bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Type 2 Diabetes Risk based on Lifestyle Factors
Authors: Dong Weiyi
Published: July 2023

Diabetes is a global chronic disease, and the number of patients and medical expenses are increasing. It is predicted that by 2045, the number of adult diabetics in the world will reach 693 million, and the expenditure on health care will increase to 958 billion US dollars. Diabetes can be divided into two types: type 1 and type 2, of which type 2 diabetes accounts for 95% of all diabetes cases. In this study, principal component analysis and stochastic forest algorithm are used to evaluate the performance of the model by using large-scale survey data and ROC curve, which provides a new idea for diabetes prediction research. The results show that the stochastic forest algorithm performs well in dealing with high-dimensional data, while the principal component analysis method can reduce the dimension of high-dimensional data, reduce redundant features and improve the prediction ability of the model. Therefore, the method proposed in this paper can help medical researchers to make better use of diabetes data for research, and provide a new method for diabetes risk prediction.
Research Progress of α-glucosidase Inhibitors of Medicinal and Edible Homologous Plants and Their use in the Treatment of Diabetes
Authors: Jiao Zhe
Published: July 2023

Diabetes is one of the common diseases in modern society, which cannot be cured. It has a significant impact on the lives of patients. Type II diabetes patients mainly rely on oral hypoglycemic drugs and insulin injection to maintain blood sugar balance. While traditional hypoglycemic medications such as insulin, biguanides, and sulfonylureas may reduce blood glucose for a brief period of time, it often has some side effects and can lead to body dependence. Therefore, as a safe and economical natural hypoglycemic plant polysaccharide, it has received more and more attention. Natural active ingredients with glucosidase inhibitory activity from plants will become potential resources for research and treatment of type II diabetes. A large number of medicinal and food homologous plants have been identified as an important source of glucosidase inhibitors, and have received widespread attention. This article focuses on homologous medicinal and edible plant species and their application in diabetes treatment, here, we reviewed functional components with glucosidase inhibitory activity in homologous medicinal and edible plants and their state of research. In this paper, we hope to provide a theoretical benchmark for the development of drugs for type II diabetes from natural plants.
Prediction of the Absolute Configuration of Chiral Drugs by NMR Spectroscopy
Authors: Tianxing Shi
Published: July 2023

An NMR-based method was developed for the prediction of the absolute configuration of chiral carboxylic acids exemplified by over-the-counter drug naproxen. In this method, a pair of diastereomers were generated by purification and chiral derivatization of naproxen, whose absolute configuration was predicted by the difference in chemical shifts due to the magnetic shielding effects of phenyl functional group. The paper further illustrated the role of the related methods of Mosher ester analysis in the determination of absolute stereochemistry of carboxylic acids in addition to the derivatization and analysis of chiral secondary alcohols and amines.
Establishment and analysis of biodecomposition model based on cellular automata
Authors: Wang Haoxiang
Published: July 2023

In order to explore the relationship between the number and type of species in a plant community and the adaptability of the community to drought environment, we built an optimized cellular automata model to simulate the iteration of individuals in the community. We simplified the model and added random parameters to focus on the correlation between these two quantities, which is also the methodological philosophy of our global model architecture. After many simulations, we found that: the adaptability of community to drought with the increase of species number, effects of species type on community adaptability 3. In the face of current pollution and habitat loss, our model has some implications for strategies to increase long-term community survival.
Study on Chemical Composition Analysis and Methods of Ancient Glass Relics
Authors: An Jiaran , Si Yuqing , Cui Ziyi
Published: July 2023

In this paper, the relationship between the surface weathering of glass artifacts and the type, decoration, and color of glass was visualized by using a columnar table analysis[1], and the relationship between the three factors on the surface weathering was tested separately for significance by using a chi-square test; in addition, a multi-factor ANOVA was used to analyze whether the three factors of the type, decoration, and color of glass and their interaction on the surface weathering of glass artifacts had The significant relationships were analyzed by multivariate ANOVA. Then, the relative contributions of different types of glass to the prediction accuracy of the model were ranked by importance according to the proportion of chemical components before and after weathering at each sample point through random forest, and the chemical components were selected according to the importance ranking, and after removing most of the unimportant chemical components, the final retained chemical components were used to construct the final random forest regression model to obtain the laws of their classification, and finally the corresponding The results were obtained. The chemical components such as calcium oxide, copper oxide, iron oxide, and nitrogen oxide were drawn for visual analysis according to their importance, and we compared the derived results with the known literature and found that they were consistent with the results classified by the known literature.
Japanese Dietary Habits and Their Impact on Health
Authors: Wu Jiayi , Wu Jiayi
Published: July 2023

