Exploring Science Academic Conference Series
The Conference Proceedings Series publishes peer-reviewed papers presented at the Exploring Science conferences. These proceedings are a valuable resource for the scientific community, providing access to the latest research findings, innovative methodologies, and interdisciplinary studies. Key Features of the Proceedings: Peer-Reviewed Content – All submitted papers undergo a rigorous peer-review process to ensure academic quality and validity. Multidisciplinary Scope – Covers a wide range of fields, including but not limited to: Physical Sciences (Physics, Chemistry, Materials Science) Life Sciences (Biology, Biochemistry, Medicine) Engineering & Technology (AI, Robotics, Renewable Energy) Environmental Sciences (Climate Change, Sustainability) Social Sciences & Humanities (Psychology, Economics, Education) Open Access & Indexing – Many proceedings are published open access, ensuring wide visibility. Some are indexed in major databases like Scopus, Web of Science, Google Scholar, and others. Digital & Print Formats – Available in both digital (PDF/eBook) and print versions for accessibility. Benefits for Authors & Participants: Publication Opportunity – Presenters can publish their work in a recognized proceedings series. Networking – Connect with global experts and potential collaborators. Career Advancement – Enhances academic profiles for researchers and students. Impactful Research Dissemination – Contributes to the global scientific discourse. Submission Guidelines: Abstracts and full papers must follow the conference’s template. Submissions should present original, unpublished research. Deadlines and review timelines are announced on the official conference website. For more information, visit the official Exploring Science Conference website or contact the organizing committee.
Pharmacoeconomic Evaluation of an Olanzapine-Containing Quadruple Antiemetic Regimen for the Prevention of CINV: An Efficacy-Cost Analysis
This study systematically evaluates the efficacy, safety, and cost-effectiveness of an olanzapine-containing quadruple antiemetic regimen compared with the standard triple regimen for preventing chemotherapy-induced nausea and vomiting (CINV) associated with moderately to highly emetogenic chemotherapy. The aim is to provide evidence-based guidance for optimizing clinical CINV management. The quadruple regimen significantly improved the complete response rate for delayed-phase CINV to 83%–86% and increased the overall no-nausea rate to 78.7%, demonstrating superior efficacy compared with the standard triple regimen. Adding olanzapine addressed the pharmacological limitations of the standard regimen by enhancing delayed-phase coverage and controlling refractory nausea through multi-receptor antagonism. Pharmacoeconomic analysis indicates that, despite the additional cost of olanzapine, the quadruple regimen reduces the need for rescue medications and complication management costs due to its enhanced efficacy, yielding cost savings of 300–500 yuan (RMB) per treatment cycle and a superior cost-effectiveness ratio. Incorporating low-dose olanzapine into the standard triple regimen offers a more effective and cost-efficient approach to preventing CINV in patients receiving moderately to highly emetogenic chemotherapy. Future research should focus on developing individualized predictive models and validating real-world effectiveness in diverse patient populations.
chemotherapy-induced nausea and vomiting, olanzapine, antiemetic regimen, cost-effectiveness analysis, pharmacoeconomics
Copyright (c) 2026 Qiyi Zhang (Author) https://creativecommons.org/licenses/by/4.0
Vol. 12 (2026): Proceedings of the 2026 International Conference on Life Sciences and Health Engineering (IC-LSHE 2026)
It is our distinct pleasure to invite you to the 2026 International Conference on Life Sciences and Health Engineering (IC-LSHE 2026) in the intellectually vibrant city of Oxford, UK. This premier event is meticulously designed to serve as a global nexus for leading academics, pioneering researchers, clinical practitioners, industry experts, and policy makers. The central theme of IC-LSHE 2026, "Converging Technologies for Transformative Healthcare," underscores our commitment to fostering interdisciplinary dialogue. The traditional boundaries between biology, medicine, engineering, and data science are rapidly dissolving. This conference is dedicated to exploring the fertile ground at these intersections, where groundbreaking discoveries are translated into tangible solutions for the world's most pressing health challenges.
Copyright (c) 2026 IC-LSHE Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Vol. 11 (2026): Proceedings of the 2nd International Conference on Social Sciences and Cultural Studies (ICSSCS 2026)
The 2nd International Conference on Social Sciences and Cultural Studies (ICSSCS 2026) is a premier global platform for scholars, researchers, policymakers, and industry experts dedicated to advancing knowledge in social sciences and cultural studies. Held in the historic city of Barcelona, from May 18-19, 2026, the conference offers a unique opportunity to share insights, foster collaborations, and explore innovative solutions to societal challenges.
