No Fermionic Wigs for BPS Attractors in 5 Dimensions

We analyze the fermionic wigging of 1/2-BPS (electric) extremal black hole attractors in N=2, D=5 ungauged Maxwell-Einstein supergravity theories, by exploiting anti-Killing spinors supersymmetry transformations. Regardless of the specific data of the real special geometry of the manifold defining the scalars of the vector multiplets, and differently from the D=4 case, we find that there are no corrections for the near--horizon attractor value of the scalar fields; an analogous result also holds for 1/2-BPS (magnetic) extremal black string. Thus, the attractor mechanism receives no fermionic corrections in D=5 (at least in the BPS sector).Comment: 24 pages, LaTeX2

A stochastic cellular automaton model to describe the evolution of the snow-covered area across a high-elevation mountain catchment

Variations in the extent and duration of snow cover impinge on surface albedo and snowmelt rate, influencing the energy and water budgets. Monitoring snow coverage is therefore crucial for both optimising the supply of snowpack-derived water and understanding how climate change could impact on this source, vital for sustaining human activities and the natural environment during the dry season. Mountainous sites can be characterised by complex morphologies, cloud cover and forests that can introduce errors into the estimates of snow cover obtained from remote sensing. Consequently, there is a need to develop simulation models capable of predicting how snow coverage evolves across a season. Cellular Automata models have previously been used to simulate snowmelt dynamics, but at a coarser scale that limits insight into the precise factors driving snowmelt at different stages. To address this information gap, we formulate a novel, fine-scale stochastic Cellular Automaton model that describes snow coverage across a high-elevation catchment. Exploiting its refinement, the model is used to explore the interplay between three factors proposed to play a critical role: terrain elevation, sun incidence angle, and the extent of nearby snow. We calibrate the model via a randomised parameter search, fitting simulation data against snow cover masks estimated from Sentinel-2 satellite images. Our analysis shows that

Microarray gene expression profiling of neural tissues in bovine spastic paresis

Abstract: Background: Bovine Spastic Paresis (BSP) is a neuromuscular disorder which affects both male and female cattle. BSP is characterized by spastic contraction and overextension of the gastrocnemious muscle of one or both limbs and is associated with a scarce increase in body weight. This disease seems to be caused by an autosomal and recessive gene, with incomplete penetration, although no genes clearly involved with its onset have been so far identified. We employed cDNA microarrays to identify metabolic pathways affected by BSP in Romagnola cattle breed. Investigation of those pathways at the genome level can help to understand this disease. Results: Microarray analysis of control and affected individuals resulted in 268 differentially expressed genes. These genes were subjected to KEGG pathway functional clustering analysis, revealing that they are predominantly involved in Cell Communication, Signalling Molecules and Interaction and Signal Transduction, Diseases and Nervous System classes. Significantly enriched KEGG pathway's classes for the differentially expressed genes were calculated; interestingly, all those significantly under-expressed in the affected samples are included in Neurodegenerative Diseases. To identify genome locations possibly harbouring gene(s) involved in the disease, the chromosome distribution of the differentially expressed genes was also investigated. Conclusions: The cDNA microarray we used in this study contains a brain library and, even if carrying an incomplete transcriptome representation, it has proven to be a valuable tool allowing us to add useful and new information to a poorly studied disease. By using this tool, we examined nearly 15000 transcripts and analysed gene pathways affected by the disease. Particularly, our data suggest also a defective glycinergic synaptic transmission in the development of the disease and an alteration of calcium signalling proteins. We provide data to acquire knowledge of a genetic disease for which literature still presents poor results and that could be further and specifically analysed in the next future. Moreover this study, performed in livestock, may also harbour molecular information useful for understanding human diseases

A comprehensive CFD model for the biomass pyrolysis

The present work addresses the study of the pyrolysis of biomass particle, with the aim to improve the comprehensive mathematical model of the thermochemical processes involving solids decomposition. A new CFD model for the biomass pyrolysis was developed at the particle scale in order to properly describe the relative role of reaction kinetics and transport phenomena. The model is able to solve the Navier-Stokes equations for both the gas and solid porous phase. The code employs the open-source OpenFOAM® framework to effectively manage the computational meshes and the discretization of fundamental governing equations. The mathematical algorithm is based on the PIMPLE method for transient solver and exploit the operator-splitting technique that allows the separation of the transport and the reactive term in order to handle complex computational geometries minimizing the computational effort. The model was tested with experimental data for both reactive and non-reactive conditions. The code is able to provide correct information about temperature distribution within the particle, gas, tar and char formation rates

