As the number of electric vehicles continues to grow globally, more and more advanced technologies are being applied to electric vehicles. Energy recovery technology is a crucial part of them. The basic principle of how electric vehicles work is to convert chemical energy into electrical energy, and then convert electronic energy into kinetic energy. Actually, the ability of deceleration is necessary for electric vehicle. However, large amount of energy may lose due to this period. So, people developed energy in order to recover this energy.This study contain the introduction of two different types of energy recovery system, electronic energy recovery system and mechanical energy recovery system. Including two major advancements in this field in recent decades which contribute a lot in electronic recovery system. In addition, I introduced some latest research on mechanical energy recovery system which utilized hydro-mechanical technology. Most of my analysis are based on ECE (European standard operating conditions) . Worth noticing that more areas for expansion of this technology are also mentioned in my essay such as rotorcrafts and motor-driven tanks which are the latest products.
{"title":"Research on Energy Recovery System","authors":"Ziheng Huang","doi":"10.61173/zp1k4554","DOIUrl":"https://doi.org/10.61173/zp1k4554","url":null,"abstract":"As the number of electric vehicles continues to grow globally, more and more advanced technologies are being applied to electric vehicles. Energy recovery technology is a crucial part of them. The basic principle of how electric vehicles work is to convert chemical energy into electrical energy, and then convert electronic energy into kinetic energy. Actually, the ability of deceleration is necessary for electric vehicle. However, large amount of energy may lose due to this period. So, people developed energy in order to recover this energy.This study contain the introduction of two different types of energy recovery system, electronic energy recovery system and mechanical energy recovery system. Including two major advancements in this field in recent decades which contribute a lot in electronic recovery system. In addition, I introduced some latest research on mechanical energy recovery system which utilized hydro-mechanical technology. Most of my analysis are based on ECE (European standard operating conditions) . Worth noticing that more areas for expansion of this technology are also mentioned in my essay such as rotorcrafts and motor-driven tanks which are the latest products.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"78 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452220","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 recent years, the fusion of advanced technologies with virtual reality has opened new cultural preservation and engagement avenues. This dissertation explores the innovative application of Neural Radiance Fields (NeRF) technology in transcending the boundaries of physical museums and transporting their treasures into the metaverse. While classical computer vision has seen substantial progress, a developing intersection exists between NeRF and cultural heritage preservation. This study bridges this gap by introducing an approach that amalgamates NeRF techniques with the rich cultural wealth of museums.The conventional museum experience is extended into the metaverse through a novel methodology that leverages NeRF’s capabilities. The core objective is to enable individuals to explore digitized museum artifacts with unparalleled realism. NeRF technology captures intricate visual details and enables immersive interactions by rendering scenes with volumetric precision, transforming how cultural artifacts are experienced and understood.This dissertation delves into the technical intricacies of integrating NeRF technology into the metaverse. The implementation involves the reconstruction of 3D artifact models. The results underscore the potential of NeRF to reshape the cultural heritage landscape by bridging the gap between traditional museums and the boundless possibilities of the metaverse.
{"title":"Moving museums into the Metaverse","authors":"Ruier Zhang","doi":"10.61173/ttjnt195","DOIUrl":"https://doi.org/10.61173/ttjnt195","url":null,"abstract":"In recent years, the fusion of advanced technologies with virtual reality has opened new cultural preservation and engagement avenues. This dissertation explores the innovative application of Neural Radiance Fields (NeRF) technology in transcending the boundaries of physical museums and transporting their treasures into the metaverse. While classical computer vision has seen substantial progress, a developing intersection exists between NeRF and cultural heritage preservation. This study bridges this gap by introducing an approach that amalgamates NeRF techniques with the rich cultural wealth of museums.The conventional museum experience is extended into the metaverse through a novel methodology that leverages NeRF’s capabilities. The core objective is to enable individuals to explore digitized museum artifacts with unparalleled realism. NeRF technology captures intricate visual details and enables immersive interactions by rendering scenes with volumetric precision, transforming how cultural artifacts are experienced and understood.This dissertation delves into the technical intricacies of integrating NeRF technology into the metaverse. The implementation involves the reconstruction of 3D artifact models. The results underscore the potential of NeRF to reshape the cultural heritage landscape by bridging the gap between traditional museums and the boundless possibilities of the metaverse.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"75 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452245","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}
This paper presents a model that integrates a BERT encoder with a Capsule network, eliminating the traditional fully connected layer designed for downstream classification tasks in BERT in favor of a capsule layer. This capsule layer consists of three main modules: the representation module, the probability module, and the reconstruction module. It transforms the final hidden layer output of BERT into the final activation capsule probabilities to classify the text. By applying the model to sentiment analysis and text classification tasks, and comparing the test results with various BERT variants, the performance across all metrics was found to be superior. Observing the model’s handling of multiple entities and complex relationships, sentences with high ambiguity were extracted to observe the probability distribution of all capsules and compared with RNN-Capsule. It was found that the activation capsule probabilities for BERT-Capsule were significantly higher than the rest, and more pronounced than RNN-Capsule, indicating the model’s exceptional ability to process ambiguous information.
{"title":"Text classification by BERT-Capsules","authors":"Minghui Guo","doi":"10.61173/wcg0nf17","DOIUrl":"https://doi.org/10.61173/wcg0nf17","url":null,"abstract":"This paper presents a model that integrates a BERT encoder with a Capsule network, eliminating the traditional fully connected layer designed for downstream classification tasks in BERT in favor of a capsule layer. This capsule layer consists of three main modules: the representation module, the probability module, and the reconstruction module. It transforms the final hidden layer output of BERT into the final activation capsule probabilities to classify the text. By applying the model to sentiment analysis and text classification tasks, and comparing the test results with various BERT variants, the performance across all metrics was found to be superior. Observing the model’s handling of multiple entities and complex relationships, sentences with high ambiguity were extracted to observe the probability distribution of all capsules and compared with RNN-Capsule. It was found that the activation capsule probabilities for BERT-Capsule were significantly higher than the rest, and more pronounced than RNN-Capsule, indicating the model’s exceptional ability to process ambiguous information.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"6 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958840","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}
Sentiment analysis has a wide range of applications in the fields of opinion analysis, sentiment dialog, and product reviews. However, the sentiment information expressed in texts under different topics varies greatly; for example, a model that performs well on a movie review set has poor model classification on a social platform review set due to inconsistent recognition of antiphonal phrases, different expression of emoji sentiment, and missing contextual information. In this paper, the authors focus on tens of thousands of latest reviews of Chinese takeout platforms Meituan and Elema, and use the LSTM model in deep learning to double classify the data (positive and negative). This paper analyzes the performance of LSTM models in the field of sentiment analysis of takeout reviews and concludes that domain-specific text sentiment analysis requires specific analysis.
{"title":"Sentiment Analysis by Double Classification of Takeaway Platform Reviews Based on Deep Learning LSTM Models","authors":"Yunzhi Liao","doi":"10.61173/vcrwtn65","DOIUrl":"https://doi.org/10.61173/vcrwtn65","url":null,"abstract":"Sentiment analysis has a wide range of applications in the fields of opinion analysis, sentiment dialog, and product reviews. However, the sentiment information expressed in texts under different topics varies greatly; for example, a model that performs well on a movie review set has poor model classification on a social platform review set due to inconsistent recognition of antiphonal phrases, different expression of emoji sentiment, and missing contextual information. In this paper, the authors focus on tens of thousands of latest reviews of Chinese takeout platforms Meituan and Elema, and use the LSTM model in deep learning to double classify the data (positive and negative). This paper analyzes the performance of LSTM models in the field of sentiment analysis of takeout reviews and concludes that domain-specific text sentiment analysis requires specific analysis.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959270","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}
According to Einstein’s general theory of relativity, gravity is described as the curvature of space-time caused by gravitational sources, and to prove the existence of gravitational waves, many scientific research institutes around the world have begun to build equipment to try to detect gravitational waves in the vast background signals of the universe. At present, gravitational waves have been detected by many scientific research institutions and scientists. Now, the detection of gravitational waves has shifted to the direction of high precision and accuracy. This paper starts with the laser interferometer, the most basic instrument for gravitational wave detection. It expounds on the latest development progress and key technologies of gravitational wave detection in the current physical world, including noise suppression and gravitational wave detection spacecraft projects. In terms of laser interferometers, this paper describes their principle and key technologies and puts forward the difficult problems that still need to be tackled and studied. In terms of noise suppression, this paper describes the noise and interference that may be generated and how to suppress the noise to avoid interfering with the accuracy of the experiment. It also points out the shortcomings of current noise suppression techniques. For gravitational wave detection spacecraft projects, this paper first focuses on several key projects in the world, then describes the technologies and shortcomings of spacecraft, and puts forward the direction of improvement and in-depth research on these technologies. This paper aims to summarize the key technologies of gravitational wave detection in the world today and point out the direction of its future development.
{"title":"Research on the Innovation and Development of Gravitational Wave Detection Technology","authors":"Fengxi Li","doi":"10.61173/3cagf967","DOIUrl":"https://doi.org/10.61173/3cagf967","url":null,"abstract":"According to Einstein’s general theory of relativity, gravity is described as the curvature of space-time caused by gravitational sources, and to prove the existence of gravitational waves, many scientific research institutes around the world have begun to build equipment to try to detect gravitational waves in the vast background signals of the universe. At present, gravitational waves have been detected by many scientific research institutions and scientists. Now, the detection of gravitational waves has shifted to the direction of high precision and accuracy. This paper starts with the laser interferometer, the most basic instrument for gravitational wave detection. It expounds on the latest development progress and key technologies of gravitational wave detection in the current physical world, including noise suppression and gravitational wave detection spacecraft projects. In terms of laser interferometers, this paper describes their principle and key technologies and puts forward the difficult problems that still need to be tackled and studied. In terms of noise suppression, this paper describes the noise and interference that may be generated and how to suppress the noise to avoid interfering with the accuracy of the experiment. It also points out the shortcomings of current noise suppression techniques. For gravitational wave detection spacecraft projects, this paper first focuses on several key projects in the world, then describes the technologies and shortcomings of spacecraft, and puts forward the direction of improvement and in-depth research on these technologies. This paper aims to summarize the key technologies of gravitational wave detection in the world today and point out the direction of its future development.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958555","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}
Industrial defect detection plays a crucial role in modern manufacturing. Identifying and addressing inferior products contributes to enhancing product quality, strengthening product competitiveness, and increasing customer satisfaction. Existing surveys of industrial defect detection are relatively scarce and struggle to reflect the latest development trends. Therefore, this article provides a more detailed and in-depth survey of industrial defect detection technologies. The article first reviews the development history of industrial defect detection methods. It then covers three aspects: the concept of general anomalies, concepts related to image anomaly detection, and industrial defects, providing an overview of industrial defect detection in these areas. It also summarizes the current state of development, as well as the advantages and disadvantages of each aspect. Additionally, the article identifies the limitations of industrial detection methods in practical industrial applications. Finally, it looks forward to the future development trends and potential research directions in this field, aiming to inspire future research.
{"title":"A Survey: Industrial Anomaly Detection based on Data Mining","authors":"Jinrui Li","doi":"10.61173/p6g5je55","DOIUrl":"https://doi.org/10.61173/p6g5je55","url":null,"abstract":"Industrial defect detection plays a crucial role in modern manufacturing. Identifying and addressing inferior products contributes to enhancing product quality, strengthening product competitiveness, and increasing customer satisfaction. Existing surveys of industrial defect detection are relatively scarce and struggle to reflect the latest development trends. Therefore, this article provides a more detailed and in-depth survey of industrial defect detection technologies. The article first reviews the development history of industrial defect detection methods. It then covers three aspects: the concept of general anomalies, concepts related to image anomaly detection, and industrial defects, providing an overview of industrial defect detection in these areas. It also summarizes the current state of development, as well as the advantages and disadvantages of each aspect. Additionally, the article identifies the limitations of industrial detection methods in practical industrial applications. Finally, it looks forward to the future development trends and potential research directions in this field, aiming to inspire future research.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"9 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958579","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}
Magnetic resonance imaging (MRI) has emerged as an indispensable noninvasive technique in materials research, offering comprehensive insights into the interior composition of diverse materials while preserving their integrity. The primary objective of this study is to investigate the utilization of magnetic resonance imaging to examine porous materials, biomaterials, polymers, and composites. This research aims to emphasize the benefits of MRI in the context of non-destructive testing and analysis. Magnetic resonance imaging (MRI) is advantageous due to its capacity to provide exceptional spatial resolution, facilitating the observation of minute structures inside porous materials. This capability significantly contributes to comprehending fluid dynamics and the distribution of pores within such materials. Within the field of biomaterials, magnetic resonance imaging plays a pivotal role in the examination of tissue interactions and drug delivery systems. This imaging technique provides high-resolution visualizations essential for the meticulous research of cellular-level phenomena. The significance of technology in the realm of polymers and composite materials is noteworthy, as it plays a crucial role in facilitating the identification of heterogeneities and the analysis of phase distribution. Nevertheless, various issues need improvement, including signal strength, resolution, and the reaction of materials to magnetic fields. It is advisable to employ advanced imaging techniques, implement signal improvements, and make material-specific adjustments to address these constraints.
{"title":"MRI Applications and Research in Materials Science","authors":"Yu Chen","doi":"10.61173/9d34fb93","DOIUrl":"https://doi.org/10.61173/9d34fb93","url":null,"abstract":"Magnetic resonance imaging (MRI) has emerged as an indispensable noninvasive technique in materials research, offering comprehensive insights into the interior composition of diverse materials while preserving their integrity. The primary objective of this study is to investigate the utilization of magnetic resonance imaging to examine porous materials, biomaterials, polymers, and composites. This research aims to emphasize the benefits of MRI in the context of non-destructive testing and analysis. Magnetic resonance imaging (MRI) is advantageous due to its capacity to provide exceptional spatial resolution, facilitating the observation of minute structures inside porous materials. This capability significantly contributes to comprehending fluid dynamics and the distribution of pores within such materials. Within the field of biomaterials, magnetic resonance imaging plays a pivotal role in the examination of tissue interactions and drug delivery systems. This imaging technique provides high-resolution visualizations essential for the meticulous research of cellular-level phenomena. The significance of technology in the realm of polymers and composite materials is noteworthy, as it plays a crucial role in facilitating the identification of heterogeneities and the analysis of phase distribution. Nevertheless, various issues need improvement, including signal strength, resolution, and the reaction of materials to magnetic fields. It is advisable to employ advanced imaging techniques, implement signal improvements, and make material-specific adjustments to address these constraints.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"20 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140450339","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 the current era of rapid development of science and technology, the recognition and classification of digital images is the key to solving many problems, such as the application of license plate recognition, document digitization, and remote sensing image surface classification. Based on the MNIST handwritten numerical data set collected by the National Institute of Standards and Technology (NIST), this report uses Python language and PyTorch programming framework to construct a convolutional neural network (CNN) structure and practice and experience the image classification of handwritten digits in the MNIST data set.
{"title":"The Image Classification Algorithm Was Implemented on The MNIST Data Set","authors":"Wayuan Xiao","doi":"10.61173/6xfeq893","DOIUrl":"https://doi.org/10.61173/6xfeq893","url":null,"abstract":"In the current era of rapid development of science and technology, the recognition and classification of digital images is the key to solving many problems, such as the application of license plate recognition, document digitization, and remote sensing image surface classification. Based on the MNIST handwritten numerical data set collected by the National Institute of Standards and Technology (NIST), this report uses Python language and PyTorch programming framework to construct a convolutional neural network (CNN) structure and practice and experience the image classification of handwritten digits in the MNIST data set.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"102 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140451432","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}
Multi-target tracking technology is a key problem in many application areas, including robotics, video surveillance, and autonomous driving, and its purpose is to find tracking targets that match the characteristics in a continuous image or sensing sequence information and to form a reasonable trajectory for each target. This paper proposed a method that combines the two main existing approaches for multi-target tracking by applying the Kalman filter for motion model prediction to support the neural network target tracking under poor visibility and target shield.
{"title":"The framework of multi-target tracking based on neural network and motion model prediction","authors":"Tianyang Li","doi":"10.61173/jpjpht67","DOIUrl":"https://doi.org/10.61173/jpjpht67","url":null,"abstract":"Multi-target tracking technology is a key problem in many application areas, including robotics, video surveillance, and autonomous driving, and its purpose is to find tracking targets that match the characteristics in a continuous image or sensing sequence information and to form a reasonable trajectory for each target. This paper proposed a method that combines the two main existing approaches for multi-target tracking by applying the Kalman filter for motion model prediction to support the neural network target tracking under poor visibility and target shield.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"18 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140450354","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}
Currently, silicon is the most widely used semiconductor material. However, in recent years, the development of integrated circuits has encountered more and more limitations, among which the physical characteristics of single-crystal silicon materials are an important reason. With the expansion of integrated circuit scale and the continuous reduction of manufacturing processes, silicon has gradually reached its physical limit. At the same time, silicon has a higher calorific value and a higher performance loss. Therefore, people are actively seeking alternatives to silicon in integrated circuits. This paper reviews the characteristics of three new semiconductor materials, graphene, silicon carbide, gallium nitride, and current research on their applications. Silicon carbide and gallium nitride materials have shown outstanding performance in power-integrated circuits, while graphene has many applications. However, they still have many defects before being used on a large scale. In short, in integrated circuit applications, when new materials replace silicon, many problems remain to be solved.
{"title":"Prospects for silicon being replaced by other materials in integratedcircuit applications","authors":"Yuxuan Liu, Ziyu Wang, Sichen Lv","doi":"10.61173/5hw0q872","DOIUrl":"https://doi.org/10.61173/5hw0q872","url":null,"abstract":"Currently, silicon is the most widely used semiconductor material. However, in recent years, the development of integrated circuits has encountered more and more limitations, among which the physical characteristics of single-crystal silicon materials are an important reason. With the expansion of integrated circuit scale and the continuous reduction of manufacturing processes, silicon has gradually reached its physical limit. At the same time, silicon has a higher calorific value and a higher performance loss. Therefore, people are actively seeking alternatives to silicon in integrated circuits. This paper reviews the characteristics of three new semiconductor materials, graphene, silicon carbide, gallium nitride, and current research on their applications. Silicon carbide and gallium nitride materials have shown outstanding performance in power-integrated circuits, while graphene has many applications. However, they still have many defects before being used on a large scale. In short, in integrated circuit applications, when new materials replace silicon, many problems remain to be solved.","PeriodicalId":438278,"journal":{"name":"Science and Technology of Engineering, Chemistry and Environmental Protection","volume":"24 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140451262","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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