bluuebunny/crossref_metadata_2025 huggingface dataset:
Public Goods Provision and Chinese Economic Development
Authors: Lau Lawrence J.
Published: June 2022

The provision of more and better public goods is part of the Chinese efforts to emphasize the quality rather than the quantity of economic growth. Provision of public goods such as education, public health, elderly care, environmental preservation, protection, and restoration, basic research, infrastructure, social safety net, and alleviation of poverty has been vastly expanded in China since the establishment of the People’s Republic of China in 1949, with significant positive results in all these areas. The provision of public goods has also raised the potential GDP of the Chinese economy through its effects on increasing the aggregate demand for investment and consumption, the productivity of the labor force, and the rate of return on other fixed-asset investments.

Public goods are by their very nature mostly “local”, and their provision is consistent with the Chinese “dual-circulation development strategy” with primary focus on domestic circulation. The provision of public goods also constitutes a form of redistribution in kind, e.g., clean air and water, which can be enjoyed by everyone, and hence also directly advances the goal of ”common prosperity”. Despite significant increase in the degree of income inequality in China since the mid-1980s, the welfare of all Chinese people has improved significantly.

However, the provision of public goods frequently results in negative value-added at market prices, and therefore reduces, rather than enhances, the rate of growth of measured GDP. Finally, increasing the provision of public goods can be a significant source of growth of the domestic aggregate demand for both consumption and investment, over and above what increases in household income alone are able to provide. Maintaining an adequate growth of aggregate demand is essential for continued Chinese economic prosperity.
An introduction to
Recent Advances in Understanding Gold Deposits: from Orogeny to Alluvium:
the importance of multi-method approaches and developing a characterization
Authors: Torvela Taija , Chapman Rob , Lambert-Smith James
Published: June 2022

Gold occurs in many settings but the dynamic nature of Earth's crust means overlapping and overprinting deposit styles are common. Characterization of mineralization from an early stage becomes important particularly where the mineralization is complex, in order to maximize exploration and project development success, and mining productivity. Various techniques are used at different stages of a project to characterize gold deposits. This Special Publication
Recent Advances in Understanding Gold Deposits: from Orogeny to Alluvium
offers a cross-section of some specific techniques to investigate a variety of gold deposit types. In this Introduction, we briefly compare the most common gold deposit types, summarize the techniques used to investigate them and discuss some of the most important outstanding questions regarding understanding gold deposits. This is followed by a summary of each paper in this Special Publication. Whilst the 15 papers in this book can only showcase some of the many approaches available, they do highlight both the breadth of the available techniques and their utility in deposit characterization. Several papers include suggestions for avenues for fruitful further research, including a paper suggesting a new approach to classifying orogenic gold deposits, and a paper describing archaeological applications of natural gold analyses.
A Survey on Memory-centric Computer Architectures
Authors: Gebregiorgis Anteneh , Du Nguyen Hoang Anh , Yu Jintao , Bishnoi Rajendra , Taouil Mottaqiallah , Catthoor Francky , Hamdioui Said
Published: June 2022

Faster and cheaper computers have been constantly demanding technological and architectural improvements. However, current technology is suffering from three technology walls: leakage wall, reliability wall, and cost wall. Meanwhile, existing architecture performance is also saturating due to three well-known architecture walls: memory wall, power wall, and instruction-level parallelism (ILP) wall. Hence, a lot of novel technologies and architectures have been introduced and developed intensively. Our previous work has presented a comprehensive classification and broad overview of memory-centric computer architectures. In this article, we aim to discuss the most important classes of memory-centric architectures thoroughly and evaluate their advantages and disadvantages. Moreover, for each class, the article provides a comprehensive survey on memory-centric architectures available in the literature.
EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In Situ Code Search and Recommendation
Authors: Li Xingjun , Zhang Yizhi , Leung Justin , Sun Chengnian , Zhao Jian
Published: June 2022

Using computational notebooks (e.g., Jupyter Notebook), data scientists rationalize their exploratory data analysis (EDA) based on their prior experience and external knowledge, such as online examples. For novices or data scientists who lack specific knowledge about the dataset or problem to investigate, effectively obtaining and understanding the external information is critical to carrying out EDA. This article presents EDAssistant, a JupyterLab extension that supports EDA with in situ search of example notebooks and recommendation of useful APIs, powered by novel interactive visualization of search results. The code search and recommendation are enabled by advanced machine learning models, trained on a large corpus of EDA notebooks collected online. A user study is conducted to investigate both EDAssistant and data scientists’ current practice (i.e., using external search engines). The results demonstrate the effectiveness and usefulness of EDAssistant, and participants appreciated its smooth and in-context support of EDA. We also report several design implications regarding code recommendation tools.
Generic Techniques for Building Top-
k
Structures
Authors: Rahul Saladi , Tao Yufei
Published: June 2022

A
reporting query
returns the objects satisfying a predicate
q
from an input set. In
prioritized reporting
, each object carries a real-valued weight (which can be query dependent), and a query returns the objects that satisfy
q
and have weights at least a threshold τ. A

top-
k
query

finds, among all the objects satisfying
q
, the
k
ones of the largest weights; a
max query
is a special instance with
k
= 1. We want to design data structures of small space to support queries (and possibly updates) efficiently.