Copyright (c) 2026 ICSSCS Conference Organizing Committee (Author) https://creativecommons.org/licenses/by/4.0
Determinants of U.S. Health Insurance Charges: Evidence from Multivariate Regression
U.S. national health expenditures accounted for 17.3% of GDP, raising persistent concerns among insurers, policymakers, and households. Using the Medical Cost Personal Dataset (n=1,338), which includes demographic and lifestyle variables, this study examines the determinants of individual health insurance charges. A multivariate regression framework was used to estimate the effects of age, body mass index (BMI), smoking status, number of children, sex, and region. The results indicate that smoking increased the predicted charges by more than $23,000, while age and BMI also had statistically significant positive effects. In contrast, sex and regional differences were not significant after controlling for other factors. The model explained approximately 74% of the variance in charges. These findings align with prior evidence on lifestyle-related risks and provide support for risk-adjusted premium design, highlighting the policy relevance of smoking cessation and obesity prevention.
health insurance charges, multivariate regression, smoking, body mass index, risk-based pricing, risk adjustment
Copyright (c) 2026 Pengyuan Qian (Author) https://creativecommons.org/licenses/by/4.0
Criminal Regulatory Approaches to Deepfake-Related Offenses: Focusing on the Crime of Fraud
Coronary artery disease (CAD), which is driven primarily by atherosclerosis, represents a major global health burden. This review explores the dynamic evolution of the CAD immune microenvironment through single-cell RNA sequencing (scRNA-seq), revealing cellular heterogeneity and interactions. On the basis of the American Heart Association’s histological classification of atherosclerotic lesions, we systematically summarize discoveries supported by scRNA-seq across disease stages (types I-V) from early monocyte recruitment and lipid-resistant macrophage subpopulations (e.g., CD52-hi macrophages) in initial lesions to intensified inflammation involving T-cell activation, NK cell differentiation, and macrophage polarization in fatty streaks and intermediate lesions and late-stage fibrosis and T-cell clonal expansion leading to plaque instability. Key findings highlight immune imbalance exacerbated by comorbidities such as diabetes and systemic lupus erythematosus (SLE), with potential biomarkers (e.g., JUN) and therapeutic targets (e.g., the CCL4‒CCR5 axis and SPP1+ macrophages). Methodological limitations, such as the lack of spatial information and challenges in inferring causal relationships, are discussed. Future prospects, such as spatial transcriptomics integration, multiomics approaches, and AI-assisted precision medicine, are also proposed. This review highlights the transformative role of scRNA-seq in advancing CAD pathology toward precision diagnostics and therapies.
single-cell RNA sequencing, coronary artery disease, immune microenvironment, atherosclerosis, cellular heterogeneity
Copyright (c) 2026 Sixuan Xiang (Author) https://creativecommons.org/licenses/by/4.0
Progress in Single-Cell RNA Sequencing Data Analysis and Its Applications in the Study of Tumor Heterogeneity
Tumors are heterogeneous diseases, and their transition to malignancy is often subtle; however, advanced computational tools can elucidate their development and drug resistance. Single-cell RNA sequencing offers high-resolution insights into cellular and molecular changes, enhancing our understanding of cancer dynamics. This review summarizes the important technological breakthroughs since the birth of single-cell sequencing technology and the recent research results concerning tumor heterogeneity and "precision medicine" through single-cell RNA sequencing. Overall, the future of single-cell RNA sequencing in tumor treatment is undoubtedly promising.
application, research progress, single-cell sequencing, tumor heterogeneity
Copyright (c) 2026 Qingsong Cai (Author) https://creativecommons.org/licenses/by/4.0
Deciphering Treatment Resistance in NSCLC with Single-Cell Sequencing Technology
Acquired resistance to targeted and immune therapies severely limits the success of non-small cell lung cancer (NSCLC) treatment. Single-cell sequencing technologies now empower researchers to dissect this resistance at unprecedented resolution, moving beyond the averaging limitations of bulk genomics. This review highlights how single-cell and spatial multiomics approaches reveal key mechanisms of NSCLC resistance, from rare drug-tolerant subpopulations and cellular plasticity to immunosuppressive niches and metabolic adaptation within the TME. We also discuss emerging strategies-such as liquid biopsy and AI-driven data integration-that hold promise for translating these insights into more effective therapeutic interventions.
single-cell RNA sequencing, tumor heterogeneity, drug resistance, tumor microenvironment, NSCLC
Copyright (c) 2026 Na Li (Author) https://creativecommons.org/licenses/by/4.0
Research on Rare Genetic Disease Targeted Drug Delivery System Based on CRISPR-Cas9 Technology
The treatment of rare genetic diseases remains a significant challenge, and while CRISPR-Cas9 technology offers a powerful therapeutic pathway, its success is entirely contingent on the development of efficient and safe targeted delivery systems. Current delivery methods are hampered by substantial flaws. Viral vectors, such as AAV, have limited packaging capacity and carry mutation risks, whereas lentiviral vectors pose concerns regarding immunogenicity and carcinogenicity. Meanwhile, non-viral vectors like LNPs suffer from poor targeting specificity, electroporation is restricted to in vitro use, and direct injection is notoriously inefficient. To address these critical limitations, this study investigates three advanced delivery platforms. These include Virus-Like Particles (VLPs), exemplified by the safe and programmable RIDE system with demonstrated efficacy in Huntington’s disease mouse models; smart nanoparticles designed to respond to specific physiological triggers like pH or receptors for precise organ targeting, such as in liver-directed therapy for PH1; and cell-mediated systems utilizing engineered hematopoietic stem cells for diseases like sickle cell anemia. Concurrently, the research tackles persistent issues of targeting precision, immunogenic responses, and editing efficiency through strategic ligand optimization and the application of high-fidelity Cas9 variants. Collectively, this work provides valuable insights and a strategic framework to advance the clinical potential of CRISPR-Cas9 for treating rare genetic diseases.
CRISPR-Cas9, rare genetic diseases, targeted drug delivery systems, VLPs, smart responsive nanoparticles
Copyright (c) 2026 Ximing Qiao (Author) https://creativecommons.org/licenses/by/4.0
Comparative Study of Early Diabetes Risk Stratification Based on Machine Learning Algorithms
Objective: This study aims to systematically compare the performance of multiple machine learning models in diabetes risk prediction and identify key risk factors, thereby providing data-driven decision support for early diabetes screening. Methods:Using the UCI Pima Indians Diabetes dataset, five models-logistic regression, K-nearest neighbors, support vector machine, decision tree, and random forest-were trained and evaluated. Model performance was comprehensively assessed via metrics including AUC-ROC, precision, and recall, with feature importance analysis employed to elucidate core diabetes risk factors.Results: The random forest model demonstrated superior performance across multiple metrics (AUC = 0.8167). Plasma glucose was consistently identified as the strongest predictor, with body mass index (BMI) and age also emerging as significant contributors. Conclusion: The random forest model exhibits robust performance and effective capture of feature interactions, making it well-suited for early diabetes prediction with considerable potential for clinical application.
diabetes prediction, machine learning, feature importance, random forest, model comparison
Copyright (c) 2026 Tianze Zhang (Author) https://creativecommons.org/licenses/by/4.0
Innovative Explorations of Microbial Synthetic Biology in Spermidine Production
Spermidine, a polyamine with bioactivities such as anti-aging and cardiovascular protection, holds broad application prospects in the fields of biomedicine and health products. It can delay the aging process by regulating cellular mechanisms and simultaneously exert a positive protective effect on cardiovascular health, thus attracting widespread attention from the industrial community. However, traditional production methods have long restricted the industrialization of spermidine. The chemical synthesis method has issues including insufficient safety of raw materials and catalysts, numerous by-products, low purity, and environmental pollution. The natural extraction method, on the other hand, results in high product costs due to the low content of active ingredients in raw materials and low extraction efficiency, making it difficult to meet market demand. The emergence of microbial synthetic biology technology has brought a new breakthrough to spermidine production. Through metabolic engineering of microbial chassis, optimization of strain performance, and combination with adaptive fermentation processes, the yield and purity of spermidine have been significantly improved. At present, relevant enterprises have promoted industrialization attempts, and costs have been reduced to a certain extent. Nevertheless, challenges still exist in aspects such as strain stability, economics of large-scale production, and industry certification. Continuous technological improvement is required in the future to promote its large-scale application.
spermidine, microbial synthetic, biocomputing, metabolic network
Copyright (c) 2026 Yitong Zhou (Author) https://creativecommons.org/licenses/by/4.0
With the rapid development of artificial intelligence, especially generative models and multimodal large models, medical artificial intelligence has gradually moved from the early era of single-modal image recognition and text classification to the era of multimodal modeling. Medical data is inherently multimodal, including various modalities such as images, clinical texts, structured data, and genetic and signal information. For instance, in the diagnosis of lung diseases, multimodal models can integrate chest CT images with patients’ electronic health records (EHR) and medical history texts to quickly locate the lesions and generate preliminary diagnostic suggestions; in the orthopedic treatment scenarios, the models can combine X-ray images with surgical record texts to assist doctors in formulating personalized surgical plans. How to effectively integrate these modalities, and conduct tasks such as diagnostic assistance, report generation, multi-round questioning, pathological explanation and reasoning, has been a focus of research in recent years. This paper systematically reviews the development path of medical multimodal models, summarizes the changes in the capabilities of mainstream methods and the limitations of datasets, and looks forward to the challenges and future trends for practical deployment in clinical settings.
multimodal data fusion, healthcare, artificial intelligence, clinical implementation, cross-modal modeling
Copyright (c) 2026 Qiyuan Zhang (Author) https://creativecommons.org/licenses/by/4.0
Hi-C Scaffolding Challenges in Non-Model Lepidoptera
The limited availability of high-quality genomic resources for non-model Lepidoptera severely hampers research on their evolutionary adaptations and restricts their application in biodiversity conservation and agroforestry. This study systematically addresses a critical bottleneck-quality degradation in Hi-C scaffolding-that undermines the structural integrity of Lepidoptera genome assemblies. The findings show that intrinsic genomic features, such as high heterozygosity and abundant repetitive sequences, coupled with technical noise in Hi-C data and algorithmic limitations, frequently lead to large-scale structural errors (including misjoins, inversions, and translocations) that are not easily detected by conventional assembly metrics. To address these challenges, the paper proposes an integrated framework that combines haplotype-resolved assembly, manual curation, and multi-dimensional evaluation. This framework integrates optimized experimental and computational workflows, stringent quality control procedures, and standardized evaluation criteria to establish a comprehensive quality assurance system. Additionally, this paper emphasizes the importance of community collaboration and data transparency in fostering reproducible and scalable genomic research. Together, these advances are expected to significantly enhance the reliability and applicability of genomic resources for non-model Lepidoptera, providing a robust genetic foundation for evolutionary biology, comparative genomics and integrated pest management.
