Ethyl Glucuronide in Scalp and Non-head Hair: An Intra-individual Comparison

Aims: Analysis of ethyl glucuronide (EtG), a minor metabolite of ethanol, is a valid tool for the assessment of social and chronic excessive alcohol consumption. Standardized analysis of EtG is usually done in head hair. As head hair cannot always be provided, alternative hair matrices become more and more interesting. Therefore, a study was performed that compared the intra-individual EtG concentrations in scalp hair and non-head hair (chest, arm, leg and axillary hair). Methods: Hair samples were collected from 68 subjects undergoing an expert assessment for fitness to drive. Aqueous extracts of the hair matrix were cleaned by solid-phase extraction, using an Oasis MAX column. EtG was first derivatized with perfluoropentanoic anhydride and then quantified by GC-MS/MS in negative chemical ionization mode, using EtG-d5 as internal standard. Results: For categorizing drinking behaviour, the two EtG cut-off values recommended by the Society of Hair Testing were applied for all different hair types. For chest, arm and leg hair, correct classification ratios were >83%. This corresponds to sensitivity values >78% and specificities >75%. Such values indicate together with φ coefficients (rφ) > 0.7 a high correlation of the categorization of the drinking behaviour based on these body hair EtG concentrations compared with the indexing based on scalp hair EtG-values. However, it must be taken into consideration that the time frame represented by non-head hair may extend way back. Conclusions: These results indicate that chest, arm and leg hair can be a valid alternative to assess the drinking behaviour of a subject if head hair is not available; whereas axillary hair is not suitable as alternative matri

Nowcasting wind using machine learning from the stations to the grid

Presentación realizada en la 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Application of long-range weather forecasts to agricultural decision problems in Europe

Agriculture can benefit substantially from long-range weather forecasts, for the month or the season, which can help to optimize farming operations and deal more effectively with the adverse impacts of climate variability, including extreme weather events. In the context of climate change, long-range weather forecasts also represent key elements for the development of adaptation strategies. In spite of an undeniable potential, long-range forecasts issued for instance by the European Centre for Medium-Range Weather Forecasts (ECMWF) have yet to find widespread application in European agriculture. To address partially the question of why this is the case, the performance of the ECMWF monthly ensemble forecasting system was examined. It was noted that predictability is currently limited to about 3 weeks for temperature and 2 weeks for precipitation and solar radiation. This may appear deceptive at first sight, but it was noticed that precipitation forecasts over a month are, overall, at least as valuable as information obtained from observed climatology. Encouraged by this finding, the possibility of using monthly forecasts to predict soil water availability was tested. In an operational context, this could serve as a basis for scheduling irrigation. Positive skills were found for lead times of up to 1 month. It was concluded that more systematic investigations of the possibilities offered by long-range forecasts should be undertaken in the future. However, this will require additional efforts to increase the quality of the forecasts, design appropriate application tools and promote the dissemination of the outcome within the agriculture communit

The decay b -> s g at NLL in the Standard Model

I present the Standard Model calculation of the decay rate for b -> s g (g denotes a gluon) at next-to-leading logarithms (NLL). In order to get a meaningful physical result, the decay b -> s g g and certain contributions of b -> s \bar{f} f (where f are the light quark flavours u, d and s) have to be included as well. Numerically we get BR^(NLL) = (5.0 +/- 1.0) * 10^{-3} which is more than a factor 2 larger than the leading logarithmic result BR^(LL) = (2.2 +/- 0.8) * 10^{-3}. Further, I consider the impact of this contribution on the charmless hadronic branching ratio BRc, which could be used to extract the CKM-ratio |V_(ub)/V_(cb)| with more accuracy. Finally, I have a short look at BRc in scenarios where the Wilson coefficient C_8 is enhanced by new physics.Comment: 7 pages including 5 postscript figures; uses epsfi

