bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of adaptive bushing based on field grading materials
Authors: Zhao Xiaolei , Hu Jun , Yuan Zhikang , He Jinliang 
Published: April 2021

AbstractElectrical bushings, as important power system components, are critical to the reliability of power systems. The capacitance‐graded bushing is a type of high voltage bushing that is most commonly used in current power systems. However, the limitations of and problems with capacitance‐graded bushings are exposed when the operating voltage level increases. A new direct current wall bushing with adaptive field grading materials is proposed, based on the core features of the problem that the bushing is intended to solve. The main insulation structure of the proposed DC wall bushing with adaptive field grading materials comprises three parts: a field grading layer, a current limiting layer and an electrode extension layer. The proposed bushing has characteristics that include a simple structure, small size and environmental friendliness, and it also meets the requirements of existing power systems. The proposed bushing also conforms to the intended development direction for future power equipment and will aid in the development of power systems for operation at higher voltages.
On temperature‐dependent small‐signal modelling of GaN HEMTs using artificial neural networks and support vector regression
Authors: Jarndal Anwar , Husain Saddam , Hashmi Mohammad 
Published: April 2021

AbstractMachine learning‐based efficient temperature‐dependent small‐signal modelling approaches for GaN high electron mobility transistors (HEMTs) are presented by the authors here. The first method is an artificial neural network (ANN)‐based and makes use of the well‐known multilayer perceptron (MLP) architecture whereas the second technique is developed using support vector regression (SVR). The models are trained on a large set of measurement data obtained from a 2‐mm GaN‐on‐silicon device operating under varying operating conditions (bias voltages and ambient temperatures) over a wide frequency range of 0.1 to 20 GHz. An excellent agreement is found between the measured and the simulated S‐parameters for both models over the entire frequency range. It is identified that the training process and prediction capability of ANN is superior to SVR. However, the SVR is more robust when compared to the artificial neural network (ANN) in term of its sensitivity to local minima and uniqueness of the final solution. Subsequently, the performances of the proposed ANN‐ and SVR‐based models are improved by incorporating particle swarm optimization (PSO) in the model development process. The PSO improves the uniqueness of the ANN model whereas it enhances the performance of the SVR by optimising its control parameters. The proposed models exhibit very good accuracy and scalability.
Network coverage using MI waves for underwater wireless sensor network in shadowing environment
Authors: Debnath Sunandita 
Published: April 2021

AbstractIn high permittivity media, such as underground, underwater, and biological tissues the electromagnetic (EM) wave fails to provide reliable communication due to high path loss. On the other hand, magnetic induction (MI) waves have the permeability similar to air in high permittivity medium. Unlike EM wave, in MI‐based communication the effect of shadow fading on the obtainable transmission distance is not much pronounced. However, it may not be realistic to ignore the fading effect at higher frequencies considering a constant path loss while designing the link model. A channel model for underwater MI communication is proposed in this research work. The proposed channel considers the fading effect due to presence of large obstacles such as large underwater structures or seamounts, and old sunken ship on the coverage performance of an underwater wireless sensor network (UWSN).
Lasers that learn: The interface of laser machining and machine learning
Authors: Mills Benjamin , Grant‐Jacob James A. 
Published: April 2021

AbstractLaser machining is a highly flexible non‐contact fabrication method used extensively across academia and industry. Whilst simulations based on fundamental understanding offer some insight into the processes, both the highly non‐linear interactions between laser light and matter and the variety of materials involved mean that theoretical modelling is not particularly applicable to practical experimentation. However, recent breakthroughs in machine learning have resulted in neural networks that are capable of accurate and rapid modelling of laser machining at a scale, speed, and precision well beyond those of existing theoretical approaches with applications including 3‐D surface visualisation and real‐time error correction. A perspective at the intersection of laser machining and machine learning is presented, followed by a discussion of future milestones and challenges for this field.
Changing flight heading during pass to enhance SAR change detection performance
Authors: Vu Viet T. , Pettersson Mats I. , Palm Bruna G. , Alves Dimas I. , Gomes Natanael R. 
Published: April 2021

