Hydrogen jet diffusion modeling by using physics-informed graph neural network and sparsely-distributed sensor data

Efficient modeling of jet diffusion during accidental release is critical for operation and maintenance management of hydrogen facilities. Deep learning has proven effective for concentration prediction in gas jet diffusion scenarios. Nonetheless, its reliance on extensive simulations as training data and its potential disregard for physical laws limit its applicability to unseen accidental scenarios. Recently, physics-informed neural networks (PINNs) have emerged to reconstruct spatial information by using data from sparsely-distributed sensors which are easily collected in real-world applications. However, prevailing approaches use the fully-connected neural network as the backbone without considering the spatial dependency of sensor data, which reduces the accuracy of concentration prediction. This study introduces the physics-informed graph deep learning approach (Physic_GNN) for efficient and accurate hydrogen jet diffusion prediction by using sparsely-distributed sensor data. Graph neural network (GNN) is used to model the spatial dependency of such sensor data by using graph nodes at which governing equations describing the physical law of hydrogen jet diffusion are immediately solved. The computed residuals are then applied to constrain the training process. Public experimental data of hydrogen jet is used to compare the accuracy and efficiency between our proposed approach Physic_GNN and state-of-the-art PINN. The results demonstrate our Physic_GNN exhibits higher accuracy and physical consistency of centerline concentration prediction given sparse concentration compared to PINN and more efficient compared to OpenFOAM. The proposed approach enables accurate and robust real-time spatial consequence reconstruction and underlying physical mechanisms analysis by using sparse sensor data

Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages

Study of the B−→Λc+Λˉc−K−B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} decay

The decay $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ is studied in proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV using data corresponding to an integrated luminosity of 5 $\mathrm{fb}^{-1}$ collected by the LHCb experiment. In the $\Lambda_{c}^+ K^{-}$ system, the $\Xi_{c}(2930)^{0}$ state observed at the BaBar and Belle experiments is resolved into two narrower states, $\Xi_{c}(2923)^{0}$ and $\Xi_{c}(2939)^{0}$, whose masses and widths are measured to be $$m(\Xi_{c}(2923)^{0}) = 2924.5 \pm 0.4 \pm 1.1 \,\mathrm{MeV}, \\ m(\Xi_{c}(2939)^{0}) = 2938.5 \pm 0.9 \pm 2.3 \,\mathrm{MeV}, \\ \Gamma(\Xi_{c}(2923)^{0}) = \phantom{000}4.8 \pm 0.9 \pm 1.5 \,\mathrm{MeV},\\ \Gamma(\Xi_{c}(2939)^{0}) = \phantom{00}11.0 \pm 1.9 \pm 7.5 \,\mathrm{MeV},$$ where the first uncertainties are statistical and the second systematic. The results are consistent with a previous LHCb measurement using a prompt $\Lambda_{c}^{+} K^{-}$ sample. Evidence of a new $\Xi_{c}(2880)^{0}$ state is found with a local significance of $3.8\,\sigma$, whose mass and width are measured to be $2881.8 \pm 3.1 \pm 8.5\,\mathrm{MeV}$ and $12.4 \pm 5.3 \pm 5.8 \,\mathrm{MeV}$, respectively. In addition, evidence of a new decay mode $\Xi_{c}(2790)^{0} \to \Lambda_{c}^{+} K^{-}$ is found with a significance of $3.7\,\sigma$. The relative branching fraction of $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ with respect to the $B^{-} \to D^{+} D^{-} K^{-}$ decay is measured to be $2.36 \pm 0.11 \pm 0.22 \pm 0.25$, where the first uncertainty is statistical, the second systematic and the third originates from the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb public pages

Measurement of the ratios of branching fractions R(D∗)\mathcal{R}(D^{*}) and R(D0)\mathcal{R}(D^{0})

The ratios of branching fractions $\mathcal{R}(D^{*})\equiv\mathcal{B}(\bar{B}\to D^{*}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(\bar{B}\to D^{*}\mu^{-}\bar{\nu}_{\mu})$ and $\mathcal{R}(D^{0})\equiv\mathcal{B}(B^{-}\to D^{0}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(B^{-}\to D^{0}\mu^{-}\bar{\nu}_{\mu})$ are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0 fb${ }^{-1}$ of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode $\tau^{-}\to\mu^{-}\nu_{\tau}\bar{\nu}_{\mu}$. The measured values are $\mathcal{R}(D^{*})=0.281\pm0.018\pm0.024$ and $\mathcal{R}(D^{0})=0.441\pm0.060\pm0.066$, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is $\rho=-0.43$. Results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb public pages

A study of CP violation in B-+/- -&gt; DK +/- and B-+/- -&gt; D pi(+/-) decays with D -&gt; (KSK +/-)-K-0 pi(-/+) final states

