Academic Community Research Articles

They returned to the Chinese mainland, and what? A collaborative autoethnographic study on the career transitions of Chinese international doctorates from the West to the east

ABSTRACT The purpose of this study is to analyse the approaches of three Chinese international doctoral degree holders who received their doctoral education in the West in tackling the challenges of relocation and career transition to the East after completing their doctoral degrees. Through a collaborative autoethnographic research from June 2020 to December 2022, from the theoretical perspectives of institutional theory, the study reveals four tactics of institutional arbitrage, driven by resources, purpose, practice, and legitimacy, allowed the Chinese international doctoral degree holders to extend their international academic networks to the local context, adapt research purposes to fit national needs, introduce effective practices from the international academic community to local institutions, and advocate for the legitimacy of cutting-edge research in the local academic community. Through these tactics, the multiple logics of profession, corporation, market, and state were found to be strengthened in the institutional environment, thus supporting their career development. The study further proposes to view Chinese international doctoral degree holders’ relocation to China as an important component of the international mobility of knowledge workers, contributing to the formation of global scholarship, rather than a simple return journey to their homeland.

Global Framework for Scientific Event: A 32-Country and 118-Expert Collaborative Effort

Introduction Despite numerous efforts to define and categorize scientific events, a consensus remains elusive, complicating the understanding and classification of such events. The current study aims to establish clear, consensus-based definitions for various types of scientific events by examining their definitions and purposes from the perspectives of scholars across different countries. Methods The current study used a descriptive survey design to gather responses from scholars across 32 countries about their perceptions of scientific events. The sample size was determined using G*power, requiring 80 participants; 118 were recruited. Data was collected via a Google Forms survey with a 9-point Likert scale. Invitations were emailed. The Content Validity Index (CVI) was used to assess item relevance, with all items achieving a CVI above 0.78. Data analysis involved descriptive statistics, and ethical considerations regarding voluntary participation and confidentiality were observed. Results This study validated the definitions of ten event types, each achieving a CVI above 0.78. The scientific events were categorized into conferences, symposiums, workshops, courses, summits, panels, meetings, fast-track reviews, annual reviews, and seminars. Course and panel garnered the highest level of consensus with a CVI of 0.983. Fast-track review also demonstrated a strong agreement among participants, with 82.2% expressing their concurrence with the provided definition. Conclusion The study establishes consensus-based definitions for scientific events, enhancing standardization in the academic community and underscoring the importance of precise categorization.

Using Open Access AI to Improve the Academic Lifecycle for the Botswana Academic Community.

Using Open Access AI to Improve the Academic Lifecycle for the Botswana Academic Community.

A veracity dissemination consistency-based few-shot fake news detection framework by synergizing adversarial and contrastive self-supervised learning

With the rapid growth of social media, fake news (rumors) are rampant online, seriously endangering the health of mainstream social consciousness. Fake news detection (FEND), as a machine learning solution for automatically identifying fake news on Internet, is increasingly gaining the attentions of academic community and researchers. Recently, the mainstream FEND approaches relying on deep learning primarily involves fully supervised fine-tuning paradigms based on pre-trained language models (PLMs), relying on large annotated datasets. In many real scenarios, obtaining high-quality annotated corpora are time-consuming, expertise-required, labor-intensive, and expensive, which presents challenges in obtaining a competitive automatic rumor detection system. Therefore, developing and enhancing FEND towards data-scarce scenarios is becoming increasingly essential. In this work, inspired by the superiority of semi-/self- supervised learning, we propose a novel few-shot rumor detection framework based on semi-supervised adversarial learning and self-supervised contrastive learning, named Detection Yet See Few (DetectYSF). DetectYSF synergizes contrastive self-supervised learning and adversarial semi-supervised learning to achieve accurate and efficient FEND capabilities with limited supervised data. DetectYSF uses Transformer-based PLMs (e.g., BERT, RoBERTa) as its backbone and employs a Masked LM-based pseudo prompt learning paradigm for model tuning (prompt-tuning). Specifically, during DetectYSF training, the enhancement measures for DetectYSF are as follows: (1) We design a simple but efficient self-supervised contrastive learning strategy to optimize sentence-level semantic embedding representations obtained from PLMs; (2) We construct a Generation Adversarial Network (GAN), utilizing random noises and negative fake news samples as inputs, and employing Multi-Layer Perceptrons (MLPs) and an extra independent PLM encoder to generate abundant adversarial embeddings. Then, incorporated with the adversarial embeddings, we utilize semi-supervised adversarial learning to further optimize the output embeddings of DetectYSF during its prompt-tuning procedure. From the news veracity dissemination perspective, we found that the authenticity of the news shared by these collectives tends to remain consistent, either mostly genuine or predominantly fake, a theory we refer to as “news veracity dissemination consistency”. By employing an adjacent sub-graph feature aggregation algorithm, we infuse the authenticity characteristics from neighboring news nodes of the constructed veracity dissemination network during DetectYSF inference. It integrates the external supervisory signals from “news veracity dissemination consistency” to further refine the news authenticity detection results of PLM prompt-tuning, thereby enhancing the accuracy of fake news detection. Furthermore, extensive baseline comparisons and ablated experiments on three widely-used benchmarks demonstrate the effectiveness and superiority of DetectYSF for few-shot fake new detection under low-resource scenarios.