In recent years, the impact of dietary patterns on human health has come under an increasing attention. A wealth of data suggests that healthy dietary patterns reduce the risk of diet-related diseases. Japan currently has the longest average life span in both sexes in the world. Japanese diet has always been known to be healthy, and its unique dietary habits are one of the key reasons for the longevity of the Japanese people. This study examines the relationship between certain non-communicable diseases' incidence and mortality and the Japanese diet. According to this study, in women, eating a lot of soy products is related to a reduction of breast cancer risk. however, relations between soy foods and prostate cancer in men is still debatable. Consumption of soy and fish is linked to a reduction in cardiovascular disease. The high-sodium diet in Japan is related to the increase of stroke and gastric cancer risks, while green tea and coffee are related to a lower risk of stroke. There are few studies that analyses the association between dietary patterns and related diseases in Japan, and this study provides ideas for analyzing the relationship between diet and health in Japan.
The Role of the Mediterranean Diet on Health and Its Inspiration for Chinese Diet
Authors: Dou Yidan
Published: July 2023

Health problems in China are becoming increasingly serious, as people's consumption levels increase, the drawbacks of imbalanced dietary intake continue to emerge. On the list of the world's best diets, Mediterranean Diet has been champion for five consecutive years. As the top, the Mediterranean diet has a unique and superior dietary pattern. Many studies have shown that the MD helps to lower rates of chronic diseases. Therefore, this paper explores the advantages of MD’s nutritional structure and its positive impact on chronic diseases, in order to find new solutions for the Chinese diet to be more beneficial to human health. After introducing the origin and development of the Mediterranean diet, this paper explains the main dietary rules and food composition of the Mediterranean diet, and analyzes the advantages of its nutritional structure by combining specific food groups with their nutrient content. MD’s positive effects on different kinds of chronic diseases, such as cancer and diabetes, are presented through a synthesis of studies. Then the current status and shortcomings of the Chinese diet are analyzed, and the possibilities for the development of the Chinese diet are further explored by comparing the two different dietary patterns. It is hoped that this paper will contribute to a healthier Chinese diet in the future.
Research and analysis on the preparation of C4 olefin by ethanol coupling method based on regression algorithm with computer technology
Authors: Lu Bofan , Yu Zhixuan , Chen Lu , Qin Rongshui
Published: July 2023

Computer technology has been used to study the different combination of catalyst and temperature on the ethanol conversion (one-way conversion rate of 100% per unit time ethanol (alcohol intake - ethanol residue)/ethanol intake) and C4 olefin selectivity (the proportion of all products of C4 olefin), it is necessary to decompose the catalyst composition into several indicators. The effects of several indexes on ethanol conversion and C4 olefins selectivity were analyzed by computer technology. Canonical correlation analysis was used to study them, and the coefficient before each component represented the degree of importance. The results showed that the higher the catalyst combination temperature, the higher the weight of hydroxyapatite, the higher the ethanol conversion and C4 olefins selectivity, and vice versa. When the C4 olefin yield (its value is the selectivity of C4 olefin to ethanol conversion) is as high as possible, the multiple linear stepwise regression function of catalyst parameters and temperature on C4 olefin yield is obtained. MATLAB software was used to calculate the function formula. The absolute value of coefficients determines the importance of each index to C4 olefin yield. Combined with chemical reaction, when the mass ratio is 200:200 and the temperature is 400℃, the yield of C4 olefins can be maximized, and the yield of C4 olefins is 44.728%. Through the establishment of decision tree regression model by computer technology, it can be intuitively concluded that when the temperature is lower than 350℃, the best experimental condition is the C4 olefins yield of 17.264%.
Study on the succession of plant communities under the influence of drought
Authors: Zhang Jiayang , Liu Chuanli , Wang Ke
Published: July 2023

Drought has a strong impact on the growth of plant populations, and the number of different species in a plant community plays an important role in the drought adaptation of the community's progeny. In this paper, a mathematical model is developed to analyze how the number of different species in a plant community under a long-term irregular drought cycle is adaptive to drought in future generations, and thus how the plant community benefits from it. This paper uses biomass to quantify the growth of plant populations and changes in plant communities, and uses a large amount of visual data for visual analysis. First, a logistic growth model of plant population biomass change is developed based on temperature accumulation, precipitation and interspecific competition as influencing factors. The correlation coefficients were then obtained using the collected plant population biomass to predict the changes in total plant biomass and species richness, and the interspecific competition model was then used to calculate the values of total community biomass after final stabilization based on logistic, and the system of differential equations was solved using the Runge-Kutta Methods was finally concluded that the biomass of the community after final stabilization showed a positive correlation with the number of species and species type, and showed a strong positive correlation with species type and a weak positive correlation with species number.
Glucose Biosensor: Basic Principles, Evolution and Practical Application
Authors: Huang Qijia
Published: July 2023

Glucose biosensors can detect the abnormal level of blood glucose levels, which plays an essential role for the initial phase of treatment in clinical practice. The association between fast plasma glucose (FPG) levels and presence of retinopathy to identify the threshold of glucose level with specific value which help to confirm whether or not getting the disease. Glucose biosensors measure the concentration of blood glucose by using the linear relationships between different molecules’ consumption or formation. Glucose biosensors can still be altered by reducing used potentials, increasing durability and enhancing reproducibility. Some practical applications of glucose biosensors in day-to-day life include healthcare, pharmacy, food, agriculture and so on. In food products, the concentration of glucose can be an indicator of its quality and help control the amount of sugar consumed. In other areas, the measurement of changes in glucose levels when interacting with drugs will lead to different strategies used for treatments. This article contains the basic principles of glucose biosensor, some benefits and drawbacks of four generations and related applications.
Review on COVID-19 Detection Method Based on Deep Learning
Authors: Wang Yuhao
Published: July 2023

This article discusses the use of deep learning in combating Covid-19. It has been four years since Covid-19 ravaged the world. We hope to help researchers and medical staff through our investigation. Deep learning is a very effective method, which has a wide range of functions. Deep learning neural network is very suitable for fighting against covid. We will also introduce the information of this article in detail. Because neural network is mainly used to fight against CT in tumor and other diseases. First, I introduce the main image types. This is mainly CT type. I introduced the main features of this figure.Then we introduce the problem of image classification. Here we use convolutional neural network and its optimization scheme. At last, the problem of object recognition and image segmentation is introduced. It is hoped that it will aid in the rapid adoption of deep learning in COVID-19 research. I also try my best to contribute to this research. In recent years, in-depth learning has played a vital role in all fields. Especially in medicine, we hope that through this report, we can give you a deeper understanding of deep learning.
Corrosion Prediction and Case Study of Plate Heat Exchangers Based on Artificial Intelligence
Authors: Liang Dong
Published: July 2023

Corrosion can cause downtime, equipment damage, and safety hazards. To address this challenge, researchers are turning to artificial intelligence (AI) to predict corrosion events of heat exchangers Shell and tube heat exchanger is an important equipment in nuclear power, chemical industry and other industries. The working conditions of such equipment are often complex, and many nozzles on the cylinder and head are connected with pipes or various instruments. In this paper, the local stress analysis is carried out for the nozzle of the container by using the finite element analysis method, the distribution of stress intensity at the nozzle is obtained, and the safety of the nozzle area of the container under the specific nozzle load is verified.
The Application of VR Technology in Film Production
Authors: Li Jiaming
Published: July 2023

In recent years, with the continuous maturity of virtual reality technology, the scope of application in various fields has been expanding. VR technology has gradually moved to the film industry. Since 2014, VR technology has become a new round of discussion and practice in the film industry. This paper will conduct an in-depth discussion on the production status and influence of VR films from three aspects: the actual production of VR films, the challenges VR technology brings to the current film industry, and the prospect of VR technology application in the film industry. This paper also researches the practical application and influence of VR technology in film production. The future development prospect of VR in film is discussed and analyzed, and the relevant conclusion and summary are made at the end of the article.
Lithium battery life prediction based on deep learning
Authors: Ye Liuyang , Xiang Yuewen , Fang Guanbin
Published: July 2023

Li-ion batteries have the advantages of high efficiency, high energy density and long life, having developed rapidly in recent years. However, it is tough to accurately predict the decline trend of Li-ion battery capacity, which limits the further improvement of their service life and safety. In this study, a method of using wavelet denoising to preprocess the data and predicting the life of Li-ion battery based on whale optimization algorithm combined with long short-term memory network (WOA-LSTM) is proposed. In this paper, two sets of data sets B0005 and B0006 of NASA 's public data set are used. The original battery capacity data is subjected to wavelet transform and noise reduction to remove noise and redundant information. The calculation results of SNR and RMSE are 48.1119 and 0.006225, respectively. Then LSTM (Long short-term memory), RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit) and WOA-LSTM are used to predict the remaining useful life of the data set RUL (Remaining Useful Lifetime, RUL), and the data error is compared, showing that in the results of MAE, RMSE and MAPE three prediction error indicators. The prediction results of WOA-LSTM in B0005 and B0006 show the minimum prediction errors, which are 0.0563,0.0710,0.0415 in B0005 data set and 0.0583,0.0831,0.0454 in B0006 data set. Compared with the standard LSTM model, RNN model and GRU model, the error indexes of the model are decreased, which are 7 %, 4 % and 3 % respectively, which has great advantages. This method can provide a reliable predictive analysis method for battery design and fault diagnosis.
Research on Methods and Applications Related to Question-and-Answer Dialogue Systems
Authors: Zhao Xuanzheng
Published: July 2023

