Journal of Trends in Applied Science and Advanced Technologies
Journal of Trends in Applied Science and Advanced Technologies (JTASAT) is an international peer-reviewed, open access journal.
CYBERSECURITY GOVERNANCE MECHANISM OF INDUSTRIAL CONTROL SYSTEMS UNDER THE BACKGROUND OF INTELLIGENT MANUFACTURING
With the advancement of intelligent manufacturing, industrial control systems (ICSs) are transforming towards IT/OT convergence and cloud-edge collaboration, leading to the disintegration of the boundaries of traditional closed industrial control networks and a surge in security risks. The existing governance mechanisms still suffer from such problems as an imperfect system, ambiguous rights and responsibilities, and disconnection between technical and management practices. Based on the risk governance theory and the whole life cycle concept, and in alignment with the IEC 62443 standard and domestic policies on industrial control security protection, this paper constructs an ICS cybersecurity governance mechanism featuring a three-dimensional governance framework, closed-loop full-process operation and multi-stakeholder collaborative linkage. The mechanism clarifies the rights and responsibilities of the government, enterprises, suppliers and other parties, designs a full-process system including risk identification, protection implementation, monitoring and early warning, emergency response, and continuous improvement, and proposes an implementation path of three-dimensional integration of technology, management and operation. It also divides the implementation steps into three phases: basic construction, in-depth construction and optimization and upgrading, with supporting guarantee measures in four aspects: policy, technology, resources and collaboration. This mechanism can effectively reduce the incidence of security incidents and improve the efficiency of emergency response, providing theoretical and practical support for the safe and stable operation of ICSs in intelligent manufacturing scenarios.
Copyright (c) 2026 WenTao Liu https://creativecommons.org/licenses/by/4.0
AUTOMATIC GENERATION OF AESTHETIC CHAOTIC ATTRACTORS WITH MULTIPLE CYCLIC AND DIHEDRAL SYMMETRIES
This paper presents a novel and robust computational framework for the automatic generation of visually compelling chaotic attractors that exhibit multiple, distinct cyclic or dihedral symmetries within a single image plane. Building upon the foundational concept of equivariant functions, our approach ingeniously combines normalization, geometric translation, and scale transformation techniques to partition the phase space into concentric ring-like regions, each governed by its own symmetry group. To address the critical challenge of rendering aesthetically pleasing visualizations from the inherently complex and dense orbit data, we introduce a significant enhancement to the traditional frequency-based coloring scheme. Our improved algorithm employs a color percentage distribution technique, which allows for precise, priori control over the color palette and its spatial allocation in the final image. This not only streamlines the artistic creation process but also provides a more consistent and controllable method for scientific visualization. The proposed methodology is computationally efficient and highly versatile, capable of producing a vast array of intricate and beautiful patterns. We provide detailed implementation protocols, including specific parameter sets and pseudocode, to ensure full reproducibility. The results demonstrate that our system can reliably generate high-resolution images showcasing up to four-fold or higher symmetries, constrained primarily by computational resolution limits, thereby opening new avenues for both mathematical art and the study of symmetric dynamical systems.
Copyright (c) 2026 PeiChang Ouyang; LiHua Liu; WeiQing Li https://creativecommons.org/licenses/by/4.0
UNSUPERVISED ANOMALY DETECTION IN MICROSERVICES USING AUTOENCODERS AND TEMPORAL PATTERNS
The increasing complexity and scale of microservice-based architectures have introduced new challenges in monitoring and anomaly detection. Traditional supervised learning methods often require extensive labeled data, which is impractical in dynamic and evolving environments. This paper presents an unsupervised anomaly detection framework based on autoencoders and temporal pattern modeling to identify abnormal behavior in microservice systems. By learning the reconstruction error of multivariate time-series data collected from microservice performance metrics, the model effectively distinguishes between normal and anomalous states. To capture temporal dependencies, we incorporate long short-term memory (LSTM) networks into the autoencoder architecture, enabling the detection of both point anomalies and contextual anomalies. Experimental evaluations on synthetic and real-world datasets demonstrate that our approach achieves high detection accuracy, low false positive rates, and robustness to unseen failure modes, making it suitable for real-time monitoring in production environments.
Copyright (c) 2025 Amelia Ford https://creativecommons.org/licenses/by/4.0
ANOMALY DETECTION IN API TRAFFIC USING UNSUPERVISED LEARNING FOR EARLY THREAT PREVENTION
The growing complexity and volume of API-based communication in modern web services have made API gateways increasingly vulnerable to attacks such as abuse, fraud, and volumetric threats. Traditional rule-based or signature-based detection methods struggle to identify novel or evolving attack patterns in real time. This paper proposes an unsupervised learning-based framework for early anomaly detection in API traffic to address these limitations. Leveraging clustering algorithms and autoencoders, the system learns the normal patterns of API usage without labeled data and flags deviations as potential threats. The approach is designed to be protocol-agnostic and scalable across diverse microservice architectures. Empirical evaluation using real-world API traffic datasets shows that our method achieves high detection accuracy and low false positive rates while significantly reducing manual configuration effort. The findings suggest that unsupervised learning is a promising direction for proactive, adaptive API threat detection.
Copyright (c) 2025 Peter Novak; Karolina Svoboda https://creativecommons.org/licenses/by/4.0
TEMPORAL GRAPH NEURAL NETWORKS FOR SEQUENTIAL ANOMALY DETECTION IN REAL-TIME E-COMMERCE STREAMS
The exponential growth of e-commerce transactions has created an urgent need for sophisticated anomaly detection systems capable of identifying fraudulent activities, system malfunctions, and unusual behavioral patterns in real-time data streams. Traditional anomaly detection approaches fail to capture the complex interdependencies between entities and the temporal evolution of their relationships within e-commerce ecosystems. This paper presents a novel framework that integrates Temporal Graph Neural Networks (TGNNs) with advanced graph representation learning techniques to address sequential anomaly detection in real-time e-commerce environments. Our approach leverages the structural modeling capabilities of Graph Neural Networks (GNNs) while incorporating temporal dynamics through specialized attention mechanisms and incremental learning strategies. The framework employs a multi-scale graph construction process that captures both local neighborhood structures and global network patterns, enabling the identification of anomalous nodes and subgraphs that deviate from established community structures. We introduce an adaptive random walk strategy inspired by Node2Vec that balances breadth-first and depth-first exploration to capture diverse types of anomalous patterns across different temporal scales. Comprehensive evaluation on three large-scale e-commerce datasets demonstrates significant performance improvements, with our method achieving 17.2% enhancement in F1-score and 14.6% improvement in Area Under Curve (AUC) compared to state-of-the-art approaches, while maintaining sub-second inference times suitable for real-time deployment.
Copyright (c) 2025 Sophia Walker; Luis Alvarez https://creativecommons.org/licenses/by/4.0
CONSTRUCTION OF ELECTRONIC COMPONENT DETECTION SYSTEM BASED ON CNN AND OPTIMIZATION OF PASSIVE AUTOFOCUS TECHNOLOGY
Autofocus technology is crucial in many fields, but traditional passive autofocus methods face issues such as low convergence speed, easy misjudgment, and focus breathing. Meanwhile, electronic component detection requires high accuracy and adaptability to practical scenarios. To address these problems, this study constructs an end-to-end electronic component detection baseline and explores the optimization of passive autofocus technology. First, we synthesized images of four electronic components and generated classification datasets as well as multi-object detection datasets. We adopted grayscale downsampling for feature extraction and combined standardization preprocessing with a Support Vector Classifier (SVC) for model training and testing. Additionally, we conducted a comparative analysis between the Convolutional Neural Network (CNN) and Vision Transformer (ViT) models. Experimental results show that the CNN-based detection system has reliable recognition performance for components with distinct morphological features. Compared with ViT, CNN exhibits better adaptability to small datasets, lower computational complexity, and stronger local feature capture capabilities, making it more suitable for practical application scenarios with limited hardware resources. This study provides a feasible baseline for electronic component detection and lays a foundation for the subsequent optimization of passive autofocus technology.
