Pinnacle Academic Press Proceedings Series
Pinnacle Academic Press Proceedings Series (PAPPS) is an international, refereed, double-blind peer-reviewed, open-access journal that compiles the proceedings of academic conferences, workshops, and symposia across various disciplines. The series highlights groundbreaking research, emerging trends, and innovations presented by scholars, researchers, and practitioners in their respective fields. With a focus on high-quality contributions, the series serves as a valuable resource for the academic community, facilitating the dissemination of knowledge, fostering collaboration, and offering insights into the latest developments in science, technology, and academia.
Attention-Based Multimodal Emotion Recognition for Fine-Grained Visual Ad Engagement Prediction on Instagram
This paper presents a novel Attention-Based Multimodal Framework (ABMF) for emotion recognition and fine-grained engagement prediction in Instagram advertisements. Traditional approaches to advertisement assessment rely primarily on unimodal analysis and fail to capture the nuanced relationship between emotional content and engagement behaviors. The proposed framework integrates visual, textual, and metadata features through cross-modal attention mechanisms that dynamically identify emotionally salient components across modalities. We construct and annotate the Instagram Advertisement Emotion Dataset (IAED) containing 10,000 sponsored posts with valence-arousal ratings and engagement metrics. Experimental results demonstrate that ABMF achieves significant improvements over state-of-the-art baselines, with 12.1% reduction in valence MAE and 7.1% improvement in engagement prediction MAP. The research reveals distinct relationships between emotional dimensions and specific engagement behaviors: high arousal content generates 78.6% higher share rates while positive valence drives 62.7% more likes compared to negative content. The findings provide quantifiable insights for optimizing emotional content in advertisements based on campaign objectives. The cross-modal attention mechanism enables precise identification of engagement-driving features, offering Instagram advertisers a computational approach to predict and enhance user engagement through targeted emotional content design.
Copyright (c) 2025 Xin Lu, Zihan Li (Author) https://creativecommons.org/licenses/by/4.0
Research on the Performance of Zhongding Group's Acquisition of German AMK Under Strategic Transformation Objectives
People's demand for cars has been increasing in recent years, in response to the country's call to save non-renewable resources. The sustainable development goals promote the rapid development of the new energy automobile industry, which provides new opportunities for the auto parts industry. Chinese parts and components enterprises have insufficient competitiveness in the high-end product market, high labour costs in the low-end product market, weak strength and insufficient research and development capabilities, so to take a place in the competition of different product markets and gain a larger market share, mergers and acquisitions (M&A) are an important way to learn about advanced productivity, market competitiveness and brand advantages. In this context, we evaluate the performance of the strategic behaviours of Zhongding's M&A of AMK in Germany, hoping that it can provide overseas M&A experience for domestic companies in the same industry. In this paper, we take the M&A of AMK as the research object, collate relevant literature at home and abroad, analyse the development trend of China's auto parts on the basis of the actual situation of both parties to the merger and acquisition, briefly analyse the motivation of the merger and acquisition and the process of the merger and acquisition, and combine the company's part of the financial indicators before and after the merger and acquisition, and use the method of financial and non-financial indicators to carry out a comprehensive analysis of the performance of M&A. Finally, the study concludes that the M&A has enlarged the operation scale of Zhongding, which has given the company a new growth point in revenue and further promoted the company's concept of diversified operation. However, the company's profitability did not reach the expected improvement, and the operating income and gross profit margin of air suspension system continued to decline. Due to the high production cost of air suspension system, it is unable to enter the low-end market and its service life is not long, which leads to the unstable development ability of the company. In this regard, it is proposed to reduce the production cost, popularise the middle and low-end automobile market, and prolong the service life, so as to enable the company to develop in a sustainable manner. It is a reference for other enterprises in the industry to carry out M&A.
Copyright (c) 2025 Ziye Li (Author) https://creativecommons.org/licenses/by/4.0
Application of Artificial Intelligence in Inventory Decision Optimization for Small and Medium Enterprises: An Inventory Management Strategy Based on Predictive Analytics
Small and medium enterprises (SMEs) face significant challenges in inventory management due to limited resources and dynamic market conditions. This research investigates the application of artificial intelligence technologies to optimize inventory decisions for SMEs using predictive analytics. The study develops a comprehensive AI-driven inventory management system that integrates machine learning algorithms with traditional inventory control theories. Through experimental validation using real-world SME data, the proposed framework demonstrates substantial improvements in inventory turnover rates and cost reduction. The research proposes a novel predictive analytics architecture specifically designed for resource-constrained environments, addressing key limitations of existing inventory management systems. Results indicate that AI-enabled inventory strategies can enhance operational efficiency by 34% while reducing inventory holding costs by 28%. The findings provide practical insights for SME decision-makers seeking to implement AI technologies in their inventory management processes. This research advances the understanding of AI applications in supply chain optimization for small business environments.
