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This study investigates the relationship between institutional investor association and stock price crash risk, using data from all listed non-financial sector companies in the Chinese capital market. The findings indicate a significant positive correlation between institutional investor association and stock price crash risk. Moreover, property rights and agency costs play significant moderating roles in this relationship. Specifically, the impact of institutional investors on stock price crash risk is more pronounced in non-state-owned enterprises (non-SOEs) than in state-owned enterprises (SOEs). Furthermore, this impact is more pronounced in firms with high agency costs and prominent agency problems compared to firms with low agency costs. This research contributes to financial regulators being able to identify better and prevent stock price crashes, ensuring the stability of investors' returns from their invested enterprises.

Employment of various innovative tools for education in the post-epidemic era is an urgent issue of improving the quality and efficiency of education and the relevance of educational material. The study aims to investigate the impact of the use of drones and virtual reality in educational models of student learning in the post-pandemic period. The study was conducted using a quantitative method, a sociological survey, a pedagogical experiment, and mathematical processing of the results. The study involved fourth-year students (n=748) studying in the following specialties: geography and ecology (n=119), engineering, robotics (n=135), architecture and urban planning (n=120), agriculture, agronomy (n=118), information technology (IT) (n=139), journalism (n=117), and teachers (n=258) with more than 5 years of experience. For the educational process of students of each specialty, the scope of application of drones and virtual reality has been developed. Classes with the use of drones and virtual reality were held two times a week for one academic semester. Afterward, students were interviewed using an adapted questionnaire to study their attitudes toward virtual reality technologies and drones for learning. Students of all specialties positively evaluated various aspects of the use of drones and virtual reality in their educational process. The use of drones helps to improve student engagement and visual attention and has a positive impact on learning efficiency and student satisfaction with the learning process. Teachers approve of the use of drones and virtual reality in teaching but point out that further development of the methodology for training stakeholders in the educational process, infrastructure, improvement of curricula, and consideration of various aspects of technology use is needed to effectively implement drones and virtual reality in the learning process. The practical significance of this study is to present aspects of the use of drones in teaching students of certain specialties and to study the impact of the effectiveness of the use of these technologies on the opinions of students and teachers.

A wireless sensor network (WSN) is a wireless network composed of sensor nodes, widely used in military, industrial, and agricultural fields. In these practical application scenarios, due to the limited routing search ability of wireless sensor nodes and the small coverage range of a WSN, it faces the problem of a relatively short network lifecycle. Therefore, increasing a WSN’s coverage range and reducing node energy consumption are of high value. This article proposes a method of using the ant colony algorithm (ACOD) to calculate the optimal routing of a WSN, the K-means algorithm for clustering, and the whale algorithm (WOA) and a backpropagation (BP) neural network to increase the coverage range of a WSN. After multiple experiments, the total energy consumption of WSN nodes has been effectively reduced, the coverage range of the WSN has been increased, and the lifecycle of the WSN has been extended.

This study examines paper-level metrics in the literature on topics related to environmental, social and governance (ESG) to provide a research agenda for hotspots and trends. Based on 755 papers on ESG in the Web of Science Core Collection database from 2004 to 2021, we use VOSviewer and CiteSpace to present a bibliometric review of publications, citation structure, authors, universities, countries, journals, and keywords on the topic. Additionally, the philosophy of the ESG system, factors affecting ESG, the financial outcomes of ESG, the association between ESG and corporate social responsibility (CSR), and ESG investing are presented as research hotspots. Furthermore, three research trends are identified: research on the influencing factors and economic consequences of ESG in the context of emerging markets, mechanism analysis of ESG’s impact on the capital market, and further research on ESG information disclosure and ESG ratings. Our study enriches ESG theory and provides new paths for researchers and practitioners.

Environment, Social and Governance (ESG) has attracted increasing research interest for sustainable management and decision-making in recent years. In order to further understand the research status and trend, the purpose of this study is to adopt bibliometric and visual methods to analyze the knowledge structure and the evolution path in the research field of ESG. Based on 1735 papers on ESG in the Web of Science Collection database from 2004 to 2021, this study uses CiteSpace and VOSviewer to present a bibliometric overview of publications, citation structure, authors, universities, countries/regions, journals, and keywords that work on this Topic. Results indicate that scholars are increasingly regarding ESG as an integrated sustainability analysis framework and paying more attention to the connection among its different dimensions. According to the growth trend, ESG may remain a hot research topic for the next few years. The USA is the most productive country followed by England and China. Journals on ESG research with higher effective factors have higher co-citation frequency and higher publications. Sustainable development, corporate governance, corporate social responsibility, ESG, and ESG ratings as the research hotspots are presented. Additionally, this study provides an overview of the ESG research trends for future researchers. This may aid researchers in understanding current patterns and future study paths, allowing future authors to perform their studies more successfully.

