Intensifier-Verb Collocations in Academic English by Chinese Learners Compared to Native-Speaker Students

It is difficult for L2 English learners in general, and especially Chinese learners of English, to form idiomatic collocations. This article presents a comparison of the use of intensifier-verb collocations in English by native speaker students and Chinese ESL learners, paying particular attention to verbs which collocate with intensifiers. The data consisted of written production from three corpora: two of these are native English corpora: the British Academic Written English (BAWE) Corpus and Michigan Corpus of Upper-Level Student Papers (MICUSP). The third one is a recently created Chinese Learner English corpus, Ten-thousand English Compositions of Chinese Learners (TECCL). Findings suggest that Chinese learners of English produce significantly more intensifier-verb collocations than native speaker students, but that their English attests a smaller variety of intensifier-verb collocations compared with the native speakers. Moreover, Chinese learners of English use the intensifier-verb collocation types just-verb, only-verb and really-verb very frequently compared with native speaker students. As regards verb collocates, the intensifiers hardly, clearly, well, strongly and deeply collocate with semantically different verbs in native and Chinese learner English. Compared with the patterns in Chinese learner English, the intensifiers in native speaker English collocate with a more stable and restricted set of verb collocates.Peer reviewe

The Upconversion Luminescence of Ca3Sc2Si3O12:Yb3+,Er3+ and Its Application in Thermometry

To develop novel luminescent materials for optical temperature measurement, a series of Yb3+- and Er3+-doped Ca3Sc2Si3O12 (CSS) upconversion (UC) phosphors were synthesized by the sol– gel combustion method. The crystal structure, phase purity, and element distribution of the samples were characterized by powder X-ray diffraction and a transmission electron microscope (TEM). The detailed study of the photoluminescence emission spectra of the samples shows that the addition of Yb3+ can greatly enhance the emission of Er3+ by effective energy transfer. The prepared Yb3+ and Er3+ co-doped CSS phosphors exhibit green emission bands near 522 and 555 nm and red emission bands near 658 nm, which correspond to the 2H11/2→4 I15/2, 4S3/2→4 I15/2, and 4F9/2→4 I15/2 transitions of Er3+, respectively. The temperature-dependent behavior of the CSS:0.2Yb3+,0.02Er3+ sample was carefully studied by the fluorescence intensity ratio (FIR) technique. The results indicate the excellent sensitivity of the sample, with a maximum absolute sensitivity of 0.67% K−1 at 500 K and a relative sensitivity of 1.34% K−1 at 300 K. We demonstrate here that the temperature measurement performance of FIR technology using the CSS:Yb3+,Er3+ phosphor is not inferior to that of infrared thermal imaging thermometers. Therefore, CSS:Yb3+,Er3+ phosphors have great potential applications in the field of optical thermometry

Dynamical phase transition in quantum neural networks with large depth

Understanding the training dynamics of quantum neural networks is a fundamental task in quantum information science with wide impact in physics, chemistry and machine learning. In this work, we show that the late-time training dynamics of quantum neural networks can be described by the generalized Lotka-Volterra equations, which lead to a dynamical phase transition. When the targeted value of cost function crosses the minimum achievable value from above to below, the dynamics evolve from a frozen-kernel phase to a frozen-error phase, showing a duality between the quantum neural tangent kernel and the total error. In both phases, the convergence towards the fixed point is exponential, while at the critical point becomes polynomial. Via mapping the Hessian of the training dynamics to a Hamiltonian in the imaginary time, we reveal the nature of the phase transition to be second-order with the exponent $\nu=1$, where scale invariance and closing gap are observed at critical point. We also provide a non-perturbative analytical theory to explain the phase transition via a restricted Haar ensemble at late time, when the output state approaches the steady state. The theory findings are verified experimentally on IBM quantum devices.Comment: 11+35 pages, comments are welcome

A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models

The current state of quantifying uncertainty in chemical transport models (CTM) is often limited and insufficient due to numerous uncertainty sources and inefficient or inaccurate uncertainty propagation methods. In this study, we proposed a feasible methodological framework for CTM uncertainty analysis, featuring sensitivity analysis to filter for important model inputs and a new reduced-form model (RFM) that couples the high-order decoupled direct method (HDDM) and the stochastic response surface model (SRSM) to boost uncertainty propagation. Compared with the SRSM, the new RFM approach is 64% more computationally efficient while maintaining high accuracy. The framework was applied to PM2.5 simulations in the Pearl River Delta (PRD) region and found five precursor emissions, two pollutants in lateral boundary conditions (LBCs), and three meteorological inputs out of 203 model inputs to be important model inputs based on sensitivity analysis. Among these selected inputs, primary PM2.5 emissions, PM2.5 concentrations of LBCs, and wind speed were identified as key uncertainty sources, which collectively contributed 81.4% to the total uncertainty in PM2.5 simulations. Also, when evaluated against observations, we found that there were systematic underestimates in PM2.5 simulations, which can be attributed to the two-product method that describes the formation of secondary organic aerosol

Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation