bluuebunny/crossref_metadata_2025 huggingface dataset:
Softening-Spring Phenomenon in Large Amplitude Vibration of Two-Layer Bi-Material Beams
Authors: Hao Na , Ke Liao-Liang
Published: March 2022

The purpose of this study is to analyze the nonlinear vibration behavior of two-layer bi-material beams by using the Euler beam theory and Timoshenko beam theory. It is assumed that material properties satisfy a power law distribution along the thickness direction. The differential quadrature (DQ) method in conjunction with an iterative algorithm is employed to solve the nonlinear governing equations for two-layer bi-material beams. Numerical results are presented to show the influence of Young’s modulus ratio and thickness ratio on nonlinear frequency ratio–amplitude curves of two-layer bi-material beams. It is observed that the nonlinear frequency ratio–amplitude curves are unsymmetrical with the hinged end because of bending-extension coupling effect in two-layer beams. It is also observed that the hinged–hinged two-layer beams may occur the softening-spring nonlinearity when the amplitude of oscillation is small. This softening-spring nonlinearity is dependent on the thickness ratio, Young’s modulus ratio, vibration amplitude, layer number and boundary condition. The greater the difference of material properties between the two layers is, the more obvious the softening-spring nonlinearity becomes.
Damping and Frequency Response Characteristics of Functionally Graded Fiber-Reinforced Composite Cylindrical Shells
Authors: Dai Qiyi , Liu Yunfei , Qin Zhaoye , Chu Fulei
Published: March 2022

This paper presents research on the modal damping and frequency response of functionally graded fiber-reinforced composite cylindrical shells considering the internal damping. Based on the Love shell theory and energy approach, the dynamical equations of cylindrical shells are established. In the process of variable separation, the Haar wavelet series and trigonometric functions are respectively to represent the axial and circumferential modes. Based on the microscopic damping prediction method and multi-cell model of hybrid materials, the equivalent damping and elastic characteristics of composite materials are determined. Then the damping and frequency response characteristics are solved by the complex modulus method. The present analysis is validated by comparing the results with those in the literature and finite element simulation. The effects of fiber content and distribution, lamination and geometry configuration on damping and frequency response properties are further analyzed. The analysis show that the damping decreases monotonically with the increase of fiber volume fraction. The damping behaviors may be improved by changing the fiber distribution type and stacking sequence. Increasing the internal damping of composites can obviously reduce the vibration amplitude in the resonance region, especially in the high frequency range.
Machine Learning to Identify Dynamic Properties of Railway Track Components
Authors: Kaewunruen Sakdirat , Sresakoolchai Jessada , Stittle Henry
Published: March 2022

Investigating the condition of rail track components is important for track maintenance and developing a greater understanding of track design. Railway inspection can be destructive and non-destructive approaches. In the railway industry, the non-destructive approaches are preferred because they retain the track in operation thus significantly reducing the cost of fault testing. One of the non-destructive approaches is using machine learning which is applied in this study. Field measurements and advanced analysis of results are used to extract track properties. This study creates, tunes and examines the validity of different machine learning techniques. The aim is to extract the dynamic track properties from the in-field measurements without needing the intermediary steps, saving both time and effort. Contributions of this study demonstrate that machine learning techniques have the potential to save cost and time for railway inspection. Moreover, the accuracy is satisfied. The following models are produced: Linear Regression, K-Nearest Neighbors, Gradient Boosting and a Convolutional Neural Network. We observe the limitations of linear regression and tune the remainder, producing three models with low errors.
Comparative effects of tensioning and sliding neural mobilization on peripheral and autonomic nervous system function: A randomized controlled trial
Authors: Alharmoodi Budour Yousif , Arumugam Ashokan , Ahbouch Amal , Moustafa Ibrahim M.
Published: March 2022

Background: Although different types of neural mobilization (NM) exercises induce different amounts of longitudinal nerve excursion and strain, the question whether the increased longitudinal stress and nerve excursion from sliding or tensioning intervention may subtly affect the neural functions has not been answered yet.

Objective: To compare the effects of tensioning NM versus sliding NM of the median nerve on peripheral and autonomic nervous system function.

Methods: In this randomized controlled trial, 90 participants were randomly assigned to tensioning NM, sliding NM, or sham NM. The neurophysiological outcome measures included peak-to-peak amplitude of the dermatomal somatosensory evoked potential (DSSEP) for dermatomes C6, C7, C8, and T1. Secondary outcome measures included amplitude and latency of skin sympathetic response. All outcome measures were assessed pretreatment, immediately after the two weeks of treatment and one week after the last session of the treatment.

Results: A 2-way repeated measures ANOVA revealed significant differences between the three groups. The post hoc analysis indicated that tensioning NM significantly decreased the dermatomal amplitude for C6, C7, C8, and T1 ([Formula: see text]). Sympathetic skin responses in the gliding NM group showed lower amplitudes and prolonged latencies post-treatment when compared to tensioning NM group ([Formula: see text]). In contrast, no significant changes were observed in the DSSEPs and skin sympathetic responses for participants in the sham treatment group ([Formula: see text]).

