bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Randomized Trial of Voice-Generated Inpatient Progress Notes: Effects on Professional Fee Billing
Authors: White Andrew A. , Lee Tyler , Garrison Michelle M. , Payne Thomas H. 
Published: June 2020

Abstract
Background Prior evaluations of automated speech recognition (ASR) to create hospital progress notes have not analyzed its effect on professional revenue billing codes. As ASR becomes a more common method of entering clinical notes, clinicians, hospital administrators, and payers should understand whether this technology alters charges associated with inpatient physician services.
Objectives This study aimed to measure the difference in professional fee charges between using voice and keyboard to create inpatient progress notes.
Methods In a randomized trial of a novel voice with ASR system, called voice-generated enhanced electronic note system (VGEENS), to generate physician notes, we compared 1,613 notes created using intervention (VGEENS) or control (keyboard with template) created by 31 physicians. We measured three outcomes, as follows: (1) professional fee billing levels assigned by blinded coders, (2) number of elements within each note domain, and (3) frequency of organ system evaluations documented in review of systems (ROS) and physical exam.
Results Participants using VGEENS generated a greater portion of high-level (99233) notes than control users (31.8 vs. 24.3%, p < 0.01). After adjustment for clustering by author, the finding persisted; intervention notes were 1.43 times more likely (95% confidence interval [CI]: 1.14–1.79) to receive a high-level code. Notes created using voice contained an average of 1.34 more history of present illness components (95% CI: 0.14–2.54) and 1.62 more review of systems components (95% CI: 0.48–2.76). The number of physical exam components was unchanged.
Conclusion Using this voice with ASR system as tested slightly increases documentation of patient symptom details without reliance on copy and paste and may raise physician charges. Increased provider reimbursement may encourage hospital and provider group to offer use of voice and ASR to create hospital progress notes as an alternative to usual methods.
Severe Diabetic Ketoacidosis in a Child with Type-1 Diabetes, Asthma, and COVID-19
Authors: Vasconez Wilson A. , Bustamante Escobar Carmen L. , Agarwal Nisha , Solano Juan P. , Sanchez Janine E. 
Published: June 2020

AbstractLittle is known about the association between novel coronavirus disease 2019 (COVID-19) and type-1 diabetes in children. A 16-year-old female patient with history of type-1 diabetes was admitted for life threatening diabetic ketoacidosis (DKA). She recovered from the DKA after 24 hours of insulin infusion and rehydration. On day 2, she was diagnosed with COVID-19. The DKA relapsed and required restarting insulin. She developed leukopenia, neutropenia, and high ferritin. Upon recovery, she was discharged for self-quarantine. Severity of DKA in children with COVID-19 is multifactorial. Clinical suspicion of COVID should be heightened in patients who present with unexplainedly severe DKA.
Does mental fatigue impair physical performance? A replication study
Authors: Holgado Darías , Troya Esther , Perales José C. , Vadillo Miguel A. , Sanabria Daniel 
Published: June 2020

AbstractThe aim of this study is to replicate the hypothesis that mental fatigue impairs physical performance in a pre‐registered (https://osf.io/wqkap/) within‐subjects experiment. 30 recreationally active adults completed a time‐to‐exhaustion test (TTE) at 80% VO2max in two separate sessions, after completing a mental fatigue task or watching a documentary for 90 min. We measured power output, heart rate, (session) rating of perceived exertion (RPE) and subjective mental fatigue. Bayes factor analyses revealed extreme evidence supporting the alternative hypothesis that the mental fatigue task was more mentally fatiguing than the control task, BF01 = 0.009. However, we found moderate‐to‐strong evidence for the null hypothesis (i.e., no evidence of reduced performance) for average time in TTE (BF01 = 9.762) and anecdotal evidence for the null hypothesis in (session) RPE (BF01 = 2.902) and heart rate (BF01 = 2.587). Our data seem to challenge the idea that mental fatigue has a negative influence on exercise performance. Although we did succeed at manipulating subjective mental fatigue, this did not impair physical performance. However, we cannot discard the possibility that mental fatigue may have a negative influence under conditions not explored here, e.g., individualizing mentally fatiguing tasks. In sum, further research is warranted to determine the role of mental fatigue on exercise and sport performance.
Plasmonic biosensor based on excellently absorbable adjustable plasmon-induced transparency in black phosphorus and graphene metamaterials
Authors: Liu Chao , Li Hongjian , Xu Hui , Zhao Mingzhuo , Xiong Cuixiu , Li Min , Ruan Banxian , Zhang Baihui , Wu Kuan 
Published: June 2020

