Yuancheng Si’s research while affiliated with Huzhou University and other places

This study explores the determinants of the opening price gap rate (diffrate) in the Chinese stock market using a range of statistical models, including linear models with regularization terms, generalized linear models (GLMs), generalized additive models (GAMs), and Long Short-Term Memory (LSTM) models. Emphasizing predictive accuracy, interpretability, and practical applicability, the findings reveal that GAMs with regularization terms outperform other approaches in forecasting opening price gap rate. The results identify several critical factors, including domestic market indices, global liquidity conditions, and market activity measures. By providing a comprehensive framework for understanding and modeling opening price dynamics, this work lays a robust foundation for future research and practical applications in market risk management and predictive analytics.

Although adamantinomatous craniopharyngioma (ACP) is a tumour with low histological malignancy, there are very few therapeutic options other than surgery. ACP has high histological complexity, and the unique features of the immunological microenvironment within ACP remain elusive. Further elucidation of the tumour microenvironment is particularly important to expand our knowledge of potential therapeutic targets. Here, we performed integrative analysis of 58,081 nuclei through single-nucleus RNA sequencing and spatial transcriptomics on ACP specimens to characterize the features and intercellular network within the microenvironment. The ACP environment is highly immunosuppressive with low levels of T-cell infiltration/cytotoxicity. Moreover, tumour-associated macrophages (TAMs), which originate from distinct sources, highly infiltrate the microenvironment. Using spatial transcriptomic data, we observed one kind of non-microglial derived TAM that highly expressed GPNMB close to the terminally differentiated epithelial cell characterized by RHCG, and this colocalization was verified by asmFISH. We also found the positive correlation of infiltration between these two cell types in datasets with larger cohort. According to intercellular communication analysis, we report a regulatory network that could facilitate the keratinization of RHCG ⁺ epithelial cells, eventually causing tumour progression. Our findings provide a comprehensive analysis of the ACP immune microenvironment and reveal a potential therapeutic strategy base on interfering with these two types of cells.

In this study, we employ statistical analysis, hypothesis testing, and regression models to investigate the characteristics of opening price gap rates and price gaps in the stock market indices of Mainland China, utilizing historical data. To clarify, while both ’opening price gap rate’ and ’price gaps’ are central to our analysis, they represent distinct concepts. The opening price gap rate refers to the rate at which a stock’s opening price differs from its previous closing price, indicating initial market sentiment and potential momentum for the trading day. In contrast, price gaps, as defined in technical analysis, are specific chart patterns formed by two adjacent candlesticks on consecutive trading days. These patterns are characterized either by one candlestick’s low being higher than the following day’s high, or one candlestick’s high being lower than the following day’s low, creating a "blank" area on the price chart. This signifies a price range with no trading activity and is a crucial indicator of market sentiment and potential directional moves. Our study tested and validated thirteen related hypotheses. The findings reveal a significant correlation between the directionality of price gaps and the fluctuations in opening price gap rates, highlighting key characteristics of the market. Notably, price gaps significantly impact daily changes in trading volume and turnover. Furthermore, we validated the efficacy of the opening price gap rate as a stock-picking factor through back-testing. This research offers a new perspective for understanding stock market behaviors and has considerable implications for investment decisions and market analysis.

This study presents a comprehensive review of the Basel regulatory framework's development, with an emphasis on the progression from the inaugural Basel I Accord to the definitive Basel III, and its integration into the regulatory practices of the Chinese banking system. It scrutinizes the impact of heightened regulatory standards on the capital adequacy and risk management strategies of Chinese banks, delving into the nuances of risk-weighted asset calibration and credit risk oversight. Through an illustrative case study, the research elucidates the strategic adaptations undertaken by Chinese financial institutions in response to these international norms.The study concludes by critically assessing the Basel reforms, contemplating their profound impact on fortifying the global banking infrastructure and supporting China's pursuit of financial stability. It considers how the reforms have reshaped risk management practices and capital adequacy standards, potentially bolstering the resilience of banks against economic shocks. The reflection extends to the Chinese financial system's adaptation to these international norms, highlighting the strategic shifts undertaken by domestic banks to align with global regulatory expectations while addressing the unique challenges of China's economic landscape. The paper emphasizes the importance of these reforms in enhancing the overall health and stability of financial markets, both internationally and within the context of China's rapidly evolving financial sector.

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness , and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algo-rithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.

This paper examines the progression and implementation of customer segmentation theory in banking, focusing particularly on an improved RFM (Recency, Frequency, Monetary) model. It highlights the essential role of identifying high-value customers for the sustained growth and success of commercial banks. The study elucidates the pivotal role of Customer Relationship Management (CRM) and detailed management in the realm of retail banking as crucial for achieving success. By analyzing real-case scenarios from the Chinese banking sector, this paper illuminates the evolving nature of customer segmentation theory and its varied applications. The objective is to provide insightful and practical recommendations for commercial banks aiming to develop or enhance their customer segmentation frameworks. This research contributes both to the theoretical understanding of customer segmentation and offers a pragmatic guide for banks seeking to improve their customer management approaches in a market that is becoming increasingly competitive.

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index’s opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model’s proficiency in linear trend analysis and the deep learning models’ capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index’s opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

The stock price process in China is full of uncertainty hence the stock indices were introduced to serve as indicators of the financial market. How to capture the statistical characteristics of Chinese stock indices returns by the method of parametric distributions fitting could be useful in the fields of econometrics and risk management. In this paper, we use a wider range of parametric distributions to model four main Chinese stock indices. We find a generalization of the Student’s t distribution is shown to provide the best fit.

How to capture the statistical characteristics of Chinese stock indices returns by the method of parametric distributions fitting could be useful in the fields of econometrics and risk management.In this paper we use a wider range of parametric distributions to model four main Chinese stock indices. We find a generalization of the Student’s t distribution is shown to provide the best fit.