bluuebunny/crossref_metadata_2025_split huggingface dataset:
Management of primary sclerosing cholangitis and its complications: an algorithmic approach
Authors: Prokopič Michal , Beuers Ulrich 
Published: December 2020

AbstractPrimary sclerosing cholangitis (PSC) is a rare cholestatic liver disease, characterized by multiple strictures and dilatations of the intra- and extrahepatic bile ducts, leading to progressive liver fibrosis, in 10–15% cholangiocarcinoma, and ultimately end-stage liver disease. The pathogenesis is poorly understood, but (epi-)genetic factors, mechanisms of innate and adaptive immunity, toxic effects of hydrophobic bile acids, and possibly intestinal dysbiosis appear to be involved. The strong link with inflammatory bowel disease (IBD) is associated with a markedly enhanced risk of colorectal cancer which next to cholangiocarcinoma represents the most serious diagnostic challenge in long-term PSC management. Despite extensive research, no medical treatment has been proven so far to prolong the time to liver transplantation (LTx), which remains the effective treatment in late-stage disease. Recurrence of PSC after LTx is observed in up to 20% of patients. Here, we briefly summarize actual views on PSC pathogenesis and provide an algorithmic approach to diagnostic procedures and recommendations for the management of PSC and its complications. We describe promising treatment options subject to current clinical trials.
Anomaly detection in a fleet of industrial assets with hierarchical statistical modeling
Authors: Dhada Maharshi , Girolami Mark , Parlikad Ajith Kumar 
Published: December 2020

AbstractAnomaly detection in asset condition data is critical for reliable industrial asset operations. But statistical anomaly classifiers require certain amount of normal operations training data before acceptable accuracy can be achieved. The necessary training data are often not available in the early periods of assets operations. This problem is addressed in this paper using a hierarchical model for the asset fleet that systematically identifies similar assets, and enables collaborative learning within the clusters of similar assets. The general behavior of the similar assets are represented using higher level models, from which the parameters are sampled describing the individual asset operations. Hierarchical models enable the individuals from a population, comprising of statistically coherent subpopulations, to collaboratively learn from one another. Results obtained with the hierarchical model show a marked improvement in anomaly detection for assets having low amount of data, compared to independent modeling or having a model common to the entire fleet.
Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids
Authors: He Xiaolong , He Qizhi , Chen Jiun-Shyan , Sinha Usha , Sinha Shantanu 
Published: December 2020

Abstract
As characterization and modeling of complex materials by phenomenological models remains challenging, data-driven computing that performs physical simulations directly from material data has attracted considerable attention. Data-driven computing is a general computational mechanics framework that consists of a physical solver and a material solver, based on which data-driven solutions are obtained through minimization procedures. This work develops a new material solver built upon the local convexity-preserving reconstruction scheme by He and Chen (2020) A physics-constrained data-driven approach based on locally convex reconstruction for noisy database. Computer Methods in Applied Mechanics and Engineering 363, 112791 to model anisotropic nonlinear elastic solids. In this approach, a two-level local data search algorithm for material anisotropy is introduced into the material solver in online data-driven computing. A material anisotropic state characterizing the underlying material orientation is used for the manifold learning projection in the material solver. The performance of the proposed data-driven framework with noiseless and noisy material data is validated by solving two benchmark problems with synthetic material data. The data-driven solutions are compared with the constitutive model-based reference solutions to demonstrate the effectiveness of the proposed methods.
New Inversion Approach for Interpreting Gravity Data Caused by Dipping Faults
Authors: Elhussein Mahmoud 
Published: December 2020

