Validating performance of automotive materials at high strain rate for improved crash design

This paper investigates sources of performance variability in high velocity testing of automotive crash structures. Sources of variability, or so called noise factors, present in a testing environment, arise from uncertainty in structural properties, joints, boundary conditions and measurement system. A box structure, which is representative of a crash component, is designed and fabricated from a high strength Dual Phase sheet steel. Crush tests are conducted at low and high speed. Such tests intend to validate a component model and material strain rate sensitivity data determined from high speed tensile testing. To support experimental investigations, stochastic modeling is used to investigate the effect of noise factors on crash structure performance variability, and to identify suitable performance measures to validate a component model and material strain rate sensitivity data. The results of the project will enable the measurement of more reliable strain rate sensitivity data for improved crashworthiness predictions of automotive structures

On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning

There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs. In response to this disquiet counterfactual explanations have become massively popular in eXplainable AI (XAI) due to their proposed computational psychological, and legal benefits. In contrast however, semifactuals, which are a similar way humans commonly explain their reasoning, have surprisingly received no attention. Most counterfactual methods address tabular rather than image data, partly due to the nondiscrete nature of the latter making good counterfactuals difficult to define. Additionally generating plausible looking explanations which lie on the data manifold is another issue which hampers progress. This paper advances a novel method for generating plausible counterfactuals (and semifactuals) for black box CNN classifiers doing computer vision. The present method, called PlausIble Exceptionality-based Contrastive Explanations (PIECE), modifies all exceptional features in a test image to be normal from the perspective of the counterfactual class (hence concretely defining a counterfactual). Two controlled experiments compare this method to others in the literature, showing that PIECE not only generates the most plausible counterfactuals on several measures, but also the best semifactuals.Comment: 4 figures, 9 page

Intelligent and adaptive tutoring for active learning and training environments

Active learning facilitated through interactive and adaptive learning environments differs substantially from traditional instructor-oriented, classroom-based teaching. We present a Web-based e-learning environment that integrates knowledge learning and skills training. How these tools are used most effectively is still an open question. We propose knowledge-level interaction and adaptive feedback and guidance as central features. We discuss these features and evaluate the effectiveness of this Web-based environment, focusing on different aspects of learning behaviour and tool usage. Motivation, acceptance of the approach, learning organisation and actual tool usage are aspects of behaviour that require different evaluation techniques to be used

Benchmarking calculations of excitonic couplings between bacteriochlorophylls

Excitonic couplings between (bacterio)chlorophyll molecules are necessary for simulating energy transport in photosynthetic complexes. Many techniques for calculating the couplings are in use, from the simple (but inaccurate) point-dipole approximation to fully quantum-chemical methods. We compared several approximations to determine their range of applicability, noting that the propagation of experimental uncertainties poses a fundamental limit on the achievable accuracy. In particular, the uncertainty in crystallographic coordinates yields an uncertainty of about 20% in the calculated couplings. Because quantum-chemical corrections are smaller than 20% in most biologically relevant cases, their considerable computational cost is rarely justified. We therefore recommend the electrostatic TrEsp method across the entire range of molecular separations and orientations because its cost is minimal and it generally agrees with quantum-chemical calculations to better than the geometric uncertainty. We also caution against computationally optimizing a crystal structure before calculating couplings, as it can lead to large, uncontrollable errors. Understanding the unavoidable uncertainties can guard against striving for unrealistic precision; at the same time, detailed benchmarks can allow important qualitative questions--which do not depend on the precise values of the simulation parameters--to be addressed with greater confidence about the conclusions

Plant Oils and Products of Their Hydrolysis as Substrates for Polyhydroxyalkanoate Synthesis