Copyright (c) 2025 Jialu Wang (Author) https://creativecommons.org/licenses/by/4.0
Privacy-Preserving Federated Learning Framework for Multi-Institutional Healthcare Data Analytics with Differential Privacy and Homomorphic Encryption
Healthcare data analytics across multiple institutions faces significant privacy challenges due to regulatory requirements and data sensitivity concerns. This paper presents a comprehensive privacy-preserving federated learning framework specifically designed for multi-institutional healthcare data analytics, integrating differential privacy mechanisms with homomorphic encryption techniques. The proposed framework addresses critical limitations in existing approaches by implementing adaptive privacy budget allocation strategies and secure gradient aggregation protocols tailored for healthcare environments. The system architecture incorporates four primary components: local training nodes with privacy protection modules, secure aggregation servers, communication orchestrators, and privacy management systems. Differential privacy implementation utilizes sophisticated noise injection mechanisms with epsilon values optimized between 0.5 and 1.2, while homomorphic encryption ensures secure gradient aggregation across participating institutions. Experimental evaluation on diverse healthcare datasets containing over 2.5 million patient records demonstrates model accuracy retention exceeding 94% while maintaining rigorous privacy guarantees. Performance analysis reveals successful convergence within 85-120 training rounds with computational overhead remaining below 15% compared to centralized approaches. The framework exhibits optimal scalability for networks encompassing up to 20 healthcare entities. Privacy-utility trade-off evaluation confirms superior performance compared to existing federated learning approaches in healthcare contexts. Compliance verification demonstrates adherence to HIPAA and GDPR requirements, establishing practical feasibility for real-world healthcare implementations while advancing collaborative medical research capabilities.
Copyright (c) 2025 Xiaotong Shi (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Analysis Methods for Multi-Channel Marketing Data Based on Anomaly Detection Algorithms
Multi-channel marketing environments generate massive volumes of heterogeneous data across digital platforms, social media, e-commerce channels, and traditional advertising mediums. The complexity and velocity of these data streams present significant challenges for marketing professionals seeking to extract actionable insights for strategic decision-making. This research proposes an integrated framework for intelligent analysis of multi-channel marketing data through advanced anomaly detection algorithms. The methodology combines machine learning techniques with statistical approaches to identify irregular patterns, emerging trends, and potential threats within marketing datasets. Our framework incorporates data preprocessing pipelines, algorithm optimization strategies, and real-time monitoring capabilities designed specifically for marketing intelligence applications. Experimental validation demonstrates superior performance across multiple industry sectors, with anomaly detection accuracy rates exceeding 94.2% and false positive rates maintained below 3.8%. The proposed system successfully identifies critical marketing anomalies including fraudulent activities, campaign performance deviations, and consumer behavior shifts across diverse data sources. Implementation results reveal significant improvements in marketing ROI optimization, with participating organizations reporting average performance gains of 23.7% in campaign effectiveness and 18.9% reduction in marketing waste through early anomaly identification and mitigation strategies.
Copyright (c) 2025 Yisi Liu (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Computational Resource Optimization: A Hybrid Deep Reinforcement Learning Framework for Enhancing Large-Scale Model Efficiency
This paper presents an AI-driven approach to computational resource optimization aimed at enhancing efficiency and resource utilization in large-scale AI model training and inference processes. As AI models continue to grow exponentially in size, computational resource demands have increased dramatically, with studies indicating that resource utilization efficiency in large model training and deployment typically falls below 60%, resulting in significant cost waste and energy consumption. The proposed framework leverages reinforcement learning and predictive analytics techniques to implement intelligent resource allocation and task scheduling across di-verse hardware environments (GPUs, CPUs, specialized accelerators). The multi-layered architecture incorporates four core components: resource monitoring, workload prediction, adaptive scheduling, and dynamic optimization, capable of adjusting resource configurations dynamically based on workload characteristics and hardware capabilities. Experimental evaluation across 32 large-scale AI training and inference scenarios demonstrated an average 38.7% throughput improvement and 42.3% energy consumption reduction compared to traditional scheduling meth-odds. Field validation through four industry case studies further confirmed the practical value of this approach in financial services, e-commerce, healthcare, and industrial sectors, achieving an average 31.2% increase in resource utilization and 24.8% reduction in operational costs. Economic analysis indicates substantial return on investment (169% average over 6 months) through improved computational efficiency and reduced infrastructure expenses. These research findings have significant strategic implications for enhancing core competitiveness in high-performance computing and AI infrastructure, contributing to reduced dependency on third-party computational resources, accelerated AI innovation cycles, and advancement of green computing initiatives.
Copyright (c) 2025 Xiaoying Li (Author) https://creativecommons.org/licenses/by/4.0
Skulls across Civilizations: A Comparative Study of Eastern and Western Imagery of Death in Literature and Art
The skull, as a universal symbol of mortality, has traversed time and culture to become a profound site of meaning in both Eastern and Western civilizations. This study offers a comprehensive comparative analysis of skull imagery across literature and visual art, tracing its philosophical, religious, and aesthetic evolutions from antiquity to the contemporary era. Drawing on Chinese Daoist, Buddhist, and Confucian thought, and juxtaposing these with Christian theology, Gothic literature, and European art history, the paper uncovers how each tradition approaches the existential dilemma of life and death through distinct symbolic systems. Eastern traditions often employ the skull as a medium of transcendence and natural harmony, while Western representations emphasize sin, vanity, and redemption. Through interdisciplinary analysis using literature review, iconography, and image interpretation, the paper reveals how the skull transforms from a symbol of horror into a cultural prism refracting human concerns with meaning, impermanence, and spiritual inquiry. Ultimately, despite cultural divergences, the skull serves as a shared metaphor for humanity's poetic and philosophical confrontation with mortality.
