bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tidal tails of open star clusters as probes of early gas expulsion
Authors: Dinnbier František , Kroupa Pavel 
Published: July 2020

Context.Star clusters form in the densest parts of infrared dark clouds. The emergence of massive stars expels the residual gas that has not formed stars yet. Gas expulsion lowers the gravitational potential of the embedded cluster, unbinding many of the cluster stars. These stars then move on their own trajectories in the external gravitational field of the Galaxy, forming a tidal tail.Aims.We investigate, for the first time, the formation and evolution of a tidal tail that forms due to expulsion of primordial gas. We contrast the morphology and kinematics of this tail with that of another tidal tail that forms by gradual dynamical evaporation of the star cluster. We intend to provide predictions that can determine the dynamical origin of possibly observed tidal tails around dynamically evolved (age  ≳  100 Myr) galactic star clusters by theGaiamission. These observations might estimate the fraction of the initial cluster population that gets released in the gas expulsion event. The severity of the initial gas expulsion is given by the star formation efficiency and the timescale of gas expulsion for the cluster when it was still embedded in its natal gas. A study with a more extended parameter space of the initial conditions is performed in the follow up paper.Methods.We provide a semi-analytical model for the tail evolution. The model is compared against direct numerical simulations usingNBODY6.Results.Tidal tails released during gas expulsion have different kinematic properties than the tails gradually forming due to evaporation; the latter kind have been extensively studied. The gas expulsion tidal tail shows non-monotonic expansion with time, where longer epochs of expansion are interspersed with shorter epochs of contraction. The tail thickness and velocity dispersions vary strongly, but not exactly periodically, with time. The times of minima of tail thickness and velocity dispersions are given only by the properties of the galactic potential, and not by the properties of the cluster. The estimates provided by the (semi-)analytical model for the extent of the tail, the minima of tail thickness, and velocity dispersions are in a very good agreement with theNBODY6 simulations. This implies that the semi-analytic model can be used to estimate the properties of the gas expulsion tidal tail for a cluster of a given age and orbital parameters without the necessity of performing numerical simulations.
The milliarcsecond-scale radio structure of AB Doradus A
Authors: Climent J. B. , Guirado J. C. , Azulay R. , Marcaide J. M. , Jauncey D. L. , Lestrade J.-F. , Reynolds J. E. 
Published: July 2020

Context. The fast rotator, pre-main sequence star AB Dor A is a strong and persistent radio emitter. The extraordinary coronal flaring activity is thought to be the origin of compact radio emission and other associated phenomena, such as large slingshot prominences.
Aims. We aim to investigate the radio emission mechanism and the milliarcsecond radio structure around AB Dor A.
Methods. We performed phase-referenced VLBI observations at 22.3 GHz, 8.4 GHz, and 1.4 GHz over more than one decade using the Australian VLBI array.
Results. Our 8.4 GHz images show a double core-halo morphology, similar at all epochs, with emission extending at heights between 5 and 18 stellar radii. Furthermore, the sequence of the 8.4 GHz maps shows a clear variation of the source structure within the observing time. However, images at 1.4 GHz and 22.3 GHz are compatible with a compact source. The phase-reference position at 8.4 GHz and 1.4 GHz are coincident with those expected from the well-known milliarcsecond-precise astrometry of this star, meanwhile the 22.3 GHz position is 4σ off the prediction in the north-west direction. The origin of this offset is still unclear.
Conclusions. We have considered several models to explain the morphology and evolution of the inner radio structure detected in AB Dor A. These models include emission from the stellar polar caps, a flaring, magnetically-driven loop structure, and the presence of helmet streamers. We also investigated a possible close companion to AB Dor A. Our results confirm the extraordinary coronal magnetic activity of this star, capable of producing compact radio structures at very large heights that have so far only been seen in binary interacting systems.
Sonografie – von der Forschung zur physiotherapeutischen Praxis
Authors: Hides Julie A. , Mendis Dilani , Franettovich Smith Melinda M. 
Published: July 2020

