bluuebunny/crossref_metadata_2025_split huggingface dataset:
New technologies available for livestock carcass classification and grading.
Authors: López-Campos Ó. , Prieto N. , Juárez M. , Aalhus J. L. 
Published: May 2019

Abstract
Livestock classification and grading systems are a key tool to evaluate carcass attributes and form the basis of trade amongst producers, packers and retailers. Also, these systems provide a better understanding of the products and market trends. One of the recurrent challenges in carcass classification and grading is the development of objective assessments for traits of economic value. Latest technology developments have improved the precision and accuracy of estimating carcass yields and predicting quality attributes and have the potential to be implemented as real-time management tools to optimize product fabrication and provide feedback to producers. This review outlines some of the most popular technologies available for classification and grading purposes in livestock, including those intended for carcass composition assessments, such as ultrasound, bioelectrical impedance analysis, optical probes, video image analysis, dual energy X-ray absorptiometry and computed tomography, and those for quality attributes evaluations, such as near infrared reflectance, hyperspectral imaging and Raman spectroscopy. The present review will also provide an overview of the recent research studies in the literature using these new technologies for carcass merit and quality assessments. Most of the technologies available are based on electromagnetic or mechanical energies that interact with the tissues at the atomic or molecular level, resulting in attenuated signals that can be detected and analysed quantitatively. Accuracy and repeatability along with cost, practicality and reliability will be important criteria in the successful uptake of these new technologies by the livestock industry. The complexity of the evaluations to place products in an optimum market pathway will eventually result in the deployment of a combination of technologies, working together to provide integral carcass attribute descriptions. Published reports and patents were also considered in the review process.
Patterning of diamond with 10 nm resolution by electron-beam-induced etching
Authors: Dergianlis Vasilis , Geller Martin , Oing Dennis , Wöhrl Nicolas , Lorke Axel 
Published: May 2019

Abstract
We report on mask-less, high resolution etching of diamond surfaces, featuring sizes down to 10 nm. We use a scanning electron microscope (SEM) together with water vapor, which was injected by a needle directly onto the sample surface. Using this versatile and low-damage technique, trenches with different depths were etched. Cross sections of each trench were obtained by focused ion beam milling and used to calculate the achieved aspect ratios. The developed technique opens up the possibility of mask- and resist-less patterning of diamond for nano-optical and electronic applications.
Deep learning-based electroencephalography analysis: a systematic review
Authors: Roy Yannick , Banville Hubert , Albuquerque Isabela , Gramfort Alexandre , Falk Tiago H , Faubert Jocelyn 
Published: May 2019

Abstract

Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. Objective. In this work, we review 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain–computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations. Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to (1) the data, (2) the preprocessing methodology, (3) the DL design choices, (4) the results, and (5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. Results. Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours, while the number of samples seen during training by a network varies from a few dozens to several millions, depending on how epochs are extracted. Interestingly, we saw that more than half the studies used publicly available data and that there has also been a clear shift from intra-subject to inter-subject approaches over the last few years. About

of the studies used convolutional neural networks (CNNs), while

used recurrent neural networks (RNNs), most often with a total of 3–10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was

across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies and emphasize the need for more reproducible research. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly. A planned follow-up to this work will be an online public benchmarking portal listing reproducible results.
Numerical analysis of blood flow in intracranial artery stenosis affected by ischemic stroke using Finite Element method
Authors: Fatahillah A , Setiawani S , Mandala A S , Suharto S , Murtikusuma R P , Safrida L N , Hussen S , Adawiyah R 
Published: May 2019

Abstract
Intracranial stenosis is a narrowing an artery inside the brain. This causes reduced blood flow to the cerebrum and damaged neurons in a short span of time due to reduced oxygenation. Intracranial stenosis can cause ischemic stroke due to a plaque occlusion. This research uses a mathematical model that expresses the velocity of arterial flow of an intracranial stenosis causing ischemic stroke that is expressed by the momentum equation and solved using the finite element method. The analysis of blood velocity is simulated using MATLAB and FLUENT software. This study looks at the effect of plaque thickness and initial speed of blood flow during an intracranial stenosis. The results shows that the larger the plaque thickness, the smaller the area reduction, so the greater the increases blood velocity and the higher the initial velocity of blood that given, the greater the increase of the initial velocity of blood in the artery Intracranial stenosis. According to the MATLAB simulation, blood velocity with 80% plaque thickness increases blood velocity to 1.8665 m/sec at rest and 1.8825 m/sec at exercises. When the initial velocity of blood was 0.2 m/sec, it increases to 0.5712 m/sec at rest and 0.70058 m/sec at exercises.
Ultradiscrete Lotka-Volterra system computes tropical eigenvalue of symmetric tridiagonal matrices
Authors: Fukuda Akiko , Watanabe Sennosuke , Hanaoka Ayumi , Iwasaki Masashi 
Published: May 2019

Abstract
Some of authors’ recent study shows that the time evolution of the integrable ultradiscrete Toda equation computes eigenvalue of tridiagonal matrices over min-plus algebra, where min-plus algebra is a semiring with two binary operations: ⊕ : = min and ⊗ : = +. In this paper, we rst present a Backlund transformation between the ultradiscrete Toda equation and the ultradiscrete Lotka-Volterra system. Using the Backlund transformation, we show that the ultradiscrete Lotka-Volterra system can also compute eigenvalue of symmetric tridiagonal matrices over min-plus algebra.
Numerical simulation of tsunami propagation with Finite Difference Method and Runge Kutta 4th Order Method (study case : south coast of Java island)
Authors: Priyatno Putra Firdaus , Nurul Fajar Shaffiani , Aditya Prima , Apriliani Erna , Sadjidon  , Khusnul Arif Didik 
Published: May 2019

Abstract
Tsunamis are disasters that cause so much damage. In the history of the tsunami, Indonesia has been hit by tsunami several times, based on data from the National Disaster Management Agency (BNBP), as of 1629 - 2007, Indonesia was recorded 184 times affected by large and small tsunami disasters. Based on these data, it is very important for Indonesia to increase security against the tsunami disaster. Also, the tsunami in Indonesia occurred due to earthquakes, volcanic eruptions, and landslides in the sea. This study discussed about to the tsunami with a location located in the southern of Java, precisely in the area of Central Java, considering that in 2017 there was friction between the Indies and Eurasian plates which caused an earthquake measuring 6.9 magnitude, but did not cause a tsunami. In this study, a tsunami wave propagation simulation was carried out with the aim of knowing how long it would take the waves to the shoreline and how high the waves would be when they were on the shoreline. The model used is a tsunami model created by Imamura by solving differential equations using finite difference and Runge-Kutta 4th order. Based on the simulation results with the initial determination of a 5 m tsunami wave centered at a distance of 25 km from the shoreline, Tsunami waves with a height of 8.7 m will arrive at the shoreline at 140 seconds. With wave height is 8.7 m at the shoreline, the water will enter the land or can also be called the tsunami disaster. At that time, residents are expected to have been evacuated from the coastal area. In addition, based on numerical simulations that have been carried out, information is obtained that the computation time of the method is faster than the computation time of the Runge-Kutta method with differences in altitude results that are quite small and can be tolerated. This shows that the Finite Difference method can be applied to tsunami disaster mitigation software because it has a fairly fast computing time compared to the Runge-Kutta 4th order method.
The commutative ring for prime near-rings by involving derivation
Authors: Reza Habibi Mochammad , Winda Setyawati Dian , Baihaqi Komar 
Published: May 2019

Abstract
A near-ring is an extension of the ring without having to fulfill the commutative of the sum and left distributive of the addition and multiplication operations. This paper shows that a prime and zero symmetric near-ring with derivation on near-ring satisfies proposed conditions is a commutative ring.
Clustering the basic human category indicator levels in Banten region
Authors: Asri Y , Palupiningsih P , Haryono  , Reviansya R M 
Published: May 2019

Abstract
Indonesia has an inequality in human development, education provides the highest inequality. Loss that occurs in the education category reaches 20.8 percent. In the categories of decent living standards reaches 17.3 percent, and in the health category 16.5 percent. Most volunteers and the society take responsive action after getting information from the media or from the local communities. Therefore, this study aims to find out which areas have problems on indicators of basic human categories in a certain year. Regions will be grouped into three cluster groups, regions with low values, regions with medium values, and regions with high values. K-Means is an algorithm used to classify regions. The results of this study are, knowing the areas that have the lowest, medium, and highest values based on indicators of basic human categories in 2013 to 2017. Pandeglang and Serang Regencies are the lowest indicators such as life expectancy with a center point of 62.928 in 2013 and up to 64.03 in 2017, mean years of school is 6.299 to 6.666, per capita expenditure is 8411.72 to 9065.33. The cities of South Jakarta, East Jakarta, and Central Jakarta, areas that are at the highest indicators such as life expectancy with a center point of 72.938 to 73.107, expected years of school is 12.673 to 13.438, mean years of school is 10.469 to 10.851, and per capita expenditure with a center point of 17942.57 up to 18856.4.
Model reduction analysis using Hankel Norm Approximation on discrete-time linear system with the special shape of matrix A
Authors: Khusnul Arif Didik , Adzkiya Dieky , Khoirul Roziq Mohammad , Aditya Prima , Nur Fadhilah Helisyah 
Published: May 2019

Abstract
A system can describe real problems that occur in the environment. The system is the result of modeling of real problems. Based on the results of real problem modeling, it often produces a linear system with a special shape of matrix A. In general, to represent the actual conditions of the real problem accurately, a system with a large order is required. While on the other hand, in terms of analysis and computation of the system, it is not desired to have a system with a large order. Therefore, we propose to use a model reduction that produces a system with small orders but without significant errors. One of the commonly used model reduction methods is Hankel Norm Approximation (HNA). In this research, we analyze the process of model reduction with the HNA method in the discrete-time linear system which has special forms of the matrix A. The process of reduction of this model begins with the formulation of the initial system with a special shaped matrix A. Next, we analyze the nature of stability, controllability and observability of the initial system. Then a balanced system is formed and followed by model reduction using HNA. Based on the results of the model reduction simulation using HNA, it was found that the reduced system with HNA has the same performance and properties as the initial system, which is stable, controllable, and observable. Besides that, HNA model reduction is suitable for use at high frequencies and has a fast computation time.
An object-based classification of mangrove land cover using Support Vector Machine Algorithm
Authors: Rosmasita  , Siregar Vincentius P , Agus Syamsul B , Jhonnerie Romie 
Published: May 2019

Abstract
Accurate mapping of mangrove is necessary for effective planning and management of ecosystem and resources, due to the function of mangrove as a provider of natural products The use of satellite remote sensing to map mangrove has become widespread as it can provide accurate, effecient, and repeatable assessments. The type of remote sensing that is based on imaging using the pixel method sometimes results in the misclassification of the imaging due to the “salt and pepper effects”. The aim of this study to use approach support vector machine (SVM) algorithm to classification mangrove land cover using sentinel-2B and Landsat 8 OLI imagery based on object-based classification method (OBIA). The field observation was done using Unmanned Aerial Vehicle (UAV) at Liong River, Bengkalis, Riau Province. The result by show overall accuracy classification using Sentinel-2B was better than Landsat 8 OLI imagery the value of 78.7% versus 62.7% and them were different significantly 7.23%.
Monitoring long-term shoreline changes along the coast of Semarang
Authors: Dewi Ratna S 
Published: May 2019

Abstract
Shoreline is defined as the intersection of land and water which provides economic and social security to coastal communities. Semarang is having approximately 20 km shorelines. It faces multiple threats as a result of for instance urban development, industrialization, land reclamation, land subsidence and erosion. In the present study, an attempt was made to monitor the accretion and erosion processes from multi-temporal images from 1988 up to 2017. Fuzzy approach and OTSU method were made to extract the shoreline and DSAS (Digital Shoreline Analysis System) was used to analyse the changes and estimated the shoreline change rate. From change detection results, a general trend of continuous changes of shoreline can be detected representing a vulnerable shoreline affected by human activities. The results of this study can be used to prioritize action and to develop a suitable adaptation measure.
Printculture of Surgical Pathology and Autopsy Specimens
Authors: McMullen Phillip D , Tesic Vera , Pytel Peter 
Published: May 2019

Abstract

Objectives
Printculture is a method of microbiologic assessment previously described for use in the autopsy setting. We sought to compare printculture of surgical and autopsy pathology specimens to standard microbiology culture using matrix-assisted laser desorption/ionization–time of flight (MALDI-TOF)–based colony identification.

Methods
Printculture was performed on 18 frozen samples with corresponding standard culture results. The results of MALDI-TOF identification of colonies recovered by printculture were compared with standard cultures, and percent concordance was calculated.

Results
There was 95.8% concordance to standard culture methods for cases with infections and 100% concordance for cases without infection. The pattern of growth was found to aid in the distinction between contamination and true infection.

Conclusions
Printculture allows the identification of microorganisms from routinely frozen tissues and provides a bridge between microbiology and histomorphology through the identification of associated histologic features of infection. This technique can be successfully integrated into autopsy and surgical pathology workup of potentially infected tissues.
The Triple Plane, the Bra-Flap, and the Inverted Bra-Flap Modified Dual Plane Techniques for Breast Augmentation
Authors: Bracaglia Roberto , Servillo Maria , Fortunato Regina , Gentileschi Stefano 
Published: May 2019

Abstract

Background
Breast augmentation (BA) is a very common procedure performed for a wide range of indications. The short-term and long-term outcomes are strongly correlated with the choice of the correct implant pocket, which should be tailored to the anatomic features of the breast.

Objectives
The aim of this study was to report the safety and efficacy of the triple-plane technique and Bra-flap modified dual-plane techniques for BA.

Methods
From January 1995 to January 2016, 605 patients underwent BA procedures that utilized the triple-plane technique or Bra-flap modified dual-plane techniques. Patient evaluation was performed preoperatively and postoperatively at 6 and 12 months and every 5 years thereafter. The occurrence of ptosis and implant malposition, as well as breast animation deformity, were assessed. Patient satisfaction was evaluated with the BREAST-Q Augmentation Module.

Results
The average patient age was 39.3 years. The follow-up period ranged from 24 months to 20 years. The triple-plane technique was performed in 450 patients, the Bra-flap modified dual-plane technique in 97, and the inverted Bra-flap modified dual-plane technique in 58. No cases of double-bubble deformity or implant bottoming-out were observed. Regarding animation deformity, 209 patients presented with mild to moderate distortion, whereas no patients presented with severe distortion. The BREAST-Q questionnaire reported significant postoperative improvements in all scales.

Conclusions
In over 20 years of experience, the triple-plane technique has proven to be a reliable procedure that offers natural and long-lasting results. The Bra-flap and inverted Bra-flap modified dual-plane techniques are efficient options to expand the range of breast conditions treated.

Level of Evidence: 4
Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Contained and Competitive Methods
Authors: Ebrahimpoor Mitra , Spitali Pietro , Hettne Kristina , Tsonaka Roula , Goeman Jelle 
Published: May 2019

AbstractStudying sets of genomic features is increasingly popular in genomics, proteomics and metabolomics since analyzing at set level not only creates a natural connection to biological knowledge but also offers more statistical power. Currently, there are two gene-set testing approaches, self-contained and competitive, both of which have their advantages and disadvantages, but neither offers the final solution. We introduce simultaneous enrichment analysis (SEA), a new approach for analysis of feature sets in genomics and other omics based on a new unified null hypothesis, which includes the self-contained and competitive null hypotheses as special cases. We employ closed testing using Simes tests to test this new hypothesis. For every feature set, the proportion of active features is estimated, and a confidence bound is provided. Also, for every unified null hypotheses, a $P$-value is calculated, which is adjusted for family-wise error rate. SEA does not need to assume that the features are independent. Moreover, users are allowed to choose the feature set(s) of interest after observing the data. We develop a novel pipeline and apply it on RNA-seq data of dystrophin-deficient mdx mice, showcasing the flexibility of the method. Finally, the power properties of the method are evaluated through simulation studies.
New algorithms for detecting multi-effect and multi-way epistatic interactions
Authors: Ansarifar Javad , Wang Lizhi 
Published: May 2019

Abstract

Motivation
Epistasis, which is the phenomenon of genetic interactions, plays a central role in many scientific discoveries. However, due to the combinatorial nature of the problem, it is extremely challenging to decipher the exact combinations of genes that trigger the epistatic effects. Many existing methods only focus on two-way interactions. Some of the most effective methods used machine learning techniques, but many were designed for special case-and-control studies or suffer from overfitting. We propose three new algorithms for multi-effect and multi-way epistases detection, with one guaranteeing global optimality and the other two being local optimization oriented heuristics.

Results
The computational performance of the proposed heuristic algorithm was compared with several state-of-the-art methods using a yeast dataset. Results suggested that searching for the global optimal solution could be extremely time consuming, but the proposed heuristic algorithm was much more effective and efficient than others at finding a close-to-optimal solution. Moreover, it was able to provide biological insight on the exact configurations of epistases, besides achieving a higher prediction accuracy than the state-of-the-art methods.

Availability and implementation
Data source was publicly available and details are provided in the text.
Fitting the US National Park Service for Change
Authors: Schwartz Mark W , Redford Kent H , Leslie Elaine F 
Published: May 2019

AbstractThe US National Park Service (NPS), which manages over 85 million acres and over 400 units, contends with myriad external drivers of ecosystem change that threaten parks. Stressors such as invasive species, habitat fragmentation, warming climates and rising sea level, raise the potential that parks will not attain or sustain their congressionally designated missions. Using invasive animals as a focal example of such changes, we suggest the NPS consider increasing active management of resources, participating in cooperative ecoregional management, increasing the use of public participation, and using formal decision support tools. We illustrate how these management approaches are currently underused. Acknowledging that invasive species are but one of a suite of problems that are threatening to overwhelm park management capacity we believe that the approaches we outline generalize to myriad problems facing the NPS.