Identification of relevant environmental descriptors

Based on previous experiences and a literature review the most relevant environmental descriptors were selected and tested by means of field experiments. These included: (i) the temperature-humidity index (THI), (ii) the cattle stocking system (rotational stocking, continuous stocking or strip-grazing), (iii) the botanical composition of the grasslands, (iv) the net grassland productivity in terms of Net Energy (NE) and/or energy-corrected milk (ECM) per unit grassland area, and (v) the behaviour of dairy cows within grazing herds as recorded with SensOor® technology

Identification and quantification of cell gas evolution in rigid polyurethane foams by novel GCMS methodology

Producción CientíficaThis paper presents a new methodology based on gas chromatography-mass spectrometry (GCMS) in order to separate and quantify the gases presented inside the cells of rigid polyurethane (RPU) foams. To demonstrate this novel methodology, the gas composition along more than three years of aging is herein determined for two samples: a reference foam and foam with 1.5 wt% of talc. The GCMS method was applied, on one hand, for the accurate determination of C5H10 and CO2 cell gases used as blowing agents and, on the other hand, for N2 and O2 air gases that diffuse rapidly from the surrounding environment into foam cells. GCMS results showed that CO2 leaves foam after 2.5 month (from 21% to 0.03% for reference foam and from 17% to 0.03% for foam with 1.5% talc). C5H10 deviates during 3.5 months (from 28% up to 39% for reference foam and from 29% up to 36% for foam with talc), then it starts to leave the foam and after 3.5 year its content is 13% for reference and 10% for foam with talc. Air diffuses inside the cells faster for one year (from 51% up to 79% for reference and from 54% up to 81% for foam with talc) and then more slowly for 3.5 years (reaching 86% for reference and 90% for foam with talc). Thus, the fast and simple presented methodology provides valuable information to understand the long-term thermal conductivity of the RPU foams.Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (grants MAT2015-69234-R and RTC-2016-5285-5)Junta de Castilla y Leon (grant VA275P18)Agencia austriaca para la promoción de la investigación (grant 850697

Identification of Quantitative Trait Loci Determining Vegetative Growth Traits in Coffea Canephor

Recently the use of molecular markers has been successfully applied for some crops. For coffee, new opportunities have been opened since Nestlé R&D Centre in collaboration with ICCRI completed the first genetic map of Coffea canephora. This study was aimed both to evaluate the phenotypic trait and also to identify the quantitative trait loci (QTLs) controlling the vegetative growth in Robusta coffee. Present study used three C. canephora populations and six genetic maps developed based on these populations using simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) markers. A total of 17 different quantitative data were used for the detection of QTLs on each of three populations. Present result showed that most of these traits were not heritable. The nine vegetative traits have been identified and distributed over seven different linkage groups. Due to some QTLs determining one given trait were overlapping on the same linkage group and were coming from the same favourable parent, a total of 19 QTLs detected for vegetative traits might finally be considered as only 12 QTLs involved. However, only two of them were shared for different traits. One involved for the number/length of primary branches and width of the canopy while the other for length of internodes and width of canopy. These two QTLs might determine the size of the tree canopy in this species

Particle identification

Particle IDentification (PID) is fundamental to particle physics experiments. This paper reviews PID strategies and methods used by the large LHC experiments, which provide outstanding examples of the state-of-the-art. The first part focuses on the general design of these experiments with respect to PID and the technologies used. Three PID techniques are discussed in more detail: ionization measurements, time-of-flight measurements and Cherenkov imaging. Four examples of the implementation of these techniques at the LHC are given, together with selections of relevant examples from other experiments and short overviews on new developments. Finally, the Alpha Magnetic Spectrometer (AMS 02) experiment is briefly described as an impressive example of a space-based experiment using a number of familiar PID techniques.Comment: 61 pages, 30 figure

Steganographer Identification

Conventional steganalysis detects the presence of steganography within single objects. In the real-world, we may face a complex scenario that one or some of multiple users called actors are guilty of using steganography, which is typically defined as the Steganographer Identification Problem (SIP). One might use the conventional steganalysis algorithms to separate stego objects from cover objects and then identify the guilty actors. However, the guilty actors may be lost due to a number of false alarms. To deal with the SIP, most of the state-of-the-arts use unsupervised learning based approaches. In their solutions, each actor holds multiple digital objects, from which a set of feature vectors can be extracted. The well-defined distances between these feature sets are determined to measure the similarity between the corresponding actors. By applying clustering or outlier detection, the most suspicious actor(s) will be judged as the steganographer(s). Though the SIP needs further study, the existing works have good ability to identify the steganographer(s) when non-adaptive steganographic embedding was applied. In this chapter, we will present foundational concepts and review advanced methodologies in SIP. This chapter is self-contained and intended as a tutorial introducing the SIP in the context of media steganography.Comment: A tutorial with 30 page

Complementarity and Identification

This paper examines the identification power of assumptions that formalize the notion of complementarity in the context of a nonparametric bounds analysis of treatment response. I extend the literature on partial identification via shape restrictions by exploiting cross-dimensional restrictions on treatment response when treatments are multidimensional; the assumption of supermodularity can strengthen bounds on average treatment effects in studies of policy complementarity. This restriction can be combined with a statistical independence assumption to derive improved bounds on treatment effect distributions, aiding in the evaluation of complex randomized controlled trials. Complementarities arising from treatment effect heterogeneity can be incorporated through supermodular instrumental variables to strengthen identification in studies with one or multiple treatments. An application examining the long-run impact of zoning on the evolution of urban spatial structure illustrates the value of the proposed identification methods.Comment: 46 page