AbstractDue to the wavelength‐resolution characteristics, wavelength‐resolution synthetic aperture radar (SAR) is considered for the change detection application aiming to achieve high detection probability and low false alarm rate. However, wavelength‐resolution SAR change detection suffers from the problems caused by specular reflection of the elongated structure in the SAR scene, the dependency of radar cross‐section on incident angle of electromagnetic wave, the interference due to antenna backlobe and so forth. Hardware and software solutions are therefore desired to deal with these problems. The authors propose to change flight heading during passes as a natural solution. The proposed solution is tested with the CARABAS data using a typical SAR change detection method. The achieved change detection results show the good performance of the proposed solution, for example, the detection probability is up to 100% with the false alarm rate of 0.5 per square kilometre.
Modelling the radar signature of rotorcraft
Authors: Point Guillaume , Degurse Jean‐François , Savy Laurent , Montécot Marc , Milin Jean‐Luc 
Published: April 2021

AbstractThe problem of radar helicopter detection and classification requires simple and fast modelling of rotorcraft signature to aid the design of specific waveforms or to provide quality training data to machine learning algorithms. Here, we present a modular parametric model for the baseband radar cross‐section of helicopters in the narrowband approximation. Every significant element of the complex rotorcraft signature is identified and its signature is reproduced faithfully. The full model is validated by comparing simulated data with real radar helicopter data. Particularly important features, such as the overall time‐frequency response, blade flash shape and blade flash duration, are shown to be reconstructed very accurately.
Robust low‐rank Hankel matrix recovery for skywave radar slow‐time samples
Authors: Zhang Baiqiang , Xie Junhao , Zhou Wei 
Published: April 2021

AbstractIn skywave radar, the slow‐time samples received in a certain range‐azimuth cell are usually processed for signal analysis and target detection. Particularly, to extract the principal components, such as sea clutter and target signal, in slow‐time samples, the Hankel matrix structure and subspace‐based methods are often applied, which can also reduce the additive Gaussian noise. In the real environment, the slow‐time samples sometimes are seriously contaminated by the transient interference, affecting the performance of useful signal reconstruction. The robust low‐rank matrix recovery methods have been applied to solve this problem, where the transient interference is assumed to be sparse, following the Laplace distribution. However, the Hankel structures of useful signal and transient interference have not been fully utilized in the conventional methods, which can enhance the reconstruction performance. Here, the robust low‐rank Hankel matrix recovery problems is reformulated for skywave radar slow‐time samples and solve them with the inexact augmented Lagrange multiplier method. Additionally, the low‐rank Hankel matrix completion problem is discussed, where the location of the transient interference is determined. The experimental results have demonstrated the good performance of our proposed methods and also some interesting conclusions are obtained.
FDG-PET basierte Bestrahlungsplanung beim Lungenkarzinom – Konsequenzen aus der PET-Plan-Studie
Authors: Moser Lutz 
Published: April 2021

ZusammenfassungDie 2020 veröffentlichten Ergebnisse der PET-Plan-Studie haben zum ersten Mal randomisiert kontrolliert eine Nichtunterlegenheit einer FDG-PET basierten involved-field-Bestrahlung beim lokal fortgeschrittenen nicht-kleinzelligen Lungenkarzinom im Vergleich zur elektiven Lymphabflussbestrahlung gezeigt. In Kombination mit einer isotoxischen Dosiseskalation resultieren daraus eindrucksvoll geringe Lokalrezidivraten. Die aus der Studie abgeleiteten technischen Voraussetzungen der Bestrahlungsplanung erfordern ein Qualitätsniveau, dessen Umsetzung beim Lungenkarzinom angestrebt werden sollte und in Zukunft ein Vorbild für die Therapie bei anderen soliden Tumoren sein kann.
Positionspapier des Deutschen Netzwerk Versorgungsforschung (DNVF) zur anwendungsbegleitenden Datenerhebung nach Sozialgesetzbuch V
Authors: Niemeyer Anna , Kluge Sandra , Gurisch Carolin , Hoffmann Wolfgang , Kostuj Tanja , Olbrich Kerstin , Rau Henriette , Rauchensteiner Stephan , Stausberg Jürgen 
Published: April 2021

