Toward improved streamflow forecasts: Value of semidistributed modeling

The focus of this study is to assess the performance improvements of semidistributed applications of the U.S. National Weather Service Sacramento Soil Moisture Accounting model on a watershed using radar-based remotely sensed precipitation data. Specifically, performance comparisons are made within an automated multicriteria calibration framework to evaluate the benefit of "spatial distribution" of the model input (precipitation), structural components (soil moisture and streamflow routing computations), and surface characteristics (parameters). A comparison of these results is made with those obtained through manual calibration. Results indicate that for the study watershed, there are performance improvements associated with semidistributed model applications when the watershed is partitioned into three subwatersheds; however, no additional benefit is gained from increasing the number of subwatersheds from three to eight. Improvements in model performance are demonstrably related to the spatial distribution of the model input and streamflow routing. Surprisingly, there is no improvement associated with the distribution of the surface characteristics (model parameters)

An interpretable machine learning framework for dark matter halo formation

We present a generalization of our recently proposed machine-learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact of the initial density and tidal shear fields on the formation of haloes over the mass range 11.4 ≤ log (M/M⊙) ≤ 13.4. The algorithm is trained on an N-body simulation to infer the final mass of the halo to which each dark matter particle will later belong. We then quantify the difference in the predictive accuracy between machine-learning models using a metric based on the Kullback–Leibler divergence. We first train the algorithm with information about the density contrast in the particles’ local environment. The addition of tidal shear information does not yield an improved halo collapse model over one based on density information alone; the difference in their predictive performance is consistent with the statistical uncertainty of the density-only based model. This result is confirmed as we verify the ability of the initial conditions-to-halo mass mapping learnt from one simulation to generalize to independent simulations. Our work illustrates the broader potential of developing interpretable machine-learning frameworks to gain physical understanding of non-linear large-scale structure formation

In-car distractions and automated driving: A preliminary simulator study

As vehicles with automated driving features become more common, drivers may become ever more tempted to engage in secondary in-car tasks. We report on the results of a driving simulator study that investigated whether the presence of an in-car video would make drivers more likely to switch on an automated driving system so that they can watch the video. Results show an increase in automated driving mode usage when a video was playing compared to when it was not playing. The presence of this in-car video also made participants slower at reacting to frequent red traffic lights, which the automated driving mode did not detect and were the responsibility of the driver to respond to. These results suggest that in-car distractions are a critical concern for the safe and responsible use of automated driving systems

Prioritizing consumers in smart grid: A game theoretic approach

This paper proposes an energy management technique for a consumer-to-grid system in smart grid. The benefit to consumers is made the primary concern to encourage consumers to participate voluntarily in energy trading with the central power station (CPS) in situations of energy deficiency. A novel system model motivating energy trading under the goal of social optimality is proposed. A single-leader multiple-follower Stackelberg game is then studied to model the interactions between the CPS and a number of energy consumers (ECs), and to find optimal distributed solutions for the optimization problem based on the system model. The CPS is considered as a leader seeking to minimize its total cost of buying energy from the ECs, and the ECs are the followers who decide on how much energy they will sell to the CPS for maximizing their utilities. It is shown that the game, which can be implemented distributedly, possesses a socially optimal solution, in which the sum of the benefits to all consumers is maximized, as the total cost to the CPS is minimized. Numerical analysis confirms the effectiveness of the game. © 2010-2012 IEEE

Genetic variations in GBA1 and LRRK2 genes: Biochemical and clinical consequences in Parkinson disease

Variants in the GBA1 and LRRK2 genes are the most common genetic risk factors associated with Parkinson disease (PD). Both genes are associated with lysosomal and autophagic pathways, with the GBA1 gene encoding for the lysosomal enzyme, glucocerebrosidase (GCase) and the LRRK2 gene encoding for the leucine-rich repeat kinase 2 enzyme. GBA1-associated PD is characterized by earlier age at onset and more severe non-motor symptoms compared to sporadic PD. Mutations in the GBA1 gene can be stratified into severe, mild and risk variants depending on the clinical presentation of disease. Both a loss- and gain- of function hypothesis has been proposed for GBA1 variants and the functional consequences associated with each variant is often linked to mutation severity. On the other hand, LRRK2-associated PD is similar to sporadic PD, but with a more benign disease course. Mutations in the LRRK2 gene occur in several structural domains and affect phosphorylation of GTPases. Biochemical studies suggest a possible convergence of GBA1 and LRRK2 pathways, with double mutant carriers showing a milder phenotype compared to GBA1-associated PD. This review compares GBA1 and LRRK2-associated PD, and highlights possible genotype-phenotype associations for GBA1 and LRRK2 separately, based on biochemical consequences of single variants

