bluuebunny/crossref_metadata_2025_split huggingface dataset:
Superflares on the late-type giant KIC 2852961
Authors: Kővári Zs. , Oláh K. , Günther M. N. , Vida K. , Kriskovics L. , Seli B. , Bakos G. Á. , Hartman J. D. , Csubry Z. , Bhatti W. 
Published: July 2020

Context. The most powerful superflares reaching 1039 erg bolometric energy are from giant stars. The mechanism behind flaring is thought to be the magnetic reconnection, which is closely related to magnetic activity (including starspots). However, it is poorly understood how the underlying magnetic dynamo works and how the flare activity is related to the stellar properties that eventually control the dynamo action.
Aims. We analyze the flaring activity of KIC 2852961, a late-type giant star, in order to understand how its flare statistics are related to those of other stars with flares and superflares, and to understand the role of the observed stellar properties in generating flares.
Methods. We searched for flares in the full Kepler dataset of KIC 2852961 using an automated technique together with visual inspection. We cross-matched the flare-like events detected by the two different approaches and set a final list of 59 verified flares during the observing term. We calculated flare energies for the sample and performed a statistical analysis.
Results. The stellar properties of KIC 2852961 are revised and a more consistent set of parameters are proposed. The cumulative flare energy distribution can be characterized by a broken power law; that is to say, on the log-log representation the distribution function is fitted by two linear functions with different slopes, depending on the energy range fitted. We find that the total flare energy integrated over a few rotation periods correlates with the average amplitude of the rotational modulation due to starspots.
Conclusions. Flares and superflares seem to be the result of the same physical mechanism at different energy levels, also implying that late-type stars in the main sequence and flaring giant stars have the same underlying physical process for emitting flares. There might be a scaling effect behind the generation of flares and superflares in the sense that the higher the magnetic activity, the higher the overall magnetic energy released by flares and/or superflares.
CS Cha B: A disc-obscured M-type star mimicking a polarised planetary companion
Authors: Haffert S. Y. , van Holstein R. G. , Ginski C. , Brinchmann J. , Snellen I. A. G. , Milli J. , Stolker T. , Keller C. U. , Girard J. 
Published: July 2020

Context. Direct imaging provides a steady flow of newly discovered giant planets and brown dwarf companions. These multi-object systems can provide information about the formation of low-mass companions in wide orbits and/or help us to speculate about possible migration scenarios. Accurate classification of companions is crucial for testing formation pathways.
Aims. In this work we further characterise the recently discovered candidate for a planetary-mass companion CS Cha b and determine if it is still accreting.
Methods. MUSE is a four-laser-adaptive-optics-assisted medium-resolution integral-field spectrograph in the optical part of the spectrum. We observed the CS Cha system to obtain the first spectrum of CS Cha b. The companion is characterised by modelling both the spectrum from 6300 Å to 9300 Å and the photometry using archival data from the visible to the near-infrared (NIR).
Results. We find evidence of accretion and outflow signatures in Hα and OI emission. The atmospheric models with the highest likelihood indicate an effective temperature of 3450 ± 50 K with a log g of 3.6 ± 0.5 dex. Based on evolutionary models, we find that the majority of the object is obscured. We determine the mass of the faint companion with several methods to be between 0.07 M⊙ and 0.71 M⊙ with an accretion rate of Ṁ = 4 × 10−11±0.4 M⊙ yr−1.
Conclusions. Our results show that CS Cha B is most likely a mid-M-type star that is obscured by a highly inclined disc, which has led to its previous classification using broadband NIR photometry as a planetary-mass companion. This shows that it is important and necessary to observe over a broad spectral range to constrain the nature of faint companions.
Single-Center Outcomes with Rapid Deployment Aortic Valve Replacement
Authors: Schlömicher Markus , Bechtel Matthias , Useini Dritan , Naraghi Hamid , Haldenwang Peter Lukas , Moustafine Vadim , Strauch Justus T. 
Published: July 2020

Abstract
Objectives Rapid-deployment valves can reduce procedural times and may facilitate minimally invasive surgery. In our institution, more than 500 patients underwent rapid deployment aortic valve replacement (AVR).
Methods A total of 510 patients underwent rapid deployment AVR between March 2012 and September 2017, of whom 270 patients underwent isolated AVR and 240 underwent AVR with concomitant procedures. The cumulative follow-up time was 1,444 patient-years, the median follow-up time 2.8 years, respectively
Results An early all-cause mortality of 3.5% (n = 18) was seen with a cumulative survival of 91.9 ± 2.2% after 12 months. Mean cross-clamp times were 37 ± 19 minutes for isolated AVR and 93 ± 29 minutes for AVR with concomitant procedures. The rate of new pacemaker implantation was 7.8% (n = 40). No case of structural degeneration occurred in the follow-up. Three (0.6%) cases of endocarditis were registered.
Conclusions Rapid deployment AVR can be performed safely with low complication rates and good hemodynamic results. Therefore, the relevance in aortic valve surgery can be stressed.
Growth and characterization of homoepitaxial β-Ga2O3 layers
Authors: Brooks Tellekamp M , Heinselman Karen N , Harvey Steve , Khan Imran S , Zakutayev Andriy 
Published: July 2020

Abstract

β-Ga2O3 is a next-generation ultra wide bandgap semiconductor (E

g
= 4.8–4.9 eV) that can be homoepitaxially grown on commercial substrates, enabling next-generation power electronic devices among other important applications. Analyzing the quality of deposited homoepitaxial layers used in such devices is challenging, in part due to the large probing depth in traditional x-ray diffraction (XRD) and also due to the surface-sensitive nature of atomic force microscopy (AFM). Here, a combination of evanescent grazing-incidence skew asymmetric XRD and AFM are investigated as an approach to effectively characterize the quality of homoepitaxial β-Ga2O3 layers grown by molecular beam epitaxy at a variety of Ga/O flux ratios. Accounting for both structure and morphology, optimal films are achieved at a Ga/O ratio of ∼ 1.15, a conclusion that would not be possible to achieve by either XRD or AFM methods alone. Finally, fabricated Schottky barrier diodes with thicker homoepitaxial layers are characterized by J–V and C–V measurements, revealing an unintentional doping density of 4.3 × 1016 cm− 3–2 × 1017 cm−3 in the epilayer. These results demonstrate the importance of complementary measurement methods for improving the quality of the β-Ga2O3 homoepitaxial layers used in power electronic and other devices.
Antimicrobial Surfaces of Metal Halides Immobilized on Polymeric Materials
Authors: Carolina Caicedo , Flórez López Edwin , John Vergara , Jhon Guateque , Yenny Ávila-Torres 
Published: July 2020

Abstract
Chitosan is a biopolymer extracted from the shell of crustaceans. It is currently possible that this be modified to improve its bacterial properties. In this context, in the present work new materials were synthesized as electro-spun films, where the best conditions were establish that led to biocidal activity against to Staphylococcus aureus ATCC 6538. The addition of PVA to chitosan improves electrospun conditions since the chitosan alone cannot stabilize the voltage. The best interaction with water was resolved at 16 % PVA, increasing the hydrophobic capacity of the film. The ion metallic interacts with chitosan matrix through acetyl group, and the alcohol and ammine group do not participate in electronic interaction. Finally, this work shows that the metal ion increases the biocidal properties of chitosan as a thin film.
Synthesized Nanoparticles Based on Functionalized Metal Oxides with Chiral ligand
Authors: Jhonatan Corrales , Florez-López Edwin , Henry Riascos , Lorena Calambás , Yuberth García , Anderson Nasamuez , Yenny Ávila-Torres 
Published: July 2020

Abstract
The research reports the synthesis of α- MnO2 and CuO nanoparticles obtained from laser ablation method. Based on the properties as semiconductors, the oxides as bactericity systems for Escherichia coli ATCC 11229 and Staphylococcus aureus ATCC 6538 have been tested. The influence on plasmon was evaluated, in relationship to the energy, time and wavelength of the laser used. The dispersion of nanoparticles for manganese oxide were in function of the energy, and its frequency oscillation is greater than copper oxide, due to the effects of charge transfer. The effect of a chiral organic compound was estimated in situ, generating stability and reproducible biocidal properties. Finally, it is important to mention that the biological activity was mediated by the semiconductor capacity. Control experiments will be necessary for to continue for the understanding of the mechanism.
Electrochemical monitoring of the oxidative degradation of glyphosate with UV/H2O2
Authors: Castro-Narváez Sandra , Vielma-Balanta Libia , Quiñonez Jairo-Nelson , Jaramillo-Aguirre Alonso 
Published: July 2020

AbstractA voltammetric method optimized for the sensitive and reliable study of the UV/H2O2oxidation of glyphosate (N- (phosphonomethyl) glycine) (GPS). The cell consisted of three electrodes in a compartment, silver wire coated with silver chloride as a pseudo reference electrode, platinum electrode as working electrode and stainless steel as auxiliary electrode. GPS cyclic voltammetric analysis suggests the formation of a GPS-Pt complex on the electrode surface, with an enhanced signal in perchloric acid between 1.3 to -1.3V; sensitive to protonation of the amino group, no response observed at pH values greater than 5.00. The oxidative degradation of GPS with UV/H2O2is evident at pH 3.0, it exhibits first order pseudo kinetics with a half-life of 40 ± 4min. Two degradative routes are estimated depending on the concentration of H2O2: at low concentrations (H2O2/GPS 1: 100 ratio), the formation of sarcosine and then glycine is favored; at H2O2/GPS 1:20 and 1:10 ratios predominate the formation of glycine.
Decision Support System for Determining Floating Dock Locations in Maluku Islands Using AHP-TOPSIS
Authors: Tukan M , Camerling B J , Afifudin M T , Hozairi  
Published: July 2020

AbstractThe number of shipyards that can carry out maintenance and repair work in the province of Maluku is very small, causing many docking queues, causing many companies to choose to dock outside the Maluku province, even though Maluku has a large and good area to build a shipyard. To increase the number and capacity of ship docking sites, the floating docking system was chosen to be developed because it is cheaper and very flexible to be moved because Maluku has two different wind seasons that affect the ship docking process. This study aims to determine the right location for the development of floating docking development in Maluku Province. This study uses a combination of two methods namely AHP-TOPSIS. AHP is used to determine the level of importance between criteria, criteria consisting of aspects of meteorology, geography and oceanography, environmental and population aspects, and aspects of facilities and infrastructure. The results of priority analysis through the weighting of criteria from several experts with the AHP approach to the selection of the best alternative regions using the TOPSIS method. The AHP-TOPSIS calculation results give the first priority value in the Dobo region with a total of 41%, the second in the Saumlaki region with a total of 27%, the third in the Tual region with a total of 24%, and the fourth is the Tiakor region around 8%.
Deep Contextual of Document Using Deep LSTM Meet Matrix Factorization to Handle Sparse Data: Proposed Model
Authors: Hanafi  , Suryana N , Basari ASH 
Published: July 2020

Abstract
Recommender system is important tool in big data era. It has responsible to make suggestion about product or service automatically for web application or mobile. In everyday utility, we cannot escape for information about food, travelling, social network, ticketing, news and etc. What the best choice for customer necessary is recommender system task to provide relevant information. Collaborative filtering is most useful recommender system technique in which considering user behaviour in the past to calculate recommendation. The first generation of collaborative filtering exploit statistical approach to calculate product recommendation. However, traditional collaborative filtering facing serious problem in scalability, accuracy and shortcoming in large data. Model based in the second generation of collaborative filtering to produce product recommendation where this model rely on matrix factorization to produce recommendation. Model based proven better performance over memory based. However, model-based performance degrades significantly when met with sparse data due the number of rating are very small. This problem popular called sparse data problem. Several methods proposed by researchers to handle sparse data problem. Mostly of them exploit text document to increase recommendation performance. However, majority of model fail to gain text document understanding. This study proceeds ongoing process with several stage. First, develop model to interpreted text document using LSTM aims to capture contextual understanding of document. Second, integrated LSTM with matrix factorization. This step aims to produce rating prediction considering text document of the product. The first step completely finished. According to experiment report, this model success to capture contextual of the document then transform into 2D space text document representation. For the further research, we are going to integrated with matrix factorization and evaluation result of rating prediction using RMSE metric evaluation.
Deep learning-based object detection and geographic coordinate estimation system for GeoTiff imagery
Authors: Pratama B M , Gunawan D , Gultom R A G 
Published: July 2020

Abstract
A deep learning-based system has been created to autonomously analyze GeoTiff aerial imagery in order to retrieve information about objects type and their geographic coordinates. This research focuses on applying a Convolutional Neural Network (CNN) to detect objects and estimate the geographic coordinate of airplanes, ships and cars in those images. The system prototype was tested to measure the accuracy and precision for object detection. Furthermore, a Mean Absolute Error (MAE) analysis is done to the system to measure object coordinate estimation performance. The accuracy and precision for object detection of the system prototype are 81,05% and 93,29%, respectively. The system has MAE values which vary from 0,000012° to 0,000034° for object coordinate estimation.
Generate Contextual Insight of Product Review Using Deep LSTM and Word Embedding
Authors: Hanafi  , Suryana N , Basari ASH 
Published: July 2020

AbstractNowadays, in every day live, majority people face in many internet options. For example, what meal to eat, what news to read, what vehicle to ride, what the best path to travelling, what the best group in social network to joint, what the best video to watch, what the best video in YouTube to watch and etc. The best way to recommend the internet content to customer is by using recommender system. Recommender system calculate product recommendation by detecting user behaviour in the past. The user behaviour in the past was being variable to compute similarity between many customers. One of the majority user behaviour is in the term of document. Most of document interpret model in recommender system use traditional NLP model such as TF-IDF, LDA model. According to NLP point of view, traditional NLP face the weakness in contextual understanding. Aims to handle the problem on above, we proposed novel model to generate contextual understanding by involve two important aspect considered subtle word and word sequence. We implemented word embedding based on GLOVE and detecting word sequential using RNN-LSTM. According to qualitative evaluation report, our model successful to capture contextual insight of the document of movie review by IMDB. This model suitable to integrated with latent factor based on matrix factorization to generate product recommendation in Collaborative filtering model.
Information System of Agricultural Commodities Mapping Based on Machine Learning
Authors: Nur D , Riyanti C , Olivya M 
Published: July 2020

Abstract
Climate change that occurs from year to year affects the agricultural sector. People who work in agriculture need to know the compatibility of plants with climate conditions in the region. Back Propagation Neural Network (BPNN) is a multilayer Artificial Neural Network (ANN) that is used to train neural networks with input in the form of precipitation, relative humidity, and temperature data. The training produced a model that was able to classify climate data based on plant growth requirements. The model for Soybean got the highest average accuracy of 96.53% and 90.87% for Rice. Variables that influence training include the number of neurons in the hidden layer, the value of learning rate, the number of folds, and the number of epochs. Prediction results generated from the model are used as a reference to display markers on maps that can be accessed by users.
Predicting Rainfall Intensity using Naïve Bayes and Information Gain Methods (Case Study: Sleman Regency)
Authors: Sena I G W , Dillak J W , Leunupun P , Santoso A J 
Published: July 2020

Abstract
Climate change, which has an impact on environmental problems in tropical countries, is still a severe problem, and efforts to prevent and manage it are continuously pursued. Indonesia, as a tropical country with topographical conditions and strategic geographical position, causes Indonesia to have different weather and climate patterns. Climatologically there are significant differences between the rainy season and the dry season. Both these seasons can bring blessings but also disasters if not appropriately handled, flooding in the rainy season and drought in the dry season. High rainfall can cause floods and landslides, whereas if using excess water in the rainy season can be a solution for water shortages in the dry season. The purpose of this study is to predict the rainfall intensity with the Naïve Bayes method and what parameters are considered the most dominant causes of heavy rainfall using the information gain method. The source of the data in this study came from BMKG data, which was during the rainy season between October to March from 2016 - 2019 in the Sleman Regency. The results of this study showed that the Naïve Bayes method could be used to predict rainfall intensity in Sleman Regency. Also, the most influential parameter on rainfall intensity is the average temperature with an information gain value of 0.047811028.
Detecting Ictal and Interictal Condition of EEG Signal using Higuchi Fractal Dimension and Support Vector Machine
Authors: Wijayanto I , Hadiyoso S , Aulia S , Atmojo B S 
Published: July 2020

