bluuebunny/crossref_metadata_2025 huggingface dataset:
Gut Microbial Dysbiosis Associated with Type 2 Diabetes Aggravates Acute Ischemic Stroke
Authors: Chen Xiaojiao , Wu Qiheng , Gao Xuxuan , Wang Huidi , Zhu Jiajia , Xia Genghong , He Yan , Song Wei , Xu Kaiyu
Published: December 2021

We demonstrated an additive effect of type 2 diabetes (T2D) and acute ischemic stroke (AIS) on AIS with T2D (AIS_T2D) patient gut microbiota dysbiosis, and gut dysbiosis associated with T2D was positively correlated with stroke severity in AIS patients. Through animal experiments, we found that cerebral injury was exacerbated by fecal microbiota transplantation from T2D patients compared with that from healthy controls, which was associated with a reduction in short-chain fatty acid (SCFA)-producing bacteria.
Determining posterior vitreous structure by analysis of images obtained by AI-based 3D segmentation and ultrawidefield optical coherence tomography
Authors: Ohno-Matsui Kyoko , Takahashi Hiroyuki , Mao Zaixing , Nakao Noriko
Published: December 2021

Aims

To determine the three-dimensional (3D) structure of the vitreous fluid including the posterior precortical vitreous pockets (PPVP), Cloquet’s canal and cisterns in healthy subjects by AI-based segmentation of the vitreous of swept-source optical coherence tomography (OCT) images. In addition, to analyse the vitreous structures over a wide and deep area using ultrawidefield swept-source OCT (UWF-OCT).

Methods

Ten eyes of six patients with the mean age was 40.7±8.4 years and the mean refractive error (spherical equivalent) was −3.275±2.2 diopters were examined.

Results

In the UWF OCT images, the structure of the vitreous was observed in detail over 23 mm wide and 5 mm area. AI-guided analyses showed the complex 3D vitreous structures from any angle. Cisterns were observed to overlie the PPVP from the anterior. The morphology and locations of the cisterns varied among the subjects but tended to be similar in the two eyes of one individual. Cisterns joined the PPVPs superior to the macula to form a large trunk. This joined trunk was clearly seen in 3D images even in eyes whose trunk was not detected in the B scan OCT images. In some eyes, the vitreous had a complex appearance resembling an ant nest without large fluid-filled spaces.

Conclusions

A combination of UWF-OCT and 3D imaging is very helpful in visualising the complex structure of the vitreous. These technologies are powerful tools that can be used to clarify the normal evolution of the vitreous, pathological changes of vitreous and implications of vitreous changes in various vitreoretinal diseases.
Testing behaviour and positivity for SARS-CoV-2 infection: insights from web-based participatory surveillance in the Netherlands
Authors: McDonald Scott A , Soetens Lucia C , Schipper C Maarten A , Friesema Ingrid , van den Wijngaard Cees C , Teirlinck Anne , Neppelenbroek Nienke , van den Hof Susan , Wallinga Jacco , van Hoek Albert Jan
Published: December 2021

Objectives

We aimed to identify populations at a high risk for SARS-CoV-2 infection but who are less likely to present for testing, by determining which sociodemographic and household factors are associated with a lower propensity to be tested and, if tested, with a higher risk of a positive test result.

Design and setting

Internet-based participatory surveillance data from the general population of the Netherlands.

Participants

Weekly survey data collected over a 5-month period (17 November 2020 to 18 April 2021) from a total of 12 026 participants who had contributed at least 2 weekly surveys was analysed.

Methods

Multivariable analyses using generalised estimating equations for binomial outcomes were conducted to estimate the adjusted ORs of testing and of test positivity associated with participant and household characteristics.

Results

Male sex (adjusted OR for testing (ORt): 0.92; adjusted OR for positivity (ORp): 1.30, age groups<20 (ORt: 0.89; ORp: 1.27), 50–64 years (ORt: 0.94; ORp: 1.06) and 65+ years (ORt: 0.78; ORp: 1.24), diabetics (ORt: 0.97; ORp: 1.06) and sales/administrative employees (ORt: 0.93; ORp: 1.90) were distinguished as lower test propensity/higher test positivity factors.

Conclusions

The factors identified using this approach can help identify potential target groups for improving communication and encouraging testing among those with symptoms, and thus increase the effectiveness of testing, which is essential for the response to the COVID-19 pandemic and for public health strategies in the longer term.
Targeting ER protein TXNDC5 in hepatic stellate cell mitigates liver fibrosis by repressing non-canonical TGFβ signalling
Authors: Hung Chen-Ting , Su Tung-Hung , Chen Yen-Ting , Wu Yueh-Feng , Chen You-Tzung , Lin Sung-Jan , Lin Shuei-Liong , Yang Kai-Chien
Published: December 2021

Background and objectives

Liver fibrosis (LF) occurs following chronic liver injuries. Currently, there is no effective therapy for LF. Recently, we identified thioredoxin domain containing 5 (TXNDC5), an ER protein disulfide isomerase (PDI), as a critical mediator of cardiac and lung fibrosis. We aimed to determine if TXNDC5 also contributes to LF and its potential as a therapeutic target for LF.

