bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Non-metallic Elements B, Si and Te on the Formation, Mechanical Properties and Microstructure of Cu-Zr-based Amorphous Alloys
Authors: Xiao Kaiyuan
Published: December 2022

In recent years, the B2 CuZr crystals have been successfully introduced into the matrix of amorphous alloy, resulting in the formation of bulk metallic glass composite. The in situ fabricated amorphous composites have exhibited not only good plasticity but high yield strength in conjunction with obvious work-hardening behavior, which has attracted great attention. However, until now microalloying elements are mainly metallic elements and less studies have been conducted on the non-metallic elements on the glass-forming ability and mechanical properties of Cu-Zr-based metallic glasses and their composites. In this work, the influence of non-metallic elements B, Si and Te on the structural evolution and the mechanical properties in Cu-Zr-based metallic glass matrix composites were studied. It was found the that the prepared bulk metallic glass matrix composites show high strength and plasticity at room temperature. This work provides a good experimental basis for future application.
The Analysis of Incomplete Business Data
Authors: Weng Longao , Wang Rongxuan
Published: December 2022

Missing values can dramatically reduce the accuracy and availability of missing data, especially when analyzing business data. A common method to deal with the missing data is simply deleting the samples containing missing attributes. However, this will lead to bias and invalid conclusions since some data are too important to be omitted easily. Therefore, we should use certain methods to complete the data set instead of deleting data with missing values. In this paper, we compared several data imputation methods by adopting them to deal with six benchmark business data sets. The result provides us with guidance when dealing with incomplete business data.
Analysis of Obstacles in Marketing of Small and Medium-sized New Energy Enterprises Based on Fuzzy-DEMATEL Model
Authors: He Ziyi , Yang Chen , Chen Mengmeng
Published: December 2022

With the vigorous development of technological revolution and industrial transformation, new energy enterprises have undergone historic changes. In order to speed up the transformation of small and medium-sized new energy enterprises and improve their market competitiveness, this paper studies the marketing obstacles of small and medium-sized new energy enterprises. Through literature review and investigation, this paper determines 22 indicators that affect the marketing of China's new energy enterprises, constructs the method of Fuzzy-DEMATEL (Fuzzy Decision Laboratory Analysis), reduces the defects of subjective scoring through Fuzzy method, and calculates the relationship between the influencing factors in the index system based on DEMATEL. The results show that the key factors hindering the marketing of new energy enterprises are the irrationality of making relevant decisions, the lack of a complete system of marketing management, the lack of awareness of marketing management, and the imperfect marketing system.
Analysis on The Influence Mechanism of Corporate Stock Price Based on Lasso RBF Neural Network
Authors: Jiang Chenyi , Xing Peixue , Shi Xiangtian
Published: December 2022

In order to study the correlation between stock prices and financial indicators of Chinese listed companies, this paper selects representative and relevant data from CSMAR to study 12 indicators as input layers, which reflect the solvency, profitability and management ability of the companies. Based on the lasso algorithm, the data dimension is reduced to 5 indicators; a lasso RBF neural network is constructed and the neural network is trained to obtain the company's stock price simulation. The results show that the model has good robustness and the accuracy of the stock price fitting is 93%. Among them, the main contribution of net investment cash flow to stock price is 25.46, which provides some suggestions for stock price prediction analysis.
Research on component analysis and identification of ancient glass products based on cluster analysis
Authors: Zhang Hengyi , Guo Hongchen , Zhang Hao
Published: December 2022

In this paper, we study the ancient glass component analysis and category to identify problems, through the establishment of principal component analysis, cluster analysis and multiple regression model mathematical model, using SPSS software, realized by analyzing the chemical composition content on the glass before and after weathering type classification problem and for different categories of the relationship between the chemical composition of glass sample problem solving, According to the change rate, the chemical composition content before weathering is predicted, and the predicted value before weathering is obtained.
Component Identification and Prediction of Ancient Glass Products Based on Decision Tree Model and SVM Model
Authors: Chen Jin , Jia Ruoyi , Yan Yaxin
Published: December 2022

The study of the patterns of chemical composition of glass objects is an important research method of classifying ancient glass objects. In this paper , we firstly selected suitable indicators and sought classification boundary lines through a decision tree model to discover the classification pattern of ancient glass and verify its accuracy . Secondly, it was sub-classified and divided by means of hierarchical clustering and k-means mean clustering to realise the work of categorisation of known components . Finally an SVM model was built to obtain a confusion matrix map to achieve the classification prediction of unknown components.
K-means++ clustering-based composition analysis and identification model for glass products
Authors: Li Zhuoyuan , Kang Beier , Zhang Ruijie
Published: December 2022

