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.
From Lab to Market: A Strategic Market Entry Framework for a Cool-Roof Paint Startup in Chennai, India
The global cool-roof industry is experiencing significant growth driven by climate change and sustainability imperatives, with India representing a particularly promising yet competitive market due to its extreme heat vulnerability and nascent supportive policies. This study investigates the strategic market entry challenges and opportunities for Dewpoint Technologies, an Australian startup possessing patented cool-roof paint with unique self-cleaning and passive water-collection properties. Through a qualitative case study methodology, we conducted an in-depth analysis of the competitive landscape in Chennai, India, benchmarking six incumbent firms using the BMC framework alongside financial data from public companies. Our analysis reveals that while Dewpoint's core cooling efficacy is comparable to competitors, its self-cleaning and water-harvesting capabilities constitute a defensible competitive advantage in a water-scarce context like Chennai. However, the startup lacks the manufacturing capacity, distribution networks, and market knowledge of established players. Consequently, we propose a phased market entry strategy centered on a dual approach of targeted niche marketing and strategic partnership. We identify the underserved public and educational building sectors as the primary niche and recommend a private-label manufacturing and distribution agreement with Solo Paints, a Chennai-based eco-friendly paint manufacturer, to mitigate risks and costs. The strategy further includes pursuing sustainability certifications, leveraging Indo-Australian research grants for initial funding, and adopting a premium pricing strategy justified by its differentiated value proposition. This study provides an empirically-grounded strategic framework for technology startups seeking to navigate entry into complex, competitive emerging markets, highlighting the critical interplay between leveraging unique capabilities, identifying unmet market needs, and forging strategic local partnerships.
Copyright (c) 2026 Jialu Xu, Liana Berzins, Diana Thilay, Dianin Thilay, Yutan Yang (Author) https://creativecommons.org/licenses/by/4.0
A Comparative Study of the Business Models and Social Impacts of Shared Mobility in China and Southeast Asia
This study examines the divergent development of shared mobility in Asia through a comparative analysis of China's bicycle-sharing model and Southeast Asia's motorcycle-based ride-hailing model. It investigates their contrasting socio-economic origins, evolving business models, and distinct societal impacts. Utilizing a comparative case study methodology focused on China exemplified by Ofo and Mobike and Southeast Asia represented by Indonesia's Go-Jek, the research draws on secondary data from industry reports and academic literature. Findings reveal the Chinese model, built on mature mobile payments and manufacturing, transitioned from capital-fueled expansion to state-regulated rationalization, grappling with urban spatial conflicts. Conversely, the Southeast Asian model, rooted in local motorcycle culture, evolved into multi-service super-apps, facing core challenges related to platform labor rights. The study concludes that these models are not mere replications of Western prototypes but represent context-dependent adaptive innovations, offering crucial insights into the interplay between digital economies and socio-cultural structures in Asia.
Copyright (c) 2026 Kaiyuan Sun (Author) https://creativecommons.org/licenses/by/4.0
The Integrated Application of Virtual and Augmented Reality Technologies in Sports Education
With the rapid advancement of Virtual Reality (VR) and Augmented Reality (AR) technologies, smart sports education is undergoing transformative changes. This paper systematically explores the integrated application of VR and AR in sports education, focusing on practical cases and effectiveness in skill training, tactical rehearsals, and physical fitness assessment. The study demonstrates that VR and AR provide immersive, personalized, and data-driven training environments that significantly enhance athletes' learning outcomes and engagement. Meanwhile, challenges such as hardware costs, interaction experience, instructor training, and data privacy are critically analyzed. The paper further discusses future development trends and promotion strategies, emphasizing the importance of interdisciplinary collaboration and technological innovation to support sustainable advancement in smart sports education.
