bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cip1 tunes cell cycle arrest duration upon calcineurin activation
Authors: Flynn Mackenzie J. , Benanti Jennifer A. 
Published: June 2022

Significance
To ensure their survival, cells arrest the cell division cycle when they are exposed to environmental stress. The duration of this arrest is dependent upon the time it takes a cell to adapt to a particular environment. How cells adjust the amount of time they remain arrested is not known. This study investigates the role of the phosphatase calcineurin in controlling cell cycle arrest duration in yeast. We show that calcineurin lengthens arrest by prolonging Hog1-dependent activation of the poorly characterized cyclin-dependent kinase inhibitor Cip1. Cip1 only impacts cell cycle arrest in response to stressors that robustly activate calcineurin, suggesting that Cip1 is a context-specific regulator that differentially adjusts the length of arrest depending on the particular stressor.
The aryl hydrocarbon receptor instructs the immunomodulatory profile of a subset of Clec4a4+eosinophils unique to the small intestine
Authors: Wang Wei-Le , Kasamatsu Jun , Joshita Satoru , Gilfillan Susan , Di Luccia Blanda , Panda Santosh K. , Kim Do-Hyun , Desai Pritesh , Bando Jennifer K. , Huang Stanley Ching-Cheng , Yomogida Kentaro , Hoshino Hitomi , Fukushima Mana , Jacobsen Elizabeth A. , Van Dyken Steven J. , Ruedl Christiane , Cella Marina , Colonna Marco 
Published: June 2022

SignificanceEosinophils contribute to type 2 immunity against helminths and allergens. The small intestine harbors eosinophils with incompletely understood pathophysiological roles. Here, we show that intestinal eosinophils include two subsets. One expresses the inhibitory receptor Clec4a4 and the inhibitory ligand PD-L1 and is unique to the small intestine; the other manifests a proinflammatory phenotype. Both subsets are blood derived. Remarkably, Clec4a4+eosinophils were instructed by the aryl hydrocarbon receptor (AHR), a ligand-activated transcription factor that imprints many gut immune cells. Selective AHR deletion in eosinophils depleted Clec4a4+eosinophils, augmented innate lymphocytes producing type 2 cytokines, and enhanced helminth clearance. We conclude that Clec4a4+eosinophils have immunomodulatory functions, which could be harnessed for the therapy of food allergies and eosinophilic gastrointestinal disorders.
The influence of acoustic radiation force beam shape and location on wave spectral content for arterial dispersion ultrasound vibrometry
Authors: Capriotti Margherita , Roy Tuhin , Hugenberg Nicholas R , Harrigan Hadiya , Lee Hon-Chi , Aquino Wilkins , Guddati Murthy , Greenleaf James F , Urban Matthew W 
Published: June 2022

Abstract

Objective. Arterial dispersion ultrasound vibrometry (ADUV) relies on the use of guided waves in arterial geometries for shear wave elastography measurements. Both the generation of waves through the use of acoustic radiation force (ARF) and the techniques employed to infer the speed of the resulting wave motion affect the spectral content and accuracy of the measurement. In particular, the effects of the shape and location of the ARF beam in ADUV have not been widely studied. In this work, we investigated how such variations of the ARF beam affect the induced motion and the measurements in the dispersive modes that are excited. Approach. The study includes an experimental evaluation on an arterial phantom and an in vivo validation of the observed trends, observing the two walls of the waveguide, simultaneously, when subjected to variations in the ARF beam extension (F/N) and focus location. Main results. Relying on the theory of guided waves in cylindrical shells, the shape of the beam controls the selection and nature of the induced modes, while the location affects the measured dispersion curves (i.e. variation of phase velocity with frequency or wavenumber, multiple modes) across the waveguide walls. Significance. This investigation is important to understand the spectral content variations in ADUV measurements and to maximize inversion accuracy by tuning the ARF beam settings in clinical applications.
Conditional recurrence plots for the investigation of sawteeth pacing with RF modulation
Authors: Peluso Emmanuele , Murari Andrea , Craciunescu Teddy , Lerche Ernesto , Gaudio Pasquale , Gelfusa Michela , Gallart Daniel , Taylor David 
Published: June 2022