Design

Histological and transcriptome analyses on human cirrhotic livers were performed. Col1a1-GFPTg, Alb-Cre;Rosa26-tdTomato and Tie2-Cre/ERT2;Rosa26-tdTomato mice were used to determine the cell type(s) where TXNDC5 was induced following liver injury. In vitro investigations were conducted in human hepatic stellate cells (HSCs). Col1a2-Cre/ERT2;Txndc5fl/fl (Txndc5cKO) and Alb-Cre;Txndc5fl/fl (Txndc5Hep-cKO) mice were generated to delete TXNDC5 in HSCs and hepatocytes, respectively. Carbon tetrachloride treatment and bile duct ligation surgery were employed to induce liver injury/fibrosis in mice. The extent of LF was quantified using histological, imaging and biochemical analyses.

Results

TXNDC5 was upregulated markedly in human and mouse fibrotic livers, particularly in activated HSC at the fibrotic foci. TXNDC5 was induced by transforming growth factor β1 (TGFβ1) in HSCs and it was both required and sufficient for the activation, proliferation, survival and extracellular matrix production of HSC. Mechanistically, TGFβ1 induces TXNDC5 expression through increased ER stress and ATF6-mediated transcriptional regulation. In addition, TXNDC5 promotes LF by redox-dependent JNK and signal transducer and activator of transcription 3 activation in HSCs through its PDI activity, activating HSCs and making them resistant to apoptosis. HSC-specific deletion of Txndc5 reverted established LF in mice.

Conclusions

ER protein TXNDC5 promotes LF through redox-dependent HSC activation, proliferation and excessive extracellular matrix production. Targeting TXNDC5, therefore, could be a potential novel therapeutic strategy to ameliorate LF.
Latin Americans and US Hispanics show Differences in IBD Phenotype: A Systematic Review with Meta-Analysis
Authors: Avalos Danny Juan , Satiya Jinendra , Contreras Alberto , Trivedi Shivani , Alvarado Luis , Dodoo Christopher , Dwivedi Alok Kumar , Zuckerman Marc J.
Published: December 2021

Latin America has experienced a rise in the prevalence and incidence of inflammatory bowel disease (IBD). Differences in IBD phenotype between Hispanics in Latin America and those in the USA have not been described. We conducted a systematic review with meta-analysis of population-based and cohort studies comparing the phenotype of ulcerative colitis (UC) and Crohn's disease (CD) in Latin Americans and US Hispanics. A systematic search was conducted up to March 2019 using MEDLINE, EMBASE and Google Scholar. Inclusion criterion includes studies describing IBD phenotype in Latin Americans or in US Hispanics. Exclusion criterion includes prevalence or incidence studies not describing phenotype. A random effects model was chosen “a priori” for analysis of pooled proportions. A total of 46 studies were included from Latin America and 7 studies from the USA. The predominant IBD subtype in Latin America was UC with a more balanced UC:CD ratio noted in Puerto Rico (0.53) and Brazil (0.56). UC-related extensive colitis was more common in US Hispanics (0.64) than in Latin Americans (0.38), p<0.001. CD phenotype was similar between US Hispanics and Latin Americans. UC is the predominant IBD subtype in Latin America, with the exception of Puerto Rico and Brazil which demonstrate a more balanced UC:CD ratio. In UC, extensive colitis was more frequently seen in US Hispanics than in Latin Americans. CD phenotype was similar in both US Hispanics and Latin Americans.
Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review
Authors: Chen Haiyang , He Yu
Published: December 2021

Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.
Efficacy and Safety of Shengmai Preparation Combined with Western Medicine for Coronary Heart Disease: A Systematic Review and Meta-Analysis
Authors: Li Yujuan , Li Dan , Jin Xiao , Yang Shengjie , Zhao Ran , Wu Min
Published: December 2021