ZusammenfassungIm Rahmen der frühen Nutzenbewertung kann der Gemeinsame Bundesausschuss (G-BA) seit 2019, ermächtigt durch das Gesetz für mehr Sicherheit in der Arzneimittelversorgung (GSAV), für bestimmte Arzneimittel eine anwendungsbegleitende Datenerhebung fordern. Daraus ergeben sich besondere Herausforderungen, insbesondere im Spannungsfeld zwischen methodischer Anforderung und praktischer Umsetzbarkeit. Das Positionspapier gibt hierzu eine Übersicht und greift dabei die durch das Bundesministerium für Gesundheit (BMG) definierten Rahmenvorgaben sowie die seitens des G- BA formulierten Verfahrensweise auf. Im Anschluss werden mögliche Lösungsansätze diskutiert und Empfehlungen für die Umsetzung aus der Perspektive der Versorgungsforschung gegeben.
Von der Verhaltensprävention zur Verhältnisprävention – 10 Jahre Deutsche Allianz Nichtübertragbare Krankheiten DANK
Authors: Garlichs Dietrich , Bitzer Barbara 
Published: April 2021

ZusammenfassungVor 10 Jahren wurde die „Deutsche Allianz Nichtübertragbare Krankheiten“ DANK gegründet, ein Zusammenschluss von inzwischen 24 medizinischen Fachgesellschaften, um die Politik zu mobilisieren, der Welle der nichtübertragbaren Krankheiten mit besserer Primärprävention zu begegnen.Die aktuelle Corona-Krise zeigt, dass die Politik in der Lage ist, drastische Maßnahmen, wie einen Lockdown von Wirtschaft und Gesellschaft, zu verfügen, um gesundheitliche Gefahren abzuwehren. Sie zeigt auch, wie wichtig ein dauerhaftes Zurückdrängen der chronischen Krankheiten ist. Die weitaus meisten COVID-19-Toten hatten mindestens eine schwere Vorerkrankung. Ohne die hohe Zahl der Vorerkrankungen in Deutschland wäre die Zahl der Corona-Opfer sehr wahrscheinlich deutlich niedriger und die Notwendigkeit drastischer Einschränkungen des zivilen Lebens geringer gewesen. Den Tsunami der nichtübertragbaren Krankheiten zurückzudrängen, ist also auch aus diesem Grunde dringend notwendig.Die mehr als 10 Jahre andauernde Debatte über die Notwendigkeit einer einfach verständlichen Lebensmittelkennzeichnung und die jetzt erfolgte Zulassung des Nutri-Scores ist ein gutes Beispiel, dass es nicht reicht, wissenschaftlich fundierte Lösungen zu veröffentlichen – man muss auch dafür werben, relevante Informationen immer wieder im politischen Raum kommunizieren und falls nötig, politischen Druck aufbauen. DANK hat in 10 Jahren gezeigt, dass die Wissenschaft dazu in der Lage ist. Eine wesentliche Leistung von DANK bestand darin, wichtige medizinische Fachgesellschaften zu zahlreichen gesundheitspolitischen Initiativen zu motivieren und hinter den konkreten Forderungen nach mehr Verhältnisprävention zu einen.
Establishment of bovine expanded potential stem cells
Authors: Zhao Lixia , Gao Xuefei , Zheng Yuxuan , Wang Zixin , Zhao Gaoping , Ren Jie , Zhang Jia , Wu Jian , Wu Baojiang , Chen Yanglin , Sun Wei , Li Yunxia , Su Jie , Ding Yulin , Gao Yuan , Liu Moning , Bai Xiaochun , Sun Liangzhong , Cao Guifang , Tang Fuchou , Bao Siqin , Liu Pentao , Li Xihe 
Published: April 2021

Significance
Bovine embryonic stem cells and pluripotent stem cells hold the potential to substantially advance biotechnology and agriculture. We report the establishment of bovine expanded potential stem cells (bEPSCs) from preimplantation embryos of both wild-type and somatic cell nuclear transfer (SCNT). EPSCs have broader developmental potential to generate embryonic and extraembryonic cell lineages. bEPSCs express high levels of pluripotency genes, propagate robustly in single cell passaging, are genetically stable, and permit efficient precise gene editing. They differentiate in vitro and in chimeras to both the embryonic and extraembryonic cell lineages. Importantly, genetically modified bEPSCs can be used as donors in SCNT or cloning.
Effects of attaching elastic bands to the waist and heels on drop jumps
Authors: Peng Hsien‐Te , Song Chen‐Yi , Chen Zong‐Rong , Lai Chien‐Ting , Gu Chin‐Yi , Wang Li‐I. 
Published: April 2021