A first study of CP violation in the decay modes $B^\pm\to [K^0_{\rm S} K^\pm \pi^\mp]_D h^\pm$ and $B^\pm\to [K^0_{\rm S} K^\mp \pi^\pm]_D h^\pm$, where $h$ labels a $K$ or $\pi$ meson and $D$ labels a $D^0$ or $\overline{D}^0$ meson, is performed. The analysis uses the LHCb data set collected in $pp$ collisions, corresponding to an integrated luminosity of 3 fb$^{-1}$. The analysis is sensitive to the CP-violating CKM phase $\gamma$ through seven observables: one charge asymmetry in each of the four modes and three ratios of the charge-integrated yields. The results are consistent with measurements of $\gamma$ using other decay modes

Measurement of Upsilon production in collisions at root s=2.76 TeV

The production of $\Upsilon(1S)$, $\Upsilon(2S)$ and $\Upsilon(3S)$ mesons decaying into the dimuon final state is studied with the LHCb detector using a data sample corresponding to an integrated luminosity of 3.3 $pb^{-1}$ collected in proton-proton collisions at a centre-of-mass energy of $\sqrt{s}=2.76$ TeV. The differential production cross-sections times dimuon branching fractions are measured as functions of the $\Upsilon$ transverse momentum and rapidity, over the ranges $p_{\rm T} Upsilon(1S) X) x B(Upsilon(1S) -> mu+mu-) = 1.111 +/- 0.043 +/- 0.044 nb, sigma(pp -> Upsilon(2S) X) x B(Upsilon(2S) -> mu+mu-) = 0.264 +/- 0.023 +/- 0.011 nb, sigma(pp -> Upsilon(3S) X) x B(Upsilon(3S) -> mu+mu-) = 0.159 +/- 0.020 +/- 0.007 nb, where the first uncertainty is statistical and the second systematic

A capacitive-inductive dual modality imaging system for non-destructive evaluation applications

For many non-destructive evaluation (NDE) applications, more information can be obtained by using different techniques, especially when the techniques are sensitive to different types of defects. However separate inspections are not always practical due to time and cost constraints. Therefore, an inspection system combing more than one modalities would have many advantages. A dual modality imaging system is thus proposed which can automatically switch between capacitive imaging and inductive imaging modes. Instead of using a physical combination of two sensors, the proposed system employs a coplanar coil pair as a sensor, and the modality switching is done by changing the wiring schemes through program-controlled switch box. After a single scan over the specimen under test, two types of images, namely capacitive and inductive, can be obtained by the proposed system. For an insulated metallic structure, the capacitive image contains the defect information in the insulation layer and on the top surface of the conducting layer, while the inductive image contains the defect information within the conducting region. The proposed integration of the two imaging modalities in a single system does not introduce any interference between the modes and provides more information on the defects with a reduced testing time and production cost on hardware and software compared to using two NDE techniques separately. A theoretical explanation of the imaging mechanisms for the capacitive and inductive modes are provided. The results of finite element modelling show perturbation of the probing fields due to defects in the two imaging modes. Experimental results from a dual modality imaging system are also presented, demonstrating detection of defects in insulator-metal hybrid structures to verify the effectiveness of this approach

16S Ribosomal Ribonucleic Acid Gene Polymerase Chain Reaction in the Diagnosis of Bloodstream Infections: A Systematic Review and Meta-Analysis.

We aim to evaluate the accuracy of the 16S ribosomal ribonucleic acid (rRNA) gene polymerase chain reaction (PCR) test in the diagnosis of bloodstream infections through a systematic review and meta-analysis.A computerized literature search was conducted to identify studies that assessed the diagnostic value of 16S rRNA gene PCR test for bloodstream infections. Study quality was assessed using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We calculated the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and their 95% confidence intervals (95% CI) for each study. Summary receiver operating characteristic (SROC) curve was used to summarize overall test performance. Statistical analysis was performed in Meta-DiSc 1.4 and Stata/SE 12.0 software.Twenty-eight studies were included in our meta-analysis. Using random-effect model analysis, the pooled sensitivity, specificity, PLR, NLR, and DOR were 0.87 (95% CI, 0.85-0.89), 0.94 (95% CI, 0.93-0.95), 12.65 (95% CI, 8.04-19.90), 0.14 (95% CI, 0.08-0.24), and 116.76 (95% CI, 52.02-262.05), respectively. The SROC curve indicated that the area under the curve (AUC) was 0.9690 and the maximum joint sensitivity and specificity (Q*) was 0.9183. In addition, heterogeneity was statistically significant but was not caused by the threshold effect.Existing data suggest that 16S rRNA gene PCR test is a practical tool for the rapid screening of sepsis. Further prospective studies are needed to assess the diagnostic value of PCR amplification and DNA microarray hybridization of 16S rRNA gene in the future