Copyright (c) 2026 Gangzhi Na (Author) https://creativecommons.org/licenses/by/4.0
Increasing Income Generating through Fashion Entrepreneurial Product Development
Product development is a crucial aspect of business implementation in the fashion industry. This study aims to describe the process of entrepreneurial product development in the fashion sector to increase income generating business units in the Fashion Design Education study program at Surabaya State University. The product development process carried out in this study is included in the category of new product development which consists of the stages of planning, design, manufacturing, and launching. Based on the results of product feasibility tests with experts, the percentage of product quality with very good qualifications based on intrinsic, marketing, functional, and social-communicative aspects was obtained with an overall average value of 87.24%. The results obtained after conducting product development are total revenue increases compared to before. The main factors that increase revenue are derived from modifications to product packaging, modifications to product design and types, and product characteristics.
Copyright (c) 2025 Mita Yuniati, Inty Nahari, Deny Arifiana (Author) https://creativecommons.org/licenses/by/4.0
Career Readiness: An Exploratory and Confirmatory Factor Analysis
This study investigates the career readiness of economics education students by examining four critical constructs: digital literacy, entrepreneurial mindset, employability skills, and ICT skills. Adopting a quantitative approach, Exploratory Factor Analysis (EFA) was carried out during the pilot test stage with 120 students, while Confirmatory Factor Analysis (CFA) involved 455 students from various regions in Indonesia. The EFA results identified 18, 13, 19, and 26 items that met the factor loading criteria for each construct. Subsequent CFA refinement reduced these to 9, 13, 15, and 22 items, respectively. The findings emphasize that career readiness in economics education is shaped by an interplay of digital competence, entrepreneurial orientation, transferable employability skills, and proficiency in ICT. These competencies serve as essential foundations for students to adapt to rapidly changing labor markets and technological transformations. More importantly, the study underscores the need to reframe the role of educators in strengthening pathways that equip students not only as job seekers but also as job creators. By integrating these dimensions into teaching and learning practices, higher education can foster graduates who are capable, adaptable, and resilient in facing future career challenges.
Copyright (c) 2025 Dian Rachmawati, Sheerad Sahid, Mohd. Izwan Mahmud (Author) https://creativecommons.org/licenses/by/4.0
Green Garage with HCS: Advancing Automotive Local Mechanic Innovation for Sustainable Technology and Green Economy in Indonesia’s Emerging Urban Areas
This study aims to implement the Integrated Green Garage Empowerment Model, transforming local workshops into green technology entrepreneurs through Hydrocarbon Crack System (HCS) fabrication training and digital marketing on the Shopee marketplace. The Participatory Action Research (PAR) method was employed, involving eight local workshops in the Batang Integrated Industrial Estate as case studies and co-researchers. The results showed a significant increase in technical competency, with an average score of 85.1 (in the very competent category), covering the ability to fabricate, install, and test HCS systems on motorcycles. Meanwhile, digital marketing training yielded an average score of 74.0 (competent category), with the potential to increase market reach by up to 500% and achieve revenue growth of 35–45% per year. This transformation creates a local economic potential worth IDR 67.5 billion per year and creates 240 direct green jobs. These findings confirm that integrating energy-efficient technology and digitalizing business operations can strengthen the independence of local workshops and accelerate the transition to a green economy based on innovation and community.
Copyright (c) 2025 Bayu Ariwibowo, Isma Widiaty, Sena Mahendra, Fahmy Zuhda Bahtiar, Nindita Erwanti (Author) https://creativecommons.org/licenses/by/4.0
From Green Skills to Sustainable Workforce: Mapping Two Decades of Research on Vocational Education for Sustainability Transition
The global shift toward a green economy has repositioned vocational education as a central mechanism for sustainable workforce development. This study maps two decades (2005–2025) of research on green skills and sustainability transition in vocational education to identify publication trends, thematic structures, and emerging research directions. Using a bibliometric approach, 527 Scopus-indexed documents were analyzed through VOS viewer and the Bibliometric R package to produce co-occurrence networks, thematic maps, and temporal evolution visualizations. The findings indicate a clear shift from early studies focused on human capital, labour markets, and technical training toward sustainability-oriented and eco-digital competencies. Five dominant thematic clusters: green skills, vocational education, workforce development, innovation policy, and Industry 4.0, demonstrate the growing intersection between digital transformation and environmental sustainability. European countries, especially the UK and Germany, continue to lead global scholarship, while increasing contributions from Asia reflect expanding contextual diversity. Overall, this study highlights how vocational education has transformed from an economic instrument into a strategic platform for sustainability transition. The convergence of digitalization and green competencies forms the basis of emerging eco-digital TVET, underscoring the need for integrated pedagogical and policy approaches, particularly in developing countries.