ABSTRACTThis study aimed to investigate the effects of the external load of elastic bands attached to the waist and heels to enhance the pre‐activation of leg extensor muscles on drop jumps (DJs). Twelve male college athletes volunteered for this study. Eight cameras and two force platforms were used to collect data. Each subject performed DJs with elastic band loads of 0% and 20% body weight (BW) attached to the waist and heels during the airborne and landing phases from 40‐ and 50‐cm drop heights. Repeated measures of two‐way analysis of variance were performed with two loads of the elastic bands and two heights of the platform for each dependent biomechanical variable. Jump height, reactive strength index, leg stiffness, hip, knee flexion, and ankle plantarflexion angles at the initial foot contact and ankle dorsiflexion range of motion (ROM) significantly increased with 20% BW loads. The peak ground reaction force of impact, eccentric work, and hip flexion range of motion significantly decreased with 20% BW loads. The use of the elastic bands as accentuated loading during the airborne and landing phases of DJs can induce pre‐activation of the joint extensors of the lower extremity to achieve stretch‐shortening cycle benefits and performance and reduce the ground impact for the lower extremity.

Highlights
Attaching elastic bands to the waist and heels enables the following during drop jumps.
The joint extensors of the lower extremities act as a counterbalance to the pull from the elastic bands.
The performance of the drop jump was improved.
The ground impact was reduced.
Proton radioactivity within the generalized liquid drop model with various versions of proximity potentials
Authors: Xin Yu-Qi , Deng Jun-Gang , Zhang Hong-Fei 
Published: April 2021

Abstract
In the present work, we systematically investigate the proton radioactivity half-lives of spherical proton emitters adopting a generalized liquid drop model (GLDM) with 16 different proximity potentials, of which the proximity potential Prox.77-13 gives the closest results to the experimental data. Combined with the previous conclusion that the GLDM with proximity potential Prox.77-13 can also best describe α decay half-lives, which makes the model more uniform and consistent. Further, we use the proximity potential Prox.77-13 in GLDM to predict the proton radioactivity half-lives of 14 spherical proton emitters that are allowed energetically but not yet observed experimentally or specifically quantified. Finally, we research the Geiger−Nuttall law for proton radioactivity. The results reveal that the Geiger−Nuttall law can also be well used to study the proton radioactivity half-lives of isotopes with the same orbital angular momentum l.
Prediction of Heart Diseases using Random Forest
Authors: Pal Madhumita , Parija Smita 
Published: April 2021

Abstract
The process of discovering or mining information from a huge volume of data is known as data mining technology. Today data mining has lots of application in every aspects of human life. Applications of data mining are wide and diverse. Among this health care is a major application of data mining. Medical field has get benefited more from data mining. Heart Disease is the most dangerous life-threatening chronic disease globally. The objective of the work is to predicts the occurrence of heart disease of a patient using random forest algorithm. The dataset was accessed from Kaggle site. The dataset contains 303 samples and 14 attributes are taken for features of the dataset. Then it was processed using python open access software in jupyter notebook. The datasets are classified and processed using machine learning algorithm Random forest. The outcomes of the dataset are expressed in terms of accuracy, sensitivity and specificity in percentage. Using random forest algorithm, we obtained accuracy of 86.9% for prediction of heart disease with sensitivity value 90.6% and specificity value 82.7%. From the receiver operating characteristics, we obtained the diagnosis rate for prediction of heart disease using random forest is 93.3%. The random forest algorithm has proven to be the most efficient algorithm for classification of heart disease and therefore it is used in the proposed system.
A LiDAR System Simulator Using Parallel Raytracing and Validated by Comparison with a Real Sensor
Authors: Gusmão G F , Barbosa C R H , Raposo A B , de Oliveira R C 
Published: April 2021

