14,305 research outputs found

    Balancing loading mass and gravimetric capacitance of NiCo−layered double hydroxides to achieve ultrahigh areal performance for flexible supercapacitors

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    Delivering high areal capacitance (CA) at high rates is crucial but challenging for flexible supercapacitors. CA is the product of areal loading mass (MA) and gravimetric capacitance (CW). Finding and understanding the balance between MA and CW of supercapacitor materials is significant for designing high-CA electrodes. Herein, we have systematically studied the correlation between MA and CW of the nanosheet arrays of NiCo−layered double hydroxide (NiCo−LDH), which were electrodeposited on carbon cloth with different heights to adjust the MA, accompanied by the interlayer distance regulation to improve the CW. The optimal CW performance is achieved at the best charge transfer kinetics for each of MA series. The NiCo−LDH electrode with the suitable MA (2.58 ​mg ​cm−2) and the relatively high CW (1918 F ​g−1 ​at 5 ​A ​g−1 and 400 ​F ​g−1 ​at 150 ​A ​g−1) present a high CA of 4948 ​mF ​cm−2 ​at 12.9 ​mA ​cm−2 and a record-high 1032 ​mF ​cm−2 among LDHs-based flexible electrodes at an ultrahigh current density of 387 ​mA ​cm−2. The corresponding flexible supercapacitor coupled with activated carbon delivers a high energy density of 0.28 ​mWh cm−2 ​at an ultrahigh power density of 712 ​mW ​cm−2, showing great potential applications

    Extracting the speed of sound in the strongly interacting matter created in ultrarelativistic lead-lead collisions at the LHC

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    International audienceUltrarelativistic nuclear collisions create a strongly interacting state of hot and dense quark-gluon matter that exhibits a remarkable collective flow behavior with minimal viscous dissipation. To gain deeper insights into its intrinsic nature and fundamental degrees of freedom, we extracted the speed of sound in this medium created using lead-lead (PbPb) collisions at a center-of-mass energy per nucleon pair of 5.02 TeV. The data were recorded by the CMS experiment at the CERN LHC and correspond to an integrated luminosity of 0.607 nb1^{-1}. The measurement is performed by studying the multiplicity dependence of the average transverse momentum of charged particles emitted in head-on PbPb collisions. Our findings reveal that the speed of sound in this matter is nearly half the speed of light, with a squared value of 0.241 ±\pm 0.002 (stat) ±\pm 0.016 (syst) in natural units. The effective medium temperature, estimated using the mean transverse momentum, is 219 ±\pm 8 (syst) MeV. The measured squared speed of sound at this temperature aligns precisely with predictions from lattice quantum chromodynamic (QCD) calculations. This result provides a stringent constraint on the equation of state of the created medium and direct evidence for a deconfined QCD phase being attained in relativistic nuclear collisions

    Multiplicity dependence of σψ(2S)/σJ/ψ\sigma_{\psi(2S)}/\sigma_{J/\psi} in pppp collisions at s=13\sqrt{s}=13 TeV

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    International audienceThe ratio of production cross-sections of ψ(2S)\psi(2S) over J/ψJ/\psi mesons as a function of charged-particle multiplicity in proton-proton collisions at a centre-of-mass energy s=13\sqrt{s}=13 TeV is measured with a data sample collected by the LHCb detector, corresponding to an integrated luminosity of 658 pb1^{-1}. The ratio is measured for both prompt and non-prompt ψ(2S)\psi(2S) and J/ψJ/\psi mesons. When there is an overlap between the rapidity ranges over which multiplicity and charmonia production are measured, a multiplicity-dependent modification of the ratio is observed for prompt mesons. No significant multiplicity dependence is found when the ranges do not overlap. For non-prompt production, the ψ(2S)toJ/ψ\psi(2S)-to-J/\psi production ratio is roughly independent of multiplicity irrespective of the rapidity range over which the multiplicity is measured. The results are compared to predictions of the co-mover model and agree well except in the low multiplicity region. The ratio of production cross-sections of ψ(2S)\psi(2S) over J/ψJ/\psi mesons are cross-checked with other measurements in di-lepton channels and found to be compatible

    Table_1_Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China.docx

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    ObjectiveInsulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the “common soil” of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings.MethodsWe analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models.ResultsThe LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc.ConclusionThe ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.</p

    CEPC Technical Design Report -- Accelerator