Coupled THM analysis of long-term anisotropic convergence in the full-scale micro tunnel excavated in the Callovo-Oxfordian argillite

The main purpose of this paper is to analyse the convergence measurements of the ALC1604 in situ heating test carried out in the Callovo-Oxfordian claystone formation (COx) in the Meuse/Haute-Marne underground research laboratory (MHM URL). The concept of the test consists of horizontal micro-tunnel, equipped with a steel casing. The micro-tunnel is excavated in the direction of the horizontal principal major stress (sH). In situ observations showed anisotropic convergence with the maximum and minimum values in the horizontal and vertical directions, respectively. Coupled THM numerical analyses have been carried out to provide a structured framework for interpretation, and to enhance understanding of THM behaviour of Callovo-Oxfordian claystone. However, a special mechanical constitutive law is adopted for the description of the time-dependent anisotropic behaviour of the COx. The simulation of the test using this enhanced model provides a satisfactory reproduction of the THM long-term anisotropic convergence results. It also provides a better understanding of the observed test response.Postprint (published version

Thermo-hydro-mechanical simulation of a full-scale steel-lined micro-tunnel excavated in the callovooxfordian claystone

The paper presents an interpretation of the full-scale ALC1604 in situ heating test carried out in Callovo-Oxfordian claystone (COx) in the Meuse/Haute-Marne underground research laboratory (MHM URL). The MHM URL is a site-specific facility planned to study radioactive waste disposal in the COx. The thermo-hydro-mechanical (THM) behaviour of the host rock is significant for the design of the underground radioactive waste disposal facility and for its long-term safety. When subjected to thermal loading, the Callovo-Oxfordian claystone of low permeability (~10-20-10-21 m2) exhibits a strong pore pressure response that significantly affects the hydraulic and mechanical behaviour of the material. The observations gathered in the in situ test have provided an opportunity to examine the integrated thermo-hydromechanical (THM) response of this sedimentary clay. Coupled THM numerical analyses have been carried out to provide a structured framework for interpretation, and to enhance understanding of THM behaviour of COx. Numerical analyses have been based on a coupled theoretical formulation that incorporates a constitutive law specially developed for this type of material. The law includes a number of features that are relevant for a satisfactory description of the hydromechanical behaviour. By performing the numerical analysis, it has been possible to incorporate anisotropy of material parameters and of in situ stresses. The performance and analysis of the in situ tests have significantly enhanced the understanding of a complex THM problem and have proved the capability of the numerical formulation to provide adequate predictive capacity

Precision and Sensitivity in Detailed-Balance Reaction Networks

We study two specific measures of quality of chemical reaction networks, Precision and Sensitivity. The two measures arise in the study of sensory adaptation, in which the reaction network is viewed as an input-output system. Given a step change in input, Sensitivity is a measure of the magnitude of the response, while Precision is a measure of the degree to which the system returns to its original output for large time. High values of both are necessary for high-quality adaptation. We focus on reaction networks without dissipation, which we interpret as detailed-balance, mass-action networks. We give various upper and lower bounds on the optimal values of Sensitivity and Precision, characterized in terms of the stoichiometry, by using a combination of ideas from matroid theory and differential-equation theory. Among other results, we show that this class of non-dissipative systems contains networks with arbitrarily high values of both Sensitivity and Precision. This good performance does come at a cost, however, since certain ratios of concentrations need to be large, the network has to be extensive, or the network should show strongly different time scales

Alterations in T1 of normal and reperfused infarcted myocardium after Gd-BOPTA versus GD-DTPA on inversion recovery EPI.

