Numerical modelling for the interpretation of a laboratory mock-up experiment of bentonite/granite interface

AbstractPerformance assessment of a deep geological repository requires understanding diffusion and determining diffusion parameters under real conditions because diffusion is a key transport mechanism in hosting geological formation. FEBEX (Full-scale Engineered Barrier Experiment) is a demonstration and research project dealing with the bentonite engineered barrier designed for sealing and containment of the high-level radioactive waste repository. To support field investigations of FEBEX in situ test, a large–scale laboratory mock-up experiment (MUE) is being performed at CIEMAT facilities to study tracer migration at the bentonite/granite interface. Numerical models of MUE are presented here for HTO, 36Cl- and 137Cs+. Experiments are modeled with 2-D axi-symmetric finite element grids and are solved with CORE2D V4. Model results indicate that numerical solutions with reference parameters reproduce measured data for HTO and 36Cl- but show large discrepancies for 137Cs+. Relevant diffusion and retention parameters are identified by sensitivity analysis for tracer concentrations in borehole, bentonite and granite, respectively. Interpretation of 137Cs+ data measured in the tracer chamber is perfomed by taking into account the uncertainties in initial activity C0 and initial time t0. Optimum values of C0 and t0 are obtained. The best fit is obtained with De-filter equal to 2.03.10-10 m2/s and Kd-bentonite equal to 5m3.Kg-1

Acute and sub-acute toxicity evaluation of Enterococcus faecalis HZNU P2 isolated from peacock faeces in vivo

The sub-acute toxicity of E. faecalis HZNU P2 was investigated in rats fed with different doses for 14 days. To evaluate the acute oral toxicity of E. faecalis HZNU P2, rats were fed with E. faecalis HZNU P2 at a high dose of 2×1011 CFU kg−1 for 10 days. Results showed that there were no abnormal clinical signs in any of the groups during the experiment. There were no significant differences in live weight gain among rats fed with E. faecalis HZNU P2, compared to those in control group. Macroscopic or microscopic examinations of organs revealed no abnormalities, indicating that E. faecalis HZNU P2 did not adversely affect the health of rats. Results of this study demonstrated that digestion of E. faecalis HZNU P2 in rats did not show any obvious signs of toxicity

Entropy-corrected new agegraphic dark energy in Horava-Lifshitz cosmology

We study the entropy-corrected version of the new agegraphic dark energy (NADE) model and dark matter in a spatially non-flat Universe and in the framework of Ho\v{r}ava-Lifshitz cosmology. For the two cases containing noninteracting and interacting entropy-corrected NADE (ECNADE) models, we derive the exact differential equation that determines the evolution of the ECNADE density parameter. Also the deceleration parameter is obtained. Furthermore, using a parametrization of the equation of state parameter of the ECNADE model as $\omega_{\Lambda}(z)=\omega_0+\omega_1 z$, we obtain both $\omega_0$ and $\omega_1$. We find that in the presence of interaction, the equation of state parameter $\omega_0$ of this model can cross the phantom divide line which is compatible with the observation.Comment: 20 pages, 2 figures, to appear in 'Astrophysics and Space Science

White-box optimization from historical data

Contains fulltext : 122450.pdf (preprint version ) (Open Access)BENELEARN 2013: Proceedings of the 22nd Belgian-Dutch Conference on Machine Learning, Nijmegen, 3 june 201

Participation behavior and social welfare in repeated task allocations

Task allocation problems have focused on achieving one-shot optimality. In practice, many task allocation problems are of repeated nature, where the allocation outcome of previous rounds may influence the participation of agents in subsequent rounds, and consequently, the quality of the allocations in the long term. We investigate how allocation influences agents' decision to participate using prospect theory, and simulate how agents' participation affects the system's long term social welfare. We compare two task allocation algorithms in this study, one only considering optimality in terms of costs and the other considering optimality in terms of primarily fairness and secondarily costs. The simulation results demonstrate that fairness incentivizes agents to keep participating and consequently leads to a higher social welfare

Auction optimization using regression trees and linear models as integer programs

In a sequential auction with multiple bidding agents, the problem of determining the ordering of the items to sell in order to maximize the expected revenue is highly challenging. The challenge is largely due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned regression models to integer linear programs (ILP), which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings

Fair task allocation in transportation

Task allocation problems have traditionally focused on cost optimization. However, more and more attention is being given to cases in which cost should not always be the sole or major consideration. In this paper we study a fair task allocation problem in transportation where an optimal allocation not only has low cost but more importantly, it distributes tasks as even as possible among heterogeneous participants who have different capacities and costs to execute tasks. To tackle this fair minimum cost allocation problem we analyze and solve it in two parts using two novel polynomial-time algorithms. We show that despite the new fairness criterion, the proposed algorithms can solve the fair minimum cost allocation problem optimally in polynomial time. In addition, we conduct an extensive set of experiments to investigate the trade-off between cost minimization and fairness. Our experimental results demonstrate the benefit of factoring fairness into task allocation. Among the majority of test instances, fairness comes with a very small price in terms of cost