bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extensive Mendelian randomization study identifies potential causal risk factors for severe COVID-19
Authors: Sun Yitang , Zhou Jingqi , Ye Kaixiong 
Published: December 2021

Abstract
Background
Identifying causal risk factors for severe coronavirus disease 2019 (COVID-19) is critical for its prevention and treatment. Many associated pre-existing conditions and biomarkers have been reported, but these observational associations suffer from confounding and reverse causation.

Methods
Here, we perform a large-scale two-sample Mendelian randomization (MR) analysis to evaluate the causal roles of many traits in severe COVID-19.

Results
Our results highlight multiple body mass index (BMI)-related traits as risk-increasing: BMI (OR: 1.89, 95% CI: 1.51–2.37), hip circumference (OR: 1.46, 1.15–1.85), and waist circumference (OR: 1.82, 1.36–2.43). Our multivariable MR analysis further suggests that the BMI-related effect might be driven by fat mass (OR: 1.63, 1.03–2.58), but not fat-free mass (OR: 1.00, 0.61–1.66). Several white blood cell counts are negatively associated with severe COVID-19, including those of neutrophils (OR: 0.76, 0.61–0.94), granulocytes (OR: 0.75, 0.601–0.93), and myeloid white blood cells (OR: 0.77, 0.62–0.96). Furthermore, some circulating proteins are associated with an increased risk of (e.g., zinc-alpha-2-glycoprotein) or protection from severe COVID-19 (e.g., prostate-associated microseminoprotein).

Conclusions
Our study suggests that fat mass and white blood cells might be involved in the development of severe COVID-19. It also prioritizes potential risk and protective factors that might serve as drug targets and guide the effective protection of high-risk individuals.
Predicting aircraft taxiing estimated time of arrival by cluster analysis
Authors: Tang Xinmin , Ji Xiaoqi , Liu Jinan 
Published: December 2021

AbstractIn order to predict the aircraft taxiing estimated time of arrival (ETA) to reduce scene conflicts and improve the operating efficiency of airports, the Kalman filter algorithm is employed to preprocess historical trajectory data for an airport scene. For the first time, in order to measure the distance between trajectory samples, the aircraft taxiing period and the number of aircraft in a scene are used as sample features for aircraft taxiing ETA prediction. The dynamic time warping algorithm is employed to extract the different features of the trajectory. The Euclidean distance between two sample features is taken as their similarity. The initial clustering centre is determined based on the divided difference maximum principle by using the K‐means algorithm to cluster samples and selecting the cluster with the highest matching degree according to the time period of the aircraft under planning and the number of aircraft operating in the scene. The trajectory sequence for the sample at the cluster centre and the static path planned by the tower are combined to predict the aircraft taxiing ETA. The accuracy of this method at predicting the aircraft taxiing ETA was demonstrated in simulations by comparing and analysing actual trajectory data with predicted taxiing ETA.
Der Tuberositas-Transfer im kindlichen Alter
Authors: Wagner Daniel 
Published: December 2021

ZusammenfassungDie patellofemorale Instabilität (PFI) ist eine typische Erkrankung im Kindes- und Jugendalter. Aufgrund des hohen Reluxationsrisikos ist eine gezielte Risikoanalyse notwendig. Die Patella alta und eine veränderte Zugrichtung des Streckapparates, die über den TTTG-Abstand ermittelt werden kann, stellen auch im jungen Alter Hauptrisikofaktoren der PFI dar. Die zunehmend evidenzbasierte Indikationsstellung unterscheidet sich nicht wesentlich von der Erwachsener. Aufgrund der Gefahr einer gestörten Wachstumslenkung kann bei offenen Fugen ein Tuberositas-Transfer jedoch nicht erfolgen, sodass die operativen Techniken den Gegebenheiten angepasst werden müssen. Eine Korrektur der Patella alta und eines erhöhten TTTG-Abstands ist mit weichteiligen distalen Eingriffen ohne Beeinträchtigung der Wachstumsfugen auch im Kindes- und Jugendalter möglich und verbessert die Ergebnisse gegenüber isolierten MPFL-Rekonstruktionen bei Überschreiten von Grenzwerten der Risikofaktoren und korrekter Indikationsstellung.
Functional Assessment of Hearing Aid Benefit: Incorporating Verification and Aided Speech Recognition Testing into Routine Practice
Authors: Sydlowski Sarah A. , King Michelle , Petter Karen , Bachmann Meagan Lewis 
Published: December 2021