Copyright (c) 2025 Atika Atika, Isma Widiaty, Lilis Widaningsih (Author) https://creativecommons.org/licenses/by/4.0
Improving Automatic Essay Assessment Through Cosine Similarity Leveraging a Semantic Corpus
Automatic Essay Scoring (AES) represents an effective solution for facilitating automated evaluation of written essays by mitigating evaluator subjectivity and accelerating the assessment process. Nonetheless, a persistent challenge lies in achieving high accuracy due to limitations in semantic understanding. This study employs Cosine Similarity as a baseline approach and further integrates a semantic data corpus to enhance the representation of textual meaning. Empirical results demonstrate that relying solely on Cosine Similarity captures predominantly lexical-level similarities, yielding limited correlation with human scoring. The incorporation of a semantic corpus substantially improves the system’s capacity to recognize synonyms and linguistic variations, thereby enhancing the sensitivity and reliability of the scoring process. Despite these improvements, the findings underscore the necessity for further corpus refinement, evaluation on larger and more diverse datasets, and assessment using multiple correlation metrics. Overall, this study provides a substantive contribution to the development of AES systems that are more accurate, consistent, and closely aligned with human judgment, thereby advancing the field of automated educational assessment.
Copyright (c) 2025 Fitria Ekarini, Septian Eko Prasetyo, Anan Nugroho, Alfian Ardhiansyah, Clarita Aprilliani, Fakhri Ahmda Kurnia (Author) https://creativecommons.org/licenses/by/4.0
Sustainability Competencies in Beauty-Focused Vocational Programs: Pedagogical Strategies and Industry Expectations - A Systematic Literature Review
The integration of sustainability competencies into vocational education programs has become increasingly critical as industries worldwide shift toward environmentally conscious practices. Beauty-focused vocational programs, which encompass sectors such as cosmetology, skincare, and personal care services, face unique challenges in incorporating green education principles while meeting industry expectations for skilled professionals. This systematic literature review examines the current state of sustainability competencies in beauty-focused vocational programs, evaluates pedagogical strategies employed to develop these competencies, and analyzes industry expectations for sustainable practices among graduates. Following PRISMA guidelines, a comprehensive search was conducted across academic databases, including Scopus, for articles published between 2020-2025. Keywords included "green education," "sustainable education," "education for sustainable development," combined with "TVET," "vocational education," and "technical and vocational education and training." The initial search yielded 70 articles, which were systematically screened and filtered to 17 articles that met the inclusion criteria. The review identified three key themes: (1) emerging sustainability competencies required in beauty-focused vocational programs, including environmental awareness, sustainable product knowledge, and waste reduction practices; (2) innovative pedagogical strategies such as project-based learning, experiential learning, and industry partnerships; and (3) evolving industry expectations for graduates who can balance aesthetic excellence with environmental responsibility. While progress has been made in integrating sustainability into vocational education, significant gaps remain in beauty-focused programs. The findings suggest a need for more systematic integration of green competencies, enhanced industry-education partnerships, and development of specialized curricula that address the unique sustainability challenges of the beauty industry.