non-model lepidoptera, Hi-C scaffolding, genome assembly, quality assessment, haplotype phasing
Copyright (c) 2026 Xueting Wang (Author) https://creativecommons.org/licenses/by/4.0
Advances in the Influence of Wnt on Mesenchymal Stem Cell Differentiation
Mesenchymal stem cells (MSCs) possess self-renewal ability and high differentiation potential. It can be used to construct in vitro models to assist with pathological and pharmacological research, as well as applications in tissue engineering. The core process in the osteogenic differentiation of MSCs is osteogenesis, which is the process of bone tissue formation primarily carried out by osteoblasts. Chondrocytes and skeletal muscle cells are also involved in the process of bone injury repair. As a group of highly conserved signaling proteins, Wnt family proteins affect the direction of MSC differentiation through autocrine or paracrine mechanisms. This review summarizes the influence of Wnt in differentiation process, as well as the relevant factors in the Wnt signaling pathway for directed differentiation during cell culture, aims to provide new solutions and guidance for precise treatment.
Wnt, mesenchymal stem cells, directed differentiation
Copyright (c) 2026 Jiaying Sun (Author) https://creativecommons.org/licenses/by/4.0
Predictive and Analytical Methods for Determining Viral Variation: Evolution and Frontier Breakthroughs
The continuous mutation of viruses poses a persistent and severe challenge to global public health security, disease prevention and control, and biomedical research and development. The accurate prediction and in-depth analysis of viral mutation trends and their potential impacts are of critical strategic importance for constructing effective epidemic early warning systems and guiding the targeted development of vaccines and drugs. In recent years, the rapid development of artificial intelligence and high-throughput sequencing technologies has significantly enhanced our ability to monitor and infer viral evolutionary paths. In particular, the application of machine learning models in predicting mutation hotspots and functional impacts has opened new avenues for research. Moreover, interdisciplinary approaches combining genomics, structural biology, and computational modeling provide deeper insights into the mechanisms driving viral evolution. This paper aims to systematically review the evolution of methods for viral mutation prediction and analysis, delve into frontier technological breakthroughs in the field, objectively analyze the advantages and limitations of existing approaches, and provide an outlook on future directions. Furthermore, the integration of real-time surveillance data with predictive models is emphasized as a key factor in improving the timeliness and accuracy of mutation alerts. The goal is to offer a comprehensive and valuable reference for scientific research and practical applications in related fields, thereby assisting human society in better responding to the complex problems arising from viral variation.
sequence alignment, phylogenetic tree, statistical model
Copyright (c) 2026 Mingshuo Zhang (Author) https://creativecommons.org/licenses/by/4.0
Research on the Impact of Digital-Real Industrial Technology Integration on the Development of New Quality Productive Forces in Enterprises
This study, based on data from 2015-2024 regarding digital-real industrial technology integration and samples of new quality productive forces in Shanghai and Shenzhen A-share listed enterprises, systematically examines the impact effects, influencing mechanisms, and heterogeneity characteristics of digital-real industrial technology integration on the development of new quality productive forces in enterprises. The research finds that digital-real industrial technology integration can significantly promote the development of new quality productive forces in enterprises; this impact is primarily achieved through three mechanism pathways: alleviating corporate financing constraints, enhancing innovation efficiency, and improving operational efficiency. Heterogeneity tests further reveal that the influence of digital-real industrial technology integration on the development of new quality productive forces in enterprises exhibits distinct differences at the enterprise, regional, and industry levels. At the enterprise level, technology-intensive, labor-intensive, and high-profit enterprises are more likely to foster the development of new quality productive forces through digital-real industrial technology integration. At the regional level, enterprises in the southern region and economically underdeveloped areas derive greater benefits from digital-real industrial technology integration. At the industry level, enterprises in high-tech sectors and those with high degrees of monopoly are better positioned to achieve the development of new quality productive forces through digital-real industrial technology integration. The study demonstrates that digital-real integration can promote the development of new quality productive forces in enterprises by alleviating financing constraints and elevating innovation and operational efficiency, while exhibiting heterogeneity effects at the enterprise, regional, and industry levels. These findings expand the theoretical connotations of the digital economy and high-quality development, providing empirical evidence and decision-making references for enterprises’ differentiated transformations, governments’ targeted policies, and optimized resource allocation.
