Irrigating Cork Oaks Trees – First Insights on Growth and Stripping

Cork oak (Quercus suber L.) trees have a high environmental value already well documented in the literature. Also, its socio-economical value is recognized due to their ability to produce cork, which is renewable every 9 years. However, high cork oak mortality rates are being observed since last decades in all Mediterranean basis. The lack of regeneration and well-structured forest stands with trees of different ages are compromising the cork production in the short term future. Since cork is the most profitable forest product in Portugal, a closer involvement of applied research with producers is important. Our studies regarding irrigation and fertigation application in cork oak trees intend to evaluate different treatments for a faster tree growth, reducing the time until the first cork stripping. Our intention with this presentation is to show the first pointers from irrigated cork oaks with 16 years old (irrigated since plantation). Comparable measurements and parameters will be presented between cork oak growing in irrigated and non-irrigated plots, including some cork formation analysis. Our studies also include cork quality laboratory analysis which are being processed. Irrigated cork oaks annual increment growth is significantly higher than control. Also, some indicators from eco-physiology show the effect of irrigation on transpiration rates of the trees, allowing a continuous growth even during dry seasons. First results are promising regarding tree growth performance leading to a shorter first time stripping period. Non irrigated cork oaks only in their 20’s reach 70 cm at breast height (CAP). Due to their water availability since plantation, 130 monitored irrigated trees of 16 years old presented more than 70 cm of CAP and were stripped for the first time this year. Also, some irrigated adult trees from the same plot were stripped. Continuous structural and functional data were acquired during this process and some results will also be presented

Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

Accurate pressure drop estimation in forced boiling phenomena is important during the thermal analysis and the geometric design of cryogenic heat exchangers. However, current methods to predict the pressure drop have one of two problems: lack of accuracy or generalization to different situations. In this work, we present the correlated-informed neural networks (CoINN), a new paradigm in applying the artificial neural network (ANN) technique combined with a successful pressure drop correlation as a mapping tool to predict the pressure drop of zeotropic mixtures in micro-channels. The proposed approach is inspired by Transfer Learning, highly used in deep learning problems with reduced datasets. Our method improves the ANN performance by transferring the knowledge of the Sun & Mishima correlation for the pressure drop to the ANN. The correlation having physical and phenomenological implications for the pressure drop in micro-channels considerably improves the performance and generalization capabilities of the ANN. The final architecture consists of three inputs: the mixture vapor quality, the micro-channel inner diameter, and the available pressure drop correlation. The results show the benefits gained using the correlated-informed approach predicting experimental data used for training and a posterior test with a mean relative error (mre) of 6%, lower than the Sun & Mishima correlation of 13%. Additionally, this approach can be extended to other mixtures and experimental settings, a missing feature in other approaches for mapping correlations using ANNs for heat transfer applications

Hair analysis following chronic smoked-drugs-of-abuse exposure in adults and their toddler: a case report

<p>Abstract</p> <p>Introduction</p> <p>Over the past two decades, the study of chronic cocaine and crack cocaine exposure in the pediatric population has been focused on the potential adverse effects, especially in the prenatal period and early childhood. Non-invasive biological matrices have become an essential tool for the assessment of a long-term history of drug of abuse exposure.</p> <p>Case report</p> <p>We analyze the significance of different biomarker values in hair after chronic crack exposure in a two-year-old Caucasian girl and her parents, who are self-reported crack smokers. The level of benzoylecgonine, the principal metabolite of cocaine, was determined in segmented hair samples (0 cm to 3 cm from the scalp, and > 3 cm from the scalp) following washing to exclude external contamination. Benzoylecgonine was detectable in high concentrations in the child's hair, at 1.9 ng/mg and 7.04 ng/mg, respectively. Benzoylecgonine was also present in the maternal and paternal hair samples at 7.88 ng/mg and 6.39 ng/mg, and 13.06 ng/mg and 12.97 ng/mg, respectively.</p> <p>Conclusion</p> <p>Based on the data from this case and from previously published poisoning cases, as well as on the experience of our research group, we conclude that, using similar matrices for the study of chronic drug exposure, children present with a higher cocaine concentration in hair and they experience more serious deleterious acute effects, probably due to a different and slower cocaine metabolism. Consequently, children must be not exposed to secondhand crack smoke under any circumstance.</p

