Performances of a Newly High Sensitive Trilayer F/Cu/F GMI Sensor

We have selected stress-annealed nanocrystalline Fe-based ribbons for ferromagnetic/copper/ferromagnetic sensors exhibiting high magneto-impedance ratio. Longitudinal magneto-impedance reaches 400% at 60 kHz and longitudinal magneto-resistance increases up to 1300% around 200 kHz.Comment: 4 pages, 6 figures, Sensors and Actuators A (in review

Study of the Radiation Hardness Performance of PiN diodes for the ATLAS Pixel Detector at the SLHC upgrade

We study the radiation tolerance of the silicon and GaAs PiN diodes that will be the part of the readout system of the upgraded ATLAS pixel detector. The components were irradiated by 200 MeV protons up to total accumulated dose 1.2×1015 p/cm2 and by 24 GeV protons up to 2.6×1015 p/cm2. Based on obtained results, we conclude that radiation hardness does not depend on the sensitive area or cut off frequency of PiN diodes. We identify two diodes that can be used for the SLHC upgrade

The DUNE Far Detector Interim Design Report Volume 1: Physics, Technology & Strategies Deep Underground Neutrino Experiment (DUNE)

The Deep Underground Neutrino Experiment (DUNE) will be a world-class neutrino observatory and nucleon decay detector designed to answer fundamental questions about the nature of elemen tary particles and their role in the universe. The international DUNE experiment, hosted by the U.S. Department of Energy’s Fermilab, will consist of a far detector to be located about 1.5 km underground at the Sanford Underground Research Facility (SURF) in South Dakota, USA, at a distance of 1300 km from Fermilab, and a near detector to be located at Fermilab in Illinois. The far detector will be a very large, modular liquid argon time-projection chamber (LArTPC) with a 40 kt (40 Gg) fiducial mass. This LAr technology will make it possible to reconstruct neutrino interactions with image-like precision and unprecedented resolution