Copyright (c) 2025 Delta Apriyani, Lilis Widaningsih, Sudjani Sudjani (Author) https://creativecommons.org/licenses/by/4.0
The Analysis of Technological Literacy Capabilities in Automotive Learning Using a Minicar Vechicle Simulator at a Vocational School
This research wants to explain the technological literacy abilities of vocational school students in automotive learning using a minicar vehicle simulator. Descriptive method was used in this research. The research period is January-June 2025. The research was conducted at a state vocational high school in Bandung and Cimahi of the city. The samples came from nine groups (schools) of students as a population, then group was taken as a sample with a total sample of 360 students. Data collection techniques use tests. The test is a technological literacy test on basic vehicle knowledge aspects. Technological literacy has indicators of the ability to: identify problems, explain phenomena and utilize scientific evidence. The technological literacy test refers to material about vehicles in general. The research instrument was validated by seven teachers as experts. Data analysis using percentages was carried out by calculating students' correct answers on test. The results of the study show that: (1) Students' technological literacy skills in the aspect of identifying problems achieved 25%, which is classified as "poor"; (2) In the aspect of explaining scientific phenomena, 43% achieved, which is included in the "fairly good" category; (3) In the aspect of using scientific evidence, 32% achieved, which is included in the “not good”.
Copyright (c) 2025 Wahid Munawar, Sriyono Sriyono, Ramdhani Ramdhani , Ismadi Rajab (Author) https://creativecommons.org/licenses/by/4.0
Development of a Technopreneurship-Based Teaching Factory to Prepare Competencies Vocational School Graduates
Vocational high schools (SMK) play a strategic role in producing competent and competitive graduates who meet the needs of industry and the business world. The challenges of globalization and technological developments require learning that is not only oriented towards technical skills but also technology-based entrepreneurship (technopreneurship). Teaching factories (Tefa) are one effort to improve the quality of education in the era of industry 4.0 and 21st-century learning. Teaching factories are the basis for a new model of integration between the academic world and industry. Only 5% of SMK have implemented teaching factories. The research method used a systematic literature review. The results of the study indicate that the development of teaching factories integrated with technopreneurship can create learning experiences and environments in accordance with industrial culture. Technopreneurship-based teaching factories also improve student competencies in both soft and hard skills. This model not only prepares SMK graduates who are ready to work but also encourages the development of technopreneurship. The integration of teaching factories and technopreneurship is expected to strengthen the role of SMK in increasing innovation in vocational education.
Copyright (c) 2025 Sunyoto Sunyoto, Rizki Setiadi, Shohihatur Rohman (Author) https://creativecommons.org/licenses/by/4.0
Student Academic Writing Analysis for AI-Based Adaptive Learning System Development, Case Study: Building Engineering Education Students
The advancement of Artificial Intelligence (AI) technology in higher education has been suboptimal in tackling the challenges of student academic writing, as current systems primarily concentrate on technical enhancements and have yet to effectively aid students in formulating logical arguments and coherently organizing ideas. This research seeks to evaluate students' academic writing abilities to inform the development of an AI-driven learning system tailored to their specific requirements. A descriptive quantitative methodology was employed to gather data via online questionnaires from 50 students enrolled in the Building Engineering Education Study Program. The instruments were predicated on five dimensions: writing structure, language proficiency, argumentation and logic, reference utilization, and technical writing skills, utilizing a 1-5 Likert scale. The findings indicate that PTB students fall within the "Very Capable" classification, attaining an average score of 82%. The utilization of references garnered the greatest score at 87%, although writing structure and logical argumentation remain at the lowest level, scoring 79%. The findings reveal difficulties in shifting from descriptive technical writing to argumentative academic discourse, underscoring the necessity for an AI-driven learning system that incorporates a corpus-based instructional method to deliver comprehensive feedback on the formation of coherent argumentation structures. The study advocates for the creation of a comprehensive AI system prototype and its extension to other engineering programs to enhance the academic literacy of students across the nation.
