bluuebunny/crossref_metadata_2025_split huggingface dataset:
Walking on thin ice
Authors: Khuder Baraa , Petrić Bojana 
Published: September 2021

Abstract
Researcher reflexivity is important throughout the research process, especially in ethnographic enquiry. This chapter provides a reflexive account of our research experiences with exiled Syrian academics, whose efforts to re-establish their academic careers in their new contexts and publish in English as an Additional Language we have been following for more than two years. Data are taken from a longitudinal multiple-case study using ethnography as deep theorizing (Lillis, 2008). In this chapter we provide a reflexive account of five research methodological aspects: participant recruitment, conducting and analysing interviews, Text Histories, textual representation, and ethics in collaborative ethnography. We discuss how aspects of the researcher’s experience and position interact with the research process.
Rainfall-Runoff Short-Term Forecasting Method Based on LSTM
Authors: Chen Yuan , Xu Jian 
Published: September 2021

Abstract
A rainfall-runoff forecasting method based on Long Short Term Memory (LSTM) was proposed in this study, which can extract the trend characteristics of runoff time series data through introducing daily rainfall data collected at related upstream stations and making use of the advantages of LSTM in saving long-term sequence feature information and avoid vanishing gradient, and identify the nonlinear mapping relationship in between, thus establishing a short-term runoff forecasting model. In this study, 24-hour and 5-day short-term forecasting models were established based on the runoff data collected at Danba Hydrologic Station in Dadu River Basin and historical rainfall collected at three upstream stations (Xiaojin, Dawei, and Fubian River). The experimental results showed that the forecasting models performed well during the inspection period. In 24-hour forecasting, RMSE was 98.016 and MAE was 45.709, which were 73.993 and 32.699, respectively in 5-day forecasting, indicating better performance and increased forecasting accuracy than simple LSTM model.
Design of Handwritten Numeral Recognition System Based on BP Neural Network
Authors: Liu Jianghai , Hong Jie 
Published: September 2021

Abstract
In the case of pattern recognition, handwritten numeral recognition is an important research topic in pattern recognition, and it has a very wide application in today’s information society. However, the research on numeral recognition is still in the development stage, and the recognition effect is not ideal. A handwritten numeral recognition method based on BP neural network is proposed. Firstly, the image is grayed, binarized, smoothed, denoised and normalized to extract the pixel value; Then the designed BP neural network is trained, compared with the expected results and expected structure, and the BP neural network is adjusted and modified; Finally, the trained neural network is obtained. Experiments show that the accuracy of this method for handwritten digit recognition is 85.88%.
Key Clips and Key Frames Extraction of Videos Based on Deep Learning
Authors: Chen Junyu , Peng Ganlan , Peng Yuanfang , Fang Mu , Chen Zhibin , Li Jianqing , Lan Liang 
Published: September 2021

Abstract
The surveillance camera network covering the city, while protecting the safety of people’s lives and property, generate a large amount of surveillance video data every day, but few videos contain useful information. Surveillance cameras in sparsely crowded areas may capture most of the video in the background. A large number of surveillance videos bring greater storage pressure, and also increase the difficulty of the staff’s work. In this paper, we use the auto-encoder network to extract features of video frames. By comparing the feature differences between video frames, we can automatically select key video clips and key frame images that contain useful information to slim down the surveillance video. Through experiments on actual cameras, we found that this method can achieve the purpose of reducing the pressure of storage and traversal.
Research on Multi-objective Trajectory Prediction Algorithm Based on Driving Intent Classification
Authors: Ma Wenda , Xie Hui 
Published: September 2021

