bluuebunny/crossref_metadata_2025_split huggingface dataset:
What happened before?
Authors: Valegård P.-G. , Waters L. B. F. M. , Dominik C. 
Published: June 2021

Context. Planets form in circumstellar disks around pre-main-sequence stars. A key question is, how do the formation and evolution of protoplanetary disks depend on stellar mass? Studies of circumstellar disks at infrared and submillimeter wavelengths around intermediate-mass Herbig Ae/Be stars have revealed disk structures such as cavities, gaps, and spiral arms. The Herbig Ae/Be stars represent an older population of intermediate-mass pre-main-sequence stars. Since these evolve toward the main sequence on timescales comparable to those of typical disk dissipation, a full picture of disk dispersal in intermediate-mass pre-main-sequence stars must include the intermediate-mass T Tauri (IMTT) stars.
Aims. We seek to find the precursors of the Herbig Ae/Be stars in the solar vicinity within 500 pc from the Sun. We do this by creating an optically selected sample of IMTT stars from the literature, here defined as stars of masses 1.5 M⊙≤ M*≤ 5 M⊙ and with a spectral type between F and K3.
Methods. We used literature optical photometry (0.4–1.25 μm) and distances determined from Gaia DR2 parallax measurements together with Kurucz stellar model spectra to place the stars in a HR diagram. We employed Siess evolutionary tracks to identify IMTT stars from the literature and derived masses and ages. We used Spitzer spectra to classify the disks around the stars into Meeus Group I and Group II disks based on their [F30/F13.5] spectral index. We also examined the 10 μm silicate dust grain emission and identified emission from polycyclic aromatic hydrocarbons (PAH). From this, we built a qualitative picture of the disks around the IMTT stars and compared this with available spatially resolved images at infrared and submillimeter wavelengths to confirm our classification.
Results. We find 49 IMTT stars with infrared excess. The identified disks are similar to the older Herbig Ae/Be stars in disk geometries and silicate dust grain population. The detection frequency of PAHs is higher than from disks around lower mass T Tauri stars but less frequent than from Herbig Ae/Be disks. Spatially resolved images at infrared and submillimeter wavelengths suggest gaps, and spirals are also present around the younger precursors to the Herbig Ae/Be stars.
Conclusions. Comparing the timescale of stellar evolution toward the main sequence and current models of protoplanetary disk evolution, the similarity between Herbig Ae/Be stars and the IMTT stars points toward an evolution of Group I and Group II disks that are disconnected and represent two different evolutionary paths.
Cydalima perspectalis (box tree moth).
Authors: Kenis Marc , Leuthardt Florine , Li Hong-mei 
Published: June 2021

Abstract

The box tree moth, Cydalima perspectalis, is native to East Asia (Inoue et al., 1982). It was first recorded in Europe in 2007, in southwest Germany and the Netherlands (Krüger, 2008; Straten and Muus, 2010). Since then it has been recorded in many other European countries, and climate models predict further spread of the species in Europe, invading most areas except for Northern Fenno-Scandinavia, Northern Scotland and high mountain regions (Nacambo et al., 2014). In the newly invaded regions, C. perspectalis larvae feed on the leaves of box trees, Buxus spp., resulting in defoliation, which can kill the trees. The most significant damage, however, can be from the larvae attacking the bark of box trees causing the trees to dry out and die. Besides cultural and economic effects, the most serious threat from C. perspectalis is on the natural Buxus populations (Kenis et al., 2013). The species is easily introduced accidentally with its host plant, which is extensively traded over Europe and therefore presents a serious threat (Leuthardt et al., 2010; Straten and Muus, 2010).
Uraba lugens (eucalypt leaf skeletonizer).
Authors: Berndt Lisa 
Published: June 2021

Abstract

U. lugens was first considered a serious pest of natural eucalypt forests in Western Australia in 1983 when the first severe outbreak occurred there (Strelein, 1988). Prior to that it was widely known as a pest of eucalypt forests in eastern Australia (Campbell, 1962; Harris, 1974). As these natural forests are or were managed for timber production, it is considered an economically important pest in its native range. Damage to amenity trees is also a common problem, although few trees are killed by this defoliation (Anonymous, 1979).U. lugens was recorded as invasive in New Zealand by Crabtree (1997). In its exotic range it has become a significant pest of amenity trees, particularly Lophestemon confertus (Australian brush box), which is commonly planted in some parts of Auckland City (Kriticos et al., 2007).
Beta‐alanine did not improve high‐intensity performance throughout simulated road cycling
Authors: Perim Pedro , Gobbi Nathan , Duarte Breno , de Oliveira Luana Farias , Costa Luiz Augusto Riani , Sale Craig , Gualano Bruno , Dolan Eimear , Saunders Bryan 
Published: June 2021

ABSTRACTThis study investigated the effect of beta‐alanine supplementation on short‐duration sprints and final 4‐km simulated uphill cycling time‐trial performance during a comprehensive and novel exercise protocol representative of the demands of road‐race cycling, and determined if changes were related to increases in muscle carnosine content. Seventeen cyclists (age 38 ± 9 y, height 1.76 ± 0.07 m, body mass 71.4 ± 8.8 kg, V̇O2max 52.4 ± 8.3 ml·kg−1·min−1) participated in this placebo‐controlled, double‐blind study. Cyclists undertook a prolonged intermittent cycling protocol lasting 125 min, with a 10‐s sprint every 20 min, finishing with a 4‐km time‐trial at 5% simulated incline. Participants completed two familiarization sessions, and two main sessions, one pre‐supplementation and one post‐supplementation following 28 days of 6.4 g·day−1 of beta‐alanine (N=11) or placebo (N=6; maltodextrin). Muscle biopsies obtained pre‐ and post‐supplementation were analysed for muscle carnosine content. There were no main effects on sprint performance throughout the intermittent cycling test (all P>0.05). There was no group (P=0.69), time (P=0.50) or group x time interaction (P=0.26) on time‐to‐complete the 4‐km time‐trial. Time‐to‐completion did not change from pre‐ to post‐supplementation for BA (−19.2 ± 45.6 s, P=0.43) or PL (+2.8 ± 31.6 s, P=0.99). Beta‐alanine supplementation increased muscle carnosine content from pre‐ to post‐supplementation (+9.4 ± 4.0 mmol·kg−1dm; P<0.0001) but was not related to performance changes (r=0.320, P=0.37). Chronic beta‐alanine supplementation increased muscle carnosine content but did not improve short‐duration sprint performance throughout simulated road race cycling, nor 4‐km uphill time‐trial performance conducted at the end of this cycling test.Highlights
Performance during prolonged cycling events often depends on the ability to maintain an increased power output during higher intensity periods. Thus, cyclists are likely heavily dependent on their ability to resist fatigue during these periods of high‐intensity activity.
Meta‐analytical data show beta‐alanine to be an effective supplement to improve exercise outcomes, but little work exists on its efficacy during dynamic actions that are common during prolonged cycling.
Beta‐alanine supplementation increased muscle carnosine content but did not generate improvements in the performance of high‐intensity cycling (10‐s sprints or 4‐km uphill time‐trial) during a simulated road race cycling protocol.
These data suggest that short duration sprints (≤10 s) and longer duration (>10 min) high‐intensity activity throughout endurance cycling may not be improved with beta‐alanine supplementation despite increases in muscle carnosine content.
New insights into correlated materials in the time domain—combining far-infrared excitation with x-ray probes at cryogenic temperatures
Authors: Mankowsky Roman , Sander Mathias , Zerdane Serhane , Vonka Jakub , Bartkowiak Marek , Deng Yunpei , Winkler Rafael , Giorgianni Flavio , Matmon Guy , Gerber Simon , Beaud Paul , Lemke Henrik Till 
Published: June 2021

Abstract
Modern techniques for the investigation of correlated materials in the time domain combine selective excitation in the THz frequency range with selective probing of coupled structural, electronic and magnetic degrees of freedom using x-ray scattering techniques. Cryogenic sample temperatures are commonly required to prevent thermal occupation of the low energy modes and to access relevant material ground states. Here, we present a chamber optimized for high-field THz excitation and (resonant) x-ray diffraction at sample temperatures between 5 and 500 K. Directly connected to the beamline vacuum and featuring both a Beryllium window and an in-vacuum detector, the chamber covers the full (2–12.7) keV energy range of the femtosecond x-ray pulses available at the Bernina endstation of the SwissFEL free electron laser. Successful commissioning experiments made use of the energy tunability to selectively track the dynamics of the structural, magnetic and orbital order of Ca2RuO4 and Tb2Ti2O7 at the Ru (2.96 keV) and Tb (7.55 keV) L-edges, respectively. THz field amplitudes up to 1.12 MV cm−1 peak field were demonstrated and used to excite the samples at temperatures as low as 5 K.
E-Voting using Blockchain
Authors: Gupta Suraj Pratap , Mani Tripathi Ankur 
Published: June 2021

Abstract
A vote gives a citizen of a democratic country the power to elect a representative which forms a government which is of the people, by the people, and for the people. Voting forms one of the fundamental pillars of modern democracy. Conducting a transparent, verifiable, and unbiased election is a challenging task for the election commission. Earlier ballot papers were used for conducting elections but it has to face many issues like booth capturing or damaging of ballot papers of the booths where there were fewer chances of winning for a candidate. Currently, EVMs are used for conducting the election in India but there are many reports that EVMs are not fully tamperproof. EVM is not a universal acceptance across the world. Blockchain is a distributed tamperproof technology which can help in conducting election transparent and tamperproof. Here in this paper, an approach for voting using blockchain and Paillier encryption is proposed. It implies the tamperproof property of blockchain and additive homomorphic property of Paillier encryption to build a voting architecture that will make the election process trans-parent and tamperproof. Next, algorithms for registration, voting, and result declaration has been mentioned along with the results of Paillier encryption for voting.
RETRACTED: A Survey of image pre-processing techniques for medical images
Authors: Prabu Shankar K C , Prayla Shyry S 
Published: June 2021

Abstract
Medical Images cannot be utilized similarly it was procured. The obtained images must be Pre-Processed and Segmented. Real confinements are there in medical image preprocessing as far as multifaceted nature when contrasted with preparing different kinds of images. Confinements in image acquisitions make image division a repetitive procedure. The most significant objective of segmenting medical images is in performing activities on medical images for distinguishing designs and in recovering data. This survey speaks primarily about medical image handling. At that point many methods have been proposed to deal with portions of Medical images. The similar investigation of different processing procedures has been analysed. The overview thus gives subtleties of mechanized division strategies, explicitly talked about with regards to scanned medical images. The rationale is in analysing the issues experienced in dividing the scanned medical images, and the respective benefits and confinements techniques as of now accessible for segmentation of medical images.
Comparative Analysis of Machine Learning Methods to Detect Chronic Kidney Disease
Authors: Roy Madhusree Sankar , Ghosh Ritama , Goswami Diyali , Karthik R 
Published: June 2021

AbstractChronic Kidney disease (CKD) is a lifelong health hazard that can cause the failure of kidneys. Symptoms of this develop slowly and are not obvious. Early detection of Chronic Kidney Disease can lead to significant progress in finding the cure for this disease. Through this study, we aim to employ ML techniques for the prediction and diagnosis of Chronic Kidney Disease. The findings obtained from our predictive analysis combined with the expertise of healthcare professionals can help in making an accurate prognosis. For this, we have used a dataset containing data from 400 individuals acquired from the University of California Irvine (UCI) repository. Various feature selection techniques have been used to optimize the number of features affecting Chronic Kidney Disease. Subsequently, these desirable features are chosen and used in different ML models and their accuracy, sensitivity is compared. Multiple Machine learning algorithms have been explored such as Logistic Regression, Naïve Bayes, KNN, SVM, Decision Trees, Random Forest Classifier, and Extra Trees Classifier. It was concluded that Decision Trees using information gain gave six optimal features and the Extra Trees Classifier model gives the best accuracy of 99.36 % with Extra Trees Classifier having one of the least execution times.
Student Result Prediction in Covid-19 Lockdown using Machine Learning Techniques
Authors: Jeslet D. Santhi , Komarasamy Dinesh , Hermina J. Jenita 
Published: June 2021

Abstract
Covid-19 virus has moved the world to a static state. This has confused the education field in conducting the end examinations. The school education has declared the results of a few classes as pass without conducting the end examinations. But, this may not be possible for all the education system, especially for final year college student results. For this reason, the prediction of result based on their previous performance is essential. Prediction can be effectively achieved by using machine learning algorithms. Machine learning automatically learns and improves from example and experience. Supervised machine learning algorithms Logistic regression and SVM are used for this work. The data set of 1460 students result of a college is considered for the study. Finally, the trained machine predicts accurately whether the student is eligible to acquire the degree or not and the same is viewed in the college portal.
Auto-Off ID: Automatic Detection of Offensive Language in Social Media
Authors: Geetha R , Karthika S , Sowmika Chaluvadi Jwala , Janani Bharathi M 
Published: June 2021

Abstract
As the popularity of social media grows, computer-mediated anonymity allows users to engage in activities that they would not do in real life. This makes users vulnerable to abuse through Internet platforms. Due to the enormous number of social media data, it is not possible to manually filter out the overflow of abusive content in online communities and social networking sites. The research work proposes a multi-level classification model that deploys various machine and deep learning models to effectively identify offensive content in a tweet. The proposed Auto-Off ID system is designed to build a system that classifies tweets as offensive or non-offensive; filters out and classifies offensive tweets as either targeted or non-targeted; filters out targeted tweets and identify mentions of individuals and organizations who have been bullied. The study is supported by the text analysis features with lexicon features using LIWC, POS tags for primary and secondary users, Twitter Tag Scores (TTS). This system is evaluated using a diverse choice of machine learning and deep learning models from which it is proved that C-LSTM outperform with an accuracy of 91.72% for offensive language identification; LDA + Logistic Regression training with SVM accuracy of 90.87% for offensive tweet classification.
EEG based mental state analysis
Authors: Ajith Krishnapriya , Menaka R , Kumar Shani S 
Published: June 2021

Abstract
This work proposes a method for mental state analysis from the Electroencephalogram (EEG) signal data using Convolutional Neural Network (CNN). In recent years, deep learning techniques, particularly CNN have become a very popular topic. Emotions also play a very crucial role in our daily life. Emotions affect a person’s cognition, behaviour and decision-making. In this paper, we analyse the EEG signals and classify them into various emotions. The result would be a real-time EEG-based emotion recognition system. The DEAP dataset, which is a multimodal dataset, has been used in this study for analysis of human mental states.
Analysis of QoS Parameters using Prediction Algorithms on CSO-based Energy-Efficient Reliable Sectoring Scheme in WSN
Authors: Wategaonkar Dhanashri Narayan , Nagaraj S V 
Published: June 2021

AbstractThe maintaining of the Quality of Service(QoS) parameter in wireless sensor networks (WSN) is becoming a challenging task. The limitations are like achieving QoS parameters in a distributed network, constraints in sensing parameters, natural disasters, and battery life of sensor nodes need to overcome. Here in this paper few of the QoS parameters are analysed to increase the lifetime of sensor networks by partitioning a network into various sectors. A novel optimized sectoring scheme is proposed for optimal election of sector head (SH) for a given network. A Cat Swarm Optimization based Energy-Efficient Reliable sectoring Scheme with prediction algorithms (P-CSO-EERSS) is also proposed in this paper. The use of prediction algorithms is for early detection of node lifetime to improve the overall lifespan of a network. Along with this QoS parameters such as packet delivery ratio and energy consumption are analysed and compared with existing clustering algorithms. According to implementation results, it has been shown that the prediction based CSO-EERSS algorithm returns better QoS parameters as compare to existing cluster schemes.
Information Hiding in Audio Steganography using LSB Matching Revisited
Authors: Shanthakumari R , Devi E M Roopa , Rajadevi R , Bharaneeshwar B 
Published: June 2021

Abstract
Audio file steganography can be used as an effective and efficient method to hide messages. LSB based approach is the simplest steganographic technique among the various types for hiding secret information in audio formats. The approach is decided independent of the relationship between audio samples and the size of the secret messages. Efficiency of the output message is reduced when low data rate is used for hiding the message in lower power audio samples. The performance of data hiding in high power of audio is discussed in this paper. To identify the higher frequency, range from a given sample carrier and to hide the messages in the identified range, the LSBMR algorithm is used. The carrier wave is pre-processed in order to reduce the possibilities of intrusion detection. Experimentation of the adaptive steganography is performed on the files that are in. wav format. PSNR and MSE parameters are used for performance analysis. The threshold value of the audio carrier is depending upon the length of the message is used. The content of the secret information and threshold value of the carrier audio decides the number of samples for evaluation.
Affirmation of the Separate Rate-law Method for Determining vmax and KM of the Michaelis Reaction
Authors: Patiha  , Saraswati T E , Wahyuningsih S , Firdaus M , Masykur A 
Published: June 2021

Abstract
The maximum-velocity, v
max, and the Michaelis-constant, KM
, are the essence of the Michaelis-Menten equation. The value of v
max is determined when the reaction is in zeroth-order and KM
is in first-order. With a slight change in the notion of v
max concept, both were used as the basis for a new determination method called Separate Rate-law Method (SRlM). As yet, the Lineweaver-Burk and Eadie-methods were the most commonly used. Both combine zeroth-order and first-order data. The purpose of this study was to validate the quality of all methods. The research was carried out theoretically. All were tested by applying them to the same literature data, and the results were compared. The assessment is based on the v
max and KM
values obtained; doubling [E] will double v
max but the same KM
. The results show that the two present methods are inaccurate. Both give v
max and KM
values even if the reaction is always first-order. Based on the integral method, two variants of the new method, SRlMIM
, and initial rate, SRlMIRM
, were also introduced. All new methods give more accuracy, which results in the method accuracy of SRlM ≈ SRlMIM
> SRlMIRM
.
Coating of colloidal silica on glass surfaces with bbk nano silica as raw material
Authors: Khairunnisa S , Wahyudi K , Wijayanti R B , Marzuki A , Suryana R 
Published: June 2021

Abstract
Due to the colloidal silica has many applications such as coating, a catalyst precursor, a nano-size filler, investment casting, semiconductor wafer polishing, and an inorganic binder, it still attracting to be studied. The colloidal silica layer has been coated on a glass substrate using the spray-coating process. Two kinds of colloidal silica were prepared, i.e., the colloidal silica formed by mixing of nano-silica from Balai Besar Keramik (BKK) and the silane hexamethyldisilazane (HMDS), and the colloidal silica polishing Mastermet (0.06 mm) commercial as a comparison. The HMDS percent was varied at 10, 20, and 30 vol.%. The contact angle measurement obtained the hydrophobic properties of the samples, and a UV-Vis spectrophotometer measured the transmittance samples. A water contact angle of 94.9° is achieved at 10% HMDS. For all samples, the transmittance of the before and after coated glass substrate can reach an average of 89% to 90%. From this study, it is concluded that the BBK nano-silica has met the commercial requirements that are the hydrophobic property in the contact angle and the transmittance.
Safety analysis TMSR-500 in terms of the temperature reactivity coefficient of the fuel and the moderator using the MCNP6 software
Authors: Manik F R L , Suharyana  , Anwar F , Riyatun  , Khakim A 
Published: June 2021

Abstract
TMSR-500 is a type of fourth-generation reactor that is technologically advanced with a full actinide recycling system and produces low fuel waste. This research focuses on calculating and analyzing the temperature reactivity coefficient of fuel and moderator in the TMSR-500 core designed by Martingale, Inc. All calculations performed using the MCNP6 software and the ENDF / B-VII.I continuous energy nuclear data library. The fuel material for TMSR-500 is liquid salt NaF-BeF2-ThF4-UF4 with enrichment of U-235 of 19.75%, and the moderator material is graphite. The variation of temperature increase in fuel and moderator starts from 293.6 K to 2500 K. As the reactor temperature increases, the value of fuel reactivity decreased, the fuel temperature reactivity coefficient is negative (-2.4879 ± 0.42) pcm/K. At the stage of calculating the moderator temperature reactivity coefficient, the calculation results show a positive value (0.0019 ± 0.0016) pcm/K as the moderator temperature increases. Based on the study of the temperature reactivity coefficient of the fuel and the moderator, then the reactor is in a safe state in terms of the overall calculation results, which indicate the total temperature reactivity coefficient is negative (-2.4883 ± 0.42) pcm/K.