In the face of more and more network data information, search engines have gradually become the main retrieval method to obtain relevant information knowledge. However, in today's increasingly explosive development of information on the Internet, by contrast, traditional search engines have problems such as semantic understanding and complicated answers. Therefore, question answering systems are more important. The automatic question answering system generally adopts natural language processing related technologies. When users ask questions, the system automatically judges and gives answers. It involves computer linguistics, machine learning, artificial intelligence and other popular technology research. According to different classification criteria, the automatic question answering system is roughly divided into open field automatic question answering system and stereotyped automatic question answering system.. This thesis investigates methods and applications related to question-and-answer dialogue systems. On the methodological side, we introduce commonly used datasets and the principles and techniques of text, speech and visual question and answer systems, and analyse in detail the excellent example ChatGPT. In terms of applications, we present the application of Q&A dialogue systems in search engines, smart campuses. There is some reference value.
Research on Traffic Sign Recognition Method Based on Generating Countermeasures Network
Authors: Liang Guoxun
Published: July 2023

Nowadays, intelligent assisted driving technology is developing rapidly. In the field of intelligent assisted driving technology, traffic sign recognition is a crucial area which focuses on identifying input images and outputting corresponding traffic sign types in the images. However, due to the limitations of optical hardware in the image acquisition system or the poor environment in which the image acquisition system operates, the collected images may not be well recognized by classifiers used for traffic sign classification, despite containing traffic sign information. This could be due to low resolution of images, unclear images, and excessive noise. This article presents a modified generative adversarial network for reconstructing high-resolution traffic signs, specifically suited for the reconstruction of traffic sign images. The generated images and interpolated images are then sent into a convolutional neural network, which is a classifier, to complete the task of classifying traffic signs. The accuracy of the two sets of data is compared, and it is concluded that the classification accuracy of images reconstructed by the generative adversarial network is generally 15% higher than that of images processed by the interpolation method. This study not only proves that the low resolution of images containing traffic sign information can negatively affect subsequent traffic sign classification tasks, but also demonstrates that traffic sign recognition methods based on generating adversarial networks can indeed improve classification accuracy.
Intelligent Community Scheme Based on Vehicle Design
Authors: Huang Chihan , Xiong Junling
Published: July 2023

With the help of cutting-edge technologies, we propose a feasible intelligent community scheme concept by designing vehicles with different functions. Firstly, we send a questionnaire to the public and use the AHP method to analyze. We find that residents have a greater demand for leisure travel, medical security and safety protection. Secondly, we design three different means of transportation, namely intelligent drones, intelligent medical vehicles and intelligent leisure vehicles to solve the problem. In each design, its importance and its innovation points are detailed. Then, in order to meet the intelligent needs of residents, we design the intelligent community APP, which is divided into the elder version and the younger version, to provide residents with more convenient and comprehensive services. The scheme we proposed improved the automation and intelligence of the community, thus providing a more comfortable environment.
Research On Lexical Data Set Analysis Based on Decision Tree Model
Authors: Du Yufei
Published: July 2023

In order to predict the future number of users and development prospects of "World" game, this paper analyzes the terminology used in game data sets. Firstly, descriptive statistical analysis was used to analyze the existing data, and ARIMA model was used to estimate the data during the forecast period, so as to obtain the interval estimation and point estimation of the number of results. Secondly, EXCEL is used to calculate the percentage of each word, and analysis of variance model is used to get the attribute influence between words. Thirdly, the training set and test set were analyzed through machine learning, and the mapping model was established. "EERIE" was input as the word vector to obtain the prediction results. Finally, the relationship between the decision tree model and the actual expected break time is established, and the difficulty of EERIE is evaluated. The results show that with the word vector of EERIE as input, when the model accuracy is 55.56%, the prediction result is (1,2,3,4,5,6,X)-(6,12,21,33,25,3,0). The prediction difficulty was medium, and the accuracy of the model was 80%.
Characteristic Analysis and Application of the Optoelectronic Neural Network
Authors: Chen Yuhan
Published: July 2023

In recent years, optoelectronic neural networks have garnered significant attention due to their potential to facilitate high-speed and efficient information processing by employing light-based signals to transmit and process data. This review provides an overview of the current state-of-the-art in optoelectronic neural networks, including their design principles, fabrication techniques, and applications. The article also presents five different methods for constructing optoelectronic neural networks, which offer insights into current ONN research and solutions to overcome the limitations of traditional neural networks. Furthermore, the review discusses three different applications of ONNs, including basic tasks such as data classification, speech recognition, and image recognition, as well as hardware accelerators and SNN algorithms for object detection. The promising potential of optoelectronic neural networks in transforming various fields, such as artificial intelligence, image recognition, and data processing, is also highlighted. As research in this area continues to advance, further breakthroughs in optoelectronic neural networks are anticipated.
The Performance Comparison between PoSpace and PoW
Authors: Wang Zheng
Published: July 2023