The efficacy and safety of Shengmai preparation combined with Western medicine (SMP–WM) to treat coronary heart disease (CHD) were reviewed. Twenty-five randomized controlled trials of SMP–WM treatment for CHD were retrieved from seven databases and other trial sources between their inception and April 10, 2021. The risk of bias domains was accessed by Cochrane Collaboration’s tool, and the data were statistically analyzed using RevMan 5.3 and Stata 12.0 software. The majority of included studies had a low or unclear risk of overall bias. Total mortality was not reduced (RR = 0.39, 95% CI: 0.13–1.19, [Formula: see text] = 0.10), but the cardiovascular events (RR = 0.35, 95% CI: 0.22–0.54, [Formula: see text] < 0.01), weekly frequency (SMD = −2.38, 95% CI: −2.89 – −1.88, [Formula: see text] < 0.01), and duration (SMD = −3.24, 95% CI: −3.76 – −2.71, [Formula: see text] < 0.01) of angina pectoris attacks were significantly decreased by SMP–WM. The SMP–WM combination exerted antiplatelet activity by reducing platelet adhesion (SMD = −0.97, 95% CI: −1.49 – −0.45, [Formula: see text] = 0.0003) and the platelet reactivity index (SMD = −1.75, 95% CI: −2.04 – −1.46, [Formula: see text] < 0.01). SMP–WM could protect endothelial function by increasing nitric oxide (SMD = 1.28, 95% CI: 0.54–2.02, [Formula: see text] < 0.01) and decreasing endothelin (SMD = −1.26, 95% CI: −1.85 – −0.66, [Formula: see text] < 0.01). The combination also improved hemorheology by reducing whole blood viscosity (SMD = −1.59, 95% CI: −2.32 – −0.85, [Formula: see text] < 0.01), plasma viscosity (SMD = −0.65, 95% CI: −0.86 – −0.45, [Formula: see text] < 0.01), and fibrinogen (SMD = −4.21, 95% CI: −4.58 – −3.83, [Formula: see text] < 0.01). The SMP–WM combination favorably impacts cardiovascular events, angina symptoms, endothelial function, platelet aggregation, and blood viscosity, with comparable safety to that of routine Western medicine. Further investigation is required to enhance the strength of the evidence.
Falcarindiol Stimulates Apoptotic and Autophagic Cell Death to Attenuate Cell Proliferation, Cell Division, and Metastasis through the PI3K/AKT/mTOR/p70S6K Pathway in Human Oral Squamous Cell Carcinomas
Authors: Park Kyung-Ran , Leem Hyun Hee , Kwon Yoon-Ju , Kwon Il Keun , Hong Jin Tae , Yun Hyung-Mun
Published: December 2021

Human oral squamous cell carcinomas (OSCCs) have high cancer mortality and a 5-year survival rate lower than that of most other carcinomas. New therapeutic strategies are required for the treatment and prevention against OSCCs. An approach to cancer therapy using plant-derived natural compounds has been actively in progress as a trend. Falcarindiol (FALC), or its isolated form Ostericum koreanum Kitagawa (O. koreanum), is present in many food and dietary plants, especially in carrots, and this compound has a variety of beneficial effects. However, biological activity of FALC has not been reported in OSCCs yet. This study aimed to demonstrate the antitumor effects of FALC against OSCCs, YD-10B cells. In this study, FALC was selected as a result of screening for compounds isolated from various natural products in YD-10B cells. FALC suppressed cell growth, and FALC-induced apoptotic cell death was mainly accompanied by the dephosphorylation of PI3K, AKT, mTOR, and p70S6K. The apoptotic cell death was also associated with autophagy as evidenced by the expression of Beclin-1, the conversion of LC3-II, and the formation of autophagosome. FALC-induced autophagy was accompanied by MAPKs including ERK1/2 and p38. Furthermore, FALC caused the antimetastatic effects by inhibiting the migration and invasion of YD-10B cells. Taken together, the findings suggest the potential value of FALC as a novel candidate for therapeutic strategy against OSCCs.
Bifurcations in a Time-Delayed Birhythmic Biological System with Fractional Derivative and Lévy Noise
Authors: Zhang Wenting , Xu Wei , Guo Qin , Zhang Hongxia
Published: December 2021

The birhythmic oscillation is of great significance in biology and engineering, and this paper presents a bifurcation analysis in a time-delayed birhythmic oscillator containing fractional derivative and Lévy noise. The numerical method is used to explore the influence of various parameters on the bifurcation of the birhythmic system, and the role of fractional derivative and Lévy noise in inducing or inhibiting birhythmicity in a time-delayed birhythmic biological system is examined in this work. First, we use a numerical method to calculate the fractional derivative, which has a fast calculation speed. Then the McCulloch algorithm is employed to generate Lévy random numbers. Finally, the stationary probability density function graph of the amplitude is obtained by Monte Carlo simulation. The results show that the fractional damping and Lévy noise can effectively control the characteristics of the birhythmic oscillator, and the change of the parameters (except the skewness parameter) can cause the system bifurcation. In addition, this article further discusses the interaction of fractional derivative and time delay in a birhythmic system with Lévy noise, proving that adjusting parameters of time delay can lead to abundant bifurcations. Our research may help to further explore the bifurcation phenomenon of birhythmic biological system, and has a practical significance.
Difference Double Sequence of Complex Uncertain Variables Defined by Orlicz Function
Authors: Datta Debasish , Tripathy Binod Chandra
Published: December 2021

In this paper, we introduce the difference double sequence of complex uncertain variables defined by Orlicz function. We study some of their properties like solidness, symmetricity, and completeness and prove some inclusion results.
Fine-Grained Semantic Image Synthesis with Object-Attention Generative Adversarial Network
Authors: Wang Min , Lang Congyan , Liang Liqian , Feng Songhe , Wang Tao , Gao Yutong
Published: December 2021

