bluuebunny/crossref_metadata_2025 huggingface dataset:
Watchful Eye or Just a Veil? Common Institutional Ownership and Environmental Information Disclosure Quality
Authors: Ding Hao
Published: July 2023

As an important channel for the external stakeholder to understand the environmental performance of enterprises, corporate environmental information disclosure is not only an effective way to exercise social supervision but also an important mechanism to promote corporate environmental governance. However, Under China’s current environmental information disclosure model, irregular disclosures, ambiguity, and selective disclosure practices remain prevalent. Hence, it is crucial to seek solutions to improve the quality of environmental information disclosure. In recent years, the phenomenon of common institutional ownership has become increasingly widespread in the capital markets and has a significant impact on the strategic decisions of companies. This paper selects Chinese A-share listed firms from 2010-2021 as a research sample to examine the impact of common institutional ownership on the quality of environmental information disclosure. The study found that common institutional ownership can improve the quality of environmental information disclosure. The higher the degree of their linkage and the greater the shareholding, the more pronounced the synergistic effect. The findings remained valid after testing using propensity score matching (PSM) and changing the sample period. Heterogeneity analysis shows that the facilitating effect of common institutional ownership on the quality of environmental information disclosure is more pronounced in high-polluting firms and firms which stay in the growth and maturity stage. This paper enriches the research on the economic consequences of common institutional ownership in China and provides management implications for improving the environmental information disclosure system and promoting “genuine green” corporate social responsibility.
Global Industries’ Hedge Strategy Under China-US Trade War
Authors: Sheng Chuan
Published: July 2023

Human use trade to develop economy and technology til nowadays.Trade would benefit both parties but trade war is a movement that not worth for both parties. China-US trade war began since 2018, it has effect huge amount of global industries and global economy. Both countries’ currency and those who has close business relationship countries’ currency exchange rate were influenced. Both country kept put increasing tariffs on their products so that huge amount of global industries had to seek different hedge strategies to reduce foreign exchange risks or eliminate them to offset foreign currency exchange risk. This research paper would first introduce the definition of the trade and trade war, then analyze what was happening during China-US trade war, next use examples from 10-K to show how US tech giants use financial strategy to hedge their risk exposure and state possible situations might facing when hedging. Last conclude with four ways to reduce reduce and forecast the foreign currency exchange rate risks.
Financial Analysis of Hyatt Hotels Corporation
Authors: Qi Yutong
Published: July 2023

Due to the epidemic, as well as the implementation of restrictions, the hotel industry has had a significant impact. Taking Hyatt Hotel as the entry point, this paper analyzed Hyatt Hotel's operating status and recovery from the epidemic by comparing the important accounting elements and financial ratio in different dimensions with the other three hotels, based on the financial data from their annual reports (Intercontinental Hotels Group, Carnival Corporation, and H World Group). Through the research, it is found that with the reduction of the restrictive policy, Hyatt recovered well from the epidemic, especially in the factors of net income gained the highest profit among the peers, and the ROE which has a much higher level than its peers at the same time they maintain a low long-term debt ratio. Also, its comprehensive operating condition was greater than that of its peers. However, there are still opportunities and challenges in the future, and it still needs to wait and see what happens next.
The Synergistic Power of Fintech and Digital Transformation
Authors: Qian Chenyi
Published: July 2023

Through the process of transforming from traditional methods to digitalization, the financial industry has proved that digital transformation has improved the efficiency of the financial industry. The development and needs of the times have promoted the innovative development of the financial industry, leading to the emergence of financial technology. Fintech has optimized some of the disadvantages of traditional finance, making it a technology that modern humans rely on, leading to changes in the financial industry. This paper analyzes aspects of digitalization such as online payments, P2P lending markets, artificial intelligence advisors, and blockchain. For the different parts, the differences between them and the traditional methods are discussed. Whether these technologies are mature or stable, their impact on the financial industry and some regulatory problems are analyzed. Future directions of this industry are summarized by exploring more different strengths, weaknesses, and implications for the financial industry from these sections.
ESG Scores and Environmental Protection Investment: Evidence from Listed Companies in China
Authors: Fan Xinyu , Dong Yilan
Published: July 2023

The ESG concept has increasingly permeated the day-to-day management and strategic choices of companies in recent years. Within industries, regions and company sizes, all organizations have been allocating more resources to improving ESG. How does ESG scores affect corporate environmental investment? Exploring this question allow us to better understand the activities of corporate environmental investment, also be aware of the effectiveness of government's environmental regulation and industry regulation. Based on a panel of 615 Chinese listed corporates from 2019-2021 from CSMAR, the paper found: (1) For EPI of A-share listed companies, the amount was generally sufficient from 2019 to 2021, which is incompatible with previous discoveries. (2) There is a statistically significant relationship between the score of ESG and the amount of EPI. (3) The additional effect of centralized shareholding on the positive relationship remains unclear after robustness test. The findings above not only broaden the research perspective of EPI and ESG, but also enrich academic research on environmental management.
Credit risk contagion and firm investment efficiency: From the perspective of credit debt defaults
Authors: Wang Kaitong
Published: July 2023

Frequent credit debt defaults can constitute credit risk contagion causing systemic financial risk across the macroeconomy. In this paper, we find that credit risk contagion that occurs in a city or industry has a negative influence on the investment efficiency of firms within the scope, and this effect is amplified as the size of credit debt defaults grows. This conclusion is still robust after a series of tests. The mechanism analysis results show that credit risk contagion reduces firms' investment efficiency by affecting their bank lending, trade credit along the supply chain, credit allocation towards mortgage loans, and financial constraints. Furthermore, we find that private firms and firms located in regions with decentralized banking sectors are less affected by credit risk contagion, which validates the impact of firm ownership and regional banking sector concentration. Finally, we according to the test result of firm performance reveal that credit risk contagion increases the prudence of firm investment decisions, making this contraction of firm investment show a positive effect. This paper provides both theoretical support and policy insights for strengthening credit debt market regulation and preventing systemic financial risks.
Financial Analysis and Reporting of Construction Enterprise: An analysis of CRH plc
Authors: Li Siyi
Published: July 2023

A financial analysis statement is a report that examines a company's financial health and performance in detail. The report will often contain a review of the business's financial reports, such as the balance sheet, income statement, and cash flow statement, in addition to other financial measures and ratios used to assess the company's financial status. It is a vital tool since it may assist managers in making decisions, give an objective assessment of the company's performance, permit comparison with industry benchmarks, and aid in regulatory compliance. Real solutions to human problems are provided through construction enterprises. Many projects are executed by multiple contractors, and a single project may include a variety of techniques and methods. It is classified as an asset-heavy business, which may offer distinct advantages over other types of enterprises. CRH plc is a multinational conglomerate of diverse construction materials companies headquartered in Dublin, Ireland. It makes and distributes a wide range of building goods. It is a traditional representative of a construction enterprise. We may use it as a model by studying its financial performance.
Research on withdrawal behavior of foreign venture capital under the industry development and changes-- Take SoftBank Group as an example
Authors: Ge Chuyuan
Published: July 2023

In recent years, due to SoftBank Group's adjustment of strategic layout, the actual proportion of investment in China has decreased. As a leader in the investment industry, SoftBank Group has helped a large number of budding Chinese enterprises such as Alibaba and Didi Chuxing expand rapidly and achieve extremely high market shares, making an important contribution to the development of China's modern service industry. Therefore, this paper intends to explore the reasons for "SoftBank's withdrawal from China" event, which will help better utilize foreign investment to promote the development of the tertiary industry and improve the contribution of the tertiary industry to China's economic progress.
Extraction Methodologies and Applications of Renewable Polymers
Authors: Li Yinuo
Published: July 2023

Since there are large scales of production in modern industries all over the world, non-biodegradable materials would cause serious environmental issues when dealing with pollution treatments. To reduce environmental impact, it is said that producing renewable polymers is an effective method to maintain production yields without harming the environment. Renewable polymers are polymeric materials derived from renewable carbon wastes, and reused or biodegraded at the end of life cycles. This article introduces four examples of common renewable polymers with different bio-based platforms by comparing their structures, extraction methodologies, and applications respectively. The opportunities for sustainable polymer development would be based on performances while applying to industrial and commercial use. Although some renewable polymers are still not widely used on a grand scale due to yields or costs, the researchers would continue to investigate methods to improve the situation. Because of the appealing advantages of the production of renewable polymers, the future of manufacturing would be significantly beneficial to both environment and the whole economy.
Analysis of Indoor Path Planning Techniques for Wheeled Mobile Robots
Authors: Wu Lei
Published: July 2023

Path planning is crucial for autonomous mobile robots to navigate in unknown settings. Achieving autonomous navigation in indoor environments without collisions with static or dynamic obstacles, reaching the destination quickly, and meeting the requirements of specific work scenarios are essential challenges. Over the years, various path planning algorithms have been proposed in the literature, each with advantages and limitations. This paper provides a brief overview of advanced indoor path planning algorithms for wheeled mobile robots, listing classical algorithms as well as recent advancements. These algorithms' fundamental principles, features, and computational complexities are discussed and analyzed. Furthermore, this paper highlights some challenges and open research questions in path planning. This survey aims to provide readers with a concise review of existing indoor wheeled mobile robot path planning techniques and their applications, to inspire future research in this vital field of robotics.
Research on aircraft automation warning model based on gradient boosting tree algorithm
Authors: Liu Xingmin , Su Bowen , Zhang Caiyi , Liu Haixu
Published: July 2023

Flight safety is the basis for the survival and development of civil aviation transportation industry, and with the rapid development of China's civil aviation industry, the study of aircraft safety issues is becoming more and more important. Serious flight accidents can lead to great threat to life as well as economic loss, so this paper focuses on flight safety issues, and analyzes and investigates flight key parameters segment data, aircraft overruns, and flight parameters data. In this paper, based on the relevant data from QAR , t-test was performed on the processed data through data analysis and data cleaning to ensure the reliability of the data. And the principal component components of the key data were extracted using the random forest algorithm, and the 6-bit eigenvalues were selected as the key parameters, which had the greatest influence on the landing G value of the aircraft. Then, the data set was divided according to the ratio of 8:2, and the pilot qualification was used as the label value, and the machine learning classification prediction was performed by the gradient boosting tree GBDT algorithm to obtain the pilot qualification prediction model, and the highest accuracy reached 76.3% in the test set. This paper also compares the extreme gradient boosting XGBoost algorithm, with the highest current parameter reaching 73.4%. In this paper, the gradient boosting tree GBDT is chosen to construct a flight crew qualification assessment model.
DSP optimization technique based on deep learning to account for the effects of fiber nonlinearity
Authors: Yang Jing
Published: July 2023

The first section of this work is the classification of digital signal processing (DSP) techniques for adjusting, minimizing, and utilizing fiber nonlinearities in coherent fiber optic transmission systems. Then, different experimental optimization methods based on the Volterra series of Deep Neural Networks (DNN) and the Digital Back Propagation (DBP) methods are contrasted with the aid of specific instances. Concrete examples reinforce the techniques. The findings demonstrate the viability of a DSP strategy using deep learning for inexpensive optical comms, resulting in significant performance gains and computational efficiencies. The optimal DSP, it is further determined, should balance minimizing transmission flaws with minimizing additional distortions brought on by ineffective linear or nonlinear correction steps as a result of in-line amplifier noise. It is demonstrated that, in addition to improving system performance, machine learning (ML) improves analytical reasoning, provides deeper mathematical comprehension of fiber nonlinearity correction, and advances theoretical knowledge in related fields.
Design study of detachable car accident alarm
Authors: Wu Yongjia , Yuan Shuqi , Ma Yinuo , Yao Keke
Published: July 2023

The current market alarm has limitations such as non-removable, high cost, and many restrictions on the alarm. The removable, embedded installation structure of this design makes the car accident alarm have the advantage of high applicability, and the combination of cell phone APP terminal and embedded hardware design achieves effective reduction of waste of public resources, lower cost and resistance to product popularity. In terms of reliability, the design can detect whether the sensor is damaged, and ensure that the sensor can still correctly determine that the vehicle has been in a car accident when it is damaged, which is more reliable than most alarms. When receiving information about a car accident from the embedded hardware, an alert will be sent through the user's mobile device in time to remind the user. Through the cell phone's built-in GPS positioning technology, the application will automatically obtain information such as the vehicle's current location and send that information to emergency contacts.
Research on Intelligent Design Method and Technology of Grenade Driven by Digital Twin
Authors: Yi Che , Liu Liwei , Bo Hao , Wang Wanjiong , Bin Yang
Published: July 2023

In order to change the design method of grenade based on hand drawing and traditional CAD numerical calculation, solve the problem of long design cycle caused by repeated design because the result of grenade design does not meet the tactical index, and promote the transformation of grenade design to the direction of intelligence, based on the concept and architecture of digital twin, a five-layer structure model of the intelligent design system of grenade driven by digital twin is established, and the relationship and connotation of each layer are analyzed: the preliminary realization scheme of applying digital twin technology and intelligent design technology to grenade design is discussed, which provides theoretical reference for subsequent practical application.
SARS-CoV-2 detection with type I-A CRISPR “FALCON” system
Authors: Wang Anna , Wang Virgia
Published: July 2023

CRISPR-Cas systems are currently applied in the detection of infectious diseases as a rapid and accurate alternative for the traditional RT-PCR (reverse-transcription polymerase chain reaction) test. The type I-A CRISPR-Cas nucleic acid detection system has been proven to achieve efficient detection through a 15-minute one-pot reaction at 85°C. For this project, a type I-A system “FALCON” (Fast Agent in Limited COVID Nucleic acid detection) was created to detect SARS-CoV-2 and its variants. The system’s guide RNA is designed with the N gene of SARS-CoV-2. When tested with samples containing SARS-CoV-2 pseudovirus, the system’s embedded Cas3 enzyme is activated upon guide RNA recognition and collaterally cleaves FQ (fluorophore-quencher) reporters, producing a fluorescent sample where the amount of fluorescence is proportional to the viral load. In addition, FALCON’s results are highly consistent with those of RT-PCR but requires much less instrumentation and shorter testing time, and can differentiate among virus variants. FALCON can be developed into commercial SARS-CoV-2 self-testing kits, or used in detection and differentiation of other diseases with a modified guide RNA.
Exploring New Therapeutic Approaches for Rheumatoid Arthritis Based on Basic Signaling Pathways
Authors: Chen Yuxuan , Du Qingya , Xi Mengtian
Published: July 2023

RA is an incurable systemic inflammation the main features are infiltration of pro-inflammatory factors with joint swelling and pain as the main clinical manifestation. Studies have shown that there are many causes of RA, among which immune factors are the most important pathogenesis of rheumatoid arthritis. At present, emerging studies have found that PI3K / AKT / mTOR signaling axis is abnormal in patients with RA, which opens up new ideas for the treatment of RA through the normal expression of signal transduction by targeted drugs. Janus kinase family ( JAKs ), are up-regulated in non-receptor protein tyrosine kinase cytokine signaling ( SOCS ) inhibitors, suggesting that the existence of SOCS is most possibly to constitute the main machine-made of negative adjustment of JAK / STAT signaling. In RA patients, there is a correlation between the activation of mTOR signaling and the quantity of osteoclasts. The recently discovered cytokine IL-22 has been shown to significantly increase the number of fibroblasts isolated from the skin of the patients with psoriasis. The dual inhibitor of P1-3K / mTOR, NVP-BEZ235, effectively blocks the spread. This article mainly explores and discusses new targeted drugs such as tofacitinib and Ruxolitinib by discussing new pathological mechanisms and conventional diagnosis and treatment methods of RA. Since RA is one of the most representative diseases of skeletal immune diseases, the study of ra abnormal signaling pathways to discuss related treatment methods provides valuable insights into the disease and other inflammatory and autoimmune diseases.
The role of circadian rhythm in breast cancer, lung cancer, and colorectal cancer
Authors: Yue Xiaoyan
Published: July 2023

Circadian rhythm is a biological rhythm system with a self-regulating function and a 24-hour cycle that is synchronized with the alternation of day and night and is driven by circadian clock genes and clock-control genes. The circadian clock governs and controls processes like immune, metabolism, endocrine, cell division and proliferation, apoptosis, and sleep, allowing cells, tissues, and organs to carry out different living activities in an organized and coordinated way. The root cause of tumors is gene mutations that cause the body to lose its normal regulation of cell growth. According to studies, cancer and problems of biological rhythm are tightly linked. Related epidemiological studies are related to the occurrence and inhibition of tumors by circadian rhythms the molecular mechanism of action has become a hot issue. In this paper, combined with relevant research at home and abroad, we focus on the specific association between circadian rhythm and the high occurrence of breast cancer, lung cancer, and colorectal cancer in three populations, as well as the part circadian clocks play at the molecular level in tumor development and progression, and discuss the timing of cancer. Therapeutics is a novel therapeutic field that has the potential to increase efficacy and minimize negative effects. A sounder theoretical foundation for the identification, management, and prognosis of clinical cancers will be made possible by the clarification of the circadian clock in cancer.
The role of microglia in Alzheimer’s Disorder
Authors: Guo Xiaoyang , Lan Haishan , Liao Jinbin
Published: July 2023

Alzheimer's disease (AD) is a neurodegenerative disorder with insidious onset and gradual development. Its symptoms include loss of memories, impaired language, reduced spatial perception, mood swings, and reduced ability to perform calculations and abstract thinking. AD is the most common type of dementia in the aged.  Around the globe, more than 40 million people suffer from AD, and the number continues to rise. At present, there are many hypotheses about the pathogenesis of Alzheimer's disease, but the specific pathogenesis is not clear.  Currently, due to unclear pathogenesis, this disease has no specific treatment methods, mainly symptomatic treatment and supportive treatment. Nowadays, most of the treatments being studied focus on reducing levels of neurotoxic Aβ and Tau. However, these therapeutic targets appear to be secondary and not causally related to the development of AD. Microglia, as one of the most significant immune cells in the central nervous system (CNS) has shown considerable clinical efficacy against neurodegenerative diseases. Furthermore, one of the causes of AD progression also includes neuroinflammation mediated by malfunctioning microglia cells. Microglia may exhibit great therapeutic benefits in treating AD. There are currently three approaches to ameliorate pathological changes in AD patients through microglia: modifying microglia to reduce neurological damage caused by dysfunction, targeting microglia immune receptors to improve their immune response, and targeting microglia-mediated inflammatory response to reduce inflammatory damage.
Applications of bioinformatics in the post-genome era
Authors: Tang Yuming
Published: July 2023

Bioinformatics uses various informatics technical tools to study biological data that are difficult to process by general methods, to predict and compare biological information, etc. In the post-genomic era, the application of bioinformatics is highlighted and the methods of information processing have been expanded through innovation based on the original ones. In the post-genomic era, bioinformatics has attracted tremendous interest from medical and industrial communities worldwide, and it studies various biological genome sequences, single nucleotide polymorphism analysis, comparative genomics and protein structures, playing a profound role in biology, medicine, agriculture and information technology.
Research on the Predictive Accuracy of Diabetes based on the BRFSS Database
Authors: Zhao Zhan
Published: July 2023

To study the factors that influence the disease of people with diabetes based on the BRFSS database, and to find out whether the BRSFF database has the ability to accurately predict whether an individual has the disease. This work adopts the method of data mining based on the 2015 BRFSS dataset, and uses R language to mine the relevant data in the hope of discovering the relationship between the BRFSS dataset and diabetics. The main data mining methods used include regression analysis, boxplot observation and chi-square test. The study found that the BRFSS dataset can accurately predict whether people will have diabetes, and the factors affecting the disease of diabetics in the BRFSS dataset are found. And it can be roughly observed whether these factors have a large or small impact on whether an individual has diabetes. The study provides some ideas for using BRSFF to study the associated effects of diabetes in people with diabetes, broadening the way to study people with diabetes.
Application of nanomaterial-modified electrochemical sensors in pesticide residue detection
Authors: Hu Xing , Dang Wenyu , Jiang Yenan
Published: July 2023

Nowadays, the frequent application of insecticides leads to pesticide remnants detected in plentiful amounts of many foods, such as fruits and edibles. Even though the remains of the compounds produced by pesticides are tracked, the tremendous toxicity is sufficient to endanger human life and health. Thus, detecting pesticide residue in different vegetable and fruit foods is essential. Determine the number of leftover pesticides in different vegetable and fruit foods to avoid serious illnesses brought about by exposure to pesticides so that people’s physical and mental health and product quality and safety are ensured. Therefore, this article is about nano-porous materials based-electrochemical sensors used to detect pesticide residues in food. This article briefly introduces the mechanism and composition of electrochemical sensors, the classification of electrochemical nanomaterials, and the utilization of nanomaterials in electrochemical sensors. Nano-porous materials have a high specific surface area, adsorption, and catalytic properties to ensure accurate, accurate, and reliable detection. This article discusses metal nanomaterials with variable sizes, shape, surface, and physical and chemical features and their implementation in the detection of electrochemical sensors. It also introduces metal oxide nanomaterials with the characteristics of cheap and easy preparation and the detection range of this material. It also introduces the properties of carbon nanomaterials, carbon nanotubes, graphene, and carbon dots. Subsequently, this article summarizes the progress of nano-porous materials based on electrochemical sensors in pesticide residues. It also introduces the opportunities and challenges that nanomaterial-modified electrochemical sensors will face in pesticide detection and looks forward to reducing the harm of pesticides to the environment and human health in the future.
High-Performance Monolithic Integration of III-V QD Lasers on Si Substrates
Authors: Li Jun
Published: July 2023

With the development of digital processes, the speed and efficiency of information exchange has increased to a great extent. Silicon-based photonics allows for large-scale photonic integration through CMOS manufacturing processes, the advantages of which lie in the low cost, low energy consumption and high quality of such processes. Currently, silicon-based monolithic integrated quantum dot lasers have achieved lifetimes comparable to those of heterogeneous integrated lasers and are even available for commercial applications. The silicon photonic platform also offers low-loss passive devices, as well as high-speed optical modulators and photodetectors. However, device performance suffers due to differences in physical properties between group III-V lasers and substrate materials, an important factor limiting the development of silicon-based monolithic integrated quantum dot lasers. This paper reviews the respective characteristics of silicon-based monolithic and heterogeneous integration. The advantages of monolithically integrated III-V quantum dot lasers over heterogeneous integration are highlighted, as well as the challenges, solutions and recent developments.
Exploration of the composition of ancient glass artifacts based on optimization algorithms
Authors: Liu Shuwen , Hu Fei
Published: July 2023

The Silk Road facilitated exchanges between East and West, where glass played a significant role. However, ancient glass artifacts are susceptible to weathering, leading to changes in their chemical composition. This article explores the relationship between weathering and explanatory variables of glass type, color, and decoration. It presents a method for predicting the weathering degree of glass artifacts and their chemical composition content before weathering. Descriptive statistics are used to calculate the weathering degree and variation of each chemical component content, allowing for quantification of the impact of weathering on the proportion of chemical components. Logistic regression is used to obtain regression coefficients of the weathering degree and other attributes. Various measures, including correlation coefficients and significance tests, are calculated to assess the predictive power of the explanatory variables. The proposed method is valid for predicting the chemical composition proportion data before weathering, which aids further research in the archaeological field. Overall, this article provides a comprehensive approach to understanding the effect of weathering on ancient glass artifacts and predicting their chemical composition content before weathering.
The Effects of Dietary Nutrients on Obesity
Authors: Chen Guangye , Chen Guangye
Published: July 2023

Obesity is a major public health problem affecting people of all ages, genders and ethnicities around the world. It is characterized by an abnormal or disproportionate amount of fat that can be detrimental to people’s health. There are many factors relating to obesity, such as pressure, sedentary life, dietary and so on. One of the most relevant factors in people’s lives is their daily diet. The objective of this paper is to analyze the repeatedly observed link between obesity and carbohydrate, low vitamin D levels and protein intake. Besides, this paper also focuses on how dietary nutrients affect people's obesity. Upon ingestion of these three nutrients by the human body, a potential mechanism of digestion and subsequent metabolic processes can be elucidated. Subsequent to digestion, the effects on the human body can be observed. Thus, a thorough understanding of the potential mechanisms and their resulting effects can be achieved. This article provides important insight into the effects of carbohydrate, vitamin D and protein on obesity. It also provides a valuable contribution to the understanding of the complex relationship between nutrition and obesity.
Experimental study on bonding performance of polypropylene fiber modified rubber concrete and deformed steel bar
Authors: Zhu Chenhui
Published: July 2023

In order to study the binding properties of deformed steel rods and rubber concrete modified with polypropylene fiber, four groups of bonding specimens were designed with polypropylene fiber volume ratio as a variable. The effect of polypropylene fiber on bond strength and bond slip curve was investigated by drawing test. It was found that the bond strength increased with the increase of polypropylene fiber volume fraction in the range of 0 ~ 1.5 %. Based on the experimental data, the constitutive relationship between deformed steel bar and polypropylene fiber modified rubber concrete was established. It provides a theoretical basis for further research on polypropylene fiber modified rubber concrete.
Research on Predictive Model Based on Ensemble Learning
Authors: Zhu Jingyi , Zhang Anbo , Zheng Haixin
Published: July 2023

Ensemble learning completes learning tasks by building and combining multiple learners. The use of ensemble learning can make accurate prediction. This paper used the dataset publicly available on kaggle platform. Firstly, this paper preprocessed and performed descriptive statistics on the dataset, based on which this research constructed the prediction model. Three ensemble learning models Random Forest, AdaBoost, and LightGBM were selected to study the data. To prevent overfitting, a 10-fold cross-validation method was used to train the classifiers and the models were tuned using the grid search method. Finally, the three models were compared in terms of Accuracy, Precision, Recall, F1-score, ROC curve and AUC values. The comparison shows that all three models have good performance, and the accuracy of all model predictions are higher than 80%. However, there is a slight difference in classification ability among the models. Random Forest performs best, with an Accuracy of 86.94, Precision of 85.91, Recall of 93.10, F1-score of 0.8936, and AUC of 0.8906. All evaluation indexes are higher, which also verify the feasibility of using ensemble learning algorithms in prediction.
The Study of Performance for Face Detection Based on Multiple Representative Convolutional Neural Networks
Authors: Foun Ming Him
Published: July 2023

Due to the diversity of deep learning models, choosing the suitable model for a specific task can be rather onerous. In this paper, the performance of three deep convolutional neural networks, namely VGG16, ResNet50, and MobileNetV2 on face detection were compared. Each model was trained on a dataset of 11,900 images from the FDDB dataset that included various face sizes and orientations with multiple augmentations, including color alteration, blurring, and flipping. The final layers of the models were modified into a binary classification model and a regression model indicating face found and coordinates of the facial bounding box. The models were trained on the same basis of 40 epochs with batch size 64 with binary cross entropy loss and DIoU loss and a learning rate of 0.0001 with a learning rate decay of 0.8 per epoch. The experimental results demonstrated that VGG16 outperformed ResNet50 and MobileNetV2 in terms of accuracy, with VGG16 achieving the highest  score of 0.9240, followed by ResNet50 with a score of 0.8568, and MobileNetV2 with an accuracy of 0.6028. The results suggest that VGG16 is a more suitable choice for face detection applications than ResNet50 and MobileNetV2, while ResNet50 and MobileNetV2 may provide higher accuracy for other image recognition tasks or real time face detections. The findings in this paper can contribute to the selection of appropriate deep learning models for face detection.
Addressing Posterior Collapse in Variational Autoencoders with β-VAE
Authors: Wang Ziyang
Published: July 2023

Posterior collapse is a pervasive issue in Variational Autoencoders (VAEs) that leads to the learned latent representations becoming trivial and devoid of meaningful information. To address this problem, this paper presents a novel β-VAE approach, which incorporates a hyperparameter β to strike an optimal balance between the reconstruction loss and the KL divergence loss. By conducting a comprehensive series of experiments and drawing comparisons with existing methods, robust evidence is provided that the proposed β-VAE method effectively mitigates posterior collapse and yields more expressive and informative latent representations.The experimental setup involves various architectures and datasets to demonstrate the versatility and efficacy of the β-VAE approach in diverse settings. Additionally, ablation studies are performed to investigate the impact of different β values on the model's performance, elucidating the role of this hyperparameter in controlling the trade-off between reconstruction quality and latent representation expressiveness. Furthermore, the disentanglement properties of the learned latent space are analyzed, which is a crucial aspect of VAEs, especially when applied to complex, real-world data.In-depth analysis of the results offers valuable insights into the underlying mechanisms of β-VAE, thereby contributing to a more profound understanding of VAEs and their inherent limitations. The findings not only establish the effectiveness of the β-VAE method in preventing posterior collapse but also pave the way for future research on improving VAEs' performance in various applications. Potential future work could explore alternative techniques for balancing the competing objectives of reconstruction and latent representation learning or delve into the theoretical properties of β-VAE, providing a more rigorous foundation for this approach.
Analysis of the composition of ancient glass products based on Spearman's correlation coefficient
Authors: Hou Yibo
Published: July 2023

Ancient glass stored underground for a long time has undergone different degrees of weathering, which has led to changes in its chemical composition and affected the correct judgment of its category. Based on this, this paper uses various algorithms such as Spearman correlation coefficient, chi-square test and cluster analysis to establish a comprehensive model for glass composition evaluation. The results show that: (1) The surface weathering degree of glass artifacts is significantly correlated with their types; (2) The content of Lead oxide can be used to make a general distinction between high-potassium glass and lead-barium glass; (3) Among the known glass samples, high-potassium glass can be divided into three categories and lead-barium glass can be divided into four categories; (4) According to the reasonableness test, it is known that the model has good reliability and can provide guidance for the study of ancient glass.
A study of glass classification and identification based on K-means clustering and Euclidean distance
Authors: Kong Yaxuan , Liu Zijun , Tan Wenyong , Wu Wenbo
Published: July 2023

Glass is buried in the process of weathering, resulting in a large number of exchanges of its chemical composition. To classify and identify the glass of unknown composition, firstly, the existing classification law of glass was analyzed, and the subclasses of glass types were divided based on principal component analysis and K-means cluster analysis, and the number of subclasses was determined according to the P value of cluster significance, and the high potassium glass was divided into 3 classes and the lead-barium glass was divided into 4 classes. Subsequently, a glass classification model was constructed for the unknown component glasses, and after obtaining the glass classification, the classification model of glass mean was constructed by Euclidean distance based on K-means clustering results, and the subclass classification results of all unknown samples were obtained.
A study on the discrimination and identification of ancient glassware types based on cluster analysis
Authors: Zheng Liyang , Tao Shuangyang , Zhang Hua , Liu Siyang , Dong Xuan , Li Jingyuan
Published: July 2023

The classification and identification of ancient glass artifacts is complicated due to the exchange of internal elements, which can be influenced by a multiplicity of different factors, such as weathering, erosion, and so on. To classify and identify the types of glass artifacts, this paper uses cluster analysis (K-Means) to study the main chemical composition of high potassium glass and lead-barium glass. It is found that the chemical composition of different glass ( potassium oxide, barium oxide, lead oxide) and subclasses types are significantly different, and the content of lead oxide in lead-barium glass also has a large difference. Using the clustering centers obtained by cluster analysis (K-Means), the eight unknown types of glass were first determined visually and briefly based on the different proportions of potassium oxide, barium oxide, and lead oxide. As for accurate classification, the Euclidean distances between the unknown components and the sample centroids were calculated separately, and the shortest Euclidean distances were identified by comparison, and then categorized for identification.
The Application of Nanomaterials in Energy Materials
Authors: Wang Chen , Zhang Jiarui , Wu Zonghan
Published: July 2023

With the continuous development of industry, nanomaterials had become one of the most active research fields due to their unique electronic, optical, and mechanical properties. In this review, we systematically reviewed the research progress of nanomaterials in recent years. Specifically, we explored the applications of nanomaterials in oilfield development, battery performance, and solid waste treatment. And we pointed out the existing problems in the current research.
Research on the Composition Analysis and Identification of Ancient Glass Products Based on Decision Tree and K-means Clustering Algorithm
Authors: Hu Wenxuan , Ke Yuping , Liu Yueyang , Dong Xingzhi , Hu Chaohao
Published: July 2023

Ancient glass is susceptible to weathering by environmental influences, resulting in changes in its chemical composition and proportions, which affects the correct judgment of its category by archaeologists. In order to correctly analyze and identify the composition of ancient glass artifacts, firstly, this paper establishes a decision tree model to derive the basis for determining the type of glass artifacts. Secondly, the aggregation coefficients of high potassium glass and lead-barium glass were plotted separately in this paper, and the K-value of clustering was determined to be 2 according to the elbow rule. After using principal component analysis to reduce the dimensionality of the chemical composition content data, the K-means clustering algorithm was used to classify the subclasses of high potassium glass and lead-barium glass respectively. Finally, the established model was used to identify the chemical composition and its type of unknown class of glass artifacts.
Advances in biomass-derived carbon composites as electrode materials for supercapacitors
Authors: Cai Yingpeng , Luo Yexin
Published: July 2023

