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 Submissions should present original, unpublished research.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.
Design of Lightweight Vision System for Agricultural Drones: Real-time Identification of Crop Diseases under Complex Lighting Conditions
Global food security is facing multiple pressures such as shrinking arable land and extreme weather, driving agriculture towards high precision and intelligence. Unmanned aerial vehicles (UAVs), as an aerial monitoring platform, provide important technical support for crop disease monitoring with their mobility and imaging capabilities. However, the complex lighting conditions in the field have led to a decline in image quality, and the limited computing power of the embedded platform has severely restricted the accuracy and real-time performance of disease identification. For this reason, this paper systematically reviews the latest progress in lightweight visual model design and light enhancement methods. In terms of model lightweighting, channel pruning, quantization techniques and knowledge distillation strike an effective balance between accuracy and efficiency; In terms of illumination enhancement, from traditional Retinex algorithms to physically guided generative adversarial networks, there is a significant improvement in image quality and model robustness. Future research could focus on areas such as polarization imaging and federated learning to further enhance the generalization ability and reliability of the system in complex environments.
agricultural drones, lightweight vision systems, crop disease identification, complex lighting robustness, edge computing deployment
Copyright (c) 2026 Yinlai Zhang (Author) https://creativecommons.org/licenses/by/4.0
Parkinson’s Disease: Calcium Dysregulation as a Multilevel Mechanism of Selective Neuronal Vulnerability, with Implications for Therapeutic Strategy
Parkinson’s disease (PD) is a prevalent neurodegenerative disorder characterised by the selective degeneration of dopaminergic neurons in the substantia nigra pars compacta. Despite extensive investigation into genetic and environmental risk factors, the mechanisms underlying this selective neuronal vulnerability remain incompletely understood. Increasing evidence suggests that calcium dysregulation represents a unifying pathogenic mechanism linking molecular, cellular, and systems-level processes in PD. This review synthesises evidence demonstrating how the intrinsic reliance of substantia nigra dopaminergic neurons on calcium-dependent autonomous pacemaking imposes a sustained metabolic burden that predisposes these neurons to degeneration. It examines how genetically diverse PD-associated mutations, including those affecting PINK1/Parkin, LRRK2, and GBA, converge on disrupted calcium homeostasis through distinct cellular pathways involving mitochondrial buffering, lysosomal calcium signalling, and organelle quality control. At the cellular level, chronic calcium dysregulation drives oxidative stress, α-synuclein aggregation, and impaired autophagy, forming a self-reinforcing cycle of neuronal dysfunction. These processes extend beyond individual neurons, promoting neuroinflammation and basal ganglia circuit reorganisation that ultimately underlie motor symptom expression. The review further discusses the therapeutic implications of this multilevel framework, highlighting why broad calcium channel blockade has shown limited clinical efficacy. It argues that future disease-modifying strategies will require greater molecular and temporal precision, as well as integration with systems-level neuromodulation approaches that reduce activity-dependent calcium and metabolic burden. Overall, calcium dysregulation emerges as a central integrator of vulnerability across biological scales in Parkinson’s disease, offering a coherent framework for understanding pathogenesis and guiding therapeutic innovation.
Parkinson’s disease, calcium dysregulation, selective neuronal vulnerability, dopaminergic neurons, α-synuclein pathology
Copyright (c) 2026 Weijie Lai (Author) https://creativecommons.org/licenses/by/4.0
A Discussion on the Similarities Between Mechanical Metamaterials and Soft Matter and the Possibilities for Their Combined Development
Mechanical metamaterials and soft matter, as frontier branches of materials science, extend the performance boundaries of conventional materials through artificial structural design and intrinsic responsive behavior, respectively. This paper briefly introduces these two fields and examines their similarities. It further discusses the possibility of their innovative integrated development as an approach to addressing certain limitations in each field, such as employing the stimulus responsiveness of soft matter to overcome the functional static nature of mechanical metamaterials, and utilizing the structural design paradigm of mechanical metamaterials to enhance the response speed and fatigue resistance of soft matter.
mechanical metamaterials, soft matter, similarity, integrated development
Copyright (c) 2026 Jiarui Zheng (Author) https://creativecommons.org/licenses/by/4.0
Applications of Machine Learning in Additive Manufacturing
The research aims to solve common challenges in additive manufacturing processes, including strong multi-parameter coupling, difficulty in suppressing defects, and unstable performance, while overcoming the limitations of traditional empirical trial-and-error and physics-based modeling approaches and advancing additive manufacturing toward intelligent, automated, and closed-loop upgrades. The methodology centers on machine learning’s data-driven paradigm, integrating various algorithms such as neural networks, random forests, and support vector machines. From the full life cycle of additive manufacturing, the study systematically explores technical pathways and application modes in process monitoring, defect control, parameter optimization, performance prediction, structural design, and material development. Results show that machine learning can effectively construct the “process–structure–performance–defect” mapping relationship in additive manufacturing. In online monitoring, defect identification accuracy exceeds 95%; process development cycles can be shortened from weeks to days, and new material R&D cycles from months to weeks. High-precision prediction of component mechanical properties is also achieved, with some models attaining R² values above 0.9. The conclusion is that machine learning provides a full-process data-driven optimization solution for additive manufacturing and effectively addresses many pain points of traditional methods. Current applications in this field still face challenges such as insufficient data quality and quantity and poor model interpretability. Future efforts should focus on physics-informed machine learning, digital-twin closed-loop control, and other directions, while advancing standardization, to promote the engineering implementation and scaled production of intelligent additive manufacturing and provide support for the development of high-end equipment, biomedicine, and other fields.
machine learning, additive manufacturing, process optimization, defect detection
Copyright (c) 2026 Zicheng Qu (Author) https://creativecommons.org/licenses/by/4.0
Limitations of AI-Generated Content and Strategies for Enhancement
With the rapid advancement of artificial intelligence (AI) technologies, tools such as Doubao and DeepSeek have become indispensable elements in everyday life. Many users turn to these AI platforms for secondary editing of images and videos or to generate visuals that align with specific textual descriptions. However, the outcomes frequently fall short of expectations: the generated content often bears little resemblance to the user’s prompts and is riddled with unacceptable flaws. To mitigate these issues and enable AI tools to produce satisfying results that meet given specifications, this paper explores the underlying principles of AI content generation. It proposes methods to enhance the accuracy of outputs and reduce inherent defects, concluding with a discussion of the study’s limitations.
AI-generated content (AIGC), limitations, enhancement strategies
Copyright (c) 2026 Weihang Gao (Author) https://creativecommons.org/licenses/by/4.0
Demand-Oriented Renovation Strategies for Public Spaces in University Dormitory Squares: A Case Study of the LZH Campus at North China University of Water Resources and Electric Power
As a key node in the campus public space system, the small squares in dormitory areas carry important functions for students’ informal learning, social interaction, and leisure relaxation, making them one of the significant public activity spaces on campus. As high-frequency daily public spaces frequently used by students, the current quality of university dormitory area small squares stands in sharp contradiction with students’ growing diversified and individualized needs. Based on demand theory, environmental behavior studies, participatory design, and other theoretical foundations, this study employs methods such as literature analysis, case studies, and field surveys to systematically investigate the existing problems in current dormitory small squares, including underutilized facilities, single-function layouts, insufficient vitality, and lack of humanistic care. The research shows that in the renovation process, priority must be given to responding to the common “basic needs” related to safety, shelter, and wayfinding, while strategically integrating and elevating the differentiated “expectation needs” concerning leisure, socialization, and aesthetic enhancement. The core lies in adopting a student demand-oriented approach and implementing four major strategies—functional compounding, flexible boundaries, natural integration, and smart empowerment—to construct a “dormitory living room” that promotes interaction and stimulates vitality. This study provides a theoretical framework and practical pathway for the “micro-renewal” of existing public activity spaces in university dormitory areas. It holds significant reference value for improving the quality of campus living environments and promoting student community integration.
demand-oriented, environmental behavior research, micro-renewal, public space renovation
Copyright (c) 2026 Jiaxuan Yu (Author) https://creativecommons.org/licenses/by/4.0
Unveiling the Mechanisms Between Writing Style and Academic Impact: A Multidimensional and Machine Learning Perspective
Writing style, as a key non-content factor influencing academic dissemination, remains underexplored in terms of its underlying mechanisms with scientific impact. Drawing on an explainable machine learning perspective, this study investigates the effects of multidimensional writing style features on citation behavior. 23,644 paper abstracts from the Web of Science (WoS) in the fields of computer science, management, and biomedicine were analyzed, extracting four categories of features: readability, emotional and subjective expression, lexical richness, and coherence of expression. These were integrated with 17 baseline indicators to construct an analytical framework. Through ablation experiments, feature importance analysis, and multiple regression, we systematically evaluated the independent contributions and directional effects of each style dimension. The results reveal that readability exerts the strongest predictive power among writing style dimensions and plays the most substantial role in citation behavior. Concise and clear expression significantly enhances citations. Additionally, positive emotional and subjective expressions contribute to improved dissemination outcomes, whereas overly complex vocabulary and excessive cohesion may exert negative effects. This study provides empirical evidence for understanding the relationship between writing style and academic impact, offering practical implications for scholarly writing, journal peer review, and research management.
writing style, scientific impact, academic writing, machine learning, science of science
Copyright (c) 2026 Ruixuan Yu (Author) https://creativecommons.org/licenses/by/4.0
Commonsense-Guided VLMs for 2D Fully UnobservedObject Detection
This paper focuses on the task of 2D fully unobserved object detection, which requires inferring the presence and spatial distribution of targets that are entirely absent from the visible frame. Despite the potential of Vision-Language Models (VLMs), they often lack the spatial precision required for fine-grained 2D reasoning, while alternative diffusion-based methods incur prohibitive computational costs. To address these challenges, we propose CIUL, a novel reasoning framework that synergizes VLMs with structured prior knowledge through two core innovations: (1) Object-Oriented Commonsense Integration, which constructs an automated knowledge base to provide robust semantic constraints via typical spatial arrangements; and (2) Lightweight In-Context Learning, a paradigm that enables the model to adaptively refine its reasoning logic for unobserved regions using local visual cues from a single image without extensive retraining. Experimental results on the NYU Depth V2 benchmark demonstrate that our approach significantly out-performs existing baselines across key metrics, including 2D Region-wise Accuracy and Normalized Cross-Entropy, effectively bridging the gap between low-level perception and high-level commonsense reasoning.
unobserved object detection, vision-language models, commonsense reasoning, in-context learning, spatial-semantic distribution
Copyright (c) 2026 Ye Liu (Author) https://creativecommons.org/licenses/by/4.0
Preparation Technologies and Applications of Graphene-Based Paper Electronic Devices
Graphene-based paper electronic devices achieve the synergistic unification of high performance and low cost by virtue of graphene's unique advantages of ultra-high carrier mobility and excellent flexibility, and the characteristics of paper-based materials such as wide availability and biodegradability as well as a cost only 1/10,000 that of crystalline silicon, becoming a core research direction in the field of green flexible electronics. This paper systematically reviews mainstream preparation technologies such as paper-based laser-induced graphene (LIG), inkjet printing, and chemical vapor deposition (CVD), focusing on the application progress of devices in sensors, flexible devices, energy, and optoelectronics. Finally, it analyzes current technical bottlenecks and looks forward to future optimization directions, providing references for academic research and industrialization transformation in this field.
graphene, paper electronic devices, preparation technologies, flexible electronics, application research
Copyright (c) 2026 Jiabao Zhang (Author) https://creativecommons.org/licenses/by/4.0
A New Paradigm of Active Defense System Based on Large Language Models
With the continuous evolution of cyber attack technologies and the persistent expansion of attack scales, traditional passive defense systems are confronting unprecedented challenges. As a pivotal breakthrough in the field of artificial intelligence, Large Language Models (LLMs) offer a novel technical pathway for the construction of active defense systems. This paper systematically reviews LLM technologies tailored to active defense systems. Firstly, it briefly delineates the fundamental principles of LLMs. Secondly, it conducts an in-depth exploration of the core technical mechanisms of LLMs and focuses on analyzing their unique advantages in the defense domain. Subsequently, it elaborates on the technical implementation and superiorities of LLMs in key application scenarios, including cyber attack forensics, automated incident response, and code auditing. Finally, it examines the critical challenges encountered by LLMs in the field of cyber security and prospects future research directions. This paper profoundly reveals the core pathway and implementation mechanism underlying the paradigm shift from “passive response” to “active defense” in the cyber security domain driven by LLMs, thereby providing systematic technical references for academic research and engineering practice in this field.
large language models (LLMs), active defense, cyber security, threat detection, intelligent security
Copyright (c) 2026 Shuijing Xiang (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Semantic Disambiguation Techniques in Dialogue Systems
With the rapid advancement of Artificial Intelligence (AI), human-computer dialogue systems have emerged as a prominent application in the AI field, where semantic disambiguation plays a critical role. This paper provides a comprehensive survey and analysis of semantic disambiguation techniques in Natural Language Processing. It not only examines the strengths and limitations of traditional approaches but also investigates state-of-the-art methods that are shaping the field. Through a systematic review, this study outlines the overall development trajectory of disambiguation technologies and identifies key challenges that remain unresolved. While significant breakthroughs have been achieved in semantic disambiguation, this review reveals that substantial challenges persist in areas such as contextual understanding, cross-domain adaptation, and real-time processing. Overall, this paper emphasizes the growing importance of integrating knowledge-driven and data-driven approaches, highlighting the potential of hybrid models that combine linguistic rules, contextual embeddings, and large language models. The findings aim to offer valuable insights for future research directions and practical implementations in evolving dialogue systems.
human-computer dialogue systems, artificial intelligence, word sense disambiguation, natural language processing
Copyright (c) 2026 Xinyue Liu (Author) https://creativecommons.org/licenses/by/4.0
Comprehensive Influence of Meteorology on Air Quality
In view of the increasingly serious air pollution problem in China in recent years, especially the significant threat of PM2.5 to public health, this study focuses on the key driving factor of meteorological conditions, aiming to reveal the specific impact path of PM2.5 concentration in Beijing, and provide a solid theoretical basis and practical guidance for scientific formulation of provention and control strategies and effective response to climate change. Spearman correlation analysis, multiple linear regression and stepwise regression were used in the empirical analysis. The results showed that the air temperature was the most important meteorological factor affecting the concentration of PM PM. There was a significant negative correlation between the air temperature and the concentration of PM Yu. (Standardized coefficient Beta = -0. 691, p < 0.05), and the concentration of PM °C. Decreased by 0. 975 μg/m ³ when the air temperature increased by 1 °C. In addition, it is found that there is a serious multicollinearity problem among the meteorological factors, which may be the main reason why other factors except temperature are not significant in the regression model. The final regression model was significant (F = 4.941, p = 0.002). This study provides a quantitative basis for understanding the impact of meteorological conditions on air quality, and suggests that temperature should be used as a key indicator for short-term pollution warning, while providing a methodological reference for subsequent studies to overcome the multicollinearity problem.
PMPM., meteorological factors, correlation analysis, multiple linear regression, stepwise regression
Copyright (c) 2026 Haoran Shuai (Author) https://creativecommons.org/licenses/by/4.0
The Application and Progress of Multimodal Models in the Medical Field
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
Single-cell RNA sequencing reveals the dynamic evolution of the immune microenvironment in coronary artery atherosclerosis
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
Research on the Construction of “Micro-Majors” in Universities under the “Double Thousand” Plan to Enhance Employability of College Students
In the context of industrial upgrading, rapid development of artificial intelligence, and the digital economy, college graduates face the dual challenges of “major–job” mismatch and insufficient interdisciplinary competencies. In response to the national “stable employment” strategy, the Ministry of Education launched the “Double Thousand” Plan for Enhancing College Students’ Employability in 2025, aiming to establish 1,000 “micro-majors” and 1,000 vocational skills training courses within three years. Through a “small credits, high focus, refined curriculum” approach, the plan helps students quickly address knowledge and skill gaps to achieve high-quality and full employment. This paper employs bibliometric and content analysis methods to review core domestic and international literature from 2015 to 2025. Domestic research on micro-major construction mainly focuses on engineering and business disciplines, proposing two main models—“university-led” and “MOOC-led”—and emphasizing the critical role of university–enterprise collaboration. International experiences highlight interdisciplinary integration, blended online–offline teaching, and international cooperative education, providing diverse paradigms for micro-major development. However, existing studies still exhibit blind spots, including insufficient attention to humanities and social sciences, uneven regional resource allocation, and the absence of quality evaluation systems. Future research should break disciplinary boundaries, address these issues by exploring curriculum domain construction systems, resource allocation systems, and quality evaluation systems for micro-majors, while promoting their “globalization” to contribute Chinese solutions to higher education reform and modernization of social governance.
Double Thousand Plan, micro-major, employability of college students
Copyright (c) 2026 Yiran Wang (Author) https://creativecommons.org/licenses/by/4.0
How to credibly investigate causal relationships is a central issue in contemporary econometrics. This paper systematically reviews the logical chain of modern causal inference: from the paradigm shift of the credibility revolution, to the counterfactual framework for defining causality, to the assumptions and limitations of OLS regression, then to the concept of identification as the bridge connecting data to truth, and finally to the art of research design. At each stage, a study on the “Citation Trap” serves as a running case example to concretize abstract methodological principles. The analysis shows that a credible causal inference study begins with a clear “what if” question, approximates the ignorability assumption through control variables and fixed effects, and constructs a complete chain of evidence by ruling out alternative explanations and testing mechanism implications. The conclusion is that the essence of econometrics lies not in complex models or techniques, but in rigorous research design that makes assumptions plausible and conclusions robust to scrutiny.
causality, credibility revolution, counterfactual framework, identification, research design
Copyright (c) 2026 Yawen Zhan (Author) https://creativecommons.org/licenses/by/4.0
The ‘Carbon Premium’ in the Stock Market: Empirical Evidence on Climate Transition Risk Pricing
As green development gradually gains momentum and global climate governance accelerates, pricing climate transition risks has become an important issue. Based on data from Chinese listed companies from 2010 to 2020, this paper constructs indicators for corporate carbon emission intensity and climate transition risk exposure. Using Fama-MacBeth regression and constructing carbon risk factors, it tests the existence of the “carbon premium” in the stock market and discusses its influencing factors and transmission mechanisms. The research findings are as follows: First, the stock market exhibits a significant “negative carbon premium”. Second, the “carbon premium” is influenced by industry differences, being more pronounced in high-carbon industries. To improve market pricing efficiency and guide effective capital allocation, this paper recommends that the government improve the standardized climate information disclosure system.
carbon premium, climate transition risk, carbon emission, asset pricing
Copyright (c) 2026 Yiya Du (Author) https://creativecommons.org/licenses/by/4.0
Human–AI Co-Creation in Film Production: Media Innovation and Aesthetic Governance in AIGC-Supported Filmmaking
Generative artificial intelligence (AIGC) is rapidly transforming film production by introducing new modes of media innovation and aesthetic experimentation. Rather than functioning merely as a technical substitute, AIGC reconfigures creative workflows through diverse forms of human–AI co-creation. This paper examines how aesthetic decision-making, creative control, and cultural meaning are negotiated within AIGC-supported film production. Drawing on a comparative case study of the Chinese AI-assisted short film The Cipher in the Notes and the Singapore–Malaysia co-produced feature film Madame Ching, the study analyzes two contrasting production pathways: constraint-based generation for historical reconstruction and prompt-driven generation for stylized visual innovation. Using textual analysis and production documentation, the paper compares the cases across four dimensions: stages of technological intervention, human–AI division of labor, configuration of aesthetic control, and cultural–semantic calibration. The findings reveal that AIGC enables novel forms of visual experimentation and accelerates aesthetic iteration, while simultaneously shifting the director’s role toward the governance of generative aesthetics. However, both cases expose shared challenges related to realism, cultural specificity, and ethical governance. By foregrounding media innovation and aesthetic processes, this study contributes to ongoing discussions on creative authorship, visual culture, and responsible AI adoption in contemporary media production.
AIGC, media innovation, film aesthetics, human–AI co-creation, creative governance
Copyright (c) 2026 Shiyu Huang (Author) https://creativecommons.org/licenses/by/4.0
A New Interpretation of Blossoms from the Perspective of Thirdspace Theory: On the Reception and Dissemination of Jin Yucheng’s Blossoms
With the television series Blossoms directed by Wong Kar-wai sparking a widespread cultural phenomenon, the spatial narrative strategies of Jin Yucheng’s original novel and its effects in cultural dissemination have garnered significant attention in academic circles. Grounded in the “Thirdspace” theoretical framework from cultural geography, this study integrates textual analysis with empirical research to examine the strategies of spatial construction in the novel Blossoms and its pathways of dissemination. At the textual level, the author employs non-traditional narrative techniques through illustrations to construct a spatial landscape characterized by synchronicity and diachronicity, thereby establishing an intertextual relationship between literary fiction and urban geography. At the level of dissemination, the spatial landscape of Blossoms functions as a cultural medium: audiences, through accessible landscape symbols in real physical spaces, activate nostalgic imaginations of Shanghai, thereby constituting the city as a vast site of nostalgia. By analyzing the internal mechanisms through which literary space is transformed into a space of cultural memory, this article seeks to provide a reasoned explanation for this cross-disciplinary and cross-cultural dissemination phenomenon.
urban culture, Blossoms, Shanghai, Thirdspace, nostalgic narrative, hypertext
Copyright (c) 2026 Zihan Ni (Author) https://creativecommons.org/licenses/by/4.0
Digital Trade and the International Division of Labor: A Literature Review and Analytical Framework from a Global Value Chain Perspective
Driven by the rapid expansion of the digital economy, digital trade has emerged as a pivotal force restructuring international trade and reconfiguring global production networks. Distinct from traditional models centered on trade in goods, digital trade-facilitated by cross-border data flows, digital services, and platform-based transactions-has profoundly reshaped the international industrial division of labor and Global Value Chain (GVC) governance. Based on a systematic review of existing literature, this paper synthesizes key perspectives on the mechanisms through which digital trade reshapes industrial specialization and the resulting governance transformations. The analysis reveals that by reducing transaction costs, facilitating technology diffusion, and amplifying platform network effects, digital trade drives the evolution of international specialization from inter-industry division toward task-based and functional models. Consequently, GVC governance logic is shifting from control-oriented models centered on ownership and contracts toward digital governance based on rules, platforms, and data factors. However, challenges such as constraints on cross-border data flows, value distribution imbalances under platform dominance, the digital divide, and the fragmentation of international rules pose risks to the stability of digital value chains. Finally, this paper discusses future research directions and policy implications to support digital trade governance and the sustainable development of global value chains.
digital trade, international division of labor, global value chains, value chain governance, digital platforms
Copyright (c) 2026 Guangyi Wang (Author) https://creativecommons.org/licenses/by/4.0
Research on the Legal Coordination Dilemmas and Countermeasures in Cross-Border Securities Trading Regulatory Cooperation: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area
The rapid flow of global capital and the widespread adoption of digital trading infrastructure have driven exponential growth in the scale of cross-border securities transactions. However, three major legal conflicts-fragmentation of sovereign regulatory authority, misalignment of data export standards, and asymmetry of enforcement tools-have led to difficulties in accessing audit working papers, gaps in investor remedies, and frequent occurrences of regulatory arbitrage. Using the Guangdong-Hong Kong-Macao Greater Bay Area as a case study, this article applies comparative law and game theory methods to systematically examine the root causes of obstacles in legal coordination for cross-border securities transactions. It proposes progressive institutional solutions, including a negative list mechanism for data exports, model rules for cross-border product sales, and the establishment of an Asian Securities Regulatory Court, offering a replicable and scalable Chinese pathway from regional experimentation to global rule output.
cross-border securities transactions, financial regulation, legal coordination, Guangdong-Hong Kong-Macao greater bay area
Copyright (c) 2026 Zhangxiao Bao (Author) https://creativecommons.org/licenses/by/4.0
Chemical Biology Approaches in Drug Target Discovery: From Affinity Probes to Thermal Stability Analysis
Drug target discovery is a critical step in new drug research and development, and chemical biology techniques and theories provide powerful tools to advance this process. This review summarizes two major categories of drug target discovery methods—affinity probes and thermal stability analysis—and their recent research progress, highlighting the role of chemical biology approaches. It also aims to deepen the author’s understanding of future directions for advanced study in chemical biology. The review focuses on activity-based protein profiling (ABPP) and affinity-based protein profiling (AfBPP) as representative affinity probe technologies, as well as label-free thermal stability techniques including DARTS, CETSA, and TPP, with extensions to related developments. Both categories of drug target discovery technologies have distinct advantages, and their combined use enables a complete workflow from theory to practice and from discovery to validation. Chemical biology methods play a positive role in drug target identification.
chemical biology, drug target discovery, affinity probe, thermal stability analysis, ABPP, AfBPP, DARTS, CETSA, TPP
Copyright (c) 2026 Xinning Kang (Author) https://creativecommons.org/licenses/by/4.0
Noninvasive Brain Stimulation Techniques for the Recovery of Unilateral Visual Impairment After Stroke: A Systematic Review
According to data from the Global Stroke Report 2025, stroke accounted for 10.72% of all deaths worldwide in 2021. Monocular visual field defects represent one of the most prevalent and severe cognitive impairments following stroke, with a prevalence rate reaching up to 30%. Current conventional treatments for monocular visual field defects yield limited efficacy, whilst non-invasive brain stimulation techniques are advancing rapidly. With the continuous development of these non-invasive brain stimulation technologies, an increasing body of research indicates that they may offer certain restorative effects for post-stroke hemianopia. To synthesize the effects of noninvasive brain stimulation techniques in the recovery of unilateral visual impairment after stroke. A comprehensive search was conducted in six databases, including PubMed, Web of Science, Cochrane Library, CNKI, Wan fang, and VIP, as of January 3, 2026. The articles were screened based on the exclusion criteria. The included articles were analyzed based on their impact on visual function improvement, quality of life, and adverse effects. Sixteen studies were analyzed, demonstrating that noninvasive brain stimulation significantly improved visual function and quality of life for patients. Additionally, adverse effects were minimal. Noninvasive brain stimulation technology shows promise in the recovery of unilateral visual impairment after stroke. However, further multi-center, large-sample, high-quality randomized controlled trials are necessary to verify its efficacy and safety.
noninvasive brain stimulation, stroke, unilateral visual impairment
Copyright (c) 2026 Xinyi Zhang, Yuxia Ma (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Annual Report Tone on Corporate Financing: A Case Study of Tesla, Inc.
Corporate annual reports serve not only as key carriers of financial data but also as critical tools for management to engage in impression management through linguistic strategies. This study aims to deeply explore the impact of annual report tone, i.e., the emotional sentiment in the Management Discussion and Analysis section, on corporate financing costs. Taking Tesla, Inc., a representative multinational company, as the research subject, the study quantifies the positivity of tone in Tesla’s annual reports from 2019 to 2024 using the Loughran-McDonald financial sentiment word list. For the first time, it combines this with contemporaneous changes in the company’s bond credit spreads and bank loan interest rates to comparatively analyze the pathways through which tone differences affect financing costs. The results show that when management adopts excessively optimistic tone without sufficient performance support, it exacerbates information asymmetry, prompting creditors to demand higher risk premiums and thereby increasing financing costs. In contrast, a prudent and neutral tone helps build market trust and reduce financing costs. This study provides reference for companies in selecting linguistic strategies during information disclosure and offers investors a new perspective for interpreting textual information in annual reports.
annual report tone, impression management, financing costs, information disclosure
Copyright (c) 2026 Zhujun Zhang (Author) https://creativecommons.org/licenses/by/4.0
U-Shaped Associations Between Metabolic Indices and Depression in Middle-aged and Older Adults: A Data-Driven Cross-Sectional Study from CHARLS
Objective: While the relationship between metabolic dysregulation and depression is well-established, its specific nature in the elderly remains obscure, partially due to the limitations of traditional metrics like BMI in distinguishing visceral fat. This study aimed to systematically investigate the associations between novel composite metabolic indices and depressive symptoms in community-dwelling Chinese middle-aged and older adults, with a particular focus on non-linear threshold effects. Methods: This cross-sectional analysis utilized nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS), comprising 10,195 participants aged ≥45 years. Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10). Four novel indices were calculated: China Visceral Adiposity Index (CVAI), Lipid Accumulation Product (LAP), Metabolic Score for Insulin Resistance (METS-IR), and Metabolic Score for Visceral Fat (METS-VF). Besides conventional statistics, we employed multivariable logistic regression to decode the independent associations, with a particular focus on identifying non-linear threshold effects. Results: After adjusting for sociodemographic, lifestyle, and clinical confounders, CVAI, LAP, METS-IR, and METS-VF allexhibited significant U-shaped nonlinear associations with depressive symptoms. Contrary to linear expectations, moderate levels of these indices (typically corresponding to the third quartile) were associated with the lowest risk of depression, conferring a protective effect that diminished or disappeared at extremely high or low levels. Conclusion: In Chinese community-dwelling middle-aged and older adults, moderate visceral adiposity or insulin resistance may be associated with a reduced risk of depression, demonstrating a U-shaped threshold effect. These findings underscore the value of utilizing composite metabolic indices as computational biomarkers for precise mental health screening in aging populations.
digital economy, labor market structure, skill-biased technological progress
Copyright (c) 2026 Yajie Liang (Author) https://creativecommons.org/licenses/by/4.0
Corporate ESG Performance and Audit Fees: Evidence from Chinese A-Share Listed Companies
Corporate ESG performance has emerged as a core indicator for evaluating a firm's sustainability and risk profile. Given the growing emphasis on risk-based approaches to audit pricing, how auditors assess the risks associated with corporate ESG performance remains a critical issue that warrants in-depth exploration. This study reveals that superior corporate ESG performance is significantly associated with lower audit fees. Mechanism tests show that strong ESG performance effectively mitigates corporate operating risk. This mitigation significantly reduces the audit risks perceived by auditors, which in turn optimizes audit procedures and ultimately lowers audit fees. Heterogeneity analysis reveals that this negative relationship is more pronounced in firms audited by non-Big Four accounting firms and those that do not hold shares in financial institutions. This study provides novel empirical evidence for audit pricing mechanisms from an ESG perspective and enriches the existing literature on the economic consequences of corporate ESG performance.
ESG performance, audit fees, operating risk, mechanism test
Copyright (c) 2026 Yaofei Zhu (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Negative Public Sentiment on Stock Price Volatility: A Case Study of Qiaqia Food
In the digital economy era, the deep integration of digital technologies and social media has enabled negative public sentiment to break through spatiotemporal constraints, with its dissemination speed and impact scope expanding exponentially. Its diffusion intensifies market information asymmetry, triggers short-term violent fluctuations in stock prices, and has become a critical risk variable affecting the stable operation of the capital market. Previous traditional studies have mostly relied on linear assumptions to investigate the relationship between negative public sentiment and stock prices, failing to fully reveal the complex dynamics potentially induced by investors’ irrational behavior. To address this gap, this study takes Qiaqia Food Co., Ltd., a representative enterprise in China’s food industry, as the research object. Through manually collected data and empirical analysis methods, it innovatively constructs a quadratic polynomial regression model to deeply explore the nonlinear mechanism by which negative public sentiment affects stock price volatility and to empirically examine the moderating role of media authority attributes. The following conclusions are drawn: there exists a significant inverted U-shaped relationship between the intensity of public sentiment and cumulative abnormal returns (CAR). Moreover, after controlling for sentiment intensity, reports from authoritative media can effectively buffer market panic. The theoretical contributions of this study are reflected at two levels. First, by constructing a quadratic polynomial model, it breaks through the previous analytical paradigm that relied on linear relationships, providing a basis for quantifying the dynamic changes in the impact of public sentiment. Second, it analyzes the moderating effect of media authority attributes, laying a solid foundation for subsequent in-depth research.
negative public sentiment, stock price volatility, nonlinear relationship, quadratic polynomial model, media authority attribute
Copyright (c) 2026 Hexiang Jia (Author) https://creativecommons.org/licenses/by/4.0
Research on the Development and Optimization of Financial Investment in the Context of Big Data
With the deep integration of big data and artificial intelligence technologies, the field of financial investment is undergoing a profound paradigm shift. This study takes the practical performance of the domestic large model DeepSeek in the “Alpha Arena” AI Trader Competition as an example, and through literature review and empirical analysis, systematically explores the technical logic and optimization pathways of intelligent financial investment. The study finds that the DeepSeek model, by integrating multi-source heterogeneous data, employing nonlinear deep learning algorithms, and implementing dynamic risk control mechanisms, has achieved a fundamental transformation of investment strategies from experience-driven to data-intelligence-driven. Based on this, the present study proposes a five-dimensional optimization framework encompassing data factor circulation, enhancement of algorithm transparency, transformation of market structure, reconstruction of regulatory adaptability, and full-cycle risk management, aiming to provide theoretical support and practical guidance for the digital transformation of financial institutions.
big data, financial investment, DeepSeek, artificial intelligence, optimization pathway
Copyright (c) 2026 Shang Chen (Author) https://creativecommons.org/licenses/by/4.0
Analysis of the Impact of Changes in Population Age Structure on Educational Fiscal Expenditure: Research Based on Provincial Panel Data from 1990 to 2023
Against the backdrop of a deepening aging population, the allocation of public finances between pensions and education has become increasingly prominent. Using panel data from 31 Chinese provinces between 1990 and 2023, this study employs a two-way fixed effects model and a mediation effect model to examine how population aging affects local government education expenditure, its mechanisms, and spatiotemporal heterogeneity. The results indicate that population aging significantly suppresses education spending: a one-percentage-point increase in the old-age dependency ratio leads to an average reduction of about 453,000 yuan in provincial education expenditure. This effect is mainly driven by the fiscal crowding-out of social security spending, which accounts for up to 49% of the mediation effect. Regional heterogeneity is notable, with western China being a “high-risk area,” while the eastern region mitigates the adverse impact through higher urbanization and stronger fiscal capacity. Temporal analysis shows that the negative effect intensified significantly after the 2010s, aligning with rising pension insurance pressures. Policy recommendations are proposed to help address aging challenges and ensure sustainable educational investment.
population age structure, education finance, population aging intergenerational competition, mediating effect
Copyright (c) 2026 Menglin Yang (Author) https://creativecommons.org/licenses/by/4.0
A Report on E-commerce Business Model of Pinduoduo
Pinduoduo, a leading Chinese e-commerce company founded in 2015, has achieved rapid growth through its innovative social commerce model, which leverages group buying and social media integration to offer competitive prices and foster community engagement. This report provides a comprehensive analysis of Pinduoduo’s business model, highlighting its customer value proposition, revenue streams, competitive landscape, and market strategy. It emphasizes Pinduoduo’s strengths, including its user-friendly interface, low entry barriers for sellers, and successful penetration of rural markets. The report also identifies key opportunities for future growth, recommending improvements in user experience, product range expansion, supply chain management, marketing efforts, and sustainability practices to ensure continued success in China’s dynamic e-commerce market.
e-commerce, Pinduoduo, business model, market strategy
Copyright (c) 2026 Jing Xiao (Author) https://creativecommons.org/licenses/by/4.0