bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comprehensive Functional Analysis in Patients after Atrial Switch Surgery
Authors: Vollmer Daniela , Hornung Andreas , Wiegand Gesa , Apitz Christian , Latus Heiner , Hofbeck Michael , Sieverding Ludger 
Published: April 2021

Abstract
Background Long-term course after atrial switch operation is determined by increasing right ventricular (RV) insufficiency. The aim of our study was to investigate subtle functional parameters by invasive measurements with conductance technique and noninvasive examinations with cardiac magnetic resonance imaging (CMR).
Methods We used invasive (pressure–volume loops under baseline conditions and dobutamine) and noninvasive techniques (CMR with feature tracking [FT] method) to evaluate RV function. All patients had cardiopulmonary exercise testing (CPET).
Results From 2011 to 2013, 16 patients aged 28.2 ± 7.3 (22–50) years after atrial switch surgery (87.5% Senning and 12.5% Mustard) were enrolled in this prospective study. All patients were in New York Heart Association (NYHA) class I to II and presented mean peak oxygen consumption of 30.1 ± 5.7 (22.7–45.5) mL/kg/min. CMR-derived end-diastolic volume was 110 ± 22 (78–156) mL/m2 and RV ejection fraction 41 ± 8% (25–52%). CMR-FT revealed lower global systolic longitudinal, radial, and circumferential strain for the systemic RV compared with the subpulmonary left ventricle. End-systolic elastance (Ees) was overall reduced (compared with data from the literature) and showed significant increase under dobutamine (0.80 ± 0.44 to 1.89 ± 0.72 mm Hg/mL, p ≤ 0.001), whereas end-diastolic elastance (Eed) was not significantly influenced (0.11 ± 0.70 to 0.13 ± 0.15 mm Hg/mL, p = 0.454). We found no relevant relationship between load-independent conductance indices and strain or CPET parameters. Conductance analysis revealed significant mechanical dyssynchrony, higher during diastole (mean 30 ± 4% baseline, 24 ± 6% dobutamine) than during systole (mean 17 ± 6% baseline, 19 ± 7% dobutamine).
Conclusions Functional assessment of a deteriorating systemic RV remains demanding. Conductance indices as well as the CMR-derived strain parameters showed overall reduced values, but a significant relationship was not present (including CPET). Our conductance analysis revealed intraventricular and predominantly diastolic RV dyssynchrony.
Implementation and data processing of a five-channel microwave interferometer with high temporal resolution and low noise on Sino-UNIted Spherical Tokamak
Authors: Su Pengjuan , Zhong Heng , Tan Yi , Gao Zhe , Zai Yingqi , Liu Haiqing , Zeng Long , Zhu Xiang , Ling Bili , Cheng Zhengbo , Liu Wenbin , Wang Shouzhi , Wang Binbin 
Published: April 2021

A five-channel microwave interferometer with high temporal resolution and high phase resolution has been developed for measuring electron density profiles and fluctuations on the Sino-UNIted Spherical Tokamak. A correction algorithm, based on the low signal amplitude detection, is proposed to eliminate the fringe jump errors. The correction algorithm achieves an accuracy of 92%. The density profile is reconstructed through the Park-matrix method, with the geometry of magnetic surfaces calculated by the equilibrium fitting. The reconstructed density profiles for discharges with supersonic molecular beam injection are in good agreement with the results of another 94 GHz single-channel horizontal interferometer and the Langmuir probes. The temporal resolution of the system is 0.5 µs and the line-integrated electron density resolution is 1 × 1015 m−2, which benefits from the single sideband modulation technique. Therefore, the multichannel interferometer system is capable of studying the details of the high-frequency (up to 200 kHz) density fluctuation such as that in the minor disruption event.
Local controllability and optimality
Authors: Avakov E. R. , Magaril-Il’yaev G. G. 
Published: April 2021