Copyright (c) 2025 HaoYang Nie https://creativecommons.org/licenses/by/4.0
UNDERSTANDING TRAFFIC ACCIDENTS: AN IN-DEPTH ANALYSIS OF HUMAN FACTORS, ECONOMIC IMPACTS, AND TRANSMISSION PATHWAYS IN TAICHUNG CITY
Traffic accidents represent a significant public safety concern in Taiwan region, particularly in Taichung City, where accident rates have consistently increased over the past five years. The research aims to explore accident hotspots, analyze the relationship between driving behavior and accident types, construct an analytical model of influencing factors, and propose effective accident prevention measures. Using a multifaceted approach that combines literature review and empirical data analysis, this study identifies key determinants of traffic accidents, including human factors, economic conditions, vehicle types, and road attributes. The findings reveal that human factors, particularly violations of traffic rules and age-related vulnerabilities, significantly influence accident severity. Additionally, economic disparities correlate with accident distribution, highlighting the need for tailored policy interventions. The study concludes with practical recommendations for enhancing traffic safety through improved education, stricter law enforcement, and infrastructure development. By integrating these strategies, local and central governments can effectively reduce the incidence of traffic accidents and their associated social costs.
Copyright (c) 2025 I-Ching Lin; Ya- Hui Hsieh https://creativecommons.org/licenses/by/4.0
FACTORS INFLUENCING PURCHASE INTENTIONS IN THE PACKAGING DESIGN OF H CATERING COMPANY’S SELF-HEATING HOTPOT
To investigate the causal mechanism linking H Catering Company's self-heating hotpot packaging design to consumer purchase intent and enhance product market competitiveness, this study employs empirical research based on 302 valid questionnaire responses, incorporating grey association analysis. Findings reveal that corporate employees aged 41 and above constitute the core consumer demographic, exhibiting highest satisfaction with packaging functionality and practicality, followed by visual elements and material safety. Grey relational analysis further confirmed the ranking of packaging design dimensions influencing purchase intent: functional utility > visual elements > material safety. This indicates functional utility as the core factor in consumer decision-making. Consequently, three-dimensional optimisation recommendations are proposed: Functional Utility Dimension: Design foldable stacking structures, concealed handles, and colour-changing water level indicators for diverse scenarios to enhance operational convenience. For visual elements, establish a differentiated visual system for flavour variations while enhancing information transmission efficiency and emotional resonance. Regarding material safety, upgrade food-grade inner packaging and puncture-resistant outer bags for heating packs, alongside improving safety guarantees for reusable materials to balance security and environmental sustainability. This research provides theoretical and practical references for optimising catering packaging design and boosting purchase intent.
Copyright (c) 2025 Yuan Lin; WenChao Pan; Ran Liu; HaiLin Wang https://creativecommons.org/licenses/by/4.0
LEARNING-BASED DYNAMIC RESOURCE ALLOCATION FOR SERVERLESS COMPUTING WITH GRAPH NEURAL NETWORKS
Serverless computing has emerged as a transformative paradigm in cloud infrastructure, offering dynamic resource provisioning and pay-per-use economics that significantly reduce operational overhead for application developers. However, the inherent challenges of serverless architectures, including unpredictable workload patterns, heterogeneous resource demands, and stringent quality-of-service requirements, necessitate intelligent resource allocation mechanisms that can adapt to rapidly changing conditions. This paper proposes a novel learning-based approach that leverages Graph Neural Networks (GNNs) to model the complex dependencies and resource relationships in serverless computing environments. Our framework captures the intricate structural patterns of function invocations, resource utilization, and inter-function dependencies through graph representations, enabling more effective resource allocation decisions. The GNN-based model employs a deep reinforcement learning architecture where an intelligent agent learns optimal policies through continuous interaction with the serverless environment. Through comprehensive experimental evaluation, we demonstrate that our approach achieves superior performance compared to traditional heuristic-based methods including Shortest Job First (SJF), Tetris, and Packer algorithms, reducing average job slowdown by approximately 40% under high load conditions while maintaining robust performance across varying workload intensities. The proposed system exhibits strong scalability with efficient training on graphs containing up to 10 million edges and demonstrates excellent generalization capabilities across diverse workload patterns.
Copyright (c) 2025 RuiWen Zhang https://creativecommons.org/licenses/by/4.0
THE PREDICTION AND INFLUENCING FACTORS OF BREAST CANCER RECURRENCE BASED ON RANDOM FOREST
Breast cancer (BC) is one of the most common malignant tumors in women. In 2022, it has become the second most common cancer after lung cancer. Although medical technology has made great progress in recent years and the survival rate of breast cancer patients has been greatly improved, according to research, about 40% of patients still relapse after treatment. Constructing a breast cancer recurrence prediction model and finding factors that affect breast cancer recurrence are of great significance for clinical treatment and prolonging patient survival. This study used the TCGA dataset and randomly divided the patients into training and test sets in a ratio of 8:2. Seven algorithms, including decision tree, logistic regression, support vector machine, K nearest neighbor, random forest, neural network, and adaptive boosting, were used to construct the model, and the performance of each model was evaluated. The results showed that the random forest model had the best effect, with an accuracy of 97.77%, a sensitivity of 94.23%, a specificity of 99.21%, a false positive rate of 0.79%, an F1 of 96.08%, and an AUC value of 96.72%. The features obtained by the model classification were ranked according to their importance. The top three features were: Age_at_Initial_Pathologic_Diagnosis_nature2012, lymph_node_examined_count and number_of_lymphnodes_Postive _by_he. The model provides more robust feature importance analysis results, providing an important reference for clinicians in breast cancer recurrence risk assessment and individualized treatment decision-making.