Conclusions: A tensioning NM on the median nerve had a possible adverse effect on the neurophysiology variables of the nerves involved in the neural mobilization. Thus, tensioning NM with the current parameters that place increased stress and strain on the peripheral nervous system should be avoided.
Investigation on transition of RMB forward exchange rate pricing mechanism based on error correction model with structural mutation
Authors: Wang Hua , Zhu Junjun
Published: March 2022

The offshore RMB market has been showing a momentum of rapid development. However, the impact of this market on the RMB exchange rate has been less studied. This paper constructs an error correction model with structural mutations and focuses on the turning point for RMB Forward Rate, during the time before and after September 2011. The model is constructed considering the micro-institutional differences between domestic and offshore RMB forward exchange rates, the impact of spreads, the impact of spot exchange rates and international financial market shocks such as the VIX index of the US dollar index. We also apply the error correction model with abrupt changes method to select the model which is innovative. According to the results of the model, the domestic RMB foreign exchange derivatives market and the offshore RMB foreign exchange derivatives market jointly played a leading role in price discovery in the determination of both short-term and long-term RMB forward exchange rates, and we also found September 2011 was an important structural change point for the RMB forward curves both domestic and abroad. Before that period, interest rate parity did not play a positive role and the NDF exchange rate occupied the dominant role in price discovery; the domestic RMB forward exchange rate and the overseas NDF exchange rate were both driven by speculative factors.
Quantitative Stimulated Raman Scattering Microscopy: Promises and Pitfalls
Authors: Manifold Bryce , Fu Dan
Published: March 2022

Since its first demonstration, stimulated Raman scattering (SRS) microscopy has become a powerful chemical imaging tool that shows promise in numerous biological and biomedical applications. The spectroscopic capability of SRS enables identification and tracking of specific molecules or classes of molecules, often without labeling. SRS microscopy also has the hallmark advantage of signal strength that is directly proportional to molecular concentration, allowing for in situ quantitative analysis of chemical composition of heterogeneous samples with submicron spatial resolution and subminute temporal resolution. However, it is important to recognize that quantification through SRS microscopy requires assumptions regarding both system and sample. Such assumptions are often taken axiomatically, which may lead to erroneous conclusions without proper validation. In this review, we focus on the tacitly accepted, yet complex, quantitative aspect of SRS microscopy. We discuss the various approaches to quantitative analysis, examples of such approaches, challenges in different systems, and potential solutions. Through our examination of published literature, we conclude that a scrupulous approach to experimental design can further expand the powerful and incisive quantitative capabilities of SRS microscopy.
Biopharmaceutical Manufacturing: Historical Perspectives and Future Directions
Authors: Szkodny Alana C. , Lee Kelvin H.
Published: March 2022

This review describes key milestones related to the production of biopharmaceuticals—therapies manufactured using recombinant DNA technology. The market for biopharmaceuticals has grown significantly since the first biopharmaceutical approval in 1982, and the scientific maturity of the technologies used in their manufacturing processes has grown concomitantly. Early processes relied on established unit operations, with research focused on process scale-up and improved culture productivity. In the early 2000s, changes in regulatory frameworks and the introduction of Quality by Design emphasized the importance of developing manufacturing processes to deliver a desired product quality profile. As a result, companies adopted platform processes and focused on understanding the dynamic interplay between product quality and processing conditions. The consistent and reproducible manufacturing processes of today's biopharmaceutical industry have set high standards for product efficacy, quality, and safety, and as the industry continues to evolve in the coming decade, intensified processing capabilities for an expanded range of therapeutic modalities will likely become routine.
Dependence of Quinone Electrochemistry on Solvent Polarity and pH
Authors: Silverstein Todd P. , Rosé Jason R.
Published: March 2022

We studied the polarographic reduction of quinones in aqueous/organic mixtures with dielectric constants (ε) from 78.4 down to 47. Added organic co-solvents were either protic or aprotic. The increase in polarographic half-wave potential,E1/2, with decliningεwas successfully fit to the Born equation down toε≈ 55. Cu2+and ubiquinone0were reduced in a single two-electron step that was moreε-sensitive when the added organic co-solvent was aprotic. Naphtho- and anthraquinone were reduced in two successive one-electron steps that were influenced identically by protic or aprotic organic co-solvents. The product of the first reduction wave was neutral semiquinone (·QH), which surprisingly, had to be protonated to ·QH2+before reduction in the second wave. Except forrH+, radii derived from fitting ourE1/2vsεresults to the Born equation were too small; in other words, non-electrostatic effects destabilized the oxidized species, greatly enhancing the Born electrostatic increase inE1/2with decliningε. Additionally, forε< 55, we observed deviation from the Born equation, which may be due to changes in solvent structure and dynamics, and solvent-solute interactions. Finally, we studied quinones incorporated into phosphatidyl choline sonicated bilayer vesicles: Ubiquinone0had two distinct irreversible two-electron reduction waves, one due to a population bound at the membrane surface, and another (whoseE1/2was negatively shifted by 150 mV) due to a population localized in the membrane interior. Ubiquinone10had a single irreversible two-electron reduction wave that was 250 mV more negative than the UQ0membrane-interior population.
Impact of intraventricular septal fiber orientation on cardiac electromechanical function
Authors: Rodríguez-Padilla Jairo , Petras Argyrios , Magat Julie , Bayer Jason , Bihan-Poudec Yann , El Hamrani Dounia , Ramlugun Girish , Neic Aurel , Augustin Christoph M. , Vaillant Fanny , Constantin Marion , Benoist David , Pourtau Line , Dubes Virginie , Rogier Julien , Labrousse Louis , Bernus Olivier , Quesson Bruno , Haïssaguerre Michel , Gsell Matthias , Plank Gernot , Ozenne Valéry , Vigmond Edward
Published: March 2022

i) A dual-layer organization of fiber orientation in the intraventricular was identified in ex-vivo hearts of large mammalsii) The RV septum has a downwardly directed superficial layer, which is continuous with the moderator band.iii) Electrically, it produced a diamond activation pattern.iv) Electromechanically, little change in pressure volume loops were noticed but stress distribution was altered.v)Fiber distribution derived from diffusion tensor imaging should be considered for an accurate strain and stress analysis.
A Random Forest Method for Identifying the Effectiveness of Innovation Factor Allocation
Authors: Xu Mo , Qi Yawei , Tao Changqi , Zhang Shangfeng
Published: March 2022

This paper makes a new attempt to identify the effectiveness of innovation factor allocation with a random forest method. This method avoids the evaluation bias of the relative effectiveness caused by the noneffective selection of production frontier in the nonparametric DEA method. It does not refer to other optimal subjects but shifts the focus to the judgment of its own effectiveness. In addition, it also gets rid of the constraints of the model and variables in the parameter SFA method, ensuring the reliability of the measurement results by resampling thousands of times. The data is collected from 30 provinces in China from 2009 to 2018. The findings show the innovation factor allocation in more than half of the provinces is not fully effective. It indicates that how to make use of innovation factor inputs to achieve the actual innovation output higher than own optimal levels is currently still in a period of exploration in China. To further improve innovation factor allocation efficiency, it deeply analyzes the impacts of innovation factor inputs and finds out the important innovation factor inputs. Furthermore, this study presents the nonlinear characteristics and optimal combination of important innovation factor inputs. According to this, it offers the detailed suggestions about how to adjust current important innovation factor inputs for each province in order to greatly enhance the effectiveness of innovation factor allocation in the future.
Experimental Study on the Pore Structure of Middle- and Low-Rank Coal and Its Influence on Methane Adsorption
Authors: Jia Gaini , Yang Ming , Zhang Xuebo , Liu Lei
Published: March 2022

The pore characteristics of coal have an important influence on the coal adsorption of methane. The liquid nitrogen adsorption method was used to study the pore structure of low- and middle-rank coal samples from two aspects: pore specific surface area and pore shape. Low-field nuclear magnetic resonance (NMR) technology was used to study the difference in methane adsorption of coal samples under the same adsorption conditions. The influence and mechanism of the pore structure of middle- and low-rank coal samples on gas adsorption are discussed and analyzed. The results show the following: (1) There are differences in the adsorption capacity of medium- and low-rank coal samples. Within 1 h of methane inflation, the methane adsorption capacity of middle-rank coals such as Henan Pingdingshan (PDS) and Shanxi Wangzhuang (WZ) and Xinjiang low-rank coals (XJ) increases by 36.4%, 31.7%, and 35.8%, respectively, and as the inflation time is extended to the cumulative inflation of 10 h, the order of the increase in methane adsorption capacity is still PDS>XJ>WZ. (2) The pore types of the experimental coal samples are mostly wedge-shaped pores and ink bottle-shaped pores. The order of the BET specific surface area of the coal samples is in the order of WZ>PDS> XJ, which means that the WZ samples can provide more adsorption sites for gas molecule adsorption, but under the same adsorption conditions, the WZ sample has the least amount of gas adsorbed. Further analysis shows that the order of the tortuosity of the coal sample is WZ>XJ>PDS, which means that the WZ sample has a large pore tortuosity, a long channel for methane molecules to diffuse into the internal pores of the coal sample, and large diffusion resistance. It is the main reason why the methane adsorption capacity of WZ coal samples is less than that of PDS and XJ coal samples in the same adsorption conditions.
A Mode of Intelligent Equipment Monitoring Optimization Based on Dynamic Programming Algorithm
Authors: Zheng Zhilei , Jiang Chuan
Published: March 2022

With the continuous development of the national economy and scientific productivity, urban construction and people’s living standards are also getting higher and higher. Although people enjoy increasingly convenient life, the demand for intelligence is getting higher and higher. Digital intelligent equipment has the functions of data collection, calculation and analysis, diagnostic and early warning, and communication functions. Analyze the status quo and existing problems of the development of intelligent equipment, as well as analyze and research key monitoring technologies in the use and development of digital intelligent equipment and provide optimal solutions for intelligent equipment hardware development requirements, software development, and model algorithms. Intelligent equipment monitoring is related to all aspects of people’s livelihood, and its intelligent development is related to the public role in this field in the future. Accurate results of monitoring can provide data support for schools, research institutions, the public, and the government. At the same time, it is also an important basis for formulating social policies. At present, the commonly used monitoring method usually adopts time series algorithm. Through literature review, it is found that the algorithm has the problem of distortion of correct data, which affects the accuracy of monitoring results. Based on the above reasons, this article combines the wavelet function with the planning algorithm and proposes a dynamic programming algorithm, which removes the redundant monitoring data in turn and clusters the distortion monitoring data with the wavelet function, which improves the accuracy and computational efficiency of the algorithm and gives full play to the monitoring of intelligence. The simulation results using MATLAB show that the planning algorithm can eliminate 90% of redundant monitoring data and improve the extraction rate of characteristic monitoring data. At the same time, the accuracy of the planning algorithm reaches 95%, and the calculation time is less than 25 s, which is better than the static planning algorithm. Therefore, the dynamic programming algorithm can better utilize the intelligence, convenience, and efficiency of the equipment to optimize the monitoring model.
Research on Moral Education Function of Music Art in College Students Based on Bayesian Learning Algorithm
Authors: Lu Meng , Pengcheng Du , Yanfeng Song
Published: March 2022

While paying attention to students’ acquisition of knowledge, we should also pay attention to students’ ideological and moral education. Bayesian network is a probabilistic graphical model that was developed in the 1980s. Therefore, it is urgent to establish the correct outlook on life and values of the motherland in the future. Music is one of the main categories of aesthetic education, which not only plays a role in cultivating students’ talents and skills, but also has the function of moral education, so it has its special teaching position. It provides the means of knowledge representation, reasoning, and learning in uncertain environment. Based on Bayesian learning algorithm, this paper studies the moral education function of music art in college students. Bayesian network can effectively carry out multivariate joint prediction, causal reasoning, expression of uncertain knowledge, pattern recognition, image processing, and causal data mining. Among them, the method based on score search regards structural learning as a combinatorial optimization problem, and score function and search method are two important factors that affect the learning effect.
Problems and Countermeasures of Financial Risk in Project Management Based on Convolutional Neural Network
Authors: Wei Ran , Ding Dewen
Published: March 2022

Under the background of market economy, engineering projects are faced with a lot of financial risks. If we cannot prevent them effectively, it will undoubtedly bring serious negative impact to the entire engineering management work. Therefore, it is particularly important to actively manage risks, identify and evaluate risks in a timely and correct manner, manage risks efficiently, and minimize risk losses. At the same time, the development of wireless communication technology has brought many new branches of engineering project management. Some problems in the process of risk management are often not handled by traditional empirical calculation or mathematical methods, so it is necessary to find an appropriate way to define and describe the nonlinear relationship between a large number of uncertain causes and risk losses. In order to match the changes in the background of the development of wireless communication technology, this paper studies the financial risk problems and countermeasures in the engineering management of convolutional neural networks. The financial risk prediction model in network engineering management is constructed, and the volume neural network algorithm referenced by it is tested. The test results are highly consistent with the expert assessment. In the research process, the combination of questionnaire survey and mathematical analysis method was adopted, the extreme value of risk factors was determined by questionnaire survey, and then the accuracy of prediction was verified by a mathematical model. After many calculations, it has been proved that the convolutional neural network simulation system based on the scientific node selection method has greatly improved the accuracy of risk assessment.
Two Different Transplant Preconditioning Regimens Combined with Irradiation and Chemotherapy in the Treatment of Childhood Leukemia: Systematic Review and Meta-Analysis
Authors: Wang Xiangwen , Mu Dan , Geng Anyang , Zhao Anqi , Song Yiyuan
Published: March 2022

Objective. To observe the therapeutic effect and the incidence of adverse reactions of total body irradiation plus cyclophosphamide (TBI/CY) and busulfan plus cyclophosphamide (BU/CY) in the treatment of pediatric hematopoietic stem cell transplantation. Methods. By searching the Cochrane Library, PubMed, Web of Knowledge, Embase, Chinese Biomedical Literature Database (CBM), and screening randomized controlled trials (RCTs), quality evaluation and data extraction were performed for the included literature, and meta-analysis was performed for RCTs included at using Review Manager 5.2 software. Results. A total of 10160 patients were enrolled in 15 RCTs, including 5211 patients in the TBI/CY group and 4949 patients in the BU/CY group. Meta-analysis showed that there was a statistical difference in transplant failure rate (OR = 1.56, 95% CI (1.23, 1.97), P = 0.0002, I2 = 56%, Z = 3.69), transplant mortality (OR = 1.45, 95% CI (1.24, 1.68), P < 0.00001, I2 = 76%, Z = 4.80), transplantation long-term disease-free survival rate (OR = 1.52, 95% CI (1.09, 2.12), P = 0.01, I2 = 0%, Z = 2.50), and transplantation adverse reactions (OR = 1.28, 95% CI (1.08, 1.52), P = 0.004, I2 = 0%, Z = 2.85). Conclusion. Meta-analysis showed that TBI/CY combined pretreatment regimen was more effective than BU/CY regimen alone in the treatment of pediatric hematologic transplantation, with a lower incidence of adverse reactions and significant long-term survival efficacy.
Internet of Medical Things (IoMT) and Reflective Belief Design-Based Big Data Analytics with Convolution Neural Network-Metaheuristic Optimization Procedure (CNN-MOP)
Authors: Sampathkumar A. , Tesfayohani Miretab , Shandilya Shishir Kumar , Goyal S. B. , Shaukat Jamal Sajjad , Shukla Piyush Kumar , Bedi Pradeep , Albeedan Meshal
Published: March 2022

In recent times, the Internet of Medical Things (IoMT) is a new loomed technology, which has been deliberated as a promising technology designed for various and broadly connected networks. In an intelligent healthcare system, the framework of IoMT observes the health circumstances of the patients dynamically and responds to backings their needs, which helps detect the symptoms of critical rare body conditions based on the data collected. Metaheuristic algorithms have proven effective, robust, and efficient in deciphering real-world optimization, clustering, forecasting, classification, and other engineering problems. The emergence of extraordinary, very large-scale data being generated from various sources such as the web, sensors, and social media has led the world to the era of big data. Big data poses a new contest to metaheuristic algorithms. So, this research work presents the metaheuristic optimization algorithm for big data analysis in the IoMT using gravitational search optimization algorithm (GSOA) and reflective belief network with convolutional neural networks (DBN-CNNs). Here the data optimization has been carried out using GSOA for the collected input data. The input data were collected for the diabetes prediction with cardiac risk prediction based on the damage in blood vessels and cardiac nerves. Collected data have been classified to predict abnormal and normal diabetes range, and based on this range, the risk for a cardiac attack has been predicted using SVM. The performance analysis is made to reveal that GSOA-DBN_CNN performs well in predicting diseases. The simulation results illustrate that the GSOA-DBN_CNN model used for prediction improves accuracy, precision, recall, F1-score, and PSNR.
Design of Fault Prediction System for Electromechanical Sensor Equipment Based on Deep Learning
Authors: Ding Yongtao , Wu Hua , Zhou Kaixiang
Published: March 2022

With the increasing complexity, scale, and intelligentization of modern equipment, the maintenance cost of equipment is increasing day by day. Moreover, once an unexpected major failure occurs, it will cause loss and damage to production, economy, and safety. Based on the considerations of system reliability and safety, fault prediction has gradually become a hot topic in the field of reliability. As a new branch of machine learning, deep learning realizes deep abstract feature extraction and expression of complex nonlinear relations by stacking deep neural networks and makes its methods solve bad problems in many traditional machine learning fields. The improvement and excellent results have been achieved. This article first introduces the model structure and working principle of the classic deep learning model noise reduction autoencoder and combines the feature extraction results of the experimental data of electromechanical sensor equipment and the model characteristics to analyze that this type of model can be trained using only normal samples. Under the restriction, the reason for abnormal features can also be correctly filtered. Then, in order to suppress the overfitting of training, the dropout layer is used in this article. The dropout layer will make the hidden layer nodes to be dropped with probability p according to the set probability. Because the lost nodes are random, each training is equivalent to training a new model. It achieves an effect similar to independently training the model and then superimposing it. Experiments prove that the dropout layer is very effective in solving overfitting. Finally, this paper conducts experimental verification on the improved algorithm. The results show that the improved model has a certain improvement in accuracy under the limited training algebra. Under the same training parameters, the accuracy is increased by approximately 2.44%, and the improved model has a better training effect and can be used for electromechanical effective prediction of sensor equipment failure.
Cryptanalysis and Improvements on Quantum Key Agreement Protocol Based on Quantum Search Algorithm
Authors: Abulkasim Hussein , Alabdulkreem Eatedal , Karim Faten , Ahmed Nada , Jamjoom Mona , Hadjouni Myriam , Abbas Safia
Published: March 2022

Recently, Huang et al. (2021) presented a quantum key agreement schemeto securely negotiate on a secret key employing the properties of a quantumsearch algorithm. First, the authors proposed the two-party quantum key agreement, and then they extended their work to the three-party case. Huang et al.‘s protocol employs the unitary operation and single-particle measurements to negotiate on a secret key without using complex quantum technologies such as quantum memory or entangled quantum particles. The authors claimed that their protocol is secure and efficient. However, this work shows that Huang et al.‘s protocol has a significant pitfall, where the private key of one user could be easily leaked to the attackers. Hence, the properties of security and fairness are not achieved. Accordingly, thetwo-party and three-party of Huang et al.’s protocol have been reviewed, and an improvementto address the shortcoming is suggested.
The Clinical Effects of Intravascular Ultrasound-Guided Percutaneous Coronary Intervention in Patients with Chronic Total Occlusion: A Meta-Analysis
Authors: Zhong Zhaoshuang , Zhao Long , Chen Kaiming , Xia Shuyue
Published: March 2022

Background. The clinical effects of intravascular ultrasound (IVUS)-guided percutaneous coronary intervention (PCI) in patients with chronic total occlusion (CTO) lesions remain unclear. Methods. We identified all full-text published studies that compared the effects of IVUS-guided CTO-PCI with angiography-guided CTO-PCI by searching electric databases including PubMed, Embase, Cochrane Library, and ISI Web of Science from the establishment to Nov 2021. There was no language limitation. The endpoints included the incidence of major adverse cardiac events (MACE), cardiac death, all-cause death, myocardial infarction (MI), and target vessel revascularization (TVR). Results. Five studies involving a total of 2320 patients were included in this meta-analysis. Compared to the angiography-guided group, IVUS-guided PCI showed no significant reduction in the incidence of MACE (I2 = 27.4%,

P

 = 0.239; RR 0.929, 95% CI 0.765 to 1.128,

P

 = 0.457), cardiac death (I2 = 0.0%,

P

 = 0.459; RR 0.574, 95% CI 0.299 to 1.103,

P

 = 0.096), all-cause death (I2 = 0.0%,

P

 = 0.964; RR 0.677, 95% CI 0.395 to 1.163,

P

 = 0.158), MI (I2 = 46.7%,

P

 = 0.131; RR0.836, 95% CI 0.508 to 1.377,

P

 = 0.482), and TVR (I2 = 21.2%,

P

 = 0.279; RR 0.929, 95% CI 0.679 to 1.272,

P

 = 0.648). Conclusions. IVUS-guided PCI demonstrated no significant benefit on MACE, cardiac death, all-cause death, MI, and TVR in patients with CTO lesions. However, given the study’s limitations, additional high-quality RCTs are needed.
Inferences for Stress-Strength Reliability Model in the Presence of Partially Accelerated Life Test to Its Strength Variable
Authors: El-Sagheer Rashad M. , Tolba Ahlam H. , Jawa Taghreed M. , Sayed-Ahmed Neveen
Published: March 2022

We focus on estimating the stress-strength reliability model when the strength variable is subjected to the step-stress partially accelerated life test. Based on the assumption that both stress and strength random variables follow Weibull distribution with a common first shape parameter, the inferences for this reliability system are constructed. The maximum likelihood, two parametric bootstraps, and Bayes estimates are obtained. Moreover, approximate confidence intervals, asymptotic variance-covariance matrix, and highest posterior density credible intervals are derived. A simulation study and application to real-life data are conducted to compare the proposed estimation methods developed here and also check the accuracy of the results.
Study on the Coupling Relationship between Environmental Quality and Green Financial Economy Based on the Grey Correlation Analysis Model
Authors: Ma Rui , Chen Liang
Published: March 2022

Since the reform and opening up, there has been a great demand vacancy in the market. The development of green finance can just meet many market demands. Let it stand out among many financing schemes. As an innovative financial model that can effectively promote the global economy, green finance is of great significance to the global ecological economy and environmental state. In this article, we will discuss the economic coupling relationship between environmental quality and green finance. Because we lack a relatively perfect economic evaluation system, therefore we integrate green finance and environmental quality indicators, introduce them into relevant and suitable evaluation system indicators, and mainly analyze and judge based on the grey relational analysis model. The results show the following: (1) investigate the environmental quality and green financial comprehensive benefit development index of A, B, and C cities, judge the lagging types of the three cities, and evaluate the specific development status of the cities. (2) Collect data and calculate the coupling degree and coordination degree of cities. Their coupling levels and coordination types are summarized, respectively. (3) The higher the correlation between urban environmental quality and green financial indicators, the closer the relationship between the two indicators and the greater the mutual influence. (4) Sort them by grey relational degree. The correlation between Y1 and Z3 is the least, which is 0.55 and 0.552, respectively. Y3 can be correlated up to 0.655. The results of this paper are very good, and we get the result that green finance can promote the effective improvement of environmental quality.
Research on Contagion and the Influencing Factors of Personal Credit Risk based on a Complex Network
Authors: Sui Xin , Wen Hongmei , Gao Jing , Lu Shaopeng
Published: March 2022

With the digital transformation of commercial banks and the online transfer of credit transactions, the relationship between credit subjects tends to be complex, and personal credit risk management is facing challenges. The contagiousness of personal credit risk and the relationship among credit subjects are poorly understood in the field of credit risk management. This study used an SEIR infectious disease model to reveal the mode and structure of associated credit risk based on the theory and method of complex networks, and it explored the status and influencing factors of associated credit risk in a scale‐free network using simulation experiments. The research findings indicate that the coefficient of latency, the coefficient of risk contagion, the coefficient of direct recovery, the coefficient of latent transformation, the coefficient of immune recovery, and the structure of the association network have a significant influence on the scale and threshold of associated credit risk. This study reveals comprehensive reasons for the outbreak of credit risk clusters and provides a new perspective for financial institutions, such as commercial banks, to help them manage credit risk.
Fuzzy Risk Assessment of Tunnels’ Undercrossing Existing Structures
Authors: Wang Chong-xu , Li Jia-Wen , Wang Hao
Published: March 2022

Numerical calculation is usually employed to analyse the safety risks of tunnel construction under existing structures. However, due to the fuzziness and randomness of the construction environment, the simulation results fluctuate significantly. Given the fuzziness and randomness encountered during the safety risk assessment of tunnels undercrossing existing structures, this study analyses and summarises the previous experiences and selects a relevant algorithm of the cloud model to solve the uncertainty problem during the assessment. The study proposes three forms of cloud models to evaluate the risks of tunnels undercrossing existing structures and performs a comparative analysis between them. Finally, the effectiveness and feasibility of the cloud model calculation method are verified through a case study. The research shows that (1) the accuracy of the one-dimensional and two-dimensional cloud models was high, and the difference was small, indicating that the cloud model method used to determine the risk level of tunnels underpassing existing structures was reasonably effective, (2) since the soft ‘and’ two dimensions operated computation, the distribution probability was dispersed, making the soft ‘and’ computation more affected by the index, resulting in lower accuracy than the one-dimensional and two-dimensional cloud models, and (3) compared with numerical calculation, the cloud model calculation was more convenient, reflecting the relationship between the indices and the relationship between the indices and the complex whole. The evaluation results used the comprehensive evaluation probability to determine the risk level so that the evaluation results were reliable.
Evaluation and Monitoring of Endometrial Cancer Based on Magnetic Resonance Imaging Features of Deep Learning
Authors: Tao Jingxiong , Wang Yi , Liang Yi , Zhang Aohua
Published: March 2022

This study was aimed to compare and analyze the magnetic resonance imaging (MRI) manifestations and surgical pathological results of endometrial cancer (EC) and to explore the clinical research of MRI in the diagnosis and staging of EC. Methods. 80 patients with EC admitted to the hospital were selected as the research objects. The ResNet network was used to optimize the network. When the depth was added, the accuracy of the model was improved, the network parameters were iteratively updated, and the damage function of the minimized network was obtained. The recognition efficiency of MRI images was analyzed using three network modes: shallow CNN network, Res‐Net network, and optimized network. The images of EC patients were analyzed, and a quantitative and timed MRI was achieved using simulated datasets in deep learning neural networks, which provided the basis for the formulation of single‐scan MRI parameters. All patients underwent preoperative MRI examination using coronal and sagittal T1WI and T2WI imaging. The results showed that the accuracy and specificity of T2 weighted imaging and enhanced scanning in MRI were 88.75% and 95%, respectively. Sensitivity was 87.5%, negative predictive value was 93.75%, and positive predictive value was 86.25%. By MRI examination, 80 cases of EC in patients with stage I diagnosis were 72 cases, accounting for 90%, with endometrial thickening and uneven enhancement. In conclusion, the MRI manifestations of EC are diversified, and MRI has a high value for the staging of EC. MRI examination is conducive to improving diagnostic accuracy.
Music Audio Rhythm Recognition Based on Recurrent Neural Network
Authors: Chen Bo
Published: March 2022

Music rhythm detection and tracking is an important part of music understanding system and music visualization system. Based on the important position of rhythm in music expression and the wide range of multimedia applications, rhythm extraction has become an important hotspot in computer music analysis. In the field of audio recognition research, deep learning can automatically learn the features of audio and extract the rhythm of music. This paper takes music audio rhythm recognition as the main research object and carries out a series of researches with deep learning GRU neural network as the main technical support. A residual network is introduced into the GRU network model, and it is found that when the residual network is at 50 layers, the model has the highest accuracy for audio rhythm extraction. After adjusting the model parameters through experiments, this paper concludes that the average recognition accuracy of the ResNet_50-GRU model for recognizing the rhythm of the music audio in the MSD, AudioSet, and FMA data sets is 92.5%.
Study on the Risk Factors of Pulmonary Infection after Laparoscopic Surgery and Analysis of the Detection Results of Drug-Resistant Bacteria
Authors: Zhai Tingting , Zhang Liwei , Sun Jing , Li Yuanchun , Hou Jie , Du Fengxia
Published: March 2022

This study aimed to investigate and analyze the risk of pulmonary infection after laparoscopic surgery and the detection results of drug-resistant bacteria. With the laparoscopic technology developing rapidly in recent years and people’s minimally invasive concept improving continuously, laparoscopic radical surgery has been widely used in the treatment of a variety of diseases. Laparoscopic surgery has the probability of causing complications. In order to avoid this, the risk factors after surgery were analyzed, and the drug-resistant bacteria were analyzed for accurate prevention and treatment. A total of 600 patients who underwent elective laparoscopic surgery in our hospital from January 2017 to September 2021 were included in the study. The risk factors and pathogen distribution of pulmonary infection were analyzed. The risk factors of pulmonary infection after laparoscopic surgery were hypoproteinemia, diabetes mellitus, pulmonary disease history, and perioperative blood transfusion. The main pathogens were Klebsiella pneumoniae, Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Streptococcus pneumoniae. In clinical work, relevant nursing intervention measures can be developed for the above factors, so as to reduce the incidence of pulmonary infection. This study finds the risk factors for pulmonary infection after surgery, and the common drug-resistant bacteria has an indicative and guiding effect on the formulation of nursing management measures.
Comparison of Protective Effects of Shenmai Injections Produced by Medicinal Materials from Different Origins on Cardiomyocytes
Authors: Zhao Min , Chen Qiufang , Wang Hong , Xu Wanfeng , Yang Jiayong , Cao Lijuan
Published: March 2022