Adiabatic Quantum Computing for Multi Object Tracking

Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. The association step naturally leads to discrete optimization problems. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. Adiabatic quantum computing (AQC) offers a solution for this, as it has the potential to provide a considerable speedup on a range of NP-hard optimization problems in the near future. However, current MOT formulations are unsuitable for quantum computing due to their scaling properties. In this work, we therefore propose the first MOT formulation designed to be solved with AQC. We employ an Ising model that represents the quantum mechanical system implemented on the AQC. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers. Finally, we demonstrate that our MOT problem is already solvable on the current generation of real quantum computers for small examples, and analyze the properties of the measured solutions

End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

End-to-end approaches to autonomous driving commonly rely on expert demonstrations. Although humans are good drivers, they are not good coaches for end-to-end algorithms that demand dense on-policy supervision. On the contrary, automated experts that leverage privileged information can efficiently generate large scale on-policy and off-policy demonstrations. However, existing automated experts for urban driving make heavy use of hand-crafted rules and perform suboptimally even on driving simulators, where ground-truth information is available. To address these issues, we train a reinforcement learning expert that maps bird's-eye view images to continuous low-level actions. While setting a new performance upper-bound on CARLA, our expert is also a better coach that provides informative supervision signals for imitation learning agents to learn from. Supervised by our reinforcement learning coach, a baseline end-to-end agent with monocular camera-input achieves expert-level performance. Our end-to-end agent achieves a 78% success rate while generalizing to a new town and new weather on the NoCrash-dense benchmark and state-of-the-art performance on the more challenging CARLA LeaderBoard

Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing

Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include sampling- and lattice-based algorithms and model predictive control, suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. To circumvent this trade-off, a 2-step procedure is motivated: first learning of a controller during offline training based on an arbitrarily complicated mathematical system model, before online fast feedforward evaluation of the trained controller. The contribution of this paper is the proposition of a simple gradient-free and model-based algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). In particular, (i) simultaneous training on separate deterministic tasks with the purpose of encoding many motion primitives in a neural network, and (ii) the employment of maximally sparse rewards in combination with virtual velocity constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl

Climate change scenarios in use: heat stress in Switzerland

Under hot conditions the human body is able to regulate its core temperature via sweat evaporation, but this ability is reduced when air humidity is high. These conditions of high temperature and high humidity invoke heat stress which is a major problem for humans, in particular for vulnerable groups of the population and people under physical stress (e.g. heavy duty work without appropriate cooling systems). It is generally expected that the frequency, duration and magnitude of such unfavorable conditions will increase with further climate warming. In this respect, climate services play a crucial role by putting together climatological information and adaptation solutions to reduce future heat stress. We here assess the recently developed CH2018 scenarios for Switzerland (https://www.climate-scenarios.ch) in terms of heat stress conditions including their future projections. For this purpose, we characterize future extreme heat conditions with the use of climate analogs. By doing so, we attempt to produce more accessible climate information which might foster the use and understanding of regional-scale climate scenarios. Here heat stress is expressed through the Wet Bulb Temperature (TW), which is a relatively simple proxy for heat stress on the human body and which depends non-linearly on temperature and humidity. It is assessed in terms of single-day events and heat stress spells. Projections based on the CH2018 scenarios indicate increasing heat stress over Switzerland, which is accentuated towards the end of the century. High heat stress conditions might be about 3?5 times more frequent for an emission scenario without mitigation (RCP 8.5) than for the mitigation scenario (RCP 2.6) by the end of the 21st century. The projected increase of heat stress results in more and longer heat stress spells, thus highlighting the importance of timely and precise prevention strategies in the context of heat-health action plans. Spatial climate analogs based on heat stress spells in Switzerland greatly vary depending on the emission scenario and are found in Central Europe under a mitigation scenario and in southern Europe under unmitigated warming.Financial support for this work is provided by the HEAT-SHIELD Project (European Commission HORIZON 2020, research and innovation programme under the grant agreement 668786). A.C. acknowledges support from Project COMPOUND (TED2021-131334A-I00) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR