International Journal of Advance in Applied Science Research
International Journal of Advance in Applied Science Research (ISSN: 3065-9965), is a peer-reviewed scholarly journal. It publishes annually and continuous publication schedule is kept so that articles are published online as soon as the final proofs are approved by the authors from 2022-2024. Due to the increase in the number of submitted manuscripts to the journal, the International Journal of Contemporary Education and Teaching Research will publish 12 issues (once a month) in a year starting from January 2025. It aims to promote rapid communication and dialogue among researchers, scientists, and engineers working in the areas of clinical medicine. It is hoped that this journal will prove to be an important factor in raising the standards of discussion, analyses, and evaluations relating to clinical medicine programs.
A Framework for the Implementation and Optimization of Classified Cybersecurity Protection in Electric Power Critical Information Infrastructure
Amidst the era of rapid information technology advancement and the deep integration of digital systems with critical infrastructure, the power industry faces increasingly severe cybersecurity challenges. The stability and security of the power grid, as a national critical infrastructure, are paramount to societal and economic well-being. This paper systematically examines the application and implementation of the Cybersecurity Level Protection (CLP) framework within the power industry. It begins by summarizing the core concept of the CLP scheme and its associated legal, regulatory, and standard requirements. A critical analysis of the current cybersecurity posture in the power sector reveals significant shortcomings, including an incomplete multi-layered security protection architecture, insufficient security awareness among personnel, and inadequate emergency response capabilities for cyber incidents. In response to these identified gaps, this paper provides a detailed discussion on the strategic implementation of the CLP framework. The discussion focuses on four pivotal areas: the precise and rational classification of information systems according to mandated protection levels; the systematic construction of a robust, tiered technical security protection system encompassing network, host, and application security; the establishment of a comprehensive security management system with clear policies, responsibilities, and accountability mechanisms; and the enhancement of continuous security operation, maintenance, and monitoring capabilities. Synthesizing these analyses, the paper ultimately proposes a holistic set of improvement measures across four interconnected dimensions: (1) Technology, advocating for the deployment of advanced threat detection, intrusion prevention, and security analytics platforms; (2) Management, emphasizing rigorous risk assessment, mandatory security training, and strict compliance audits; (3) Emergency Response and Recovery, focusing on the development of detailed incident response plans, regular drills, and reliable data backup strategies; and (4) Cooperation and Sharing, promoting information sharing and collaborative defense initiatives within the industry and with national cybersecurity agencies. The overarching aim of these integrated strategies is to comprehensively elevate the cybersecurity maturity of the power industry, thereby fundamentally ensuring the safe, stable, and resilient operation of the power system in the face of evolving cyber threats.
Copyright (c) 2025 Zhang Zhang https://creativecommons.org/licenses/by-nc/4.0
Towards a Modular Paradigm: Developing and Deploying Artificial Intelligence in Embedded Software Systems
The rapid advancement of artificial intelligence (AI) has ushered in a new era of innovation for embedded systems, leading to the widespread deployment of embedded AI software across diverse domains such as smart homes and industrial automation. This proliferation, however, concurrently imposes stringent demands on software flexibility to accommodate evolving functionalities, heterogeneous hardware, and dynamic operational environments. In this context, modular design has emerged as a pivotal architectural paradigm. Modular embedded AI software effectively addresses these challenges by decomposing complex, monolithic systems into a cohesive set of independent, self-contained, and highly cohesive modules. This decomposition significantly enhances development efficiency by enabling parallel development, simplifying debugging, and facilitating component reuse. Furthermore, it substantially improves system maintainability, allowing for targeted updates, bug fixes, or algorithm replacements within specific modules without necessitating a full system overhaul. This inherent adaptability empowers the software to respond more effectively to rapidly changing market demands and technological upgrades. Crucially, a well-defined modular architecture provides a standardized framework for the efficient integration and deployment of diverse AI algorithms, allowing developers to plug in, test, and compare different models for perception, decision-making, or control with minimal friction. This paper elaborates on the core principles, architectural patterns, and implementation methodologies for constructing modular embedded AI systems. It argues that the strategic adoption of modularity is not merely a software engineering best practice but a critical enabler for building robust, scalable, and future-proof intelligent embedded systems capable of sustaining the next wave of innovation at the intersection of AI and edge computing.
Copyright (c) 2025 Kangming Xu https://creativecommons.org/licenses/by-nc/4.0
A Blockchain-Enabled Security System for Enhancing Data Integrity and Access Control in Hospital Communication Networks
The relentless iteration of medical information technology has positioned the hospital computer communication system as the central nervous system of modern healthcare infrastructure. This system is entrusted with the storage and transmission of highly sensitive data, including patient diagnostic information, comprehensive electronic medical records (EMRs), and critical clinical decision support plans. However, the inherent complexity of hospital network environments, coupled with the continuous evolution of cyber-attack methodologies, has led to increasingly prominent security vulnerabilities within medical communication networks. Incidents of data leakage or tampering pose a direct threat to patient privacy and safety, while the manipulation of diagnostic and treatment information can severely disrupt the continuity of care, potentially leading to life-threatening risks in extreme scenarios. Blockchain technology, characterized by its decentralization, immutability, and cryptographic security, presents an innovative pathway to address these inherent security flaws. Its distributed ledger structure ensures data integrity and provides a transparent, auditable trail for all transactions. This paper, building upon this premise, conducts a systematic study on the construction of a robust security protection system for hospital computer communication networks, supported by blockchain technology. We propose a framework that leverages blockchain to create a secure, tamper-proof repository for access logs and data transaction records, ensuring the traceability and non-repudiation of all data access events. The study details the architectural integration of blockchain within existing hospital information systems, focusing on modules for identity and access management, secure data sharing between departments, and automated audit compliance. By providing a reference model that enhances data confidentiality, integrity, and availability, this research aims to contribute to the development of more resilient and trustworthy medical communication infrastructures, ultimately safeguarding patient welfare and the operational continuity of healthcare services.
Copyright (c) 2025 Ruihan Dong https://creativecommons.org/licenses/by-nc/4.0
An Attention-Guided Dynamic Stride Projection Attack Framework
Black-box transfer attacks represent a critical paradigm in adversarial machine learning, whereby adversarial examples crafted against a surrogate (source) model are deployed to deceive unknown target models. This approach serves as a vital tool for conducting security audits and enhancing the robustness of deep neural networks. A primary challenge, however, lies in the tendency of iterative attack methods to overfit the specific characteristics of the source model, thereby diminishing their cross-model transferability. To mitigate this issue, this paper proposes a novel Dynamic Step-length Projection Attack method based on Attention Guidance (DSP-Attack). The core of our method is twofold. First, it introduces a dynamic mechanism that adaptively adjusts the projection step size during the iterative perturbation generation process. This is motivated by the observation that initial perturbations often possess higher transferability; the proposed strategy thus prioritizes these early stages by employing a larger effective step size, while strategically curtailing potentially overfitting and ineffective perturbations in later iterations. Second, the method incorporates an attention guidance mechanism, derived from the source model's gradient-weighted class activation mapping, to focus the perturbation budget on regions that the model deems most salient for its predictions. This ensures that the adversarial modifications are applied to semantically meaningful and model-sensitive areas, thereby increasing the likelihood of the attack transferring to other architectures. Comprehensive experiments on the ImageNet dataset demonstrate the superior efficacy of our approach. The proposed DSP-Attack achieves significant performance improvements in transferability across a diverse set of target models, including ResNet, VGG, and DenseNet architectures, outperforming several state-of-the-art baseline methods. These findings affirm that jointly optimizing the attack trajectory via dynamic step-length control and spatial attention guidance is a potent strategy for crafting highly transferable adversarial examples.
Copyright (c) 2025 Yiming Chen https://creativecommons.org/licenses/by-nc/4.0
The Evolving Landscape of Network Information Security: Emerging Challenges and Trends in Maintenance Technologies
The accelerating global digital transformation has elevated network information security and maintenance from a technical concern to a core issue underpinning the stability of the digital economy and society. This paper provides a systematic review of the core technical framework constituting modern cybersecurity defenses. It meticulously examines foundational technologies including data encryption—encompassing symmetric (AES), asymmetric (RSA/ECC), and the emerging paradigm of homomorphic encryption—alongside intrusion detection/prevention systems (IDS/IPS) utilizing both signature-based and anomaly-based detection, firewalls, and the principles of zero-trust architecture. The analysis critically evaluates their respective application scenarios and inherent limitations. The contemporary threat landscape, however, presents formidable challenges that test these traditional defenses. These include sophisticated Advanced Persistent Threats (APTs), the exponentially expanding attack surface presented by vulnerable Internet of Things (IoT) devices, insidious software supply chain attacks, and the looming threat of quantum computing to current public-key cryptosystems. The severity is quantified by an average annual global cost of data breaches reaching $4.35 million (IBM, 2023). In response to this evolving landscape, this paper explores the profound integration prospects of emerging technologies. It investigates how Artificial Intelligence (AI) enables proactive threat intelligence analysis and automated incident response; how blockchain's immutability can fortify identity management and ensure data integrity; and how privacy-computing techniques can enable data utilization without exposing raw information. Synthesizing these insights, the paper proposes a forward-looking development direction: the construction of an "intelligent + dynamic" comprehensive defense system. This paradigm emphasizes deep technological integration (e.g., AI-driven blockchain analytics), strategic coordination between active defense mechanisms and passive detection capabilities, and a holistic approach that combines technical measures with legal and regulatory collaborative governance. The conclusions aim to provide a theoretical reference and architectural blueprint for building a more secure, resilient, and trustworthy future network ecosystem.
Copyright (c) 2025 Xiaodong Liu https://creativecommons.org/licenses/by-nc/4.0
A Locally Autonomous Driving System Driven by Collaborative AI Vision and IoT Monitoring for Path Planning and Decision-Making
This paper presents a novel domestic autonomous driving system design that synergistically integrates AI vision and IoT monitoring technologies. The proposed platform adopts a multi-sensor fusion architecture, combining edge computing with lightweight model deployment to establish a robust closed-loop "perception-decision-control" system. At the core of the system lies the YOLOv5 object detection model, which enables real-time and accurate identification of dynamic obstacles. Enhanced by Beidou-3/GPS dual-mode high-precision positioning, the system ensures precise localization even in challenging environments. Furthermore, 5G/Beidou short-message dual-channel communication facilitates seamless data transmission, enabling remote monitoring and control. Through these advanced technologies, the system achieves dynamic obstacle avoidance, adaptive speed regulation, and efficient route planning, significantly enhancing operational safety and efficiency. The solution offers a highly reliable and cost-effective domestic alternative for industrial autonomous driving. By leveraging AI vision and IoT monitoring, the system reduces reliance on expensive proprietary sensors and infrastructure, making it accessible to a broader range of applications. The closed-loop design ensures rapid response to changing environmental conditions, while the lightweight model deployment minimizes computational overhead. This innovative approach not only addresses the challenges of autonomous driving in complex industrial settings but also provides a scalable framework for future advancements. The proposed system represents a significant step forward in the development of affordable and efficient domestic autonomous driving solutions.
Copyright (c) 2025 Yuanbin Zhu, Bin Liu https://creativecommons.org/licenses/by-nc/4.0
Towards Joint Energy Efficiency and Performance Optimization: A Scheduling Strategy for Green-Aware Heterogeneous Computing
Against the backdrop of unprecedented growth in global computing demands and the rapid expansion of the artificial intelligence (AI) industry, the energy consumption and carbon emissions associated with computing infrastructure have emerged as pivotal challenges, significantly impeding the green transformation of the digital economy. The escalating reliance on high-performance computing (HPC) systems, data centers, and edge computing devices to support AI-driven applications, big data analytics, and cloud services has led to a substantial increase in energy use, exacerbating environmental concerns. This surge in energy demand not only strains power grids but also contributes to greenhouse gas emissions, undermining sustainability efforts in the digital sector. A critical challenge in this context is the spatio-temporal mismatch between resource scheduling and green energy supply within heterogeneous computing power networks. These networks, which integrate diverse computing resources such as CPUs, GPUs, and specialized accelerators, often operate in environments where renewable energy sources like solar and wind are intermittent. The variability in renewable energy generation, coupled with the dynamic nature of computing workloads, creates a complex scheduling problem. Traditional resource allocation strategies, which predominantly prioritize performance metrics such as latency and throughput, fail to account for the availability and utilization of green energy. Consequently, computing nodes frequently rely on non-renewable energy sources, leading to higher carbon footprints and inefficiencies in energy use. To address these challenges, this paper proposes a green energy-aware computing scheduling strategy designed to optimize the spatio-temporal coordination between heterogeneous computing resources and renewable energy supply. The strategy is grounded in a comprehensive sensing framework that integrates three key dimensions: task latency sensitivity, computational preference characteristics, and node-level green energy reserves. By analyzing task latency sensitivity, the strategy identifies workloads that can tolerate flexible execution times, enabling them to be scheduled during periods of abundant renewable energy. Computational preference characteristics, which include the type of processing required (e.g., CPU-intensive or GPU-intensive tasks), are used to match tasks with the most suitable computing nodes, thereby enhancing overall efficiency. Node-level green energy reserves are continuously monitored to ensure that tasks are allocated to nodes with sufficient renewable energy availability, minimizing reliance on non-renewable sources.
Copyright (c) 2025 Ling Wu https://creativecommons.org/licenses/by-nc/4.0
Deconstructing Deep Learning: An Analysis of its Intellectual Architecture and Methodological Evolution
Deep learning has revolutionized fields such as computer vision and natural language processing, yet its theoretical foundation remains an area of active research. This paper provides a comprehensive review of the core theories underpinning deep learning, encompassing the principles of neural networks, the backpropagation algorithm, and the expressive characteristics of deep architectures. We analyze four prominent neural network architectures: feedforward neural networks, which serve as the foundational framework; convolutional neural networks (CNNs), which achieve breakthroughs in image processing through localized receptive fields; recurrent neural networks (RNNs), which excel in sequence modeling; and Transformers, which leverage self-attention mechanisms to enhance performance across diverse tasks. Furthermore, we systematically trace the evolution of optimization algorithms, from stochastic gradient descent (SGD) to advanced variants such as Adam, and discuss regularization techniques including Dropout and batch normalization. The paper also examines emerging trends, such as the rise of large-scale pre-trained models and frontier technologies like model compression. However, we highlight persistent challenges, including the escalating demand for computational resources and the inherent lack of interpretability in deep learning models. Addressing these issues is crucial for advancing the field and ensuring its sustainable development.
Copyright (c) 2025 Huatian Li https://creativecommons.org/licenses/by-nc/4.0
A Framework for 5G-Enabled Medical Informatization: Key Application Scenarios and Implementation Barriers
The rapid evolution of 5G technology has emerged as a transformative force in the realm of medical informatization, offering unprecedented opportunities for innovation in healthcare service delivery models. This article provides a comprehensive overview of the specific applications of 5G technology across four critical domains within the medical sector: remote healthcare, medical Internet of Things (IoT), big data analytics, and smart hospital construction. In remote healthcare, 5G technology enables real-time, high-quality video consultations and telemedicine services, bridging geographical gaps and improving access to specialist care. The medical IoT leverages 5G's low-latency and high-bandwidth capabilities to facilitate seamless communication between wearable devices, sensors, and healthcare systems, enhancing patient monitoring and management. Big data analytics powered by 5G allows for the efficient processing and analysis of vast amounts of medical data, enabling predictive analytics and personalized treatment plans. Smart hospital construction, meanwhile, benefits from 5G's ability to support interconnected and automated systems, optimizing operational efficiency and patient experiences. Despite these advancements, the integration of 5G technology into medical informatization is not without challenges. Key issues include weak network infrastructure in certain regions, which can hinder the seamless deployment of 5G-enabled services, data security risks due to the increased connectivity and data exchange, and a shortage of interdisciplinary talent capable of navigating the complexities of 5G and healthcare integration. To address these challenges, this article proposes targeted suggestions aimed at fostering the deep integration of 5G technology with the medical industry. These include investing in robust network infrastructure to ensure widespread coverage, implementing advanced cybersecurity measures to protect patient data, and developing educational programs to cultivate a workforce proficient in both 5G technology and healthcare applications. By addressing these challenges, the medical industry can harness the full potential of 5G technology to revolutionize healthcare delivery, improve patient outcomes, and drive innovation in the sector.
Copyright (c) 2025 Xiaole Zhang https://creativecommons.org/licenses/by-nc/4.0
The Intelligent Transformation Paradigm: Architectural Evolution and Strategic Pathways for the Digital Enterprise
In the era of digital transformation, enterprises face profound challenges in optimizing their digital system architecture and navigating the pathway toward intelligent transformation. This article systematically addresses these challenges by identifying and tackling core pain points that hinder current enterprise digital transformation efforts, including efficiency bottlenecks, insufficient technological adaptability, and lagging organizational change. To overcome these obstacles, we propose a comprehensive transformation framework that emphasizes "technology empowerment, business restructuring, and organizational collaboration." This framework is designed to facilitate a holistic approach to digital transformation, ensuring that technological advancements are seamlessly integrated with business processes and organizational structures. Through a rigorous literature analysis and multi-industry case studies, we reveal the three key elements that are critical for effective digital architecture: modularity, scalability, and data-driven design. Modularity allows for flexible and adaptable system configurations, enabling enterprises to respond swiftly to changing market demands. Scalability ensures that the digital infrastructure can grow and evolve alongside the organization, accommodating increased workloads and expanding operational scope. Data-driven design leverages advanced analytics and machine learning techniques to inform decision-making processes, thereby enhancing the efficiency and accuracy of business operations. Furthermore, the article explores the gradual path of intelligent transformation, highlighting the importance of a phased approach that allows enterprises to build upon their existing digital capabilities while integrating new technologies. Research findings indicate that enterprises can achieve operational efficiency improvement, cost optimization, and competitiveness reconstruction through the deep integration of systematic architecture upgrades and intelligent tools. By adopting this integrated approach, organizations can not only survive but thrive in the digital wave, positioning themselves as leaders in their respective industries. In conclusion, this article provides a theoretical framework and practical guidance for enterprise digital transformation, offering valuable insights into how organizations can navigate the complexities of digital transformation and achieve sustainable growth. The proposed framework and pathway serve as a roadmap for enterprises seeking to leverage digital technologies to enhance their competitive edge and drive long-term success.
Copyright (c) 2025 Zeping Li https://creativecommons.org/licenses/by-nc/4.0
A Laser Lidar-Based Body Turning Detection Method in VR Interaction
The traditional key-based operation modes of virtual reality (VR) interaction often suffer from insufficient immersion and limited hand operation, which hinder the natural and intuitive user experience. To address these challenges, this paper proposes a novel body turning detection method based on laser lidar technology. By aligning the laser lidar with the human back, the method systematically collects distance data in a 4×4 grid format. The collected data is then processed using mean value analysis to enhance accuracy and robustness. Based on the data relationships, the method effectively judges the turning direction and further calculates the body turning speed to control the platform speed in real-time. Experimental results demonstrate that this method can accurately and efficiently realize human body turning detection and speed regulation. By providing technical support for liberating hand operations, the method significantly enhances the realism and immersion of VR intelligent interaction. This innovative approach not only improves the user experience but also opens new avenues for the development of more advanced and user-friendly VR systems. The findings contribute to the broader field of human-computer interaction, offering practical insights for the design and optimization of VR technologies.
Copyright (c) 2025 Shufang Chen https://creativecommons.org/licenses/by-nc/4.0
Research on the Application of Al in the Integrity Audit of Inspection and Testing Reports
In the rapidly evolving landscape of professional services, the integration of artificial intelligence (AI) into critical workflow processes has become a transformative force, particularly in domains where accuracy and efficiency are paramount. This paper presents a comprehensive investigation into the application of AI-driven intelligent systems for the integrity review of inspection and testing reports, a process that serves as a cornerstone for quality assurance and regulatory compliance across diverse industries. By leveraging the advanced capabilities of the Deepseek large-scale language model through the Coze platform, we have developed an innovative AI-based review entity specifically designed to automate and enhance the validation of inspection and testing documentation. The core functionality of this intelligent entity lies in its ability to perform automated identification and verification of critical information embedded within inspection reports. This includes, but is not limited to, meticulous checking of data accuracy, ensuring logical consistency across report sections, and identifying potential discrepancies or anomalies that may compromise the integrity of the findings. The system employs sophisticated natural language processing techniques to parse and understand complex technical documents, enabling it to extract and analyze key metrics, measurements, and conclusions with a high degree of precision. Additionally, the entity is capable of cross-referencing data with established industry standards and regulatory requirements, thereby ensuring compliance and reducing the risk of oversight. To evaluate the efficacy of this AI-driven approach, we conducted a series of comparative experiments between the AI review entity and traditional manual review methods. The experimental results demonstrate a substantial improvement in review efficiency, with the AI system achieving a 40% reduction in processing time compared to conventional manual procedures. Equally important, the error rate in report verification was significantly diminished by approximately 30%, underscoring the system's enhanced reliability in maintaining report integrity. These findings are particularly noteworthy given the critical importance of inspection and testing reports in various industries, where even minor errors can have profound implications for quality control, regulatory compliance, and decision-making processes. The study not only showcases the immediate benefits of this AI-powered solution but also highlights its broader potential for revolutionizing professional service industries. By automating routine verification tasks, the system allows human experts to focus on more complex analysis and interpretation, thereby optimizing resource allocation and enhancing overall productivity. Furthermore, the consistent and objective nature of AI-driven review processes mitigates the risk of human bias or oversight, contributing to more robust and trustworthy documentation practices.
Copyright (c) 2025 Le Sun https://creativecommons.org/licenses/by-nc/4.0
Research on the Application of Artificial Intelligence Pattern Recognition Technology in University Training Laboratory
In the context of rapid technological advancements and accelerated innovation processes, higher education institutions are undergoing profound informatization transformations. University training rooms, as core venues for practical teaching, confront significant challenges including low management efficiency, suboptimal resource allocation, and inadequate security monitoring. These issues hinder the effective utilization of training resources and impede the quality of hands-on learning experiences. Artificial intelligence (AI) pattern recognition technology presents a promising solution for the intelligent upgrade of training rooms, leveraging advanced capabilities such as image recognition, speech processing, and biometric analysis. By integrating AI-driven pattern recognition, training rooms can achieve enhanced equipment management through real-time monitoring and predictive maintenance, improved safety monitoring via intelligent surveillance and anomaly detection, optimized resource allocation based on usage patterns and demand forecasting, and innovative teaching models enabled by adaptive learning environments. This paper explores the specific applications of AI pattern recognition in these domains, analyzing its implementation pathways and potential challenges. The discussion aims to provide theoretical insights and practical guidance for the intelligent construction of university practical teaching platforms, fostering more efficient, secure, and adaptable learning environments.
Copyright (c) 2025 Mingyun Lei https://creativecommons.org/licenses/by-nc/4.0
The Deep Learning Paradigm for Plant Image Classification: A Systematic Evaluation of Architectural Efficacy
The advent of deep learning, particularly Convolutional Neural Networks (CNNs), has heralded a paradigm shift in image analysis. CNNs possess a hierarchical architecture capable of automatically learning discriminative feature representations directly from raw pixel data, thereby surpassing the limitations of manual feature engineering. While CNNs have demonstrated remarkable success in general object recognition, their application to specialized domains like plant science warrants a more nuanced and thorough investigation. Many existing studies either employ overly simplistic datasets or fail to provide a comprehensive methodological breakdown that includes critical steps like data augmentation and hyperparameter optimization. This study aims to address this gap by systematically constructing, training, and evaluating a deep learning pipeline for plant image classification. The primary objective is not merely to apply a CNN but to conduct a rigorous performance evaluation of the ResNet-18 architecture, elucidating the impact of strategic choices in dataset preparation, transfer learning, and parameter tuning on the final classification efficacy. Regarding Algorithm Selection and Model Configuration, the ResNet-18 architecture was chosen for its proven efficacy and efficient depth, which mitigates the vanishing gradient problem through its residual learning blocks. Instead of training from scratch, we leveraged Transfer Learning. The model was initialized with pre-trained weights from the ImageNet dataset, capitalizing on its rich repository of general visual features. The final fully connected layer was replaced to match the number of plant species in our dataset. This approach significantly accelerates convergence and improves performance, especially with datasets of moderate size. This study conclusively demonstrates that a CNN-based approach, specifically utilizing the ResNet-18 architecture with transfer learning and comprehensive data augmentation, can achieve state-of-the-art performance in the complex task of plant image classification. The attained accuracy of 98% significantly surpasses what is typically feasible with traditional methods. The research provides a reproducible blueprint for applying deep learning in specialized domains, highlighting the critical importance of a well-designed pipeline from data preparation to model optimization. The implications extend beyond botany, offering a template for image-based classification challenges in other scientific fields. Future work will involve scaling the model to a larger number of species, exploring the integration of multi-modal data (e.g., hyperspectral imagery), and deploying the model in a real-time, mobile application for field use by botanists and agriculturalists.