Shenmai injection is mainly used for the treatment of heart-related diseases, including coronary heart disease, viral myocarditis, chronic cor pulmonale, and shock in Asia. Medicinal materials from different origins produce Shenmai injections for clinical use, and their protective effects on cardiomyocytes may vary with the choice of raw materials. In this study, we compared the protective effects of Shenmai injections produced from different raw materials on cardiomyocytes. Results showed that the protective effects of various Shenmai injections on hypoxia-reoxygenation-induced cardiomyocyte injury were mainly attributed to total ginsenosides extract, with few differences between them. However, the protective effects of different Shenmai injections on doxorubicin and oxidative stress-induced cardiomyocyte injury were significantly different; the protective effects of Shenmai injection with Zhejiang Ophiopogon japonicus as raw material were significantly better than those with Sichuan Ophiopogon japonicus, consistent with our previous research results. Our study reveals the different cardiomyocyte protective effects of Shenmai injections produced by medicinal materials from different origins, laying a scientific foundation for their clinical selection.
Solid Lipid Nanoparticles Enhance Protective Effect of Rutin against STZ‐Induced Neurotoxicity in PC12 Cells through Autophagy Suppression
Authors: Nouri Zeinab , Sajadimajd Soraya , Bahrami Gholamreza , Moradi Sajad , Abdi Fereshteh , Farzaei Mohammad Hosein , Arkan Elham
Published: March 2022

Rutin (Rut) has been identified as a neuroprotective compound with displayed beneficial effects in Alzheimer’s disease. However, low bioavailability and solubility are the major concerns pertaining to the use of Rut. Aberrant function of autophagy has been found as a well‐established participant in the pathogenesis of neuronal degeneration. In the present study, Rut and Rut‐loaded solid lipid nanoparticles (Rut‐SLNs) were used to protect rat PC12 cells against streptozotocin‐ (STZ‐) induced neurotoxicity. Rut‐SLNs were fabricated by a solvent evaporation‐ultrasonic method. Depending on the experimental patterns including pretreatment, cotreatment, and posttreatment, PC12 cells were exposed to STZ and desired doses of the SLNs, Rut, and Rut‐SLNs. The viability of the cells, the mitochondrial membrane potential (MMP), and the expression of miR‐21, miR‐22, Akt, ATG5, Beclin1, and LC3 were evaluated by using MTT assay, rhodamine 123 fluorescent dye, and qRT‐PCR, respectively. SLN and Rut‐SLNs possess the smooth surface with an average size of 117.2 and 176.9 nm, respectively, with a negative zeta potential. The encapsulation efficiency and loading capacity of Rut in SLNs were 90.32% and 49.1%, respectively. The nanoformulation revealed a sustained drug release in vitro up to 72 h and followed Higuchi kinetics. Rut‐SLNs displayed a neuroprotective effect by augmenting the viability of PC12 cells and increasing MMP. In addition, Rut‐SLNs suppressed autophagy which was stimulated by STZ whereas, the free Rut demonstrated lower effect. Taken together, these results clearly indicated that Rut‐SLNs could be a good candidate for the prevention of neurodegenerative diseases.
A Study on Chinese-English Machine Translation Based on Transfer Learning and Neural Networks
Authors: Li Congli
Published: March 2022

The existing Chinese-English machine translation has problems such as inaccurate word translation and difficult translation of long sentences. To this end, this paper proposes a new machine translation model based on bidirectional Chinese-English translation incorporating translation knowledge and transfer learning, and the components of this model include a recurrent neural network-based translation quality assessment model and a self-focused network-based model. The experimental results demonstrate that our method works better on the dataset of machine translation quality assessment task for Chinese-English translation with more information, and the Pearson correlation coefficient of its quality assessment feature vector (such as word prediction vector representation) is higher.
CircNR3C1 Alleviates Gastric Cancer Development by Inactivating AKT/mTOR
Authors: Wang Luben , Guo Zhen , Guo Baoliang , Gao Fangkai , Liu Xiangdong , Xu Youchao , Wang Yang
Published: March 2022

Differential level and regulatory effect of circNR3C1 in gastric cancer (GC) were determined. The differential levels of circNR3C1 in clinical samples of GC were determined. The association of circNR3C1 level with pathological indicators of GC was analyzed. After intervening circNR3C1 levels in gastric cancer cells, proliferative and migratory changes were investigated. Furthermore, we measured AKT and mTOR protein levels in GC cells intervened by circNR3C1. Finally, the role of AKT/mTOR in GC cell phenotypes regulated by circNR3C1 was explored. circNR3C1 was markedly lowly expressed in GC cells and tissues. A low level of circNR3C1 predicted high incidences of lymphatic or distant metastasis of GC. Knockdown of circNR3C1 enhanced proliferation and migration abilities in BGC-823 cells, whereas overexpression of circNR3C1 yielded the opposite results in AGS cells. circNR3C1 downregulated mTOR and AKT in GC cells. In addition, induction of the AKT activator could reverse the attenuated proliferative and migratory potentials in GC cells overexpressing circNR3C1. On the contrary, induction of the AKT inhibitor reversed the stimulated malignant phenotypes of GC with circNR3C1 knockdown. circNR3C1 inhibits GC to proliferate and migrate by inactivating the AKT/mTOR signaling. It is also closely linked to GC metastasis.