As electrode materials (EMs) for supercapacitors (SCs), biomass-derived carbon composites (BDCC) are frequently used because of their plentiful supply, renewable properties, affordable manufacturing prices, large specific surface area (SSA), and porous structure with straightforward synthesis methods. The performance of SCs can be extremely boosted by BDCC, which has a wide range of potential applications. This paper outlines the advances in synthesis methods and application of BDCC as EMs for SCs and introduces the characteristics of these methods. Thereafter, the advances in biomass-derived carbon combined with carbon nanotubes (CNTs), graphene, and conductive polymers as EMs and the peculiarities and electrochemical properties (SSA, energy density, cycle stability, specific capacitance), as well as some research achievements over the past few years are reviewed. Finally, the future orientations, challenges, and prospects of BDCC as EMs for SCs are discussed.
Advances of biology sorter based on laminar flow
Authors: Chen Zhihan , Ou Zijie , Zhang Ziren
Published: July 2023

Now the biological sorters are widely applied in biology and medicine to separate specific sample. Each kind of the sorters are fitting different kind of situations and having a large difference between their cost, efficiency and other factor. In order to help researchers to find a suitable bio-sorters for their research, in this paper, four kind of passive bio-sorters that based on laminar flow were introduced, and their strengths and limitations were discussed. They share common strengths, such as simple design, low cost and so on. These makes them easy to be generalized and applied. Some possible improvements of the sorters were proposed, such as more detailed channels and external electromagnetic field to make the result more efficiency.
Comparison of paper and electronic devices and measures to reduce carbon emission
Authors: Liang Xindan , Yang Xiaotong , Zhang Wanyu
Published: July 2023

"Carbon neutrality" goals have become a topic of growing concern in recent years, but the way students read is already causing a lot of carbon emissions to the environment. This article explores the two ways students use paper books and electronic products to read, and compares the annual carbon emissions of these two ways. The results show that traditional paper reading is more low-carbon and environmentally friendly, and has a wider audience, but the carbon emissions of both methods cannot be underestimated. This paper further explores relevant measures that can be taken to minimize the carbon emissions of reading to the environment, such as companies can find more environmentally friendly alternative materials, the government can appropriately increase recycling prices to promote the recycling of waste paper and electronic products, users can improve own reading habits to improve the utilization of paper and electronics, etc. This paper looks forward to finding better solutions to the massive carbon emissions caused by the use of paper books and electronic products, and hopes to mitigate the environmental hazards associated with the use of paper books and electronic products.
Land Use Simulation of Qiantang River Basin At Optimal Spatial Scale Based on PLUS Model
Authors: Shi Yinan , Zhou Pengfei
Published: July 2023

The coordinated spatial planning of the nation's territory can be accomplished on the basis of future land use change simulation, and increasing forecast accuracy can aid in properly identifying the trend of these changes. To stimulate the future space distribution in the Qiantang River Basin in a refined manner, this paper selects data on land cover and related influencing factors for the years 2010 and 2020, and screens out the optimal spatial scale by integrating various precision indicators such as Kappa and OA. On this basis, the PLUS model is applied to simulates the regional spatial pattern in 2030. The consequences show that a 30 m cell and a 3×3 neighborhood are the optimal spatial scales for simulating space changes in the region. The simulation results have high accuracy. In the Qiantang River Basin, it is anticipated that by 2030, forest land will predominate over all other land uses, with development land moving further from the city's core. The study believes that in the future, the prediction of land development in the Qiantang River Basin can be improved in terms of stimulation model and accuracy, so as to effectively serve the regional land space planning.
Analysis of Marine Plastic Pollution and Environmental Problems
Authors: Shi Yiyang
Published: July 2023

Marine plastic pollution has been an increasing problem for many years, and plastic products in use today are a crucial source of ocean pollution. While an increasing number of studies have announced the environmental, social and economic influences of marine plastic pollution in a multitude of ways, little research has focused on policy and general assembly tools for reducing plastic pollution. This paper concentrates on the analysis of marine litter sources and effects and policies, with current actions being taken in various countries and locations around the world. Existing knowledge provides a strong basis for useful action. However, policies and other initiatives to date have been largely inadequate. While some approaches are effective, some issues remain in practice. Such as poorly enforced and technically expensive legislation, and a lack of public education. Furthermore, the extents and dynamics of all pioneering spirits are distinctly different, and at present there is a lack of coordination at all levels and close cooperation between them.