The efficient capital markets hypothesis (EMH) posits that security prices incorporate all available information in capital markets. Nevertheless, real stock markets often exhibit speculative behavior due to information asymmetry and the limited rationality of investors. This paper employs statistical analysis, a multiple regression approach, and robustness tests to investigate the impact of investor attention and accounting information comparability on stock returns. We collected monthly data from all Chinese A-share stocks listed on the main board of the Shanghai Stock Exchange for the period 2017–2021. Our findings reveal a significant positive correlation between current investor attention and current monthly stock returns and a significant negative correlation between lagged investor attention and current monthly stock returns. Moreover, accounting information comparability serves as a substantial moderator, amplifying the positive effect of current investor attention on current stock returns and mitigating the negative impact of lagged investor attention. We investigate the indicator of accounting information comparability from the perspective of investor attention. Significantly, we use accounting information comparability as a moderating variable for the first time to assess its influence on stock returns. Our results demonstrate that accounting information comparability significantly contributes to mitigating excessive share price declines and stimulating share price increases. This discovery also acts as an internal driver for listed companies to proactively improve accounting information comparability.

Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. This paper employs a literature review approach to summarize the research progress on microseismic signal identification methods and techniques over the past decade. The advantages and limitations of commonly used identification methods are systematically analyzed and summarized. Extensive discussions have been conducted on cutting-edge machine learning models, such as convolutional neural networks (CNNs), and their applications in waveform image processing. These models exhibit the ability to automatically extract relevant features and achieve precise event classification, surpassing traditional methods. Building upon existing research, a comprehensive analysis of the strengths, weaknesses, opportunities, and threats (SWOT) of deep learning in microseismic event analysis is presented. While emphasizing the potential of deep learning techniques in microseismic event waveform image recognition and classification, we also acknowledge the future challenges associated with data availability, resource requirements, and specialized knowledge. As machine learning continues to advance, the integration of deep learning with microseismic analysis holds promise for advancing the monitoring and early warning of geological engineering disasters.

This study explores the significance of Thai Halal food as a means of exerting influence for Thailand in the Chinese market. The research explores consumer preferences and market trends by utilising sentiment analysis and machine learning models like BiLSTM, XGBoost, and GBR. The halal industry, with a global value of $2.3 trillion, has immense growth opportunities, especially in China, thanks to the Belt and Road Initiative. The research emphasizes the widespread appeal of Thai cuisine, featuring popular dishes such as Massaman curry and Tom Yum Goong, as well as the importance of government support and strategic marketing to maximize cultural assets. Analysing consumer reviews and social media discussions helps identify important market drivers and potential investment opportunities for promising products. The results suggest that focusing on popular categories such as processed foods, fresh foods, and convenience items can help to optimise revenue. Forecasting models analyse consumer sentiment and sales trends to offer valuable strategic insights for long-term growth in the Chinese market. Combining different sources of strength, such as faith power, soft power, and digital power, creates a powerful foundation for Thailand to expand its cultural and economic influence. [ABSTRACT FROM AUTHOR]

Traffic accidents have a significant impact on daily life, causing head injuries like skull fractures, brain damage, and so on. Many people fail to follow the safety regulations, such as riding a motorcycle without a helmet. The use of machine learning in brain haemorrhage research is extremely challenging since it involves the collection of patient data from computed tomography (CT) scan images. This study proposes a novel region-based segmentation approach for improving the accuracy and efficiency of CT automated 3D image processing in the analysis of brain injuries. It is quite challenging to create a highly efficient superpixel method which maintains a strategic distance from the segmentation and limited clusters of the pixels in respect to the intensity boundaries. The approach reduces computational costs, and the model achieves 97.79% accuracy in segmenting brain haemorrhage images. This study also guides the direction of future research in this domain. [ABSTRACT FROM AUTHOR]