Abstract
Both black phosphorus (BP) and graphene metamaterials support surface plasmon resonance that is very sensitive to the local change of dielectric properties in the system. We design a novel BP ribbons and graphene sheet hybrid structures based on tunable mid-infrared plasmon-induced transparency. The destructive interference of two excited state modes in the structure leads to a pronounced induced transparent window, and the excellent absorption of the two channels is obtained. The transmission characteristics of the hybrid structures are theoretical calculation, and the results are in good agreement with the numerical simulation curve. The dynamic adjustment function can be realized by adjusting the carrier density of BP and the Fermi level of graphene, and the excellent absorption performance not affected within the adjustment range. Moreover, we found that the optical phase is rapid flips at the plasmon resonance position. Therefore, a sensing medium layer is added on top of the structure to implement a high-sensitivity plasmonic biosensor. This work may provide potential applications to plasmonic devices based on two-dimensional metamaterial.
Step-edge assisted large scale FeSe monolayer growth on epitaxial Bi2Se3 thin films
Authors: Fikáček J , Procházka P , Stetsovych V , Průša S , Vondráček M , Kormoš L , Skála T , Vlaic P , Caha O , Carva K , Čechal J , Springholz G , Honolka J 
Published: June 2020

Abstract
Enhanced superconductivity of FeSe in the 2D limit on oxide surfaces as well as the prediction of topological superconductivity at the interface to topological insulators makes the fabrication of Fe-chalcogenide monolayers a topic of current interest. So far superconductive properties of the latter are mostly studied by scanning tunneling spectroscopy, which can detect gaps in the local density of states as an indicator for Cooper pairing. Direct macroscopic transport properties, which can prove or falsify a true superconducting phase, are yet widely unexplored due to the difficulty to grow monolayer films with homogeneous material properties on a larger scale. Here we report on a promising route to fabricate micron-scale continuous carpets of monolayer thick FeSe on Bi2Se3 topological insulators. In contrast to previous procedures based on ultraflat bulk Bi2Se3 surfaces, we use molecular beam epitaxy grown Bi2Se3 films with high step-edge densities (terrace widths 10–100 nm). We observe that step edges promote the almost strainless growth of coalescing FeSe domains without compromising the underlying Bi2Se3 crystal structure.
Poisson GSTAR model: spatial temporal modeling count data follow generalized linear model and count time series models
Authors: Wardhani L P , Setiawan  , Suhartono  , Kuswanto H 
Published: June 2020

Abstract
This paper discusses the formation of one temporal spatial model, the Generalized Space Time Autoregressive (GSTAR) model, if the data of model is count data. The development of the GSTAR model is an update or problem completion of the count data which tends to be stationary and non-normal / Gaussian data, because GSTAR model is assumed normal distributed and stationarity. GSTAR modelling for count data refers to the Time series model for count data, which are the Generalized Autoregressive Moving Average (GARMA) model and modeling Count Time Series which has the Generalized Linear Model (GLM) concept. The model formed is called the Poisson GSTAR model
An introduction of the GE/BC embedded meshless method by using an ODE problem as example
Authors: Wu Nan-Jing , Lin Yen-Ting 
Published: June 2020

Abstract
In this paper, the GE/BC embedded meshless method is illustrated by using a 1D boundary value problem as an example. The formulation for solving one dimensional ordinary differential equation is listed. Comparison with conventional collocation method is also demonstrated. The GE/BC embedded meshless method performs much better, especially in the computation of the derivative at boundary nodes.
Convolutional neural network modeling for classification of pulmonary tuberculosis disease
Authors: Andika Lingga Aji , Pratiwi Hasih , Sulistijowati Handajani Sri 
Published: June 2020

Abstract
Tuberculosis (TB) is caused by bacteria (Mycobacterium tuberculosis) that most often affect the lungs so called Pulmonary Tuberculosis or PTB. Diagnosis using a chest radiograph image manually by doctor requires a long time, even difficult to detect PTB. Convolutional neural network (CNN) is a deep learning method that adopts the performance of human brain neurons called neural network and convolution functions to classify images. CNN can also help classify PTB based on chest radiograph images. This study uses data from the National Library of Medicine, Maryland, USA in collaboration with Shenzhen No.3 People’s Hospital, Guangdong Medical College, Shenzhen, China including 663 images entered into two classes, normal and PTB. This study uses adaptive momentum optimization (Adam) which serves to improve the accuracy of the model. The classification results of the models built were 99.19% for training data and 80.60% for validation data with 75 epochs, and accuracy in the test data was 84% which means that the model was able to qualify 84% of the test data into normal classes and PTB appropriately. 25 correctly classified as normal lungs, 5 incorrectly and 26 correctly classified as PTB and 5 incorrect.
The Effect Of Wind Turbine On Sea Flow
Authors: Anggriani I , Adnyani L P , Millah N 
Published: June 2020

Abstract
Indonesia is a maritime country that had quite extensive territorial waters, one of which was located in the East Kalimantan region. Despite having a large enough water area, only 30 percent of new marine products were used. Marine resources, especially fisheries in Indonesia, were very abundant but had not been utilized to the full, so it needs to be made offshore cages with the concept of aquaculture. Pembangkit Listrik Tenaga Bayu (PLTB) was renewable alternative energy for the coastal region. To support the development of this technological innovation, this study discussed the movement of ocean currents under the influence of wind turbines. The governing equation consists of the Shallow Water Equation and the wind turbine model. After the governing equation is formed, the model is solved numerically and the effect of wind turbines on sea flow can be observed. The influence of wind turbines on sea currents is large enough that it needs to be reviewed in the planning of developing wind turbines in the offshore sea area.
Counting of Bacterial Colonies of the Low Quality Image Using Perona-Malik Diffusion Filters and Image Morphology Operators
Authors: Hamdani Ibnu Mansyur , Anam Syaiful , Shofianah Nur 
Published: June 2020

Abstract
Microbiology has an important role in solving human problems that are complex in many fields, such as environment, medical, and biotechnology. One of the important researches in microbiology fields is the counting of the number of bacterial colonies. This research is very important to estimate the number of bacterial cells in each millilitre or gram sample. However, the manual counting of bacterial colonies is a time-consuming task and is also very tedious. To help microbiologists, an automatic counting method of bacterial colonies based on bacterial colonies images is needed to be developed. However, the bacterial colonies images taking by the phones camera has low-quality. They are noise presence and lack of contrast, so the pre-processing method is necessary to be developed. This paper proposes the pre-processing method by using modified Perona-Malik Diffusion (PMD) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The modified PMD filter is used to reduce noise, while the CLAHE method is used to enhance the contrast. Moreover, the morphology operators are used for the petri dish extraction and bacterial colonies counting process. The experimental results show that the proposed method has better performance than the original PMD based on the average values of precision, recall, and F-measure.
A random search method for finding ‘K ≥ 2’ number of ranked optimal solution to an assignment problem
Authors: Kumar Santosh , Al-Hasani Ali , Al-Rabeeah Masar , Eberhard Andrew 
Published: June 2020

Abstract
A need for an optimal solution for a given mathematical model is well known and many solution approaches have been developed to identify efficiently an optimal solution for a given situation. For example, one such class of mathematical models with industrial applications have been classified as mathematical programming models (MPM). The main idea behind these models is to find the optimal solution described by those models and interpret the solution back with respect to the given industrial situation. However, the same is not true for a ‘K’number of ranked optimal solutions, where K ≥ 2. Mathematically, the Kth best solution, K ≥ 2, deals with determination of the 2nd, 3rd, 4th or in general the Kth best solution. This Kth best solution, K ≥ 2, suddenly becomes much more demanding with respect to computational requirements, which increases with the increase in the value of K. This paper first identifies difficulties associated with determination of ranked solutions and later develops a random search method to find ranked optimal solutions in the case of an assignment problem. We test the efficiency of the proposed approach by executing the random search method on a number of different size assignment problems.
Missile Control Design for Moving Target using Model Predictive Control
Authors: Asfihani Tahiyatul , Mufidah Mirzaq Khoirul , Subchan Subchan , Adzkiya Dieky 
Published: June 2020

Abstract
In this paper, Model Predictive Control (MPC) is proposed to control a missile such that the missile reaches the moving target in minimum time. The dynamics of missile is a non-linear model. MPC is applied to linearized missile dynamics. In this study, we conduct some simulations, namely with state constraints and without state constraints. We also observe the influence of weights and prediction horizon to the time needed to reach the target. The simulation results show that the fastest time to reach the target is 20 seconds. This is achieved when the prediction horizon is 10 and the state constraints are present.
Morphological contour decomposition and reconstruction by using asymmetric SEs
Authors: Lu Minhao , Idogawa Tomoyuki 
Published: June 2020

Abstract
We had proposed an algorithm based on mathematical morphology to decompose and reconstruct an image by using two contours [4]. These contours are defined as the edge and the second edge (i.e., the edge of the image which has been removed an edge once) of the original image. Since these contours have informations of boundary and internal direction of image, the original image can be reconstructed exactly from them. However, there was a restriction in this algorithm that the structuring element (SE) used for making contours must be symmetric. In this paper, we will show an expansion of the algorithm to be able to use asymmetric SEs under a certain condition and also show some applications of this method.
CPT Symmetry Test at KLOE-2
Authors: De Lucia E 
Published: June 2020

Abstract
The entanglement in the neutral kaon pairs produced at the DAΦNE ϕ-factory is a unique tool to test discrete symmetries and quantum coherence at the utmost sensitivity, in particular strongly motivating the experimental searches of possible CPT violating effects, which would unambiguously signal New Physics. KLOE and KLOE-2 data sets with about 2.4×1010
ϕ-meson produced represent the largest sample ever collected at the ϕ-meson peak. The lepton charge asymmetry measured in K

S
semileptonic decays with 1.7 fb−1 of KLOE data, improving the statistical uncertainty of present result by about a factor two, has been presented together with the test of CPT in transitions in ϕ → K

S

K

L
→ π
±
e

∓

ν, 3π
0 and K

S

K

L
→ π
+
π
−, π
±
e

∓

ν decays.
Flavour physics and CP violation at KLOE-2
Authors: Selce A 
Published: June 2020

Abstract
The KLOE-2 experiment at the upgraded e
+
e
− DAΦNE collider of the INFN Laboratori Nazionali di Frascati completed its data taking campaign at the end of March 2018, collecting 5.5 fb−1 at the center of mass energy corrisponding to the mass of the ϕ-meson. Together with the data set of its predecessor KLOE, the acquired data sample of 8 fb−1 corresponds to 2.4×1010
ϕ-meson produced: the largest sample ever collected at the ϕ(1020) at e
+
e
− colliders.
KLOE-2 Collaboration activities are now focused on data reconstruction and analysis, continuing the KLOE long-standing tradition of flavour physics precision measurements in the kaon sector, to probe CKM unitarity and search for dark force mediator. Latest results on K

S
rare decays will be presented and discussed in the framework of Flavour Physics and CP Violation tests, focusing on the measurement of K

S
semileptonic branching ratios, using 1.7 fb−1 KLOE data, and the search for the pure CP-violating K

S
→ 3π
0 decay.
Normalization Studies on the 2016-2018 Data for the KOTO Experiment
Authors: Hutcheson Melissa A. 
Published: June 2020

Abstract
The KOTO experiment is searching for the rare decay of the neutral kaon,

K
L
0

→

π
0

ν

ν
¯

. KOTO collected data from 2016 to 2018 and accumulated 3.1 × 1019 Protons on Target (POT). Results on the normalization analysis of this dataset are described. Three normalization modes,

K
L
0

→
3

π
0

,

K
L
0

→
2

π
0

, and

K
L
0

→
2
γ

, are used to calculate the

K
L
0

flux and the number of kaons at the beam exit. The data is well reproduced by the Monte Carlo and the total number of kaons is (7.14 ± 0.05) × 1012. This gives a Single Event Sensitivity (SES) of 6.9 × 10−10.
Implementation of single shot detector for object finding in drone platform
Authors: Faza Alfarih , Darma Surya 
Published: June 2020

Abstract
The developing of object detection for many purpose has come to various techniques. Some of those works also implement to solve our daily life problem. Those development which are state-of-the-art are mostly applicable with many pros than others. In this paper we use one of state-of-the-art of object detection to control a quadcopter i.e. Single Shot MultiBox Detector (SSD). SSD is used to detect an object as quadcopter target for approach mission. SSD also use to keep an eye of the target. The target is represented in shape of ROI location. This ROI or a bounding box location is used as feedback of the control system of quadcopter which will guide the quadcopter to approach. The mission is considered as success if the quadcopter is stopped at minimum range 1 meter toward target. This works shows the successful of object detection implementation by serving IMU responses and measured distance to object responses.
Stroke severity classification based on EEG signals using 1D convolutional neural network
Authors: Yunita Dewi Fitria , Faza Alfarih , Prajitno Prawito , Kusuma Wijaya Sastra 
Published: June 2020

Abstract
Acute Ischemic Stroke (AIS) is one kind of stroke that occurs the most. Stroke itself is the number one cause of death that can reduce blood flow and deprive the oxygen into the brain. Early diagnosis can help patients getting faster medical treatment thus avoid unwanted damage to the brain. Electroencephalogram (EEG) is an alternative tool for diagnosing AIS to the standard tools as in MRI or CT-scan. In this research, we try to classify stroke severity with 1 dimensional CNN (Convolutional Neural Network). The proposed method calculates the power spectral density (PSD) of EEG recordings from normal and stroke subjects, as the model’s inputs and extracts feature automatically using CNN. The final feature-maps were trained in fully connected layer to classify 4 classes: normal, mild, moderate and severe stroke. The research is conducted to reach the possible optimum computing time with accuracy reached to 97.3% for 64 s segmentation, and 50 convolutional filters with 1x120 kernel size. This result is obtained by using the EEG signal from 4 channels: C3, C4, O1, and O2.
Rain detection in image using convolutional neural network
Authors: Luqman Hakim Arif , Prawito  
Published: June 2020

Abstract
Weather is a phenomenon occurs in the earth’s atmosphere. Weather affects human daily activities, especially outdoor activities. Weather observations including rainfall observation in Indonesia conducted by Meteorology, Climatology, and Geophysical Agency (BMKG). BMKG facing a major problem in terms of rainfall data spatial density. The insufficient amount and unevenly distributed rainfall measurement instrument, are two main factors contributing to rainfall data special density problems. One of the very prominent methods to gain a larger amount of rainfall measurement location is using the image obtained from existing Closed Circuit Television (CCTV) spread over vast areas, especially in the Jakarta region. The approach to recognize and classify the rainfall in a certain area from the CCTV image used in this research is the Convolutional Neural Network (CNN) method. The image data was taken from CCTV located in Kamal, Kalideres, West Jakarta. The images taken is split into two categories, the one that shows a rainy day and the one that shows a clear day. These two categories of images will be used as sample data to train CNN, an effort to obtain a suitable model. By using the CNN method, it’s possible to recognize and classify the rainfall condition within an image based on the model. Python is an open-source programming language that widely used nowadays to run CNN. The image classification using this CNN, scored approximately 98.30% of accuracy, which means that the model is optimal to recognize and classify rainfall conditions in a certain area based on the CCTV images.