Semantic image synthesis is a new rising and challenging vision problem accompanied by the recent promising advances in generative adversarial networks. The existing semantic image synthesis methods only consider the global information provided by the semantic segmentation mask, such as class label, global layout, and location, so the generative models cannot capture the rich local fine-grained information of the images (e.g., object structure, contour, and texture). To address this issue, we adopt a multi-scale feature fusion algorithm to refine the generated images by learning the fine-grained information of the local objects. We propose OA-GAN, a novel object-attention generative adversarial network that allows attention-driven, multi-fusion refinement for fine-grained semantic image synthesis. Specifically, the proposed model first generates multi-scale global image features and local object features, respectively, then the local object features are fused into the global image features to improve the correlation between the local and the global. In the process of feature fusion, the global image features and the local object features are fused through the channel-spatial-wise fusion block to learn ‘what’ and ‘where’ to attend in the channel and spatial axes, respectively. The fused features are used to construct correlation filters to obtain feature response maps to determine the locations, contours, and textures of the objects. Extensive quantitative and qualitative experiments on COCO-Stuff, ADE20K and Cityscapes datasets demonstrate that our OA-GAN significantly outperforms the state-of-the-art methods.
Variable-Temperature Native Mass Spectrometry for Studies of Protein Folding, Stabilities, Assembly, and Molecular Interactions
Authors: Laganowsky Arthur , Clemmer David E. , Russell David H.
Published: December 2021

The structures and conformational dynamics of proteins, protein complexes, and their noncovalent interactions with other molecules are controlled specifically by the Gibbs free energy (entropy and enthalpy) of the system. For some organisms, temperature is highly regulated, but the majority of biophysical studies are carried out at room, nonphysiological temperature. In this review, we describe variable-temperature electrospray ionization (vT-ESI) mass spectrometry (MS)-based studies with unparalleled sensitivity, dynamic range, and selectivity for studies of both cold- and heat-induced chemical processes. Such studies provide direct determinations of stabilities, reactivities, and thermodynamic measurements for native and non-native structures of proteins and protein complexes and for protein–ligand interactions. Highlighted in this review are vT-ESI-MS studies that reveal 40 different conformers of chymotrypsin inhibitor 2, a classic two-state (native → unfolded) unfolder, and thermochemistry for a model membrane protein system binding lipid and its regulatory protein.
Hierarchical Mg-Birnessite Nanowall Arrays with Enriched (010) Planes for High Performance Aqueous Mg-Ion Batteries
Authors: Shi Zhengyi , Xue Liang , Wu Jianghua , Guo Qiubo , Xia Qiuying , Ni Mingzhu , Wang Peng , Savilov Serguei V. , Aldoshin Sergey M. , Zan Feng , Xia Hui
Published: December 2021

Birnessite MnO2 is a promising cathode material for aqueous Mg-ion batteries due to its layered structure with large interlayer distance. However, the two-dimensional growth mode of birnessite induces nanosheet morphology with preferred growth of inactive (001) planes with sluggish ion transport kinetics. In this work, a high Mg content birnessite with hierarchical nanowall arrays morphology is prepared by in situ electro-conversion using spinel Mn3O4 nanowall arrays. The electro-conversion Mg-birnessite (ECMB) nanowall arrays are assembled by ultrasmall nanosheets with reduced (001) planes but increased active (010) planes, affording enriched open intercalation channels and shortened Mg2+ diffusion length. Consequently, the ECMB cathode exhibits a large specific reversible capacity of about 255.1 mAh g−1 at a current density of 200 mA g−1, and outstanding cycling stability with 73.6% capacity retention after 3000 cycles. Finally, a 2.2 V aqueous full cell is constructed by using ECMB as positive electrode and polyimide as negative electrode, which achieves a high energy density of 65.2 Wh kg−1 at a power density of 96 W kg−1. This work demonstrates effective crystal plane modulation for Mg-birnessite to achieve superior Mg2+ storage in aqueous batteries.
Remote Sensing Big Data Analysis of the Lower Yellow River Ecological Environment Based on Internet of Things
Authors: Liu Yuntong , He Kuan , Qin Fen
Published: December 2021