AbstractPotential cochlear implant (CI) candidates arrive to the clinic with a variety of hearing loss configurations, hearing aid history, and aided capabilities. CI candidacy is primarily determined based on aided speech recognition capability, which relies on benefit derived from use of hearing aids. Therefore, contemporary evaluation for CI candidacy should incorporate a battery of testing to determine abilities and limitations and must be predicated on appropriate verification of the hearing aid fitting. However, recent reports, including a retrospective chart review of patients presenting to Cleveland Clinic for CI evaluation, suggest that a significant subset of patients may be using inappropriately fit or programmed amplification. Thus, a combination of simulated real-ear measurements and aided speech recognition testing is essential for fully assessing the effect of amplification and ultimately determination of CI candidacy. Furthermore, waiting to incorporate these tools until CI candidacy is suspected may delay timely identification of problems or need to change technology. Utilization of evidence-based decision drivers ultimately leads clinicians to timely patient-specific interventions which may include surgical intervention or other amplification options. As audiology moves into a healthcare era in which payers consider the benefit of our services to overall health and well-being, demonstrating timely, optimal outcomes using thorough, multifactorial evaluation is essential.
A highly sensitive gas chromatograph for in situ and operando experiments on catalytic reactions
Authors: Golder Katharina M. , Böller Bernhard , Stienen Günter , Sickerling Joern , Wintterlin Joost 
Published: December 2021

We describe an automated gas sampling and injection unit for a gas chromatograph (GC). It has specially been designed for low concentrations of products formed in catalytic in situ and operando experiments when slow reactions on single crystal models are investigated. The unit makes use of a buffer volume that is filled with gas samples from the reactor at a reduced pressure. The gas samples are then compressed by He to the injection pressure of 1000 mbar and pushed into two sample loops of the GC, without major intermixing with He. With an additional cryo trap at one of the GC column heads, the design aims at concentrating the gas samples and focusing the peaks. The performance is characterized by experiments on the Fischer–Tropsch synthesis, using H2/CO mixtures (syngas) at 200 and 950 mbar and a Co(0001) single crystal sample as model catalyst. Chromatograms recorded during the reaction display sharp, well separated peaks of saturated and unsaturated C1 to C4 hydrocarbons formed by the reaction, whereas the syngas matrix only gives moderate signals that can be well separated from the product peaks. Detection and quantification limits of 0.4 and 1.3 ppb, respectively, have been achieved and turnover numbers as low as 10−5 s−1 could be measured. The system can be combined with all known analysis techniques used in in situ and operando experiments.
Contested compensation: the politics, economics and legal nuances of compensating white former commercial farmers in Zimbabwe
Authors: Moyo Philani 
Published: December 2021

ABSTRACT
In July 2020, the government of Zimbabwe and white former commercial farmers signed a Global Compensation Deed agreement of US$3.5 billion. Under this deal, and in line with Section 295 (3) of the constitution, white former farmers are ‘entitled to compensation from the State only for improvements that were on the land when it was acquired’. This article questions the political, financial and legal rationale of this agreement. First, it argues that the compensation deal is ultra vires since there is no enabling act of parliament to support it as required by the constitution. Consequently, this deal is tenuous and insidious. Second, Zimbabwe’s economic implosion and colossal foreign debt will make it difficult for international financial institutions to extend credit lines. Third, this deal reverses some land reform outcomes, thus raising political tensions. Fourth, these political tensions are swelling into resistance against the deal by war veterans and the opposition.
The influence of axial offset of fusion splicing on a large mode area fiber based oscillator
Authors: Zhang Hanwei , Yang Baolai , Wang Peng , Xi Xiaoming , Shi Chen , Wang Xiaolin 
Published: December 2021