Copyright (c) 2025 Yongshen Liu https://creativecommons.org/licenses/by-nc/4.0
A Data-Driven Framework for Cloud MES Implementation in Smart Manufacturing Environments
Modern manufacturing production has increasingly embraced intelligent and automated development, driven by advancements in technologies such as artificial intelligence, robotics, and the Internet of Things. However, many small and medium-sized manufacturing enterprises (SMEs) face significant challenges in achieving full-scale intelligent manufacturing due to constraints in technological capabilities and financial resources. These limitations hinder their ability to compete effectively in the evolving market landscape, where efficiency, flexibility, and innovation are critical for survival. The rapid proliferation of intelligent technologies, including Industry 4.0 and smart factory solutions, underscores the urgency for SMEs to adapt and integrate these tools into their operations. Failure to do so may result in a loss of market share and reduced competitiveness. To address this issue, this paper proposes a cloud-based Manufacturing Execution System (MES) design scheme tailored for intelligent manufacturing workshops. By leveraging cloud computing services, SMEs can overcome their own infrastructure limitations by renting cloud servers from providers, thereby enabling cost-effective access to advanced MES functionalities. This approach facilitates the gradual intelligent transformation of manufacturing workshops, ensuring that SMEs can enhance productivity, reduce operational costs, and improve decision-making without substantial upfront investment. The cloud MES system not only fills the technological gaps but also empowers SMEs to achieve sustainable growth in the competitive manufacturing sector.
Copyright (c) 2025 Zhi Hu https://creativecommons.org/licenses/by-nc/4.0
Enhancing Operational Efficiency and Accuracy: A Usability Study of HMI Design for Apple Sorting Systems
In the rapidly evolving landscape of industrial automation, human-machine interface (HMI) design plays a pivotal role in the development of control systems, significantly influencing operational efficiency, user experience, and system maintainability. This paper presents a comprehensive HMI design scheme tailored for an automatic apple sorting system, which integrates advanced automation components such as frequency converters, pneumatic cylinders, programmable logic controller (PLC) units, and sensors. The proposed HMI design encompasses four critical modules: the operation interface, which provides a visually intuitive and user-friendly platform for system control; I/O input and output management, ensuring seamless data exchange between the system and external devices; and PLC system diagnosis, enabling real-time monitoring and troubleshooting of system components. The design philosophy emphasizes clarity, aesthetics, and user-centric functionality, offering a streamlined operation process that aligns with end-user requirements. Furthermore, the modular architecture of the HMI facilitates convenient maintenance and future upgrades, thereby enhancing the system's adaptability and longevity. Experimental validations demonstrate that the proposed HMI design not only improves the overall performance of the automatic apple sorting system but also reduces operational complexity and training time for users. This study contributes to the broader field of industrial automation by providing a scalable and effective HMI solution that can be adapted to similar applications.
Copyright (c) 2025 Cungui Yang https://creativecommons.org/licenses/by-nc/4.0
Evaluating the Efficacy of Innovative AI Applications in Civil Aviation Safety and Maintenance Operations
The civil aviation industry has witnessed unprecedented growth in recent years, driven by increasing global connectivity and demand for air travel. However, this rapid expansion has also brought heightened concerns regarding safety assurance and operational efficiency, which are now recognized as pivotal factors for sustainable development in the sector. In response to these challenges, artificial intelligence (AI) technology has emerged as a transformative force, leveraging its advanced capabilities in data processing, predictive modeling, and automated decision-making to revolutionize traditional aviation practices. This study explores the innovative applications of AI in civil aviation safety and operational maintenance, focusing on four key areas: flight risk prediction, air traffic management, predictive maintenance, and airport resource scheduling. By analyzing real-world case studies and synthesizing existing literature, the research demonstrates how AI-driven solutions can enhance aviation safety through early risk detection and mitigation, optimize air traffic flow to reduce congestion and delays, predict equipment failures to minimize downtime, and efficiently allocate airport resources to improve overall operational performance. The findings highlight the significant potential of AI to not only improve safety standards but also reduce operational costs, thereby contributing to the long-term viability and competitiveness of the civil aviation industry.
Copyright (c) 2025 Cheng Zheng https://creativecommons.org/licenses/by-nc/4.0
Development and Validation of a Multispectral Vision System for In-Situ Detection of Pesticide Residues on Agricultural Produce
In response to the pressing demand for rapid and accurate detection of pesticide residues on the surface of fruits and vegetables, this study proposes an innovative visual detection system based on multispectral fusion. The system integrates an improved deep convolutional neural network (DCNN) with a Transformer architecture, enabling precise feature extraction from multispectral images. By leveraging the complementary strengths of these models, the system achieves enhanced classification accuracy and robustness against environmental variations. Furthermore, an adaptive weighted fusion algorithm, combined with improved kernel principal component analysis (KPCA), is employed to optimize the quality of spectral data fusion. This approach effectively mitigates the impact of noise and enhances the discriminative power of the features. To address the challenge of real-time detection, a parallel computing architecture based on CUDA and a heterogeneous hardware configuration scheme are designed. This implementation significantly accelerates the processing speed, enabling rapid analysis of large-scale datasets. Additionally, system stability is enhanced through redundant hardware design, intelligent fault-tolerant mechanisms, and optimized data transmission protocols. These measures ensure reliable operation under diverse environmental conditions, making the system suitable for field applications.