This paper collects data on the ecological environment of the lower Yellow River through an IoT approach and provides an in‐depth analysis of the ecological remote sensing big data. An impervious fusion of multisource remote sensing data cooperation and multimachine learning algorithm cooperation is proposed. The water surface extraction method has improved the extraction accuracy of the construction land and rural settlements in the Yellow River Delta. The data system, big data management platform, and application scenarios of the environmental data resource center are designed specifically, respectively. Based on the spherical mesh information structure to sort out environmental data, an environmental data system containing data characteristics such as information source, timeliness, and presentation is formed. According to the characteristics of various types of environmental data, the corresponding data access, storage, and analysis support system is designed to form the big data management platform. Strengthen the construction of ecological interception projects for farmland receding water. Speed up the construction of sewage treatment facilities. Carry out waste and sewage pipeline network investigation, speed up the construction of urban sewage collection pipeline network, and improve the waste and sewage collection rate and treatment rate. The management platform adopts the Hadoop framework, which is conducive to the storage of massive data and the utilization of unstructured data. Combined with the relevant national policy requirements and the current environmental protection work status, the application scenarios of environmental big data in environmental decision‐making, supervision, and public services are sorted out to form a complete data resource center framework. Gray correlation analysis is used to identify the key influencing factors of different types of cities to elaborate the contents of the construction of water ecological civilization in different types of cities and to build a framework of ideas for the construction of urban water ecological civilization to improve the health of urban water ecological civilization. To realize the sustainable development of the lower reaches of the Yellow River, blind logging and reclamation should be avoided in the process of land development, and more efforts should be made to protect tamarisk scrub and reed scrub, which are vegetation communities with positive effects on the regional ecological environment. In urban planning, the proportion of green area and water area within the city should be reasonably increased, so that the city can develop towards a livable city that is more conducive to human‐land harmony and sustainability.
Optimized Design of Multilines Center of Subway AFC System via Distributed File System and Bayesian Network Model
Authors: Fang Hui , Jiang Jiandi , Lin Feng , Zhang Wei
Published: December 2021

Automatic fare collection system (AFCS) is a modern, automatic, networked toll collection system for rail transit ticket sales, collection, billing, charging, statistics, sorting, and management. To realize the subway transit networking operation, this paper designs the subway AFCS based on a distributed file system (DFS), namely, Gluster File System (GlusterFS). Firstly, the multiline center (MLC) in the subway AFCS is designed to analyze the status and current situation of distributed file processing in subway MLC system; secondly, the relevant technical theories are summarized, the Bayesian Network (BN) theoretical model and DFS are explored, and the principles of four DFS are comparatively analyzed; thirdly, the architecture and cluster mode of GlusterFS is expounded, and then based on GlusterFS, the architecture of subway AFCS is discussed. This paper presents several innovation points: first, the subway AFCS is designed based on GlusterFS by analyzing and aiming at the functional requirements, performance requirements, and safety requirements of the MLC subway system; second, the safety risk analysis (SRA) of AFCS is conducted from six security requirements, and a Web scanning system is designed to ensure the system data security. Finally, the design scheme is used to analyze the subway passenger flow and power consumption. The results demonstrate that the design scheme can competently adapt to three different application scenarios. Through comparison of two deployment modes of the Web scanning system, the data security Web scanning system can ensure the safe operation of the AFCS. Furthermore, the statistical analysis of subway passenger flow and power supply data shows that the proposed scheme can support the smooth operation of the subway system, which has significant practical value.
Combination of Traditional Chinese and Western Medicine in Sports in Pharmaceutical Health
Authors: Zhangyanhui  , cui Huang , Genglei
Published: December 2021

The health problems caused by insufficient exercise are becoming more and more serious, which is a serious threat to human health, especially the increase in patients with chronic diseases, which brings a heavy burden to medical and health care, and even triggers a public health crisis. In order to cope with the health threats people face, how to promote the synergistic effect of sports and medical health under the background of the integration of Chinese and Western medicine has become an important issue in building a healthy China life cycle. This article is aimed at studying the application of integrated traditional Chinese and Western medicine in pharmacy and health. Through the research of multiproblem collaborative governance of sports and medicine, the mechanism to promote multidisciplinary collaborative governance was established, and the realization of the integration of sports and medicine was promoted, extending some college students in a university in a certain city and studied the experiment of the influence of exercise on health. Experiments showed that 34.8% of students who exercise almost daily were in very healthy conditions, and 47.8% of healthy students were also unhealthy or prone to illness. The 62.1% of students exercising three to four times a week are healthy, proving that sports has a certain impact on human health and has a certain reference significance for the integration of sports and medicine.
Two-Phase Flow Model for Numerical Investigation of Impact of Water Retention on Shale Gas Production
Authors: Xu Ershe , Yu Lingjie , Fan Ming , Chen Tianyu , Pan Zhejun , Tan Yuling , Cui Guanglei
Published: December 2021