Abstract
The trajectory prediction of the multi-object vehicle ahead plays a huge role in improving the vehicle’s driving safety and improving the planning and traffic efficiency of the vehicle. However, due to the uncertainty of the driving intention of the multi-object vehicle and the uncertainty of the vehicle dynamics, its trajectory prediction faces a huge challenge. First, the fuzzy C-means (FCM) method is used for multi-object trajectories. After offline training is carried out using the relevant information of the vehicle trajectory, the driving intention can be automatically fuzzy classification. Secondly, the long short-term memory (LSTM) method uses the history and current trajectory information of multi-object vehicles ahead to predict the future trajectory under different driving intentions. Then, according to the fuzzy classification results of the driving intention, the predicted trajectories are merged, and the trajectory prediction of the multi-objective vehicle ahead in the next 1s is realized through an iterative method. Finally, use real vehicle data for experimental verification. The results show that 92.1% of the lateral distance prediction error in the tenth step is less than 0.13m. The maximum distance prediction error is 0.54m. In the longitudinal distance, the prediction error of 94.5% is less than 0.6m. The maximum distance prediction error is 1.0m.
Modulation Recognition of Composite Signal Based on ResNet and Frequency Domain Graph
Authors: Wang Lijun , Han Yu , Ge Hongfang , Zhao Yongkuan , Shen Lei 
Published: September 2021

Abstract
Aiming at the problem of composite signal recognition in measurement and control system, a modulation recognition method of composite signal is proposed based on clipped internal modulation signal frequency image and residual network. Residual network has the advantages of fast convergence and high recognition accuracy, which is used to realize the recognition of different modulation modes and different bandwidth signals. The experimental results show that, compared with the traditional decision tree algorithm and the constellation based CNN recognition algorithm, the recognition performance of the proposed method is improved by more than 3dB for the variable bandwidth composite modulation signal set (BPSK-FM and QPSK-FM with internal modulation bandwidth of 128kHz, BPSK-FM and QPSK-FM with internal modulation bandwidth of 2kHz and 2FSK-FM with internal modulation bandwidth of 32kHz and 2kHz).
Lanczos Algorithm for 2DPCA
Authors: Wang Xuansheng , Li Huazhong , Zhou Yanbing , Zheng Hongying 
Published: September 2021

Abstract
The traditional PCA algorithm (Principle Component Analysis) can obtain the feature space of face image and realize face recognition by expanding the face image matrix into vectors in face recognition. 2DPCA (Two-dimensional Principle Component Analysis) doesn’t need to spread the image matrix into one-dimensional vectors. The covariance matrix is constructed directly by using two-dimensional image matrix, so that the calculation of eigenvalue and eigenvector is simplified. We apply the Lanczos algorithm to 2 DPCA in this paper for Indian faces. The numerical experiments show that our method runs much faster and gets better recognition rates than the traditional 2DPCA algorithm.
Lump Solutions to the (2+1)-Dimensional Asymmetrical Nizhnik-Novikov-Veselov-Like Equation
Authors: Zhou Fan , Li Wenting , Jiang Kun 
Published: September 2021

AbstractIn this paper, based on Hirota bilinear method, the prime number p=3 is applied to the generalized bilinear differential operator to construct a novel (2+1)-dimensional ANNV-like equation. With the help of intelligent calculation, the solutions of three types of ANNV-like equations in the form of positive quadratic functions are obtained. 3D plots, contour plots, and density plots with specific values are drawn in maple. Analyze the spatiality, locality and trajectory of the lump solution over time.
Acoustic Emission Data Analysis of Tank Settlement
Authors: Fan Guotao , Zhao Yongtao 
Published: September 2021

Abstract
The uneven settlement around the tank and the corrosion of the bottom are main factors affecting the safe operation of the tank, so it is necessary to monitor the settlement and corrosion of the tank in real time to prevent and eliminate the hidden danger in time. In this paper, the large crude oil storage tank is taken as the research object to carry out uneven settlement monitoring and acoustic emission dynamic detection. By analyzing the measured settlement data and acoustic emission characteristic parameters, the author explores the influence of tank settlement on acoustic emission signal of tank floor corrosion, so as to provide reference for the extension of service life of storage tank and anti-corrosion work of storage tank.
The Design of Intelligence Analysis Platform Based on Big Data
Authors: Shi Ziyan , Zhao Guolin , Hu Qiaolin 
Published: September 2021

Abstract
There are many problems in the current intelligence analysis work. Data islands and data transmission monitoring problems have severely restricted the further development of intelligence analysis work. To promote and improve the level of intelligence analysis work, this article mainly introduces how to apply big data to intelligence analysis. It enters into the analysis of the problems faced by the current intelligence analysis platform, focusing on the importance of big data technology for intelligence analysis. The structure and key technologies of big data can effectively solve various problems existing in intelligence analysis work. The structure of the intelligence analysis platform based on big data and the structure of the Hadoop+Spark+Steam platform are designed, including intelligence analysis and intelligence analysis. Functional modules such as data management and intelligence storage. This platform design of this article can effectively improve the effect and efficiency of intelligence analysis, and has very high practical value.
A trajectory planning algorithm for a multi-degree-of-freedom parallel robot based on NURBS research and simulation
Authors: Wang Zhiqiang , Wang Zhe , Xun Panpan , Shi Yuhui , Su Han , Jiang Han 
Published: September 2021

Abstract
Parallel robots are small in size and high in accuracy, so they are widely used in processing and production industries. In order to give full play to the characteristics of high-speed motion of parallel robots, trajectory planning has become a hot spot in the industry. This paper proposes a trajectory planning algorithm based on NURBS. Firstly, the algorithm uses the characteristics of NURBS continuous and derivable to fit interpolation points, and then we obtain a smooth and continuous robot motion trajectory. Secondly, It uses interpolation algorithm to discretize the curve to get the robot motion sequence. Finally, It completes the entire motion process of the robot in three-dimensional space. Experiments show that the algorithm can be applied to the parallel robot control system, which can give full play to the performance of the parallel robot and ensure the stable and high-speed operation of the robot.
Research on Control Strategy for Vibration and Noise Reduction of Permanent Magnet Synchronous Motor
Authors: Yuan Liwei , Tian Guanzhi , Wei Juan , Li Guoqiang , Zhao Zengwu , Jiang Liting 
Published: September 2021

Abstract
In order to reduce the vibration and noise of permanent magnet synchronous motors, this paper proposes a control strategy by analyzing the relationship between the vibration and noise of the permanent magnet synchronous motor and the current harmonics of the inverter based on actual project applications. Some methods include random switching frequency modulation technology, random pulse position modulation technology and dead zone compensation technology on current harmonics are analyzed respectively in this paper. Then this paper proposed a mixed control strategy, which combined with random switching frequency-random and pulse position-dead zone compensation methods. Matlab tools are used to simulation and verification the Mixed control strategy. Simulation and experimental results show that this control strategy can effectively reduce the total underwater noise level of permanent magnet synchronous motors.
The Dynamic analysis of Planetary roller screw in Tree-climbing robot
Authors: Yang Xi , Yan Mengxuan , Zhou Yuyao , Liu Rui 
Published: September 2021

Abstract
In the tree-climbing robot, the sawing module is lifted and lowered by the planetary roller lead screw, so the dynamic analysis of the planetary roller lead screw is very important. In this paper, the stiffness of the mechanical joint of planetary roller lead screw is calculated according to the fractal theory, and the stiffness calculation formulas between roller and ring gear, roller and lead screw are obtained. In the dynamic simulation, the stiffness of each contact pair is set according to the formula, and two groups of models with consistent and inconsistent roller contact stiffness are established. The first six order modal analysis results of the two groups of models are compared, and the influence of uneven roller contact stiffness distribution on the whole planetary roller lead screw is obtained.
Optimization of the aerodynamic characteristics of a NACA air intake based on DoE and numerical methods
Authors: Zhu D X , Yang Y , Yu Z J , Li Z P , Dong L K 
Published: September 2021

Abstract
The Design of Experiment (DoE) method combined with numerical simulations is employed to optimize the aerodynamic characteristics of a National Advisory Committee for Aeronautics (NACA) air intake. The drag force, total pressure recovery coefficient, mass flow rate at the outlet, and Mach number at the outlet are used to evaluate the performance of the air intake based on computational fluid dynamics (CFD) numerical methods. Three critical geometric parameters, namely the ramp angle a, ramp divergence angle θ, and turning corner radius R, are considered as variables in the DoE design. Results under the baseline conditions indicated that axial vortexes are formed near the side edge close to the ramp wall. DoE analysis indicated that above four performance parameters are largely influenced by the ramp angle a, while the ramp divergence angle θ has negligible effects. A small radius of the turning corner is beneficial to enhance the pressure recovery coefficient and the mass flow rate. The Mach number at the outlet declines and the velocity uniformity on a certain section increases with decreasing value of ramp angle a. Meanwhile, larger total pressure recovery coefficient, mass flow rate, and vortex tube length are acquired under a smaller value of a. Thus, the aerodynamic performance of this air intake can be improved by a set of optimized geometric parameters.
Research on steel ball surface defect detection device based on machine vision
Authors: Dong Chenchen , Zhu Weiwei , Wei Jinling , Zhou Hangchao , Chen Feng 
Published: September 2021

Abstract
Based on machine vision technology, aiming at the characteristics of steel ball surface defect detection, a set of steel ball surface defect detection device is designed and built. The device designs a V-shaped groove unfolding mechanism which is composed of a turntable, an inner ring and an outer ring, through the intermittent rotation of the inner ring and the outer ring at different speeds, the steel ball can be rapidly deployed in the whole region, so the image collected by the system can cover the whole surface of the steel ball. In order to solve the halo phenomenon of steel ball picture and the mapping of surrounding objects, based on the principle of diffuse lighting, the bowl-shaped dome light source with adjustable brightness is selected, the quality of steel ball surface image and the effective area of detection are effectively improved. Based on LabVIEW software platform, a software is developed to collect and process the surface image of steel ball and identify the surface defect features. Test surface the device can accurately identify whether there are defects on the tested steel ball surface.
The Zoom Effect: A Google Trends Analysis
Authors: Thawanyarat Kometh , Francis Shannon , Kim Trudy , Arquette Connor , Morrison Shane , Nazerali Rahim 
Published: September 2021

Abstract

Background
Increased video-chatting, stimulated by the COVID-19 pandemic, has been correlated with increased appearance concerns. Initial lockdown restrictions correlated with a decrease in aesthetic/cosmetic plastic surgery case volumes.

Objectives
The authors aimed to delineate public interest in aesthetic procedures surrounding the COVID-19 pandemic via Google Trends. They hypothesized that because of the pandemic, public interest in plastic surgery procedures increased, especially localized above the shoulder.

Methods
Trends in the United States for given search terms and volumes were gathered via Google Trends between January 2015 and March 2021. The search volumes were normalized, and a bivariate regression analysis of panel data was then applied to the aggregate trendlines to determine if a statistically significant change in search volume occurred following the stay-at-home orders.

Results
The following search terms had statistically significant (P &lt; 0.000) increases in search volumes after February 2020: blepharoplasty, Botox, brachioplasty, breast implant removal, breast reduction, brow lift, buccal fat removal, hair transplantation, lip augmentation, mentoplasty, otoplasty, platysmaplasty, rhinoplasty, and thighplasty. Chi-squared analysis demonstrated a statistically significant association (chi-squared = 4.812, P = 0.028) between increases in search volume and above-the-shoulder procedures.

Conclusions
Public interest in above-the-shoulder surgical procedures statistically significantly increased following February 2020 compared with below-the-shoulder procedures. Continued examination of specific procedure trends and determining correlations with more accurate procedural datasets will provide increased insight into consumers’ mindsets and to what extent video conferencing plays a role in the public’s interest in pursuing aesthetic surgery.
Capn4 aggravates angiotensin II-induced cardiac hypertrophy by activating the IGF-AKT signalling pathway
Authors: Cao Yuanping , Wang Qun , Liu Caiyun , Wang Wenjun , Lai Songqing , Zou Huaxi , Tao Ende , Wang Fudong , Wan Li 
Published: September 2021

Abstract
Capn4 belongs to a family of calpains that participate in a wide variety of biological functions, but little is known about the role of Capn4 in cardiac disease. Here, we show that the expression of Capn4 was significantly increased in Angiotensin II (Ang II)-treated cardiomyocytes and Ang II-induced cardiac hypertrophic mouse hearts. Importantly, in agreement with the Capn4 expression patterns, the maximal calpain activity measured in heart homogenates was elevated in Ang II-treated mice and oral coadministration of SNJ-1945 (calpain inhibitor) attenuated the total calpain activity measured in vitro. Functional assays indicated that overexpression of Capn4 obviously aggravated Ang II-induced cardiac hypertrophy, whereas Capn4 knockdown resulted in the opposite phenotypes. Further investigation demonstrated that Capn4 maintained the activation of the insulin-like growth factor (IGF)-AKT signalling pathway in cardiomyocytes by increasing c-Jun expression. Mechanistic investigations revealed that Capn4 directly bound and stabilized c-Jun and knockdown of Capn4 increased the ubiquitination level of c-Jun in cardiomyocytes. Additionally, our results demonstrated that the antihypertrophic effect of Capn4 silencing was partially dependent on the inhibition of c-Jun. Overall, these data suggested that Capn4 contributes to cardiac hypertrophy by enhancing the c-Jun-mediated IGF-AKT signalling pathway and could be a potential therapeutic target for hypertrophic cardiomyopathy.
Multisystem inflammatory syndrome in children
Authors: Greenway Andrew , Macklay Holly , Xuereb Kevin , Gibbons Linda 
Published: September 2021

Abstract:
Multisystem inflammatory syndrome in children is a previously unrecognized and potentially catastrophic illness that appears in children who have been exposed to or diagnosed with COVID-19. As healthcare agents and members of the community, nurses are positioned to assist in identifying children who may experience previously unrecognized complications of infection from the SARS-CoV-2 virus.
The impact of COVID-19 on patient safety attendant use
Authors: Shields Lisa B.E. , Lawson Tiffany P. , Flanders Kimberly 
Published: September 2021

Abstract:
Patient safety attendants (PSAs) provide constant direct observation to patients who have cognitive impairments or thoughts. Some estimates report that an acute care hospital in the United States may spend more than $1 million annually on PSAs, an expenditure often not reimbursed. With no national defined standards to regulate or monitor PSA use, this study sought to determine the impact of COVID-19 on a PSA reduction program in a large Midwestern healthcare system.
Therapeutic alliance in mental health care
Authors: Martinez Angel Johann Solorzano 
Published: September 2021

Abstract:
The field of psychiatric nursing provides unique opportunities for nurse-patient interactions. Nurses working in inpatient psychiatric settings face constant challenges that may hinder the development of meaningful nurse-patient interactions. Peplau's theory of interpersonal relations emphasized the importance of nurse-patient relationships in nursing care. Therapeutic alliance can help promote nurse-patient relationships and may help nurses and patients achieve their goals. This article presents several phases of a concept-building process that illustrates the importance of therapeutic alliance and its application in mental health care.
COVID-19 in complex common variable immunodeficiency patients affected by lung diseases
Authors: Milito Cinzia , Soccodato Valentina , Auria Stefania , Pulvirenti Federica , Quinti Isabella 
Published: September 2021

Purpose of review
In the general population, the risk of severe COVID-19 is associated with old age, male sex, hypertension, obesity and chronic diseases. Chronic lung diseases are listed as additional risk factors for hospitalization and ICU admission. The purpose of this review is to define whether chronic lung diseases, such as bronchiectasis and interstitial diseases, represent a risk for a severe SARS-CoV-2 infection in patients affected by common variable immunodeficiency (CVID), the most common symptomatic primary antibody defect.

Recent findings
CVID patients with SARS-CoV-2 infection have been reported since the beginning of the pandemic with a wide range of clinical presentations ranging from asymptomatic to mild/moderate and severe COVID-19. The meta-analysis of 88 CVID cases described in large cohorts and case reports demonstrated that CVID patients with chronic lung involvement have an increased risk for severe COVID-19 in comparison to CVID without lung diseases (50 vs. 28%, relative risk 1.75, 95% confidence interval 1.04--2.92, P = 0.043). Differently from the general population, age and metabolic comorbidities did not represent a risk factor for severe course in this patient's population.

Summary
Underlying chronic lung diseases but not age represent a risk factor for severe COVID-19 in CVID. Prompt therapeutic intervention should be adopted in SARS-CoV-2 positive CVID patients with chronic lung diseases independently of their age.