An Empirical Study of Shadowing in College English Listening Teaching

Shadowing is one of the main means of simultaneous interpreting practice, and many studies have combined it with English listening teaching. This paper takes first-year students as the research object, studies the students’ cognitive psychology of shadowing by designing a questionnaire, and explores the practicality of the method by comparing the pre and post-test scores. The results show that shadowing plays a positive role in English listening teaching

Prospective study of lung function and abdominal aortic aneurysm risk: The Atherosclerosis Risk in Communities study

Abstract Background and aims No prospective study has investigated whether individuals with respiratory impairments, including chronic obstructive pulmonary disease (COPD) and restrictive lung disease (RLD), are at increased risk of abdominal aortic aneurysm (AAA). We aimed to prospectively investigate whether those respiratory impairments are associated with increased AAA risk. Methods In 1987–1989, the Atherosclerosis Risk in Communities (ARIC) study followed 14,269 participants aged 45–64 years, without a history of AAA surgery, through 2011. Participants were classified into four groups, “COPD” [forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) <lower limit of normal (LLN)], “RLD” (FEV1/FVC ≥ LLN and FVC < LLN), “respiratory symptoms with normal spirometry” (without RLD or COPD), and “normal” (without respiratory symptoms, RLD or COPD, reference group). Results During the 284,969 person-years of follow-up, 534 incident AAA events were documented. In an age, sex, and race-adjusted proportional hazards model, individuals with respiratory impairments had a significantly higher risk of AAA than the normal reference group. After adjustment for AAA risk factors, including smoking status and pack-years of smoking, AAA risk was no longer significant in the respiratory symptoms with normal spirometry group [HR (95% CI), 1.25 (0.98–1.60)], but was still increased in the other two groups [RLD: 1.45 (1.04–2.02) and COPD: 1.66 (1.34–2.05)]. Moreover, continuous measures of FEV1/FVC, FEV1 and FVC were associated inversely with risk of AAA. Conclusions In the prospective population-based cohort study, obstructive and restrictive spirometric patterns were associated with increased risk of AAA independent of smoking, suggesting that COPD and RLD may increase the risk of AAA. Highlights • No prospective study has examined the association between lung function and abdominal aortic aneurysm (AAA). • We examined this association using a prospective population-based study in the US. • Chronic obstructive pulmonary disease (COPD) and restrictive diseases patterns were associated with increased AAA risk. • This study suggested COPD and restrictive lung diseases may increase AAA risk

Understanding the Complexity of Temperature Dynamics in Xinjiang, China, from Multitemporal Scale and Spatial Perspectives

Based on the observed data from 51 meteorological stations during the period from 1958 to 2012 in Xinjiang, China, we investigated the complexity of temperature dynamics from the temporal and spatial perspectives by using a comprehensive approach including the correlation dimension (CD), classical statistics, and geostatistics. The main conclusions are as follows (1) The integer CD values indicate that the temperature dynamics are a complex and chaotic system, which is sensitive to the initial conditions. (2) The complexity of temperature dynamics decreases along with the increase of temporal scale. To describe the temperature dynamics, at least 3 independent variables are needed at daily scale, whereas at least 2 independent variables are needed at monthly, seasonal, and annual scales. (3) The spatial patterns of CD values at different temporal scales indicate that the complex temperature dynamics are derived from the complex landform

Pyrolysis treatment of nonmetal fraction of waste printed circuit boards : Focusing on the fate of bromine

Advanced thermal treatment of electronic waste offers advantages of volume reduction and energy recovery. In this work, the pyrolysis behaviour of nonmetallic fractions of waste printed circuit boards was studied. The fate of a bromine and thermal decomposition pathway of nonmetallic fractions of waste printed circuit boards were further probed. The thermogravimetric analysis showed that the temperatures of maximum mass loss were located at 319°C and 361°C, with mass loss of 29.6% and 50.6%, respectively. The Fourier transform infrared Spectroscopy analysis revealed that the spectra at temperatures of 300°C–400°C were complicated with larger absorbance intensity. The nonmetallic fractions of waste printed circuit boards decomposed drastically and more evolved products were detected in the temperature range of 600°C–1000°C. The gas chromatography–mass spectrometry analysis indicated that various brominated derivates were generated in addition to small molecules, such as CH4, H2O and CO. The release intensity of CH4 and H2O increased with temperature increasing and reached maximum at 600°C–800°C and 400°C–600°C. More bromoethane (C2H5Br) was formed as compared with HBr and methyl bromide (CH3Br). The release intensity of bromopropane (C3H7Br) and bromoacetone (C3H5BrO) were comparable, although smaller than that of bromopropene (C3H5Br). More dibromophenol (C6H4Br2O) was released than that of bromophenol (C6H5BrO) in the thermal treatment. During the thermal process, part of the ether bonds first ruptured forming bisphenol A, propyl alcohol and tetrabromobisphenol A. Then, the tetrabromobisphenol A decomposed into C6H5BrO and HBr, which further reacted with small molecules forming brominated derivates. It implied debromination of raw nonmetallic fractions of waste printed circuit boards or pyrolysis products should be applied for its environmentally sound treating.© 2020 Sage. The article is protected by copyright and reuse is restricted to non-commercial and no derivative uses. Users may also download and save a local copy of an article accessed in an institutional repository for the user's personal reference.fi=vertaisarvioitu|en=peerReviewed

Surface free energy and mechanical performance of LDPE/CBF composites containing toxic-metal free filler

Heavy-metal contamination in children's toys is a widespread problem, and the international community has issued a series of safety standards to restrict and control the use of toxic metals in toys. In this work, a colored filler (CBF) was prepared using pearl oyster shell (POS) as the green raw material and azo dye as the colorant. Its surface properties were subsequently studied in comparison to those of POS powder using the inverse gas chromatography method. The dispersion surface free energy profiles for both CBF and POS showed that this component contributed the major part (> 70%) to the total surface free energy. The CBF possessed lower polar surface free energy and was relatively more hydrophobic. It also showed a lower thermodynamic work of cohesion, allowing its better dispersion in a low density polyethylene (LDPE) matrix. Mechanical performance studies showed that adding CBF could significantly increase the tensile strength, elastic modulus, flexural strength and flexural modulus of LDPE composites. The absence of toxic metals coupled with excellent mechanical performance makes the CBF an ideal candidate as a filler for children's toys fabrication.The authors gratefully acknowledge financial support from the National Natural Science Foundation of China (Grant nos. 51606055 and 41373121) and Zhejiang Provincial Natural Science Foundation of China (Grant no. LY14D010009).info:eu-repo/semantics/publishedVersio

Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network

Deep sparse networks are widely investigated as a neural network architecture for prediction tasks with high-dimensional sparse features, with which feature interaction selection is a critical component. While previous methods primarily focus on how to search feature interaction in a coarse-grained space, less attention has been given to a finer granularity. In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks. To explore such expansive space, we propose a decomposed space which is calculated on the fly. We then develop a selection algorithm called OptFeature, which efficiently selects the feature interaction from both the feature field and the feature value simultaneously. Results from experiments on three large real-world benchmark datasets demonstrate that OptFeature performs well in terms of accuracy and efficiency. Additional studies support the feasibility of our method.Comment: NeurIPS 2023 poste