In this work, a triple-porosity, two-phase flow model was established to fill the knowledge gap of previous models focusing on gas production characteristics while ignoring the impacts of water injection. The proposed model considers the water flow in the fracture systems and clay minerals and the gas flow in the organic matter, inorganic pore, and fracture systems. The proposed model is solved using a finite element approach with COMSOL Multiphysics (Version 5.6) and verified with field data. Then, the evolutions of the intrinsic and relative permeabilities during water injection and gas production are examined. Furthermore, the impacts of water injection time and pressure are investigated. Good verification results are obtained; the goodness-of-fit value is 0.92, indicating that the proposed model can replicate both the water stimulation and the gas production stages. The relative gas permeability declines during water injection but recovers in the gas depletion stage. Furthermore, the intrinsic permeability increases during the water injection stage but decreases during the gas production stage. A higher water injection pressure and longer injection time would enlarge the intrinsic permeability, thus improving flow capacity. However, it would reduce gas relative permeability, thereby hindering gas flow. The shale gas production characteristic is controlled by the two abovementioned competing mechanisms. There exists a perfect combination of water injection pressure and injection time for achieving the maximum profitability of a shale gas well. This work can give a better understanding of the two-phase flow process in shale reservoirs and shed light on the field application of hydraulic fracturing.
[Retracted] Research on Automobile Assembly Line Optimization Based on Industrial Engineering Technology and Machine Learning Algorithm
Authors: Shi Xiaorui , Cui Wei , Zhu Ping , Yang Yanhua
Published: December 2021

Aiming at the lack of search depth of traditional genetic algorithm in automobile assembly line balance optimization, an improved genetic algorithm based on bagging integrated clustering is proposed for balance optimization. Through the integrated learning of several K‐means algorithm based learners through bagging, a population clustering analysis method based on bagging integrated clustering algorithm is established, and then, a dual objective automobile assembly line balance optimization model is established. The population clustering analysis method is used to improve the intersection link of genetic algorithm to improve the search depth. The effectiveness and search performance of the improved genetic algorithm in solving the double objective assembly line balance problem are verified in an example.
Analysing the Impact of Pedestrian Network Centrality on Segment-Level Pedestrian Density
Authors: Pearce Daniel M. , Matsunaka Ryoji , Oba Tetsuharu
Published: December 2021

Studies have shown that street network centrality measures are capable of explaining a significant proportion of pedestrian activity. These studies typically employ street centreline networks that differ significantly from the networks that pedestrians use to traverse the built environment. Presently, centrality approaches are rarely applied to dedicated pedestrian network (DPNs). This creates uncertainty regarding their ability to explain pedestrian activity when derived from DPNs. This study addresses that gap by investigating the extent to which centrality metrics derived from DPNs can explain observed pedestrian densities, both alone and when controlling for other built environment variables in metro station environments in Asia. In total, four DPNs were created centred on metro stations in Bangkok, Manila, Osaka, and Taipei chosen to represent different urban typologies. Multivariate results show that centrality metrics alone explain a mere 6–24% of observed pedestrian densities when calculated on DPNs. When all factors are considered, the contribution of centrality remained consistent in most study sites but is somewhat reduced with land-use variables and proximity to rail transit revealed as the strongest predictors of pedestrian density. Pedestrian design factors were also frequently associated with pedestrian density. Finally, stronger associations between centrality and pedestrian densities were observed in the denser, more complex pedestrian environments. These findings provide insight into the performance of centrality measures applied to DPNs expanding pedestrian network research in this area.
Outlier Detection Based on Multivariable Panel Data and K‐Means Clustering for Dam Deformation Monitoring Data
Authors: Song Jintao , Zhang Shengfei , Tong Fei , Yang Jie , Zeng Zhiquan , Yuan Shuai
Published: December 2021

A dam is a super‐structure widely used in water conservancy engineering fields, and its long‐term safety is a focus of social concern. Deformation is a crucial evaluation index and comprehensive reflection of the structural state of dams, and thus there are many research papers on dam deformation data analysis. However, the accuracy of deformation data is the premise of dam safety monitoring analysis, and original deformation data may have some outliers caused by manual errors or instruments aging after long‐time running. These abnormal data have a negative impact on the evaluation of dam structural safety. In this study, an analytical method for detecting outliers of dam deformation data was established based on multivariable panel data and K‐means clustering theory. First, we arranged the original spatiotemporal monitoring data into the multivariable panel data format. Second, the correlation coefficients between the deformation signals of different measuring points were studied based on K‐means clustering theory. Third, the outlier detection rules were established through the changes of the correlation coefficients. Finally, the proposed model was applied to the Jinping‐I Arch Dam in China which is the highest dam in the world, and results indicate that the detection method has high accuracy detection ability, which is valuable in dam safety monitoring applications.
Research on Recommendation of Personalized Exercises in English Learning Based on Data Mining
Authors: Zhou Lina , Wang Chunxia
Published: December 2021