Abstract
The concept of local controllability is introduced for a dynamical system; sufficient conditions for such controllability are presented. As a consequence, necessary conditions for a local infimum in an optimal control problem are obtained. These strengthen Pontryagin’s maximum principle and extend it to more general classes of problems.
Bibliography: 8 titles.
A strengthening of the Bourgain-Kontorovich method: three new theorems
Authors: Kan I. D. 
Published: April 2021

Abstract
Consider the set

of irreducible denominators of the rational numbers representable by finite continued fractions all of whose partial quotients belong to some finite alphabet

. Let the set of infinite continued fractions with partial quotients in this alphabet have Hausdorff dimension

satisfying

. Then

contains a positive share of positive integers. A previous similar result of the author of 2017 was related to the inequality

; in the original 2011 Bourgain-Kontorovich paper,

.
Bibliography: 28 titles.
Stress-dependent activation of myosin in the heart requires thin filament activation and thick filament mechanosensing
Authors: Park-Holohan So-Jin , Brunello Elisabetta , Kampourakis Thomas , Rees Martin , Irving Malcolm , Fusi Luca 
Published: April 2021

Significance
The efficiency of the heart as a pump depends on an autoregulatory mechanism, the Frank–Starling law of the heart, that potentiates the strength of contraction in response to an increase in ventricular filling. Disruption of this mechanism compromises the ability of the heart to pump blood, potentially leading to heart failure. We used fluorescent probes on myosin in heart muscle cells to investigate the molecular basis of the Frank–Starling mechanism. Our results show that the stronger contraction of heart muscle at longer lengths is due to a calcium-dependent interfilament signaling pathway that links stress sensing in the myosin-containing filaments with calcium activation of the actin-containing filaments. This pathway can potentially be targeted for treating heart failure.
Black holes in asymptotic safety with higher derivatives: accretion and stability analysis
Authors: Zuluaga Fabián H. , Sánchez Luis A. 
Published: April 2021

Abstract
We review steady spherically symmetric accretion onto a renormalization group improved Schwarzschild space-time, which is a solution to an asymptotically safe theory (AS) containing high-derivative terms. We use a Hamiltonian dynamical system approach for the analysis of the accretion of four types of isothermal test fluids: ultra-stiff fluid, ultra-relativistic fluid, radiation fluid, and sub-relativistic fluid. An important outcome of our study is that, contrary to the claim in a recent work, there are physical solutions for the accretion of an ultra-relativistic fluid in AS, which include subsonic, supersonic, and transonic regimes. Furthermore, we study quantum corrections to the known stability of the accretion in general relativity (GR). To this end, we use a perturbative procedure based on the continuity equation with the mass accretion rate being the perturbed quantity. Two classes of perturbations are studied: standing and traveling waves. We find that quantum gravity effects either enhance or diminish the stability of the accretion depending on the type of test fluid and the radial distance to the central object.
Effective field theory approach to lepton number violating τ decays *
Authors: Liao Yi , Ma Xiao-Dong , Wang Hao-Lin 
Published: April 2021

Abstract
We continue our endeavor to investigate lepton number violating (LNV) processes at low energies in the framework of effective field theory (EFT). In this work we study the LNV tau decays

, where

and

denote the lowest-lying charged pseudoscalars

. We analyze the dominant contributions in a series of EFTs from high to low energy scales, namely the standard model EFT (SMEFT), the low-energy EFT (LEFT), and the chiral perturbation theory (

). The decay branching ratios are expressed in terms of the Wilson coefficients of dimension-five and -seven operators in SMEFT and the hadronic low-energy constants. These Wilson coefficients involve the first and second generations of quarks and all generations of leptons; thus, they cannot be explored in low-energy processes such as nuclear neutrinoless double beta decay or LNV kaon decays. Unfortunately, the current experimental upper bounds on the branching ratios are too weak to set useful constraints on these coefficients. Alternatively, if we assume the new physics scale is larger than 1 TeV, the branching ratios are well below the current experimental bounds. We also estimate the hadronic uncertainties incurred in applying

to

decays by computing one-loop chiral logarithms and attempt to improve the convergence of chiral perturbation by employing dispersion relations in the short-distance part of the decay amplitudes.
Improved Qsofa Algorithm Based on PPG Wearable Device
Authors: Liu Yepeng , Yu Xiaoxiao , Luo Xin , Liu Ziyu , He Yu 
Published: April 2021

Abstract
This paper designs an improved Qsofa algorithm based on photoplethysmography measurement technology. The algorithm is mainly used to achieve accurate measurement of the subject’s heart rate and blood pressure and a more refined Qsofa health score. In terms of health data processing, this paper uses a combination of data threshold and mean stabilization algorithms to significantly improve measurement accuracy. In terms of health analysis, this paper greatly reduces the difficulty of the GCS coma evaluation method in the traditional Qsofa algorithm, and at the same time makes the final score of the evaluation more detailed in the same level, so as to more accurately show the health status of the subjects.
Chongqing Transportation Network Simulation and Research Based on Floyd Algorithm
Authors: Long Xin 
Published: April 2021

Abstract
In recent years, research on complex networks is in full swing, and transportation networks are also an important part of the research on complex networks. Since the 21st century, people’s living standards have increased substantially, transportation plays a key role in our lives, and public transportation is an indispensable product of transportation in modern cities, and it greatly facilitates people’s life and travel. This time, the Floyd algorithm is used to improve and apply the complex network, and MATLAB and pajek software are used to simulate, and the empirical research on Chongqing’s bus network is carried out. It is concluded that Chongqing’s bus network has small-world characteristics. With certain help, it can provide reference for the construction plan for relevant people.
An Analysis Method of Ship Traffic Section Flow Based on Big Data
Authors: Yi-fan Liu 
Published: April 2021

Abstract
Traffic flow has important research value for traffic planning and traffic management. In waterway traffic, the cross-sectional flow of ships also has important application value in ship safety management, navigation rule making, bridge and wharf construction, and ship driver decision-making. In order to fully explore the characteristics of ship traffic flow in the big data environment, this paper studies the analysis method of ship cross-section flow based on big data. First of all, in order to ensure the accuracy of calculation and reduce the calculation time to improve calculation efficiency, based on AIS big data optimization data processing method, complete data storage strategy and construction of data management mechanism. Then, the ship section flow analysis algorithm is proposed and implemented. Finally, based on the ship section flow analysis algorithm, a ship section flow analysis and statistics system is constructed to complete the flow calculation and information statistics. The research results of this paper make the relevant analysis of ship traffic flow more convenient and faster, and have greater application value for waterway traffic planning and management.
Numerical Simulation of Axial Pressure Bearing Capacity of Building Concrete Short Column Based on Finite Element Analysis
Authors: Musen Lin 
Published: April 2021

Abstract
Based on the ANSYS finite element platform, considering the geometric parameters and calculation method of the short column Axis material of concrete building, a numerical model of the Axial bearing capacity of concrete building under the finite element analysis is established. On the basis of the model, the load displacement curves of concrete bearing concrete bearing short column Axial pressure under the characteristics of different straps are analyzed, the standard values are set, and the concrete compression strength is set. The concrete conditions and numerical model simulation are established. Feasibility analysis, Through the test results and its specific test content to judge, put forward the test results of the matching method and strategy, give a personal opinion and test description.
Mobile Robot Path Planning Algorithm Based on Improved A Star
Authors: Zhang Lei , Li Yuan 
Published: April 2021

Abstract
With the rapid development of artificial intelligence, path planning has become one of the key technologies in the application of mobile robots. Although the A star(A*)path planning algorithm can plan a collision-free path, there are still shortcomings such as the path is not optimal, too many redundant points, and not smooth enough. Aiming at the deficiencies of the A* algorithm, an improved A* algorithm is proposed. Firstly improved A* algorithm divides the distance between adjacent nodes in the path planned by A* algorithm. Then we use the two-way optimization method to delete useless nodes, short the path length and reduce the number of inflection points. Finally, a cubic spline function is introduce d to smooth the path. The simulation results show that the improved A* algorithm is effectively optimized in terms of path length, number of inflection points and smoothness.
Research on Intelligent Blending Method of Matcha Based on Improved PID Control Algorithm
Authors: Chen Kai , Yu Jie , Hao Miao , Zhang Chengmei , Yang Bing , Tan Hong , Wang Yajie 
Published: April 2021

Abstract
Most of the existing Matcha blending is done manually, which is inefficient and leads to uneven tea products. To solve this problem, this paper proposes an intelligent blending method based on improved PID control algorithm. In this paper, considering the various factors that affect the quality of tea, without affecting the quality of the product, taking the lowest cost of the enterprise as the constraint goal, the basic combination model is constructed by using PID control algorithm. Since the selection of PID parameters will affect the output of the model, this paper introduces particle swarm optimization algorithm to optimize the PID controller parameters globally, so that the PID controller can achieve the ideal control index, and finally determine the optimal combination of Matcha. The experimental results show that the method proposed in this paper can effectively realize the intelligent blending of Matcha, and maximize the cost of tea factory, effectively improve the income of Matcha products, which is conducive to the further development of tea companies.
A comparison between the wrapper and hybrid methods for feature selection on biology Omics datasets
Authors: Guo Chirui , Li Qiuhan 
Published: April 2021

Abstract
Classification problem in biological Omics data has gained in popularity in recent years. In consideration of the high dimension of the Omics dataset, the importance of feature selection technology becomes apparent. Feature selection can not only improve the classification accuracy but also avoid overfitting which is a common issue in machine learning. There are three main categories of feature selection methods: filter methods, wrapper methods, and hybrid methods. Yet it is difficult for researchers to choose among them. In this study, we conducted a comprehensive comparison for filter methods, wrapper methods, and hybrid methods. Specifically, we selected information gain as the filter method, genetic algorithm, and binary particle swarms optimization as the wrapper methods, IG-GA and IG-BPSO as the hybrid methods for comparison. The experimental results show that the IG-BPSO, a hybrid method, has the highest classification accuracy among these feature selection methods on three Omics datasets.
A Deep Learning-Based Hybrid Feature Selection Approach for Cancer Diagnosis
Authors: Wu Haoran 
Published: April 2021

Abstract
Feature selection plays an important role in machine learning-based classification tasks, especially in high dimensional data, such as biological omics datasets. Recent research has begun to explore the use of deep learning to accomplish this task as a step in feature representation. In this research, we developed a deep learning-based hybrid feature selection approach combing Sparse Autoencoder (SAE) and Logistic Regression-Recursive Features Elimination (LR-RFE) and evaluated our method on TCGA miRNA datasets. The results show that our proposed hybrid method achieves a better performance compared to other comparison methods.
Research Photocatalytic Water-splitting based on big data mining technology
Authors: Li Hexi , Chen Gengyun , Xiang Fengshun 
Published: April 2021

Abstract
Visible photocatalytic water splitting is an effective way to solve the global energy and environmental crisis, and its green and environmentally friendly properties are worth researching. In this paper, we extracted 8395 articles from the core collection of Web of Science from 1991 to 2020. Visual analysis of the data by various software, such as Excel, Origin, Citespace V, reflected the following results. First, the number of related papers has increased exponentially, reaching a peak in 2018, with a total of 1508; next, among all countries, China has published the most papers and cooperated most closely with other countries; additionally, in terms of journals, Applied Catalysis B: Environment has the largest number of publications in the field of visible light catalytic cracking of water, and Journal of the American Chemical Society has the highest number of co-citations in this field, researchers can focus on these journals; finally, MAEDA K, ZHANG J, and WANG Y are ideal collaborators in this field. “Application and modification of graphite phase carbon nitride,” “application and modification of Z-scheme photocatalyst,” and “band structure engineering” are the hottest research frontiers in this field and represent the future development direction of this field.
SDAE-based feature selection method for biological Omics data
Authors: Jiang Zhangqi 
Published: April 2021

Abstract
The advancement of Omics technology has led to a surge in molecular and cell profiling data for mechanism study. Large amount of data and complex data structure pose a great challenge to data analysis. Modern machine learning methods such as deep learning are expected to take advantage such big data for accurate disease prediction or other related tasks. However, large feature number may bring large amount redundant information and adversely affect the accuracy of a classifier. To this end, feature selection methods can remove redundant information and help the model achieve higher accuracy by selecting informative features. In this paper, we propose a two-step deep learning-based method combining stacked denoising autoencoders (SDAE) with SVM-RFE to accomplish the task of feature selection. We compared our method with other related methods and the results showed that our approach achieved a better performance than other methods when using the TCGA datasets.
Multi-Scale Context Enhanced Network for Monocular Depth Estimation
Authors: Wang Wenju , Ning Yue 
Published: April 2021

Abstract
Monocular depth estimation is a classical computer vision task. At present, most CNN methods cannot effectively combine high-level and low-level features, leading to the loss of details and blurring of boundaries. To solve the problem, we propose a Multi-Scale Context Enhanced Network (MCEN) to learn more abundant context and expand its receptive field for high-accuracy estimation. Our method employs CRE-HRNet (Context and Receptive Enhanced High-Resolution Network) with four branches ranging from low-dimension to high-dimension features to obtain richer contextual information and extract multi-scale features. It then uses RM (Refinement Module) adopting the residual dilated convolution to retains detailed information and improve the receptive field. Finally, non-local block enables our network to capture the longdistance context through its special non-local operation. Experiments with the NYU Depth V2 dataset show its outstanding performance.
Research on Multi-Angle Face Detection Method Based on Improved YOLOV2 Algorithm
Authors: Zhang Huanping , Cao Hanhua , Liu Zhendan , Zhou Yuhuai , Wang Yujuan 
Published: April 2021

Abstract
Face detection based on the YOLOV2 algorithm can achieve a higher level of accuracy and show a faster detection speed. Therefore, the YOLOV2 algorithm appears more frequently in the field of target real-time detection. The traditional YOLOV2 algorithm uses the Darknet-19 network. Based on this, this paper builds a new network by merging the residual network (ResNet). In addition, in order to further make up for the defects exposed by the YOLOV2 algorithm in face detection, we change the weight of the loss function and implement an image enhancement scheme on the training set. The YOLOV2 algorithm and the improved YOLOV2 algorithm are compared and tested on the Wider Face dataset, and then we use face data sets from different angles to test the performance of the algorithm. According to the analysis of the experimental results, the improved YOLOV2 algorithm can show higher accuracy and robustness in multi-angle face detection.
Application and Optimization of Algorithm Recommendation in Mobile Audio APP
Authors: Zhang Jieyi , Kong Dehua 
Published: April 2021

Abstract
A major feature of mobile audio applications compared with traditional radio stations is that they can enhance the direct connection between users and programs through capturing and analyzing all kinds of data, and make personalized recommendation and accurate transmission to users. Algorithmic recommendation system has different classification, different mobile audio applications based on user positioning and product characteristics, flexible use of a variety of algorithm recommendation methods in the platform. At the same time, the negative effects of algorithm recommendation can not be ignored.
Research on target tracking algorithm based on information entropy feature selection and example weighting
Authors: Bing Huang Chao , Hui Li Ting 
Published: April 2021

Abstract
An improved algorithm based on information entropy feature selection and sample weighting is proposed to solve the drift problem when the multi-instance learning (MIL) target tracking algorithm is updating classifier. Algorithm based on Boosting framework, adopt the method of MIL as sample selection, the positive and negative samples collected around the target area, to extract the image feature is compressed and weak classifier. Based on maximum entropy principle from a number of weak classifier to select several optimal classifier, the resulting strong classifier can be used to track the location of the next frame target. The results show that the algorithm is robust to target occlusion and fast motion.