LDVI-SLAM: A Lightweight Monocular Visual-Inertial SLAM System for Dynamic Environments Based on Motion Constraints

Abstract Traditional Simultaneous Localization and Mapping (SLAM) systems are typically based on the assumption of a static environment. However, in practical applications, the presence of moving objects significantly reduces localization accuracy, limiting the system's versatility. To address the challenges of SLAM systems in dynamic environments, the academic community often employs computationally intensive methods such as deep learning, and some algorithms rely on expensive sensors (e.g., LiDAR or RGBD cameras) to obtain depth information. These factors increase computational complexity or hardware costs, complicating practical deployment. To improve localization accuracy and adaptability of SLAM systems in dynamic scenarios while maintaining low deployment costs, this paper proposes a dynamic environment robust monocular inertial SLAM system named LDVI-SLAM. The system uses more cost-effective sensors—monocular cameras and Inertial Measurement Unit (IMU)—along with lightweight computational methods. In LDVI-SLAM, first, the reliability of IMU data is verified. Then, using the ego-motion information provided by the IMU, along with epipolar constraint and an improved Rotation-aware Flow Vector Bound (R-FVB) constraint, dynamic feature points are eliminated. Additionally, this paper proposes a continuous tracking across interval frames method to enhance the distinction between static and dynamic feature points. Experimental results demonstrate that LDVI-SLAM performs effectively in dynamic environments and is easy to deploy. On the VIODE dataset, experimental results show that compared to the deep learning-based DynaSLAM, this method reduces the RMSE (Root Mean Square Error) of absolute trajectory error by 10.3%. Moreover, in terms of speed, under the same computing power, the single-frame processing speed of this method is comparable to ORB-SLAM3 and is two orders of magnitude faster than DynaSLAM, significantly outperforming deep learning-based SLAM algorithms. Experiments on the OMD dataset further prove that this method effectively avoids the risk of semantic classification errors, demonstrating better robustness and generality.

Quantitative Assessment of Market Conditions and Marketing Strategies in the Financial Products Sector: A Case Study of XX Insurance Company, Beijing

The financial products sector in China has undergone significant transformation, driven by economic reforms, technological advancements, and evolving consumer preferences. This study provides a quantitative assessment of market conditions and marketing strategies within this sector, with a specific focus on XX Insurance Company, Beijing. Utilizing a cross-sectional survey of 200 respondents in Beijing, the research examines key factors influencing market dynamics, evaluates the effectiveness of XX Insurance Company’s marketing strategies, and identifies the main drivers of consumer behavior. The findings reveal a generally favorable market environment, with economic growth, regulatory changes, and competitive dynamics as primary influencers. Notably, product features, competitive pricing, strong brand reputation, and excellent customer service emerged as significant factors affecting consumer decisions. The study highlights the importance of digital presence, customer service, and innovative product offerings in enhancing marketing effectiveness. Based on the insights gained, several recommendations are proposed to optimize the company’s marketing strategies and improve its market positioning. This research contributes valuable knowledge to both the academic community and industry practitioners, offering practical guidance for navigating the competitive landscape of China's insurance market.