La modélisation stochastique des étiages: une revue bibliographique

La croissance continue de la population mondiale et l'augmentation du niveau de vie dans certaines parties de la planète exercent une pression de plus en plus forte sur la demande quantitative et qualitative de la ressource hydrique, nécessitant ainsi une gestion plus adéquate. Afin d'évaluer la fiabilité d'un système de ressources en eau et de déterminer son mode de gestion durant un étiage, il est utile d'avoir un outil de modélisation. Nous présentons ici une synthèse des travaux de modélisation réalisés dans le cadre de l'approche stochastique. Nous faisons d'abord le point sur la différence entre une sécheresse et un étiage, termes qui sont souvent confondus dans les publications, pour ensuite en présenter quelques indicateurs. L'approche stochastique peut être subdivisée en deux catégories: l'étude fréquentielle et les processus stochastiques. La plupart des études d'analyse de fréquence ont pour objet de calculer des débits d'étiage critiques xT correspondant à une certaine période de retour T, tel que P(X<xT)=1/T. L'approche par les processus stochastiques consiste à modéliser les événements de déficit ou les variables d'intérêt sans utiliser directement des modèles de débit. L'analyse de fréquence des débits ne tient pas compte des durées et émet des hypothèses trop simplistes de stationnarité. L'analyse des séquences permet l'obtention des lois de durées uniquement pour des processus de débits très simples. L'avantage de l'approche des processus ponctuels par rapport à l'analyse des séquences est qu'elle permet d'étudier des processus complexes, dépendants et non stationnaires. De plus, les processus ponctuels alternés permettent la modélisation des durée et la génération synthétique des temps d'occurrence des séries de surplus et de déficit. Nous présentons dans cet article les travaux de modélisation des étiages basés sur l'analyse fréquentielle, la théorie des séquences et sur les processus ponctuels. Nous n'avons pas inclus les études qui développent des distributions des faibles débits à partir de modèles physiques, ni les études de type régional.The increasing pressure on the water resources requires better management of the water deficit situations may it be unusual droughts or yearly recurring low-flows. It is therefore important to model the occurrence of these deficit events in order to quantify the related risks. Many approaches exist for the modeling of low-flow/drought events. We present here a literature review of the stochastic methods. We start by clarifying the difference between low-flows and droughts, two terms which are often used interchangeably. We then present some low-flow and drought indicators. The stochastic approach may be divided into two categories: Frequency analysis and stochastic processes. Most frequency analysis studies aim to assign to a flow value X a cumulative frequency, either directly using empirical distribution functions, or by fitting a theoretical distribution. This allows the computation of a critical flow xT corresponding to a return period T, such that P(X<xT)=1/T. These studies use mostly the annual minima of daily flows where the hydrological data is assumed independent and identically distributed. It is also common to analyze Qm, the annual minimum of the m-consecutive days average flow, m being generally 7, 10, 30, 60, 90, or 120 days, and to adopt as critical flow the m-day average having a return period of T years. The distributions which are used include the Normal, Weibull, Gumbel, Gamma, Log-Normal (2), Log-Pearson (3), Generalized Extreme Value, Pearson type 3, and Pearson type 5 distributions (GUMBEL, 1954; MATALAS, 1963; BERNIER, 1964; JOSEPH, 1970; CONDIE and NIX, 1975; HOANG, 1978; TASKER, 1987; RAYNAL-VILLASENOR and DOURIET, 1987; NATHAN and MCMAHON, 1990; ADAMCZYK, 1992).The approach using stochastic processes for low-flows may be direct (analytical) or indirect (experimental) (YEVJEVICH et al., 1983). The indirect approach (not described in this literature review) consists of obtaining flow models, generating synthetic flows and then empirically studying certain drought variables obtained from the synthetic data. The direct approach models deficit events and related variables without explicitely modeling flows. The stochastic processes are of two types and differ in the way that randomness is introduced in the model: ·- State modeling: The process may be modeled as a probabilistic transition between various states (Markov processes for example). The states of the process {Xt } are obtained from the hydrological observations {Yt } using thresholds. The number of states of {Xt } is finite and run series analysis may be used to study the properties of the drought parameters; or- Event modeling: The concept of random occurrence of an event is introduced, where an event is a transition between surplus and deficit and vice-versa. In this approach, stochastic point processes are appropriate. A deficit event is then considered a rare event and is characterized by its occurrence time.We review the low-flows studies based on frequency analysis, run series analysis and on point processes. However, we do not include the physically-based models nor the regional analysis studies.Run series analysis is applied to processes derived from flows and thresholds. A two-state process is obtained and Markov processes are often applied. The variables of interest are the duration of a deficit defined by the run length of series below the threshold (RL), the severity corresponding to the deficit volume over a negative run of length n (RSn), and the intensity In defined by the ratio RSn /RL (SALDARRIGA and YEVJEVICH, 1970; SEN, 1977; MILLAN and YEVJEVICH, 1971; MILLAN, 1972; SEN, 1980A; SEN, 1980B; SEN, 1980C; GÜVEN, 1983; MOYÉ et al., 1988; SEN, 1990). It is often assumed that the flow process is either independent or autoregressive of order 1 and that it is stationary except for SEN, 1980B.Point processes are based on the notion of the occurrence of an event. They are defined by the occurrence time tj of an event ej. We present a classification of some of the pertinent processes and their relation to each other. These include the Poisson process, both homongeneous and non-homogeneous, the renewal process, the doubly stochastic process and the self-exciting process. These processes are well suited for obtaining models of deficit durations (NORTH, 1981; LEE et al., 1986; ZELENHASIC et SALVAI, 1987; CHANG, 1989; MADSEN and ROSBJERG, 1995; ABI-ZEID, 1997). The advantage of this approach is its ability to take into account nonstationarity where alternating surplus-deficit point processes are defined from daily flow data. ABI-ZEID (1997) proposed a physically-based alternating non-homogeneous Poisson process that takes into account precipitation and temperature, and defined low-flow risk indices computed from these developed models.In conclusion, we remark that frequency analysis does not take into account well the duration aspcets and uses simplifying stationnarity hypothesis. Series analysis provides duration distributions for simple flow processes. The advantage of point processes is that they can model complex, dependent and non-stationary processes. Furthermore, alternating point processes can be used to model deficit durations and generate synthetic data such as occurrences of deficit and surplus events. We argue that the duration of low-flows is an important issue which has not received a lot of attention