digital-real industrial technology integration, new quality productive forces in enterprises, financing constraints, innovation efficiency, operational efficiency
Copyright (c) 2026 Lu Xie (Author) https://creativecommons.org/licenses/by/4.0
Causes and Regulation Strategies of English Listening Anxiety Among Non-English Major College Students: An Analysis Based on Literature Review
English listening anxiety is one of the core affective barriers that constrain the effectiveness of second language acquisition among non-English major college students. Based on relevant studies published in domestic and international core journals and dissertations from 2011 to 2025, this paper systematically reviews the current status, causes, and regulation strategies of English listening anxiety in this population. The findings indicate that listening anxiety levels in this group are generally moderate or higher, with significant differences across dimensions such as grade level and place of origin. The causes can be attributed to the interaction between subjective factors related to individual learners and objective factors such as teaching and materials. Existing regulation strategies encompass two main dimensions: learner autonomous regulation and teacher instructional intervention. However, limitations persist, including restricted research samples and insufficient practicality of strategies. Future research should broaden perspectives, strengthen analyses of multi-factor interactions, and enhance empirical validation of strategies, thereby providing theoretical and practical references for alleviating listening anxiety among non-English major college students and improving the quality of listening instruction.
non-English major college students, English listening anxiety, causes of anxiety, regulation strategies, second language acquisition
Copyright (c) 2026 Yuhan Tang (Author) https://creativecommons.org/licenses/by/4.0
Design of a Multimodal Virtualization PTSD Therapy Interactive System Based on AIGC
Currently, VR exposure therapy for Post-Traumatic Stress Disorder (PTSD) remains primarily based on single modalities, which struggles to deliver sufficient immersive experiences and emotional responses, leading to certain limitations in treatment efficacy. To simulate multi-sensory channels in human natural interactions and overcome the single-sensory constraints of traditional exposure therapy, this paper explores the application of AIGC technology in the personalized generation of virtual scenes to enhance immersion and engagement in treatment. By systematically dissecting the intrinsic mechanisms of VR exposure therapy, this study proposes a personalized content adaptation method driven by patients’ real-time states and integrates thermal control arrays, muscle electrical stimulation, and user visual behavior analysis technologies to construct a multimodal interaction framework, encompassing coordinated multi-channel synergies such as haptic feedback, auditory resonance, and visual immersion. Building on this foundation, a multimodal virtualized PTSD therapy interactive system based on AIGC is designed and implemented. This research offers new perspectives for PTSD treatment, advancing exposure therapy toward multimodality, intelligence, and universality, while holding significant engineering application value in fields such as neurorehabilitation and psychological therapy.
AIGC, multimodal, virtual reality, PTSD, interaction design, digital therapeutics
Copyright (c) 2026 Yinan Shang (Author) https://creativecommons.org/licenses/by/4.0
A Study of the “Two-Way Interaction” Governance Model in Ethnic Region
Grassroots governance in ethnic regions is a crucial part of the national governance system, facing the core challenge of reconciling the structural tension between the standardized logic of bureaucracy and local knowledge systems. Existing research, often trapped in the dichotomy of “state-centrism” and “society-centrism,” fails to adequately explain the cultural adaptation dilemmas in policy implementation. Drawing on Giddens’ “Structuration Theory,” this paper constructs an analytical framework of “Symbiotic Interactive Governance” to elucidate the complex mechanism of “bidirectional interaction” between the state and society at the grassroots level. The study argues that effective governance in ethnic regions is not a process of one-way embedding or passive adaptation, but rather an active process of institutional innovation and strategic evolution. Through the synergistic advancement of three pathways, “institutional integration,” “strategic innovation,” and “elite consolidation,” grassroots actors can transform external tensions into endogenous drivers, ultimately achieving a “symbiotic” order that moves from tension balance to value co-creation. This research offers a new theoretical perspective for understanding the mutually constitutive evolution of state-society relations in contemporary China and provides a reference for advancing the modernization of governance in ethnic regions.
ethnic regions, grassroots governance, bidirectional interaction, symbiotic governance, structuration theory
Copyright (c) 2026 Boxi Wang (Author) https://creativecommons.org/licenses/by/4.0
A Review of Real-Time Risk Detection for Adolescent Cybersecurity: Technological Advances, Challenges, and Prospects
Amid the wave of digitization, the internet has become a core context for adolescents’ lives and learning. The scale of underage netizens in China has reached 193 million, with an internet penetration rate of 97.2%, essentially achieving saturated coverage. The prevalence of short-form video platforms and smart devices has rendered online risks more concealed, prevalent among younger children, and cross-contextual. Reports indicate that 34.7% of adolescents have encountered cyberbullying, and 28.3% have been exposed to harmful information, urgently necessitating the construction of precise and efficient real-time risk detection systems. This study aims to systematically review the research, core technologies, and existing bottlenecks in the field of real-time risk detection for adolescent cybersecurity, providing references for theoretical deepening and practical implementation. The research method adopts a literature review, conducting in-depth analysis around the sample characteristics, technical methods, and governance mechanisms in domestic and international research. The findings indicate that the field has formed a “technology development-governance coordination-literacy cultivation” framework. Traditional technologies suffer from imbalances between timeliness and accuracy. Dual frameworks combining “reinforcement learning + deep learning” and multimodal fusion technologies offer effective paths to resolve core contradictions, yet research gaps remain in areas such as adaptation for younger age groups, cross-context migration, and human-computer collaboration. The conclusion points out that future efforts need to focus on technological innovation, scenario expansion, and governance coordination to build a refined and intelligent protection system.
