A Study on Foreignizing Translation of Culture-Loaded Words in Chinese Food Culture

Chinese food culture is rich and colorful. It is not only the axis of the Eastern food culture, but also benefits the whole world and shines in the world culture. With the development of tourism, cultural exchanges have become more frequent, and the pursuit of food in China and the West has also risen to a higher level. Therefore, translating food culture-loaded words accurately can not only promote international cultural exchanges but also further enhance the international competitiveness of Chinese food culture. This paper is composed of four chapters. Chapter One makes a general introduction to Chinese food culture and foreignizing translation. Chapter Two is a detailed analysis of the application of foreignizing translation in culture-loaded words. Chapter Three shows the prospect of the translation of culture-loaded words. Chapter Four is the conclusion of this paper, which summaries the importance of translating culture-loaded words in Chinese food culture

Synthesis of Bimetallic Nickel and Cobalt Phosphide Nanostructures for Electrocatalysis

Transition metal phosphides have become a new class of materials to be considered as promising catalysts for a number of applications including electrochemical hydrogen evolution (HER). Electrocatalysis in hydrogen evolution is a heavily studied field due to the increasing desire to develop more efficient and more cost-effective catalysts for clean hydrogen production. This project develops a chemical approach to synthesizing bimetallic cobalt-nickel phosphide nanostructures for their use in HER. The synthesis is accomplished by thermal decomposition of the metal precursors in the presence of carbon monoxide (CO) and trioctyl phosphine (TOP). The resulting nanostructures were characterized using transition electron microscopy for morphology, x-ray diffraction for composition, and inductively coupled plasma mass spectrometry for elemental concentration. The results show that bimetallic nanorods are formed, while the aspect ratio of the nanorods can be controlled by the CO injection temperature. Both CO and TOP are the key components for the formation of bimetallic nanorods, as well as the presence of both metal precursors. Additionally, a range of different nanostructures can be formed by varying the reaction conditions. These nanostructures are evaluated by linear sweep voltammetry in an alkaline electrolyte for HER. The results show that some of the phosphide nanostructures have potential to out-perform the common standard, Pt/C, at high current densities. Some correlations between the performance and composition/morphology of the nanostructures are analyzed and discussed. This study offers a solution-based chemical method for the shape-controlled synthesis of metal phosphide nanostructures that opens up the opportunities to tune their catalytic activity in various applications

Epidemiology Of Giant Cell Arteritis Related Hospital Admissions In The United States From 2007-2016

The primary objective of this retrospective cross-sectional study is to investigate the national and regional incidence, epidemiology, and clinical characteristics of Giant Cell Arteritis (GCA) related hospital admissions in the United States (US) from 2007 to 2016. The secondary objectives are to investigate the rate of systemic complications, ocular involvement, resource utilization, and predictors of mortality in GCA. The Nationwide Inpatient Sample was queried to identify all patients hospitalized with an ICD 9 or ICD 10 code for GCA between 2007-2016. Incidence was calculated using US Census data, and risk factors for in-hospital mortality were analyzed with logistic regression. A weighted total of 200,533 GCA related hospital admissions were included. The overall national incidence of GCA related hospital admissions was 6.42 per 100,000 population and 19.81 per 100,000 population for those ≥50 years. The median age was 80 years. The incidence was 3 times higher in women than men (3.43 vs. 1.33 per 100,000 population) and 2 times higher in Caucasians than African Americans (7.52 vs. 3.75 per 100,000 population). The most common systemic comorbidity was hypertension (73.2%), followed by hyperlipidemia (42.0%), and diabetes mellitus (33.2%). Autoimmune disorders were common: 23% of patients had thyroid disease, 14.6% had polymyalgia rheumatica, and 5.2% had rheumatoid arthritis. 18% of GCA patients had ocular involvement, 8.6% had stroke or cerebral arteritis, and 2.87% had aortic dissection/aneurysm or myocarditis. The in-hospital mortality was 2.7%. Age \u3e75 years (OR, 1.99; 95% CI, 1.85 – 2.13; p \u3c0.001), stroke (OR, 1.83; 95% CI, 1.68 – 1.98; p \u3c0.0001), and aortic compromise (OR, 1.76; 95% CI, 1.54 – 1.99; p \u3c0.0001) were significant predictors of mortality. Notably, there was no increase in mortality in patients with ocular involvement or autoimmune disease. In the US, Giant Cell Arteritis preferentially affects older individuals, females, and Caucasians. Approximately one fifth of cases had ocular involvement during the same hospital admission. Stroke, aortic compromise, and increased age are associated with higher mortality risk

Synthesis of Bimetallic Nickel and Cobalt Phosphide Nanostructures for Electrocatalysis