Copyright (c) 2025 Guishen Ding https://creativecommons.org/licenses/by-nc/4.0
An Intelligent Sorting and Decision-Making Platform for Express Packaging Waste Utilizing Computer Vision
This paper explores the transformative application of artificial intelligence (AI) in the domain of express packaging waste recycling, presenting a comprehensive analysis of an AI-driven technical architecture. The architecture integrates key technologies such as intelligent identification and classification, dynamic path optimization and scheduling, and data-driven prediction and decision-making. These technologies enable precise sorting of waste materials, efficient logistics planning, and proactive resource allocation, significantly enhancing operational efficiency. Furthermore, the paper elaborates on AI-empowered recycling process reengineering, which includes expanding the recycling capabilities of intelligent express lockers, establishing automated sorting and processing centers, and implementing blockchain-based traceability and credit systems. These innovations streamline waste collection, reduce manual intervention, and ensure transparency in recycling processes. In addition, the paper examines the operation mechanism of the AI-driven recycling system, focusing on the design of multi-party collaborative incentive mechanisms, dynamic pricing and market regulation, and risk early warning and emergency response systems. By fostering collaboration among stakeholders, adjusting pricing based on real-time demand, and mitigating potential risks, the system promotes sustainable resource management. Through these advancements, AI not only revolutionizes express packaging waste recycling by improving efficiency and reducing costs but also contributes to environmental sustainability by minimizing waste and optimizing resource utilization.
Copyright (c) 2025 Chong Wei https://creativecommons.org/licenses/by-nc/4.0
Extending the Theory of Organ Projection: A Media Aesthetic Study of Ultra-High-Definition Image Creation
The emergence of Ultra-High-Definition (UHD) imaging technology is fundamentally reshaping the visual foundations of film and television production, introducing a paradigm shift that transcends mere technical advancements. This transformative wave necessitates rigorous theoretical examination to address the aesthetic implications it engenders. Drawing upon Ernst Kapp's "organ projection theory" as a foundational framework, this study posits that UHD technology represents a radical extension of human sensory perception, fundamentally reconstructing two pivotal aesthetic dimensions: "realism" and "immersion." Through a meticulous analysis of the technical characteristics of UHD and their correspondence with the "projection-style" enhancement of the senses, this paper elucidates the profound theoretical implications of this technological evolution. By examining specific cinematic texts and literary evidence, the research demonstrates how UHD's high-resolution imagery, expanded color gamut, and enhanced dynamic range contribute to a heightened sense of presence and emotional engagement, thereby redefining audience experience. The study further explores the underlying techno-sensory-cultural logic of this transformation, offering insights into the broader implications for creative practice and academic inquiry in the UHD era. These findings provide a speculative reference for both creators and researchers seeking to navigate the aesthetic challenges and opportunities presented by this technological revolution.
Copyright (c) 2025 Linhui Dong https://creativecommons.org/licenses/by-nc/4.0
The Impact of AI Algorithm Optimization on Content Quality Enhancement in Short Video and Micro-Film Production
The rapid evolution of artificial intelligence (AI) is fundamentally reshaping the landscape of short-form video and micro-film production. This paper provides a systematic analysis of the pivotal role played by AI algorithm optimization in enhancing the overall quality of this dynamic media format. We specifically investigate its transformative impact across three core dimensions: content creation, post-production efficiency, and end-user engagement. By leveraging advanced deep learning architectures, including Generative Adversarial Networks (GANs) for visual synthesis, Convolutional Neural Networks (CNNs) for scene understanding and aesthetic enhancement, and Recurrent Neural Networks (RNNs) for narrative structure prediction, AI-driven tools are enabling unprecedented capabilities. These innovations facilitate not only a surge in video generation speed and a significant uplift in image resolution and stability but also the development of more coherent narrative logic and deeply immersive interactive experiences. However, the integration of AI also presents challenges, such as the risk of stylistic homogenization and emerging ethical concerns regarding deepfakes and data provenance. This study concludes that the strategic optimization of AI algorithms is a critical driver for quality advancement, pushing the boundaries of creative expression and setting new technical benchmarks for the industry. Future work should focus on developing more explainable and controllable AI systems that empower, rather than replace, human creativity.
Copyright (c) 2025 Shan Ge https://creativecommons.org/licenses/by-nc/4.0
Architecture Design and Performance Analysis of MPLS VPN in Meteorological Wide-Area Networks
The continuous advancement of meteorological observation technologies has led to an exponential growth in data volume, necessitating highly efficient and robust data transmission networks to improve forecasting accuracy and timeliness. Concurrently, meteorological services are undergoing a strategic shift towards intensification and structural flattening to enhance operational responsiveness. However, the conventional three-tier (provincial-municipal-county) tree-structured network architecture has emerged as a critical bottleneck, characterized by inherent latency and single points of failure that impede data flow and service agility. To address these limitations, this paper proposes and describes the implementation of a Multi-Protocol Label Switching (MPLS) Virtual Private Network (VPN) architecture to reconstruct the foundational meteorological network. This modernized design establishes a secure, three-layer mesh network that enables logical direct connectivity between any two nodes across the administrative hierarchy. The results demonstrate that this MPLS VPN-based mesh topology significantly enhances the stability, reliability, and data transmission efficiency of the meteorological private network. By facilitating seamless, low-latency communication and providing robust traffic engineering capabilities, this network transformation effectively supports the evolving demands of data-intensive and time-sensitive modern meteorological operations, paving the way for more advanced and reliable weather prediction services.
Copyright (c) 2025 Weishi Chen https://creativecommons.org/licenses/by-nc/4.0
Exploring the Role of Artificial Intelligence Technology in Enhancing Public Services in the Field of E-Government
As a pivotal catalyst driving the new wave of technological revolution and industrial transformation, artificial intelligence (AI) has emerged as a critical enabler for economic and social development, fundamentally reshaping government administration and public service delivery models. In the domain of e-government, AI technologies provide comprehensive capabilities including rapid data perception, intelligent decision-making support, and automated process execution. These technological advancements are instrumental in optimizing administrative approval workflows, advancing the standardization of government services, facilitating open data sharing initiatives, and significantly enhancing government decision-making capacity. The innovative application of AI in e-government systems operates at multiple levels to create transformative effects. At the technical level, AI-driven solutions improve service quality through intelligent automation and predictive analytics. Institutionally, these technologies facilitate government function transformation by enabling data-driven policy formulation and administrative reform. At the safeguard level, AI applications contribute to enhanced social governance capacity through improved risk assessment and crisis management capabilities. The integration of AI into e-government systems thus effectively increases the quality and efficiency of public service delivery, while simultaneously providing more convenient and efficient service options for both enterprises and citizens. This technological transformation represents a significant leap forward in modernizing governance structures and optimizing public service provision.
Copyright (c) 2025 Zhengqiang Ma https://creativecommons.org/licenses/by-nc/4.0
Redefining Electronic Information Engineering: The Cross-Disciplinary Fusion and Its Impact on Signal Processing Paradigms
As the fundamental enabler of modern electronic information systems, signal processing technology plays a pivotal role in driving continuous advancements in full data-lifecycle management through paradigm-shifting innovations. Recent breakthroughs in the artificial intelligence (AI) technology cluster, particularly of deep-learning algorithms with heterogeneous computing architectures, have fundamentally reshaped the theoretical boundaries and practical dimensions of classical signal processing methodologies. This transformative convergence has enabled unprecedented capabilities in real-time data analysis, adaptive filtering, and intelligent decision-making, thereby redefining the efficiency and scalability of electronic information systems. Against this backdrop, this paper systematically examines the evolution and innovation trajectory of information processing technology within electronic information engineering. By integrating theoretical analysis with empirical case studies, we elucidate the synergistic effects of AI-driven signal processing techniques on system performance optimization, energy efficiency, and fault tolerance. Furthermore, we explore emerging challenges such as computational complexity, data privacy concerns, and hardware constraints, while proposing potential solutions based on hybrid computing frameworks and edge intelligence paradigms. The insights derived from this study not only contribute to the academic discourse on advanced signal processing but also provide actionable guidance for researchers and practitioners seeking to leverage cutting-edge technologies in electronic information engineering. By fostering interdisciplinary collaboration between AI, signal processing, and hardware design, this work aims to catalyze further innovation in the field.
Copyright (c) 2025 Honggang Li https://creativecommons.org/licenses/by-nc/4.0
Towards Clinical Deployment: A Deployment-Oriented Lightweight Transformer for Low-Latency Medical Image Segmentation
This paper presents a comprehensive investigation into the system design of lightweight Transformer architectures specifically tailored for medical image segmentation tasks. The standard Transformer model, while demonstrating remarkable performance in various domains, suffers from excessive parameters and high computational complexity when applied to medical imaging, which often involves high-resolution volumetric data. To address these challenges, we propose a series of lightweight improvements including: (1) a sparse attention mechanism that reduces computational burden by focusing on relevant regions of the image, (2) a modular design approach that enables flexible configuration of network components based on task requirements, and (3) parameter sharing and pruning techniques that eliminate redundant connections while maintaining model accuracy. The proposed system demonstrates significant advantages in clinical applications, particularly in real-time surgical navigation and telemedicine scenarios. By efficiently operating on resource-constrained devices such as portable ultrasound machines and mobile diagnostic platforms, the system enables precise medical image analysis with minimal latency. This technological advancement provides crucial technical support for the development of precision medicine and inclusive healthcare, offering potential solutions for resource-limited settings and remote healthcare delivery.
Copyright (c) 2025 Haoyue Liu https://creativecommons.org/licenses/by-nc/4.0