Abstract
Fiber oscillators have the potential for achieving high power, high beam quality lasers with simple and compact structure, of which the fusion splicing point is an important aspect to the laser output characteristics. A model taking into account the axial offset of the splicing point and spatial mode competition has been proposed to analyze the mode interaction of a large mode area fiber based oscillator. The calculated results show that the axial offset of the output side fusion point has the main influence on the laser output beam quality, but the axial offset would not obviously reduce the optical efficiency, especially when the value is smaller than 2 μm. The influence of cavity parameters on the laser output characteristics under the existence of splicing point with axial offset has also been discussed. This model can provide a method for analyzing the mode dynamic that may be helpful for understanding the mode interactions in fiber oscillators.
Role of electron correlations in some Weyl systems
Authors: Aryal Niraj , Manousakis Efstratios 
Published: December 2021

Abstract
We are discussing a model to understand previously obtained results on Weyl semimetals as realized in MoTe2 using DFT and DFT+U calculations. The model is motivated from general principles and we use it to investigate the effects of Coulomb correlations originating from the localized nature of the Mo-d orbitals. We find that such correlations can eliminate or create pairs of Weyl points as the strength of the Coulomb interaction is varied. The effect of the spin-orbit coupling (SOC) is to split each Weyl point, which is assumed present in the absence of SOC, into pairs of spin-chiral partners.
Some aspects of a two-pore translocation problem
Authors: Bhattacharya Aniket 
Published: December 2021

Abstract
We report Brownian dynamics (BD) simulation results for a coarse-grained (CG) model semi-flexible polymer threading through two nanopores. Particularly we study a “tug-of-war” situation where equal and opposite forces are applied on each pore to avoid folds for the polymer segment in between the pores. We calculate mean first passage times (MFPT) through the left and the right pores and show how the MFPT decays as a function of the off-set voltage between the pores. We present results for several bias voltages and chain stiffness. Our BD simulation results validate recent experimental results and offer avenues to further explore various aspects of multi-pore translocation problem using BD simulation strategies which we believe will provide insights to design new experiments.
Universal finite-size scaling function for coarsening in the Potts model with conserved dynamics
Authors: Janke Wolfhard , Majumder Suman , Das Subir K. 
Published: December 2021

Abstract
We study kinetics of phase segregation in multicomponent mixtures via Monte Carlo simulations of the q-state Potts model, in two spatial dimensions, for 2 ≤ q ≤ 20. The associated growth of domains in finite boxes, irrespective of q and temperature, can be described by a single universal finite-size scaling function, with only the introduction of a nonuniversal metric factor in the scaling variable. Our results show that although the scaling function is independent of the type of transition, the q-dependence of the metric factor hints to a crossover at q = 5 where the type of transition in the model changes from second to first order.
Research on Similar Animal Classification Based on CNN Algorithm
Authors: Zeng Peiyi 
Published: December 2021

Abstract
Animal image classification with CNN (convolutional neural network) is commonly investigated in aera of image recogniation and classification, but major studies focus on species pictures classification with obvious distinctions. For example, CNN is usually employed to distinghish images between dogs and cats. This article puts the effort on similar animal images classification by applying simple 2D CNN via python. It focus on the binary classification for snub-nosed monkeys and normal monkeys. This distinguishment is hard to be done manually in a short time. For constructing complete convolutional neural network, some preparations are done in advance, such as the database construction and preprocess. The database is constructed by python crawler (downloading from google images), with 800 and 200 images for each class respectively as train data and test data. The pre-work includes image resizing, decoding and standardization. After that, the model is trained and then tested for verifying the model reliability. The training accuracy is 96.67% without any abnormality. On the basis of successful training, the test accuracy almost coincides with train accuracy in each 50 generations and plots in a graph. It indicates similar trends and results for them in the whole process. Because of this, CNN model in the study can help people identify rare animals in time and then people can effectively protect them. Therefore, CNN will be helpful in field of animal conservation, especially for rare species.
A Survey of Deep Learning Approaches on Wind Turbine Condition Monitoring
Authors: Li Sijia 
Published: December 2021

Abstract
Current physics-based wind turbine monitoring methods often need extra sensors installed on wind turbines, thus increasing the operation and maintenance (O&M) cost. Besides, physical methods are only effective under some constraints. The real effectiveness needs to be further checked in real conditions. Recent advances in data acquisition systems allow collection of large volumes of operational data of wind turbines. Learning knowledge from the data allows us to do monitoring in another direction. In this paper, a survey of deep learning algorithms applied to wind turbine condition monitoring is given. Compared with original data, more meaning features were extracted through feature extraction of deep learning. Monitoring these new signals, outliers were detected by applying suitable control charts. Several industrial cases confirmed the effectiveness and efficiency of these frameworks.
Recognition of flying small target based on enhanced quadratic time-frequency analysis
Authors: Guo Jia , Huang Xiaohong 
Published: December 2021

Abstract
UAVs (Unmanned Aerial Vehicles, UAVs) are flying targets that sail at low altitudes, are slower and smaller in size. Nowadays, the task of detecting and distinguishing flying small targets is very difficult, so how to efficiently recognize flying small targets in real time is a key issue of current research. In order to solve this problem, this paper proposes a method of using pseudo-WVD and image fusion to represent the characteristics of UAVs. First, the SMMWR (Single-mode millimeter wave radar, SMMWR) equipment is used to collect the echo signals of various types of UAVs, and at the same time, the two-dimensional FFT is used to extract the target micro-motion signals in the distance dimension. Secondly, PWVD is used to generate time-frequency graphs of different window functions. Finally, the images fused based on principal component analysis are sent to AlexNet for training. The result proves that the accuracy of recognition rate based on AlexNet can be 93.75%.
Research on restoration method of nuclear pulse current signal of semiconductor detector based on artificial neural networks
Authors: Qin Zhang , Qin ZhangJian , Zhang JingLong , Qi XinTe 
Published: December 2021

Abstract
The charge pulse generated by semiconductor detector caused by nuclear event carries nuclide and nuclear reaction information, but the amplified charge pulse amplitude is obviously weak and the noise is so large. Aiming at the difficulty of obtaining the charge signal pulse generated by the detector, a method for recovering the nuclear pulse current signal of semiconductor detector is proposed. Pulse recovery is divided into two parts: pulse shape recovery and pulse amplitude recovery. Point at the pulse shape, a shape recognition network of nuclear pulse current signal based on deep learning is proposed. For pulse amplitude,it can be obtained by Mexican straw hat wavelet forming algorithm. This algorithm can eliminate the baseline fluctuation caused by pulse stacking. The proposed shape recognition network of nuclear pulse current signal is composed of classifier and regressor. The classifier is used to judge whether the data contains a complete rising edge. The data containing the complete rising edge is sent to the regressor for prediction, so as to obtain the parameters related to the current pulse shape. The precision, recall and F-Measure of the classifier in classifying the test set are 98.88%, 98.05% and 98.33%, respectively. The average absolute error of the regressor in predicting the parameters related to the current pulse shape is about 9 ns. The experimental results show that the proposed method can recover the shape and amplitude of the current signal.
Nondestructive Detection of Ceramic Products Based on Tapping Sound Signal Feature Recogition
Authors: Liu Liping , Jiang Liucheng , Qiao Lele 
Published: December 2021

Abstract
Recent studies on the test of ceramic non-destructive testing are mainly based on high cost technologies, image processing and so on, these method possesses some drawback of low efficiency, high cost and so on. What’s more, detecting whether the ceramic products by human through listening to sound of tapping is also effectless. This paper proposed a non-destructive method for ceramic products to solve this problem. This non-destructive method consists of a tapping device and a signal processing module. The tapping device will be applied to generate the tapping sound signal and the signal processing system will be applied to analysis signal. After the process of signal analysis, sample length and peak of spectrum 2 parameters is extracted, then use these parameters to train SVM, the results will be compared with BP neural network (BPNN). The result of experiment shows that SVM with different kernels of linear, poly, rbf, sigmoid respectively reach the accuracy of 96.29%, 96.29%, 46.29%, 93.82%, while BPNN reaches the accuracy of 93.21%. This result proves that SVM can effectively complete the task of identifying defective ceramics, and its performance is better than BPNN.
Research on Electromagnetic Compatibility of High Frequency Switching Power Supply Based on Harmonic Balance Finite Element Method
Authors: Bu Chunning , Zhang Zhihong , Hou Tiantian , Bai Xin , He Hong 
Published: December 2021