Abstract
Ictal and interictal periods are the most important condition which needed to find for detecting and predicting seizure condition in epileptic patients. Neurologist spends hours to analyze electroencephalogram (EEG) signals in order to find a certain pattern for diagnosing epilepsy. This manual interpretation has a high chance of error and very time consuming. In order to minimize mistakes, various studies have proposed a computer-based detection system to support the detection of ictal and interictal conditions. In this study, we extract the EEG signal pattern by using the Higuchi fractal dimension to classify the ictal and interictal conditions of EEG signals. The features are extracted from five EEG sub-bands, delta, theta, alpha, beta, and gamma band. Those features are then fed to support vector machine as the classifier using 10-cross folds validation. The experiment shows that the use of HFD and the quadratic kernel is suitable for ictal detection. While the use of cubic kernel and HFD is suitable for detecting interictal conditions.
Finite State Machines for Building Believable Non-Playable Character in the Game of Khalid ibn Al-Walid
Authors: Fathoni K , Hakkun R Y , Nurhadi H A T 
Published: July 2020

Abstract
The character recognition game Khalid ibn al-Walid is an interactive Drama game application with the perspective of First Person Shooter introducing a hero in Islam named Khalid ibn al-Walid. To make the game interesting, the Non-Playable Character must be given artificial intelligence so that it can behave intelligently and make the game atmosphere more lively. One of the simple artificial intelligence that is applied in NPC games is Finite State Machine (FSM). In this game, FSM is applied to the enemy behavior of the character Khalid ibn al-Walid. The trial results show that NPC can behave independently and smartly in Search Player, Move To Player, Wait to Attack and Attack.
A Fuzzy Logic-based Control System for Microwave Ovens
Authors: Lynn N Deborah , Sourav A I , Santoso A J 
Published: July 2020

Abstract
Conventional microwave ovens rely on users for their cooking time. In other words, the user must set the oven’s cooking time manually. This paper aims to design a fuzzy logic-based controller to resolve the above-mentioned issue by determining the cooking time of the microwave oven automatically. The time determined depends on the quantity and type of food. During the fuzzy logic process, we used the Mamdani fuzzy model. This model involves determining the crisp input, fuzzification, rule evaluation, and defuzzification, to obtain the crisp output result. Input real variables used were the type of food and quantity of food, whereas the output variable used was cooking time. A fuzzy logic toolbox on MATLAB software was used to design and simulate the whole system. Considering the IF-THEN rules and functions, we used the value of food as 50, and the type of food as 50 and we obtained the cooking time as 25 minutes. The result showed that Fuzzy models are good at determining cooking time in Microwave ovens and that their cooking time depends on the inputs. Using fuzzy logic in microwave ovens helps to maintain food quality, saves users’ time, and controls the energy used by the oven.
Automatic Guided Vehicle that detects Dactylopius Opuntiae in Cactus Pears
Authors: Luwes N J , Ogochukwu J C , Rskotsoane B P 
Published: July 2020

Abstract
Food security is one of the biggest challenges faced in Africa with drought being one of the leading factors. Cactus pear is a drought-resistant source of food for both animals and humans. They have become indigenous in South Africa and has maintained a role in the diet of the local population and animal feed. This food source needs to be monitored for Dactylopius Opuntiae. Dactylopuis is an insect that infests the Cactus Pear (Opuntia) and makes a cottony, waxy mass as shelter for the female and their eggs. Industry 4.0 led to the introduction of a subcategory namely Farming 4.0 also known as smart farming. This paper evaluates such a smart farming automatic guided vehicle (AGV) system that could navigate itself from plant to plant using Xbox Kinect and detect Dactylopius Opuntiae. Results show and discuss the navigation-speed and an example of the Dactylopius Opuntiae detection. The passive response could include an internet of things (IoT) approach that could send the GPS data of the infested plant to the farmer or an active approach could be an onsite hands-on application.