Computer Life (CPL) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of computer, network, communication science and technology, and its application in human social life across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Optimization and Practice of RED Network in Image Super Resolution Tasks
This study focuses on image super-resolution (ISR) technology, particularly exploring the application and optimization of RED networks in ISR tasks. Firstly, a RED network-based image super-resolution system was designed and implemented, verifying the effectiveness and reliability of the RED network in ISR tasks. Secondly, by adjusting the network structure and parameter configuration, the impact of different settings on model performance was analyzed in depth. The experimental results show that the RED network performs well in image super-resolution tasks, and appropriate parameter adjustments can significantly improve network performance. Finally, this study also discussed the optimization direction of RED network, providing reference for future research.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
A Review on Underwater Image Enhancement Models, Datasets and Metrics
Exploration of the underwater world is still the direction we are heading. Underwater imaging remains challenging due to blurred visibility, color deviation, and low contrast. Underwater image enhancement (UIE) represents a fundamental yet critical research challenge in the field of computer vision. Despite continuous advancements in hardware and algorithmic methodologies, there remains a lack of comprehensive summaries in this domain. To address this, we provide an overview of the research progress in UIE from the following perspectives. First, we introduce the three mainstream categories of UIE algorithms, along with the construction of datasets, including paired and unpaired datasets. Second, we conduct model training and evaluate the performance on a unified dataset, presenting results from both quantitative and qualitative perspectives. Finally, by utilizing the enhanced datasets for object detection tasks, we observe that the evaluation metrics of image enhancement and object detection do not exhibit a positive correlation.
Research on the application of GPS, total station and CAD Technology in architectural Grid
In the field of contemporary construction project management, the integrated operation of global positioning system, measurement station equipment, and computer-aided design technology has become a core tool to improve construction efficiency and accuracy. This article analyzes the application of these advanced technologies in building layout and discusses their practical effectiveness in on-site surveying, construction positioning, and deformation monitoring. Global positioning system technology can provide accurate geographic location information, improving the efficiency of construction site surveying and construction positioning. The measuring station equipment plays a crucial role in monitoring construction progress, ensuring construction quality, and conducting safety supervision. Computer aided design technology, with its efficient design capabilities and structural analysis functions, provides strong support for the rapid updating of architectural design and the improvement of construction accuracy.
Exploring the Efficacy of a ChatGPT-Based Application in Enhancing Oral English Proficiency for Chinese ESL Students
This research explores the impact of Chat GPT AI Characters on ESL learning, comparing them to traditional peer practice in language education. It investigates AI's influence on language proficiency, engagement, and response quality, drawing from literature on ESL challenges. Using mixed methods—pre- and post-test questionnaires, classroom experiments—the study evaluates two groups' language fluency, response speed, and answer quality. Thematic analysis uncovers student perspectives, while statistical methods like t-tests and correlation studies highlight group differences. Ethical considerations prioritize privacy and consent. Anticipated outcomes include improved language skills and engagement. Acknowledging limitations in sample size and cultural biases, this study illuminates AI's role in ESL education, informing future teaching practices.
Research on Teaching Practice of Big Data Platform Deployment and Operation Course with Shipping Characteristics
In the era of big data, big data technology has been widely applied, and the operation and maintenance of big data platforms, as a crucial component supporting these applications, has gradually become an indispensable part of enterprise IT architecture. The demand for talents with big data platform operation and maintenance skills is increasing. For higher vocational education institutions, how to cultivate big data platform operation and maintenance talents that meet professional requirements has become an urgent problem to be solved. In order to satisfy the enterprise demand for big data platform operation and maintenance talents and enhance students' employment competitiveness, this paper discusses the teaching content and teaching organization practice of the "Big Data Platform Deployment and Operation" course. The course adopts a project-based teaching mode, integrating the project case of "Ship Shore Power Data Analysis System" into the theoretical knowledge and technical explanations at various stages of the course. At the same time, it deeply excavates the ideological and political elements of maritime power and green environmental protection contained in the project, constructs a teaching mode with obvious shipping characteristics, and improves the quality of talent cultivation.
Big data platform deployment and operation
Research on the impact of artificial intelligence on college students' learning
With the rapid development of artificial intelligence (AI) technology, its application in the field of education is becoming more and more widespread. This study aims to explore the impact of AI on college students’ learning, focusing on its role in learning motivation, learning styles and academic performance. Through a combination of quantitative and qualitative methods, the study found that AI not only enhances students’ learning motivation and stimulates their interest, but also promotes the diversification of learning styles and helps students learn more effectively and independently. In addition, students who use AI generally perform better academically than non-users, indicating that intelligent technology has a positive impact on academic performance. The research results provide an important reference for the formulation of education policies and the improvement of educational practices.
DNA Microarray Technology Principles and Applications in Genetic Research
DNA microarray technology, a high-throughput gene analysis tool, has played a crucial role in genetic research. This paper explores the fundamental principles of DNA microarray technology, including its working mechanisms, sample preparation, and types of chips. It emphasizes the applications of DNA microarrays in detecting genetic variations and analyzing gene expression profiles, illustrating examples in single nucleotide polymorphism (SNP) analysis and genome-wide association studies (GWAS). The paper also discusses the technical details of high-throughput gene expression measurement and differential expression analysis. Additionally, it analyzes the main advantages of DNA microarray technology, such as high throughput and sensitivity, while also noting its limitations, including data interpretation complexity and technical costs. Finally, the paper looks forward to future development trends in DNA microarray technology, particularly its potential applications in personalized medicine.
Time Series Forecasting of Emission Trends Using Recurrent Neural Networks
This paper explores the application of Recurrent Neural Networks (RNNs) for forecasting emission trends, a critical aspect of addressing climate change and formulating effective environmental policies. Traditional forecasting methods, such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing, often fail to capture the complex nonlinear relationships and temporal dependencies inherent in emission data. In contrast, RNNs, particularly Long Short-Term Memory (LSTM) networks, are designed to recognize patterns in sequential data, making them well-suited for time series forecasting tasks. This study employs a comprehensive methodology that includes data collection from reputable sources, feature selection, RNN model design, and rigorous evaluation using various performance metrics. The results indicate that RNNs significantly outperform traditional forecasting techniques in terms of accuracy, providing valuable insights into future emission trajectories. The findings underscore the potential of RNNs as powerful tools for policymakers and researchers, facilitating more informed decision-making in the fight against climate change.
Application of Commonly Used Artificial Intelligence Tools in Field of Publishing
With the rapid development of technology, artificial intelligence (AI) has penetrated various industries, including the publishing industry. This article provides a detailed introduction to the current application status of commonly used AI tools in the field of publishing. Among them, the automated typesetting technology represented by Fangzheng Academic Publishing Cloud Service Platform has shown a dual leap in efficiency and quality compared to traditional typesetting methods, and has brought comprehensive innovations in intelligence, personalization, and cost control; Intelligent editing, represented by Grammarly, Heima Proofreading Software, and Shanfeng Software, has demonstrated their respective advantages in efficiency, accuracy, personalization, and technological innovation, providing strong support and assistance for editors. It is an indispensable and important tool in the field of publishing.
Spatial Graph Neural Networks for Accurate Product Demand Prediction
Accurate product demand prediction is critical for enhancing operational efficiency and strategic planning in supply chain management. Traditional forecasting methods, such as time series analysis and regression models, often fall short in capturing the complex spatial dependencies that influence consumer behavior across different geographical regions. This paper explores the application of Spatial Graph Neural Networks (SGNNs) as an innovative approach to address these limitations. SGNNs extend conventional Graph Neural Networks by integrating spatial information into the graph structure, allowing for a more nuanced understanding of how local market conditions, demographic factors, and competitive dynamics impact product demand. Our study demonstrates that SGNNs significantly outperform traditional forecasting techniques by effectively modeling spatial relationships. Comparative analyses reveal the advantages and limitations of SGNNs relative to other machine learning approaches, emphasizing the importance of spatial considerations in demand prediction. Future research directions include refining SGNN methodologies, incorporating diverse data sources, and exploring applications in various domains beyond supply chain management. This work underscores the potential of SGNNs to transform demand forecasting practices across industries, paving the way for more accurate and reliable prediction techniques.
Research on the Recognition Method of Wheat Ears Based on Image Color and Classical Model
In this paper, we study wheat images during the grouting period, and construct a dataset by extracting wheat spike features through image preprocessing, color space conversion and edge detection. CNN and AlexNet models are used to train and optimize the parameters and structure to improve the recognition accuracy. By adjusting the batch size, learning rate and training rounds, CNN performs optimally with Batch size=64, Epoch=5, and learning rate=0.0001; AlexNet also performs well with similar settings, but the training cost is larger. This study verifies the effectiveness of image processing combined with the classical model CNN for recognizing wheat sheaves, providing data and theoretical support for wheat yield prediction.
Digital image processing, edge contour detection, model structure analysis, model training method, constructive dataset.
Risks of Discrimination Violence and Unlawful Actions in LLM-Driven Robots
The integration of Large Language Models (LLMs) into robotics heralds significant advancements in human-robot interaction, enabling robots to perform complex tasks involving natural language understanding, common sense reasoning, and human modeling. However, despite their impressive capabilities, LLMs pose substantial ethical and safety concerns, particularly the risk of enacting discrimination, violence, and unlawful actions. This study conducts a comprehensive Human-Robot Interaction (HRI)-based evaluation of several highly-rated LLMs, focusing on their bias and safety criteria. Our findings reveal that LLMs exhibit significant biases across diverse demographic groups and frequently produce unsafe or unlawful responses when faced with unconstrained natural language inputs. These results underscore the urgent need for systematic risk assessments and robust ethical guidelines to ensure the responsible deployment of LLM-driven robots. We propose detailed strategies for mitigating these risks, including advanced bias detection techniques, robust safety mechanisms, and collaborative standards development. By addressing these critical issues, we aim to pave the way for the development of safer, fairer, and more reliable robotic systems.
Human-Robot Interaction (HRI), Large Language Models (LLMs), AI Ethics, Safety, Bias, Risk Assessment, Fairness
disentanglement using pre-trained features
This article proposes using pre trained features to address the challenge of detecting depression through speech. Traditional raw audio has shown low accuracy and insufficient generalization performance in depression detection. We use pre trained models that have been developed to extract features, which can be used to extract general feature representations from speech data. During the pre training process, we further decouple the speakers, introducing prior information and providing a better starting point for training downstream models. The results indicate that we achieved the best performance when using the extracted features from the CONTANTVEC pre- trained model with speaker decoupling improvement.
Pre-trained, depression detection, speech classification, speaker information.
The Text Classification Method Based on BiLSTM and Multi-Scale CNN
Text classification is an important fundamental technique for efficiently managing the huge amount of textual information on the Internet. In the past few years, due to the unprecedented success of deep learning research, the task of text categorization has gradually become the center of gravity in the field of natural language processing, and the algorithms applied to it have also transitioned over time from traditional machine learning methods to neural network models based on deep learning, as well as the emerging attention mechanisms and pre-trained language models with good results. This paper outlines the traditional machine learning methods and deep learning methods involved in text classification, comprehensively analyzes the research progress and achievements of deep learning models for text classification, and compares the advantages and disadvantages of each. Then the evaluation metrics used for text classification and commonly used labeled datasets are introduced. Finally, the challenges faced by the text classification task and the difficulties to be further researched are summarized and outlooked to provide reference and support for future researchers in this field.
Text classification; Neural networks; Deep learning; Natural language processing.
Research and Implementation of Java Code Automatic Generator Based on UML
As a model-based tool, automatic code generation technology has been known and used by more and more developers due to its efficiency and convenience. This article combines UML and automatic code generation technology, proposes the conversion rules between UML model diagrams and Java code, and uses object-oriented and modular design ideas to divide the Java code automatic generator into input layer, data verification layer, and code. Generation layer, each layer uses the form of a unified interface for data exchange. Designed and implemented a Java code automatic generator based on UML model diagram. The main contents of this paper are as follows:(1)Study the mapping rules for converting UML class diagrams and sequence diagrams into Java code, extract the elements and relationships of various model diagrams, build a metamodel of UML model diagrams, and study the mapping between metamodels and Java statement-level code Relationship, and put forward sentence-level code conversion rules.(2)Using DOM, Xpath and trufun-plato plugins to parse the metadata in UML model diagrams. Through the previous study of UML class diagrams, sequence diagrams and state diagrams to the mapping rules of Java code, the coding realized automatic generation of Java code based on UML And verified the proposed Java code mapping rules.Through tests and practical use proofs, the UML model-based automatic Java code generator can convert UML model diagrams into parts of Java code, verifying the correctness and feasibility of the rules studied.
Fraud Detection in Credit Risk Assessment Using Supervised Learning Algorithms
Our study systematically evaluates the performance of various supervised learning algorithms in credit risk assessment and fraud detection, including Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Gradient Boosting Tree, and Neural Network. The results show that in credit risk assessment, the Gradient Boosting Tree performed best with an accuracy of 90.5% and a ROC-AUC of 0.84, followed by Random Forest and Neural Network, with accuracies of 89.2% and 88.8%, and ROC-AUCs of 0.82 and 0.81, respectively. In the fraud detection task, the Neural Network performed best with an accuracy of 97.5% and a ROC-AUC of 0.88, while Gradient Boosting Tree and Random Forest achieved accuracies of 97.1% and 96.3%, and ROC-AUCs of 0.87 and 0.85, respectively. Feature importance analysis indicates that repayment history, credit limit, bill amount, and repayment amount are key features in credit risk assessment, while transaction amount, transaction time, and location are crucial for fraud detection. Data preprocessing and feature engineering played critical roles in enhancing model performance. Further optimization of model hyperparameters and addressing data imbalance issues will help improve model performance. In conclusion, ensemble learning methods and Neural Networks exhibit significant advantages in credit risk assessment and fraud detection. By employing scientific data preprocessing and feature engineering, combined with advanced machine learning algorithms, financial institutions can significantly enhance their risk management effectiveness.
Credit Risk Assessment, Fraud Detection, Supervised Learning Algorithms, Data Preprocessing, Feature Engineering.
KNN model based financial revenue forecast of Guangzhou
It is mainly carried on the analysis by the Guangzhou 2013-2020 financial revenue situation, firstly, in Guangzhou The Bureau of statistics downloads the data and preprocesses the data to determine the main factors affecting Guangzhou's fiscal revenue, then the KNN model is established to forecast the fiscal revenue of Guangzhou in 2021. The numerical results show that the method is effective.
Image-based Facial Emotion Detection SYSTEM
This paper proposes an image-based approach for emotion reco2gnition, aiming to infer human emotional states through the analysis of facial expression images. Firstly, we introduce the research background and significance of emotion recognition technology, and review current mainstream methods for emotion recognition. Secondly, we provide a detailed description of the design and implementation process of the proposed emotion recognition system, including key steps such as data preprocessing, feature extraction, and model construction. In the experimental section, we conduct systematic performance evaluations and comparative experiments using publicly available datasets, validating the effectiveness and accuracy of the proposed method. The experimental results demonstrate that our approach achieves outstanding performance in emotion recognition tasks and exhibits strong generalization capability. Finally, we discuss the limitations of the proposed method, future research directions, as well as the potential value and challenges in real-world applications. Through this research, we contribute to the further development and application of image-based emotion recognition technology.
Facaial emtion; Object detection; Face detection
Integrating Blockchain and Deep Reinforcement Learning for Secure and Efficient Supply Chain Management in Tertiary Institutions
The rapid adoption of blockchain technology has led to its increasing use in various domains, including supply chain management. However, the integration of blockchain into supply chain systems poses challenges related to security, efficiency, and regulatory compliance. This paper presents an analysis of a blockchain architecture for file sharing and management in tertiary institutions. The proposed framework combines the Soft Actor-Critic (SAC) deep reinforcement learning algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC algorithm learns adaptive policies under demand uncertainty, while the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. An experiment using real-world supply chain data demonstrated the superior performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The paper also discusses the evidentiary significance of blockchain records and the procedural implications of integrating this technology into the U.S. judicial system. The framework offers a promising solution for addressing the challenges of modern supply chains by leveraging blockchain, deep reinforcement learning, and zero-trust security principles.
Blockchain, Supply Chain Management, Deep Reinforcement Learning, Zero-Trust Security
Eye Fatigue Detection System Design and Implementation
This paper implements the detection and recognition of eye fatigue state based on artificial intelligence technology and computer vision. Through the detection and recognition of eye closure time, the system provides voice warning information to prompt the driver, so as to reduce the traffic accidents caused by fatigue driving. The system is connected to a night vision camera to achieve real-time face detection and recognition. On this basis, the system locates the driver's eye feature points and calculates the parameters corresponding to the eye feature state, which is used as the basis for eye fatigue detection. When the driver has closed eyes (non-blinking) driving, the system will issue voice warning and upload the image of fatigue state in real time using the cloud platform.
Eye Features, Fatigue Detection, Voice Alert, Computer Vision
Legal Implications and Challenges of Blockchain Technology and Smart Contracts
Blockchain technology and smart contracts have emerged as revolutionary tools with the potential to transform various industries by providing decentralized, transparent, and secure methods for recording transactions and automating contractual agreements. However, the adoption of these technologies also raises significant legal and regulatory challenges. This paper critically examines the legal implications and challenges associated with blockchain technology and smart contracts. Through an analysis of existing legal frameworks, case studies, and regulatory approaches across different jurisdictions, this study identifies key issues such as enforceability, jurisdiction, data privacy, and security. The paper also explores potential solutions and recommendations for addressing these challenges, aiming to contribute to the development of a robust legal framework that supports the responsible use of blockchain technology and smart contracts.
Blockchain, Smart Contracts, Legal Implications, Regulatory Challenges
Adaptive Neural Network Architectures for Cross-Domain Generalization
Cross-domain generalization remains a critical challenge in the field of machine learning. Traditional models often struggle to maintain performance when applied to new, unseen domains due to the variations in data distribution, known as domain shift. This paper proposes adaptive neural network architectures that dynamically adjust their structure based on the domain of the input data. Our approach leverages dynamic routing, attention mechanisms, and modular neural networks to enhance the model's adaptability and robustness. The dynamic routing mechanism enables the network to select different paths for different inputs, allowing it to adapt its processing dynamically. Attention mechanisms help the model focus on the most relevant parts of the input data, enhancing its ability to generalize across domains. Modular neural networks consist of multiple independent modules that can be selectively activated or deactivated based on the input domain. We also develop a dynamic adaptation mechanism that adjusts the network structure in real-time based on domain-specific input features. Experimental results on multiple benchmark datasets, including NEU-CLS and Lithium Electronic Surface Defect Classification (IESDC) datasets, demonstrate the effectiveness of our method. The proposed approach shows significant improvements in cross-domain performance compared to state-of-the-art models, achieving higher accuracy and robustness. Ablation studies confirm the contribution of each component to the overall performance enhancement. The findings highlight the potential of adaptive architectures in addressing the challenges of domain shift in machine learning applications.
Cross-Domain Generalization, Dynamic Routing, Attention Mechanisms, Modular Neural Networks, Domain Adaptation
The main purpose of business website design is generally to achieve the interconnection of enterprise products and services through website promotion, and to keep customers informed of the company and the latest product information provided by the company at any time. At the same time, it also provides customers with timely online services and order processing functions. The online shopping system mainly provides online shopping services centered on displaying and ordering laptops and related IT products. It is a platform for establishing more direct communication and exchange between enterprise products and customer service; Promoting one's own product is one of the main purposes for enterprises to go online; Present your products to customers, allowing them to freely choose and purchase the company's products through the website.
online shopping; JSP; MySQL; Hibernate; Struts.
The application of computer hardware and software in enterprise management
With the rapid development of information technology, computer hardware and software have been widely used in enterprise management. This paper aims to explore the application of computer hardware and software in enterprise management, first of all, the basic concept of computer hardware and software, and then analyzes the application of computer software and hardware in enterprise management, and then the computer hardware and software system in enterprise management advantage are summarized, including improve management efficiency, improve the quality of management, reduce management cost, etc. And through the commonly used enterprise management software system in enterprise management, the enterprise management, fully understand the depth of the meaning of the article, finally the computer hardware and software system in the enterprise management problems are discussed, and put forward the corresponding suggestions and countermeasures.
Computer hardware and software; enterprise management; enterprise management software; impact; differences; existing problems.
The Applications of Machine Learning Algorithms to the Field of Medical Diagnosis
With a large population base, the medical needs are also very large. Patients need a lot, and doctors have a big workload. In this case, the pressure of doctors increases accordingly. The competence and clinical experience of the doctor are at the heart of medical diagnosis. Accumulating experience requires massive practice and literature reading and comprehension. With the development of computer science and the deep study of machine learning theory, machines are gradually involved in the process of medical diagnosis. The application of machine learning algorithms has opened up a path for medical diagnosis, which can assist doctors to improve the efficiency of diagnosis and treatment. The main content of this paper is the main problems and their related machine learning algorithms, the challenges and future development.
Machine learning, medical diagnosis，Support vector meachine, Neural network.
Performance Validation of Jointly Optimized Graph Filters
In this paper, a novel jointly optimized graph filter is investigated, which is based on the vertex sampling method and the sum-of-squares integration method. It takes the frequency response of the ideal graph filter and the error value of the frequency response of the designed graph filter as the optimization objectives of the jointly optimized graph filter. Firstly, the theoretical analysis and validation of the practical performance of this method are carried out in this paper, and theoretically, we can get that the performance of the jointly optimized graph filter is improved compared with that of the non-jointly optimized graph filter. Then, the actual performance of the jointly optimized graph filter is verified using real data sets. Finally, the jointly optimized graph filter design method is compared with two benchmark graph filter design methods. It is concluded that the performance of the jointly optimized graph filter also has a good improvement over the benchmark graph filters.
Graph signal processing, graph filters, joint optimization.
Accurate spray paint design of reciprocating machine based on binocular stereo vision
There are many problems in the positioning and quality control of the traditional reciprocating paint spraying system, which seriously affect the quality and production efficiency of the paint spraying system. Because of the mechanical structure to determine the position, it is difficult to realize high-precision positioning environment illumination conditions will increase the position error of the positioning system. Therefore, we need to adopt new technologies to improve precision to solve these problems. Our team used the binocular camera and the computational science OpenCV depth recognition algorithm to realize accurate identification, built a painting model of the reciprocating machine, and designed a precise painting system based on binocular stereo vision.
Computer vision, binocular camera, Opencv, reciprocating machine.
Design of moving object detection algorithm based on RoboMaster
RoboMaster is a global high-level robot competition, in which the vision system of infantry robot is studied in this paper. In complex environment, the existing vision detection algorithms have some problems, such as unstable recognition, low detection accuracy and long running time. The improvement and optimization of the algorithm are as follows: image quality detection is carried out when brightness is bright or brightness is abnormal; gamma transform is used in the pre-processing process; maximum class difference method is used in image segmentation; SVM algorithm is used to train the selection of light strips; directional gradient histogram is used to extract features; and PNP algorithm is used to calculate the single-visual distance measurement and camera attitude. The experimental results show that the vision detection algorithm designed in the complex scene can realize the recognition of moving objects.
RoboMaster, Motion target detection, SVM, PNP attitude solution, multi-feature.
Research on the Current Situation of "567 Ceramics" in the Era of Live Broadcasting
With the deepening of internet technology and the convergence of internet traffic towards social interactions, the Douyin platform has become one of the entry points to the e-commerce platform through the social interaction between anchors and fans. The e-commerce model of "live streaming with goods" has become increasingly popular In 2020, influenced by the COVID-19 pandemic, live streaming with goods experienced explosive growth. Ceramic products have also shifted from the traditional offline ceramic market to online digital ceramic consumption. The pan-entertainment of mass media and the consumer group's shallow understanding and cultural grasp of "567 ceramics" necessitate effective communication of the traditional ceramic cultural value system. This paper takes several Douyin accounts such as "Factory 567 Ceramics", "Tu's Old Ceramic Warehouse", and "Jingxiang's Old Ceramic Warehouse" as research subjects. Based on the principle of comparability, it analyzes the current situation of live broadcasting development from five aspects: user scale, live audience portrait, sales data, work data, and return rate. It seeks to identify problems and countermeasures to aid the better and faster development of ceramic art.
Douyin Platform; 567 Ceramics; Live Streaming.
Research on the path of enhancing the international competitiveness of China's service trade
This paper aims to explore the way to improve the international competitiveness of service trade. Through the analysis of the theoretical basis and current situation of the international competitiveness of service trade, this paper puts forward the promotion path of optimizing the policy and institutional environment, improving the service quality and innovation, and strengthening the talent training and international cooperation. At the same time, this paper also analyzes the challenges of improving the international competitiveness of service trade, and puts forward the corresponding coping strategies. Through this study, it is expected to provide useful reference for improving the international competitiveness of China's service trade.
service trade; international competitiveness; promotion path