Ancient glass is susceptible to weathering by environmental influences, causing changes in its compositional proportions and thus affecting the correct judgment of its elemental categories. In this paper, the study on the analysis and identification of ancient glass composition applies gray correlation analysis, K-means++ cluster analysis and principal component analysis to analyze the relationship between weathering on the surface of cultural relics and their glass types, ornamentation, and color; the content pattern of chemical composition with and without weathering on the surface of cultural relics is statistically analyzed by glass type, and the content of its chemical composition before weathering is predicted based on the weathering point detection data. Will be high potassium glass, and lead barium glass classification laws for analysis; appropriate chemical composition of each category for subcategory division, giving the division method and division results and analysis of its reasonableness and sensitivity. Analysis of the chemical composition of unknown categories of artifacts, identification of the types, and the sensitivity of the results are analyzed. The correlations between the chemical composition of glass artifacts of different categories are analyzed and the differences in their chemical composition correlations are compared.
Azimuth-only passive positioning technology of UAV based on triangulation and iteration
Authors: Jiang Lifan , Huang Boyi , Li Zijia , Sun Guofeng
Published: December 2022

In recent years, the strategic position of bearing-only passive positioning technology has been continuously improved with the increasing application of UAV cluster flight in military and civil fields.This technology uses as few UAV signals as possible to adjust the position of the UAV formation, so as to achieve the purpose of improving the stealth of the UAV cluster and reducing the external interference.In this paper, the theory and application of the method are described in detail. There are four combinations of UAVs that abstract the demand for de-transmitting signals, and one of them is discussed in detail.
Composition analysis and identification of glass products based on Pearson correlation analysis
Authors: Cai Zhijin , Zheng Zhonghan , Jiang Xin
Published: December 2022

Ancient glass objects record the cultural exchanges between East and West along the Silk Road, but they have been subjected to varying degrees of weathering over time. First, to investigate the relationship between surface weathering and glass type, decoration and color of glass artifacts, this paper investigated the correlation using Kappa consistency test and judged the difference using chi-square test, and found that there was significant correlation and difference between surface weathering and glass type. For the statistical pattern between the presence or absence of weathering on the surface and the chemical composition content, the content of each chemical composition was compared between weathered and unweathered on the surface by visualizing its chemical composition content. Secondly, to analyze the classification pattern of high potassium and lead-barium glass, it is subclassed. In this paper, the chemical constituents relevant to the classification were first selected by Pearson correlation analysis, followed by subclassification of the two types of glass artifacts by establishing a K-Means clustering subclassification model based on the elbow method to determine the number of cluster centroids. Thirdly, in order to identify the unknown types of glass artifacts, this paper establishes a random forest regression algorithm for glass type identification model and derives the results by prediction. Finally, different categories of glass artifacts are analyzed to distinguish glass from four specific categories in terms of decoration, type, color and surface weathering, and the differences in chemical composition correlation relationships between different categories are obtained by comparing the chemical composition correlation tables of different categories.
Study on properties and composition of ancient glass based on PCA and gradient lifting tree model
Authors: Zhao Hengtian , Du Junjia , Xu Yayue , Li Yujie , Jin Yingai
Published: December 2022

In order to help archaeologists better study ancient glass, uncover the mystery of the early Silk Road, and also better protect these cultural heritage.In this paper, the properties and composition of ancient glass are studied.All types of glass samples were analyzed by PCA principal component analysis and weighted summation, and the type characteristic equation was obtained. After calculating the candidate points iteratively in MATLAB, the optimal critical eigenvalue was set to 5, and the type judgment model was established.According to get the type of the characteristic equation, the selection is related components as high potassium and the composition of class division, choose negative correlation components as lead, barium and the composition of class division, through the PCA principal component analysis of two types of glass data are reduced to 3 d in the class we USES Kmeans clustering algorithm for two types of glass, For the sensitivity analysis of subclass partitioning, we used SOBOL global sensitivity analysis method, and obtained that principal component 1 would have the greatest impact on the model output.
Research on glass classification prediction model based on machine learning
Authors: Peng Xuehan , Tong Xinyan , Hu Xinyi
Published: December 2022