Transition metal phosphides have become a new class of materials to be considered as promising catalysts for a number of applications including electrochemical hydrogen evolution (HER). Electrocatalysis in hydrogen evolution is a heavily studied field due to the increasing desire to develop more efficient and more cost-effective catalysts for clean hydrogen production. This project develops a chemical approach to synthesizing bimetallic cobalt-nickel phosphide nanostructures for their use in HER. The synthesis is accomplished by thermal decomposition of the metal precursors in the presence of carbon monoxide (CO) and trioctyl phosphine (TOP). The resulting nanostructures were characterized using transition electron microscopy for morphology, x-ray diffraction for composition, and inductively coupled plasma mass spectrometry for elemental concentration. The results show that bimetallic nanorods are formed, while the aspect ratio of the nanorods can be controlled by the CO injection temperature. Both CO and TOP are the key components for the formation of bimetallic nanorods, as well as the presence of both metal precursors. Additionally, a range of different nanostructures can be formed by varying the reaction conditions. These nanostructures are evaluated by linear sweep voltammetry in an alkaline electrolyte for HER. The results show that some of the phosphide nanostructures have potential to out-perform the common standard, Pt/C, at high current densities. Some correlations between the performance and composition/morphology of the nanostructures are analyzed and discussed. This study offers a solution-based chemical method for the shape-controlled synthesis of metal phosphide nanostructures that opens up the opportunities to tune their catalytic activity in various applications

Learning Volatility Surfaces using Generative Adversarial Networks

In this paper, we propose a generative adversarial network (GAN) approach for efficiently computing volatility surfaces. The idea is to make use of the special GAN neural architecture so that on one hand, we can learn volatility surfaces from training data and on the other hand, enforce no-arbitrage conditions. In particular, the generator network is assisted in training by a discriminator that evaluates whether the generated volatility matches the target distribution. Meanwhile, our framework trains the GAN network to satisfy the no-arbitrage constraints by introducing penalties as regularization terms. The proposed GAN model allows the use of shallow networks which results in much less computational costs. In our experiments, we demonstrate the performance of the proposed method by comparing with the state-of-the-art methods for computing implied and local volatility surfaces. We show that our GAN model can outperform artificial neural network (ANN) approaches in terms of accuracy and computational time.Comment: This is a working draf

Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning

Interacting with the actual environment to acquire data is often costly and time-consuming in robotic tasks. Model-based offline reinforcement learning (RL) provides a feasible solution. On the one hand, it eliminates the requirements of interaction with the actual environment. On the other hand, it learns the transition dynamics and reward function from the offline datasets and generates simulated rollouts to accelerate training. Previous model-based offline RL methods adopt probabilistic ensemble neural networks (NN) to model aleatoric uncertainty and epistemic uncertainty. However, this results in an exponential increase in training time and computing resource requirements. Furthermore, these methods are easily disturbed by the accumulative errors of the environment dynamics models when simulating long-term rollouts. To solve the above problems, we propose an uncertainty-aware sequence modeling architecture called Environment Transformer. It models the probability distribution of the environment dynamics and reward function to capture aleatoric uncertainty and treats epistemic uncertainty as a learnable noise parameter. Benefiting from the accurate modeling of the transition dynamics and reward function, Environment Transformer can be combined with arbitrary planning, dynamics programming, or policy optimization algorithms for offline RL. In this case, we perform Conservative Q-Learning (CQL) to learn a conservative Q-function. Through simulation experiments, we demonstrate that our method achieves or exceeds state-of-the-art performance in widely studied offline RL benchmarks. Moreover, we show that Environment Transformer's simulated rollout quality, sample efficiency, and long-term rollout simulation capability are superior to those of previous model-based offline RL methods.Comment: ICRA202

An assessment of dual audit effect and contagious effect on the audit quality of non-Big N CPA firms for Chinese companies in different markets

External auditor is an independent agent to provide assurance about the validity of financial statements prepared by management to enhance the reliability of information in financial reports. As such, audit quality has long been a concern for all stakeholders and is a topic of on-going research interest. In China, the dual audit requirement for AB share companies and AH share companies started in 2001 was abolished in 2007 and 2010 respectively. This study attempts to examine whether there are dual audit effect and contagious effect on the audit quality of non-Big N audit firms for A share companies in different markets. I focus on non-Big N audit firms since the audit quality of these firms are of greater concern. Using data from 2001 to 2012, I compare the audit quality of A share companies that also have B (or H) shares ((AB/H) with the audit quality of pure A share companies to test whether there is a dual audit effect on the audit quality of A-share financial statements. I also compare AB/H share companies which hire only non-Big N auditors with those ABIH share companies who hire non-Big N domestic auditors and Big N international auditors to test the existence of contagious effect on the audit quality of A-share companies. My findings indicate that dual audit does improve the audit quality of non-Big N audit firms for A share companies. However, there was mixed evidences on the contagious effect using different measures of audit quality. This study contributes to the literature on enhancing our understanding of the determinants of audit quality in China. It can also provide policy makers in emerging economies some useful evidence on ways to improve audit quality