Abstract
In many areas of research, from neurobiology to nuclear fusion, which investigate complex dynamical systems involving numerous, different and interconnected physical quantities, the application of advanced analysis tools based on Chaos theory and Information theory has provided significant improvements both by supporting theoretical models and by highlighting hidden relationships between quantities characterizing the observed phenomena. The present article is therefore devoted to the analysis of synchronization experiments in magnetically controlled plasmas at JET, involving a ‘target’ quantity (sawteeth) and a ‘driver’ (modulated injected radio frequency) one. The typical approach to such analysis would start from assuming that the coupling between driver and target is free from external influences. However, since sawteeth can occur naturally and constitute a confounding factor in the analysis, by relaxing such an assumption a more realistic description of the system can emerge. The use of the conditional joint recurrence plots, complemented by the conditional transfer entropy, has provided further evidences supporting the effective influence of the pacing on the fast ions populations of both the minority and of the main plasma species in H-mode plasmas. The results highlight specific physical factors affecting the efficiency of the pacing and are in agreement with modelling estimates. The analysis performed then paves the way for future studies on more recent DT pulses performed at JET, and on data from other synchronization experiments.
Anisotropic diffusion as a proxy model for the estimation of heat-loads on plasma-facing components
Authors: Knieps A , Liang Y , Drews P , Endler M , Gao Y , Geiger J , Jakubowski M , Koenig R , Niemann H , Wang F Q , Xu S , Zhou S ,   The W7-X Team
Published: June 2022

Abstract
To facilitate the estimation of heat loads on plasma-facing components in fusion devices in various different magnetic geometries, a heat load proxy model was developed based on anisotropic diffusion. In this work, this model is compared to the so-called field-line diffusion approach. To facilitate the evaluation of these models, a novel synthetic camera-based approach for obtaining heat load distributions from Monte Carlo samples was also developed and implemented. With the assistance of this synthetic camera, heat load predictions for the Wendelstein 7-X divertor were obtained and compared with infrared camera observations. It was found that the anisotropic diffusion-based model achieved a closer match to infrared camera observations, while still being suitable in computational effort for large magnetic configuration database scans.
A surrogate model of offshore wind farm annual energy production to support financial evaluation
Authors: Valotta Rodrigues Rafael , Friis-Møller Mikkel , Dykes Katherine , Pollini Nicolò , Jensen Morten 
Published: June 2022

Abstract
The preliminary financial evaluation of wind farm profitability requires fast analysis of energy production and costs while having very little specific information around the project. Early in the design process, the selection of specific wind turbines and the layout design may not yet be defined. Techno-economic and financial analysis models have been developed to use input from a small set of high-level project characteristics to estimate major cost elements and energy production for a wind farm to support quick analysis of levelized cost of energy (LCoE), or other financial metrics. Such models are typically based on prior project data and/or very simple analytical models. However, as capabilities for financial analysis of wind farms advance, so does the desire to improve the accuracy of the physical and cost modelling of the system. In this work, we develop a surrogate model of Annual Energy Production (AEP) for offshore wind farms for financial analysis applications in the early stages of development. The surrogate is developed from an parameterized engineering model and covers a large potential wind farm design space addressing different technological and site conditions. The surrogate model uncovers the underlying structure in the model in terms of input-output relationships and achieves a coefficient of determination of 0.994. The method used to develop the surrogate model can be adapted for additional dimensions of inputs as needed.
Identification of large-scale atmospheric structures under different stability conditions using Dynamic Mode Decomposition
Authors: Alcayaga Leonardo , Larsen Gunner Chr. , Kelly Mark , Mann Jakob 
Published: June 2022

Abstract
We investigate the characteristic of large-scale coherent motions over a large horizontal domain using the Dynamic Mode Decomposition (DMD) spectral analysis algorithm applied on measurements from two long-range pulsed lidars. We show the results and advantages of this methodology on six cases representative of three thermal stratification conditions at two heights relevant for wind energy: near-neutral, unstable and stable stratification at 50m and 200m above ground level. For these cases the DMD algorithm show three types of structures: streaks near the surface for near-neutral for neutral stratification, large-scale convective rolls for the unstable cases and sheet-like rotational patches for stable conditions. The DMD algorithm also shows the stationary effects of the terrain on the flow at 50m above ground level, within the atmospheric surface layer. The possibility of isolating terrain effects from coherent motions makes DMD attractive for studying complex atmospheric flow phenomena as well as to have more realistic input for wind farm flow simulations.
Farm blockage model validation using pre and post construction LiDAR measurements
Authors: Jacquet C , Apgar D , Chauchan V , Storey R , Kern S , Davoust S 
Published: June 2022

Abstract
This study discusses the characterization of farm blockage for an Onshore site. A test campaign was designed to measure the effects of farm induction upstream of a row of turbines. 5 LiDARs were dispersed on a site in North America. Measurement periods took place before and after erection of the turbines. A thorough characterization of the wind conditions is performed using the measurement from the pre-construction period. During nighttime, vertical profiles reveal the occurrence of low-level jet (LLJ) structure, and LiDAR-to-LiDAR horizontal variations in the measured wind speed are strong. Therefore, the analysis focuses on the daytime data only. Impact of farm blockage is quantified by analyzing variations of measured wind speed, relative to a LiDAR of reference, between the pre- and post-construction periods. These wind speed variations measured by the LiDARs, therefore, give insights on how the flow is distorted upstream of the row of turbines, but also within the inter-turbine space in the row. Additionally, the wind farm is simulated using a new CFD-based engineering model for blockage. Simulation results show very good agreement with the measurements, demonstrating the ability of the model to capture the underlying physics. The last part of the paper discusses the range of applicability of the test campaign results and proposes ways to further improve farm blockage characterization.
Synoptic and mesoscale winds in the complex terrain of Perdigão
Authors: Coimbra I L , Palma J M L M 
Published: June 2022

Abstract
For modeling complex atmospheric flows in wind energy studies when employing a chain of models, it is necessary to guarantee accurate wind simulations in all models that make up the chain. With this aim, we evaluated the ERA5 dataset (synoptic) and Weather Research and Forecasting (WRF) simulations (mesoscale) of the wind over Perdigão, which will later serve as input to a microscale model. The Perdigão-2017 measurement campaign is one of the latest and largest experiments over a complex terrain, thus being a valuable dataset for the validation of atmospheric models. Previous long-term simulation studies in Perdigão did not employ a highresolution reanalysis dataset for boundary conditions, nudging techniques, planetary boundary layer (PBL) sensitivity analysis, nor did compare synoptic and mesoscale data against local measurements. Here we simulated the wind in Perdigão using WRF with two one-way nested domains (9-km and 3-km horizontal resolutions) during 45 days, evaluating five PBL schemes. ERA5 reanalysis was used as input to the mesoscale simulations and for the spectral nudging in WRF’s outer domain. Data from the top anemometer of a 100-m tower, located in Perdigão’s SW ridge, were employed as reference in the evaluation of ERA5 and WRF wind speeds and directions. The hourly-averaged 3-km WRF simulations showed wind speed errors of 2.22 m/s RMSE and −0.02 m/s Bias with the YSU PBL scheme. The approximately 31-km ERA5 dataset exhibited total errors of 2.22 m/s (−0.37 m/s) RMSE (Bias). We attributed the similar ERA5 total error to its smoother wind speed values compared to WRF’s, despite its coarser resolution, and not to its faithful representation of the wind in Perdigão. WRF overestimated wind speeds mainly between the NW and N sectors as a result of its misrepresentation of the NW mountain range. Despite ERA5 coarser resolution, its smoothed terrain did not seem to greatly affect the wind near the NW direction. Additionally, both ERA5 and WRF wind speed errors vary linearly with the measured wind speed.
Impact of Turbulent Time Scales on Wake Recovery and Operation
Authors: Hodgson E L , Aa Madsen M H , Troldborg N , Andersen S J 
Published: June 2022

