New Requirements for English Majors in the Traditional Chinese Medicine Foreign Trade Industry in the New Era

In recent years, traditional Chinese medicine has shown a unique advantage in the treatment and recovery of infected patients, coupled with the gratifying situation of the import and export of traditional Chinese medicine products, and the vibrant prospects of the traditional Chinese medicine foreign trade market, the current situation of the traditional Chinese medicine foreign trade industry is quite promising. Therefore, the demand for Chinese medicine foreign trade talents by Chinese medicine foreign trade enterprises has greatly risen, which also puts forward new requirements for English majors. This paper analyzes the development trend of Chinese medicine cross-border e-commerce industry and the new requirements for English talents in the current era, discusses the necessary working ability and quality that English professionals engaged in Chinese medicine foreign trade must have in the current context, and expounds the main strategies and ways to improve the professional ability and work quality of Chinese medicine foreign trade professionals in the new era from multiple directions based on the practice of the project. This paper hopes to promote the development of the foreign trade industry of traditional Chinese medicine, and at the same time provide certain references for the cultivation of English professionals, and explore a new direction for the employment of English majors

Meeting investors’ demands in PPP project to improve enthusiasm for participating in green and low-carbon

The use of PPP scheme to guide private investors to actively participate in green and low-carbon development is conducive to filling the funding gap of domestic green and low-carbon transformation. It is important to meet the demands of investors to ensure that investors can permanently participate in PPP low-carbon projects. Due to the high financing leverage, wide coverage, and government’s right to initiate in PPP project, the investors’ demands also include the enhancement of social reputation, and the acquisition of future project market resources besides investment income. To fully understand the purpose of the investors’ participation in PPP projects and provide guidance for further analysis of behavioural influence path, the study systematically analyzes the demands of investors and develops a demand measurement scale. Firstly, based on the characteristics of PPP scheme, six investor’s demands were identified. Secondly, through theoretical analysis, the measurement items of investors’ demand were constructed, and 269 valid data were collected through questionnaire. Finally, carrying out factor analysis, reliability and validity test, the items were revised to get the formal investor demand scale. The research provides guidance for improving the demand satisfaction of investors, which is conducive to attracting private capital to participate in the green low-carbon development strategy of PPP projects, and provides financial guarantee for achieving the ‘double carbon’ goal

Cross-linked CoMoO4/rGO nanosheets as oxygen reduction catalyst

Development of inexpensive and robust electrocatalysts towards oxygen reduction reaction (ORR) is crucial for the cost-affordable manufacturing of metal-air batteries and fuel cells. Here we show that cross-linked CoMoO4 nanosheets and reduced graphene oxide (CoMoO4/rGO) can be integrated in a hybrid material under one-pot hydrothermal conditions, yielding a composite material with promising catalytic activity for oxygen reduction reaction (ORR). Cyclic voltammetry (CV) and linear sweep voltammetry (LSV) were used to investigate the efficiency of the fabricated CoMoO4/rGO catalyst towards ORR in alkaline conditions. The CoMoO4/rGO composite revealed the main reduction peak and onset potential centered at 0.78 and 0.89 V (vs. RHE), respectively. This study shows that the CoMoO4/rGO composite is a highly promising catalyst for the ORR under alkaline conditions, and potential noble metal replacement cathode in fuel cells and metal-air batteries

Challenges of refractive cataract surgery in the era of myopia epidemic: a mini-review

Myopia is the leading cause of visual impairment in the world. With ever-increasing prevalence in these years, it creates an alarming global epidemic. In addition to the difficulty in seeing distant objects, myopia also increases the risk of cataract and advances its onset, greatly affecting the productivity of myopes of working age. Cataract management in myopic eyes, especially highly myopic eyes is originally more complicated than that in normal eyes, whereas the growing population of cataract with myopia, increasing popularity of corneal and lens based refractive surgery, and rising demand for spectacle independence after cataract surgery all further pose unprecedented challenges to ophthalmologists. Previous history of corneal refractive surgery and existence of implantable collamer lens will both affect the accuracy of biometry including measurement of corneal curvature and axial length before cataract surgery, which may result in larger intraocular lens (IOL) power prediction errors and a compromise in the surgical outcome especially in a refractive cataract surgery. A prudent choice of formula for cataract patients with different characteristics is essential in improving this condition. Besides, the characteristics of myopic eyes might affect the long-term stability of IOL, which is important for the maintenance of visual outcomes especially after the implantation of premium IOLs, thus a proper selection of IOL accordingly is crucial. In this mini-review, we provide an overview of the impact of myopia epidemic on treatment for cataract and to discuss new challenges that surgeons may encounter in the foreseeable future when planning refractive cataract surgery for myopic patients