Copyright (c) 2025 Sri Rahayu, Danny Meirawan, Hanifah Indah Rahmawati, Zahra Ghinaya (Author) https://creativecommons.org/licenses/by/4.0
Monetix-Based Learning Media Development: An Application to Improve Students' Critical Thinking Abilities
The study addresses Indonesia's critical thinking skill gap in Economics education, specifically targeting the complex topics of Price Index and Inflation. It seeks to develop an application-based learning media, MONETIX, designed to enhance students’ critical thinking abilities by transforming conventional, passive learning into an interactive, student-centered experience accessible via Android smartphones. This research utilized a Research and Development (R&D) design following the ADDIE model: Analysis, Design, Development, Implementation, and Evaluation. Participants included 45 eleventh-grade students at SMAN 1 Kota Probolinggo, divided into small and large groups. Data collection involved validation questionnaires, pre-tests, post-tests, and N-Gain score analysis to measure the improvement in critical thinking skills. Expert validations and user feedback were incorporated to refine the learning media. Validation scores from material experts, media experts, and prospective users ranged from 83 to 97, indicating the media's high suitability. Implementation results showed significant improvements in critical thinking skills, with N-Gain scores categorized as high (0.79 average for the small group). Efficiency tests yielded satisfactory results reflecting the media’s positive impact on learning effectiveness and student motivation. The MONETIX application-based learning media is valid, effective, and efficient in improving critical thinking skills in Economics. This technology-integrated media offers a flexible and engaging alternative to traditional learning methods, supporting students' deeper conceptual understanding. Future research should focus on expanding interactive features and incorporating additional 4C skills beyond critical thinking.
Copyright (c) 2025 Aida Intan Amirotul Fitri, Rizza Megasari (Author) https://creativecommons.org/licenses/by/4.0
The Effect of Learning Facilities on Home Economics Education Students' Motivation: A Quantitative Study Based on Self-Determination Theory
Adequate learning facilities are crucial in practical-based academic programs to support skill development and foster students’ intrinsic motivation. This research examines the impact of learning facilities on students’ motivation in the Home Economics Education program at Universitas Negeri Semarang, using Self-Determination Theory (SDT) as the theoretical framework. The study aims to evaluate students’ perceptions of the adequacy and relevance of learning facilities and to investigate their relationship with student motivation. A quantitative survey was conducted with 51 undergraduate students using structured questionnaires with a Likert scale to measure perceptions of learning facilities and motivation. Data were analyzed using descriptive statistics, Pearson product-moment correlation, and simple linear regression. The findings reveal a strong positive correlation (r = 0.876, p < 0.01) and a high coefficient of determination (R² = 0.768) between learning facilities and student motivation. Well-equipped and accessible facilities, such as kitchens, sewing labs, and business management rooms, satisfy students’ psychological needs for autonomy, competence, and relatedness, thereby enhancing intrinsic motivation. These results highlight the importance of improving learning facilities to optimize student engagement and learning outcomes in practical-based programs, providing insights for educators and policymakers to enhance the learning environment effectively.
Copyright (c) 2025 Ming-Chang Wu, Nabila Luthfionaura, Dhidik Prastiyanto (Author) https://creativecommons.org/licenses/by/4.0
Mapping the Career Pathways of Electrical Engineering Education Graduates Using Tracer Study
Alumni tracer activities are initiatives conducted by higher education institutions to collect data on graduates’ career paths and professional development after completing their studies. This information serves as a crucial basis for assessing the alignment between the curriculum and labor market demands, while also contributing to the enhancement of educational quality. This study aims to describe the career development of alumni in the labor market, focusing on several indicators, including the percentage of alumni entering the workforce, currently employed, pursuing further studies, and engaged in entrepreneurship. The study employed a descriptive method by distributing surveys to all alumni of the Electrical Engineering Education Study Program at State University of Surabaya who graduated in 2024. The sampling technique applied was saturated sampling, in which the entire population was included as research participants. This approach provides evaluative insights for the university in curriculum development, measuring the relevance of graduates’ competencies to the needs of industry, and fulfilling a key component of the university accreditation process. One of the benchmarks used was the gold standard, referring to the proportion of alumni who are employed, pursuing higher education, or running a business. Based on the tracer study results, the gold standard for alumni of the Electrical Engineering Education Study Program in 2024 was recorded at 85.19%. Data analysis was conducted using descriptive statistics to map graduates’ profiles, distribution across employment sectors, and the relevance of acquired skills to labor market requirements.
Copyright (c) 2025 Ali Nur Fathoni, Achmad Imam Agung, Yulia Fransisca, Khusnul Khotimah, Bima Sawung Saputra (Author) https://creativecommons.org/licenses/by/4.0
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