Copyright (c) 2025 Shimiao Su (Author) https://creativecommons.org/licenses/by/4.0
Research on Qianhe Flavor Industry's Brand Image Construction from the Perspective of "Brand as Management"
This study explores the brand image construction of Qianhe Flavor Industry through the lens of the "Brand as Management" theory. By analyzing brand positioning, multi-channel communication, and brand culture, the paper demonstrates how brand strategy can serve both internal coordination and external differentiation. Using a qualitative case approach, the study highlights the importance of integrated branding in achieving market relevance and organizational alignment. The findings offer strategic insights for traditional Chinese food enterprises seeking brand renewal. Qianhe's case exemplifies how legacy brands can achieve sustainable growth through culturally rooted, digitally enhanced brand management practices.
Copyright (c) 2025 Boxuan Liu (Author) https://creativecommons.org/licenses/by/4.0
Research on User Retention Management for Content-Based Media Platforms in the Algorithmic Recommendation Era
Content-based media platforms, such as video streaming services and news applications, are confronted with fierce competition in user retention within the context of algorithmic recommendation dominance. While recommendation systems effectively enhance short-term user engagement, they often fall short in addressing long-term retention due to an over-reliance on clickstream data, a limitation that leads to the neglect of user psychological needs, dynamic changes in content quality, and the impact of social interactions. This study proposes a multimodal neural network framework designed to integrate heterogeneous data, including user behavior sequences (such as clicks and watch time), content semantic features (including text attributes and audio-visual characteristics), and social network interactions, for the purpose of predicting user retention and formulating personalized retention strategies. The framework adopts modality-specific encoders, where Transformer is used for temporal behavior data, CNN for content features, and GNN for social graphs, combined with cross-modal attention mechanisms to capture synergistic relationships such as the resonance between content and user interests. A multi-task learning objective is employed to simultaneously predict retention status (whether users remain active within 30 days) and time-to-churn, thereby enhancing practical applicability. Experimental validation based on a dataset from a video platform demonstrates that the proposed framework outperforms single-modality baseline models, with interpretability analyses identifying key factors driving retention, such as content diversity and the frequency of social engagement. This research advances user retention management by bridging behavioral analytics and user psychology, enabling content-based media platforms to reduce user churn through targeted interventions.
Copyright (c) 2025 Zhaoyu Wang (Author) https://creativecommons.org/licenses/by/4.0
Optimizing Chronic Disease Self-Management Toolkit Design: A Comparative Analysis of Existing Intervention Models
Chronic diseases represent a leading global health challenge, accounting for over 70% of annual mortality worldwide. While digital self-management toolkits have emerged as pivotal interventions for improving patient outcomes, their effectiveness remains limited by heterogeneous user engagement patterns and fragmented design methodologies. This study addresses critical gaps in chronic disease management by conducting a systematic comparative analysis of existing intervention models across four major conditions: diabetes, chronic obstructive pulmonary disease (COPD), hypertension, and heart failure. We propose a multidimensional evaluation framework examining six core components: educational content delivery, physiological monitoring mechanisms, feedback systems, social support integration, gamification elements, and clinician engagement levels. Through longitudinal assessment of 23 randomized controlled trials involving 12,834 participants, we identified three dominant toolkit archetypes with distinct performance characteristics. Our analysis demonstrates that models incorporating adaptive personalization algorithms and bidirectional clinician-patient communication channels significantly improved medication adherence and clinical biomarkers compared to standardized approaches. Furthermore, explainable artificial intelligence techniques revealed key design principles correlated with sustained engagement, including dynamic goal-setting interfaces and context-aware behavioral nudges. Validation experiments confirmed that optimized toolkits based on these principles reduced all-cause hospitalization rates by 23% during a 12-month implementation period. This research contributes to precision public health by establishing evidence-based architecture for next-generation self-management systems, ultimately bridging the gap between behavioral theory and scalable digital implementation.
Copyright (c) 2025 Zitong Zhang (Author) https://creativecommons.org/licenses/by/4.0
Invisible Frontlines: Unregulated Human-Animal Interfaces and the Risk Architecture of Emerging Zoonoses
The increasing frequency of zoonotic disease outbreaks underscores the critical role of human-animal interfaces in pathogen spillover. Despite advances in biomedical research, many high-risk interactions remain unregulated, creating gaps in global health security. This paper examines the socio-behavioral and ecological drivers of zoonotic emergence, focusing on unregulated practices such as wildlife trade, intensive farming, and habitat encroachment. By analyzing these interfaces, the study aims to identify systemic vulnerabilities in current zoonotic risk management frameworks and propose actionable policy interventions. The research employs a qualitative content analysis of peer-reviewed literature, institutional reports, and case studies to map the risk architecture of emerging zoonoses. Key findings reveal that cultural practices, economic incentives, and weak governance perpetuate high-risk human-animal interactions. Case studies of recent zoonotic outbreaks, including highly pathogenic viruses, illustrate how unregulated interfaces facilitate cross-species transmission, while existing policies often fail to address underlying socio-economic drivers. The study contributes to the One Health discourse by emphasizing the need for integrated, behavior-centered approaches to zoonotic prevention. It recommends stricter wildlife trade regulations, community-based education programs, and enhanced cross-sectoral collaboration to mitigate spillover risks. By integrating ecological, socio-behavioral, and governance dimensions, the study advances a multidimensional framework for zoonotic risk reduction, emphasizing anticipatory policy design and cross-sectoral coordination as critical complements to biomedical containment strategies.
Copyright (c) 2025 Rong Liu (Author) https://creativecommons.org/licenses/by/4.0
Retracted: Research on Intelligent Keyframe In-Betweening Technology for Character Animation Based on Generative Adversarial Networks
Character animation production faces significant efficiency challenges due to labor-intensive keyframe interpolation processes that require extensive manual intervention. This paper presents a novel intelligent keyframe in-betweening technology based on generative adversarial networks (GANs) to automate intermediate frame generation while preserving character consistency and motion quality. The proposed framework incorporates a multi-scale temporal feature extraction mechanism that captures complex motion patterns through residual connections and attention-based aggregation. An improved GAN architecture employs dual-path processing streams combining spatial and temporal information, enhanced with spectral normalization and adaptive instance normalization for stable training dynamics. The character consistency preservation algorithm integrates deep feature matching with geometric constraint enforcement to maintain visual coherence across generated sequences. Experimental validation on a comprehensive dataset of 55,000 animation sequences demonstrates superior performance with SSIM scores reaching 0.923 and temporal consistency measures achieving 0.856, representing substantial improvements over existing methodologies. User studies involving 165 participants confirm practical applicability, with professional animators rating the generated sequences at 4.19/5.00 for overall quality. The technology enables significant productivity gains in animation production workflows, achieving 30-45% cost reductions while maintaining professional quality standards, making high-quality animation more accessible across diverse commercial applications.
Copyright (c) 2025 Zi Wang, Zhong Chu (Author) https://creativecommons.org/licenses/by/4.0
Multi-Domain Adaptation for Autonomous Driving Perception under Diverse Weather
Autonomous driving perception systems are confronted with substantial robustness challenges under diverse weather conditions, where sensor data distortion caused by rain, fog, snow, or intense illumination often leads to degraded performance in critical tasks such as object detection and semantic segmentation. Existing approaches predominantly depend on single-domain models trained under ideal environmental conditions, which suffer from poor generalization across weather domains due to inherent domain shifts. This study explores the application of multi-domain adaptation techniques to enhance perception stability by integrating heterogeneous sensor data, including RGB (Red, Green, Blue) images, LiDAR (Light Detection and Ranging) point clouds, and thermal imaging, while leveraging cross-domain feature alignment mechanisms. The proposed framework employs domain-specific encoders combined with adversarial learning to mitigate weather-induced domain gaps, alongside a multi-task learning objective that simultaneously optimizes perception accuracy and domain invariance. Experimental validation demonstrates that the framework achieves superior performance compared to conventional single-domain and shallow adaptation models, with interpretability analyses revealing key weather-robust features such as thermal edge consistency and LiDAR (Light Detection and Ranging) point density patterns. Its ability to adapt to unseen weather conditions could enable reliable autonomous driving in complex real-world environments and reduce weather-related accidents. By bridging domain adaptation theory with automotive perception requirements, this work advances the translation of robust AI (Artificial Intelligence)-driven systems into practical autonomous driving applications.
Copyright (c) 2025 Jinghui Tan (Author) https://creativecommons.org/licenses/by/4.0
Remembering the City: Trauma and Modernity in Hong Kong Literature
This paper examines how contemporary Hong Kong literature engages with the city as a site of trauma, memory, and modernity. Drawing on trauma theory (Caruth, LaCapra), urban modernity (Jameson, Harvey), and memory studies (Nora), the study analyzes how literary texts reconstruct the urban space of Hong Kong as a repository of historical rupture and cultural displacement. Focusing on Dung Kai-cheung's Atlas: The Archaeology of an Imaginary City, the paper argues that fiction operates as a counter-archive, preserving what is threatened by societal changes, colonial erasure, and capitalist urban redevelopment. The city, as rendered in literature, emerges not as a stable setting but as a fragmented, haunted, and continually re-imagined space. Through symbolic reconstruction and narrative estrangement, Hong Kong writers articulate a form of literary remembrance that resists forgetting and reclaims agency in the face of spatial and temporal disorientation. The paper contributes to ongoing conversations about postcolonial identity, cultural memory, and the politics of place in Sinophone literature.
Copyright (c) 2025 Siyang Xue (Author) https://creativecommons.org/licenses/by/4.0
Research on Supply Chain Payment Risk Identification and Prediction Methods Based on Machine Learning
Supply chain payment risk management has become increasingly critical in modern global commerce, where financial disruptions can cascade through entire networks of suppliers and manufacturers. Traditional risk assessment methodologies often fail to capture the dynamic and complex nature of contemporary supply chain environments, leading to substantial financial losses and operational disruptions. This research proposes a comprehensive machine learning-based framework for identifying and predicting supply chain payment risks through advanced feature engineering and ensemble learning techniques. The study develops a multi-dimensional risk assessment model that integrates supplier financial health indicators, transaction pattern analysis, and macroeconomic variables to enhance prediction accuracy. Experimental validation using real-world procurement and payment data demonstrates significant improvements in risk detection capabilities, achieving 94.2% accuracy in payment default prediction and reducing false positive rates by 37% compared to conventional methods. The proposed framework provides actionable insights for supply chain financial managers and contributes to the advancement of AI-driven risk management solutions in enterprise environments.
Copyright (c) 2025 Yilun Li, Shengjie Min, Chenyu Li (Author) https://creativecommons.org/licenses/by/4.0
An Exploration of the Connection between Sima Xiangru's FU and Shu Cultural Elements
The abundant material and spiritual wealth of Shu, spanning millennia, vividly manifests in Sima Xiangru's fu works, transforming into a romantic breeze that still captivates readers across centuries. By disseminating his literary pieces throughout the Han Empire and to later generations, Sima Xiangru created a series of renowned fu compositions, whose grand and elaborate diction exerted an undeniable influence on the establishment of Shu's cultural paradigm. Through elegant prose that satirizes the pursuit of power and conveys admonitions with literary ingenuity, Han fu from Shu further distinguishes itself with its profound underlying themes, shining as a brilliant star in the history of Chinese civilization and serving as a guiding beacon for repeated scholarly interpretation. Therefore, this study analyzes the connection between Sima Xiangru's fu and Shu cultural elements based on his six extant fu works.
Copyright (c) 2025 Xincheng Ying (Author) https://creativecommons.org/licenses/by/4.0
Digital Platform Economies and Labor Market Polarization: An Empirical Study Using Global Microdata
The rapid expansion of the digital platform economy has transformed global labor markets, not only by introducing new forms of work but also by reshaping the structure of employment across skill levels. This paper advances existing research by integrating global microdata with cutting-edge theories of skill- and task-biased technological change, algorithmic governance, and digital exclusion. The analysis demonstrates that platform economies intensify labor market polarization through the automation of routine tasks, algorithm-driven reallocation of work, and the selective inclusion of workers based on digital access and skills. Notably, the study reveals that the impact of these mechanisms is conditioned by national institutions and welfare regimes, leading to divergent outcomes across advanced and emerging economies. By developing a comprehensive analytical framework, this paper provides new theoretical insights into how platformization restructures occupational hierarchies and offers policy directions for building more inclusive and equitable digital labor markets.
Copyright (c) 2025 Feite Liu (Author) https://creativecommons.org/licenses/by/4.0
AI-Based Pattern Recognition and Characteristic Analysis of Cross-Border Money Laundering Behaviors in Digital Currency Transactions
Digital currency transactions have become increasingly prevalent for cross-border money laundering activities, presenting significant challenges to traditional anti-money laundering (AML) frameworks. This research investigates the application of artificial intelligence techniques for identifying and analyzing patterns in cross-border money laundering behaviors within digital currency ecosystems. Through comprehensive analysis of transaction data and behavioral characteristics, this study develops a systematic approach to pattern recognition using machine learning and deep learning methodologies. The research examines various money laundering schemes, including mixing services, layering techniques, and decentralized finance exploitation. Advanced AI algorithms demonstrate superior performance in detecting suspicious transaction patterns compared to conventional rule-based systems. The findings reveal distinct behavioral signatures associated with illicit cross-border activities, enabling more effective detection and prevention strategies. This work contributes to the advancement of regulatory technology solutions and provides insights for policymakers and financial institutions in combating digital currency-based money laundering.
Copyright (c) 2025 Aixin Kang, Xiaowen Ma (Author) https://creativecommons.org/licenses/by/4.0
"I'm Forever Chinese": The Role of ICTs in Cultural Identity Maintenance among Chinese Immigrants in Transnational Families
This study explores the role of Information and Communication Technologies (ICTs) in preserving cultural and ethnic identities among Chinese immigrants in the U.S. By focusing on the case study of Haley, a second-generation immigrant, the research investigates how digital platforms like WeChat, TikTok, and YouTube facilitate the preservation of cultural heritage and promote cultural inclusion. ICTs enable real-time communication with family, participation in cultural practices, and access to traditional resources, helping bridge generational and cultural gaps. The study highlights how these platforms support dual cultural environments, balancing cultural roots with adaptation to American society. Additionally, challenges such as digital access disparities and the marginalization of older generations are discussed. The findings emphasize the transformative impact of ICTs on identity formation and provide insights into the ongoing negotiation of cultural and ethnic identity in transnational contexts. This research contributes to the broader discourse on globalization, cultural continuity, and the evolving role of technology in shaping immigrant identities.
Copyright (c) 2025 Wenjun Zhu (Author) https://creativecommons.org/licenses/by/4.0
The Digital Translation of Soundscapes: AI Assistant Gender Performativity and the Reconstruction of Sound Meaning
Artificial intelligence voice assistants increasingly shape how gender is experienced and understood through digital sound. The design of feminized AI voices reflects and reinforces cultural expectations that link femininity with service, politeness, and emotional labor. Through an interdisciplinary framework grounded in gender performativity theory, technofeminist analysis, and sound studies, this paper examines how vocal features such as pitch, tone, rhythm, and speech patterns encode social roles within AI systems. These vocal characteristics are not neutral but function as carriers of symbolic meaning, aligning technological outputs with long-standing gender hierarchies. The widespread adoption of female-voiced assistants in domestic and service-oriented applications illustrates how gendered labor is reimagined in digital form. At the same time, limited representation of non-binary and culturally diverse voices highlights a structural gap in current AI voice design. Ethical concerns emerge around the standardization of voice as both a technical product and a social interface. A more inclusive approach to voice technology requires recognition of sound as a material practice that shapes identity, agency, and interaction. Rather than treating voice as a passive output, AI systems should be developed with attention to cultural specificity, user diversity, and the symbolic implications of auditory design. Understanding voice as a site of power and representation offers a critical pathway toward more equitable and reflective technological development.
Copyright (c) 2025 Xinran Feng (Author) https://creativecommons.org/licenses/by/4.0
Mandatory ESG Information Disclosure Policy:"Safeguarding" the Development of Green Finance—An Evolutionary Game Model Based on Local Governments and Greenwashing Behavior of Heavily Polluting Enterprises
China is in the early stages of green finance development, with some listed companies engaging in greenwashing—misleading practices to appear environmentally friendly. To address this, the paper develops an evolutionary game model involving local governments and heavily polluting enterprises. It explores how mandatory ESG disclosure influences corporate strategy, finding that it can curb greenwashing when local governments enforce higher fines and higher-level governments provide stronger incentives. The case of Shendong Coal Group demonstrates the effectiveness of combining government penalties and incentives. A robust penalty system and high-level government incentives are essential for successful ESG disclosure policies to mitigate corporate greenwashing.
Copyright (c) 2025 Xinyu Zou, Zhimei Ouyang, Ruoyan Dong (Author) https://creativecommons.org/licenses/by/4.0
Application of Cell Biology and Molecular Biology Principles in Monoclonal Vaccines and Drug Delivery Systems
Advances in cell and molecular biology have revolutionized vaccine development and drug delivery. This review explores how mechanisms such as receptor-mediated uptake, genetic engineering, and immune modulation inform the design of monoclonal antibody-based prophylactics and advanced delivery systems. We examine theoretical foundations including antigen–antibody recognition, endocytosis pathways, and techniques like phage display and mRNA vaccine platforms. Practical applications discussed include monoclonal antibodies for viral prevention and treatment, structure-based immunogens, and diverse delivery systems such as nanoparticles, antibody–drug conjugates, viral vectors, and cell-derived vesicles. Case studies, including COVID-19 mRNA vaccines and RSV-targeting antibodies, demonstrate how molecular design enhances delivery efficacy. We compare delivery platforms in terms of targeting, safety, and performance, and address current challenges such as biological barriers and regulatory complexity. Looking ahead, we highlight innovations like smart materials and synthetic biology. This review underscores how cellular and molecular principles are guiding the next generation of precise and effective medical interventions.
Copyright (c) 2025 Mikarly Zhong (Author) https://creativecommons.org/licenses/by/4.0
Digital Transformation and ESG Rating Disagreement: A Dynamic Relationship Test Based on Panel VAR Model
Previous research focuses on the unidirectional relationship between digital transformation and ESG rating disagreement, even though some evidence suggests that ESG rating disagreement can influence digital transformation, which in turn reduces ESG rating disagreement. Based on the panel VAR, we study the simultaneous relationship between digital transformation and ESG rating disagreement employing data of A-share listed companies from 2011 to 2023 and ESG ratings from eight agencies. Using the panel VAR model, impulse response functions, and variance decomposition, this study provides new empirical evidence on the dynamic relationship between ESG and digital transformation. The findings reveal that ESG rating disagreement and digital transformation exhibit a symmetrically bidirectional dynamic relationship. It is further suggested that ESG rating disagreement and digital transformation exhibit lagged effects on each other.