Using Open Access AI to Improve the Academic Lifecycle for the University for Development Studies Academic Community

Using Open Access AI to Improve the Academic Lifecycle for the University for Development Studies Academic Community

Developing writing productivity in a graduate support community

Purpose This paper aims to examine how doctoral students in education and applied linguistics fields successfully navigated graduate writing demands by participating in a support community that catalyzed writing productivity, peer mentoring and feedback. Guiding graduate students’ writing processes based on scholarly interests and providing peer support are vital to scholarly productivity and transition into academia. Design/methodology/approach Following a collaborative analytic autoethnographic case study design (Adams et al., 2022; Chang et al., 2013), the authors narrated major events that impacted their writing and publication experiences. The authors visualized their entire doctoral writing experience based on the frequency of writing events that contributed to writing productivity. In data triangulation discussions, the authors reflected on writing experiences. Findings Findings show that the support community alleviated individual struggles associated with writing a dissertation and high-quality papers. Key factors contributing to scholarly growth included nonevaluative peer support, feedback and shared academic resources. Writing within the periphery of faculty research and predominantly focusing on doctoral milestones led to individual scholarly interests being overshadowed. Without structured guidance, doctoral writers may develop initiatives to alleviate individual struggles and meet academic writing demands in the disciplines. Research limitations/implications The authors recommend including structured guidance on developing writing productivity and a personal research agenda in the early stages of the doctorate. Originality/value This study offers unique examples of how a student group supported writing productivity and socialization into the academic community. It illustrates the multifaceted nature of academic writing influenced by faculty–student relationships, peers and individual initiatives. This paper provides doctoral writers and graduate programs with examples of accomplishing academic publishing goals.

Impact of campus closure during COVID-19 on lifestyle, educational performance, and anxiety levels of college students in China

BackgroundHigher education students exhibit heightened sensitivity to environmental changes as they navigate the critical transition from adolescence to adulthood. The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges to universities worldwide, exemplifying a crisis that profoundly affects the learning outcomes and psychological status of college students. Although it is known that campus lockdown has triggered dramatic changes in lifestyles, learning outcomes, and psychological statuses, in-depth knowledge of the causal relationships among these changes remains largely unclear.MethodsHere, we conducted a cross-sectional survey designed to assess the impact of campus closure during COVID-19 on lifestyle, educational performance, and anxiety levels among college students. We surveyed over 3,500 junior college, undergraduate and graduate participants from 94 colleges/universities across 30 provinces, municipalities, and autonomous regions in China.We employed structural equation modeling (SEM) to explore the relationships between changes in lifestyle, educational performance, and levels of anxiety associated with campus open or closure regulations during the COVID-19 pandemic.ResultsOur results discovered that sleep duration, physical activity involvement, and social connections were crucial for sustaining students' learning outcomes and mental health. The shift to online learning and campus lockdown exacerbated stressors, contributing to heightened anxiety (β = 0.066), disrupted sleep patterns, and enhanced physical activity (β = 0.070) and reduced learning effect (β = -0.059). Sleep patterns were disrupted by the campus lockdown, an effect mediated by the degradation of relationships among classmates. Nonetheless, the best-fitting SEM uncovered the intricate relationships among lifestyle changes, learning outcomes, and psychological status in response to sudden environmental changes (Fisher’s C = 80.949, P = 0.328). These results highlight the critical role of adaptable, supportive campus policies tailored to meet the diverse needs and interests of students during and beyond crises (Fisher’s C = 59.568, P = 0.809).ConclusionsOur study advocates for a holistic approach that addresses the multifaceted aspects of student life to cultivate a resilient academic community. This approach contributes to a deeper understanding of the effects of sudden environmental changes on students' psychological well-being and academic performance in the post-pandemic era.