Search for new Higgs bosons via same-sign top quark pair production in association with a jet in proton-proton collisions at s = 13 TeV

A search is presented for new Higgs bosons in proton-proton (pp) collision events in which a same-sign top quark pair is produced in association with a jet, via the pp→tH/A→ttc‾ and pp→tH/A→ttu‾ processes. Here, H and A represent the extra scalar and pseudoscalar boson, respectively, of the second Higgs doublet in the generalized two-Higgs-doublet model (g2HDM). The search is based on pp collision data collected at a center-of-mass energy of 13 TeV with the CMS detector at the LHC, corresponding to an integrated luminosity of 138 fb−1. Final states with a same-sign lepton pair in association with jets and missing transverse momentum are considered. New Higgs bosons in the 200–1000 GeV mass range and new Yukawa couplings between 0.1 and 1.0 are targeted in the search, for scenarios in which either H or A appear alone, or in which they coexist and interfere. No significant excess above the standard model prediction is observed. Exclusion limits are derived in the context of the g2HDM

Search for flavor changing neutral current interactions of the top quark in final states with a photon and additional jets in proton-proton collisions at s=13 TeV

A search for the production of a top quark in association with a photon and additional jets via flavor changing neutral current interactions is presented. The analysis uses proton-proton collision data recorded by the CMS detector at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of Formula Presented. The search is performed by looking for processes where a single top quark is produced in association with a photon, or a pair of top quarks where one of the top quarks decays into a photon and an up or charm quark. Events with an electron or a muon, a photon, one or more jets, and missing transverse momentum are selected. Multivariate analysis techniques are used to discriminate signal and standard model background processes. No significant deviation is observed over the predicted background. Observed (expected) upper limits are set on the branching fractions of top quark decays: Formula Presented (Formula Presented) and Formula Presented (Formula Presented) at 95% confidence level, assuming a single nonzero coupling at a time. The obtained limit for Formula Presented is similar to the current best limit, while the limit for Formula Presented is significantly tighter than previous results

Machine Learning based tool for CMS RPC currents quality monitoring

The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to $2\times 10^{34}$ $\text{cm}^{-2}\text{s}^{-1}$ are routinely achieved. The CMS RPC system performance is constantly monitored and the detector is regularly maintained to ensure stable operation. The main monitorable characteristics are dark current, efficiency for muon detection, noise rate etc. Herein we describe an automated tool for CMS RPC current monitoring which uses Machine Learning techniques. We further elaborate on the dedicated generalized linear model proposed already and add autoencoder models for self-consistent predictions as well as hybrid models to allow for RPC current predictions in a distant future

Search for a vector-like quark T′ → tH via the diphoton decay mode of the Higgs boson in proton-proton collisions at s \sqrt{s} = 13 TeV

A search for the electroweak production of a vector-like quark T′, decaying to a top quark and a Higgs boson is presented. The search is based on a sample of proton-proton collision events recorded at the LHC at = 13 TeV, corresponding to an integrated luminosity of 138 fb−1. This is the first T′ search that exploits the Higgs boson decay to a pair of photons. For narrow isospin singlet T′ states with masses up to 1.1 TeV, the excellent diphoton invariant mass resolution of 1–2% results in an increased sensitivity compared to previous searches based on the same production mechanism. The electroweak production of a T′ quark with mass up to 960 GeV is excluded at 95% confidence level, assuming a coupling strength κT = 0.25 and a relative decay width Γ/MT′ < 5%