Abstract
The advances in 3D imaging and Computer Vision are allowing for a massive acquisition of data, especially in the form of point clouds. However, scanning with these sensors can be costly and time consuming, reducing the efficiency of certain procedures, like deep learning dataset generation and forest canopy calculation. One trending solution is the creation of 3D imaging sensor simulators. This paper presents a simulator for light detection and ranging (LiDAR) systems with a parallel raytracing approach and a flexible scene creator. Finally, the simulator sensor is validated with a real LiDAR data.
Evaluating of the ultrasonic transducers’ diameter on velocity measurement in nondestructive testing
Authors: Dourado T C , Miqueleti S A , Alvarenga A V , Costa-Félix R P B 
Published: April 2021

Abstract
Nondestructive test (NDT) has been proved to be a valuable support for industry, saving money and time. International standards provide some requirements for transducers’ diameter concerning the area section of the workpiece to be inspected. This paper investigates the relationship between the ultrasonic transducer’s diameter and the area section to be measured, and its impact on the measured longitudinal ultrasonic velocity. Initial results showed a tendency of growth on the ultrasonic velocity with an increase of diameter.
Multiparametric measuring system for atmospheric monitoring
Authors: Cavalcante T L , Louzada D R , da Silva A , Monteiro E C 
Published: April 2021

Abstract
There are several studies in the literature on monitoring carbon dioxide concentrations in combination with various other parameters to assess indoor air quality. However, no study describes the monitoring of air quality in different locations of the same environment. The characterization of the spatial distribution of atmospheric parameters can contribute to more appropriate analyses, providing customized planning’s for improvements. The present work develops a multiparametric measuring system for real-time monitoring of the spatial distribution of carbon dioxide, temperature, humidity, particulate matter, volatile organic compounds, and barometric pressure. Preliminary results indicate the necessity of multiple-location measurement for appropriate air quality analyses.
Proposed model for legal control applied to stand-alone meters and measuring systems for crude oil
Authors: Junior Edisio Alves de Aguiar 
Published: April 2021

Abstract
The joint resolution ANP/Inmetro 001/2013 approves the Technical Regulation for Oil and Natural Gas Measurements, establishing the technical, constructive and metrological conditions and requirements that oil and natural gas meters and measuring systems must comply, to ensure the credibility of the measurement results. This is a singular regulation in the field of legal metrology in Brazil, and this study presents technical details that may justify the possibility of implementing differences between the legal control of meters and measuring systems used on the crude oil production. Emphasis is given to the singleness of the measuring systems employed on oil platforms, designed for unique installation conditions, on specific platforms, which limit the possibility of presenting a “prototype”, a common practice in the control of serial manufactured instruments. The possibility of a project type approval model, presented by the metering station developer is suggested.
Metrological control of electrolytic sensors for measuring angular deviations: application in monitoring displacements of engineering structures
Authors: Leaño E G , Rocha Filho P , Hernández J D , Frota M N 
Published: April 2021

Abstract
The work evaluates the metrological reliability of electrolytic sensors (electrolevels) used to determine angular deviations of elements of engineering structures. The motivation for the work resulted from its strategic application to control the stability of civil engineering structures, notably with application in geotechnics (e.g.: different types of dams of interest in engineering). The methodology used is based on the metrological consistency associated with the quality control process of a set of electrolevels submitted, simultaneously, to a careful calibration process, with traceability ensured to a standard angular deviation pattern. Mounted on a rigid bar, but pivoted at one of its ends to allow angular displacements, the output of each electrolevel is compared with the previously calibrated standard, for different controlled conditions of angular deviation imposed on the bar articulated to a center of rotation. The result of the analysis of typical calibration data for nine electrolevels allowed to show that the expression of the uncertainties associated with the calibration measurements performed constitutes an essential foundation to ensure the metrological reliability of the electrolevels, having allowed to validate a method of discard of electrolevels not meeting the allowable tolerance for a specific engineering application. Among the conclusions of the work, it was possible to qualify the statistical treatment of the levels of uncertainty as a sufficiently robust strategy to validate the electrolevels as a reliable instrument for angular displacement measurements, with a level of confidence appropriate to its application in civil engineering.