A Blockchain based Fund Management System for Construction Projects -- A Comprehensive Case Study in Xiong'an New Area China

As large scale construction projects become increasingly complex, the use and integration of advanced technologies are being emphasized more and more. However, the construction industry often lags behind most industries in the application of digital technologies. In recent years, a decentralized, peer-topeer blockchain technology has attracted widespread attention from academia and industry. This paper provides a solution that combines blockchain technology with construction project fund management. The system involves participants such as the owner's unit, construction companies, government departments, banks, etc., adopting the technical architecture of the Xiong'an Blockchain Underlying System. The core business and key logic processing are all implemented through smart contracts, ensuring the transparency and traceability of the fund payment process. The goal of ensuring investment quality, standardizing investment behavior, and strengthening cost control is achieved through blockchain technology. The application of this system in the management of Xiong'an construction projects has verified that blockchain technology plays a significant positive role in strengthening fund management, enhancing fund supervision, and ensuring fund safety in the construction process of engineering projects. It helps to eliminate the common problems of multi-party trust and transparent supervision in the industry and can further improve the investment benefits of government investment projects and improve the management system and operation mechanism of investment projects.Comment: Accepted to the 8th International Conference on Smart Finance (ICSF 2023

Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned \textit{semantics} of language tokens do the most heavy lifting during the reasoning process. Different from human's symbolic reasoning process, the semantic representations of LLMs could create strong connections among tokens, thus composing a superficial logical chain. To test our hypothesis, we decouple semantics from the language reasoning process and evaluate three kinds of reasoning abilities, i.e., deduction, induction and abduction. Our findings reveal that semantics play a vital role in LLMs' in-context reasoning -- LLMs perform significantly better when semantics are consistent with commonsense but struggle to solve symbolic or counter-commonsense reasoning tasks by leveraging in-context new knowledge. The surprising observations question whether modern LLMs have mastered the inductive, deductive and abductive reasoning abilities as in human intelligence, and motivate research on unveiling the magic existing within the black-box LLMs. On the whole, our analysis provides a novel perspective on the role of semantics in developing and evaluating language models' reasoning abilities. Code is available at {\url{https://github.com/XiaojuanTang/ICSR}}

The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research

Advances in Single-vehicle intelligence of automated driving have encountered significant challenges because of limited capabilities in perception and interaction with complex traffic environments. Cooperative Driving Automation~(CDA) has been considered a pivotal solution to next-generation automated driving and intelligent transportation. Though CDA has attracted much attention from both academia and industry, exploration of its potential is still in its infancy. In industry, companies tend to build their in-house data collection pipeline and research tools to tailor their needs and protect intellectual properties. Reinventing the wheels, however, wastes resources and limits the generalizability of the developed approaches since no standardized benchmarks exist. On the other hand, in academia, due to the absence of real-world traffic data and computation resources, researchers often investigate CDA topics in simplified and mostly simulated environments, restricting the possibility of scaling the research outputs to real-world scenarios. Therefore, there is an urgent need to establish an open-source ecosystem~(OSE) to address the demands of different communities for CDA research, particularly in the early exploratory research stages, and provide the bridge to ensure an integrated development and testing pipeline that diverse communities can share. In this paper, we introduce the OpenCDA research ecosystem, a unified OSE integrated with a model zoo, a suite of driving simulators at various resolutions, large-scale real-world and simulated datasets, complete development toolkits for benchmark training/testing, and a scenario database/generator. We also demonstrate the effectiveness of OpenCDA OSE through example use cases, including cooperative 3D LiDAR detection, cooperative merge, cooperative camera-based map prediction, and adversarial scenario generation