Designing and recasting LHC analyses with MadAnalysis 5

We present an extension of the expert mode of the MadAnalysis 5 program dedicated to the design or reinterpretation of high-energy physics collider analyses. We detail the predefined classes, functions and methods available to the user and emphasize the most recent developments. The latter include the possible definition of multiple sub-analyses and a novel user-friendly treatment for the selection criteria. We illustrate this approach by two concrete examples: a CMS search for supersymmetric partners of the top quark and a phenomenological analysis targeting hadronically decaying monotop systems.Comment: 17 pages, 3 figures, 10 tables; version accepted by EPJ

How the Readability Level of Prior Text Impacts Comprehension of Subsequent Text

The purpose of this thesis research is to explore how the difficulty of text impacts the ability to process subsequent text. The basic idea is that difficult text might deplete cognitive or attentional resources, creating difficulties for readers as they try to continue reading afterwards. A pilot study was conducted to gather preliminary data prior to the two final studies. High (easy to read) and low (difficult to read) readability passages were created and presented prior to a target passage. The results from the pilot study suggested that there may be differences in comprehension based on the readability and difficulty level of preceding text. Study one investigated how the length of the preceding passage impacted comprehension of subsequent text. A 2 readability (easy vs hard) x 2 length (short vs long) ANOVA failed to find any significant main effects or a significant interaction. Study two investigated whether differences in working memory capacity might make some people more susceptible to the effects of difficulty on comprehension on subsequent text. An ANCOVA failed to find a significant effect of readability on comprehension of the subsequent text after controlling for verbal working memory scores. Working memory scores were also not a significant predictor of comprehension scores

Bayesian methods for predicting LAI and soil water content

peer reviewedLAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: Extended Kalman Filtering (EKF), Particle Filtering (PF), and Variational Filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error (RMSE) of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation.Filtering method-based state and parameter estimation for crop model

Preparation and physicochemical characterization of films prepared with salmon skin gelatin extracted by a trypsin-aided process

The recovery of gelatins from Atlantic salmon (Salmo salar) skin for film formation and characterization was studied. Fish skins pre-treated with trypsin (250 U/g) produced the highest hydroxyproline content (7.41 ± 0.49 mg hydroxyproline/g treated skin) and yield (53.05 ± 4.38%) of gelatin, as compared to the use of saline solution. Pre-treatment with a lower concentration of trypsin (1 U/g) at a shorter pre-treatment time successfully reduced the degradation of gelatin with co-production of high molecular weight α-chains. Gelatin was further extracted by a trypsin-aided process for film formation and characterization. Films with increasing protein concentration (from 1 to 5%, w/v) exhibited higher thickness, tensile strength, and elongation at break (EAB), but a marked decrease in EAB for films with 6 and 7% (w/v). Films with 5% proteins showed higher thickness, lower tensile strength and higher EAB with increasing concentrations of glycerol (from 10 to 50% of proteins, w/w). All films exhibited high water uptake, decrease in light transmission and an increase in opacity as the protein and glycerol contents increased. Electrophoretic studies showed that the increase in the mechanical properties of the films was correlated with the increase in protein concentration, owing to the increased content of high molecular weight chain fractions. Furthermore, Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) revealed the interaction between the proteins and glycerol for all films. This study demonstrated the viability of the trypsin supplementation process to obtain salmon skin gelatin for film formation

Extraction des paramètres électriques sur les transistors CMOS de technologies avancées

Session POSTER 3L'extraction des paramètres électriques est un point clef pour la compréhension des phénomènes physiques qui régissent le fonctionnement des transistors. La réduction des dimensions impose aujourd'hui d'adapter les méthodes d'extraction préexistantes aux nouvelles générations de dispositifs. Ainsi, nous montrons comment – à partir de courbe courant-tension ID(VG) et capacité-tension Cgc(VG) – il est possible d'extraire avec précision la mobilité à bas champ (μ0) et les résistances séries (RSD) sur des transistors ultra-courts

Chaotic motion of charged particles in toroidal magnetic configurations

We study the motion of a charged particle in a tokamak magnetic field and discuss its chaotic nature. Contrary to most of recent studies, we do not make any assumption on any constant of the motion and solve numerically the cyclotron gyration using Hamiltonian formalism. We take advantage of a symplectic integrator allowing us to make long-time simulations. First considering an idealized magnetic configuration, we add a non generic perturbation corresponding to a magnetic ripple, breaking one of the invariant of the motion. Chaotic motion is then observed and opens questions about the link between chaos of magnetic field lines and chaos of particle trajectories. Second, we return to a axi-symmetric configuration and tune the safety factor (magnetic configuration) in order to recover chaotic motion. In this last setting with two constants of the motion, the presence of chaos implies that no third global constant exists, we highlight this fact by looking at variations of the first order of the magnetic moment in this chaotic setting. We are facing a mixed phase space with both regular and chaotic regions and point out the difficulties in performing a global reduction such as gyrokinetics

miRNA-dependent target regulation: functional characterization of single-nucleotide polymorphisms identified in genome-wide association studies of Alzheimer’s disease

Other miRNA targeting sites identified by less stringent analysis near rs7143400-C/G, rs2847655-T/C, rs610923-C/A and rs9909-G/C. A summary of the genes, PolymiRTSs, effects of minor alleles, targeting miRNAs and miRNA expression alterations observed in AD (when available; refer to the cited references). The grayed miRNAs were also found in the stringent screening described in Fig. 2a in the main text. (XLS 23 kb

Assessing nitrogen fertilisation strategies according to climate variability : A modelling approach

/Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de micro-capteurs en vue d’optimiser l’apport en engrais azoté

Assessing the potential of an algorithm based on mean climatic data to predict wheat yield

The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de micro-capteurs en vue d’optimiser l’apport en engrais azoté