The Scaled-Charge Additive Force Field for Amino Acid Based Ionic Liquids

Abstract. Ionic liquids (ILs) constitute an emerging field of research. New ILs are continuously introduced involving more and more organic and inorganic ions. Amino acid based ILs (AAILs) represent a specific interest due to their evolutional connection to proteins. We report a new non- polarizable force field (FF) for the eight AAILs comprising 1-ethyl-3-methylimidazolium cation and amino acid anions. The anions were obtained via deprotonation of carboxyl group. Specific cation-anion non-covalent interactions have been taken into account by computing electrostatic potential for ion pairs, in contrast to isolated ions. The van der Waals interactions have been transferred from the CHARMM36 FF with minor modifications. Therefore, compatibility between our parameters and CHARMM36 parameters is preserved. Our FF can be easily implemented using a variety of popular molecular dynamics programs. It will find broad applications in computational investigation of ILs

ACME vs PDDL: support for dynamic reconfiguration of software architectures

On the one hand, ACME is a language designed in the late 90s as an interchange format for software architectures. The need for recon guration at runtime has led to extend the language with speci c support in Plastik. On the other hand, PDDL is a predicative language for the description of planning problems. It has been designed in the AI community for the International Planning Competition of the ICAPS conferences. Several related works have already proposed to encode software architectures into PDDL. Existing planning algorithms can then be used in order to generate automatically a plan that updates an architecture to another one, i.e., the program of a recon guration. In this paper, we improve the encoding in PDDL. Noticeably we propose how to encode ADL types and constraints in the PDDL representation. That way, we can statically check our design and express PDDL constraints in order to ensure that the generated plan never goes through any bad or inconsistent architecture, not even temporarily.Comment: 6\`eme \'edition de la Conf\'erence Francophone sur les Architectures Logicielles (CAL 2012), Montpellier : France (2012

Exploding Nitromethane in silico, in real time

Nitromethane (NM) is widely applied in chemical technology as a solvent for extraction, cleaning and chemical synthesis. NM was considered safe for a long time, until a railroad tanker car exploded in 1958. We investigate detonation kinetics and reaction mechanisms in a variety of systems consisting of NM, molecular oxygen and water vapor. State-of-the-art reactive molecular dynamics allows us to simulate reactions in time-domain, as they occur in real life. High polarity of the NM molecule is shown to play an important role, driving the first exothermic step of the reaction. Presence of oxygen is important for faster oxidation, whereas its optimal concentration is in agreement with the proposed reaction mechanism. Addition of water (50 mol%) inhibits detonation; however, water does not prevent detonation entirely. The reported results provide important insights for improving applications of NM and preserving safety of industrial processes.Comment: arXiv admin note: text overlap with arXiv:1408.372

Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods

We introduce a framework to build a survival/risk bump hunting model with a censored time-to-event response. Our Survival Bump Hunting (SBH) method is based on a recursive peeling procedure that uses a specific survival peeling criterion derived from non/semi-parametric statistics such as the hazards-ratio, the log-rank test or the Nelson-Aalen estimator. To optimize the tuning parameter of the model and validate it, we introduce an objective function based on survival or prediction-error statistics, such as the log-rank test and the concordance error rate. We also describe two alternative cross-validation techniques adapted to the joint task of decision-rule making by recursive peeling and survival estimation. Numerical analyses show the importance of replicated cross-validation and the differences between criteria and techniques in both low and high-dimensional settings. Although several non-parametric survival models exist, none addresses the problem of directly identifying local extrema. We show how SBH efficiently estimates extreme survival/risk subgroups unlike other models. This provides an insight into the behavior of commonly used models and suggests alternatives to be adopted in practice. Finally, our SBH framework was applied to a clinical dataset. In it, we identified subsets of patients characterized by clinical and demographic covariates with a distinct extreme survival outcome, for which tailored medical interventions could be made. An R package `PRIMsrc` is available on CRAN and GitHub.Comment: Keywords: Exploratory Survival/Risk Analysis, Survival/Risk Estimation & Prediction, Non-Parametric Method, Cross-Validation, Bump Hunting, Rule-Induction Metho