Copyright (c) 2025 Xi Yang; WenBei Zheng; WenYun Xia https://creativecommons.org/licenses/by/4.0
GNN-DRIVEN DETECTION OF ANOMALOUS TRANSACTIONS IN E-COMMERCE SYSTEMS
The exponential growth of e-commerce platforms has transformed global trade, enabling seamless digital transactions. However, this expansion has also led to an increase in fraudulent activities, including fake transactions, money laundering, and synthetic account fraud. Traditional fraud detection systems, which rely on predefined rules or supervised learning models, struggle to adapt to evolving fraudulent tactics. This study proposes a graph neural network (GNN)-driven anomaly detection framework to improve fraud detection in e-commerce systems by leveraging the inherent graph structure of online transactions. The proposed approach models e-commerce transactions as a heterogeneous transaction graph, where nodes represent users, merchants, and transaction records, while edges encode relationships such as purchase behavior, payment connections, and review activity. The framework integrates graph convolutional networks (GCN) and graph attention networks (GAT) for spatial anomaly detection, combined with temporal graph networks to track transaction sequence patterns. Unlike traditional methods, this approach captures both structural transaction dependencies and time-based anomalies, enabling the detection of coordinated fraud schemes. Extensive experiments on real-world e-commerce transaction datasets demonstrate that the proposed model outperforms conventional fraud detection techniques, achieving a higher detection accuracy and a significantly lower false positive rate. The results highlight the effectiveness of graph-based learning in identifying complex fraud rings, transaction laundering, and fraudulent refund behaviors. This research underscores the importance of GNN-powered fraud detection in enhancing e-commerce security, providing an adaptive and scalable solution for modern digital marketplaces.
Copyright (c) 2025 HaoYu Wu; JiaYi Wang https://creativecommons.org/licenses/by/4.0
EXPLORING APPLIED SCIENCE AND TECHNOLOGY
Applied science refers to the practical application of scientific knowledge to solve real-world problems. It incorporates theories and principles from pure sciences and adapts them to create solutions in various fields. Advanced technology, on the other hand, involves the use of cutting-edge tools, techniques, and methods that enhance the capabilities of applied science. Together, they form a crucial foundation for innovation and development across multiple disciplines, including engineering, medicine, and environmental sciences.
Copyright (c) 2024 Okechukwu Chidoluo Vitus https://creativecommons.org/licenses/by/4.0
THE REFORM AND INNOVATION OF THE TEACHING MODE OF CHORAL COURSE DRIVEN BY “IDEOLOGICAL AND POLITICAL EDUCATION AND AESTHETIC EDUCATION”
The thesis focuses on the reform and innovation of choral teaching mode driven by "ideological and political education and aesthetic education". Firstly, it expounds the research background, purpose and significance, and uses a variety of research methods to carry on in-depth discussion. By combing the relevant theoretical basis and literature review, it points out that there are some problems in the current choral teaching, such as obsolete textbook content, single teaching method and low participation of students. In-depth analysis of the causes of the problems, including insufficient attention to ideological and political education, teacher professional development lag, traditional concepts and education resources distribution imbalance. The reform and innovation strategies such as updating the content of teaching materials, introducing diversified teaching methods, strengthening the principal position of students and optimizing the allocation of resources are proposed. Finally, the conclusion emphasizes the importance of "ideological and political education, aesthetic education" in chorus teaching, which provides theoretical and practical reference for promoting the development of chorus teaching, so as to cultivate high-quality vocal talents with all-round development.