Previous work has shown that a top-
k
structure can also support max and prioritized queries with no performance deterioration. This article explores the opposite direction: do prioritized queries, possibly combined with max queries, imply top-
k
search? Subject to mild conditions, we provide affirmative answers with two reduction techniques. The first converts a prioritized structure into a static top-
k
structure with the same space complexity and only a logarithmic blowup in query time. If a max structure is available in addition, our second reduction yields a top-
k
structure with no degradation in expected performance (this holds for the space, query, and update complexities). Our techniques significantly simplify the design of top-
k
structures because structures for max and prioritized queries are often easier to obtain. We demonstrate this by developing top-
k
structures for interval stabbing, 3D dominance, halfspace reporting, linear ranking, and
L
∞
nearest neighbor search in the RAM and the external memory computation models.
The Econometric Model for Causal Policy Analysis
Authors: Heckman James J. , Pinto Rodrigo
Published: June 2022

This article discusses the econometric model of causal policy analysis and two alternative frameworks that are popular in statistics and computer science. By employing the alternative frameworks uncritically, economists ignore the substantial advantages of an econometric approach, and this results in less informative analyses of economic policy. We show that the econometric approach to causality enables economists to characterize and analyze a wider range of policy problems than is allowed by alternative approaches.
Dynamical Analysis on a Malaria Model with Relapse Preventive Treatment and Saturated Fumigation
Authors: Aldila Dipo
Published: June 2022

Malaria has produced health issues in many parts of the world. One of the reason is due to the recurrence phenomenon, which can happen years after the main infection has appeared in the human body. Furthermore, the fumigation intervention, which has become a major worry in several regions of the world, has yielded unsatisfactory results, as seen by the high number of cases reported each year in several African countries. We present a novel mathematical model that integrates tafenoquine treatments to prevent relapse in the human population and saturation fumigation to control mosquito populations in this study. The endemic threshold, also known as the basic reproduction number, is calculated analytically, as is the existence and local stability of the equilibrium points. Through careful investigation, we discovered that the malaria-free equilibrium is locally asymptotically stable if the basic reproduction number is less than one and unstable if it is greater than one. According to the sensitivity analysis, the utilization of tafenoquine treatment is inversely proportional to the basic reproduction number. Although our model never exhibits a backward bifurcation at the basic reproduction number equal to one, we have demonstrated that it is possible; when the basic reproduction number is greater than one, two stable malaria-endemic equilibrium can exist. As a result, when the basic reproduction number is more than one, the final state will be determined by the initial condition of the population. As a result, enormous temporal fumigation can shift the stability of our malaria model from a big endemic size to a smaller endemic size, which is more advantageous in terms of the malaria prevention strategy. Despite the fact that this is not a case study, the numerical results presented in this article are intended to support any theoretical analysis of current malaria eradication tactics in the field.
Methods of Improving and Optimizing English Education in Colleges and Universities Assisted by Microvideo Technology
Authors: Wang Xiuhua
Published: June 2022

English teaching in general higher education is faced with great challenges. English teachers have to face a group of students from different regions, who have received different English teaching methods during middle school, and their English proficiency is uneven. At present, the teaching methods of most colleges and universities are backward, unable to make full use of modern information technology, and students have low interest in learning, poor independent learning ability, and low learning effect, especially in English. In order to comprehensively improve students’ English level, English teaching methods in colleges and universities should be completely different from the traditional teaching methods in middle schools. This paper sorts out trial research, questionnaire survey, and data analysis method and combines some problems existing in college English teaching and the characteristics of microvideo, such as short, concise, sound, and text, to apply microvideo as a teaching resource in college English grammar teaching. A trial group was set up to study the effectiveness of microvideo teaching. No students in the trial group disliked the microvideo teaching and the average score of students in the trial group was 8.992 points higher than the average score of students in the traditional group. The results show that microvideo teaching can not only increase students’ interest in learning English but also improve their English language skills.
A Dynamic Adaptive Weighted Differential Evolutionary Algorithm
Authors: Wu Kaijun , Liu Zhengnan , Ma Ning , Wang Dicong
Published: June 2022

This study proposed a dynamic adaptive weighted differential evolution (DAWDE) algorithm to solve the problems of differential evolution (DE) algorithm such as long search time, easy stagnation, and local optimal solution. First, adaptive adjustment strategies of scaling factor and crossover factor are proposed, which are utilized to dynamically balance the global and local search, and avoid premature convergence. Second, an adaptive mutation operator based on population aggregation degree is proposed, which takes population aggregation degree as the amplitude coefficient of the basis vector to determine the influence degree of the optimal individual on the mutation direction. Finally, the Gauss perturbation operator is introduced to generate random disturbance and accelerate premature individuals to jump out of the local optimum. The simulation results show that the DAWDE algorithm can obtain better optimization results and has the characteristics of stronger global optimization ability, faster convergence, higher solution accuracy, and stronger stability compared with other optimization algorithms.
Potential Diagnostic and Prognostic Values of CBX8 Expression in Liver Hepatocellular Carcinoma, Kidney Renal Clear Cell Carcinoma, and Ovarian Cancer: A Study Based on TCGA Data Mining
Authors: Lin Jie , Chen Lizhu , Wu Dingjie , Lin Jiexiang , Liu Bin , Guo Ciren
Published: June 2022

