bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of abrupt energy transfer among turbulent plasma structures using singular value decomposition
Authors: Sasaki M , Kobayashi T , Dendy R O , Kawachi Y , Arakawa H , Inagaki S 
Published: November 2020

Abstract
A method to quantify the energy transfer among turbulent structures using singular value decomposition (SVD) is presented. We apply the method to numerical turbulence data obtained from a global plasma simulation using the Hasegawa–Wakatani fluid model, in which the Kelvin–Helmholtz instability plays a dominant role. Using the SVD method, the electrostatic potential is decomposed into a background potential deformation, a zonal flow, a coherent mode and an intermittent structure. Thus there are four key structures, as distinct from the three found in conventional theory. The kinetic energy of each structure is evaluated, and the limit cycle among them is obtained. In the limit cycle, an abrupt change of the background is found to be synchronised with the period of the zonal flow. The energy transfer function of each turbulence structure, which is defined on the basis of a vorticity equation, is evaluated. This then provides physical understanding of how the limit cycle is sustained by dynamical changes in the energy transfer among structures over the its period. In addition, it is shown that the abrupt deformation of the background is caused by the non-linear self-coupling of the intermittent structure.
L-mode plasmas analyses and current ramp-up predictions for a JT-60SA hybrid scenario
Authors: Morales J , Garcia J , Giruzzi G , Artaud J-F , Piron C , Vallar M , Goodman T 
Published: November 2020

Abstract
In this work, L-mode analyses are performed in order to assess a modeling framework for the prediction of electron cyclotron resonant heating (ECRH) -assisted current ramp-up phases for JT-60SA tokamak hybrid scenario #4-2. We compare two turbulence transport models, CDBM and TGLF, using the integrated modeling code CRONOS. Model validation is performed on the basis of an L-mode ramp-up phase in a JET plasma, and a flat-top L-mode TCV (Tokamak a configuration variable) plasma with applied ECRH. Parameter scans in

Z

eff

and in-edge electron temperature (T

e
) are performed. Our results indicate effective prediction of the q profile in JET ramp-up if edge T

e
is properly captured. Indeed, our sensitivity scan demonstrates the strong impact of edge T

e
on q profile evolution. The results of CDBM and TGLF modeling show good agreement with the experimental measurements. With respect to a JT-60SA hybrid scenario, a significant amount of ECRH off-axis is required to maintain a q profile above unity across the entire plasma radius. Based on an analysis of power deposition location, we find that ECRH applied close to ρ = 0.33 allows a compromise between a q profile larger than unity, and high central T

e
. We evaluate two current ramp-up rates, where the fast rate is double that of the slower rate. The quantity of ECRH required for the fast ramp scenario is found to be greater by almost a factor of two than that required in the slow ramp scenario.
Fast neural Poincaré maps for toroidal magnetic fields
Authors: Burby J W , Tang Q , Maulik R 
Published: November 2020

Abstract
Poincaré maps for toroidal magnetic fields are routinely employed to study gross confinement properties in devices built to contain hot plasmas. In most practical applications, evaluating a Poincaré map requires numerical integration of a magnetic field line, a process that can be slow and that cannot be easily accelerated using parallel computations. We propose a novel neural network architecture, the HénonNet, and show that it is capable of accurately learning realistic Poincaré maps from observations of a conventional field-line-following algorithm. After training, such learned Poincaré maps evaluate much faster than the field-line integration method. Moreover, the HénonNet architecture exactly reproduces the primary physics constraint imposed on field-line Poincaré maps: flux preservation. This structure-preserving property is the consequence of each layer in a HénonNet being a symplectic map. We demonstrate empirically that a HénonNet can learn to mock the confinement properties of a large magnetic island by using coiled hyperbolic invariant manifolds to produce a sticky chaotic region at the desired island location. This suggests a novel approach to designing magnetic fields with good confinement properties that may be more flexible than ensuring confinement using KAM tori.
Disruption prediction using a full convolutional neural network on EAST
Authors: Guo B H , Shen B , Chen D L , Rea C , Granetz R S , Huang Y , Zeng L , Zhang H , Qian J P , Sun Y W , Xiao B J 
Published: November 2020

Abstract
In this study, a full convolutional neural network is trained on a large database of experimental EAST data to classify disruptive discharges and distinguish them from non-disruptive discharges. The database contains 14 diagnostic parameters from the ∼104 discharges (disruptive and non-disruptive). The test set contains 417 disruptive discharges and 999 non-disruptive discharges, which are used to evaluate the performance of the model. The results reveal that the true positive (TP) rate is ∼ 0.827, while the false positive (FP) rate is ∼0.067. This indicates that 72 disruptive discharges and 67 non-disruptive discharges are misclassified in the test set. The FPs are investigated in detail and are found to emerge due to some subtle disturbances in the signals, which lead to misjudgment of the model. Therefore, hundreds of non-disruptive discharges from training set, containing time slices of small disturbances, are artificially added into the training database for retraining the model. The same test set is used to assess the performance of the improved model. The TP rate of the improved model increases up to 0.875, while its FP rate decreases to 0.061. Overall, the proposed data-driven predicted model exhibits immense potential for application in long pulse fusion devices such as ITER.
Electron concentration in the non-luminous part of the atmospheric pressure filamentary discharge
Authors: Faltýnek J , Kudrle V , Šnírer M , Toman J , Jašek O 
Published: November 2020

Abstract
The non-luminous surroundings of atmospheric pressure plasma jets contain still substantial electron density which can be of practical importance, when e.g. reactant or precursor is introduced into this zone. Low signal-to-noise ratio makes the Stark broadening technique unsuitable there, and so we employed the microwave interferometry to determine the electron density during the plasma synthesis of graphene. The relatively long wavelength, tight geometry and the presence of overcritical plasma filament necessitated the use of a numerical solution of the Maxwell equations. Besides laying the groundwork of the method, the paper also discusses the importance of plasma density profile in the active filament and its surroundings. The results show that in radial distance ten times higher than a visually apparent plasma diameter, the plasma density was still around 1016–1017 m−3.
Investigation of an AlGaN-channel Schottky barrier diode on a silicon substrate with a molybdenum anode
Authors: Zhang Tao , Zhang Jincheng , Zhang Weihang , Zhang Yachao , Duan Xiaoling , Ning Jing , Hao Yue 
Published: November 2020

Abstract
We demonstrate an AlGaN-channel Schottky barrier diode (SBD) with a molybdenum anode that has excellent performance at high temperatures. The AlGaN-channel SBD shows a suppressed leakage current, compared to a GaN-channel SBD, and the leakage current only increases by 3.5 times when the temperature increases from 300 K to 425 K, while that of the GaN-channel SBD increases by 27.8 times. Meanwhile, the forward current also shows less degeneration at an elevated temperature. Combined with the 1 A forward current of a large-periphery device with a perimeter of 20 mm, the AlGaN-channel SBD shows great potential for use in next-generation power electronics, especially in extreme environments.
Creating 3D printed sensor systems with conductive composites
Authors: Lazarus Nathan , Bedair Sarah S 
Published: November 2020

Abstract
Fused filament fabrication (FFF), the printing of parts through the deposition of layers of melted thermoplastic, is one of the most widely used 3D printing processes due to its ease of use, low cost and accessibility. In this work, integration of 3D printed sensors and interconnect between embedded components into a printed part is demonstrated in an FFF process for the first time. The use of printed active materials for sensing allows interactivity with the end user through mechanisms like touch and temperature. Through dual extrusion in a low cost commercial printer, printing of both a conductive thermoplastic composite and a non-conducting filament are combined to create complex patterns. The piezoresistive and thermally responsive properties of the thermoplastic composite are used to create several different sensor modalities including a piezoresistive strain sensor, a contact switch and a resistive temperature sensor. A heated insertion technique is then developed for embedding of electrical components. The conductive thermoplastic is also used to incorporate a 3D printed circuit board in the same part, including in-line embedding within the body of the part during the printing process. The sensor performance and component embedding properties are characterized, and the process is used to print systems including relaxation oscillators and op-amp interface circuits for sensor monitoring.
Entropy-driven cell decision-making predicts ‘fluid-to-solid’ transition in multicellular systems
Authors: Barua Arnab , Syga Simon , Mascheroni Pietro , Kavallaris Nikos , Meyer-Hermann Michael , Deutsch Andreas , Hatzikirou Haralampos 
Published: November 2020

Abstract
Cellular decision making allows cells to assume functionally different phenotypes in response to microenvironmental cues, with or without genetic change. It is an open question, how individual cell decisions influence the dynamics at the tissue level. Here, we study spatio-temporal pattern formation in a population of cells exhibiting phenotypic plasticity, which is a paradigm of cell decision making. We focus on the migration/resting and the migration/proliferation plasticity which underly the epithelial-mesenchymal transition and the go or grow dichotomy. We assume that cells change their phenotype in order to minimize their microenvironmental entropy following the LEUP (Least microEnvironmental Uncertainty Principle) hypothesis. In turn, we study the impact of the LEUP-driven migration/resting and migration/proliferation plasticity on the corresponding multicellular spatio-temporal dynamics with a stochastic cell-based mathematical model for the spatio-temporal dynamics of the cell phenotypes. In the case of the go or rest plasticity, a corresponding mean-field approximation allows to identify a bistable switching mechanism between a diffusive (fluid) and an epithelial (solid) tissue phase which depends on the sensitivity of the phenotypes to the environment. For the go or grow plasticity, we show the possibility of Turing pattern formation for the ‘solid’ tissue phase and its relation with the parameters of the LEUP-driven cell decisions.
Dimensionless parameter scaling of intrinsic torque in C-Mod enhanced confinement plasmas
Authors: Rice J.E. , Cao N.M. , Tala T. , Chrystal C. , Greenwald M.J. , Hughes J.W. , Marmar E.S. , Reinke M.L. , Rodriguez Fernandez P. , Salmi A. 
Published: November 2020

Abstract
A dimensionless parameter dependence study of intrinsic torque has been performed on a database of H- and I-mode plasmas from the Alcator C-Mod tokamak. The torque was determined by comparing intrinsic angular momentum density profiles just before and just after L–H and L–I transitions. The intrinsic torque has been found to scale as

β

N

1.5

ρ

*

−
1.0

ν

*

0.1

, with the parameter ranges 0.3 ⩽ β

N
⩽ 1.5, 0.004 ⩽ ρ
* ⩽ 0.011 and 0.04 ⩽ ν
* ⩽ 0.9. Comparison with results from JET and DIII-D suggests that the intrinsic torque should be normalized by some measure of the device size. Depending upon this normalization, the estimated total intrinsic torques for ITER, SPARC and ARC are ∼20, ∼4 and ∼8 Nm, respectively.
A statistical approach for the automatic identification of the start of the chain of events leading to the disruptions at JET
Authors: Aymerich E. , Fanni A. , Sias G. , Carcangiu S. , Cannas B. , Murari A. , Pau A. , JET contributors the 
Published: November 2020

Abstract
This paper reports an algorithm to automatically identify the chain of events leading to a disruption, evaluating the so-called reference warning time. This time separates the plasma current flat-top of each disrupted discharge into two parts: a non-disrupted part and a pre-disrupted one. The algorithm can be framed into the anomaly detection techniques as it aims to detect the off-normal behavior of the plasma. It is based on a statistical analysis of a set of dimensionless plasma parameters computed for a selection of discharges from the JET experimental campaigns. In every data-driven model, such as the generative topographic mapping (GTM) predictor proposed in this paper, it is indeed necessary to label the samples needed for training the model itself. The samples describing the disruption-free behavior are extracted from the plasma current flat-top phase of the regularly terminated discharges. The disrupted space is described by all the samples belonging to the pre-disruptive phase of each disruptive discharge in the training set. Note that a proper selection of the pre-disruptive phase plays a key role in the prediction performance of the model. Moreover, these models, which are highly dependent on the training input space, may be particularly prone to degradation as the operational space of any experimental machine is continuously evolving. Hence, a regular schedule of model review and retrain must be planned. The proposed algorithm avoids the cumbersome and time-consuming manual identification of the warning times, helping to implement a continuous learning system that could be automated, despite being offline. In this paper, the automatically evaluated warning times are compared with those obtained with a manual analysis in terms of the impact on the mapping of the JET input parameter space using the GTM methodology. Moreover, the algorithm has been used to build the GTM of recent experimental campaigns, with promising results.
Natural evolution strategies and variational Monte Carlo
Authors: Zhao Tianchen , Carleo Giuseppe , Stokes James , Veerapaneni Shravan 
Published: November 2020

Abstract
A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization. The recent work of Gomes et al (2019 arXiv:1910.10675) on heuristic combinatorial optimization using neural quantum states is pedagogically reviewed in this context, emphasizing the connection with natural evolution strategies (NES). The algorithmic framework is illustrated for approximate combinatorial optimization problems, and a systematic strategy is found for improving the approximation ratios. In particular, it is found that NES can achieve approximation ratios competitive with widely used heuristic algorithms for Max-Cut, at the expense of increased computation time.
The Grassmann algebra over arbitrary rings and minus sign in arbitrary characteristic
Authors: Dor Gal , Kanel-Belov Alexei , Vishne Uzi 
Published: November 2020

An analog in characteristic

2
2

for the Grassmann algebra

G
G

was essential in a counterexample to the long standing Specht conjecture. We define a generalization

G

\mathfrak {G}

of the Grassmann algebra, which is well-behaved over arbitrary commutative rings

C
C

, even when

2
2

is not invertible. This lays the foundation for a supertheory over arbitrary base ring, allowing one to consider general deformations of superalgebras.

The construction is based on a generalized sign function. It enables us to provide a basis of the non-graded multilinear identities of the free superalgebra with supertrace, valid over any ring.

We also show that all identities of

G

\mathfrak {G}

follow from the Grassmann identity, and explicitly give its co-modules, which turn out to be generalizations of the sign representation. In particular, we show that the

n
n

th co-module is a free

C
C

-module of rank

2

n
−
1

2^{n-1}

.
A Comparison of Self- and Proxy-Reported Subjective Sleep Durations With Objective Actigraphy Measurements in a Survey of Wisconsin Children 6–17 Years of Age
Authors: Holzhausen Elizabeth A , Hagen Erika W , LeCaire Tamara , Cadmus-Bertram Lisa , Malecki Kristen C , Peppard Paul E 
Published: November 2020

AbstractChildren’s sleep quality and duration are important to overall development, health, and wellbeing. However, measuring children’s sleep is challenging, especially in situations where objective assessment is impractical. This study aimed to assess age and proxy effects in comparing subjective sleep duration with objective measures, in a community-based sample of Wisconsin children (aged 6–17 years), recruited from 2014–2017. The sample participants had a mean age of 11.4 (standard deviation, 3.3) years and 52% of them were male. We used linear mixed effects models to test for age effects in proxy- and self-report groups separately, and a quasiexperimental regression discontinuity approach to compare subjective sleep duration with objective actigraphy estimates across proxy- and self-report groups. We found evidence of systematic overestimation of sleep duration when using subjective measurements but did not find evidence of age effects in either group. Based on these analyses, we found evidence of differential overestimation by proxy- or self-report condition. Proxy reporters overestimated sleep duration by 2.3 hours (95% confidence interval: 2.2, 2.4), compared with 1.0 hour (95% confidence interval: 0.7, 1.2) for self-reporters. These findings suggest that proxy- versus self-reporting conditions are an important consideration when designing a study, and that it might be beneficial to reduce the age at which children self-report.
MOSGA: Modular Open-Source Genome Annotator
Authors: Martin Roman , Hackl Thomas , Hattab Georges , Fischer Matthias G , Heider Dominik 
Published: November 2020

Abstract

Motivation
The generation of high-quality assemblies, even for large eukaryotic genomes, has become a routine task for many biologists thanks to recent advances in sequencing technologies. However, the annotation of these assemblies—a crucial step toward unlocking the biology of the organism of interest—has remained a complex challenge that often requires advanced bioinformatics expertise.

Results
Here, we present MOSGA (Modular Open-Source Genome Annotator), a genome annotation framework for eukaryotic genomes with a user-friendly web-interface that generates and integrates annotations from various tools. The aggregated results can be analyzed with a fully integrated genome browser and are provided in a format ready for submission to NCBI. MOSGA is built on a portable, customizable and easily extendible Snakemake backend, and thus, can be tailored to a wide range of users and projects.

Availability and implementation
We provide MOSGA as a web service at https://mosga.mathematik.uni-marburg.de and as a docker container at registry.gitlab.com/mosga/mosga: latest. Source code can be found at https://gitlab.com/mosga/mosga

Supplementary information
Supplementary data are available at Bioinformatics online.
Activation of Oxytocin Receptors Excites Subicular Neurons by Multiple Signaling and Ionic Mechanisms
Authors: Hu Binqi , Boyle Cody A , Lei Saobo 
Published: November 2020

Abstract
Oxytocin (OXT) is a nonapeptide that serves as a neuromodulator in the brain and a hormone participating in parturition and lactation in the periphery. The subiculum is the major output region of the hippocampus and an integral component in the networks that process sensory and motor cues to form a cognitive map encoding spatial, contextual, and emotional information. Whilst the subiculum expresses the highest OXT-binding sites and is the first brain region to be activated by peripheral application of OXT, the precise actions of OXT in the subiculum have not been determined. Our results demonstrate that application of the selective OXT receptor (OXTR) agonist, [Thr4,Gly7]-oxytocin (TGOT), excited subicular neurons via activation of TRPV1 channels, and depression of K+ channels. The OXTR-mediated excitation of subicular neurons required the functions of phospholipase Cβ, protein kinase C, and degradation of phosphatidylinositol 4,5-bisphosphate (PIP2). OXTR-elicited excitation of subicular neurons enhanced long-term potentiation via activation of TRPV1 channels. Our results provide a cellular and molecular mechanism to explain the physiological functions of OXT in the brain.
Seroprevalence of SARS-CoV2 in IBD Patients Treated with Biologic Therapy
Authors: Berte’ Roberto , Mazza Stefano , Stefanucci Marta Rachele , Noviello Daniele , Costa Stefania , Ciafardini Clorinda , Mileti Erika , Mapelli Marina , Pasqualato Sebastiano , Pinto Sergio , Favale Agnese , Vecchi Maurizio , Neurath Markus F , Atreya Raja , Fantini Massimo Claudio , Facciotti Federica , Caprioli Flavio 
Published: November 2020

AbstractBackground and AimsA similar course of COVID-19 in patients with inflammatory bowel diseases [IBD] and in the general population has been reported. However, disease prevalence in IBD patients is presently unknown. In this prospective observational study, we aimed at determining SARS-CoV2 infection prevalence in IBD patients treated with biologic therapy.MethodsFrom IBD patients under biologic therapy and recruited from three different locations in Italy and Germany, 354 sera were evaluated for antibody presence by RBD ELISA. Control groups were: i] age-matched healthy subjects tested in the same time period in Milan, Italy; ii] healthy subjects collected in the pre-COVID era; iii] IBD patients under biologic therapy collected in the pre-COVID era.ResultsEight out of 354 patients tested positive for the anti-RBD-SARS-CoV2 IgG antibody [prevalence 2.3%]. The percentage of IgG-positive patients among those recruited from Milan was significantly higher than among those recruited from other locations [prevalence 5.4% vs 0.4%, p &lt;0.005]. IgG-positive patients reported a significantly higher incidence of fever, anosmia, and ageusia, and were more likely to have entered into close contact with COVID-19-positive subjects before the study enrolment.ConclusionsSeroprevalence of SARS-CoV2 in IBD patients treated with biologic therapy reflects values measured in the local general population. Specific symptoms and contact history with SARS-CoV2-infected individuals strongly increase the likelihood of SARS-CoV2 seropositivity.