bluuebunny/crossref_metadata_2025 huggingface dataset:
BALANÇO ENTRE REMOÇÃO E INSERÇÃO DE ÁRVORES NO BAIRRO MIRAMAR, JOÃO PESSOA, PB
Authors: Andrade dos Passos Luciana , Perez Leticia Palazzi , Nunes Lopes Vanessa
Published: December 2022

Estudos espaço-temporais, com apoio de geotecnologias e de análise quali e qualitativas, têm contribuído para estudos da paisagem, incluindo processos de perdas e ganhos da arborização urbana. Este trabalho explora a relação entre remoção e inserção de indivíduos arbóreos presentes na arborização viária, com uso simultâneo de diferentes ferramentas de análise. Os objetivos da pesquisa foram relacionar identificar a influência das transformações arquitetônicas sobre a distribuição espaço-temporal dos indivíduos arbóreos, nos planos horizontal e vertical. Foram utilizadas ferramentas de geoprocessamento e de interpretação visual de imagens de satélite, em 2011 e 2019; e em fichas de análise de elementos selecionados da paisagem alterada, para análise urbanística-arquitetônica, em 2011 e 2017, via Google Street View, em estudo de caso no bairro de Miramar, situado na Zona Leste de João Pessoa (PB). Como resultado, foi possível observar a redução de 16% no número de lotes arborizados, maior concentração de árvores em avenidas principais, e que, em relação às mudanças no uso do solo, as perdas ocorreram tanto em “edificações multifamiliares” quanto em “edificações térreas”, denotando que mesmo sem alteração de uso residencial para comercial, há substituição ou remoção total de indivíduos arbóreos.
LEVANTAMENTO QUALI-QUANTITATIVO DE ESPÉCIES ARBÓREAS DA UNIVERSIDADE DO ESTADO DE MINAS GERAIS – UEMG, UNIDADE IBIRITÉ
Authors: França Viviane Gabriela Gregório , Alves Marília Rodrigues Pereira , Lopes Ariadne Dias Caldas , Rocha Maria José Reis da
Published: December 2022

A arborização urbana desempenha importantes funções ecológicas, estéticas e ornamentais. O conhecimento das espécies arbóreas nativas e cultivadas de uma área urbana pode potencializar as chances de sucesso de ações de manejo e conservação. Neste sentido, este estudo teve como objetivo realizar um levantamento quali-quantitativo das espécies arbóreas da Universidade do Estado de Minas Gerais (UEMG), no município de Ibirité, e elaborar uma chave de identificação para as espécies. Os indivíduos com CAP ≥ 15 cm foram marcados com etiqueta enumerada, mensurados e identificados. Em uma área de dois hectares, foram inventariados 218 espécimes pertencentes a 64 espécies, 52 gêneros e 23 famílias, sendo 49,10% das espécies nativas, 45,45% cultivadas e 5,45% naturalizadas. As famílias mais representativas foram Arecaceae, com 14 espécies, Fabaceae (11), Bignoniaceae (6) e Anacardiaceae (4), sendo as demais famílias representadas por três ou menos espécies. Foram registradas espécies arbóreas importantes da flora brasileira, tais como Cedrela fissilis Vell. (cedro), espécie ameaçada de extinção, Cariniana estrellensis (Raddi) Kuntze (jequitibá) e Hymenaea courbaril L. (jatobá). O conhecimento da flora arbórea da UEMG, Unidade Ibirité, permitirá o desenvolvimento de ações educativas que visem à conscientização e à conservação da biodiversidade, tendo em vista os benefícios sociais desencadeados pela diversidade da flora associada em áreas urbanas.
AVALIAÇÃO DE RISCO DE QUEDA DE ÁRVORES DE TIPUANA TIPU (BENTH.) KUNTZE NA ‘RUA MAIS BONITA DO MUNDO’, EM PORTO ALEGRE, RS, BRASIL
Authors: Weber Andrea Saldanha , Brazolin Sérgio
Published: December 2022

Este trabalho tem como objetivo avaliar o grau de risco e propor medidas de manejo para as árvores de tipuanas, que compõe o túnel verde da rua conhecida como a ‘rua mais bonita do mundo’, em Porto Alegre, RS. Foram avaliados 26 indivíduos escolhidos aleatoriamente, através da metodologia desenvolvida pela ISA – International Society of Arboriculture, em que é realizada uma avaliação não invasiva, mediante uma inspeção visual externa da árvore, a fim de quantificar e qualificar os defeitos presentes, bem como o entorno e a existência de alvos potenciais. O resultado deste estudo revelou que 16% dos indivíduos avaliados apresentaram risco de queda baixo; 42% moderado; 38% alto e 4% máximo. Após avaliação, verificou-se que 25 árvores podem ser preservadas, desde que sejam realizadas práticas de manejo, como podas, monitoramento e adequação do canteiro. Para nove indivíduos recomendou-se uma inspeção interna com uso de equipamentos a fim de melhor averiguar o risco. Para um dos indivíduos (risco máximo), foi recomendada a supressão, tendo em vista que medidas de manejo convencionais não conseguiriam reduzir significativamente o risco de queda. Este estudo revelou a necessidade de manutenção preventiva a fim de diminuir o número de acidentes envolvendo as árvores urbanas.
ARTIFICIAL INTELLIGENCE DALAM REKRUTMEN
Authors: Zurnali Cut , Wahjono Wahjono
Published: December 2022