Background. Chromobox protein homolog 8 (CBX8), a transcriptional repressor, participates in many biological processes in various carcinomas. Cell differentiation, aging, and cell cycle progression are examples of such processes. It is critical to investigate CBX8 expression and its relationship with clinicopathological characteristics in liver hepatocellular carcinoma (LIHC), kidney renal clear cell carcinoma (KIRC), and ovarian cancer (OV) to investigate CBX8’s potential diagnostic and prognostic values. Methods. TCGA and CPTAC databases were used to compare the data between cancer and matched normal tissues on RNA and protein expression profiles and their relevant clinical information to determine the relationship between CBX8 and clinicopathological features. Kaplan–Meier analyses were used to assess CBX8 relationship’s with disease-free survival (DFS), relapse-free survival (RFS), and overall survival (OS). The multivariate Cox regression analysis was used to identify independent risk factors which affect prognosis. DNA methylation and genetic changes and their impact on prognoses were evaluated by cBioPortal and MethSurv websites. Spearman’s correlation was used to determine the relationship of CBX8 expression with somatic mutation. Tumor immune estimation resource (TIMER) was adopted to investigate the relationship between CBX8 and immune cell infiltration (ICI). CBX8-relevant genes and proteins are analyzed by EnhancedVolcano and STRING databases. The gene set enrichment analysis (GSEA) was performed to investigate the potential functions of CBX8. Results. CBX8 RNA and protein overexpression were confirmed in LIHC, KIRC, and OV (

p
<
0.05

). High CBX8 was significantly related to the clinical features and poor prognoses. The CBX8 genetic alteration rate was 3%. DNA methylation was also associated with prognoses. CBX8 closely interacted with ICI, TMB, MSI, purity, and ploidy. GO analyses revealed that CBX8-associated genes were enriched in biological processes, cell cycle regulation, and molecular functions. KEGG analyses exhibited that CBX8 was gathered in signaling pathway regulation. GSEA revealed that cell cycle, DNA replication, and Wnt signaling pathways were differentially enriched in the high CBX8 expression group. Conclusions. CBX8 could be a potential diagnostic and prognostic biomarker for LIHC, KIRC, and OV cancers.
Mechanism of Targeting the Hedgehog Signaling Pathway against Chemotherapeutic Resistance in Multiple Myeloma
Authors: Zhang Yuefeng , Yao Guoli , Yang Xinxin , Qiu Tao , Wang Sheng
Published: June 2022

Objective. The aim of this study was to explore the relationship between the Hedgehog signaling pathway and drug resistance in multiple myeloma. Methods. The human myeloma cell line RPMI 8266 was taken as the research object. An azithromycin (AZM)-resistant cell line RPMI 8226/R was constructed, and GENT61 was used to block the Hedgehog signaling pathway. Cells were rolled into RPMI 8226/S (S group), RPMI 8226/R (R group), GENT61+RPMI 8226/S (GENT61+S group), and GENT61+RPMI 8226/R (GENT61+R group). The proliferation of cells in each group was assessed, and the expression of patched homolog 1 (PTCH1), zinc finger-containing transcription factors 1 (GLI1), GLI2, hair-division associated enhancer 1 (Hes1), and sonic hedgehog factor (SHH) in each group was detected. Interleukin (IL)-6 and vascular endothelial growth factor (VEGF) were measured. Results. Compared with the S group, the expression levels of PTCH1, GLI2, Hes1, and SHH and the contents of IL-6 and VEGF in the R group were greatly increased, while the expression level of GLI1 was notably decreased (

P
<
0.05

). Compared with the R group, the GENT61+R group greatly increased cell proliferation inhibition. However, the expression levels of PTCH1, GLI2, Hes1, and SHH, and the contents of IL-6 and VEGF were notably decreased, while GLI1 expression levels were greatly increased (

P
<
0.05

). Conclusion. AZM-resistant multiple myeloma was closely associated with the Hedgehog signaling pathway activation, and blocking the Hedgehog signaling pathway can be used as a therapeutic target to improve drug resistance in multiple myeloma.
Predicament and Thinking of College Students’ Employment and Entrepreneurship under the Background of Supply-Side Reform
Authors: Yu Mei
Published: June 2022

Based on the analysis of the current employment and entrepreneurship of college students based on the supply-side structural reform, the employment and entrepreneurship guidance of college students is far from meeting the needs of the society. Therefore, the innovation of talent training mode needs to become an important goal of college development at this stage. Based on the previously mentioned background, this paper included 853 university students as research objects and their data from 2010 to 2018. First, we used the BP neural network as the starting point to conduct in-depth research, selected the sequential model algorithm based on the Keras framework, built a one-layer network and six types of eigenvalue labels to predict the development direction of college students’ employment and entrepreneurship, and evaluated the prediction accuracy of the model. This proves that the prediction effect of the model has the value of continuing in-depth research, and then, the prediction model is further optimized. Then, we added a three-layer network to the model and an SGD optimizer and used Softmax as the regression function to verify that the optimized model predicts well. The average accuracy of the prediction model constructed in this paper is 81.48%, the standard deviation is 4.34%, the Acc value of the model is stable at around 0.835, and the loss value is stable at around 0.3, which proves that the prediction model has a good prediction. It provides a set of application models that can be used for reference for the combination of BP neural network related knowledge and college students’ employment and entrepreneurship development direction prediction. Based on the background of the supply-side structural reform, this paper mainly analyzes and researches the current employment and entrepreneurial paths of college students in my country, hoping to provide reference for the cultivation of talents in colleges and universities.
Low-Carbon Awareness Information Technology of Enterprise Executives Based on Big Data and Multimodal Information Fusion
Authors: Yang Guimei
Published: June 2022

The so-called multimodal information refers to the information from different information sources on different or the same side of the same description target. These pieces of information are different in terms of storage structure, representation, semantic connotation, credibility, and emphasis, but there is a certain inevitable connection between them. This paper aims to study how to analyze and study the low carbon of enterprises with the help of multimodal information fusion based on the background of big data and construct the evolutionary neural network of the improved adaptive genetic algorithm. This paper puts forward the problem of low carbon in enterprises, which is based on multimodal information fusion, and then elaborates on the concept and related algorithms of multimodal information fusion. Information fusion has carried out case design and analysis. Through the research and analysis of enterprise low carbon and self-adaptive algorithm, it can be obtained that the neural network has reached the threshold of 3.95 after iterating for nearly 60 generations, and stopped iterating to obtain the best individual. The evolutionary neural network in this paper reaches stability after a small number of iterations and can basically achieve a certain low carbonization.
The Relationship between Cuff Pressure and Air Injection Volume of Endotracheal Tube: A Study with Sheep Trachea Ex Vivo
Authors: Wang Ruixue , Qin Xinlei , Zhou Wenyi , Zhao Yujia , Yin Jiangwen , Cao Zhenggang , Zhai Jingwen , Wang Sheng
Published: June 2022

Background. Endotracheal intubation is a widely used treatment. Excessive pressure of the endotracheal tube cuff leads to a series of complications. Here, we used tracheae of sheep to analyze the relationship between the air injection volume and endotracheal tube cuff pressure so as to guide the doctors and nurses in controlling the pressure of the endotracheal tube cuff during clinical work and minimise the risk of complications. Materials and Methods. Forty sheep tracheae were utilised and were divided into five groups according to their diameters. Different sizes of endotracheal tubes were inserted into each trachea, and the cuff pressure with the increase of air injection volume was recorded. The formulas that reflect the relationship between air injection volume and cuff pressure were obtained. Then, sheep tracheae were randomly selected; different types of tubes were inserted, and the stipulated volume of air was injected. The actual pressure was measured and compared with the pressure predicted from the formulas. Statistical analysis was conducted to verify whether the formulas obtained from the first part of the experiment were in accordance with the expert evaluation table, which consists of opinions of several experts. Results. After obtaining 15 formulas, we collected the differences between the theoretical cuff pressure and the actual cuff pressure that satisfied the expert evaluation. Relying on the formulas, the medical turntable was obtained, which is a tool that consists of two round cards with data on them. The top card has a notch. The two cards are stacked together, and as the top card rotates, the data on the bottom card can be easily seen in a one-to-one relationship. Conclusion. The formulas are capable of showing the relationship between the cuff air injection volume and pressure of endotracheal tube cuff. The medical turntable can estimate the air injection volume to ensure that the pressure stays in an acceptable range.
Deep Learning and Transfer Learning for Malaria Detection
Authors: Jameela Tayyaba , Athota Kavitha , Singh Ninni , Gunjan Vinit Kumar , Kahali Sayan
Published: June 2022

Infectious disease malaria is a devastating infectious disease that claims the lives of more than 500,000 people worldwide every year. Most of these deaths occur as a result of a delayed or incorrect diagnosis. At the moment, the manual microscope is considered to be the most effective equipment for diagnosing malaria. It is, on the other hand, time-consuming and prone to human error. Because it is such a serious global health issue, it is important that the evaluation process be automated. The objective of this article is to advocate for the automation of the diagnosis process in order to eliminate the need for human intervention in the process. Convolutional neural networks (CNNs) and other deep-learning technologies, such as image processing, are being utilized to evaluate parasitemia in microscopic blood slides in order to enhance diagnostic accuracy. The approach is based on the intensity characteristics of Plasmodium parasites and erythrocytes, which are both known to be variable. Images of infected and noninfected erythrocytes are gathered and fed into the CNN models ResNet50, ResNet34, VGG-16, and VGG-19, which are all trained on the same dataset. The techniques of transfer learning and fine-tuning are employed, and the outcomes are contrasted. The VGG-19 model obtained the best overall performance given the parameters and dataset that were evaluated.
Plasma Neurofilament Light Chain Is Associated with Cognitive Impairment after Posterior Circulation Stroke
Authors: Jiang Lianyan , Wang Zhiqiang , Wang Rongyu , Li Mao , Zhang Yaodan , Yang Dongdong
Published: June 2022

Background. Neurofilament light chain (NfL) is a biomarker for large-caliber axonal degeneration in the subcortex. The purpose of this research was to examine the relationship between plasma neurofilament light chain (pNfL) and cognitive impairment following a posterior circulation stroke. Methods. Patients over the age of 18 with their first-ever acute ischemic stroke (AIS) of the posterior cerebral circulation within 24 h of symptom onset were included from July 1, 2017, to December 31, 2019. Blood samples were collected within 48 h after the stroke. The Montreal Cognitive Assessment (MOCA) (MOCA < 26) was adopted to define poststroke cognitive impairment (PSCI) 90 days after stroke onset. Results. A total of 264 patients were analyzed in this research 101 (38.30%) patients were clinically diagnosed with PSCI. The PNfL concentration was significantly higher in the PSCI group compared with the non-PSCI group (

p
<
0.001

). The pNfL concentration (OR 1.044;

p
<
0.001

) remained to be a significant predictor for PSCI after a multivariable logistic regression analysis, even after adjusting for factors including age, sex, education background (OR 1.044;

p
<
0.001

), baseline NIHSS, infarct volume, and TOAST classification (OR 1.035;

p
<
0.001

). The diagnostic efficacy of pNfL concentration for PSCI was then explored with a ROC analysis. The optimum pNfL concentration threshold was 38.12 pg/ml, with a sensitivity of 78.20%, a specificity of 66.9%, and an AUC of 0.782 (

p
<
0.001

). Conclusion. This research showed that pNfL concentration, independent of established conventional risk factors, could predict the cognitive impairment in 90 days following posterior circulation stroke.
Study on Coercivity-Stress Relationship of X80 Steel under Biaxial Stress
Authors: Feng Shulu , Ai Zhijiu , Liu Jian , He Jiayi , Li Yukun , Peng Qifeng , Li Chengkun
Published: June 2022

Buried pipelines are prone to deformation under surface displacement, pressure occupation, and/or other actions, resulting in stress concentration. Conducting regular stress detection is an important approach to reducing pipeline accidents. This study performed experiments on measuring the coercivity stress of X80 steel, the most commonly used material for high-pressure large-diameter gas transmission pipelines. The coercivity of the specimen under different stress combinations was measured using the DC-driven coercive force measurement system, and the variation of coercive force with biaxial stress was also analyzed. The modified coercive force-stress expression of the generalized Hooke’s law was proposed; the model for the coercive force-stress relationship of X80 steel under the biaxial stress state was established based on this expression. The study indicates that the coercive force method is reliable for accurately measuring the stress of pipeline steel and evaluating the safety margin of in-service pipelines.
Reactive Oxygen Species Bridge the Gap between Chronic Inflammation and Tumor Development
Authors: Yu Weihua , Tu Yongmei , Long Zi , Liu Jiangzheng , Kong Deqin , Peng Jie , Wu Hao , Zheng Gang , Zhao Jiuzhou , Chen Yuhao , Liu Rui , Li Wenli , Hai Chunxu
Published: June 2022

According to numerous animal studies, adverse environmental stimuli, including physical, chemical, and biological factors, can cause low-grade chronic inflammation and subsequent tumor development. Human epidemiological evidence has confirmed the close relationship between chronic inflammation and tumorigenesis. However, the mechanisms driving the development of persistent inflammation toward tumorigenesis remain unclear. In this study, we assess the potential role of reactive oxygen species (ROS) and associated mechanisms in modulating inflammation-induced tumorigenesis. Recent reports have emphasized the cross-talk between oxidative stress and inflammation in many pathological processes. Exposure to carcinogenic environmental hazards may lead to oxidative damage, which further stimulates the infiltration of various types of inflammatory cells. In turn, increased cytokine and chemokine release from inflammatory cells promotes ROS production in chronic lesions, even in the absence of hazardous stimuli. Moreover, ROS not only cause DNA damage but also participate in cell proliferation, differentiation, and apoptosis by modulating several transcription factors and signaling pathways. We summarize how changes in the redox state can trigger the development of chronic inflammatory lesions into tumors. Generally, cancer cells require an appropriate inflammatory microenvironment to support their growth, spread, and metastasis, and ROS may provide the necessary catalyst for inflammation-driven cancer. In conclusion, ROS bridge the gap between chronic inflammation and tumor development; therefore, targeting ROS and inflammation represents a new avenue for the prevention and treatment of cancer.
Network Effects and Characteristics of Cross-Industrial Tail Risk Spillover in China
Authors: Wen Bohui , Gou Shangde , Wei Yuting , Hao Jing
Published: June 2022

In recent years, the Chinese capital market has suffered several violent shocks, and the characteristics of systemic risk contagion across industries and markets have become increasingly important. It brings great potential danger to the stability of financial markets. Therefore, exploring the risk spillover among the real sectors has gradually attracted the attention of scholars. This paper examines the cross-industrial tail risk spillover network in the Chinese financial market. The characteristics and the dynamic contribution of each industry in the tail risk transmission chains are explored. We use the ∆CoES-ENGDFM-LVDN model based on monthly data from 2006 to 2020 to measure the tail risk of 28 industries in China and form a cross-industrial tail risk spillover network. The results show that different industries have different levels of spillover and importance in the network. Tail risk mainly spills over from the nonfinancial sector to the financial sector. The nonbank financial industry is the main recipient of tail risk spillover and is becoming progressively more important in the risk network. In addition, with the promotion of industrial structure, emerging industries such as communications, computers, and health care have begun to play more important roles in the tail risk spillover network in China. This paper not only enriches the research in the areas of tail risk spillover and systemic risk, but also has implications for regulators to maintain financial stability and prevent financial risks.
Research on the Application of Animation Design Based on Machine Learning and Dynamic Image Index
Authors: Yin Yaodong
Published: June 2022

With the development of computer technology, animation is more and more used because of its simple, effective, and higher performance. Machine learning has become the core of artificial intelligence at present. Intelligent learning algorithms are widely used in practical problems such as evaluation. Knowledge-based automatic animation production system faces two challenges: (1) lack of learning ability and waste of data on the website; (2) the quality of animation produced that depends on the level of system designer and the inability of system users to participate in animation production.In order to solve these two problems, an active animation learning system enables the animation system to constantly learn experience and produce the most popular animation, for the first time, for animation production system design and implementation of applied research. Image retrieval technology is a research center in the field of image application. It is widely used in many fields, such as electronic commerce. Animation design will use dynamic image and machine learning to innovate.
Mechanical Properties and Accuracy Evaluation of 3D Printing Based on Value in the Munsell Color System
Authors: Huang Jianping , Ye Wanxiang , Ye Guihua , Jia Qi , Li Kelun , Kong Jiawen , Jiang Heng Bo , Lan Qing , Yu Liming
Published: June 2022

Aim. Printing parameters have been studied extensively to improve printing performance, while resins have been studied to a lesser extent. Therefore, we explore the impact of value in the Munsell color system on the mechanical properties and accuracy of printed products. Materials and Methods. Resins having different values were prepared by mixing transparent resin and color pastes. We used plate models for mechanical measurement, crown-and bridge models for accuracy measurement, and disc models for color measurement. Results. The Group E resin, which had the lowest value, had the minimum ultimate flexural strength (61.199 MPa) and a relatively low elastic modulus (19.015 MPa). Excluding the d index, no significant difference in accuracy was observed. In terms of the d index of trueness, the result of the Group E resin was significantly low. The relative errors of trueness were less than 0.0727 μm. The relative errors of precision were less than 0.0093 μm. Conclusion. The value of a resin affects the mechanical properties and accuracy of printed products. It is better to secure flexural strength by using a lower-value resin. High-value resins have relatively low accuracies.
Adhesive Property and Road Performance Evaluation of Asphalt Overlay Pavement with Geotextile Interlayer
Authors: Zhang Haiwei , Sun Bowei , Li Yongxiang , Li Yanli
Published: June 2022

Geosynthetic interlayer systems are effective techniques to control reflective cracking and have better waterproof performance in damaged asphalt pavements. The interlayer bonding performance is critical to its function. This research investigated the interfacial adhesive property and road performance of asphalt overlay with geotextile interlayer. First, shear damage energy characteristics of geotextile interlayer have been investigated by varying tack coat types (SBS-modified asphalt, virgin asphalt, and emulsified asphalt), application rates (0.3 kg/m2–1.2 kg/m2), spraying times (1–2), upper mixture paving temperature (140°C–180°C), service temperature (5°C–45°C), and soaking time (0 h–96 h). In addition, bending fatigue tests, rutting tests, and semicircular bending tests were carried out to evaluate the comprehensive road performance of the asphalt overlay. The results showed that the interface adhesive property of asphalt overlay with geotextile interlayer using SBS-modified asphalt is best when the application rate is 0.8 kg/m2. The higher paving temperature is beneficial for the geotextile interlayer adhesion performance when the SBS-modified asphalt is sprayed at one time, while the paving temperature has almost no effect when the SBS-modified asphalt is sprayed at two times. The geotextile interlayer adhesion performance is less affected by temperature and more affected by water immersion. Compared with the common asphalt overlay types, the asphalt overlay with geotextile interlayer using SBS-modified asphalt shows better overall performance.
Application of Accelerometer to Monitor Students’ Exercise Load in 50 m Round Trip
Authors: Li Hao , Xu Meng
Published: June 2022

