Pub Date : 2024-02-28DOI: 10.53469/jtpes.2024.04(02).02
Jingxiao Tian, Ao Xiang, Yuan Feng, Qin Yang, Houze Liu
This study developed a novel hybrid deep learning framework aimed at enhancing the accuracy of disease prediction using temporal data from Electronic Health Records (EHRs). The framework integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, leveraging the strength of CNNs in extracting hierarchical feature representations from complex data and the capability of LSTMs in capturing long-term dependencies in temporal information. An empirical investigation on real-world EHR datasets revealed that, compared to Support Vector Machine (SVM) models, standalone CNNs, and LSTMs, this hybrid deep learning network demonstrated significantly higher prediction accuracy in disease prediction tasks. This research not only advances the performance of predictive models in the health data analytics domain but also underscores the importance of adopting and further developing advanced deep learning technologies to address the complexity of modern medical data. Our findings advocate for a shift towards integrating complex neural network architectures in developing predictive models, potentially offering avenues for more personalized and proactive disease management and care, thereby setting new standards for future health management practices.
{"title":"Enhancing Disease Prediction with a Hybrid CNN-LSTM Framework in EHRs","authors":"Jingxiao Tian, Ao Xiang, Yuan Feng, Qin Yang, Houze Liu","doi":"10.53469/jtpes.2024.04(02).02","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(02).02","url":null,"abstract":"This study developed a novel hybrid deep learning framework aimed at enhancing the accuracy of disease prediction using temporal data from Electronic Health Records (EHRs). The framework integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, leveraging the strength of CNNs in extracting hierarchical feature representations from complex data and the capability of LSTMs in capturing long-term dependencies in temporal information. An empirical investigation on real-world EHR datasets revealed that, compared to Support Vector Machine (SVM) models, standalone CNNs, and LSTMs, this hybrid deep learning network demonstrated significantly higher prediction accuracy in disease prediction tasks. This research not only advances the performance of predictive models in the health data analytics domain but also underscores the importance of adopting and further developing advanced deep learning technologies to address the complexity of modern medical data. Our findings advocate for a shift towards integrating complex neural network architectures in developing predictive models, potentially offering avenues for more personalized and proactive disease management and care, thereby setting new standards for future health management practices.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"20 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140419731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.53469/jtpes.2024.04(02).06
Zhiming Zhao, Ning Zhang, Jize Xiong, Mingyang Feng, Chufeng Jiang, Xiaosong Wang
In the domain of e-commerce, customer reviews wield significant influence over business strategies. Despite the existence of various recommendation methodologies like collaborative filtering and deep learning, they often encounter difficulties in accurately analyzing sentiment and semantics within customer feedback. Addressing these challenges head-on, this paper introduces BERTFusionDNN, a novel framework merging BERT for extracting textual features and a Deep Neural Network for integrating numerical features. We assess the efficacy of our approach using a Women Clothing E-Commerce dataset, benchmarking it against established techniques. Our method adeptly extracts valuable insights from customer reviews, fortifying e-commerce recommendation systems by surmounting barriers associated with deciphering both textual nuances and numerical intricacies. Through this endeavor, we pave the way for more robust and effective strategies in leveraging customer feedback to optimize e-commerce experiences and drive business success.
{"title":"Enhancing E-commerce Recommendations: Unveiling Insights from Customer Reviews with BERTFusionDNN","authors":"Zhiming Zhao, Ning Zhang, Jize Xiong, Mingyang Feng, Chufeng Jiang, Xiaosong Wang","doi":"10.53469/jtpes.2024.04(02).06","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(02).06","url":null,"abstract":"In the domain of e-commerce, customer reviews wield significant influence over business strategies. Despite the existence of various recommendation methodologies like collaborative filtering and deep learning, they often encounter difficulties in accurately analyzing sentiment and semantics within customer feedback. Addressing these challenges head-on, this paper introduces BERTFusionDNN, a novel framework merging BERT for extracting textual features and a Deep Neural Network for integrating numerical features. We assess the efficacy of our approach using a Women Clothing E-Commerce dataset, benchmarking it against established techniques. Our method adeptly extracts valuable insights from customer reviews, fortifying e-commerce recommendation systems by surmounting barriers associated with deciphering both textual nuances and numerical intricacies. Through this endeavor, we pave the way for more robust and effective strategies in leveraging customer feedback to optimize e-commerce experiences and drive business success.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"47 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140418101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In modern software development and operations, DevOps (a combination of development and operations) has become a key methodology aimed at accelerating delivery, improving quality and enhancing security. Meanwhile, artificial intelligence (AI) and machine learning (ML) are also playing an increasingly important role in cybersecurity, helping to identify and respond to increasingly complex threats. In this article, we'll explore how AI and ML can be integrated into DevOps practices to ensure the security of software development and operations processes. We'll cover best practices, including how to use AI and ML for security-critical tasks such as threat detection, vulnerability management, and authentication. In addition, we will provide several case studies that show how these technologies have been successfully applied in real projects and how they have improved security, reduced risk and accelerated delivery. Finally, through this article, readers will learn how to fully leverage AI and ML in the DevOps process to improve software security, reduce potential risks, and provide more reliable solutions for modern software development and operations.
在现代软件开发和运营中，DevOps（开发与运营的结合）已成为一种关键方法，旨在加速交付、提高质量和增强安全性。与此同时，人工智能（AI）和机器学习（ML）也在网络安全领域发挥着越来越重要的作用，帮助识别和应对日益复杂的威胁。在本文中，我们将探讨如何将人工智能和 ML 集成到 DevOps 实践中，以确保软件开发和运营流程的安全性。我们将介绍最佳实践，包括如何将人工智能和 ML 用于威胁检测、漏洞管理和身份验证等安全关键任务。此外，我们还将提供几个案例研究，展示这些技术是如何在实际项目中成功应用的，以及它们是如何提高安全性、降低风险和加速交付的。最后，通过本文，读者将了解如何在 DevOps 过程中充分利用人工智能和 ML 来提高软件安全性、降低潜在风险，并为现代软件开发和运营提供更可靠的解决方案。
{"title":"Enhancing Security in DevOps by Integrating Artificial Intelligence and Machine Learning","authors":"Penghao Liang, Yichao Wu, Zheng Xu, Shilong Xiao, Jiaqiang Yuan","doi":"10.53469/jtpes.2024.04(02).05","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(02).05","url":null,"abstract":"In modern software development and operations, DevOps (a combination of development and operations) has become a key methodology aimed at accelerating delivery, improving quality and enhancing security. Meanwhile, artificial intelligence (AI) and machine learning (ML) are also playing an increasingly important role in cybersecurity, helping to identify and respond to increasingly complex threats. In this article, we'll explore how AI and ML can be integrated into DevOps practices to ensure the security of software development and operations processes. We'll cover best practices, including how to use AI and ML for security-critical tasks such as threat detection, vulnerability management, and authentication. In addition, we will provide several case studies that show how these technologies have been successfully applied in real projects and how they have improved security, reduced risk and accelerated delivery. Finally, through this article, readers will learn how to fully leverage AI and ML in the DevOps process to improve software security, reduce potential risks, and provide more reliable solutions for modern software development and operations.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"29 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140422968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.53469/jtpes.2024.04(02).04
Mengran Zhu, Ye Zhang, Yulu Gong, Changxin Xu, Yafei Xiang
Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions. This research proposes an innovative methodology combining Neural Networks (NN) and Synthetic Minority Over-sampling Technique (SMOTE) to enhance the detection performance. The study addresses the inherent imbalance in credit card transaction data, focusing on technical advancements for robust and precise fraud detection. Results demonstrate that the integration of NN and SMOTE exhibits superior precision, recall, and F1-score compared to traditional models, highlighting its potential as an advanced solution for handling imbalanced datasets in credit card fraud detection scenarios. This research contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities.
信用卡欺诈检测是金融行业面临的一项严峻挑战，需要复杂的方法来准确识别欺诈交易。本研究提出了一种结合神经网络（NN）和合成少数群体过度采样技术（SMOTE）的创新方法，以提高检测性能。该研究解决了信用卡交易数据固有的不平衡问题，重点关注技术进步，以实现稳健、精确的欺诈检测。研究结果表明，与传统模型相比，NN 和 SMOTE 的集成在精确度、召回率和 F1 分数方面都表现出了更高的水平，突出了其作为处理信用卡欺诈检测场景中不平衡数据集的先进解决方案的潜力。这项研究有助于不断努力开发有效和高效的机制，以保护金融交易免受欺诈活动的影响。
{"title":"Enhancing Credit Card Fraud Detection: A Neural Network and SMOTE Integrated Approach","authors":"Mengran Zhu, Ye Zhang, Yulu Gong, Changxin Xu, Yafei Xiang","doi":"10.53469/jtpes.2024.04(02).04","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(02).04","url":null,"abstract":"Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions. This research proposes an innovative methodology combining Neural Networks (NN) and Synthetic Minority Over-sampling Technique (SMOTE) to enhance the detection performance. The study addresses the inherent imbalance in credit card transaction data, focusing on technical advancements for robust and precise fraud detection. Results demonstrate that the integration of NN and SMOTE exhibits superior precision, recall, and F1-score compared to traditional models, highlighting its potential as an advanced solution for handling imbalanced datasets in credit card fraud detection scenarios. This research contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"112 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140422252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.53469/jtpes.2024.04(02).09
Quan Zhang, Beichang Liu, Guoqing Cai, Jili Qian, Zhengyu Jin
As the expression products of genes and macromolecules in living organisms, proteins are the main material basis of life activities. They exist widely in various cells and have various functions such as catalysis, cell signaling and structural support, playing a key role in life activities and functional execution. At the same time, the study of protein can better grasp the life activities from the molecular level, and has important practical significance for disease management, new drug development and crop improvement. Due to advances in high-throughput sequencing technology, protein sequence data has grown exponentially. The protein function prediction problem can be seen as a multi-label binary classification problem by extracting the features of a given protein and mapping them to the protein function label space. A variety of data sources can be mined to obtain protein function prediction features, such as protein sequence, protein structure, protein family, protein interaction network, etc. The initial steps are classical sequence-based methods, such as BLAST, which calculate the similarity between protein sequences and transmit annotations between proteins whose similarity scores exceed a specific threshold. This method has great limitations for protein function prediction without sequence similarity. Therefore, this paper analyzes the development prospect of bioanalysis and artificial intelligence through the application status and realization path of AlphaFold2 protein prediction algorithm based on artificial intelligence.
{"title":"Application of the AlphaFold2 Protein Prediction Algorithm Based on Artificial Intelligence","authors":"Quan Zhang, Beichang Liu, Guoqing Cai, Jili Qian, Zhengyu Jin","doi":"10.53469/jtpes.2024.04(02).09","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(02).09","url":null,"abstract":"As the expression products of genes and macromolecules in living organisms, proteins are the main material basis of life activities. They exist widely in various cells and have various functions such as catalysis, cell signaling and structural support, playing a key role in life activities and functional execution. At the same time, the study of protein can better grasp the life activities from the molecular level, and has important practical significance for disease management, new drug development and crop improvement. Due to advances in high-throughput sequencing technology, protein sequence data has grown exponentially. The protein function prediction problem can be seen as a multi-label binary classification problem by extracting the features of a given protein and mapping them to the protein function label space. A variety of data sources can be mined to obtain protein function prediction features, such as protein sequence, protein structure, protein family, protein interaction network, etc. The initial steps are classical sequence-based methods, such as BLAST, which calculate the similarity between protein sequences and transmit annotations between proteins whose similarity scores exceed a specific threshold. This method has great limitations for protein function prediction without sequence similarity. Therefore, this paper analyzes the development prospect of bioanalysis and artificial intelligence through the application status and realization path of AlphaFold2 protein prediction algorithm based on artificial intelligence.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"133 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140423401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E-commerce chatbots play a crucial role in customer service but often struggle with understanding complex queries. This study introduces a breakthrough approach leveraging the Falcon-7B model, a state-of-the-art Large Language Model (LLM) with 7 billion parameters. Trained on a vast dataset of 1,500 billion tokens from RefinedWeb and curated corpora, the Falcon-7B model excels in natural language understanding and generation. Notably, its 16-bit full quantization transformer ensures efficient computation without compromising scalability or performance. By harnessing cutting-edge machine learning techniques, our method aims to redefine e-commerce chatbot systems, providing businesses with a robust solution for delivering personalized customer experiences.
{"title":"Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization","authors":"Yang Luo, Zibu Wei, Guokun Xu, Zhengning Li, Ying Xie, Yibo Yin","doi":"10.53469/jtpes.2024.04(02).08","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(02).08","url":null,"abstract":"E-commerce chatbots play a crucial role in customer service but often struggle with understanding complex queries. This study introduces a breakthrough approach leveraging the Falcon-7B model, a state-of-the-art Large Language Model (LLM) with 7 billion parameters. Trained on a vast dataset of 1,500 billion tokens from RefinedWeb and curated corpora, the Falcon-7B model excels in natural language understanding and generation. Notably, its 16-bit full quantization transformer ensures efficient computation without compromising scalability or performance. By harnessing cutting-edge machine learning techniques, our method aims to redefine e-commerce chatbot systems, providing businesses with a robust solution for delivering personalized customer experiences.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"85 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140423751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.53469/jtpes.2023.03(10).03
Shuangshuang Liu, Ping Li
At the present stage, the degree of social information intelligence continues to increase, electronic information technology has been widely used in various industries, has become an important part of People's Daily life. Electronic information technology can also promote the development and progress of enterprises, most of the enterprises have begun to use electronic information technology in the process of development, it can be seen that the electronic information technology has considerable prospects for development, this paper analyzes the construction engineering enterprises for the use of electronic information technology, hoping to provide a certain reference to the relevant personnel. The paper firstly reviewed the classic newsvendor model and expounded the establishment and solution to the model. Then the model of the supply chain system was established based on the classical newspaper model. Finally, based on the basic theory of repurchase contract, a repurchase agreement was established. The supply chain buyback contract under the newsvendor model can not only realize the coordination of supply chain, but also realize the distribution of profit in the supply chain.
{"title":"The Application of Electronic Information Technology in Building Engineering","authors":"Shuangshuang Liu, Ping Li","doi":"10.53469/jtpes.2023.03(10).03","DOIUrl":"https://doi.org/10.53469/jtpes.2023.03(10).03","url":null,"abstract":"At the present stage, the degree of social information intelligence continues to increase, electronic information technology has been widely used in various industries, has become an important part of People's Daily life. Electronic information technology can also promote the development and progress of enterprises, most of the enterprises have begun to use electronic information technology in the process of development, it can be seen that the electronic information technology has considerable prospects for development, this paper analyzes the construction engineering enterprises for the use of electronic information technology, hoping to provide a certain reference to the relevant personnel. The paper firstly reviewed the classic newsvendor model and expounded the establishment and solution to the model. Then the model of the supply chain system was established based on the classical newspaper model. Finally, based on the basic theory of repurchase contract, a repurchase agreement was established. The supply chain buyback contract under the newsvendor model can not only realize the coordination of supply chain, but also realize the distribution of profit in the supply chain.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"31 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.53469/jtpes.2023.03(10).06
Hao Hu, Shulin Li, Jiaxin Huang, Bo Liu, Change Che
Casting defects encompass a broad spectrum of imperfections, such as blow holes, pinholes, burrs, shrinkage defects, and various metallurgical anomalies. Detecting these defects manually requires a trained eye, and even the most diligent inspectors can inadvertently overlook subtle irregularities. To address these challenges, there is a growing movement toward automation in quality control. Deep learning models, including the Xception model, are being harnessed to create a robust classification system. Such models have the capacity to analyze thousands of product images with precision, identifying defects that may elude human inspectors. Furthermore, data augmentation techniques are applied to enhance the dataset, allowing the model to generalize more effectively and improve its defect recognition capabilities.
{"title":"Casting Product Image Data for Quality Inspection with Xception and Data Augmentation","authors":"Hao Hu, Shulin Li, Jiaxin Huang, Bo Liu, Change Che","doi":"10.53469/jtpes.2023.03(10).06","DOIUrl":"https://doi.org/10.53469/jtpes.2023.03(10).06","url":null,"abstract":"Casting defects encompass a broad spectrum of imperfections, such as blow holes, pinholes, burrs, shrinkage defects, and various metallurgical anomalies. Detecting these defects manually requires a trained eye, and even the most diligent inspectors can inadvertently overlook subtle irregularities. To address these challenges, there is a growing movement toward automation in quality control. Deep learning models, including the Xception model, are being harnessed to create a robust classification system. Such models have the capacity to analyze thousands of product images with precision, identifying defects that may elude human inspectors. Furthermore, data augmentation techniques are applied to enhance the dataset, allowing the model to generalize more effectively and improve its defect recognition capabilities.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":" 65","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.53469/jtpes.2023.03(10).01
Ziyue Ding, Lingyao Jia, Linxi Tian, Xiangxiang Li
The recent pressure pipeline reform and China's opening to the outside world have become catalysts for further economic and social development. The oil fields in the northeast and the pure water projects in the southwest have benefited the residents, however, due to the working pressure problems in the pipeline transportation, which are caused by the corrosion of the pipeline materials and the various problems arising from the operation of the pipeline, resulting in long-term safety problems. Therefore, the importance of checking pipeline pressure cannot be missed. In order to ensure the safety of China's natural gas pipelines under pressure, we need to concentrate on analyzing various existing problems and quickly find solutions.
{"title":"Analysis of Common Problems and Improvement Measures of Pressure Pipeline Inspection","authors":"Ziyue Ding, Lingyao Jia, Linxi Tian, Xiangxiang Li","doi":"10.53469/jtpes.2023.03(10).01","DOIUrl":"https://doi.org/10.53469/jtpes.2023.03(10).01","url":null,"abstract":"The recent pressure pipeline reform and China's opening to the outside world have become catalysts for further economic and social development. The oil fields in the northeast and the pure water projects in the southwest have benefited the residents, however, due to the working pressure problems in the pipeline transportation, which are caused by the corrosion of the pipeline materials and the various problems arising from the operation of the pipeline, resulting in long-term safety problems. Therefore, the importance of checking pipeline pressure cannot be missed. In order to ensure the safety of China's natural gas pipelines under pressure, we need to concentrate on analyzing various existing problems and quickly find solutions.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"16 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.53469/jtpes.2023.03(10).04
Shufan Zhu, Rongyan Zhu
After decades of reform and opening up, China's development structure is constantly upgrading, and the quality of development is also constantly improving. As one of the main sources of clean energy, electric energy plays a vital role in people's production and life. Therefore, how to better serve all aspects of the society with power production technology is the current development must pay attention to the topic. This paper focuses on the analysis of the current situation of electric power production technology and its future development trend. Therefore, under the circumstances that logistics resources are limited and logistics activities are increasingly dependent on the market and industrial structure, Henan Province should scientifically plan and rationally allocate and use logistics resources in implementation of "One Belt, One Road" strategy to achieve the best input and output, improve the overall efficiency and level of logistics; vigorously develop local economy from the perspective of system coordination, realize coordination among logistics enterprises, logistics industry coordination, as well as inter-industry coordination and regional coordination.
{"title":"Analysis of the Development Trend of Electric Power Technology","authors":"Shufan Zhu, Rongyan Zhu","doi":"10.53469/jtpes.2023.03(10).04","DOIUrl":"https://doi.org/10.53469/jtpes.2023.03(10).04","url":null,"abstract":"After decades of reform and opening up, China's development structure is constantly upgrading, and the quality of development is also constantly improving. As one of the main sources of clean energy, electric energy plays a vital role in people's production and life. Therefore, how to better serve all aspects of the society with power production technology is the current development must pay attention to the topic. This paper focuses on the analysis of the current situation of electric power production technology and its future development trend. Therefore, under the circumstances that logistics resources are limited and logistics activities are increasingly dependent on the market and industrial structure, Henan Province should scientifically plan and rationally allocate and use logistics resources in implementation of \"One Belt, One Road\" strategy to achieve the best input and output, improve the overall efficiency and level of logistics; vigorously develop local economy from the perspective of system coordination, realize coordination among logistics enterprises, logistics industry coordination, as well as inter-industry coordination and regional coordination.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"95 33","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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