The consensus algorithm of Bitcoin, namely proof-of-work (PoW), has been criticized for its high energy consumption and centralization of mining power. PoW needs a lot of processing power to execute cryptographic problems and verify network transactions. In contrast, Chia's consensus mechanism is based on proofs-of-space (PoSpace), which utilizes disk space rather than computing power. PoSpace involves generating a large amount of verifiable storage space on the network and requires less energy consumption than PoW. This paper presents a comparative analysis of the algorithms used by Bitcoin, Ethereum, and Chia. The paper simulates the PoW and PoSpace processes by Java and compares both mechanisms' memory, CPU usage, and time consumption using VisualVM. The experiment results show that PoSpace costs less space and time resources than PoW. The former is more energy-efficient and decentralized.
Blockchain Technology in Various Fields: Applications, Challenges, And Future
Authors: Zhang Xun
Published: July 2023

Blockchain technology has revolutionized many industries and has great potential outside of finance. This paper explores blockchain's applications and challenges in agriculture, electricity, transportation, healthcare, and finance. Blockchain technology can track agricultural product origin, quality, and safety to improve supply chain transparency in agriculture. Blockchain can also reduce intermediaries, improve payment systems, and expand financing. Blockchain can enable decentralized electricity management, peer-to-peer energy transactions, and lower transaction costs. It could boost renewable energy integration, grid efficiency, and energy access for underserved communities. Blockchain technology can improve transportation supply chain visibility and reduce fraud by providing a shared, tamper-proof ledger to track goods and prevent unauthorized access. Blockchain can secure, interoperable, and improve patient privacy in healthcare. It could let patients share their health data with providers and researchers while protecting their privacy. Blockchain implementation is also tricky. Scalability, interoperability, compliance, and data privacy Blockchain solutions must handle large amounts of data, integrate with existing systems, comply with laws and regulations, and protect sensitive data. Further research and development are necessary to explore blockchain technology's possibilities in these fields fully.
Predicting Stock Prices of Chinese Liquor Companies with the LSTM Network: A Case Study on Shede Spirits
Authors: Li Jingqiao
Published: July 2023

This paper presents an investigation into the application of Long Short-Term Memory (LSTM) networks for predicting stock prices of Chinese liquor companies, specifically Shede Spirits. The study utilized a one-year dataset of Shede Spirits' stock prices obtained from Yahoo Finance, covering daily opening, closing, and high prices. The data was preprocessed through sorting, normalization using MinMaxScaler, and creation of input-output pairs with a lookback window of 20 days. The dataset was split into training and testing sets with an 80:20 ratio. A two-layer LSTM model with 32 hidden units per layer was constructed and implemented using PyTorch. Dropout regularization was applied between LSTM layers to prevent overfitting. Experimental results showed the LSTM model's effectiveness in predicting Shede Spirits stock prices, achieving a Root Mean Squared Error (RMSE) of 0.023 and an R^2 score of 0.89 on the test dataset. The LSTM model demonstrated superior performance in capturing non-linear patterns and long-term dependencies compared to linear regression and ARIMA methods. The training loss plot indicated convergence towards an optimal state, and the dropout regularization successfully prevented overfitting. In conclusion, this research highlights the potential of LSTM networks as a powerful tool for stock price prediction in the Chinese liquor industry, offering benefits to investors and businesses. It also emphasizes the importance of employing advanced AI techniques in financial forecasting tasks.
The Study of Evolution and Application Related to the Chat-GPT
Authors: Li Yipeng
Published: July 2023

Chat-GPT has become increasingly popular and has provided tremendous help to people in their daily life. The fundamental working principle of Chat-GPT involves the incorporation of various methods such as Natural Language Processing (NLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Transformer and Reinforcement Learning from Human Feedback model (RLHF). These methods assist Chat-GPT in understanding, predicting and computing the desired outcomes for the users. NLP is used to help machines understand and process human language. RNN is employed to facilitate the machine in comprehending the input's logic. LSTM enables the control of memory elements, allowing the machine to combine unrelated elements in memory. RLHF serves as a switch button, controlling the machine's output and improving the accuracy of results. Chat-GPT can be used for image editing, coding, translation, paper editing and other tasks. However, it is critical to exercise caution while using Chat-GPT and not to become overly reliant on it, given the potential problems it may create.
Research on glass type classification based on Logit model and K-Means clustering algorithm
Authors: Li Jiayao , Lin Cheng , Zhuang Liwei , Xu Zhilong , Liang Zhenlin , Gao Yuning
Published: July 2023

Glass artifacts have been important in human history and are often studied by archaeologists and art historians to understand the development of society, technological progress, and cultural exchange. However, the systematic classification of glass artifacts is a major challenge to their study, because glass artifacts excavated from archaeological excavations are often highly weathered, making it difficult to classify them, so a scientific and reliable method to analyze and systematically classify glass artifacts based on their detected chemical composition is of great importance to the study of human history and culture. In this paper, two methods, logit model classification and cluster analysis, were used to determine the key to distinguishing high-potassium glass from lead-barium glass by the content of PbO, SrO, SnO2, and CaO components. Next, a comparison of the three cluster analyses was used to determine the use of the k-means algorithm to further subdivide the high potassium class and the lead-barium glass artifacts into two subclasses each: high Al-Fe and high Cu-Zn; and high PbP and high Na-Zn. Finally, the sensitivity analysis of the model and the robustness of the model and the reasonableness of the results were analyzed using Pearson correlation coefficients.
Measurement of transparent liquid concentration based on ultrasonic grating method
Authors: Pan Yanrong
Published: July 2023

In this experiment, ultrasonic grating method is used to measure the concentration of transparent liquid. The principle is that the liquid forms standing waves, namely ultrasonic grating, by using a signal generator. The monochromatic parallel light diffracted after entering the ultrasonic grating, and the sound velocity could be obtained by the spacing of diffraction fringes. The experimental equipment includes three parts: light source system, ultrasonic generation system and digital observation system. The experimental test shows that the experimental device can be used to measure the concentration of sugar water, salt water and other transparent liquid, with simple operation and high measurement accuracy.
Prediction and sub-classification of glass original components based on statistical learning algorithm
Authors: Ye Jinyan , Guo Ziheng , Yu Xinyu , Song Yinghua
Published: July 2023

To address the problem that it is difficult for people to identify the information of artifacts affected by weathering, this paper takes several groups of weathered and unweathered silicate glass as the research object, and extracts the data of their chemical composition content and surface decoration, thus analyzing the correlation between decoration, glass type and weathering degree using chi-square test and KS test and studying the proportion of chemical composition of weathered glass before weathering. On this basis, L1 regularized logistic regression was used to roughly classify the initial classification, a subcategorization model was established by hierarchical clustering, and the entropy weight method was used to determine the significant indicators and the optimal number of categories for subcategorization. The results show that glass type has a significant effect on whether weathering occurs; the chemical components that have a significant effect on the determination of the type of lead-barium glass are SiO2, PbO and BaO, and the chemical components that have a significant effect on the determination of the type of high-potassium glass are SiO2 and K2O; high-potassium glass is divided into three subclasses, and lead-barium glass is divided into three subclasses.
The Role of Metal-Organic Framework Architecture in Hydrogen Storage
Authors: Luo Chenxi
Published: July 2023

In recent years, human demand for energy has been increasing. However, the transitional use of currently used energy sources such as fossil fuels, for example, has had a very serious adverse effects on the environment and people's health. The development and widespread use of new and clean energy sources is an urgent necessity. As a clean, sustainable energy source, hydrogen has gained popularity as a potential replacement for fossil fuels. However, for hydrogen to be widely adopted as an energy source, it is imperative to create ways for storing hydrogen that are both practical and affordable. Due to the adjustable pore size, large surface area, and high porosity, metal-organic frameworks (MOFs) are receiving increasing attention in hydrogen storage. This paper aims to explore the potential of MOFs used in hydrogen storage and to provide insights into the optimization of the MOF architecture to design more suitable MOFs in this field.
Advanced Metal–Organic Frameworks for Gas Separation
Authors: Guo Xiao
Published: July 2023

In the chemical process, the effective separation of small gaseous molecules with similar physical and chemical properties is of great significance. In addition, clean energy sources such as hydrogen and methane are regarded as an ideal alternative to conventional fossil fuels. However, at present, the storage technology of this kind of natural gas is not perfect. The entry of harmful substances produced by human activities into the atmosphere has become a global danger that is becoming more and more worrying. It has become an important topic for scholars and industries Development of a multi-stage channel structure with high efficiency and low energy consumption for effective separation, storage or capture of toxic and harmful substances. In order to solve this problem, we intend to study the adsorption and adsorption of MOFs in the environment aboutH2/N2, H2/CO and H2/CH4 and some other gases. Compared with molecular sieve, activated carbon and other conventional inorganic porous materials, MOFs have a broad application prospect in gas adsorption and storage with unique properties. At present, the research of metal-organic frame materials with high stability has made great progress. Through the implementation of this project, it is expected to promote its wide application in air pollution control and other aspects.
Performance Analysis of Different Types of Solid Electrolytes
Authors: Li Yifeng
Published: July 2023

The development and interest in environmentally friendly cars nowadays present new challenges to the lithium-ion batteries. With the safety problems caused by the liquid electrolyte in conventional batteries, the development of new electrolytes is becoming increasingly urgent. Based on the existing literature, this paper discusses the following: (1) the advantages and disadvantages of solid-state electrolytes; (2) Requirements for solid electrolytes; (3) Classification and characteristics of solid electrolytes; (4) Properties of different types of electrolytes; (5) Future application and research direction. Performance and possible applications of different solid-state electrolytes are presented in this paper. Specifically, it is found that inorganic solid electrolytes are the best in ion transference number, making them suitable for quick charging batteries. Polymer-based solid electrolytes perform better at electrochemical window, so they can be used to enhance the energy density of batteries. Polyethylene glycol (PEG) based composite solid electrolytes and sulfide solid electrolytes have the highest ionic conductivity, so they are optimum materials for high-rate performance batteries.
Application of CNTs Based on Their Unique Electrical Properties
Authors: Xie Yujie
Published: July 2023

Ever since carbon nanotubes (CNTs) were discovered, because of their exceptionally impressive mechanical, chemical, and electrical properties, CNTs have garnered considerable interest and investigation from experts throughout the world, accelerating the development of nanoscience and technology. The unique tubular structure of CNTs enables them to have excellent electrical properties and can be used in all aspects of electrical devices. Therefore, this paper collects all aspects of applications and applicable optimization schemes based on the electrical properties of CNTs. The main applications of the electrical properties of nanotubes are summarized. It is discovered that CNTs' electrical characteristics are primarily reflected in the electrochemical devices, photoelectric devices as well as electronic devices. CNTs have tremendous potential for applications in these fields and can greatly improve the performance of related devices and devices. The large-scale production and application of CNTs in related fields will benefit the development of energy, photoelectric and electronic devices worldwide.
Application of Heterometallic Supramolecular Cages in Treating Cancer
Authors: Cong Zihan
Published: July 2023

Supramolecular complexes rely on non-covalent interactions, enabling a host-guest structure. When metal ions are introduced into the skeleton of ‘host’, the valance electron configuration of heteroatoms result in versatile stable structures of complexes. Heterometallic supramolecular cage is a kind of stable form which is able to act as a carrier. Instead of diffusing into circulatory system directly, carriers are used to hold the drug molecule and release it at an aim position. In this case, targeted therapy, which significantly reduce the side effect of drugs, can be achieved. There have been discussions on the potential of supramolecular structures as cytotoxic agents and drug delivery systems for anticancer drugs. Several anticancer, antibacterial, and other pharmacological analogue compounds were used to investigate the guest binding capabilities of the successfully synthesized heterometallic complexes. The stability of these cages in water and when coupled with specific guest molecules was examined. These cages' capacity for cytotoxicity as well as diverse host-guest combinations were investigated.
Development and application of comprehensive evaluation model based on correlation analysis algorithm, multiple logistic regression and clustering algorithm - Taking glass composition analysis and identification as an example
Authors: Yang Sichang , Dai Luyao , Lu Changwen
Published: July 2023

Take glass as an example. In ancient times, when firing glass, in addition to the raw material quartz sand, chemical components such as stabilizer and flux need to be added. During the storage process of glass, the internal elements will exchange with the environmental elements in a large amount, resulting in the weathering of the glass surface. In this paper, the composition of ancient glass is analyzed and identified by establishing correlation analysis model, multiple logistic regression and cluster model. The results show that: (1) There is a high correlation between glass type and surface weathering. Lead barium glass is easy to be weathered, and high potassium glass is not easy to be weathered. (2) High-potassium glass and lead-barium glass have the highest classification accuracy when they are divided into two sub-categories. Therefore, glass can be divided into four categories: high-potassium I, high-potassium II, lead-barium I, and lead-barium II.
SOFC and MCFC: New Energy Technologies of The Future
Authors: Zhang Jenny
Published: July 2023

The new energy technology is the hot spot and key point to solving the current environmental problems. In this paper, two types of fuel cells, SOFC and MCFC, were introduced in order to show their contributions to the energy transition. The two kinds of fuel cells were described in detail from the aspects of working principle, power generation mechanism, engineering application, current research and future prospect. Finally, the two fuel cells were compared in terms of electrochemical reaction and outstanding advantages.
Land Use Policy Recommendations——Based on Empirical Analysis of The Effect of Agricultural Land Expansion on Greenhouse Gas Emission
Authors: Wang Zhaojie
Published: July 2023

Agricultural activities emit about 10-12% greenhouse gases each year. Moreover, the total amount is on the rise, which can significantly contribute to global warming.  To get governments to pay more attention to greenhouse gas emissions from agricultural activities, so that the necessary regulations can be implemented. This paper studies the relationship between agricultural land expansion and greenhouse gas emissions. My initial hypothesis was that ceteris paribus, the agricultural land expansion would cause a rise in greenhouse gas emissions. I obtained 25 years of data for 12 countries from the World Development Indicators Database of the World Bank. And created a panel data frame. The variables exist in it are: Total greenhouse gas emissions (kt of CO2 equivalent), Energy use (kg of oil equivalent per capita), Alternative and nuclear energy (% of total energy use), Agricultural land (% of land area), GDP per capita (current US$). In order to cope with the possible heteroscedasticity problem in the regression, several of the variables were processed logarithms. This paper used a Two-way Fixed effect model which controls fixed effects from both cross section and time. The explained variable is log (GHG emissions), and the explanatory variables are log (energy use per capita), log (GDP per capita), clean energy using rate, and percentage of agricultural land in total land. However due to the presence of multicollinearity, log (GDP per capita) was removed. This paper also tested the autocorrelation and used the Cochrane-Orcutt Iterative Process to estimate the autocorrelation coefficient to transform all variables so that solve the autocorrelation issue. The final regression model is as follows: ln(Greenhouse Gases emission)=   + 1.06*ln(Energy use per capita) – 0.009*(Alternative and nuclear energy % of total energy use) + 0.0074*(Agricultural land % of land area).   = . The overall F-test of this model is significant, and the individual t-tests of the coefficients of each variable are all significant. R2=92.18, adjR2=91.04. This shows that the explanatory power of the model is strong. This outcome is consistent with my initial hypothesis that agricultural land expansion does lead to an increase in greenhouse gas emissions. The other two coefficients are also consistent with our common sense. This result can help governments to decide on the planning of land use. If the current amount of energy used and the types of energy used do not change, a country develops agriculture, it will inevitably lead to more greenhouse gas emissions. More Importantly, it is not like industrial manufacture, normally there are few regulations and policies on agricultural GHG emissions. But the quantitative results of the model tell us that as agriculture expands, it is necessary for the government to implement regulations and policies on it.
Comprehensive Evaluation and Stepwise Regression Model of Light Pollution in Topsis Based on Entropy Weight Method
Authors: Sun Qikai , Yao Jiaming
Published: July 2023

Light pollution is a phenomenon that affects our environment. It is of great significance to make a quantitative analysis of light pollution and establish an evaluation system for energy saving, environment improvement and social and economic benefits. In order to establish a reasonable evaluation system, based on a comprehensive evaluation model of light pollution risk level based on the entropy weight method of Topsis analysis, combined a multiple linear regression model to test the significance of the indicator data and performed the White's test and the VIF test for multiple covariance on the model.Then this paper used the standard inverse stepwise regression model to solve the linear regression model, constructed a comprehensive evaluation model of light pollution.
Evaluation and Analysis Model of Light Pollution Risk Level Based on Multiple Indicators
Authors: Xu Mingshen , Wen Ruixiang , Wang Shuangliang
Published: July 2023

A new environmental problem-light pollution has gradually attracted the attention of governments all over the world. To develop a model to evaluate the risk level of light pollution， we have set up three first-level indicators which are regional development level, population, and biological environment, and set up two-level indicators under them like: urbanization rate, lighting intensity, biology diversity.etc.We use the light pollution index obtained by satellite detection, and get the final regression equation of light pollution index by ridge regression fitting with the data of 28 provinces, and then verify it with the image observed by Luojia –1, and determine the model equation of Light Pollution Index. Then we optimize the model through both geography and climate, and correct the provinces where the light pollution index fitted by the regression equation is different from the real light pollution index, making the model accuracy further improved.
A Light Pollution Risk Assessment Model Based on TOPSIS and EWM-AHP Methods
Authors: Liu Jiaxin
Published: July 2023

With the development of industrialization and urbanization, light pollution has become increasingly serious in various countries. Assessing the risk level of light pollution (LPRL) is the foundation and the most important step to study the light pollution problem. In this paper, five primary indicators and 13 secondary indicators are selected, and the indicator weights are determined by EMW and AHP. The light pollution risk levels of different regions are calculated by the Topsis method. The output results show that the LPRL is highest in urban areas, followed by suburban and rural areas, and the LPRL in protected land is lowest, and the model results are consistent with the actual situation. Finally, this paper analyzes the weights of different indicators in the LRPL model, and proposes corresponding intervention policies for the three factors with the highest weights.
A Scoring Model for Regional Light Pollution
Authors: Zhu Yun
Published: July 2023

The use of artificial light has greatly facilitated people's lives. In order to assess the level of light pollution in a region, a model based on AHP and multiple indicators is developed. Using this evaluation model, the light pollution indices of two cities, Wudalianchi Farm and Suzhou, are evaluated for more in-depth discussion and solution proposal.