Copyright (c) 2024 Han Zhou https://creativecommons.org/licenses/by/4.0
ANALYSIS OF THE MARKETING STRATEGIES OF CHINA’S BYD NEW ENERGY VEHICLES IN THE THAI MARKET
In recent years, the concept of sustainable development has been widely concerned by people, and new energy vehicles have gradually become a new hot spot in the automobile market, and the new energy vehicle market is in the stage of rapid expansion. As a leading enterprise in China's new energy automobile industry, BYD has carefully laid out overseas, and officially announced its entry into the passenger car market in Thailand in August 2022. Since then, BYD has emerged in the Thai market with its excellent new energy vehicle technology. The production and sales of BYD's new energy vehicles in the Thai market have been rising, and BYD enterprises have become the leader in the Thai new energy vehicle market. The focus of this paper is to use BYD's new energy vehicle marketing strategy in the Thai market as the research object. First, use PEST analysis method to analyze the macro environment such as the political environment, economic environment, social environment, and technical environment of BYD's new energy vehicle. Secondly, through the analysis of its consumer needs, cost, convenience, and communication strategies through the 4C marketing theory, explore its marketing model in the Thai market, analyze the results and difficulties encountered by BYD's new energy vehicle marketing strategies in the Thai market, and explore the optimization plan for BYD's new energy vehicle marketing strategy in Thailand, so as to achieve the sustainable development of BYD's new energy vehicle company in the Thai market.
Copyright (c) 2024 HongYan Li https://creativecommons.org/licenses/by/4.0
THE ROLE OF IT AUDITING IN DATA SECURITY FOCUSING ON RISK IDENTIFICATION, STRENGTHENING INTERNAL CONTROLS, AND COMPLIANCE WITH SECURITY POLICIES
In an increasingly digital era, data security has become one of the top priorities for organizations. Information Technology (IT) auditing plays a crucial role in ensuring that the information systems and data managed by companies are protected from potentially harmful threats. This paper aims to explore the role of IT auditing in enhancing data security, focusing on risk identification, strengthening internal controls, and compliance with security policies. Through a qualitative approach, this research collects data from interviews with audit professionals and case studies from several organizations. The results indicate that IT auditing not only helps identify weaknesses in security systems but also provides recommendations that can enhance data protection effectiveness. Additionally, IT auditing contributes to raising awareness of the importance of data security throughout the organization. The conclusion emphasizes that the routine implementation of IT auditing is a strategic step necessary to maintain the integrity and confidentiality of information in an increasingly complex business environment. This abstract provides a brief summary of the objectives, methodology, results, and conclusions of the research regarding the role of IT auditing in data security.
Copyright (c) 2025 Indy Misya Rumata Situmorang; Kania Jasmine Azzahra; Iskandar Muda https://creativecommons.org/licenses/by/4.0
QUANTUM TELEPORTATION: THE CURRENT STATE OF RESEARCH
Through the process of quantum teleportation, a transfer of quantum information takes place from one particle to another while maintaining zero physical movement between the two. Quantum teleportation requires two entangled particles along with projective measurements followed by exchanging two bits of classical information. The process requires quantum mechanics principles which combine superposition with entanglement. Quantum entanglement occurs when two or more particles create an inseparable relationship that cuts off a separate description of an individual particle. Experimental realizations of quantum teleportation continue to operate on different physical platforms which include photons as well as atoms and superconducting circuits. Applications of Quantum teleportation includes quantum computing, secure communication, and cryptography. In future Scientists believe that a breakthrough in quantum computing technology could make teleportation a reality, including the ability to teleport a whole human. This paper provides the present research in quantum teleportation, including the theoretical framework, experimental implementations, and potential applications. We also discuss the challenges and limitations of quantum teleportation and propose future directions for research.
Copyright (c) 2025 Alamgir Khan; Jamal Shah https://creativecommons.org/licenses/by/4.0
SUSTAINABILITY MARKETING AND ETHICAL BRANDING IN THE DIGITAL SPACE
This paper explores the role of digital marketing in promoting sustainability and ethical branding, emphasizing how these practices shape consumer perceptions and engagement. As consumer awareness of environmental and social issues grows, brands are increasingly challenged to communicate their sustainability commitments and ethical standards transparently. This study employs a mixed-methods approach, combining quantitative surveys and qualitative case studies, to analyze the effectiveness of various digital marketing strategies in fostering trust and loyalty among eco-conscious consumers. Findings highlight the critical impact of social media engagement, influencer partnerships, and educational content in building authentic brand reputations. The research also addresses challenges such as greenwashing and the need for ethical transparency, underscoring the importance of verifiable, data-backed communication. The paper concludes with practical recommendations for brands to enhance authenticity and trustworthiness in digital spaces and identifies areas for further research to deepen understanding of sustainability marketing across different cultural and regional contexts. This study contributes valuable insights for brands and marketers aiming to navigate the complexities of ethical branding in an increasingly digital and consumer-conscious world.