Copyright (c) 2025 Jingwei Li (Author) https://creativecommons.org/licenses/by/4.0
Construction of a Traditional Chinese Medicine Knowledge Base and Intelligent Inference for Precision Diagnosis and Treatment
This study presents a modular framework that integrates artificial intelligence (AI) with Traditional Chinese Medicine (TCM) to enable precision and personalized diagnosis. Addressing the semantic ambiguity and unstructured nature of TCM knowledge, we construct an ontology-driven knowledge graph capturing over 4,000 symptoms, 900 syndromes, and 1,800 herbs from classical texts, clinical guidelines, and electronic records. A four-stage inference engine, encompassing symptom clustering, Bayesian syndrome-disease mapping, rule-based prescription generation, and patient-specific adjustments, delivers explainable, adaptable recommendations. The system is deployed using a microservice architecture with Docker-based SaaS access and real-time API integration. Evaluated on 500 clinical cases and a validation set of 50 expert-reviewed cases, the framework achieved high diagnostic accuracy (Top-1: 79.4%), prescription precision (83.6%), and interpretability (mean expert rating: 4.52/5), outperforming rule-based and black-box baselines. This research advances the formalization of TCM through AI-enhanced reasoning, bridging symbolic knowledge with statistical learning to support scalable, trustworthy decision-making. Future work will extend to multimodal diagnostic integration and clinical deployment in diverse care settings.
Copyright (c) 2025 Yuehui Li, Yongqi Zhu, Qin Xu (Author) https://creativecommons.org/licenses/by/4.0
Causal Effect Analysis of Extracurricular Tutoring Based on Random Forest Propensity Score Matching: Evidence from Student Academic Performance
The prevalence of extracurricular tutoring has sparked ongoing debates regarding its causal impact on student academic performance. Traditional assessment methods often fail to address selection bias and complex nonlinear relationships inherent in educational data. This study proposes a machine learning enhanced approach, Random Forest Propensity Score Matching (RF-PSM), to overcome the limitations of conventional propensity score methods when analyzing high dimensional observational data. By leveraging random forests for propensity score estimation, the method captures intricate interactions among student characteristics while maintaining robust covariate balance. The analysis utilizes a nationally representative student performance dataset, incorporating demographic, socioeconomic, and prior academic achievement variables. Key findings reveal significant heterogeneous treatment effects: tutoring demonstrates the strongest positive impact on median performing students, whereas effects diminish for both high and low achievers. The methodological contribution lies in demonstrating RF-PSM's superior performance over logistic regression based matching through reduced bias in effect estimation. Practically, these results inform targeted educational policies by identifying student subgroups that benefit most from supplemental instruction. The study underscores the potential of combining machine learning with causal inference frameworks to derive more nuanced insights from educational big data.
Copyright (c) 2025 Yangjun Lu (Author) https://creativecommons.org/licenses/by/4.0
From Artifact to Algorithm: The Role of AI in Reimagining Curatorial Practices in Contemporary Art Museums
The rapid digitization of cultural heritage and the growing complexity of audience engagement have compelled contemporary art museums to reconsider traditional curatorial practices. While artificial intelligence has demonstrated transformative potential across various fields, its role in redefining the conceptual and operational frameworks of museum curation remains underexplored. This study examines how AI technologies, ranging from computer vision to generative models, are reshaping curation from an artifact-centered process to an algorithm-mediated practice. The research adopts a case study methodology, analyzing AI implementations across three leading institutions: the Victoria and Albert Museum, the Museum of Modern Art, and the Palace Museum’s digital lab. By synthesizing technical reports, curator interviews, and visitor feedback, the study identifies key patterns in how AI facilitates dynamic collection mapping, visitor-centric exhibition design, and generative curation. These applications reveal both the operational efficiencies gained and the emerging tensions between algorithmic automation and curatorial authority. Findings suggest that AI functions not merely as a tool but as an active collaborator in curation, introducing the concept of "algorithmic curation" as a new paradigm. However, this shift raises critical questions about authorship, bias, and the democratization of cultural interpretation. The study contributes to ongoing debates in digital museology by proposing a framework for ethical AI integration in curatorial workflows, while highlighting the need for institutional guidelines to balance innovation with cultural stewardship.
Copyright (c) 2025 Xintong Li (Author) https://creativecommons.org/licenses/by/4.0
Strategic Business Decision-Making and Future Transition of Estée Lauder: From 4IR to 5IR in the Global Luxury Cosmetics Market
This paper presents a critical analysis of the strategic business decisions undertaken by Estée Lauder Companies, a global leader in the luxury cosmetics industry. It examines the principal challenges currently faced by the company, including supply chain vulnerabilities, increasing market competition, dependence on travel retail channels, and the growing imperative for digital transformation. The paper also provides strategic recommendations to support sustainable growth and facilitate the company’s transition from the Fourth Industrial Revolution to the Fifth Industrial Revolution, emphasizing ethical innovation and human-centered technological development. Employing analytical frameworks such as PESTLE, SWOT, the BCG Matrix, and the Business Model Canvas, the paper evaluates both external environmental factors and internal operational strategies. This comprehensive approach enables a detailed examination of Estée Lauder’s efforts in innovation, supply chain optimization, talent management, sustainability implementation, and climate change adaptation. The findings indicate that despite a decline in overall revenue in the 2023 fiscal year, Estée Lauder achieved promising growth in the Asia-Pacific region, particularly in Mainland China and Hong Kong. The company has made significant progress in its sustainability agenda, including the achievement of global carbon neutrality and the use of renewable energy sources. Continued investment in product innovation and digital infrastructure is contributing to long-term competitive strength. To maintain resilience and market leadership, the paper recommends that Estée Lauder expand local production in key regions, diversify distribution channels, and enhance workforce training in advanced technologies. Strengthening transparency in sustainability reporting and prioritizing responsible use of emerging technologies will also be essential in advancing toward the Fifth Industrial Revolution. Overall, this paper provides valuable insights into how a luxury beauty brand can adapt to dynamic global market conditions through proactive and forward-looking strategic decision-making.