Teknologi AI telah mencapai fase pengembangan berkecepatan tinggi. AI memungkinkan perusahaan untuk menghapus tugas yang berulang, membosankan, dan rentan terhadap kesalahan karena ambiguitas. AI membantu departemen SDM dengan mengotomatiskan dan menyelesaikan sebagian besar tugas SDM bernilai rendah ke lingkup pekerjaan yang lebih strategis. AI memiliki potensi untuk merevolusi pengalaman karyawan dalam berbagai cara, mulai dari perekrutan hingga manajemen bakat, dengan memproses sejumlah besar data secara cepat dan akurat. Banyak organisasi sudah mengintegrasikan teknologi AI dalam proses rekrutmen karyawan baru. Secara empiris dan konseptual, dampak AI terhadap proses rekrutmen dapat ditemukan dalam banyak penelitian dan kajian literatur.
Perancangan Klasifikasi Tanaman Herbal Menggunakan Transfer Learning Pada Algoritma Convolutional Neural Network (CNN)
Authors: Purwanto Purwanto , Sumardi Sumardi
Published: December 2022

Tanaman herbal merupakan tumbuhan yang bermanfaat untuk mencegah atau menyembuhkan penyakit. Misalnya daun sirih yang bisa digunakan untuk membantu meredakan batuk. Selain dari daun, bagian tanaman herbal lainnya yang bisa digunakan adalah akar, buah, bunga, dan kulit kayunya. Penggunaan tanaman herbal sebagai obat di Indonesia sudah ada sejak zaman nenek moyang dan diwariskan secara turun temurun. Namun seiring dengan perkembangan zaman, munculnya berbagai jenis obat modern terbaru, dan kurangnya informasi mengenai tanaman herbal mengakibatkan obat dari tanaman herbal sedikit demi sedikit ditinggalkan. Oleh karena itu penulis melakukan perancangan klasifikasi tanaman herbal menggunakan transfer learning pada algoritma Convolutional Neural Network (CNN) yang dapat memudahkan masyarakat mengenali jenis tanaman herbal berdasarkan citra daunnya. Pada penelitian ini penulis membuat model CNN menggunakan transfer learning berarsitektur EfficientNet untuk melatih dataset yang terdiri dari 7 tanaman herbal, yaitu daun jambu biji, daun sirih, daun kunyit, daun kemangi, daun sirsak, daun papaya, dan lidah buaya. Pada perancangan ini, dataset dibagi menjadi 2 yaitu train set dan test set dengan rasio 80%:20% dan digunakan image augmentation sebanyak 4 lapis untuk menambah variasi gambar dan meningkatkan akurasi.
An Assessment of Methods of Tax Fraud among the Taxpayers in South West Nigeria
Authors: JIMOH Lukuman Adewale , ADEKUNLE Samuel Kayode
Published: December 2022

Raising revenue through taxes is one of the most difficult issues confronting governments in developing countries, particularly in south-west Nigeria. This has been exacerbated by the continued failure to investigate the various methods used by taxpayers to commit tax fraud. Hence, this study assessed the methods of tax fraud among the taxpayers in south west Nigeria. The population of the study comprised 504,263 taxpayers registered with the Federal Inland Revenue Service in South West Nigeria. The sample size was 400. The data collected was descriptively analysed. The study concluded that the level at which the taxpayers employed undue income reduction to engage in tax fraud, expense reclassification to raise costs, and keeping financial records for different purposes, in south-west Nigeria were on the high side. The study recommended that the government take very seriously the factors that encourage taxpayers to engage in tax fraud, which reduces their income illegally, and that taxpayers who reclassify expenses to reduce the amount of tax owed to the government should be punished accordingly.
Influência da Diabetes gestacional nos casos de Transtorno do Espectro Autista: uma revisão de literatura
Authors: Borghi Beatriz de Almeida , Araujo Karoliny Blanco , Machado Daisy
Published: December 2022