adolescent cybersecurity, real-time risk detection, reinforcement learning, deep learning, collaborative governance, multimodal fusion, digital literacy
Copyright (c) 2026 Wanrou Guo (Author) https://creativecommons.org/licenses/by/4.0
Decoding Youth Buzzwords: “Transmutation” in Contemporary Ideologies and Marxist Perspective
With the advent of the new era, numerous social ideologies have surged, exerting significant influence on the value systems of the youth demographic. The phenomenon of “transmutation,” exemplified by popular youth slang such as “lying flat” and “involution,” is widely disseminated, reflecting underlying shifts in ideological values and raising concerns over the security of mainstream ideological consensus. The Party and the state prioritize guiding youth values, with General Secretary Xi Jinping emphasizing the importance of fostering correct ideological development during adolescence. It is urgent to critically analyze the genesis of these ideological trends from a theoretical standpoint. This paper adopts a Marxist perspective to critically examine the “transmutation” phenomenon, dissecting the mechanisms of semantic substitution, value preconditioning, and propagative fracturing, ultimately revealing the intrinsic nature of capitalist ideology. It proposes a holistic approach comprising de-commodification of concepts, discourse power contention, and innovative communication strategies. Through theoretical critique, this study aims to fill gaps in analyzing the material base of social relations, thereby clarifying ideological safeguards within pedagogical practice and youth ideological dissemination endeavors.
transmutation, youth, internet buzzwords, ideology
Copyright (c) 2026 Ningxin Ji (Author) https://creativecommons.org/licenses/by/4.0
Investigation and Analysis about Multimodal Fatigue Detection
Fatigue driving accounts for a significant proportion of traffic accidents. Multimodal fatigue detection offers a new effective way to detect fatigue. This article selected six articles by some criteria and reviewed them carefully. The improvements of the method include using lightweight YOLOv8 networks to process complex images; others process multimodal EEG signals via non-smooth non-negative matrix factorization (nsNMF) and Gramian angular field imaging. Attention-based networks like MMA-Net and TMU-Net are designed to fuse EEG, EDA, PPG, and EOG signals. Additionally, LSTM-based models analyze PPG and facial features, while enhanced MTCNN and PFLD algorithms improve detection accuracy and reduce individual variability. Based on the methods, the article summarizes some challenges in the multimodal fatigue detection and proposes the future of the multimodal fatigue detection. In general, the article provides a comprehensive review to guide further research about multimodal fatigue detection.
multimodal, fatigue driving, fatigue detection, EEG multimodal, multimodal improvement
Copyright (c) 2026 Keran Huang (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Overseas Backgrounds of Directors, Supervisors, and Senior Executives on the Green Innovation Ability of Enterprises: Based on Empirical Analysis of Listed Company Data from 2014 to 2023
In the face of the dual background of the national green development strategy and the difficulty of implementing enterprise green innovation, whether and how the overseas background of directors, supervisors, and senior executives, as the core decision-makers of corporate governance, can and how their overseas background can affect enterprise green innovation has become an important issue. The purpose of this study is to empirically test the impact of the overseas background of the directors, supervisors and senior management teams on the green innovation ability of enterprises, and to explore the moderating role of whether the enterprise is a key pollution monitoring unit in the external policy situation. The research method of this study is to select the data of China’s A-share listed companies from 2014 to 2023 and construct a two-way fixed effect model for empirical analysis. Based on the company’s green innovation ability, whether directors, supervisors and senior executives have overseas backgrounds are the core variables, and a series of corporate financial and governance variables are controlled. The research hypothesis was verified by benchmark regression, moderating effect test, heterogeneity analysis and robustness test. The study found that the overseas background of directors, supervisors and senior executives has a positive but not significant impact on enterprise green innovation. Key pollution monitoring policies have a significant positive moderating effect and can strengthen the promotion of green innovation from overseas backgrounds.