Plant oils could provide a sustainable source of carbon for polyhydroxyalkanoate production as they are both renewable and inexpensive. No study to our knowledge has undertaken a comparative study of the use of major European and global commodity plants oils and products of their hydrolysis as substrates for medium chain length polyhydroxyalkanoate (mcl-PHA) production. There have been several studies which have investigated the use of plant oils and their hydrolysis products for short chain length PHA (scl-PHA) production, therefore, in this study, we have focused specifically on mcl-PHA-producing organisms. A comparison between direct growth on oils and the products of their hydrolysis is described here for several mcl-PHA-producing Pseudomonas strains. Pseudomonas putida KT2440, CA-3, GO16, Pseudomonas chlororaphis 555 were screened for their ability to utilize a range of common plant oils (olive, sunflower, rapeseed, and palm) and their hydrolysis products as sole sources of carbon and energy for growth and PHA accumulation. When the oils were supplied in shaken flask experiments, P. putida CA-3 and P. putida KT2440 showed little or no growth, while P. putida GO16 reached a cell dry weight of between 0.33 and 0.56 g L–1, and accumulated mcl-PHA to between 12 and 25 % of CDW, P. chlororaphis 555 reached a cell dry weight of between 0.67 and 0.86 g L–1, and accumulated mcl-PHA to between 27 and 34 % CDW in 48 h. In contrast, when the hydrolyzed fatty acid mixtures were supplied, all 4 strains tested grew and accumulated mcl-PHA. P. putida CA-3 and GO16 achieved the highest biomass (1.02 – 1.06 g L–1) with the majority of the hydrolyzed plant oil fatty acids, however P. chlororaphis 555 accumulated similar levels of PHA as these two strains. Despite being the strain of choice for mcl-PHA accumulation, for the majority of studies, P. putida KT2440 achieved less biomass and accumulated less PHA than other strains tested with the majority of oil-derived fatty acids. It is important to note that both biomass and PHA levels varied significantly across strain and hydrolyzed oil type. Due to the fact that P. chlororaphis 555 was able to grow and accumulate PHA from both plant oils and hydrolyzed oil fatty acids, it was selected for bioreactor trials to try to achieve high cell density and high PHA productivity using rapeseed oil and hydrolyzed rapeseed oil fatty acids. Rapeseed oil (RO) and its hydrolysis product (HROFA) were chosen for these experiments because P. chlororaphis 555 accumulated approximately 30 % mcl-PHA from both substrates, and as this oil can be produced globally, it would offer less barriers to scale-up than Palm oil. The mcl-PHA volumetric productivity with RO as the substrate was 0.53 g L–1 h–1 after 25 h with a yield of 0.22 g PHA g–1 oil, while the volumetric productivity with HROFA as the substrate was 0.54 g L–1 h–1 after 25 h with again a lower yield of 0.15 g PHA g–1 HROFA. Thus, under the fermentation conditions tested, HROFA was an inferior substrate for PHA production when compared to RO

Is attending a mental process?

The nature of attention has been the topic of a lively research programme in psychology for over a century. But there is widespread agreement that none of the theories on offer manage to fully capture the nature of attention. Recently, philosophers have become interested in the debate again after a prolonged period of neglect. This paper contributes to the project of explaining the nature of attention. It starts off by critically examining Christopher Mole’s prominent “adverbial” account of attention, which traces the failure of extant psychological theories to their assumption that attending is a kind of process. It then defends an alternative, process-based view of the metaphysics of attention, on which attention is understood as an activity and not, as psychologists seem to implicitly assume, an accomplishment. The entrenched distinction between accomplishments and activities is shown to shed new light on the metaphysics of attention. It also provides a novel diagnosis of the empirical state of play

Reconstructing 3D x-ray CT images of polymer gel dosimeters using the zero-scan method

In this study x-ray CT has been used to produce a 3D image of an irradiated PAGAT gel sample, with noise-reduction achieved using the ‘zero-scan’ method. The gel was repeatedly CT scanned and a linear fit to the varying Hounsfield unit of each pixel in the 3D volume was evaluated across the repeated scans, allowing a zero-scan extrapolation of the image to be obtained. To minimise heating of the CT scanner’s x-ray tube, this study used a large slice thickness (1 cm), to provide image slices across the irradiated region of the gel, and a relatively small number of CT scans (63), to extrapolate the zero-scan image. The resulting set of transverse images shows reduced noise compared to images from the initial CT scan of the gel, without being degraded by the additional radiation dose delivered to the gel during the repeated scanning. The full, 3D image of the gel has a low spatial resolution in the longitudinal direction, due to the selected scan parameters. Nonetheless, important features of the dose distribution are apparent in the 3D x-ray CT scan of the gel. The results of this study demonstrate that the zero-scan extrapolation method can be applied to the reconstruction of multiple x-ray CT slices, to provide useful 2D and 3D images of irradiated dosimetry gels