O Transtorno do Espectro do Autista (TEA) inclui estados clínicos variados, identificados por dificuldades de linguagem e interação social. Recentemente, pesquisas demonstraram que uma inflamação em células cerebrais pode estar associada ao desenvolvimento de uma forma grave do TEA. Tendo em vista que distúrbios gestacionais prejudicam diretamente o desenvolvimento fetal, o Diabetes Mellitus Gestacional (DMG), geralmente se desenvolve perto do terceiro trimestre de gravidez devido à uma resistência à insulina provocada pelos hormônios da gestação. Essa condição tem se apresentado como um grande fator responsável pelo surgimento do TEA nesta fase, devido ao aumento do índice glicêmico estar diretamente relacionado com as disfunções do desenvolvimento cerebral do feto. A hiperglicemia intrauterina pode afetar as conexões neurais ao formar toxinas que são formadas por meio de uma reação não enzimática entre açúcares redutores e proteínas, fosfolipídeos ou ácidos nucleicos. Portanto, realizou-se uma revisão bibliográfica sobre a influência da DMG no TEA, evidenciando sua relação de tal maneira a elucidar e otimizar tanto as áreas de pesquisas e estudos, quanto às unidades de saúde e gestantes, trabalhando na conscientização e no acompanhamento. O abordamento do TEA vem sendo muito discutido nos dias atuais, por ser denominado como um novo transtorno gera-se a necessidade de encontrar as causas para a formação deste distúrbio neuronal. Por tal razão, torna-se indispensável o estudo da formação das primeiras conexões neurais, formadas no momento da gestação, e quais questões podem acometê-las.
Design and implementation of ship data analysis platform
Authors: Zhang Juan , Lv Taizhi
Published: December 2022

As the basis and core of the management work of the maritime department, the basic data of ships is a prerequisite for doing a good job in various work. With the rapid development of economic construction, the shipping industry has put forward a higher demand for maritime services. As an important component, ship data provides a strong guarantee to meet this demand. Therefore, it is very important to strengthen the construction of the ship basic data analysis platform. In this paper, through data analysis and visualization technology, the data can be displayed intuitively in the form of charts, which can effectively improve the level of maritime supervision. The platform uses Spring Cloud framework to extract ship basic data from MySQL database, Axios to obtain JSON data transmitted from the background interface, Vue to implement bidirectional binding, Element UI to implements page layout, and Echarts to displays charts.
Coastal Wind Power Forecasting Research Scheme Design
Authors: Xu Shiwei
Published: December 2022

As global resource and environmental problems become increasingly acute and climate change becomes more and more obvious, the large-scale development and utilization of new energy sources are highly valued by all countries in the world. Wind energy is highly valued because of its many advantages. Wind power, especially offshore wind power, has become one of the key development directions of renewable energy. To address the problem that coastal wind farms are in urgent need of accurate wind power forecasting, the authors of this paper investigate and summarize the relevant research work in this field. At the same time, the coastal meteorological micro-scale differences are obvious, and a research program for coastal wind farm power forecasting is designed to lay the foundation for subsequent specific research for the characteristics of this region.
A Review of Security Research on the Internet of Things, Based on Artificial Intelligence and Blockchain
Authors: Zhang Ni
Published: December 2022

With the rapid improvement of digital technology, the Internet of things (IoT) has become a trending development direction. Its massive data interaction capabilities have drawn researchers’ attention to key security issues. This paper describes the concept of IoT, its application areas, and corresponding security problems. The use of blockchain and cryptographic algorithms is introduced, and the application of blockchain in IoT security is analyzed and discussed in detail. Drawing upon artificial intelligence, technical solutions such as using machine learning for privacy protection and intrusion detection are presented. Finally, the problems and challenges facing IoT, driven by blockchain and artificial intelligence, are discussed.
A facial expression recognition method based on Convolutional Neural Network
Authors: Huang Hongbin
Published: December 2022

This project is the implementation method of a simple static facial expression recognition (FER) project. Under the environment of Python3, the deep learning model is used to compare with the traditional model. Finally, CNN (Convolutional Neural Networks) is actually used to construct the entire system, and model evaluation is carried out on the three FER datasets FER2013, JAFFE and CK+. The project's main functions include "get a picture of a person's face" and "recognize expressions". The principles of the above functions include the following: the establishment of neural network structure, the acquisition of data sets, the model training based on data sets, the use of OpenCV to obtain the face, and the use of the model to recognize the expression. The experimental results reproduce the simple deep learning model to realize FER and verify the different effects of different data sets on the results.  Face recognition has been widely used in all aspects of life, but different purposes need different models and data collection methods, and the laboratory collection cost is high, and the amount of data is limited, so this project also discusses the data set selection methods under different purposes.
Design of Automatic Management System of Electric Power Marketing Charges and Accounts Based on Robot Process Automation
Authors: Sun Jie , Qiu Degui , Xu Zhufa
Published: December 2022

In the automation construction of marketing charge accounts in power supply enterprises, the key is the automation system of electric energy measurement. It covers all levels of measurement points and collection terminals in the whole network, and integrates measurement management, analysis, monitoring and information collection. The traditional electricity charge management of electric power enterprises is the process of manual meter reading, accounting and charging. Meter readers go to each customer every month to read the meter according to the meter reading time and meter reading card, record the number of electric energy meters on the meter reading card, and then copy the data of the meter reading card on the accounting card. Manual meter reading can't meet the current demand. Therefore, this paper will study the automatic management of electric power marketing charges based on RPA (Robot process automation), and build an automatic management system of electric power marketing charges. Through the comparative analysis of experimental data, the experimental results of the response accuracy of different systems show that this system is still more advantageous, and the accuracy of this system is the highest, with an average accuracy of 81.59%. However, the accuracy of reference method and reference method are 62.72% and 46.95% respectively. A perfect charging system based on RPA can not only improve the service level and corporate image of power supply companies, but also improve the management level of power supply companies, thus improving the economic benefits of enterprises.
Application and Innovation of Embroidery Art in Modern Fashion Design
Authors: Wang Shuran
Published: December 2022

Traditional embroidery is an important handicraft in China, which has a cultural background of thousands of years and is also one of the important components of Chinese folk art. Combining traditional embroidery art with modern fashion design can form modern fashion design and provide good materials for the long-term development of fashion design in China. Combining embroidery technology with modern fashion design can strengthen the level of modern fashion design in China and make the development of fashion design in China more national characteristics.
Research on personalized design of facial expression package based on network perspective
Authors: He Yi
Published: December 2022

The development of network technology and the popularization of smart phones promote the rapid development of new image production technology. Network expression is a unique visual carrier of information dissemination based on network culture. Nowadays, in the major network platforms, facial expression packs have emerged in large numbers and gradually become a new communication tool. We are gradually moving towards an era in which images replace language. The arrival of the personalized era also reveals the inevitability of personalized design of network expressions.
On the contemporary transformation of Inner Mongolia Grassland fine brushwork figure painting creation
Authors: Wang Zhiwei
Published: December 2022

The expression of contemporary fine brushwork figure painting on grassland theme mainly forms the mainstream with the realistic style of amorous feelings and spiritual expression. In addition, image modeling contributes to the diversification of creation methods. Now, in the face of the new era, painters should stand in the context of modern society and use the spirit of The Times to examine the grassland culture, through the image of the national customs and deeply explore the connotation of national culture, to show the current situation of national spirit and modern civilization caused by the collision of national living state and social psychology changes. Continue to walk the grassland theme meticulous figure painting creation of contemporary exploration and transformation of the road.
The design of the Moon Center based on the female perspective
Authors: Duan Huiying
Published: December 2022

With the opening of China's three child policy and the continuous development of social economy, China's industrial structure has changed constantly. As a new economic growth point, the maternity and infant industry has become a hot industry in recent years. New service industries such as Moon Sister and Moon Center have developed rapidly. The scientific and comfortable modern moon center is increasingly favored by young women. It is undeniable that the emergence of maternity care institutions has timely met the needs of women for puerperal care today. However, there are always many imperfections in the development of emerging industries at the time of their inception, and the Moon Club entered the mainland market late and is in the initial stage of industrial development. From the actual situation of the few existing Moon Centers, it only meets the immediate needs and hasty response, and fails to fundamentally improve and enhance the reasonable, humanized, scientific planning, design and research of Moon Centers. The space function and design style are not mature enough, and there is no deep concept to support it. It is a superficial form, so women's demand for the existing indoor space of postpartum care center has not been met. Therefore, the interior design of postpartum care center must start to change the original design thinking, no longer simply pursue the excessive exaggeration and blandness in style and decoration, but should design a warm and comfortable interior space that conforms to the consumption psychology and aesthetic psychology of newborn mothers based on emotional needs, especially for the functional care of related nursing spaces and its feasible humanized and emotional design.
Preface: 5th International Conference on Chemical Engineering and Advanced Materials (CEAM 2022)
Authors: Thackeray Willie , Li Jie
Published: December 2022

The 2022 5th International Conference on Chemical Engineering and Advanced Materials (CEAM 2022) was held virtually during September 24-25, 2022 due to the travel restrictions imposed because of the Covid-19 pandemic. This conference aims to provide opportunities for all participants to exchange new innovative ideas and application experiences as well as to provide a platform where possible innovators can think of new ventures, generate contacts and networks for future collaboration.

CEAM 2022 tracks include chemical engineering, biological chemistry, biological pharmacy, food science, chemical materials, nano materials, materials processing engineering and advanced materials applications. This conference brought together leaders from industry and academia to exchange and share their experiences, present research results, explore collaborations and to spark new ideas, with the aim of developing new projects and exploiting new technology in these fields. The conference has received more than 70 papers from all around the world and after a rigorous peer-review process, less than 55 papers were accepted.

On behalf of the conference organizers, we would like to thank the reviewers for their time and effort in reviewing the papers. Their feedbacks to the authors have been excellent and useful to ensure high quality papers for the conference. Our appreciation to all keynote speakers for sharing their thoughts and experiences with respect to various topics. Our sincere thanks also go to all attendees from all around the world for participating in the conference. Last but not least, we would like to thank the organizing committee members for their hard work, cooperation and insights in ensuring the success of this conference.

Scientific Committee of CEAM 2022

Chongqing, China
MOFs together with derivatives for supercapacitor applications
Authors: Liu Dingbang
Published: December 2022

With the continuous progress of science and technology, the demand for energy of new scientific and technological products is higher and higher. However, with the development of large developing countries such as China, the fossil energy on the earth is almost exhausted. In order to solve this problem, people began to develop new clean energy. In order to use new energy more reasonably and efficiently, people must develop a high-performance Supercapacitor is a new energy storage device developed at the end of last century. However, the biggest obstacle to the development of supercapacitors is that their energy density is far lower than that of traditional energy storage devices. However, the biggest obstacle to the development of supercapacitors is that their energy density is far lower than that of traditional energy storage devices. The ideal supercapacitor electrode material has the characteristics of uniform pore size distribution, high specific surface area and high porosity. As a new organic-inorganic material, MOFs have a variety of spatial porous network structures, and the derivatives synthesized with MOFs as precursors can still maintain their original porous This review mainly introduces the examples of preparing electrode materials with reasonable structure with MOFs as electrode materials or MOFs and their derivatives as electrode materials were summarized and prospected.
The synthesis, activation, and adsorption capacity of metal-organic frameworks for hydrogen storage
Authors: Xu Kaixiang
Published: December 2022

Metal-organic frameworks (MOFs) comprise both organic and inorganic parts, allowing for immense diversity and tunability for specific purposes. Hydrogen has a hopeful prospect of being a clean energy carrier because of its lightness and therefore energy density, and it has been reported that MOFs already effectively adsorb hydrogen at cryogenic temperatures. In this article, the main synthesis methods for MOFs for hydrogen storage will be outlined, including the solvothermal, sonochemical, electrochemical and microwave methods. Methods of activation and stability concerns (thermal, mechanical and moisture) will also be addressed. The main focus of the article will be on increasing hydrogen capacity in MOFs at room temperature as opposed to cryogenic. The main ways to achieve this are adjusting pore size and strengthening hydrogen-MOF interaction. Recent research has shown that the most important effects in this regard are generating active metal sites, the spillover mechanism, and adjusting organic linkers.
Metal-Organic Frameworks for Removing Radioactive Wastes in Water
Authors: Chen Taotao
Published: December 2022

Humans only got a limited number of natural resources to survive and exploit. Water, as one of the limited natural resources, has been polluted because of human rapid development in many fields of industries. Nuclear wastes, as one of the pollutants, bring serious or in-reversible harm not only to the environment but also to human health. Metal-organic frameworks (MOFs) materials could be applied to various kinds of areas and also got a huge potential in the treatment of wastewater with radioactive wastes. This article mainly talks about the research progress of MOF material used in the field of radioactive wastewater treatment and introduces the preparation and characteristics of MOF-5, HKUST-1/KCoFC, and UiO66-COOH materials. Additionally, factors including solid-liquid ratio, initial concentration, reacting time, and pH value that affect the adsorption capacity on U(VI), Cs, and Sr are analyzed. Some research on MOFs materials used to remove different kinds of radioactive substances is also highlighted. Finally, some problems that still exist are raised, and expectations and directions for future development in this field are provided.
Prediction of the chemical composition of ancient glass objects before weathering
Authors: Chen Ziyuan
Published: December 2022

This paper analyzes the differences and correlations in the composition of ancient glass artifacts and determines how they are classified. The surface weathering of known glass artifacts is analyzed in relation to their glass type, color, and decoration: and the statistical patterns of chemical composition content in different types of glass artifacts under different weathering conditions are analyzed according to glass type, and the chemical composition content before weathering is predicted by analysis of weathering point detection data.
Composition analysis and identification of ancient glass products
Authors: Li Jiaming , Geng Yongsen , Meng Fanyu
Published: December 2022

In this paper, we take the composition analysis and identification of lead-barium glass and high-potassium glass as the starting point, and analyze and solve the problems related to the weathering and chemical composition of the two glass types through machine learning and random forest. In this paper, the mean value and content interval of the given data are initially processed, and the classification basis can be roughly determined. Then the random forest classification model is used to classify them, and the importance of each feature is obtained. The K-mean clustering algorithm is adopted to solve the problem, and the reasonableness test is performed using the fuzzy clustering algorithm, and the results of both are similar, indicating good reasonableness. Finally, a random forest model was used to train the new sample data, and then a sobol sensitivity test was performed on them according to the classification results to obtain a higher sensitivity of alumina to the classification results of high-potassium glass and a higher sensitivity of lead oxide to the classification results of lead-barium glass.
Solving the problem of composition and identification of ancient glass products based on classification model
Authors: Zou Jingya , Zhu Kangyu
Published: December 2022