Copyright (c) 2024 Rakibul Hasan Chowdhury https://creativecommons.org/licenses/by/4.0
DEEP LEARNING IN SOFTWARE MANAGEMENT: A COMPREHENSIVE REVIEW
The software industry is facing growing complexities in effectively managing software development lifecycles, from navigating project planning challenges to ensuring timely resolution of software defects. In recent years, the emergence of deep learning, a powerful subset of artificial intelligence, has begun to transform the landscape of software management. This comprehensive review article provides a thorough examination of the current and future applications of deep learning in optimizing software development practices, enhancing project management capabilities, and improving the overall efficiency and quality of software delivery. By automating the analysis of complex data patterns, deep learning algorithms have demonstrated the ability to augment the decision-making capabilities of software managers, leading to more informed, data-driven, and proactive management of software projects. This review delves into the key areas where deep learning is making a significant impact, including software defect prediction, effort estimation, project risk assessment, and workflow optimization. It also explores the challenges and considerations that must be addressed to ensure the successful integration of deep learning into software management, such as data availability and quality, model interpretability, and ethical implications. Furthermore, the paper outlines emerging trends and future directions, including the integration of deep learning with other cutting-edge technologies, the shift towards continuous and adaptive software management, and the role of deep learning in fostering more collaborative and cognitive software development environments. By harnessing the power of deep learning, software management professionals can unlock new frontiers of data-driven decision-making, predictive analytics, and intelligent automation, ultimately leading to improved project outcomes, enhanced productivity, and increased customer satisfaction. This review serves as a comprehensive guide for software managers, researchers, and industry stakeholders seeking to explore the transformative potential of deep learning in revolutionizing software management practices.
Copyright (c) 2024 Aditya Raghavan https://creativecommons.org/licenses/by/4.0
THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE FINANCIAL INDUSTRY: A REVIEW
Artificial Intelligence (AI) is revolutionizing the financial industry, offering new opportunities for improved efficiency, personalized services, and enhanced decision-making. This review article provides a comprehensive overview of the impact of AI on finance, discussing its applications, challenges, and future prospects. The article explores key areas where AI is making a significant difference, including fraud detection and prevention, risk management, trading and investment, and customer service. It highlights the benefits of AI in each domain, such as real-time anomaly detection, accurate credit risk assessment, algorithmic trading, and personalized financial advice. However, the article also addresses the challenges and considerations associated with AI adoption, including regulatory compliance, data privacy, algorithmic bias, integration with legacy systems, and the talent and skills gap. Looking ahead, the article discusses emerging trends and opportunities, such as the integration of AI with blockchain, AI-driven financial inclusion, and collaborative human-machine partnerships. It also explores potential disruptions to traditional financial roles and business models. The article concludes by emphasizing the need for strategic planning, investment in research and development, and collaboration among stakeholders to harness the full potential of AI in finance while navigating its challenges.
Copyright (c) 2024 Sarah Williams; David Lee https://creativecommons.org/licenses/by/4.0
UNITIZED AND EFFICIENT INSTALLATION CONSTRUCTION TECHNOLOGY FOR LARGE-AREA CURVED HONEYCOMB ALUMINUM CEILING
This paper proposes the "pre-assembly on the ground, unitized installation" construction technology to solve the ceiling construction problems of the first phase of the Hangzhou Convention and Exhibition Center project. It aims to improve construction efficiency and accuracy. By forming a professional surveying and mapping team to monitor the construction process throughout, we implemented structural optimization and precise control, effectively reducing the on-site labor demand by more than 40%, and reducing material loss and construction waste by more than 20%. This series of innovative measures not only ensures the project's construction period and quality goals, but also provides reliable technical references and experience references for the ceiling construction of large-scale exhibition buildings.
Copyright (c) 2024 Yi Zhu; ZhiCheng Bai; YueQiang Cao; XiaoXia Zhao; YinGuang Wang; XiaoNing Feng https://creativecommons.org/licenses/by/4.0