a transparent, automated, and reproducible alternative for pre-labelled data. We validate these measures with a small-scale study with human-responders. To study the interviewer’s influence on the interviewee we utilise features informed by social psychological theory to predict interview quality based on the interviewer’s linguistic behaviour. Our psychologically informed model significantly outperforms a bag-of-words model, demonstrating the strength of a cross-disciplinary approach toward the analysis of conversational data at scale