The GBA variant E326K is associated with alpha-synuclein aggregation and lipid droplet accumulation in human cell lines

Sequence variants or mutations in the GBA gene are numerically the most important risk factor for Parkinson disease (PD). The GBA gene encodes for the lysosomal hydrolase enzyme, glucocerebrosidase (GCase). GBA mutations often reduce GCase activity and lead to impairment of the autophagy-lysosomal pathway, which is important in the turnover of alpha-synuclein, accumulation of which is a key pathological hallmark of PD. Although the E326K variant is one of the most common GBA variants associated with PD, there is limited understanding of its biochemical effects. We have characterised homozygous and heterozygous E326K variants in human fibroblasts. We found that E326K variants did not cause significant loss of GCase protein or activity, endoplasmic reticulum (ER) retention or ER stress, in contrast to the L444P GBA mutation. This was confirmed in human dopaminergic SH-SY5Y neuroblastoma cell lines over-expressing GCase with either E326K or L444P protein. Despite no loss of GCase activity, a significant increase of insoluble alpha-synuclein aggregates in E326K and L444P mutants was observed. Notably, SH-SY5Y over-expressing E326K demonstrated a significant increase in lipid droplet number under basal conditions, which was exacerbated following treatment with the fatty acid oleic acid. Similarly, a significant increase in lipid droplet formation following lipid loading was observed in heterozygous and homozygous E326K fibroblasts. In conclusion, the work presented here demonstrates that the E326K mutation behaves differently to common loss of function GBA mutations, however lipid dyshomeostasis and alpha-synuclein pathology is still evident

Machine learning cosmological structure formation

We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The algorithm infers the relationship between the initial conditions and the final dark matter haloes, without the need to introduce approximate halo collapse models. We gain insights into the physics driving halo formation by evaluating the predictive performance of the algorithm when provided with different types of information about the local environment around dark matter particles. The algorithm learns to predict whether or not dark matter particles will end up in haloes of a given mass range, based on spherical overdensities. We show that the resulting predictions match those of spherical collapse approximations such as extended Press–Schechter theory. Additional information on the shape of the local gravitational potential is not able to improve halo collapse predictions; the linear density field contains sufficient information for the algorithm to also reproduce ellipsoidal collapse predictions based on the Sheth–Tormen model. We investigate the algorithm’s performance in terms of halo mass and radial position and perform blind analyses on independent initial conditions realizations to demonstrate the generality of our results

A Large Scale Double Beta and Dark Matter Experiment: GENIUS

The recent results from the HEIDELBERG-MOSCOW experiment have demonstrated the large potential of double beta decay to search for new physics beyond the Standard Model. To increase by a major step the present sensitivity for double beta decay and dark matter search much bigger source strengths and much lower backgrounds are needed than used in experiments under operation at present or under construction. We present here a study of a project proposed recently, which would operate one ton of 'naked' enriched GErmanium-detectors in liquid NItrogen as shielding in an Underground Setup (GENIUS). It improves the sensitivity to neutrino masses to 0.01 eV. A ten ton version would probe neutrino masses even down to 10^-3 eV. The first version would allow to test the atmospheric neutrino problem, the second at least part of the solar neutrino problem. Both versions would allow in addition significant contributions to testing several classes of GUT models. These are especially tests of R-parity breaking supersymmetry models, leptoquark masses and mechanism and right-handed W-boson masses comparable to LHC. The second issue of the experiment is the search for dark matter in the universe. The entire MSSM parameter space for prediction of neutralinos as dark matter particles could be covered already in a first step of the full experiment - with the same purity requirements but using only 100 kg of 76Ge or even of natural Ge - making the experiment competitive to LHC in the search for supersymmetry. The layout of the proposed experiment is discussed and the shielding and purity requirements are studied using GEANT Monte Carlo simulations. As a demonstration of the feasibility of the experiment first results of operating a 'naked' Ge detector in liquid nitrogen are presented.Comment: 22 pages, 12 figures, see also http://pluto.mpi-hd.mpg.de/~betalit/genius.htm