With the further advancement of microelectronics innovation and sensors, sensors can be broadly implanted in cell phone gadgets, compact gadgets, and so forth. The utilization of speed increase sensors for human running checking has expansive application possibilities. From one perspective, the everyday development of the human body is firmly connected with the physical and emotional wellness of the person. Observing the day-to-day developments of the human body is of incredible importance in planning a logical running activity plan and working on actual wellbeing. On the other hand, it is also of practical value to monitor human abnormal movements. This kind of abnormal movement caused by accidental falls can bring certain harm to the human body. Real-time monitoring of the fall can provide timely assistance to the person and reduce the risk brought by the fall. This article analyzes and summarizes the research theories and common research methods in the field of 50 m round-trip movement monitoring based on the acceleration sensor. According to the process of 50 m round-trip movement pattern recognition, the data collection, preprocessing, feature extraction, and selection of 50 m round-trip movement are evaluated. The classification and recognition of each module were analyzed. This article proposes a human body motion recognition mechanism based on acceleration sensors by looking at the three trademark upsides, the wavefront edge, wavefront limit, and time stretch between the pinnacle and valley of the speed increase sensor vertical information waveform, and joining the rule of choice tree order to accomplish the activities of hunching down, taking off, and running. To get an accurate recognizable proof and recognize ways of behaving, a human fall identification calculation is proposed. This calculation removes human movement attributes throughout the fall and focuses on four sorts of falls: forward fall, reverse fall, left fall, and right fall by utilizing the connection of the three tomahawks of the speed increase sensor. The trial results show that the normal right acknowledgment pace of the human body’s 50 m full-circle running way of behaviour is more than 90%, which has specific useful application esteem.
The Long Time Behavior of Equilibrium Status of a Predator‐Prey System with Delayed Fear in Deterministic and Stochastic Scenarios
Authors: Kong Weili , Shao Yuanfu
Published: June 2022

In view of a time lag between the time that the prey perceive predator signals and make some changes or behavior responses, we establish a predator–prey system with direct and indirect predation in this paper. First, we investigate the existence, boundedness, and global asymptotical stability of the positive equilibrium status. Next, by perturbing the mortality rates of prey species and predator species, we stretch the deterministic system to the stochastic scenario and investigate the existence of stochastic process and the global asymptotical stability of the equilibrium status. The analytical findings show that fear affects the value of the equilibrium status, and stochastic disturbance affects its stability, but time delay has no effect if some conditions are satisfied, which are verified by some examples and numerical simulations.
Clinical Features of Acute Coronary Syndrome in Patients with Coronary Heart Disease and Its Correlation with Tumour Necrosis Factor in Cardiology
Authors: Guo Run , Wu Tingting , Zheng Nan , Wan Yanfang , Wang Jun
Published: June 2022

Tumour necrosis factor (TNF) levels are higher in patients who have experienced an acute ischemic stroke. Greater levels of TNF may not be linked to an increased risk of recurrent coronary events in the stable phase after myocardial ischemia (MI). Coronary atheroma is connected to endothelial and smooth muscle cells, as well as macrophages that emit the multifunctional cytokine tumour necrosis factor (TNF). Transplanted tumours become more vulnerable when TNF-α was first recognized to have a function in hemorrhagic necrosis. TNF-α has been demonstrated to induce heart failure, pulmonary edoema, and cardiomyopathy in people with advanced heart failure when it is elevated in the bloodstream. It has been postulated that prolonged overexpression of TNF-α after ischemia may contribute to poor cardiac outcomes by increasing TNF-α when the myocardium undergoes both temporary ischemia and reperfusion. A rise in TNF levels has been seen after a myocardial infarction, but it is unclear if these higher levels, found months after the initial event, are associated with an increased risk of subsequent heart attacks. We looked at TNF levels in the blood of 270 patients with coronary heart disease in the Chinese Hypertension League’s Cholesterol and Recurrent Events (CARE) experiment to see if this notion held true. Recurrent coronary syndrome and coronary mortality were monitored prospectively in the participants. The min max imbalance normalization can be used to assess a patient’s baseline characteristics, including hormone and cholesterol test results. Type 2 stimulant connection to aggregate the TNF-signaling qualities and fuzzy techniques was applied. There may now be enough preliminary evidence from the crucial bundle neural network analysis to identify the risk of coronary heart disease associated with TNF pregeneration studies. The tests were assessed using a variety of methods and performance metrics in a Matlab environment.
Analysis and Visualization of High‐Dimensional Dynamical Systems’ Phase Space Using a Network‐Based Approach
Authors: St Luce Shane , Sayama Hiroki
Published: June 2022