ZusammenfassungWeltweit setzen Physiotherapeuten die Sonografie sowohl in der klinischen Praxis als auch zu Forschungszwecken ein. Die Autorinnen erläutern die wichtigsten Anwendungsmöglichkeiten und beschreiben drei klinische Anwendungsgebiete, bei denen sich die Untersuchung per Ultraschall in der Physiotherapie lohnen könnte.
Risk Factors for Chronic Traumatic Encephalopathy: A Proposed Framework
Authors: Phelps Alyssa , Mez Jesse , Stern Robert A. , Alosco Michael L. 
Published: July 2020

AbstractChronic traumatic encephalopathy (CTE) is a progressive neurodegenerative disease that has been neuropathologically diagnosed in contact and collision sport athletes, military veterans, and others with a history of exposure to repetitive head impacts (RHI). Identifying methods to diagnose and prevent CTE during life is a high priority. Timely diagnosis and implementation of treatment and preventative strategies for neurodegenerative diseases, including CTE, partially hinge upon early and accurate risk characterization. Here, we propose a framework of risk factors that influence the neuropathological development of CTE. We provide an up-to-date review of the literature examining cumulative exposure to RHI as the environmental trigger for CTE. Because not all individuals exposed to RHI develop CTE, the direct and/or indirect influence of nonhead trauma exposure characteristics (e.g., age, sex, race, genetics) on the pathological development of CTE is reviewed. We conclude with recommendations for future directions, as well as opinions for preventative strategies that could mitigate risk.
2D range modulator for high-precision water calorimetry in scanned carbon-ion beams
Authors: Holm Kim Marina , Weber Ulrich , Simeonov Yuri , Krauss Achim , Jäkel Oliver , Greilich Steffen 
Published: July 2020

Abstract
Ionization chamber-based dosimetry for carbon-ion beams still shows a significantly higher standard uncertainty than high-energy photon dosimetry. This is mainly caused by the high standard uncertainty of the correction factor for beam quality

k

Q
,

Q
0

. Due to a lack of experimental data, the given values for

k

Q
,

Q
0

are based on theoretical calculations. To reduce this standard uncertainty,

k

Q
,

Q
0

factors for different irradiation conditions and ionization chambers (ICs) can be determined experimentally by means of water calorimetry. To perform such measurements in a spread-out Bragg peak (SOBP) for a scanned carbon-ion beam, we describe the process of creating an almost cubic dose distribution of about 6 × 6 × 6 cm3 using a 2D range modulator. The aim is to achieve a field homogeneity with a standard deviation of measured dose values in the middle of the SOBP (over a lateral range and a depth of about 4 cm) below 2% within a scanning time of under 100 s, applying a dose larger than 1 Gy. This paper describes the optimization and characterization of the dose distribution in detail.
Anode spots of low current gliding arc plasmatron
Authors: Böddeker Simon , Bracht Vera , Hermanns Patrick , Gröger Sven , Kogelheide Friederike , Bibinov Nikita , Awakowicz Peter 
Published: July 2020

Abstract
In this work a gliding arc plasmatron consisting of a filamentary discharge rotating in a nitrogen vortex flow at low DC current (I = 100 mA) is investigated. The gas flow swirl of the plasmatron is produced by six tangential gas inlets. The Reynolds number of the nitrogen flow through these tubes at the flow rate of Q = 10 slm amounts to about 2400, which is in the intermediate range. Under these conditions, the formation of micro-vortices can be caused by small gas flow disturbances like e.g. a tube edge. The operation of the GA plasmatron at these conditions is accompanied by the production of plasma spots at the anode surface, namely near the gas inlets. Melted and solidified metal is found in erosion traces left by plasma spots at the anode surface. It is established that melting of stainless steel cannot be caused by an axial current of I = 100 mA of plasma spots and an helical current is supposed. This assumption is confirmed by microscope images of eroded traces with toroidal melting areas. These experimental results corroborate a hypothesis of previous studies, concerning the gliding arc physics, about the formation of plasma objects with an axial magnetic field by the interaction of micro-vortices with the plasma channel.
Insights in the A- and B-site’s Radii Influence in the Polar Character of ABF4
Authors: Espitia Juan S. , Paez Carlos J. , Garcia-Castro A. C. 
Published: July 2020

AbstractHere, we present the study of the ferroelectricity in the ABF4materials with A = (Sr, Ba) and B = (Mg, Zn). This theoretical study was performed based-on first-principles calculations within the density-functional theory, DFT, formalism. We found that the geometric ferroelectricity in ABF4layered compounds can be tuned by changing the A-site with Sr and Ba cations and B-sites with Mg and Zn atoms. Additionally, we studied the stability of the phases as a function of the A and B cations’ occupation preference. We observed a layer-by-layer disposition, as a ground state, when the A-site has different compositions of Sr/Ba.
Non-periodic Noisy Signals Denoising Using Adaptive Neuro-Fuzzy Inference System (ANFIS)
Authors: Santoso I , Warsito A , Prakoso T , Sofwan A , Zahra A A , Christyono Y , Riyadi M A 
Published: July 2020

Abstract
Signal always occurs with noise. Since noise acts as an unwanted signal, we must clear or reduce it with some denoising method. It is relatively easy to denoise the normal distribution noise-contaminated the periodic signal. The problem ascends if a non-Gaussian noise intrudes into a non-periodic signal. The standard filter, such as DWT (discrete wavelet transforms), cannot overcome this directly and blindly. In this research, we proposed ANFIS (Adaptive Neuro-Fuzzy Inference System) as a non-periodic noisy signal denoising method. Foremost, the ANFIS trained to mimic or estimate the interfered noise, then this noise estimation used as a subtractive signal in a non-periodic noisy signal. As a result, the ANFIS can reduce the non-Gaussian noise in the various noisy non-periodic signals with minimum error better than standard DWT (Discrete Wavelet Transform).
Real Time Video Analytics Based on Deep Learning and Big Data for Smart Station
Authors: Hidayat F , Hamami F , Dahlan I A , Supangkat S H , Fadillah A , Hidayatuloh A 
Published: July 2020

Abstract
CCTV is a sophisticated tool for monitoring and surveillance. It was chosen because of the low price and ease of use. CCTV creates issues in monitoring because officers must always supervise every CCTV monitor to look for abnormal conditions. This research proposes the implementation of smart CCTV for surveillance in Bandung Station. It applied deep learning algorithm to create smart CCTV for sensing and understanding station environment such as passengers’ flow, crowded area, prohibited location and others. Big data technologies are also carried out to solve the complexity of data. The objective of this research is to improve the quality of services in Bandung Station and make the station safer, more secure and more convenient.
Collaborative Decision-Making for Human-Technology Interaction - A Case Study Using an Automated Water Bottling Plant
Authors: Coetzer J , Kuriakose R B , Vermaak H J 
Published: July 2020

Abstract
The Fourth Industrial Revolution places different rapidly advancing technologies at the centre of developing autonomous systems. Previous and current research focus on the developing and testing state-of-the-art technologies related to an autonomous environment. However, most industrial environments in the South Africa, and many other developing countries, are still making use of mixed environments. In mixed environments, automated systems and humans need to collaborate for the completion of a process. Currently, there exists very little research on how a collaborative decision-making process can be developed such that the worker’s acceptance and adaptation to the process is taken into cognizance. This research identifies the lack of collaborative decision-making processes as a research gap. This paper focuses on the research done in this field with an extensive literature review, followed by a review of potential models for human technology interaction. The paper then uses the case study of an automated water bottling plant to advance the study in collaborative decision making. The paper concludes by discussing the advantages of collaborative decision.
Generation of Synthetic Continuous Numerical Data Using Generative Adversarial Networks
Authors: Aziira A H , Setiawan N A , Soesanti I 
Published: July 2020

Abstract
Continuous numerical is a type of data which often used for unsupervised learning such as clustering. However, this valuable data often provided in a small amount because it is hard to obtain, expensive, required an expert to collect them, or not available because it contains confidential information that cannot be published. These limited data situations can be an obstacle for processing and analyzing data or restrain clustering related research in general. Therefore, there is a need to be an alternative that can replace or increase the amount of data. The proposed method is generating synthetic continuous numerical data using Generative Adversarial Networks (GANs). This study used two GAN architectures (GAN and CGAN) and focused on unlabeled continuous numerical data to provide replacement or additional data for the clustering task. The Quality of synthetic data was measured using the accuracy of the xgboost algorithm in classifying real and synthetic data. When the xgboost accuracy of perfectly realistic data is 50%, synthetic data based on CGAN achieving 63%. The result of this study shows that GAN can generate data similar enough and not significantly different from the real data.
Self Adaptive and Simulated Annealing Hyper-Heuristics Approach for Post-Enrollment Course Timetabling
Authors: Kartika H M , Ahmad M 
Published: July 2020

Abstract
One of timetabling problem in education field is Post-Enrollment Course Timetabling (PE-CTT). The challenges faced in the PE-CTT are differences in type of problems, a number of limitations, and requirements that differ from one university to another. It is difficult to find common and effective solutions. State-of-art method that can develop more general systems by using cheaper methods and still being able to solve problems is Hyper-Heuristic approach.
The Self-Adaptive Strategy is used as a strategy for selecting Low-Level-Heuristics (LLH) and Simulated Annealing as a Move Acceptance (MA) strategy to solve the course timetabling problems. Self-adaptive can increase the level of convergence to the optimal value in the optimization process. Simulated Annealing can accept solutions that are no better so that the final solution is not trapped in the local optima solution.
The contributions of this paper are state-of-art hybridization of Self-Adaptive and Simulated Annealing Hyper-Heuristic approach to solve post-enrollment course timetabling problem. The Self Adaptive and Simulated Annealing Hyper-Heuristics then compared with Simple Random and Simulated Annealing Hyper-Heuristics. Tests will be carried out on Socha dataset. The hybridization of Self Adaptive and Simulated Annealing shows competitive results.
CFD Analysis of Airflow Through Prism Obstacles Inside Solar Air Heater Channel
Authors: Diana L , Safitra A G , Ichsani D , Nugroho S 
Published: July 2020

Abstract
This study presents an analysis of airflow through prism obstacles inside a trapezoidal solar air heater channel using a computational fluid dynamics method. The purpose is to now the best configuration of gap ratio (S/H) 0.7, 1, 1.3 for 30° obstacle folded angle. The air is 2.62 m/s and the air temperature is 299 K. The channel wall boundary condition is heat flux with a constant value of 620 W/m2 is attached alongside the wall of the channel. The equation is Pressure-velocity coupling performed with SIMPLE. Renormalization-group (RNG) k-ε is chosen as a turbulent model. The simulation result presents temperature distribution, airflow vector in the middle plane of a channel. The obstacle can create backflow. It can increase the air temperature in a channel.
Machine to Machine Communication Protocol for SMART Manufacturing Units
Authors: Gericke G A , Kuriakose R B , Vermaak H J , Madsen Ole 
Published: July 2020

Abstract
This paper explores the need for a true Machine-to-Machine communication protocol without the need of intermediate servers, to allow machines to communicate with one another in a decentralized manner, while offering a lower latency and transmission time than existing protocols. The communication protocol is developed with Siemens Siematic S7-1200 modules that will communicate within different networks and benchmarked against the existing protocols. The results show that a decentralized communication protocol can be implemented with reduced latency and transmission time, while offering critical characteristics needed to realize SMART Manufacturing within real-time for a production line.
PAMELA-CL: Partition Membership Based on Lazy Classifier for Neuromarketing
Authors: Yulita I N , Sholahuddin A , Emilliano  , Novita D 
Published: July 2020

Abstract
Neuromarketing is one of the business strategies that has developed lately. The strategy studies the effect of product promotion on the brain. If the impact analysis on the brain is successfully carried out, the company can find a good and effective marketing strategy for potential customers. This study used electroencephalography (EEG) as data. 30 respondents were involved in data recording. The final goal in this study was to classify the emotions of respondents to the video simulations that were displayed. The video contains a number of products. There were 14 electrodes used for the recording process. Then the EEG data were preprocessed, and its characteristics were extracted before being classified. This study proposed PAMELA-CL for the classification. The classifier was compared with lazy classifier. The result was obtained that this new classifier has higher accuracy than the lazy classifier. The difference in accuracy between the two was above 25%. All experiments involving PAMELA-CL had accuracy above 85%. It showed that this new classifier could be recommended in solving neuromarketing problems, especially for the dataset used in this study.
The Comparison of Cloud Migration Effort on Platform as a Service
Authors: Suryawan R B , Ferdiana R , Widyawan  
Published: July 2020

Abstract
Platform as a Service offers users to easily and quickly deploy applications in the cloud environment without thinking about the availability of supporting resources for developing the application. However, migrating on-premise applications to cloud vendors still has risks ranging from the compatibility of application technology with the cloud platform to the efforts needed. One obstacle in the adoption of cloud technology is the lack of visibility in planning migration efforts. So this study aims to measure and compare the feasibility of PaaS cloud vendors based on the calculation of the effort required during the migration process through an empirical approach. Specifically, an experiment was carried out by migrating on-premise application to Amazon Elastic Beanstalk, Azure App Service, and Google App Engine. Empirical results show that Azure App Service and Amazon Elastic Beanstalk have similar complexity of effort, while Google App Engine has the highest complexity of effort.
The potential of DNA origami to build multifunctional materials
Authors: Tapio Kosti , Bald Ilko 
Published: July 2020

AbstractThe development of the DNA origami technique has revolutionized the field of DNA nanotechnology as it allows to create virtually any arbitrarily shaped nanostructure out of DNA on a 10–100 nm length scale by a rather robust self-assembly process. Additionally, DNA origami nanostructures can be modified with chemical entities with nanometer precision, which allows to tune precisely their properties, their mutual interactions and interactions with their environment. The flexibility and modularity of DNA origami allows also for the creation of dynamic nanostructures, which opens up a plethora of possible functions and applications. Here we review the fundamental properties of DNA origami nanostructures, the wide range of functions that arise from these properties and finally present possible applications of DNA origami based multifunctional materials.
The association between an increased reimbursement cap for chronic disease coverage and healthcare utilization in China: an interrupted time series study
Authors: Shen Menghan , He Wen , Yeoh Eng-Kiong , Wu Yushan 
Published: July 2020

Abstract
Hypertension and diabetes are highly prevalent in China and pose significant health and economic burdens, but large gaps in care remain for people with such conditions. In this article, drawing on administrative insurance claim data from China’s Urban Employee Basic Medical Insurance (UEBMI), we use an interrupted time series design to examine whether an increase in the monthly reimbursement cap for outpatient visits using chronic disease coverage affects healthcare utilization. The cap was increased by 50 yuan per chronic disease on 1 January 2016, in one of the largest cities in China. Compared with the year before the increase, patients with only hypertension increased their spending using chronic disease coverage by 17.8 yuan (P &lt; 0.001) or 11.6%, and those with only diabetes increased their spending using chronic disease coverage by 19.5 yuan (P &lt; 0.001) or 10.6%, with the differences almost entirely driven by spending on drugs. In addition, these two groups of patients reduced their spending using standard outpatient coverage by 13.9 yuan (P &lt; 0.001) or 5.7% and 14.9 yuan (P = 0.03) or 5.2%, respectively, and thus had no changes in total outpatient spending. Patients with both hypertension and diabetes, meanwhile, increased their spending using chronic disease coverage by 54.8 yuan (P &lt; 0.001) or 18.1% and decreased their spending using standard outpatient coverage by 16.1 yuan (P = 0.002) or 6.1%, with no changes in their probability of hospitalization. Among patients with both hypertension and diabetes who had fewer-than-average outpatient visits in 2015, the hospitalization rate decreased after the 2016 reimbursement cap increase (adjusted odds ratio = 0.702, P = 0.01). These findings suggest that increasing financial protection for patients with hypertension and diabetes may be an important strategy for reducing adverse health events, such as hospitalization, in China.