overseas background, corporate green innovation, key pollution monitoring, corporate governance
Copyright (c) 2026 Yufan Wang (Author) https://creativecommons.org/licenses/by/4.0
SMIC’s Dual Listing in A-Shares and Hong Kong Shares: Differences and Valuation Drivers from Institutional, Financial, and Market Performance Perspectives
Against the backdrop of global semiconductor supply chain restructuring and intensifying China-US tech competition, Semiconductor Manufacturing International Corporation (SMIC), a flagship of China’s semiconductor industry, implemented a dual-listing strategy with listings in Hong Kong in 2004 and on the STAR Market in 2020. This study explores differences in listing mechanisms, financial signal interpretation, and valuation logic between the two markets using a mixed-methods approach that integrates case analysis, comparative study, and empirical testing with data from 2020 to 2025. Key findings include three core aspects. First, institutional divergence is evident as the STAR Market’s registration-based system prioritizes policy support for R&D-intensive firms, while Hong Kong’s Chapter 18C emphasizes disclosure and global capital access, leading to differences in listing efficiency and financing flexibility. Second, financial interpretation bias exists where A-share investors focus on "growth scale" while Hong Kong investors prioritize "profit quality". Third, valuation drivers such as investor structure-with retail-dominated A-shares versus institutional-dominated Hong Kong shares-and policy sensitivity explain the long-term A/H premium, while technological gaps and global cycles suppress Hong Kong valuations. This study enriches the literature on dual listing for strategic tech firms and provides insights for cross-border financing, regulatory coordination, and global asset allocation.
SMIC, dual listing, institutional difference, valuation premium, financial signal, international finance
Copyright (c) 2026 Jun Ge (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Strengthening Legal Liability on the Effectiveness of Internal Control in Listed Companies
This paper utilizes the implementation of the new Securities Law in 2020 as a quasi-natural experiment, drawing on data from Chinese A-share listed companies spanning 2018-2022. It employs a multi-period difference-in-differences approach to investigate the impact of strengthening legal liability on the effectiveness of internal control in listed companies, along with the underlying mechanisms. The findings reveal that, in the full sample, the enhancing effect of the new Securities Law is not significant. However, it exhibits a significant positive impact on super-large enterprises in the top 75th percentile of asset size. Mechanism tests indicate that financial risk constraints serve as the core channel: the policy significantly reduces the leverage ratios of small and medium-sized enterprises, while in super-large enterprises, financial risks exert a significant negative influence on internal control. This study provides crucial evidence for understanding the conditional effectiveness of “harsh laws and stern punishments” style regulation, offering important theoretical and practical implications for advancing differentiated regulation and optimizing capital market governance.
internal control, legal liability, difference-in-differences
Copyright (c) 2026 Jiayi Shi (Author) https://creativecommons.org/licenses/by/4.0
ARIMA-GARCH and ARIMA-EGARCH-t Models in Fitting and Forecasting Volatility of the Shanghai Composite Index
Stock market volatility lies at the heart of risk management. Existing research indicates that A-share returns exhibit fat-tail characteristics, yet further refinement is required in balancing the model’s depiction of fat tails and asymmetry with predictive robustness. This paper utilises daily data from the Shanghai Composite Index spanning January 2010 to June 2025 as its sample. It compares the Autoregressive Integrated Moving Average-Generalised Autoregressive Conditional Heteroskedasticity (1,1)-Normal (ARIMA-GARCH (1,1)-Normal) and Autoregressive Integrated Moving Average-Exponential Generalised Autoregressive Conditional Heteroskedasticity (1,1)-t (ARIMA-EGARCH (1,1)-t) models in terms of volatility fitting and forecasting performance. Methodologically, the mean correlation was first filtered using ARIMA (2,0,2). Maximum likelihood estimation was employed for modelling, with performance evaluated through rolling forecasts. The research encompasses sequence stationarity testing and model construction, evaluating in-sample fit through metrics such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and log-likelihood values. Out-of-sample forecasting performance is measured using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Quantile Likelihood (QLIKE) values. The results indicate that the EGARCH-t model exhibits superior in-sample fit with a higher log-likelihood value (10,606.9), successfully capturing fat tails (ν=4.7236) and the leverage effect (γ=-0.0216). However, the GARCH model demonstrated greater robustness in out-of-sample forecasting, with lower error metrics such as MSE (0.000000) and RMSE (0.000523). Research indicates that model selection requires balancing goodness-of-fit with generalizability, while EGARCH-t is well-suited for capturing historical volatility mechanisms, GARCH holds greater predictive utility.
Shanghai composite index, GARCH model, EGARCH model, fitting, forecasting
Copyright (c) 2026 Qinming Yao (Author) https://creativecommons.org/licenses/by/4.0
Fintech Empowering the Real Economy: The Key Pathway to Driving High-Quality Development of China’s Economy
In the context of the transformation toward high-quality development of China’s economy in the new era, fintech, leveraging core technologies such as artificial intelligence and blockchain, reconstructs the financial ecosystem and serves as a key engine in addressing the dilemmas of “difficult financing, high costs, and narrow coverage” faced by small and medium-sized enterprises in the real economy. This article systematically delineates the internal mechanisms through which fintech empowers the real economy. By constructing a fintech index via integrated text mining methods and drawing on multi-dimensional empirical evidence, it reveals three core empowerment pathways-corporate financing, consumption investment, and resource allocation-while analyzing the specific roles of fintech in promoting economic development. It provides theoretical references for establishing a virtuous cycle of “technology-finance-industry” and holds strategic significance for cultivating new quality productive forces.