Supernova neutrino burst detection with the Deep Underground Neutrino Experiment

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UA

Prospects for beyond the standard model physics searches at the deep underground neutrino experiment: DUNE collaboration

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UA

Neutrino interaction classification with a convolutional neural network in the DUNE far detector

Documento escrito por un elevado número de autores/as, solo se referencia el/la que aparece en primer lugar y los/as autores/as pertenecientes a la UC3M.The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2-5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects.This document was prepared by the DUNE Collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. This work was supported by CNPq, FAPERJ, FAPEG and FAPESP, Brazil; CFI, Institute of Particle Physics and NSERC, Canada; CERN; MŠMT, Czech Republic; ERDF, H2020-EU and MSCA, European Union; CNRS/IN2P3 and CEA, France; INFN, Italy; FCT, Portugal; NRF, South Korea; Comunidad de Madrid, Fundación "La Caixa" and MICINN, Spain; State Secretariat for Education, Research and Innovation and SNSF, Switzerland; TÜBITAK, Turkey; The Royal Society and UKRI/STFC, United Kingdom; DOE and NSF, United States of America

Eksperimentasi Model Pembelajaran Kooperatif Tipe Stad Dan Tgt Dengan Pendekatan Kontekstual Terhadap Prestasi Belajar Dan Aspek Afektif Matematika Siswa Ditinjau Dari Kecerdasan Majemuk

The objectives of this research were to investigate: (1) whose learning achievement and affective aspect of Mathematics are better between the students with the cooperative learning model of the STAD type with contextual approach and those with the cooperative learning model of the TGT type with contextual approach; (2) whose learning achievement and affective aspect of Mathematics are better among the students with mathematical logical intelligence, visual intelligence, kinesthetic intelligence, and interpersonal intelligence; and (3) whether or not there is an interaction in each category of the cooperative learning models and the intelligence types on the learning achievement and affective aspect of Mathematics. This research used the quasi experimental research method. Its population was all of the students in Grade VII of State Junior Secondary Schools of Sukoharjo regency in Academic Year 2012/2013. The samples of the research consisted of 141 students, and they were taken by using the stratified cluster random sampling. The data of the research were gathered through test of learning achievement and questionnaire affective aspect. The proposed hypotheses of the research were tested by using a two-way MANOVA with unbalance cells at the significance level of 5%. The results of the research are as follows 1) The learning achievement in Mathematics of the students of the TGT with contextual approach is better than that STAD with contextual approach, but the affective aspect of Mathematics of the students with TGT with contextual approach is as good as that STAD with contextual approach; 2) The learning achievement in Mathematics of the students with the mathematical logical intelligence is as good as those of the students with the kinesthetic intelligence, interpersonal intelligence but better than visual intelligence, the learning achievement in Mathematics of the students with the kinesthetic intelligence is better than interpersonal intelligence. The affective aspect of Mathematics of the students with the mathematical logical intelligence is as good as that of the students with the kinesthetic intelligence, but better than visual intelligence and interpersonal intelligence, and the affective aspect of Mathematics of the students with the visual intelligence is as good as that of the students with the interpersonal intelligence. 3) There is no any interaction of effect of the cooperative learning models and the multiple intelligences on the learning achievement in Mathematics and the affective aspect of Mathematics