A Bibliometrics Portrait of Chinese Research through the Lens of China Economic Review. A research proposal.

Notwithstanding, in the last two decades, there has been a noticeable increase in published work on the research field of Chinese economy. There are few studies, which analyze the evolution of Chinese economics research, and the weight of international economics within it, by resorting to objective methods, namely bibliometrics. Giving our focus on Chinese economics related research, we select to base our empirical analysis on the “seed journal” China Economic Review (CER), which is the most important economic journal especially concerned with the issues of Chinese economy. We classify and assess all the (522) articles that were published in CER from its genesis (1989) up to December 2010. We construct three main databases: the first database as bibliographic database that contains the more than 500 articles published in CER, where we classify articles by themes (such as Macroeconomics, Microeconomics and International Economics) and types(such as formal vs. empirical); the second database includes the references of those 500 articles, which we denominate ‘roots of Chinese economics research’; and the third database, the ‘influence of Chinese economics research’, where we have all the studies that cited (more than 3000 references) the 500 articles published in CER. By undertaking an exploratory statistical analysis on the three databases - bibliographic database, ‘roots’ database and ‘influence’ database, we are able to assess three main group of issues: 1) the importance, within Chinese economics of the topic ‘international economic’; the types of research that are pursued in the period of analysis (formal vs empirical); and the most prolific authors in the area; 2) the ‘roots’ of Chinese economics, that is, who and which outlets are influencing most Chinese economics research; 3) the scope of influence of Chinese economics literature.Evolution of research, Economics, Bibliometrics, China Economic Review

Construction of Human Neuromuscular Disease-Related Gene Site-Specific Mutant Cell Line by Cas9 Mutation System

Objective: to construct human neuromuscular disease-related gene site-specific mutant cell line by Cas9 mutation system. Methods: according to the principle of CRISPR/Cas9 target design, the exon region of CXCR4 gene sequence was found in the National Center for Biotechnology Information (NCBI) of the United States. Two sgRNAs were designed. Lenticrisprv2 was used as the vector to construct the lenticrisprv2-sgrna recombinant plasmid, which was transformed into the sensitive stbl3 strain. The monoclonal sequencing was selected to verify and expand the culture of the plasmid, then it was transferred to 293T cells for packaging to a slow virus. The virus was collected and infected with 4T1 cells. The monoclonal cells were isolated and cultured by puromycin screening and limited dilution method. The genomic DNA of the selected monoclonal cells was extracted and the DNA fragment near the knockout site was amplified by PCR and sequenced. Results: one cell line had 6 deletion mutations, including DYSF mutation site of neuromuscular disease gene and HEK293T cell model knocked out by DYSF mutation site of neuromuscular disease gene. Conclusion: the recombinant plasmid targeting CXCR4 gene was obtained by CRISPR/Cas9 system, and the human neuromuscular disease-related gene site-specific mutant cell line was successfully constructed

Trusta: Reasoning about Assurance Cases with Formal Methods and Large Language Models

Assurance cases can be used to argue for the safety of products in safety engineering. In safety-critical areas, the construction of assurance cases is indispensable. Trustworthiness Derivation Trees (TDTs) enhance assurance cases by incorporating formal methods, rendering it possible for automatic reasoning about assurance cases. We present Trustworthiness Derivation Tree Analyzer (Trusta), a desktop application designed to automatically construct and verify TDTs. The tool has a built-in Prolog interpreter in its backend, and is supported by the constraint solvers Z3 and MONA. Therefore, it can solve constraints about logical formulas involving arithmetic, sets, Horn clauses etc. Trusta also utilizes large language models to make the creation and evaluation of assurance cases more convenient. It allows for interactive human examination and modification. We evaluated top language models like ChatGPT-3.5, ChatGPT-4, and PaLM 2 for generating assurance cases. Our tests showed a 50%-80% similarity between machine-generated and human-created cases. In addition, Trusta can extract formal constraints from text in natural languages, facilitating an easier interpretation and validation process. This extraction is subject to human review and correction, blending the best of automated efficiency with human insight. To our knowledge, this marks the first integration of large language models in automatic creating and reasoning about assurance cases, bringing a novel approach to a traditional challenge. Through several industrial case studies, Trusta has proven to quickly find some subtle issues that are typically missed in manual inspection, demonstrating its practical value in enhancing the assurance case development process.Comment: 38 page

Moral Hazard and Transparency in Peer-to-Peer Auto Insurance with Telematics

Peer-to-peer (P2P) insurance uses new technology to connect policyholders and brings about disruptive innovation. While P2P insurance serving people with relatively high degrees of social connection, like friends and relatives, has been theoretically and practically underpinned, there is a lack of understanding about its viability or efficiency in serving strangers with few to no social ties as moral hazard may be substantial. In this paper, we bridge the gap by empirically measuring moral hazard in a P2P auto insurance where the insured individuals are strangers. Our research findings remove an obstacle that may hinder a broad application of the P2P insurance model among large groups of individuals. Moreover, we investigate factors that mitigate moral hazard and study the impact of transparency in premium balance on driving safety. We show that the transparency allows people to learn vicariously from peers’ lessons and lets them drive more safely

Predicting Stock Price Movement Direction with Enterprise Knowledge Graph

Predicting stock price movement direction is a challenging task for financial investment. Previous researches focused on investigating the impacts of external factors (e.g., big events, economic influence and sentiments) in combination with the historical price to predict short-term stock price movement, while few researches leveraged the power of various relationships among enterprises. To bridge this gap, this research proposes power vector model and influence propagation model to mine the rich information in constructed Enterprise Knowledge Graph (EKG) for price movement prediction. In addition, Deep Neural Network (DNN) is introduced to train the model. The proposed model shows good prediction performance on the dataset of China top 500 enterprises

Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction

Traditionally, numerical models have been deployed in oceanography studies to simulate ocean dynamics by representing physical equations. However, many factors pertaining to ocean dynamics seem to be ill-defined. We argue that transferring physical knowledge from observed data could further improve the accuracy of numerical models when predicting Sea Surface Temperature (SST). Recently, the advances in earth observation technologies have yielded a monumental growth of data. Consequently, it is imperative to explore ways in which to improve and supplement numerical models utilizing the ever-increasing amounts of historical observational data. To this end, we introduce a method for SST prediction that transfers physical knowledge from historical observations to numerical models. Specifically, we use a combination of an encoder and a generative adversarial network (GAN) to capture physical knowledge from the observed data. The numerical model data is then fed into the pre-trained model to generate physics-enhanced data, which can then be used for SST prediction. Experimental results demonstrate that the proposed method considerably enhances SST prediction performance when compared to several state-of-the-art baselines.Comment: IEEE TGRS 202

Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning

Under different working conditions of gearbox, the feature extraction of fault signals is difficult, and large difference in data distribution affects the fault diagnosis results. Based on the problems, the research proposes a method based on improved MobileNetV3 network and transfer learning (TL-Pro-MobilenetV3 network). Three time-frequency analysis methods are used to obtain time-frequency distribution. Among them, short time Fourier transform (STFT) combined with Pro-MobilenetV3 network takes the shortest time and has the highest accuracy. Furthermore, transfer learning is introduced into the model, and the optimal training parameters are selected training the network. Using the dataset from Southeast University, the TL-Pro-MobilenetV3 model is compared with four classical fault diagnosis models. The experimental results show the accuracy of the method proposed can reach 100% and the training time is the shortest in two working conditions, proving the proposed model has a good performance in generalization ability, recognition accuracy and training time