Independent Directors' Opposition, Earnings Management and the Risk of Stock Price Crash

The purpose of this paper is to analyze the legal practice and theoretical basis of the implied secrecy clause in the United States, and to explore the domestic development of the implied secrecy obligation in our country. Based on this, this article uses the American case and law to establish the standard to judge whether our country's counterpart undertakes the implied confidentiality obligation. First of all, the first part of this article summarizes the existing legal framework of our country on the obligation of implied confidentiality and its challenges in judicial practice, and summarizes the views of the academic community on this issue. By combing the current situation of legislation, analyzing the problems in judicial practice, and studying the opinions of academic circles, this paper proposes that the implied obligation of confidentiality in the field of trade secrets should be affirmed clearly. Secondly, the second part of the comparative law perspective, a comparative analysis of the common law system and the civil law system on the implied provisions of the different theories, so as to provide theoretical support for our country to learn from the experience of the and the United States. Finally, the third part from the legislative basis, judicial practice and practical needs of the three dimensions, demonstrated the application of implied confidentiality obligations of the rationality and necessity, it further strengthens the importance of implied secrecy obligation in legal practice.

Generative AI Platform for Automating Social Media Posts From Urology Journal Articles: A Cross-Sectional Study and Randomized Assessment.

This cross-sectional study assessed a generative-AI platform to automate the creation of accurate, appropriate, and compelling social-media (SoMe) posts from urological journal articles. One hundred SoMe-posts from the top 3 journals in urology X (Twitter) profiles were collected from Aug-2022 to Oct-2023 A freeware GPT-tool was developed to auto-generate SoMe posts, which included title-summarization, key findings, pertinent emojis, hashtags, and DOI links to the article. Three physicians independently evaluated GPT-generated posts for achieving tetrafecta of accuracy and appropriateness criteria. Fifteen scenarios were created from 5 randomly selected posts from each journal. Each scenario contained both the original and the GPT-generated post for the same article. Five questions were formulated to investigate the posts' likability, shareability, engagement, understandability, and comprehensiveness. The paired posts were then randomized and presented to blinded academic authors and general public through Amazon Mechanical Turk (AMT) responders for preference evaluation. Median (IQR) time for post auto-generation was 10.2 seconds (8.5-12.5). Of the 150 rated GPT-generated posts, 115 (76.6%) met the correctness tetrafecta: 144 (96%) accurately summarized the title, 147 (98%) accurately presented the articles' main findings, 131 (87.3%) appropriately used emojis and hashtags 138 (92%). A total of 258 academic urologists and 493 AMT responders answered the surveys, wherein the GPT-generated posts consistently outperformed the original journals' posts for both academicians and AMT responders (P < .05). Generative-AI can automate the creation of SoMe posts from urology journal abstracts that are both accurate and preferable by the academic community and general public.

Generative AI: Is Authentic Qualitative Research Data Collection Possible?

With the advent of readily accessible generative artificial intelligence (GAI), a concern exists within the academic community that research data collected in the context of conducting doctoral dissertation research is authentic. The purpose of the present study was to explore the role of GAI in the production of new research paying particular attention to the use of GAI in collecting new data for a doctoral dissertation. This study employed qualitative methodology examining how GAI, specifically ChatGPT, responded to interview questions from a previously published article by the researchers to determine how closely chatbots mimic responses from the actual study participants. The researchers found that data integrity in qualitative research may be at risk if higher education institutions do not set clear policies and specific parameters for how doctoral research data is obtained and validated in light of GAI.

Firm Heterogeneity and Regional Choice of China's Outward Foreign Direct Investment: A Review

This literature review focuses on the impact of firm heterogeneity on China's foreign direct investment (OFDI) region selection. The research shows that enterprise heterogeneity, such as productivity, technology and capital structure, significantly affects the OFDI decision and regional choice of Chinese enterprises. These findings not only enrich the understanding of the theory of enterprise heterogeneity, but also provide practical guidance for Chinese enterprises to formulate more effective internationalization strategies, optimize the layout of production networks, and improve OFDI efficiency and risk management capabilities. Looking forward to the future, the academic community can further study the evolution trend of enterprise heterogeneity, the role of emerging markets, the specific impact of policy environment changes on OFDI, and how digital transformation affects the role of enterprise heterogeneity in OFDI decision-making.

