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We calculate the electrical conductivity of suspended and supported monolayer MoS2 at terahertz (THz) frequencies by means of EMC–FDTD, a multiphysics simulation tool combining an ensemble Monte Carlo (EMC) solver for electron transport and a finite-difference time-domain (FDTD) solver for full-wave electrodynamics. We investigate the role of carrier and impurity densities, as well as substrate choice (SiO2 or hexagonal boron nitride, hBN), in frequency-dependent electronic transport. Owing to the dominance of surface-optical-phonon scattering, MoS2 on SiO2 has the lowest static conductivity, but also the weakest overall frequency dependence of the conductivity. In fact, at high THz frequencies, the conductivity of MoS2 on SiO2 exceeds that of either suspended or hBN-supported MoS2. We extract the parameters for Drude-model fits to the conductivity versus frequency curves obtained from microscopic simulation, which may aid in the experimental efforts toward MoS2 THz applications. 

Membrane processes are widely used in industrial applications such water purification, food processing and pharmaceutical manufacturing. During their operation, the accumulation of foulants in membrane pores and on membrane surfaces lead to the reduction in flux, membrane lifetime and increase in operational cost, and the understanding of the fouling phenomenon is important for mitigating these problems. In this paper we report the application of Raman chemical imaging as a means of identify and map foulants on a membrane surface. The surface of a Polytetrafluoroethylene (PTFE) membrane was studied by Raman chemical imaging before and after fouling during desalination via membrane distillation. Information about location and concentration of three different salts namely CaSO4, BaSO4 and CaCO3 was studied. The three salts showed different distribution patterns, and their distribution was analyzed by correlation mapping and multivariate curve resolution. It was observed that CaSO4 agglomerated in specific places while the BaSO4 and CaCO3 were more distributed. Raman imaging appears to be a powerful tool for studying membrane foulants and can be effective in identifying the distribution of different species on a membrane surface. 

We measure the branching fraction of the decay $B^{-}\to D^0\rho (770{)}^{-}$using data collected with the Belle II detector. The data contain 387 million $B\overline{B}$pairs produced in $e^{+}{e}^{-}$collisions at the $\mathrm{\Upsilon }\left(4S\right)$resonance. We reconstruct $8360\pm 180$decays from an analysis of the distributions of the $B^{-}$energy and the $\rho (770{)}^{-}$helicity angle. We determine the branching fraction to be $(0.939\pm 0.021\left(\mathrm{stat}\right)\pm 0.050\left(\mathrm{syst}\right))\%$, in agreement with previous results. Our measurement improves the relative precision of the world average by more than a factor of two. Published by the American Physical Society2024 

Abstract The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME)/FRB and the Survey for Transient Astronomical Radio Emission 2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations’ O3 observing run. Here, we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts ≤1 s) we derive 50% (90%) upper limits of 10⁴⁸(10⁴⁹) erg for GWs at 300 Hz and 10⁴⁹(10⁵⁰) erg at 2 kHz, and constrain the GW-to-radio energy ratio to ≤10¹⁴−10¹⁶. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs. 

A<sc>bstract</sc> A measurement is performed of Higgs bosons produced with high transverse momentum (p_T) via vector boson or gluon fusion in proton-proton collisions. The result is based on a data set with a center-of-mass energy of 13 TeV collected in 2016–2018 with the CMS detector at the LHC and corresponds to an integrated luminosity of 138 fb⁻¹. The decay of a high-p_THiggs boson to a boosted bottom quark-antiquark pair is selected using large-radius jets and employing jet substructure and heavy-flavor taggers based on machine learning techniques. Independent regions targeting the vector boson and gluon fusion mechanisms are defined based on the topology of two quark-initiated jets with large pseudorapidity separation. The signal strengths for both processes are extracted simultaneously by performing a maximum likelihood fit to data in the large-radius jet mass distribution. The observed signal strengths relative to the standard model expectation are$$ {4.9}_{-1.6}^{+1.9} $$ ${4.9}_{-1.6}^{+1.9}$and$$ {1.6}_{-1.5}^{+1.7} $$ ${1.6}_{-1.5}^{+1.7}$for the vector boson and gluon fusion mechanisms, respectively. A differential cross section measurement is also reported in the simplified template cross section framework. 

Abstract Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.