This study tested whether Gd-BOPTA/Dimeg or Gd-DTPA exerts greater relaxation enhancement for blood and reperfused infarcted myocardium. Relaxivity of Gd-BOPTA is increased by weak binding to serum albumin. Thirty-six rats were subjected to reperfused infarction before contrast (doses = 0.05, 0.1, and 0.2 mmol/kg). delta R1 was repeatedly measured over 30 min. Gd-BOPTA caused greater delta R1 for blood and myocardium than did Gd-DTPA; clearance of both agents from normal- and infarcted myocardium was similar to blood clearance; plots of delta R1 myocardium/delta R1 blood showed equilibrium phase contrast distribution. Fractional contrast agent distribution volumes were approximately 0.24 for both agents in normal myocardium, 0.98 and 1.6 for Gd-DTPA and Gd-BOPTA, respectively, in reperfused infarction. The high value for Gd-BOPTPA was ascribed to greater relaxivity in infarction versus blood. It was concluded that Gd-BOPTA/Dimeg causes a greater delta R1 than Gd-DTPA in regions which contain serum albumin

Power aware routing algorithms (PARA) in wireless mesh networks for emergency management

Wireless Mesh Networks (WMNs) integrate the advantages of WLANs and mobile Ad Hoc networks, which have become the key techniques of next-generation wireless networks in the context of emergency recovery. Wireless Mesh Networks (WMNs) are multi-hop wireless networks with instant deployment, self-healing, self-organization and self-configuration features. These capabilities make WMNs a promising technology for incident and emergency communication. An incident area network (IAN) needs a reliable and lively routing path during disaster recovery and emergency response operations when infrastructure-based communications and power resources have been destroyed and no routes are available. Power aware routing plays a significant role in WMNs, in order to provide continuous efficient emergency services. The existing power aware routing algorithms used in wireless networks cannot fully fit the characteristics of WMNs, to be used for emergency recovery. This paper proposes a power aware routing algorithm (PARA) for WMNs, which selects optimal paths to send packets, mainly based on the power level of next node along the path. This algorithm was implemented and tested in a proven simulator. The analytic results show that the proposed power node-type aware routing algorithm metric can clearly improve the network performance by reducing the network overheads and maintaining a high delivery ratio with low latency

Effect of sulphur and silicon fertilizers on disease control and yield and quality of wheat

Published research showed there was a link between the abundance of septoria diseases (Zymoseptoria tritici and Parastagonospora nodorum) and sulphurous acid in acid rain. Historical records showed that P. nodorum was at a peak in 1970s while Z. tritici became more abundant at 1980s. Another, small scale, study showed that both pathogens were affected by sulphur, but Z. tritici was reduced more by sulphur than P. nodorum. These studies suggested it could be useful to investigate the effect of sulphur fertilizer under larger scale, field, conditions. Thus, the aim of this project was to study if sulphur fertilizer affects the abundance of these pathogens, and to study if sulphur fertilization alters the yield and quality of wheat under disease pressure. A further question was whether silicon fertilizer affects both pathogens and the yield and quality of wheat. A final aspect of this thesis was to develop a way to quantify pathogens by using qPCR assay on large field samples. A glasshouse experiment showed that all wheat cultivars were susceptible to available pure isolates of both pathogens, but the proportion of leaves infected by these isolates varied between cultivars. Cv. Paragon showed a high proportion of infected leaves in comparison with Gallant and Maris Huntsman (M. Huntsman). Three successive field experiments were conducted. Z. tritici infected all plots, so it was not possible to estimate changes in competitive effects due to the fertiliser regimes. In the field experiments a variety of measurements were used at different growth stages to assess the diseases. The proportion of leaves infected with, and disease severity of, Z. tritici were not significantly affectedly by sulphur fertilization in 2013. In two further experiments, proportion of leaves infected with, and spore concentration of, Z. tritici were reduced by sulphur fertilization. In the 2014-2015 experiment disease severity of Z. tritici was significantly reduced by sulphur fertilization. Although autumn spray and seed inoculation with P. nodorum did not succeed, a spring spray inoculation was successful in the 2014-2015 experiment. In this experiment, proportion of leaves infected and disease severity of both fungi were reduced by sulphur fertilization. but Z. tritici was reduced more than P. nodorum. These results agree with the historical trends. Yield and quality measurements results showed that sulphur fertilization increased yield and the wheat quality was improved. This result was obtained when the sulphur fertilization was split and applied at multiple times throughout the growing season of wheat (Table 6)

Bioactive molecule prediction using extreme gradient boosting

Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets