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This study aims to tackle the critical issue of detecting stock market manipulation, which undermines the integrity and stability of financial markets globally. Even enhanced with machine learning, traditional statistical methods often struggle to analyze high-frequency trading data effectively due to inherent noise and the limited availability of publicly known manipulation cases. This leads to poor model generalization and a tendency toward over-fitting. Focusing on China's securities market, our study introduces an innovative approach that employs deep learning-based high-frequency jump tests to overcome these challenges and to develop a more effective method for identifying manipulative activities.

We employed the “Jump Variation – Time-of-Day” (JV-TOD) non-parametric technique for jump tests on high-frequency data, coupled with the synthetic minority over-sampling technique (SMOTE) algorithm for re-balancing sample data. Our approach trains a deep neural network (DNN) on refined data to enhance its ability to identify manipulation patterns accurately.

Our results show that the deep neural network model, calibrated with high-frequency price jump data, identifies manipulation behavior more specifically and accurately than traditional models. The model achieved an accuracy rate of 94.64%, an F1-score of 95.26% and a recall rate of 95.88%, significantly outperforming traditional models. These results demonstrate the effectiveness of our approach in mitigating over-fitting and improving the robustness of market manipulation detection.

The proposed model provides regulatory entities and financial institutions with a more efficient tool to monitor and counteract market manipulation, thereby improving market fairness and investor protection.

By integrating the JV-TOD jump test with deep learning, this study proposed a new approach to market manipulation detection. The innovation is in its capacity to detect subtle manipulation signals that traditional methods typically overlook. Our model, which is trained on jump test data enhanced by the SMOTE algorithm, excels at learning complex manipulation patterns. This enhances both detection accuracy and robustness. In contrast to existing methods that are challenged by the noisy and intricate nature of high-frequency data, our approach shows enhanced performance in identifying nuanced market manipulations, offering a more effective and reliable method for detecting market manipulation.

This paper aims to explore the citation aging pattern of Library and Information Science (LIS) and further investigate the impact of interdisciplinary citation on the aging of scientific literature.

The study examines LIS journal articles published between 2016 and 2020. Articles were retrieved from the Web of Science (WoS) and were organized using Scopus's discipline classification system. Citation aging patterns within LIS are described using literature aging indicators. The study examines the effect of interdisciplinary citations on the literature aging pattern by comparing the half-life of LIS literature and the median age of interdisciplinary citations.

The study results show that the citation aging rate of LIS in the last five years has been slow, and the rate of slowing down is decreasing. Interdisciplinary citations are sourced from various disciplines, focusing on computer science, social sciences and business. The proportion of self-citations is declining. The Reference Diversity Index (RDI) increases from 0.690 to 0.724 between 2016 and 2020. Currently, the median age of interdisciplinary citations is higher than the LIS's half-life. It has a diminishing effect on the citation aging rate. But the median age of interdisciplinary citations is decreasing. The interdisciplinary citation may contribute to the literature aging rate in the future. The effect of interdisciplinary citation on literature aging needs to be judged dialectically.

This study still has some limitations. Due to the wide variety of citation journals in LIS, there is no database to cover all journals, so it is impossible to match all citation journals with disciplines. Therefore, it is still feasible to analyze interdisciplinary citations based on the two-eight principle for large-scale data. This approach necessarily sacrifices some of the precision of the study. However, the results of this paper can still be helpful for the development of the discipline. In addition, LIS is a discipline with solid cross-cutting properties, and this paper concludes only with this interdisciplinary discipline in mind. It is necessary to test the applicability of the findings to other disciplines.

The study explores the impact of interdisciplinary citation on literature aging from a professional communication perspective. The results reveal underlying reasons for the aging of scientific literature. These findings further enrich the study of the effect of interdisciplinary communication.

To enhance the understanding of the antecedents of students' career adaptability, this study employs the crossover model to explore the potential transfer of career adaptability from headteachers to students and the underlying mechanisms involved.

This study examined the proposed moderated mediation model using matched survey data collected from 37 headteachers and 1,598 students in Chinese higher vocational colleges.

Headteachers’ career adaptability is positively related to students’ career adaptability via students’ psychological capital. An increased frequency of headteacher–student interactions strengthened the indirect relationship between headteachers' career adaptability and students' career adaptability.

The findings suggest that, under certain conditions, headteachers’ career adaptability could be transferred to students via students’ psychological capital.