Copyright (c) 2025 Xiyao Jia (Author) https://creativecommons.org/licenses/by/4.0
ESG Investment Strategies in Emerging Markets: Implementation and Effectiveness Evaluation
The global rise of Environmental, Social, and Governance (ESG) investing has reshaped capital flows and corporate conduct in developed markets, but its application in emerging economies remains underexplored. This study examines the implementation of ESG strategies in emerging market contexts, evaluating both financial and non-financial impacts on firms, investors, and overall market development. Using a modular framework, we analyze case studies from Brazil, China, Southeast Asia, and India, identifying four distinct ESG strategy models shaped by regional institutions. Drawing on ESG scores, financial data, and policy analysis, we assess how ESG practices influence risk-adjusted returns, cost of capital, and investment inflows for 150 publicly listed firms (2015–2023) using firm-fixed effects regression models. Structured interviews with investors and sustainability officers provide qualitative insights into ESG adoption barriers and enablers. Findings show ESG-aligned firms in emerging markets demonstrate greater resilience to volatility and increased investor confidence. However, benefits are moderated by institutional quality, data transparency, and regulatory consistency. Governance-related ESG practices, such as board independence and stakeholder disclosure, exert the clearest positive effect on firm valuation. In contrast, environmental metrics often lack standardization, while social factors like labor and community engagement remain underreported. This study advances sustainable finance literature by offering a structured assessment of ESG strategies in developing markets. It calls for harmonized reporting standards, capacity-building, and ESG data interoperability. Policy recommendations include incentivizing verified disclosures, integrating ESG into credit ratings, and ESG training for corporate boards. Overall, ESG can act as both a development catalyst and market differentiator amid institutional and market challenges in emerging economies.
Copyright (c) 2025 Zexi Li (Author) https://creativecommons.org/licenses/by/4.0
Strength Regulation Strategies in Elite Volleyball Athletes: Implications for University Sports Education and Management
Strength regulation is a critical determinant of performance in elite volleyball, where athletes rely on explosive power, endurance, and dynamic force generation. However, university sports programs often lack systematic approaches to integrating scientifically validated strength training methods, leading to suboptimal athlete development and increased injury risks. This study investigates strength regulation strategies employed by elite volleyball athletes and examines their applicability within university sports education and management systems. By synthesizing case studies of professional players’ training regimens and analyzing physiological adaptation techniques, the research identifies scalable strategies for resource-constrained academic environments. The methodology combines a review of biomechanical literature with practical evaluations of low-cost training adaptations, such as modified periodization models and equipment alternatives. The findings reveal that key elite strategies, including neuromuscular activation drills, plyometric progressions, and recovery protocols, can be effectively adapted to university settings through modular programming and cross-disciplinary collaboration. Pilot implementations demonstrate measurable improvements in vertical jump performance and injury resilience among student-athletes. The study underscores the potential of structured strength regulation to enhance training efficiency, reduce long-term health risks, and bridge the gap between recreational and competitive sports pathways. These insights advocate for policy-level reforms in university sports management, emphasizing science-based training standardization and resource-sharing partnerships with professional leagues.
Copyright (c) 2025 Zhihang Bao (Author) https://creativecommons.org/licenses/by/4.0
Improving the Use of English Subjunctive Mood in Remote Conditionals: A Pedagogical Framework for Chinese Learners
This study explores the challenges Chinese learners face when using the English subjunctive mood in remote conditionals, a grammatical structure that expresses hypothetical or unreal situations. Rooted in the author's personal struggles and broader observations among Chinese postgraduate students, the paper investigates the cognitive and linguistic barriers stemming from differences between Chinese and English grammar. The study identifies key issues such as confusion between declarative and subjunctive moods, misuse of verb tenses, and modal verb errors, often caused by negative language transfer and intralingual overgeneralization. Drawing on second language acquisition theories and contrastive analysis, the paper highlights how learners’ reliance on first language structures and insufficient feedback can hinder accurate subjunctive usage. The pedagogical response proposed integrates two established models: ESA (Engage, Study, Activate) and ARC (Authentic use, Restricted use, Clarification). This integrated framework emphasizes learner motivation, contextual understanding, and structured practice. By aligning teaching strategies with learners’ pragmatic needs and cognitive processes, the study offers a systematic and communicative approach to improve both recognition and production of the subjunctive mood in English. The framework is designed to support more meaningful and accurate language use in academic and real-world contexts and to enhance learners’ grammatical competence and communicative confidence.
Copyright (c) 2025 Yuchen Zhou (Author) https://creativecommons.org/licenses/by/4.0