Glass is an important commodity on the ancient Silk Road, which is of great value to the study of ancient Chinese and Western trade. In this paper, aiming at the classification of glass cultural relics, the glass type assessment classification model and the random forest model are constructed by analyzing the proportion data of chemical composition content of existing glass samples. This paper summarizes the classification rules of two types of glass by drawing the pie chart of the proportion of each chemical component of high potassium glass and lead barium glass, and combining with a large number of literatures on glass weathering: the glass with high potassium oxide content is judged as high potassium glass; The probability of higher proportion of lead oxide and barium oxide is lead barium glass. Then, this paper selects several chemical components that occupy the main position in the composition as the standard, divides the lead barium glass and high potassium glass into three different subcategories, establishes the glass type evaluation classification model, uses C language, adopts multiple cycle logic nesting, and conducts the main division of glass categories, then introduces Di parameter to further deepen the division of glass categories, and runs each group of programs, Specific classification results can be obtained and brought into the data to obtain classification results. Finally, the sensitivity and rationality of classification results are analyzed by constructing two-dimensional descriptive statistical images of variances and influence parameters of high potassium glass and lead barium glass respectively.
A study on the classification of the composition of ancient glassware based on statistical law analysis
Authors: Pang Yuexin
Published: December 2022

Ancient glass is highly susceptible to weathering by the environment in which it is buried, and weathering can cause changes in the proportions of its chemical composition, which can affect the correct determination of its category. In this paper, we solve the problem by studying the chemical composition of ancient glass artefacts to establish a mathematical model. We first used the Spearman correlation test and obtained that the weathering of the surface of the artefacts had no relationship with the color and decoration of the glass, and a negative weak correlation with the type of glass; then we analyzed the statistical pattern of the chemical composition content of the surface of the artefact samples with and without weathering, and conducted the Mann-Whitney test on the chemical composition of the sample data. The mean values of , , , , , and in lead-barium glass were not significantly different before and after weathering, while the mean values of other oxides were significantly different, e.g. the CaO content increased from 1.89% to 4.49% after weathering; finally, the chemical composition of surface weathered glass before weathering was predicted. By analyzing the discrete degree coefficients of each component, we found that the component content is stochastic, and proposed the index of random variation to quantify the stochasticity, and combined with the inevitable variation in the weathering process to establish a mathematical model of the chemical component content before and after weathering for prediction.
A study of the composition of ancient glassware based on focal logistic regression models
Authors: Wang Hongsheng , Lin Guihuang , Tong Zanjia
Published: December 2022

Glass has been developed in China for over two thousand years and was an important commodity in the economic and trade exchanges between China and abroad along the Silk Road. Since the Han Dynasty, glassware from the West began to be imported into China via the Silk Road, and Chinese glassmaking techniques have taken on foreign technologies, thus making ancient glass made in China similar in appearance to foreign glass, but the main chemical composition of the two is different due to the different regions and the different materials used. This paper focuses on the types of ancient glass artefacts that make up ancient glass, the patterns of content of each major chemical component and the specific mathematical relationships within them. This paper establishes a correlation analysis model based on information entropy using the information gain and information gain in the decision tree algorithm to correlate the weathering of glass artefacts with their color, decoration and type, and ultimately concludes that only the decoration of high potassium glass has a high correlation with its weathering. Then a nearest neighbor analysis model was established based on the idea of KNN nearest neighbor algorithm, and a joint focal logistic regression model was used to complement the weathering of the sampling points.
Classification and identification of glass products based on K-means clustering analysis and Fisher's linear discriminant method
Authors: Chen Jiahuan , Li Yongan
Published: December 2022

Ancient glass is very susceptible to weathering due to the buried environment, and the internal elements of the weathered glass will exchange with the elements in the environment thus leading to changes in the composition ratio. In order to classify and identify glass objects, this paper analyzes the classification laws of high potassium glass and lead-barium glass based on the data of samples of known types; selects the appropriate chemical composition for each category and then divides it into subcategories, gives specific division methods to establish a classification model, and uses this model for the chemical composition of glass artifacts of unknown categories to identify the types to which they belong
Research on diabetes Prediction and Intelligent Diagnosis System Based on Machine Learning
Authors: Lin Huang , Wang Tengyi
Published: December 2022

This paper mainly focuses on the diagnosis and prediction of diabetes, and uses machine learning algorithm to study the prediction model of diabetes and intelligent consultation recommendation system. First, based on the public dataset of diabetes in UCL database, logistic regression model was used to screen variables, and four main characteristics were obtained: Pregnancies, Glucose, BMI and DPF. Secondly, four machine learning algorithms, K-nearest neighbor algorithm (KNN), naive Bayesian algorithm, decision tree and support vector machine, are used to establish the prediction model of diabetes. The model results show that the decision tree algorithm and K-nearest neighbor algorithm have high accuracy in specificity and negative case hit rate; However, the ROC curves of K-nearest neighbor algorithm and support vector machine algorithm are overlapped. Therefore, AUC value is further used to compare the prediction results of the two models. Finally, the prediction model based on support vector machine has certain advantages in prediction accuracy. Finally, taking diabetes patients as the object, on the basis of early prediction, using data mining technology, an intelligent diagnosis system for diabetes based on Java and fuzzy reasoning is constructed. The system functions include system user management, user information management, diabetes diagnosis and advice, system management and other functional modules, of which diabetes diagnosis and advice module is the core module of the system. The intelligent diagnosis system can provide basic medical diagnosis and suggestions, thus improving the diagnosis efficiency and becoming an important auxiliary tool for medical diagnosis.
Glass classification and recognition based on genetic simulated annealing algorithm and decision tree algorithm
Authors: Sun Zhijie , Guan Dayu , Zu Zhiliang , Wang Jing , Wei Hengchao , Chen Shuo
Published: December 2022

The chemical composition and ratio of ancient glasses are highly susceptible to change due to the complex burial environment. In order to determine the classification rules of lead-barium glass and potassium glass, and to subclassify lead-barium glass and potassium glass according to the appropriate chemical composition index, this paper mines the linear combination of high potassium glass and lead-barium glass by linear discriminant analysis, and calculates the clustering centers of lead-barium glass and high potassium glass subclassifications and the subclassifications of glass to be classified based on the FCM (Fuzzy-C Mean Clustering) algorithm of genetic simulated annealing algorithm. The sub-clusters of lead-barium glass and high-potassium glass, as well as the affiliation degrees of the glass samples to be classified were calculated based on the genetic simulated annealing algorithm. The model results were then examined by using the decision tree algorithm, and the results of glass artifact type identification were obtained.
Big Data Analysis of the Tourism Linkage of the Yangtze River Delta Using Stata Calculation and Improved Gravity Model
Authors: Liu Shengming
Published: December 2022

In this study, the top 11 prefecture-level cities with GDP ranking in 2020 in the Yangtze River Delta are selected as the basic research units. Based on the basic gravity model, the data selection of some variables is adjusted, and the intensity, total amount and membership degree of tourism economic links of major cities in the Yangtze River Delta are calculated by USING Stata. The study found that the tourism economic linkages of the Yangtze River Delta urban agglomeration show an evident core-edge structure, with Shanghai, Suzhou, and Hangzhou as the key linkage triangle; the peripheral cities present weaker tourism economic linkages with other cities. In the whole urban agglomeration, tourism economic linkages should be strengthened, and the synergy and integration of tourism development in the Yangtze River Delta need to be further enhanced. Therefore, it is proposed that transport facilities should be improved, innovative cooperation should be enhanced, and tourism resources should be integrated, so as to promote the deep integration and win-win development of tourism in the Yangtze River Delta region.
Forest efficiency decision-making model based on linear weighted multi-objective genetic algorithm
Authors: Yan Xin , Qiu Ruobing , Yang Jingxuan
Published: December 2022

It can not be negligible that climate change is a dire threat to countless people due to the development of our society and technology.To draw the applied range of the decision-making model of forest management plan, we set up Linear-weighted multi-objective genetic algorithm according to the ecological, economic and social factors of the forest ecosystem and determine the different weight of each factor through the entropy weight method.finally, we select the Pennsylvania Forest as an example to test our model.
Emotional state analysis based on speech recognition signals
Authors: Qin Ji
Published: December 2022

At present, as intelligent speech recognition is widely used, the importance of speech emotion recognition technology is also gradually gaining recognition. Considering the current social pressure, the generation of negative emotions has become particularly common. Therefore, it is necessary to increase the recognition of different emotions by using the speech recognition system, which has been perfected, so as to play a role in anticipation and prevention. In this paper, we focus on the negative effects of negative emotions on people's lives, and use K-nearest classifier to identify different emotions, and accurately identify them, and prevent and monitor them. Finally, the feasibility of this method is confirmed by simulating the process.
Credit Card Fraud Detection Based on Support Vector Machine
Authors: Xia Jianglin
Published: December 2022

Due to the increasing popularity cashless transactions, credit card fraud has become one of the most common frauds and caused huge harm to the financial institutions and individuals in real life. In this academic paper, the algorithm Support Vector Machine (SVM) is used to build models to deal with the credit card fraud detection problem with the performance metrics AUC and F1-score. The experiment dataset is named Credit Card Transactions Fraud Detection Dataset from the Kaggle website. After the step of preprocessing, the dataset is split into the training, testing and validation dataset with 11 numerical features and a label feature called “is_fraud”. The inner parameter “class_weight” of the SVM algorithm in Python is set as “balanced” to deal with the imbalanced datasets. The main method to find the optimized models is using the GridSearchCV function in Python library sklearn. After tuning the hyperparameters and handling the overfitting phenomenon, the optimized models for the two metrics are found. The parameter values of the best model for AUC are C=10, class_weight= “balanced”, g =0.01, kernel = “rbf”. The training AUC is 0.87 and testing AUC is 0.90. The parameter values of the final optimized model for F1-score are C=0.8, class_weight= “balanced”, g =0.06, kernel = “rbf”. The final training F-score is 0.305 and testing F-score is 0.260.
Multi-classification of Human Action Based on ResNet
Authors: Ji Shuaidong
Published: December 2022

With the development of machine learning and other related technologies, more and more excellent research on human action recognition and classification has been proposed, which significantly promotes the application of this technology in the actual situation. This paper mainly focuses on the characteristics of ResNet18, ResNet50, ResNet101, and ResNet152 in 15 human action recognition and classification tasks respectively. First, adjust and enhance the sample images of all training sets and test sets, and adjust the overall parameters according to the characteristics of the input image, so that all images can be normalized and high quality can be guaranteed at the same time; Then use the above four ResNet models to study and test with an epoch of 200, and explore the characteristics of the four models in this project by comparing the accuracy, loss, confusion matrix graphic, F1-score, and the number of epoch experienced in reaching the steady state of accuracy. The results show that in the overall effect, with the increase of the number of layers of the ResNet models, the accuracy changes in a positive correlation, and the epoch number experienced by the accuracy reaching the steady state changes in a negative correlation.
Machine Learning Methods for Credit Card Fraud Detection
Authors: He Yihong
Published: December 2022

Machine learning is an innovative and efficient tool to prevent credit card fraud, however, given the variety of machine learning models, which model is the most suitable for fraudulent transaction predictions becomes a tough question to answer. In this research, a comprehensive evaluation method is borrowed to compare performances between different machine learning models. More precisely, this research uses the Area under the ROC Curve (AUC) metric to evaluate and compare performances between four different machine learning models with the same transaction information dataset. The four models are K Nearest Neighbor, Logistic Regression, Random Forest, and Support Vector Machine. In this research, a dataset that contains over one million credit card transaction data is processed and divided into training data and testing data. After preprocessing, the same training data are fitted into four different models and being test against the same testing data. After a series of hyperparameter tuning, the AUC score of each model is obtained and compared. The comparison result indicates that Random Forest makes the most accurate and consistent predictions on fraudulent transactions in this dataset, and thus can be recommended as the primary machine learning algorithm to prevent credit card fraudulent transactions.
A Joint Network Based CNN for Yoga Pose Classification and Scoring
Authors: Yang Wenxi
Published: December 2022

Comparing to traditional rehabilitation, rehabilitation at home becomes a need during pandemic. The technique brought up in this paper allows patients and yoga fans exercise at home with low cost and comfort while can also evaluate their postures. Previous works focus either on classifying poses or scoring on the sameness between the two input branches of patients’ poses and normative poses, but they ignore the combination of them in one single network. In this study, a residual block based Siamese CNN network with classification and scoring modules is proposed, aiming at providing accurate pose matching scores and classify pose types on yoga postures simultaneously. The Siamese network takes two inputs of learner’s pose and standard pose, which are preprocessed skeleton images by OpenPose. With the addition of residual block on the first convolutional module, back propagation is facilitated, which boosts up the process of updating parameters and optimization. The model calculates total loss by summing up cosine embedding loss and cross entropy loss in which the weight parameter lambda could be modified based on need. As for the scoring module, cosine similarity is used to calculate pose resemblance on batch level. The improvement in model performance is obvious when comparing the loss and accuracy between the Siamese network with residual block and VGG-16. Experimental results indicate that the residual block based Siamese network achieves competitive performance compared to the VGG-16 and can provide scoring feedback to learner’s yoga poses.
Sensitivity Improvements of Flexible Capacitive Pressure Sensors
Authors: Xu Chenyu
Published: December 2022

In recent years, wearable electronic devices have developed rapidly and flexible sensors have been increasingly used in medical, aerospace, automotive, industrial and commercial applications. Flexible pressure sensors are critical in human-robot interaction, physiological signal monitoring and robotic haptics. Among them, capacitive type flexible pressure sensors are widely used due to their unsophisticated structure, good stability, small temperature deviation and environmentally friendly. Currently, most of capacitive sensors adopt the design with an electrical membrane inserted between two electrodes. This paper presents a comprehensive review of the development of flexible capacitive pressure sensor from the perspective of technological and engineering developments. This paper firstly introduces the capacitive pressure sensor by using the working mechanism as an entry point. Secondly, this paper discusses three general methods to increase the sensitivity of pressure sensing, including changing the dielectric microstructure, the dielectric constant, and different dielectric materials. Finally, the advantages and disadvantages of three methods for sensitivity improvement were discussed.
Implanted Electrodes and Microneedle Array Electrodes for the Neuromodulation
Authors: Zhu Huanqun
Published: December 2022

In the human body, nerve conduction plays an important role. A range of physiological reactions and movements in the human body require nerves for transmission. Nerves realize the function of conduction by transmitting bio-electrical signals. If the nerves are damaged, the biological signals will not be transmitted, so the human body's reactions cannot be carried out, causing serious consequences. Neuromodulation is an effective treatment modality for patients with nerve damage. Neuromodulation has been proven to have effective therapeutic effects in various brain and neurological diseases. Among them, the electrodes in the neuromodulator regulate the abnormalities of the brain or neurons by releasing electromagnetic pulses, which are an important part of the whole device. However, various electrodes can be used for neuromodulation, and different electrodes have different characteristics from one another. In this study, several electrodes that can be used as wearable/implantable neuromodulators and their properties will be investigated and whether there is a relationship between them.
Forest Management Strategy Model Based on EPIC and Meta-Analysis
Authors: Zhou Zirun , Sun Chuanqing , Xiao Yujie , Zhang Jiahao
Published: December 2022

First, to develop a reasonable carbon sequestration model to predict the amount of carbon dioxide sequestered by forests and their products, we constructed an EPIC-based carbon sequestration model to simulate the amount of carbon dioxide sequestered and the actual amount of carbon dioxide sequestered. forest management strategies and find the best strategies. In addition, a meta-analysis-based optimal forest resource utilization model was established, which comprehensively analyzed forests, including carbon dioxide sequestration and other natural, social and economic values.
Research status and prospect of robotic systems in the field of aerospace engineering
Authors: Zhao Yibo
Published: December 2022

Technology has undergone major changes in the field of aerospace engineering. One major field is robotics. Robotics plays an important role in the field of aerospace and has received more and more attention and attention from scholars at home and abroad. This paper reviews the development of robotic systems from the application of robotics in aerospace engineering, the advantages and disadvantages of robotic systems, and how to improve their effectiveness for better use in the future. The research results found that in terms of its application, it mainly includes welding, inspection, painting, space exploration, drilling and so on. Benefits include high precision, low operating costs and high productivity. On the other hand, it has the disadvantage of requiring a lot of space, balancing speed and accuracy, requiring skilled labor, and being prone to remote control and misuse. Its effectiveness can be increased by minimizing energy consumption to reduce operating costs and improving coordination to reduce collisions. In the future, the use of robotics seems promising as it may be used in transportation to reduce carbon emissions and transportation costs. The research in this paper can provide a reference for the future development of robotic systems.
Construction of Safety Production Standardization Information System of Ninggang
Authors: Zhang Enbo , Zhang Zhong , Xia Ying , Hu Bin , Li Jing , Geng Ting , Wang Biao
Published: December 2022

Different from other enterprises, the complexity of the production process and production environment of iron and steel enterprises has led to a very serious safety situation, which not only endangers the lives of many workers, but also has an extremely bad impact on society. It is very necessary to put forward higher requirements on safety management for iron and steel enterprises. Based on the standardization of enterprise safety production, this paper builds a safety production standardization information system with Ningbo Iron and Steel Grid Company Limited. (hereinafter referred to as Ninggang) as an example, combined with Internet intelligent tools, to assist enterprises to complete the construction of safety production standardization and improve the safety management efficiency of iron and steel enterprises.
Deep Well and Ultra-Deep Well Drilling and Completion Technology Optimization and Application of Kuche Piedmont Area
Authors: Liu Hongtao , Zhang Chongyuan , Zhao Li , Zhang Xuliang , Qin Hongde , Lv Xiaogang , Yang Junqi
Published: December 2022

In recent years, the exploration and development of China's Tarim Basin has moved towards ultra-deep and complex areas. With the increase in well depth and the influence of the geological characteristics of the Kuche Piedmont area, a series of drilling problems for 8000m-level ultra-deep wells in this area have been caused. As a result, a series of new drilling and completion technologies have been formed: ①Based on the tower standard I type well structure, the improved tower standard II-B\II-C type well structure is used to solve the drilling of two sets of salt formations. Difficulties; ②Optimizing UDM-2 oil-based drilling fluid, achieving an average hole diameter expansion rate of only 6.82%, reducing lost circulation and other complications; ③Designing and optimizing a new type of multi-edge PDC bit to significantly increase the average ROP; ④Application The industry-leading pre-measurement resistivity technology successfully predicts the salt bottom; ⑤The application of fine controlled pressure cementing technology to achieve effective sealing of high-pressure saline formations. In recent years, the Kuche Piedmont Block in the Tarim Basin has continued to tackle key problems in the application of ultra-deep well drilling and completion technologies, and has successfully reached a new level of 8000m, which has played an important role in supporting the development of deep oil and gas exploration and development business.
The Significant Factors in Refuse Classification Based on Correlation and Multiple Regression Analysis
Authors: Zheng Liqian
Published: December 2022

With the rapid growth of China's economy, great achievements have been made in various constructions. Meanwhile, huge resources and environmental damage have been paid. To reduce pollution and protect environment, the continuous implementation of mandatory waste classification policy in various cities in China, refuse classification has been a practical problem that tightly related to mankind’s live. In this article, it finds the significant factors that affect the effectiveness of the refuse classification as well as the people’s attitude toward it. Based on the questionnaire, people’s will turn positive as the classification commences and guidance takes quite an important role in the process. Refuse classification is a long-term project beneficial to the country and the people. Contemporarily, the government has been advocating waste classification, but it has not been effectively implemented in the actual operation process. On the basis of in-depth analysis of the problems existing in the implementation of garbage classification, key objects are found to form a green and environment-friendly resource conserving society. These results provide some reference for further garbage classification.
The Prediction Model of Air Quality in Changsha City Based on Artificial Neural Network
Authors: Li Sicong , Xu Wei
Published: December 2022

The air quality is a hot topic noticed by all the present society. Although modern technology has helped to collect data of the air position, the huge amount of irregular data also makes it hard to detect the pattern of air pollution. Therefore, this research is to apply data mining to the analysis of the air quality. Data mining is a comprehensive mining of information including trends, features and correlations hidden in data. Data mining can extract the value of data which was not known from numerous random data. This research is based on the daily average values of six major pollutants in Changsha from January 1, 2015, to December 31, 2017, and analyzes the relationship between the air quality and socioeconomic factors of Changsha to establish a prediction model using B-P back propagation neural network. The final model of the research fits the actual data well and predicts the future value of API precisely. However, considering the slow convergence rate and the addition of new samples which may affect the previously learnt samples, further exploration on the B-P neural network model can be launched with other mathematical theories.
The Correlation Between Temperature Inversion and Atmosphere Pollution in Beijing
Authors: Hua Shuoyan
Published: December 2022

This study aimed to evaluate the correlation between temperature inversion and air pollution in Beijing, China in year 2020. The study first analyzed the monthly trend of temperature inversion levels in 2020, finding that it has two peaks in April and October and has the lowest value in August. The writer used line charts and linear regression method to study the correlation between temperature inversion level and the daily AQI level in Beijing in 2020. The study also focused on the relationship between concentration of polluting air particles: PM2.5, PM10, SO2, CO, NO2 and temperature inversion level, finding that temperature inversion level is positively correlated with the concentration of most polluting particles. The study provided comments for governments and citizens on how to reduce the effect of the air pollution caused by temperature inversion.
Predict the frequencies of cold waves in Guangdong in the future using CanESM5 Model
Authors: Han Yixuan
Published: December 2022

In this research, we used the data of daily minimum temperatures from ensemble r1i1p1f1 of CanESM5 model under the five SSP scenarios, namely SSP 1-1.9, SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5, to calculate the frequencies of cold waves in Guangdong from 1900 to 2100, in order to make an accurate forecast of the pattern of occurrences of cold waves in Guangdong for the foreseeable future and compare it with the situations in the past. The frequencies of cold waves in Guangdong generally stayed stable at 4 from 1900 to 2014, though a slightly decline trend still can be witnessed. The frequencies of cold waves under the scenario SSP1-1.9 will show a decline trend at the beginning, but they will remain and finally increase in the mid and late twentieth century, while the frequencies of cold waves under the scenarios SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5 will generally all decline in the future. The trends of frequencies of cold waves varied in different regions of China in the past, and the decline trend in northern China was significant than that in southern China.
A Study on the Impact of Effective Use of Geographical Indications on Carbon Reduction Technology in the Production Layer of Traditional Chinese Medicine Industry in China
Authors: Zheng Chenxing , Chen Feiyu , Qin Jiaxin
Published: December 2022

The double carbon target is a solemn commitment made by the Chinese government to the world, and emission reduction and sink increase in the agricultural production process is an important way to achieve the double carbon target. However, the current status of green production in Chinese medicine is not optimistic, which is not conducive to agricultural emission reduction on the one hand, and will inevitably restrict the long-term well-being of the traditional Chinese medicine industry in driving economic growth on the other. Based on a microdata survey of Eucommia farmers in Lueyang County, Shaanxi Province, this study empirically analyzed the effect of effective use of GI on farmers' adoption of carbon emission reduction technologies using the Propensity Score Matching method and explored the degree of adoption of each carbon emission reduction technology by farmers using the entropy weighting TOPSIS. The results show that the effective use of GI has a positive impact on farmers in terms of fertilizer and medicine savings, and no impact in terms of water and energy saving. And the ease of implementation of the emission reduction measures had a positive effect on the adoption by farmers.
Study on CO2 corrosion behavior and protection technology of gas storage injection production well casing
Authors: Wang Zhou , Wang Ligang , Liu Hongfa , Yao Qi , Li Xiaolong , Wang Xiaolong
Published: December 2022

Wen 23 depleted gas reservoir gas storage is the largest underground gas storage in central and eastern China. It shoulders the important tasks of peak shaving, emergency gas supply and smooth operation of pipelines in the natural gas market in central and eastern China.During the construction period of the gas reservoir, a series of anti-corrosion measures, such as high-efficiency annular anti-corrosion protective fluid, have been designed, taking all kinds of corrosion risks of injection and production well casing into full consideration, and the anti-corrosion effect is remarkable.However, after the phase I project was constructed and put into operation, it was found that some injection and production wells had obvious leakage of annulus protection fluid. Based on the identification results of "three aspects and eleven leakage paths", it is believed that the leakage of tubing threads and packers is the main leakage path of annulus protection fluid.The natural gas produced by the original component detection of the gas reservoir contains a small amount of CO2, which may cause corrosion perforation to the downhole casing under certain circumstances.Through the corrosion perforation experiment on P110 steel selected for injection production well casing, combined with electrochemical test and surface analysis and other corrosion characterization methods, the anti-corrosion coating design for Wen 23 gas storage is completed.The research results show that the corrosion inhibition efficiency of the new salt resistant anti-corrosion coating is more than 80%, the electrochemical slow-release efficiency is more than 85%, and the anti-corrosion effect is outstanding. It can provide a strong technical guarantee for the safe, stable and stable operation of the injection production well of the gas storage.
Research on Microbial Fuel Cell in Water Treatment and Power Generation
Authors: Han Zhuolin
Published: December 2022

With the development of economics, water pollution is becoming increasingly serious, and the existing sewage treatment technology has high energy consumption. Therefore, the development of low energy consumption, safe and environmental protection technology is the trend of in further. Microbial fuel cell (MFC), as an emerging comprehensive technical method for wastewater disposal, shows great application prospects. In this work, the working principle and function of different types of MFC, the application of MFC in sewage treatment field, and the shortcomings of MFC devices to be perfected in the market are discussed.
Climate Change Policies of Canada and China
Authors: Wang Yifei , Zhan Yunqiao , Zhang Qianhan , Zhao Jianqi
Published: December 2022

The climate issue has become one of the topics of common concern today. Therefore, climate change policies have become increasingly crucial in domestic economic development and participation in international affairs. However, throughout the development of climate governance, there are similarities and differences in the formulation of climate policies between east and west countries. This paper chooses Canada as the representative of the western countries and China as the representative of the eastern countries to make a comparative analysis of the two countries’ climate governance process, the domestic climate actions, and the participation in International Climate Governance in recent years and uses the realism theory to explain. This paper finds that due to the differences in economic development and political systems in the history of Canada and China, the climate policies of the two countries develop differently. However, today, when the climate issue is becoming more urgent, the two countries face the common pressure brought by the climate issue and the common interests in solving the climate issue. Their climate change policies are highly similar.
Carbon sequestration model based on entropy weight method and genetic algorithm
Authors: Yan Xin , Qiu Ruobing , Yang Jingxuan
Published: December 2022

As the great amount of greenhouse gases release into the atmosphere, climate change gives a tremendous threat to people. To predict the amount of carbon sequestration, we establish an optimal evaluation model combined GA Model with Entropy Weight Method. Specifically, We develop a carbon sequestration model by using the entropy weight method to determine the percentage of the 6 variables. Second, we use the genetic algorithm to optimize the model.
Breakthrough in drilling and completion equipment and key technologies for 8000m-class ultra-deep wells onshore China
Authors: Yang Huohai , Fan Guotao , Jiang Xin
Published: December 2022

With the exploration and development of a group of deep ultra-deep large oil and gas fields in Tarim basin and Sichuan Basin, deep oil and gas reservoirs will be the main productivity replacement area and the main exploration focus area. Drilling and completion still face severe challenges such as high temperature and high pressure, complex pressure systems, complex accidents and poor drillability. This paper describes the technology of domestic oil and gas fields with 8000m level drilling and completion. Considering the demand of exploration and development of local oil and gas fields while standardizing the international advanced level, the insufficiency of technology and equipment improvement demand of each block are discussed, and the research direction of technology and equipment is prospected.