Glass has been used as ornaments or containers in ancient times, and in recent years, with the development of the world, the existence of glass has been paid more and more attention. Examining the composition and identification of ancient glass artifacts reflects the rise and fall of glass production in each dynasty, thus reflecting the face of each dynasty, reflecting the historical understanding and importance of glass materials, which is quite helpful for archaeological research. The problem of predicting unknown classes of glass artifacts and performing sensitivity analysis is solved by a classification model based on random forest. In this paper, we first define lead-barium glass as a positive class, and build classification models for weathered and unweathered glass based on the random forest algorithm and the traditional classification algorithm, and then train them separately. ROC curve Verify the prediction effect of random forest.
Classification and identification of ancient glass objects based on decision tree model
Authors: Zhang Jiaqi , Tang Ming , Gao Zhiyuan
Published: December 2022

Glass products were one of the important goods on the Silk Road. In this paper, a decision tree model is adopted to determine the law of classification of high potassium and lead-barium glass and to obtain the criteria for classification thus illustrating the law of classification. In order to subclassify the two categories of high potassium and lead-barium, k-means clustering is used for unsupervised classification. Once a classification model was established, the unknown classes of glass artifacts were then classified using this model.
Category identification and composition analysis of ancient glass products based on GA-BP neural network and factor analysis
Authors: Xie Juele , Ye Jiahui , Zhou Jun
Published: December 2022

Ancient glass is a witness of early trade exchanges, and its study has profound historical significance. In this paper, we analyze the nature and chemical composition data of a batch of ancient glass products to investigate the compositional differences and identification characteristics of different types of glass products. In this paper, we use relevant information to achieve the effect of identifying cultural relics based on chemical composition data by mathematical modeling for a batch of ancient glass products in China (divided into high potassium glass and lead-barium glass), and analyze the correlation between the chemical composition of different categories of glass to compare the differences between classes.
Study on Inspection Mechanism of Organic Gems Based on Laser Raman Spectroscopy
Authors: Shi Li , Chi Hui , Yu Yong , Wang Wei
Published: December 2022

Raman spectroscopy is a fast and nondestructive analysis and detection technology. Raman spectroscopy contains rich molecular structure information of substances. Based on the application status of Raman spectroscopy in the field of gemstones, this paper further explores its application technology in the qualitative identification of organic gemstones. In this paper, natural Longpai, Koba resin and rosin were tested based on laser Raman spectroscopy. The identification difficulties of high imitations of natural resins in Longpai are solved respectively. Therefore, the application of Raman spectroscopy to the identification and analysis of high imitations of organic gem amber and its natural resin has great advantages and convenience.
The classification method of different glasses is clear and composition analysis
Authors: Wang Jingxi , Zhao Menghua , Guo Jiaqi
Published: December 2022

With the development of the Silk Road, this paper mainly studies the glass category, glass pattern decoration, weathering, glass color, chemical composition content as the indicators to make statistical rules and analysis rules, and analyze and summarize the relationship between chemical components content,For question 1, it is divided into three problems. Preprocess the attached data, process its invalid data, and integrate the data. The first question first uses bivariate correlation analysis, using Spearman correlation coefficient and Pearson correlation coefficient, and then using chi-square test to test the results of the correlation analysis. The result is: there is a strong correlation between category and weathering, while there is no correlation between color, ornamentation and weathering.[1]The second question is based on the statistical analysis model of the first question, under the condition of glass type and surface weathering, and the vertical sample T test obtained the statistical law of silica, potassium oxide, calcium oxide and lead oxide. The third question established a predictive model and used multiple linear regression to obtain the prediction results.
Analysis and identification of ancient glass artifacts based on their chemical composition
Authors: Yu Haonan , Cheng Hao , Zhan Wanting
Published: December 2022

The Silk Road led to the development of ancient glass products, and common ancient glass products include high potassium glass and lead-barium glass. Ancient glass is susceptible to weathering by elemental exchange with the surrounding environment, resulting in changes in internal chemical composition and appearance. In this paper, the correlation and influence of relevant factors on the degree of weathering on the surface of glass artifacts are fully analyzed, and subclasses and unknown types are identified based on their chemical composition, while the necessary sensitivity analysis is conducted for each of these results, and finally, correlations between chemical compositions and differences in correlations are presented. This study can guide for glass heritage workers to help them better carry out their conservation work.
Preface: 2022 International Conference on Mathematical Modeling and Machine Learning (MMML 2022)
Authors: Clare Genevieve , Wang Shuai
Published: December 2022

2022 International Conference on Mathematical Modelling and Machine Learning (MMML 2022) was successfully held during 26-27 November, 2022 in Sydney, Australia. The conference intends to invite worldwide famous scientists, experts, scholars, and researchers for academic presentations.

MMML 2022 aims to bring together leading experts and scholars in the fields of mathematical modeling and machine learning to share their experience and research results. It also provides an excellent interdisciplinary platform for researchers, practitioners and educators to showcase the latest innovations, trends and concerns in these fields, as well as practical challenges encountered and adopted solutions.

This conference cordially invites high-quality research contributions to describe original and unpublished results of experimental or theoretical work in mathematical modeling and machine learning for presentation at conferences. Less than 60 articles were selected from more than 90 submissions after peer-review. Authors participated in MMML 2022 with oral presentations and posters, promoting the communication among researchers and experts from institutes and universities.

Organizing Committee of MMML 2022

Sydney, Australia
Short-Term Stock Price Prediction Algorithm Construction Based on Integrated Learning of SVR and RF with Bagging
Authors: Liu Yiqing , Ayitelieke Ayiken , Yu Jiayi
Published: December 2022

In the context of the emergence of artificial intelligence machine learning algorithms, how to handle training data for reasonable and accurate prediction of the stock future market is expected to bring effective methods, but previous traditional models are difficult to be used effectively in complex stock markets. In this paper, by exploring new stock forecasting methods such as Support Vector Regression (SVR), Random Forest (RF), and integration based on integrated learning, the model results are compared with previous traditional model results and the models are evaluated using R2 and MSE metrics. The algorithm based on Bagging integration has better robustness and generalization, in which both R2 and MSE have some improvement compared with those before integration. The research in this paper is beneficial to provide a reasonable prediction approach for stock forecasting later, which can help consumers make better quantitative trading.
Research and Analysis of the Synergy Degree of Regional Economy and Regional Labor Force Based on AHP-EWM Coupling Perspective
Authors: Fan Hongwei , Jiang Yizheng , Mai Jiahao
Published: December 2022

To measure the coordinated development level of regional economy and regional labor force, on the basis of the analysis of the regional economy and regional labor force development coordination effect, select 11 representative regional economic subsystem of order parameters and 9 representative regional labor force subsystem of order parameters. The weight is calculated by AHP-EWM coupling method to evaluate the level of coordinated development of regional economy and regional labor force. In this paper, the order parameter method of measuring system synergy degree is used to construct a synergy degree evaluation model of regional economy and regional labor force development, and the model is applied to measure the level of regional economy and regional labor force synergy development in Zhoushan city, an archipelago tourism city in China. The results indicate that the synergy degree of regional economy and regional labor force in Zhoushan city shows an M-shaped development trend of alternating increase and decrease, and the overall synergy degree is in a low level state.
A modified LSTM neural network model for stock price prediction based on auxiliary information and model average
Authors: Liu Xiangyao
Published: December 2022

LSTM neural network has been widely used in stock price forecasting, but it has some problems such as difficulty in selecting superparameters, insufficient information of original price sequence, etc. Based on this, the model propose an improved method based on functional principal component analysis and distance covariance weighted model averaging. On the one hand, the method solves the problem that the original stock price information may not be sufficient to predict future price trends by using the functional principal component basis expansion coefficient of the intraday price to capture the residual sequence between the LSTM neural network predicted value and the real stock price. On the other hand, for the superparameter selection of LSTM neural network, the method uses a model averaging method based on distance covariance, which effectively balances the variance and bias of the prediction model. Furthermore, the regression method between the residuals sequence and the basis expansion coefficient of the functional principal component is model free. The actual data analysis shows that the proposed method has a greater improvement in MSE and BE than the original LSTM. Finally, the proposed method can be viewed as an optimization framework. This method can also modify ARIMA, GARCH and other models, and it can also be applied to other time series data prediction background.
Research on High-frequency stock price prediction based on Chebyshev-Stacking and Weighted LSTM neural network [1]
Authors: Wang Yiwen , Zhang Yida , Pan Zixuan
Published: December 2022

To solve the problem of high-frequency stock price prediction, this paper proposed a prediction model based on Chebyshev-Stacking and a weighted LSTM neural network. The proposed method extracts the function characteristic information of the high-frequency stock price series through Chebyshev orthogonal polynomial basis expansion. Considering that the potential model structure between each component of the function feature vector and the residual sequence predicted by the LSTM neural network is unknown and there is a certain noise, this paper used the Stacking framework to enhance the data and weighed the bias and variance of the prediction model. In addition, since the number of predictor variable periods of the LSTM neural network is a hyperparameter, the model averaging method based on distance covariance is used for optimization. The results of actual data analysis show that the proposed method is significantly better than the original LSTM neural network in terms of mean square error, absolute error, and relative error. By selecting the different number of training sets, the robustness of the improved model is verified. Finally, the proposed method can also be used in practical applications such as daily average temperature prediction, missile trajectory prediction, and real-time monitoring of atmospheric environment quality.
A study on the classification of ancient glassware based on unsupervised learning
Authors: Wu Renjie
Published: December 2022

In the process of weathering, ancient glass objects have changed their composition ratio due to the large exchange of internal elements with external environmental elements, which affects the correct judgment of their categories. This paper establishes a relevant model for the classification and research of glass artifacts. The relationship between the surface weathering of these glass artifacts and the type, decoration, and color of the glass is analyzed; the statistical pattern of the content of chemical components with and without weathering on the surface of the artifacts is analyzed in conjunction with the type of glass, and the content of their chemical components before weathering is predicted based on the data of the weathering points. To study the classification pattern of high potassium glass and lead-barium glass; to select the appropriate chemical composition for each category for sub-category classification, to give specific division methods and division results, and to analyze the reasonableness and sensitivity of the classification results. Identify the categories to which unknown categories of glass artifacts belong, and analyze the sensitivity of the classification results. To analyze the correlations between the chemical components of glass artifact samples of different categories and compare the differences in the correlations between the chemical component contents of different categories.
Study and Identification of Ancient Glass Composition Based on Regression Equation and Statistical Analysis
Authors: Zhang Pu
Published: December 2022

Glass is valuable material evidence of the early trade along the ancient Silk Road, but the ancient glass is easily weathered by the buried environment, which leads to the change of its composition proportion, thus affecting the correct judgment of its classification. The study of the composition analysis and identification of ancient glass products is of great help to the understanding of the social culture at that time and the trade civilization between China and foreign countries. This paper mainly studies the composition analysis and identification of ancient glass products. Firstly, the chi-square test is used to analyze the correlation, the regression analysis model is established to complete the significance test, and dynamic clustering is used to classify the glass. It is concluded that high-potassium glass and lead-barium glass can be divided into three categories. The rationality and sensitivity of the classification results are analyzed by a decision tree. Finally, based on the least square method and dynamic clustering, the results show that: The comprehensive model used in this paper can accurately analyze the composition of ancient glass.
Development of a Carbon Financial Performance Assessment Model for the Power Generation Industry
Authors: Zhao Xiaoyu , Wu Siyan , Zhao Rui
Published: December 2022

In the face of global warming, sustainable development has become a universal choice for all countries in the world. China has put forward the goal of "double carbon" and promoted the low-carbon transformation of traditional energy enterprises, which has put forward higher requirements for their energy saving and emission reduction. In this context, this paper presents a comprehensive analysis of the challenges and opportunities of carbon finance development at the micro level. And for power generation enterprises, a financial performance evaluation system is constructed from three dimensions: economic, social and environmental. The data of 18 indicators of 32 listed companies in power generation industry are selected as samples, and the mathematical model is assigned by CRITIC method, and the comprehensive financial performance evaluation of the above companies is carried out by TOPSIS method. It provides theoretical support and referential decisions for low-carbon transformation of power generation enterprises, with a view to improving the comprehensive efficiency of enterprises.
Research on sales strategy of electric vehicle target customers based on machine learning algorithm
Authors: Li Zhichong
Published: December 2022

Due to the influence of various subjective and objective factors, there is great uncertainty in consumers' willingness to purchase new energy vehicles. In this study, it is hoped that based on customer satisfaction and personal characteristic information, the main factors affecting whether the target customers are willing to purchase new energy vehicles are explored, so that corresponding sales strategies can be formulated. In this study, two different embedding methods based on penalty terms and tree models are used for feature selection, the former using three models LR, LASSO and SVM, and the latter using RF and LightGBM models for a total of five models for machine learning to find out the relevant features that affect the sales of different brands, and the voting method is applied to the selected features, and the results are found. The battery technology performance of electric vehicles, comfort, annual mortgage of target customers and the ratio of auto loan to annual household income have a significant impact on the sales of all three brands. In addition, affordability, safety and customer's work situation also had different degrees of influence on sales of the different brands. A multilayer perceptron (BP neural network) prediction model is built for each brand's target customers to predict their purchase intention. The prediction accuracy of the network is improved by balancing the positive and negative samples so that the ratio of the two types of samples in the dataset is approximately 1:1. The test set is also used for real-time testing to determine the fit status of the dataset. The accuracy of the final model was above 80% on both the training and test sets, and the model predicted the 1st, 5th, 6th, 7th, 12th, and 12th samples. The model predicted the purchase intention of the 1st, 5th, 6th, 7th, 12th and 13th customers.
Classification and identification of glass artifacts based on fuzzy clustering and binary logit regression analysis
Authors: Zhang Tong , Cui Mingyu , He Nina
Published: December 2022

This paper establishes a mathematical model based on the existing data of glass types, summarizes the classification rules of high potassium glass and lead-barium glass, and selects the appropriate chemical composition and subclasses for high potassium glass and lead-barium glass. And based on the classification law, the chemical composition data of the unknown category of glass artifacts are used to identify the type to which they belong.
Analysis and identification of the composition of ancient glass products based on regression models
Authors: Li Zeyao , Zeng Yanlin , Zhang Weiting
Published: December 2022

The Silk Road was an important channel for economic exchanges between China and foreign countries in ancient times, and glass products were an important physical evidence of trade exchanges. Early glass was introduced to China in the form of bead-shaped products, and on this basis, craftsmen improved it to form the unique ancient Chinese glass. Glass products are important physical evidence of the ancient Silken Road, but they are vulnerable to weathering due to the influence of the burial environment. In this paper, we use a batch of ancient glass artifacts as the research object, and build a machine learning classification model and a clustering model based on the different chemical composition content to analyze the classification basis and the relationship between the chemical composition content of the glass artifacts and optimize the research.