Technical note: Two-component electrical-conductivity-based hydrograph separation employing an exponential mixing model (EXPECT) provides reliable high-temporal-resolution young water fraction estimates in three small Swiss catchments

<jats:p>Abstract. The young water fraction represents the portion of water molecules in a stream that have entered the catchment relatively recently, typically within 2–3 months. It can be reliably estimated in spatially heterogeneous and nonstationary catchments from the amplitude ratio of seasonal isotope (δ18O or δ2H) cycles of stream water and precipitation, respectively. Past studies have found that young water fractions increase with discharge (Q), thus reflecting the higher direct runoff under wetter catchment conditions. The rate of increase in the young water fraction with increasing Q, defined as the discharge sensitivity of the young water fraction (Sd*), can be useful for describing and comparing catchments' hydrological behaviour. However, the existing method for estimating Sd*, which only uses biweekly isotope data, can return highly uncertain and unreliable Sd* when stream water isotope data are sparse and do not capture the entire flow regime. Indeed, the information provided by isotope data depends on when the respective sample was taken. Accordingly, the low sampling frequency results in information gaps that could potentially be filled by using additional tracers sampled at a higher temporal resolution. By utilizing high-temporal-resolution and cost-effective electrical conductivity (EC) measurements, along with information obtainable from seasonal isotope cycles in stream water and precipitation, we develop a new method that can estimate the young water fraction at the same resolution as EC and Q measurements. These high-resolution estimates allow for improvements in the estimates of the Sd*. Our so-called EXPECT (Electrical-Conductivity-based hydrograph separaTion employing an EXPonential mixing model) method is built upon the following three key assumptions: We construct a mixing relationship consisting of an exponential decay of stream water EC with increasing young water fraction. This has been obtained based on the relationship between flow-specific young water fractions and EC. We assume that the two-component EC-based hydrograph separation technique, using the above-mentioned exponential mixing model, can be used for a time-source partitioning of stream water into young (transit times < 2–3 months) and old (transit times > 2–3 months) water. We assume that the EC value of the young water endmember (ECyw) is lower than that of the old water endmember (ECow). Selecting reliable values from measurements of ECyw and ECow to perform this unconventional EC-based hydrograph separation is challenging, but the combination of information derived from the two tracers allows for the estimation of endmembers' values. The two endmembers have been calibrated by constraining the unweighted and flow-weighted average young water fractions obtained with the EC-based hydrograph separation to be equal to the corresponding quantities derived from the seasonal isotope cycles. We test the EXPECT method in three small experimental catchments in the Swiss Alptal Valley using two different temporal resolutions of Q and EC data: sampling resolution (i.e. we only consider Q and EC measurements during dates of isotope sampling) and daily resolution. The EXPECT method has provided reliable young water fraction estimates at both temporal resolutions, from which a more accurate discharge sensitivity of the young water fraction (SdEXP) could be determined compared with the existing approach. Also, the method provided new information on ECyw and ECow, yielding calibrated values that fall outside the range of measured EC values. This suggests that stream water is always a mixture of young and old water, even under very high or very low wetness conditions. The calibrated endmembers revealed a good agreement with both endmembers obtained from an independent method and EC measurements from groundwater wells. For proper use of the EXPECT method, we have highlighted the limitations of EC as a tracer, identified certain catchment characteristics that may constrain the reliability of the current method and provided recommendations about its adaptation for future applications in catchments other than those investigated in this study. </jats:p&gt

Predicting mid-air gestural interaction with public displays based on audience behaviour

© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.Knowledge about the expected interaction duration and expected distance from which users will interact with public displays can be useful in many ways. For example, knowing upfront that a certain setup will lead to shorter interactions can nudge space owners to alter the setup. If a system can predict that incoming users will interact at a long distance for a short amount of time, it can accordingly show shorter versions of content (e.g., videos/advertisements) and employ at-a-distance interaction modalities (e.g., mid-air gestures). In this work, we propose a method to build models for predicting users’ interaction duration and distance in public display environments, focusing on mid-air gestural interactive displays. First, we report our findings from a field study showing that multiple variables, such as audience size and behaviour, significantly influence interaction duration and distance. We then train predictor models using contextual data, based on the same variables. By applying our method to a mid-air gestural interactive public display deployment, we build a model that predicts interaction duration with an average error of about 8 s, and interaction distance with an average error of about 35 cm. We discuss how researchers and practitioners can use our work to build their own predictor models, and how they can use them to optimise their deployment.Peer reviewe