AbstractA new inversion algorithm of gravity data utilizing the particle swarm optimization (PSO) algorithm is used to interpret dipping faults models. The PSO technique is stochastic in nature; its development was motivated by the common in‐flight performance of birds looking for food. Particles or models represent birds. Individual particles have a location and a velocity vector. The parameter value represents the position vectors. PSO is adjusted by random particles or models and searches for targets by updating generations. This algorithm determines the dipping faults different parameters (amplitude factor, depth to the center of the upper part of the layer, depth to the center of the lower part of the layer, fault dip angle, and the origin of the fault trace). Herein, the PSO algorithm is applied to noise‐free synthetic data, synthetic data contaminated with different random noise levels (5%, 10%, and 15%) and real field gravity data from Egypt. The applicability and efficiency of the PSO inversion algorithm are well demonstrated for synthetic and field gravity data. The errors of the different estimated parameters are calculated for synthetic data, also, the root mean square error is calculated for synthetic and real data. The parameters estimated from real data matches well with that resulted from different published techniques. From the results obtained by using the present technique, we can apply the proposed technique in different applications, like mining and ore exploration.
Performance of 6 Different Global Navigation Satellite System Receivers at Low Latitude Under Moderate and Strong Scintillation
Authors: de Paula E. R. , Martinon A. R. F. , Moraes A. O. , Carrano C. , Neto A. C. , Doherty P. , Groves K. , Valladares C. E. , Crowley G. , Azeem I. , Reynolds A. , Akos D. M. , Walter T. , Beach T. L. , Slewaegen J.‐M. 
Published: December 2020

AbstractAfter sunset, in the equatorial regions ionospheric plasma irregularities are generated due to the generalized Rayleigh‐Taylor instability. Under favorable conditions these irregularities develop in the equatorial region while mapping along the magnetic field lines giving rise to large plasma depletion structures called Equatorial Plasma Bubbles with embedded smaller structures on their walls. The global navigation satellite system (GNSS) L1 band frequency is sensitive to irregularities of the size of 300–400 m in the first Fresnel zone, which cause scattering and diffraction of the signal and produce amplitude and/or phase scintillation. Severe scintillation of GNSS signals can in turn cause loss of lock of the receiver code and/or carrier loops. As a result, GNSS navigation and positioning solution can be adversely affected by the ionospheric scintillation. There are multiple GNSS receivers designed to monitor scintillations. These receivers are based on different hardware designs and use different methodologies to process the raw data. When using simultaneous data from different GNSS scintillation monitors it is important to evaluate and compare their performances under similar scintillation conditions. The scintillation monitoring techniques may be useful for many applications that use GNSS signal. The aim of this work is to evaluate the performance of six different GNSS receivers located at São José dos Campos (23.1°S, 45.8°W, dip latitude 17.3°S) during moderate and strong scintillation activity. The amplitude (S4) and phase (σϕ) scintillation indexes from these receivers were analyzed and compared for the nights February 20–21 and November 27–28, 2013.
Influence of High Latitude Sudden Stratospheric Warming on Tropical Weather: Observations From a 205 MHz Stratosphere Troposphere Radar and Surface Meteorological Parameters
Authors: Remya R. , Manoj M. G. , Rakesh V. , Mohanakumar K. , Sivan C. 
Published: December 2020

AbstractThe study illustrates the evidence of dynamical coupling between the high‐latitude sudden stratospheric warming (SSW) events that occurred in three consecutive winter seasons and the concomitant changes in the wind pattern in the lower stratosphere and troposphere observed with a state‐of‐the‐art 205 MHz stratosphere‐troposphere wind profiling Radar, located at Cochin (10.03°N, 77.33°E), a tropical station in southwest peninsular India. Associated with the occurrence of SSW, the tropical region experiences a change in zonal wind speed and direction at upper tropospheric altitudes after the central day of warming. The zonal wind in the troposphere also reversed its prevailing direction. Downward propagation of zonal wind during the SSW period is noticed, which is also supported by the reanalysis data set. In the lower troposphere, vertical wind experienced sudden fluctuations with varying amplitudes soon after the peak day of the SSW event. The surface meteorological parameters in an area (8°–12°N, 74°–78°E), centered at Cochin, from ERA‐Interim are also examined. An abrupt fall in outgoing longwave radiation, followed by convection and unusual rainfall in the tropical region 5–6 days before the peak of SSW events, is a unique and exciting feature noted in the study. Detailed analysis of the past 20 years of SSW events confirmed the observational evidence of unusual rainfall over the low latitude region related with the onset of SSW. Comprehensive observational and numeric modeling studies are needed to understand the mechanism for the SSW events in high latitudes and the associated convective activity and precipitation in the low latitude region.
Increased Turbulence in the Eurasian Upper‐Level Jet Stream in Winter: Past and Future
Authors: Lv Yanmin , Guo Jianping , Li Jian , Han Yi , Xu Hui , Guo Xiaoran , Cao Lijuan , Gao Wenhua 
Published: December 2020

AbstractAnthropogenic activities have been increasingly strengthening the meridional temperature gradient in the upper atmosphere, which has a profound impact on the wind shear and turbulence in mid‐latitudes. Here we examined the trends of the wintertime jet stream and clear‐air turbulence at 250 hPa over the Eurasian. The zonal wind speed showed a significantly increasing trend in the region between 40°N and 60°N where the meridional temperature gradient between the equator and the pole were increasing sharply in the past 4 decades (1980–2019). In addition, there existed a clear trend toward stronger vertical shear at 250 hPa in the latitudinal zone between 30°N and 40°N where the zonal wind increased, while a declining trend was shown to the south of 30°N, according to three different reanalysis datasets, including ERA5, JRA55, and NCAR/NCEP, which may be related to the poleward shift of the subtropical jet stream. The turbulence, closely related to the occurrence of vertical wind shear, was found to become stronger in the latitudinal zone between 30°N and 40°N consequently. The projections based on Phase 6 of Coupled Model Intercomparison Project models show that the zonal wind speed in the mid‐latitudes will increase significantly for the period 2015–2100, especially between the latitudinal zone between 40°N and 60°N, and the corresponding vertical wind shear in this area tend to dramatically increase. The findings not only help us better understanding of turbulence occurrence in the past 40 years, but also have profound implications for the projection of turbulence occurrence under global warming over mid‐latitude regions of North Hemisphere.
Pore Size Distribution Controls Dynamic Permeability
Authors: Li Jimmy X. , Rezaee Reza , Müller Tobias M. , Sarmadivaleh Mohammad 
Published: December 2020

AbstractProbing the flow permeability of porous media with elastic waves is a formidable challenge, also because the wave‐induced oscillatory motion renders the permeability frequency dependent. Existing theoretical models for such a dynamic permeability assume that the frequency dependence is primarily controlled by a single characteristic length scale of the pore space. However, the fact that in most natural porous media there exists a distinct range of pore sizes is ignored. To overcome this limitation, we develop a dynamic permeability model that explicitly incorporates the pore size distribution. We show that the pore size distribution has a first‐order effect on the dynamic permeability. Since the pore size distribution can be deduced from techniques such as nuclear magnetic resonance, our results indicate the possibility to jointly use remote‐sensing technologies for improved permeability determination and cross‐fertilization of laboratory and in‐field techniques.
Assessing the Influence of COVID‐19 on the Shortwave Radiative Fluxes Over the East Asian Marginal Seas
Authors: Ming Yi , Loeb Norman G. , Lin Pu , Shen Zhaoyi , Naik Vaishali , Singer Clare E. , Ward Ryan X. , Paulot Fabien , Zhang Zhibo , Bellouin Nicolas , Horowitz Larry W. , Ginoux Paul A. , Ramaswamy V. 
Published: December 2020

AbstractThe Coronavirus Disease 2019 (COVID‐19) pandemic led to a widespread reduction in aerosol emissions. Using satellite observations and climate model simulations, we study the underlying mechanisms of the large decreases in solar clear‐sky reflection (3.8 W m−2 or 7%) and aerosol optical depth (0.16 W m−2 or 32%) observed over the East Asian Marginal Seas in March 2020. By separating the impacts from meteorology and emissions in the model simulations, we find that about one‐third of the clear‐sky anomalies can be attributed to pandemic‐related emission reductions, and the rest to weather variability and long‐term emission trends. The model is skillful at reproducing the observed interannual variations in solar all‐sky reflection, but no COVID‐19 signal is discerned. The current observational and modeling capabilities will be critical for monitoring, understanding, and predicting the radiative forcing and climate impacts of the ongoing crisis.
High Altitude Echoes From the Equatorial Topside Ionosphere During Solar Minimum
Authors: Derghazarian S. , Hysell D. L. , Kuyeng K. , Milla M. A. 
Published: December 2020

AbstractWe describe a new class of nonthermal plasma density irregularities observed in the postmidnight topside equatorial ionosphere under low solar flux conditions. They are distinct from irregularities associated with equatorial spread F (ESF) in terms of their morphology and because they exhibit strong spectral sidebands at the lower‐hybrid frequency. The coherent echoes were observed in a series high‐altitude radar experiments performed at Jicamarca utilizing long‐ and coded double‐pulse modes and a dual‐beam mode. The coded double‐pulse mode was used to measure the low‐frequency characteristics of the echoes with fine range resolution. Doppler shifts of the main backscatter line were observed to fall between ±150 m/s. The long‐pulse mode was employed for high‐frequency spectral analysis which revealed the presence of strong spectral sidelobes at the lower‐hybrid frequency. A dual‐beam mode was used to investigate the horizontal structure of the echoes. Zonal drift speeds of 50–70 m/s were inferred with this mode, and longitudinal dimensions of approximately 270 km were estimated. The study summarizes with a discussion of different mechanisms that may be responsible for the phenomenon and the lower‐hybrid sidebands in particular.
Propagation of Ultralow‐Frequency Waves from the Ion Foreshock into the Magnetosphere During the Passage of a Magnetic Cloud
Authors: Takahashi Kazue , Turc Lucile , Kilpua Emilia , Takahashi Naoko , Dimmock Andrew , Kajdic Primoz , Palmroth Minna , Pfau‐Kempf Yann , Soucek Jan , Motoba Tetsuo , Hartinger Michael D. , Artemyev Anton , Singer Howard , Ganse Urs , Battarbee Markus 
Published: December 2020

AbstractWe have examined the properties of ultralow‐frequency (ULF) waves in space (the ion foreshock, magnetosheath, and magnetosphere) and at dayside magnetometer stations (L = 1.6–6.5) during Earth's encounter with a magnetic cloud in the solar wind, which is characterized by magnetic fields with large magnitudes (∼14 nT) and small cone angles (∼30°). In the foreshock, waves were excited at ∼90 m Hz as expected from theory, but there were oscillations at other frequencies as well. Oscillations near 90 mHz were detected at the other locations in space, but they were not in general the most dominant oscillations. On the ground, pulsations in the approximate Pc2–Pc4 band (5 mHz–120 mHz) were continuously detected at all stations, with no outstanding spectral peaks near 90 mHz in the H component except at stations where the frequency of the third harmonic of standing Alfvén waves had this frequency. The fundamental toroidal wave frequency was below 90 mHz at all stations. In the D component spectra, a minor spectral peak is found near 90 mHz at stations located at L < 3, and the power dropped abruptly above this frequency. Magnetospheric compressional wave power was much weaker on the nightside. A hybrid‐Vlasov simulation indicates that foreshock ULF waves have short spatial scale lengths and waves transmitted into the magnetosphere are strongly attenuated away from noon.
Modeling the GABLS4 Strongly‐Stable Boundary Layer With a GCM Turbulence Parameterization: Parametric Sensitivity or Intrinsic Limits?
Authors: Audouin O. , Roehrig R. , Couvreux F. , Williamson D. 
Published: December 2020

AbstractThe representation of stable boundary layers (SBLs) still challenges turbulence parameterizations implemented in current weather or climate models. The present work assesses whether these model deficiencies reflect calibration choices or intrinsic limits in currently‐used turbulence parameterization formulations and implementations. This question is addressed for the CNRM atmospheric model ARPEGE‐Climat 6.3 in a single‐column model/large‐eddy simulation (SCM/LES) comparison framework, using the history matching with iterative refocusing statistical approach. The GABLS4 case, which samples a nocturnal strong SBL observed at Dome C, Antarctic Plateau, is used. The standard calibration of the ARPEGE‐Climat 6.3 turbulence parameterization leads to a too deep SBL, a too high low‐level jet and misses the nocturnal wind rotation. This behavior is found for low and high vertical resolution model configurations. The statistical tool then proves that these model deficiencies reflect a poor parameterization calibration rather than intrinsic limits of the parameterization formulation itself. In particular, the role of two lower bounds that were heuristically introduced during the parameterization implementation to increase mixing in the free troposphere and to avoid runaway cooling in snow‐ or ice‐covered region is emphasized. The statistical tool identifies the space of the parameterization free parameters compatible with the LES reference, accounting for the various sources of uncertainty. This space is non‐empty, thus proving that the ARPEGE‐Climat 6.3 turbulence parameterization contains the required physics to capture the GABLS4 SBL. The SCM framework is also used to validate the statistical framework and a few guidelines for its use in parameterization development and calibration are discussed.
Ensemble Methods for Neural Network‐Based Weather Forecasts
Authors: Scher Sebastian , Messori Gabriele 
Published: December 2020

AbstractEnsemble weather forecasts enable a measure of uncertainty to be attached to each forecast, by computing the ensemble's spread. However, generating an ensemble with a good spread‐error relationship is far from trivial, and a wide range of approaches to achieve this have been explored—chiefly in the context of numerical weather prediction models. Here, we aim to transform a deterministic neural network weather forecasting system into an ensemble forecasting system. We test four methods to generate the ensemble: random initial perturbations, retraining of the neural network, use of random dropout in the network, and the creation of initial perturbations with singular vector decomposition. The latter method is widely used in numerical weather prediction models, but is yet to be tested on neural networks. The ensemble mean forecasts obtained from these four approaches all beat the unperturbed neural network forecasts, with the retraining method yielding the highest improvement. However, the skill of the neural network forecasts is systematically lower than that of state‐of‐the‐art numerical weather prediction models.
Impact of visuomotor feedback on the embodiment of virtual hands detached from the body
Authors: Seinfeld Sofia , Müller Jörg 
Published: December 2020

AbstractIt has been shown that mere observation of body discontinuity leads to diminished body ownership. However, the impact of body discontinuity has mainly been investigated in conditions where participants observe a collocated static virtual body from a first-person perspective. This study explores the influence of body discountinuity on the sense of embodiment, when rich visuomotor correlations between a real and an artificial virtual body are established. In two experiments, we evaluated body ownership and motor performance, when participants interacted in virtual reality either using virtual hands connected or disconnected from a body. We found that even under the presence of congruent visuomotor feedback, mere observation of body discontinuity resulted in diminished embodiment. Contradictory evidence was found in relation to motor performance, where further research is needed to understand the role of visual body discontinuity in motor tasks. Preliminary findings on physiological reactions to a threat were also assessed, indicating that body visual discontinuity does not differently impact threat-related skin conductance responses. The present results are in accordance with past evidence showing that body discontinuity negatively impacts embodiment. However, further research is needed to understand the influence of visuomotor feedback and body morphological congruency on motor performance and threat-related physiological reactions.
Predictive Processing and Developmental Language Disorder
Authors: Jones Samuel David , Westermann Gert 
Published: December 2020

PurposeResearch in the cognitive and neural sciences has situated predictive processing—the anticipation of upcoming percepts—as a dominant function of the brain. The purpose of this article is to argue that prediction should feature more prominently in explanatory accounts of sentence processing and comprehension deficits in developmental language disorder (DLD).MethodWe evaluate behavioral and neurophysiological data relevant to the theme of prediction in early typical and atypical language acquisition and processing.ResultsPoor syntactic awareness—attributable, in part, to an underlying statistical learning deficit—is likely to impede syntax-based predictive processing in children with DLD, conferring deficits in spoken sentence comprehension. Furthermore, there may be a feedback cycle in which poor syntactic awareness impedes children's ability to anticipate upcoming percepts, and this, in turn, makes children unable to improve their syntactic awareness on the basis of prediction error signals.ConclusionThis article offers a refocusing of theory on sentence processing and comprehension deficits in DLD, from a difficulty in processing and integrating perceived syntactic features to a difficulty in anticipating what is coming next.