The concept of attractors is considered critical in the study of dynamical systems as they represent the set of states that a system gravitates toward. However, it is generally difficult to analyze attractors in complex systems due to multiple reasons including chaos, high‐dimensionality, and stochasticity. This paper explores a novel approach to analyzing attractors in complex systems by utilizing networks to represent phase spaces. We accomplish this by discretizing phase space and defining node associations with attractors by finding sink strongly connected components (SSCCs) within these networks. Moreover, the network representation of phase space facilitates the use of well‐established techniques of network analysis to study the phase space of a complex system. We show the latter by introducing a new node‐based metric calledattractivitywhich can be used in conjunction with the SSCC as they are highly correlated. We demonstrate the proposed method by applying it to several chaotic dynamical systems and a large‐scale agent‐based social simulation model.
Cervical Cancer Cells-Derived Extracellular Vesicles Containing microRNA-146a-5p Affect Actin Dynamics to Promote Cervical Cancer Metastasis by Activating the Hippo-YAP Signaling Pathway via WWC2
Authors: Wang Weiwei , Wu Lipei , Tian Jiale , Yan Wenhui , Qi Chunrun , Liu Wuchao , Xuan Shihai , Shang Anquan
Published: June 2022

Application of extracellular vesicles (EVs) for cancer treatment has been well-documented. We probed into the potential role of cervical cancer cells-secreted EVs by transferring miR-146a-5p in cervical cancer. After characterization of miR-146a-5p expression in clinical cervical cancer tissue samples, gain- and loss-of-function experiments were implemented to test the effect of miR-146a-5p on the invasion, epithelial-mesenchymal transition (EMT), and anoikis in cervical cancer cells. EVs were isolated from high-metastatic cervical cancer cells, after which their effects on the malignant behaviors of low-metastatic cervical cancer cells were assessed in a co-culture system. Luciferase assay was implemented to validate the putative binding relationship between miR-146a-5p and WWC2, followed by further investigation of downstream pathway (Hippo-YAP). Finally, nude mouse lung metastasis model was developed for in vivo validation. miR-146a-5p was elevated in cervical cancer tissues and high miR-146a-5p expression promoted the metastatic potential of cervical cancer cells through enhancing their invasiveness and anoikis resistance, and inducing EMT. Furthermore, miR-146a-5p carried by EVs secreted by highly metastatic cervical cancer cells could promote the metastasis of low-metastatic cervical cancer cells. Mechanistically, miR-146a-5p targeted WWC2 to activate YAP, by which it inhibited the phosphorylation of cofilin, and promoted the process of cofilin-mediated depolymerization of F-actin to G-actin. In vivo data demonstrated that EVs-carried miR-146a-5p promoted tumor metastasis through the WWC2/YAP axis. Cancer-derived EVs delivered pro-metastatic miR-146a-5p to regulate the actin dynamics in cervical cancer, thereby leading to cancer metastasis. This experiment highlighted an appealing therapeutic modality for cervical cancer.
Identification of Recurrent Congestion in Main Trunk Road Based on Grid and Analysis on Influencing Factors
Authors: Sun Qiuxia , Chu Guoxiang , Li Qing , Zhang Yu
Published: June 2022

To reduce the risk of traffic congestion to residents and the urban transportation system, this paper extracted frequently congested areas and major trunk roads based on the GPS (global positioning system) data of cabs and TPI (traffic performance index) data and identified traffic patterns and main trunk roads in the traffic grid, so as to analyze the evolution of traffic congestion and make effective suggestions. The results can not only enable travelers to effectively avoid peak periods and congested sections but also support the managers to optimize urban planning and implement efficient traffic management methods. The research process of this study is as follows: firstly, the research object area was divided into different grids based on one-week taxi GPS data and the distribution characteristics of taxi operations in Qingdao. Secondly, the two-dimensional grid traffic attribute information is constructed using the following two indicators: number of vehicles and the average speed of passenger trajectory. Then, the congestion discriminant model based on the three-dimensional traffic attribute information was established according to the variation rules of the number of position point in the grid. Finally, the TPI data was applied to compare and evaluate the identification results of the above two models to identify frequently congested grids and main trunk roads. The case analysis showed that the result of grid’s congestion status identification considering three-dimensional traffic attribute information (25.198%) was better than that of grid congestion state considering two-dimensional traffic attribute information (23.997%).
An Intelligent Grey Correlation Model for Online English Teaching Quality Analysis
Authors: Wei Xin
Published: June 2022

In order to improve the effect of English teaching, we study the evaluation of English teaching effect based on the grey relational model. Comparing the influencing factors of the primary index and the secondary index, the judgment matrix is constructed, the matrix weight is calculated, and the consistency is tested; after the consistency test results are determined, the variable weight comprehensive evaluation method is used to adjust the weight value of each evaluation index, and the total score is calculated by obtaining the scores of each index at the same time, so as to evaluate the teaching effect. The experimental results show that the relevance of the grey correlation method to English teaching quality is greater than 0.90, the teaching quality is good, the selection of evaluation indicators and the test-retest reliability of evaluation results are greater than 0.700, and the evaluation results are effective.