Re-examination of the Pre-Qin History of the Records of the Historian and Sima Qian’s View of Historiography from the Archaeological Perspective

In recent years, the remarkable development of archaeology, especially the excavation and publication of pre-Qin and Qin-Han documents, has provided new materials and opportunities for the study of ancient history and deepened the understanding of the Records of the Historian. Comparison of the excavated materials and the contents of the Records of the Historian reveals mutual corroboration, as well as discrepancies and doubts, prompting the academic community to reassess the research results of pre-Qin history and the academic history of the Records of the Historian. This article focuses on the pre-Qin part of the Records of the Historian, combining literature and archaeological data to explore its authenticity, and comparing the records of the Historian with the excavated data to discuss the origin of the Qin people, the harsh system and the death of the Qin, etc. The author also analyzes the life of Sima Qian and his academic and political environment, aiming to comprehensively re-conceptualize the history of the pre-Qin period and the scholarly value of the Records of the Historian and Sima Qian.

A novel fault detection and identification method for complex chemical processes based on OSCAE and CNN

The safety and reliability of chemical processes are of paramount importance. However, due to the complicated heat, mass, and momentum transfer processes coupled with chemical reactions, and the interrelated effects among various equipment units, it is difficult to accurately detect and identify faults occurring in the chemical system. This study proposes an innovative fault diagnosis framework specifically designed for chemical systems. A method based on orthogonal self-attention convolutional autoencoder (OSCAE) is constructed for fault detection. The convolutional autoencoder is combined with an orthogonal attention mechanism to extract time-series characteristic information of the operational state, thereby achieving precise detection of fault conditions. Additionally, a convolutional neural network (CNN) model uses both raw signals and data reconstructed by OSCAE to identify different faults, which enhances the features of the input and enables highly accurate fault identification. The effectiveness and robustness of the propose method is validated with simulation data of our self-established chemical distillation system and the publicly available Tennessee Eastman process system. The diagnosis efficacy in fault detection and identification is validated by applying fault detection metrics, high-dimensional feature visualizations, and confusion matrix analyses across two case studies. This paper not only provides an effective diagnostic framework for chemical systems but also offers a usable benchmark dataset for chemical distillation systems to the academic community, with the hope that the research content can enhance the stability and reliability of chemical processes.

A Study of Premodern Folk Women’s Narrative Literature in Female Script

Folk women’s literature has always been a field of little research in the literary academic community. Due to the lack of seriousness in its texts, difficulty in collecting, and insufficient representative texts, many literatures analyse these texts from cultural and social perspectives. However, this article aims to explore the literary value behind these texts and prove that folk women can also achieve self-empowerment through literary creation.

Efficient photocatalytic decomposition of PFOA over BiOI1-x with low power LED light

In recent years, the academic community has shown significant interest in per- or polyfluoroalkyl compounds (PFAS) due to their challenging degradation and potential health risks. Photocatalysis has been investigated for PFAS decomposition due to its environmentally friendly nature. In this study, BiOI with abundant iodine vacancies was synthesized through solvothermal and calcination methods (referred to as BiOI1-x), and was used for PFAS degradation with a low power UV light source. Compared to pure BiOI, BIOI1-x showed higher photocatalytic activity towards PFOA (perfluorooctanoic acid). Within 5 h under 5 W LED light irradiation, the degradation rate of PFOA reached 51.9 % with BiOI1-x calcined at 440 °C (No significant degradation of PFAS was observed with pure BiOI). Capture experiments, electron paramagnetic resonance spectroscopy, and electrochemical experiments revealed that the main active species in the system were photogenerated holes, followed by hydroxyl radicals. Furthermore, the presence of iodine vacancies significantly improved the efficiency of charge carrier separation and enhanced the photocatalytic performance. Finally, a hypothetical degradation pathway for PFOA in this system was suggested. This study achieved efficient degradation of PFAS under low power LED light (5 W), emphasizing its significant practical importance in terms of energy conservation.

Accurate and Objective Lecturer Appraisal System: Implementation of the LOPCOW Method

This research proposes the use of the Logarithmic Precursor Chain-Driven Objective Weighting (LOPCOW) method to evaluate the best lecturers in universities. The LOPCOW method ensures that the assessment covers all aspects of lecturer quality and performance, including education, research, community services, discipline, commitment, cooperation skills, and innovation. The evaluation of lecturers using the scores and ratings provided showed that CDE lecturers were the best, with the highest score of 0.715. CDE lecturers showed high consistency in all aspects assessed, especially in education, research, and community service. This was followed by MNO lecturers (0.676), STU lecturers (0.668), XYZ lecturers (0.637), and AFI lecturers (0.627). In conclusion, highly ranked lecturers showed strong dedication to the Tridharma of higher education, with consistent performance and a positive impact on the academic community and the general public. Future research should focus on developing strategies to improve lecturers' teaching quality by applying new educational technologies and evaluating their impact on student learning.