Aiming at the problems of traditional method of exercise recommendation precision, recall rate, long recommendation time, and poor recommendation comprehensiveness, this study proposes a personalized exercise recommendation method for English learning based on data mining. Firstly, a personalized recommendation model is designed, based on the model to preprocess the data in the Web access log, and cleaning the noise data to avoid its impact on the accuracy of the recommendation results is focused; secondly, the DINA model to diagnose the degree of mastery of students’ knowledge points is used and the students’ browsing patterns through fuzzy similar relationships are clustered; and finally, according to the clustering results, the similarity between students and the similarity between exercises are measured, and the collaborative filtering recommendation of personalized exercises for English learning is realized. The experimental results show that the exercise recommendation precision and recall rate of this method are higher, the recommendation time is shorter, and the recommendation results are comprehensive.
The Synergistic Reducing Drug Resistance Effect of Cisplatin and Ursolic Acid on Osteosarcoma through a Multistep Mechanism Involving Ferritinophagy
Authors: Tang Zhen , Dong Hui , Li Tian , Wang Ning , Wei Xinghui , Wu Hao , Liu Yichao , Wang Wei , Guo Zheng , Xiao Xin
Published: December 2021

Increasing evidence suggests that traditional Chinese medicine strategies are obviously beneficial for cancer treatment, but scientific research on the underlying molecular mechanisms is lacking. We report that ursolic acid, a bioactive ingredient isolated from Radix Actinidiae chinensis, has strong antitumour effects on osteosarcoma cells. Functional studies showed that ursolic acid inhibited tumour cell proliferation and promoted the apoptosis of a variety of osteosarcoma cells. Ursolic acid had a synergistic cytotoxic effect with cisplatin on osteosarcoma cells. In a mouse osteosarcoma xenograft model, low‐dose cisplatin combined with ursolic acid significantly reduced tumour growth. Notably, ursolic acid reversed weight loss in mice treated with cisplatin. Mechanistic studies showed that ursolic acid degraded ferritin by activating autophagy and induced intracellular overload of ferrous ions, leading to ferroptosis. In addition, ursolic acid enhanced the DNA‐damaging effect of cisplatin on osteosarcoma cells. Taken together, these findings suggest that ursolic acid is a nontoxic adjuvant that may enhance the effectiveness of chemotherapy in osteosarcoma.
[Retracted] Application of Neural Network Algorithm Combined with Bee Colony Algorithm in English Course Recommendation
Authors: Ren Guiting
Published: December 2021

The traditional BP neural network has the disadvantages of easy falling into local minimum and slow convergence speed. Aiming at the shortcomings of BP neural network (BP neural network), an artificial bee colony algorithm (ABC) is proposed to cross‐optimize the weight and threshold of BP network parameters. This study is mainly about the application of BP neural network algorithm in English curriculum recommendation technology. It includes the application of BP neural network algorithm in English course recommendation technology, English course teaching design mode, the application of BP neural network algorithm in English course, and the optimal combination of bee colony algorithm and BP neural network. After 4690 iterations, the neural network reaches the target accuracy, and the training is completed. At the same time, the prediction error of the model is less than 10%, which further shows that the performance of the prediction model is good. Therefore, the combination model is recommended in this paper. The results show that the optimization algorithm improves the solution accuracy and speeds up the convergence speed of the network.
The Visibility of Multisensor Fusion Technology in Public Art Design
Authors: Zhang Yan , Zhang Qi
Published: December 2021

This exploration is aimed at improving the efficiency and safety of fully automatic line operation in public space. From the perspective of multisensor fusion technology, the definition, development status, and design principles of public art, as well as the related applications of multisensor fusion technology theory and image fusion theory, are introduced by consulting relevant literature. Then, the scheme of subway platform door gap antipinch detection system based on multisensor fusion and the obstacle detection method of tram in transit are proposed. Finally, through the method of questionnaire, the passengers’ cognition of public art in subway space and the concerned components of public art in subway space are analyzed. The results show that the subway space art content loved by passengers is mainly daily life, and the degree of love can reach 28%, followed by local customs, culture, and fashion trends, with a degree of love of 23%. Besides, the problem of obstacle detection of tram in transit is also studied, and a new obstacle detection method combining visual sensor transmission detection and lidar detection is proposed. This method can quickly and accurately identify unsafe factors. Therefore, the research on the visibility of multisensor fusion technology in public art design has great reference significance for the rapid development of transportation industry.
Performance of a Three-Substep Time Integration Method on Structural Nonlinear Seismic Analysis
Authors: Zhang Jinyue , Shi Lei , Liu Tianhao , Zhou De , Wen Weibin
Published: December 2021

In this work, a study of a three substeps’ implicit time integration method called the Wen method for nonlinear finite element analysis is conducted. The calculation procedure of the Wen method for nonlinear analysis is proposed. The basic algorithmic property analysis shows that the Wen method has good performance on numerical dissipation, amplitude decay, and period elongation. Three nonlinear dynamic problems are analyzed by the Wen method and other competitive methods. The result comparison indicates that the Wen method is feasible and efficient in the calculation of nonlinear dynamic problems. Theoretical analysis and numerical simulation illustrate that the Wen method has desirable solution accuracy and can be a good candidate for nonlinear dynamic problems.
Control of Magnetic Manipulator Using Reinforcement Learning Based on Incrementally Adapted Local Linear Models
Authors: Brablc Martin , Žegklitz Jan , Grepl Robert , Babuška Robert
Published: December 2021