Abstract
With the rapid development of electronic technology, high frequency switching power supply has been widely used in various electrical equipment, and its electromagnetic interference has attracted more and more attention. In this paper, the harmonic balance finite element method is used to solve the electromagnetic compatibility of high frequency switching power supply. In this method, the coefficients of sine wave are applied to represent the waveform, and the solution is generated by linear synthesis. We propose to combine it with the finite element method to solve the steady-state response of electromagnetic field in frequency domain. It is proved the method we proposed is superior to the traditional time domain method.
Special Vehicles to Prevent Touching High-voltage Lines Monitoring and Warning Device
Authors: Zhao Tiemin , Liu Jianshu , Zheng Shiyang , Han Dongxu , Wang Junqi 
Published: December 2021

Abstract
Domestic regional special vehicles and construction personnel high-voltage electrocution accidents always occur, resulting in large-scale equipment and socio-economic losses. Based on this, in order to effectively reduce special vehicles touching high-voltage lines causing safety accidents, this paper researches and designs a safety monitoring and warning device for special vehicles touching high-voltage lines, mainly including sensor monitoring templates, signal transmission templates and monitoring and warning alert templates, through the decoding of different template principles, combined with electric field induction and wireless transmission principles, the safety distance of high-voltage lines is relevant to explore, then when Special vehicle objects into the safety distance threshold, that is, to start the information monitoring alarm, to remind the construction of special vehicle equipment and personnel away from electrocution, to ensure safe production. Through the monitoring and warning device, the rationalization, economy and safety of high-voltage line can be achieved.
Study on the Influence of Explosive Types on Rod Jet
Authors: Wang Bingzhe , Xie Liyun , Guo Zhounan 
Published: December 2021

Abstract
In order to study the influence of explosive type on the rod jet formation of energetic composite liner, the process of rod jet formation of this liner is numerically simulated by using finite element analysis and multi-material Euler algorithm. In this paper, the rod jet formation and penetration performance of the liner are studied and analyzed by combining relevant theories and simulation calculation, and the influence law of explosive type on rod jet formation and target penetration thickness is obtained. In this paper, the stability of rod jet formed by this kind of model is verified by numerical simulation, and the influence law of explosive type on rod jet is obtained by simulating the collected data such as effective mass, tip velocity and jet length of rod jet and the equivalent target penetration thickness obtained by the quasi steady incompressible ideal fluid theory of jet penetration; it provides data support and design basis for the application of new explosives in energetic composite liner in the next stage.
Hydraulic optimization of the physical parameters of a drinking water distribution system
Authors: Bonilla-Granados C A , Cely-Calixto N J , Carrillo Soto G A 
Published: December 2021

Abstract
Drinking-water distribution systems are generally designed with methodologies based on trial-and-error tests, which generate feasible results. However, these trials are not the most economical and reliable solution since they do not consider the optimization of the network. For the present work, the hydraulic model of the drinking water distribution network of San José de Cúcuta, Colombia, was optimized by applying the concept of resilience rate and minimum cost. The development of the work consisted of the hydraulic modeling of the physical components of the network in EPANET software, as well as the application of calculations of the connectivity coefficient and the unitary power of each section. With the data obtained from the modeling and calculations, the physical parameters were optimized, and the cost-benefit ratio was estimated. It was found that the current drinking water distribution system does not have a power surplus to overcome a system failure. The optimization increased the total energy surplus of the network (261%) and the resilience rate (585%). Also, the connectivity coefficient was improved with an average value of 0.95. The hydraulic optimization methodology applied resulted in a network resilient to system failures.