A STEP INTO THE FUTURE (review of the book “Russian economy: who is to blame? What to do?”)

The review provides an overview of the main ideas of the book with the catchy title “Russian Economy: Who is to Blame? What to do?”, prepared by a team of authors under the leadership of Doctor of Economics, Professor S. I. Kretov. The monograph’s breakthrough ideas are noted, including the proposal to form a new contour of the circulation sphere, based on Free Sovereign Development Certificates (USSR rubles with demurrage). The authors’ proposals will make it possible to form a sovereign circulation system, which assumes the priority of the production sector and serves its needs. The ideas expressed deserve their thoughtful critical assessment by the academic and professional community, testing in model industries and, I hope, subsequent implementation. Separately, the relevance of the proposals made back in 2021 is noted, as well as their practical value for solving the problems of developing the Russian economy.

Adjuvant pembrolizumab versus placebo in resected stage III melanoma (EORTC 1325-MG/KEYNOTE-054): long-term, health-related quality-of-life results from a double-blind, randomised, controlled, phase 3 trial

In the European Organisation for Research and Treatment of Cancer (EORTC) 1325-MG/KEYNOTE-054 study, adjuvant pembrolizumab improved recurrence-free survival and distant-metastasis-free survival in patients with resected stage III melanoma. Earlier results showed no effect of pembrolizumab on health-related quality of life (HRQOL). Little is known about HRQOL after completion of treatment with pembrolizumab, an important research area concerning patients who are likely to become long-term survivors. This study reports long-term HRQOL results. This double-blind, randomised, controlled, phase 3 trial compared adjuvant pembrolizumab with placebo in patients aged 18 years or older with previously untreated stage IIIA, IIIB, or IIIC resected cutaneous melanoma and an Eastern Cooperative Oncology Group performance status score of 1 or 0, recruited from 123 academic centres and community hospitals in 23 countries. Patients were randomly assigned (1:1) with a minimisation technique stratified for stage and geographical region to receive 200 mg of intravenous pembrolizumab or placebo every 3 weeks for up to 18 doses. Investigators, patients, and those collecting or analysing data were masked to group assignment. The primary endpoint of the trial was recurrence-free survival (reported elsewhere). HRQOL was a prespecified exploratory endpoint, measured with the EORTC Quality of Life Questionnaire-Core 30. All patients with a baseline HRQOL evaluation available who were alive 108 weeks from randomisation were included in this analysis of long-term HRQOL. Long-term HRQOL included assessments measured every 6 months between 108 weeks and 48 months after randomisation. The threshold of clinical relevance for all HRQOL scales used was an average change of 5 points. The trial is ongoing, recruitment is completed, and HRQOL data collection is finalised. This study is registered with ClinicalTrials.gov, NCT02362594, and EudraCT, 2014-004944-37. Between Aug 26, 2015, and Nov 14, 2016, 1019 patients were randomly assigned to pembrolizumab (n=514) or placebo (n=505). Completion of the HRQOL evaluation at baseline exceeded 90% (481 [94%] patients in the pembrolizumab group and 467 [92%] in the placebo group), and ranged between 60% and 90% for post-baseline timepoints. Among patients with a baseline HRQOL evaluation, 365 (39%) were female and 583 (61%) were male. The mean change from baseline to long-term HRQOL was -0·56 (95% CI -2·33 to 1·22) in the pembrolizumab group and 1·63 (-0·12 to 3·38) in the placebo group. The difference between the two groups was -2·19 (-4·65 to 0·27, p=0·081). Differences for all other scales were smaller than 5 and not statistically significant. Adjuvant pembrolizumab did not have a significant impact on long-term HRQOL compared with placebo in patients with resected stage III melanoma. These findings, together with earlier results on efficacy and HRQOL, support the use of pembrolizumab in this setting. Merck Sharp & Dohme.