Reinforcement learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on techniques that have not been used much so far: symbolic regression (SR), based on genetic programming and local modelling. Using measured data, symbolic regression yields a nonlinear, continuous‐time analytic model. We benchmark two state‐of‐the‐art methods, SNGP (single‐node genetic programming) and MGGP (multigene genetic programming), against a standard incremental local regression method called RFWR (receptive field weighted regression). We have introduced modifications to the RFWR algorithm to better suit the low‐dimensional continuous‐time systems we are mostly dealing with. The benchmark is a nonlinear, dynamic magnetic manipulation system. The results show that using the RL framework and a suitable approximation method, it is possible to design a stable controller of such a complex system without the necessity of any haphazard learning. While all of the approximation methods were successful, MGGP achieved the best results at the cost of higher computational complexity. Index Terms–AI‐based methods, local linear regression, nonlinear systems, magnetic manipulation, model learning for control, optimal control, reinforcement learning, symbolic regression.
Moisture Content on Methane Desorption Characteristics in Coal and Its Effect on Outburst Prediction
Authors: Li Peng , Cao Yaolin , Li Xuelong , Wang Fakai , Sun Zhongguang , Huang Qinke , Chen Deyou
Published: December 2021

Facts have proved that coal and gas outbursts require rapid desorption in a short period of time. Due to the limitation of experimental conditions, the methane desorption characteristics in the first few seconds (0–60 s) in coals with different moisture contents have not been fully studied at present. In this article, the initial desorption characteristics of methane in coals with different moisture contents were investigated using a self‐developed experimental setup. In order to collect enough methane pressure data for analysis and calculation, a self‐developed real‐time data acquisition system with a time interval of about 10 ms was used in the experiment to calculate the initial gas desorption amount and the initial gas velocity diffusion index (ΔP). Experiments show that coal with low water content and methane outburst is more dangerous than coal with high water content and outburst; and the degree of outburst of coal and methane decreases exponentially with the increase of moisture content.
Exchange Rate Transaction of International Trade Goods Based on Fuzzy Granulation and Deep Learning
Authors: Hu Jin , Han Li
Published: December 2021

The change of international trade goods exchange rate transaction has an impact on economic operations and economic stability. Therefore, an international trade goods exchange rate transaction based on fuzzy granulation and in-depth learning is proposed. Based on fuzzy information granulation and BP neural network, this paper analyzes the interest rate evaluation theory. For the future expectation of currency exchange rate, portfolio equilibrium determines the proportional relationship of each component in the portfolio and analyzes the impact of asset price and exchange rate change according to this relationship. Then, it points out the risk evaluation index system, calculates the risk degree of exchange rate transaction of international trade goods, and then evaluates the risk of exchange rate transaction of international trade goods. It completes the research on exchange rate transactions of international trade goods based on fuzzy granulation and in-depth learning. The experimental results show that excessive exchange rate fluctuation will bring the same proportion fluctuation to the asset price in the financial market, and the coordination between exchange rates and the coordination of exchange rate and asset price can promote the steady growth of national economy.
[Retracted] Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese‐English Parallel Corpus Noise Processing
Authors: Li Bing , Tuo Anxie , Kong Hanyue , Liu Sujiao , Chen Jia
Published: December 2021

This paper uses neural network as a predictive model and genetic algorithm as an online optimization algorithm to simulate the noise processing of Chinese‐English parallel corpus. At the same time, according to the powerful random global search mechanism of genetic algorithm, this paper studied the principle and process of noise processing in Chinese‐English parallel corpus. Aiming at the task of identifying isolated words for unspecified persons, taking into account the inadequacies of the algorithms in standard genetic algorithms and neural networks, this paper proposes a fast algorithm for training the network using genetic algorithms. Through simulation calculations, different characteristic parameters, the number of training samples, background noise, and whether a specific person affects the recognition result were analyzed and discussed and compared with the traditional dynamic time comparison method. This paper introduces the idea of reinforcement learning, uses different reward mechanisms to solve the inconsistency of loss function and evaluation index measurement methods, and uses different decoding methods to alleviate the problem of exposure bias. It uses various simple genetic operations and the survival of the fittest selection mechanism to guide the learning process and determine the direction of the search, and it can search multiple regions in the solution space at the same time. In addition, it also has the advantage of not being restricted by the restrictive conditions of the search space (such as differentiable, continuous, and unimodal). At the same time, a method of using English subword vectors to initialize the parameters of the translation model is given. The research results show that the neural network recognition method based on genetic algorithm which is given in this paper shows its ability of quickly learning network weights and it is superior to the standard in all aspects. The performance of the algorithm in genetic algorithm and neural network, with high recognition rate and unique application advantages, can achieve a win‐win of time and efficiency.