fintech, real economy, high-quality development
Copyright (c) 2026 Xinze He (Author) https://creativecommons.org/licenses/by/4.0
Consumption Behavior of the Silver-Haired Population and Market Demand Prediction
With the acceleration of global population aging, the silver economy has emerged as a crucial engine driving social and economic development. This study, based on market data for China’s silver economy from 2016 to 2024, constructs a multifaceted statistical modeling system. From dual perspectives of society and individuals, it uncovers the developmental patterns and market characteristics of the silver economy. Multiple linear regression, K-Means clustering, and random forest methods are employed to form a multidimensional analytical framework, overcoming the limitations of traditional studies that rely on single methodologies. The findings reveal that the scale of the silver economy market is positively correlated with the digital economy market size and the national proportion of the elderly population, while negatively correlated with the year-end number of participants in basic medical insurance. Similarly, from an individual perspective, the silver economy market scale is positively correlated with residents’ health literacy levels and per capita disposable income. For health-oriented consumer groups, emphasis should be placed on strengthening the layout of medical and health product lines; for era-oriented consumer groups, age-appropriate content marketing strategies need optimization; and for market-oriented consumer groups, the supply of cost-effective goods should be enhanced. Health needs and age-appropriate services are core drivers of elderly consumption behavior, suggesting that enterprises should prioritize layouts in these areas. The digital economy and the proportion of the elderly population are key drivers of the silver economy, whereas basic medical insurance exhibits a negative effect by suppressing demand for market-oriented services.
silver economy, cluster analysis, random forest, multiple linear regression
Copyright (c) 2026 Yuhang Li, Chengyu Lai, Siqi Liao (Author) https://creativecommons.org/licenses/by/4.0
Study on the Differentiated Impact of Social Media Emotion Orientation on Corporate ESG Performance
With the popularization of ESG concepts in the capital market, the impact mechanism of social media on corporate ESG performance has become a current research highlight. This article aims to systematically review the relevant research in the past 5 years, from the perspective of the impact mechanism of positive and negative social media coverage on ESG performance, summarize the differentiated paths of social media on ESG ratings and corporate ESG practices in different industries (energy, finance, etc.) and analyze the limitations of existing research in areas such as “insufficient cross industry comparison” and “lack of long-term impact tracking”.Research has found that the social media focuses on the environmental impact of high-risk industries (such as energy), while the financial industry emphasizes social and governance supervision. In the future, it is necessary to further explore the dynamic interactive mechanism of “media-public-enterprise” in the era of social media, in order to provide theoretical reference for optimizing corporate ESG.
social media, ESG dimension, heterogeneity analysis
Copyright (c) 2026 Xiaowan Huang (Author) https://creativecommons.org/licenses/by/4.0
Optimization of the Tax Business Environment and Corporate Green Transformation: Empirical Evidence from the Tax “Streamlining Administration and Delegating Power” Reform
This paper employs the pilot reform of the “streamlining administration and delegating power” in the tax system as a quasi-natural experiment. Drawing on data from A-share listed companies spanning 2013-2023, it investigates the impact of optimizing the tax business environment on corporate green innovation. The findings reveal that optimizing the tax business environment significantly enhances the level of corporate green innovation. This conclusion holds robust after a series of robustness tests, including parallel trends tests, placebo tests, and controls for contemporaneous policy interferences. Mechanism analysis indicates that optimizing the tax business environment promotes corporate green transformation through two pathways: alleviating financing constraints and reducing agency costs. Heterogeneity analysis further demonstrates that this policy effect is more pronounced in state-owned enterprises, non-heavy polluting enterprises, and regions with higher levels of economic and financial development. This study provides important theoretical foundations and policy implications for deepening the “streamlining administration and delegating power” reform and optimizing the tax business environment to stimulate corporate green innovation momentum.
tax business environment, “streamlining administration and delegating power” reform, green innovation, financing constraints, agency costs
Copyright (c) 2026 Yingxin Yang (Author) https://creativecommons.org/licenses/by/4.0
Research on Stock Realized Volatility Prediction Using Multi-Models Incorporating International Macro Factors
Predicting realized volatility (RV) is crucial for asset allocation and risk management. Existing multi-factor research on predicting realized volatility in stocks has rarely incorporated international macroeconomic factors, and the comparison of machine learning models remains limited. This paper constructs a multi-factor framework combining technical and international macroeconomic factors using monthly data from the 50 most liquid stocks in the CSI 300 Index from 2018 to 2024. Three models-ridge regression, extreme gradient boosting (XGBoost), and a multilayer perceptron (MLP)-are used to predict the next period's RV. Results show that incorporating international macroeconomic factors improves the forecast accuracy of all models, with machine learning performing even better on the test set (R²≈0.43). The study finds that incorporating international macroeconomic factors significantly improves model forecast accuracy, with machine learning outperforming linear regression models. This research provides a reference for selecting methods for volatility forecasting in different market scenarios and offers theoretical insights for investors, asset pricing, and risk management.
realized volatility, multi-factor model, linear regression, machine learning, volatility forecasting
Copyright (c) 2026 Jiarui He (Author) https://creativecommons.org/licenses/by/4.0