Abstract
Enhancing wake recovery behind wind turbines has the potential to significantly improve the power production and efficiency of large wind farms. Rather than investigating turbine control strategies, floater motion or global turbulent quantities such as turbulence intensity, this work aims to study wake stability and recovery through a focus on the turbulent scales of the inflow. Using Large Eddy Simulations of a single turbine, sinusoidal streamwise forcing is applied to the inflow with a constant amplitude and mean flow velocity, but differing time scales between 80s and 140s. For all applied time scales the turbine wake is characterised by the rolling-up of the near wake into the periodic shedding of vortex rings, and an excitation of the applied forcing frequency resulting in velocity fluctuations in the wake several times larger than that at the inflow. For shorter time scales (80s - 90s) a more aggressive and earlier wake roll-up led to a shorter near wake region, faster overall recovery and significantly improved the expected power output from 6R downstream onwards. An inflow time period of 80s gave rise to more than a 50% increase in power output of a fictive downstream turbine placed at 14R downstream, compared to an inflow time scale of 140s.
How generalizable is a machine-learning approach for modeling hub-height turbulence intensity?
Authors: Bodini Nicola , Lundquist Julie K. , Livingston Hannah , Moriarty Pat 
Published: June 2022

Abstract
Hub-height turbulence intensity is essential for a variety of wind energy applications. However, simulating it is a challenging task. Simple analytical models have been proposed in the literature, but they all come with significant limitations. Even state-of-the-art numerical weather prediction models, such as the Weather Research and Forecasting model, currently struggle to predict hub-height turbulence intensity. Here, we propose a machine-learning-based approach to predict hub-height turbulence intensity from other hub-height and ground-level atmospheric measurements, using observations from the Perdigão field campaign and the Southern Great Plains atmospheric observatory. We consider a random forest regression model, which we validate first at the site used for training and then under a more robust round-robin approach, and compare its performance to a multivariate linear regression. The random forest successfully outperforms the linear regression in modeling hub-height turbulence intensity, with a normalized root-mean-square error as low as 0.014 when using 30-minute average data. In order to achieve such low root-mean-square error values, the knowledge of hub-height turbulence kinetic energy (which can instead be modeled in the Weather Research and Forecasting model) is needed. Interestingly, we find that the performance of the random forest generalizes well when considering a round-robin validation (i.e., when the algorithm is trained at one site such as Perdigão or Southern Great Plains) and then applied to model hub-height turbulence intensity at the other location.
How long can constant wind speed periods last in the turbulent atmospheric boundary layer?
Authors: Moreno Daniela , Friedrich Jan , Wächter Matthias , Peinke Joachim , Schwarte Jörg 
Published: June 2022

Abstract
The statistical characteristics of periods of constant wind speed in the atmospheric turbulent wind are investigated. Our hypothesis is that specific circumstances of these structures in the wind may lead to not yet identified phenomena on an operating wind turbine. Therefore, the characterisation in terms of their lengths, probability of occurrence and extreme events is the focus of this study. Two data sets from atmospheric wind are analysed. Our findings reveal that the statistics of constant wind speed periods depends firstly, on the precise definition of wind speed thresholds and secondly, on the degree of turbulence. Additionally, a comparison to wind time series generated with a standard model suggests that these structures might not be properly reproduced by standard wind field models.
Development of a dynamic wake model accounting for wake advection delays and mesoscale wind transients
Authors: Foloppe B , Munters W , Buckingham S , Vandevelde L , van Beeck J 
Published: June 2022

Abstract
With tip height often above 200 meters, currently installed wind turbines are increasingly interacting with the atmospheric boundary layer. Therefore, the research intends to develop a new dynamic wake model that considers weather transient inputs. The Flow Redirection and Induction in Steady State (FLORIS) framework has been proven to be a powerful wake modelling tool even though lacking dynamical effects. An extension of the FLORIS framework is presented in order to include wake advection delays between wind turbines, time-varying and spatially heterogeneous wind conditions. The so-called Observation Points (OPs) are generated at the rotor centre location. Following a Lagrangian approach, the OPs are convected downstream, along the wake of the wind turbine, defining the wake centreline. It is during this process that the dynamics of wind turbine wake and background flow field unsteadiness are included. Finally, using the same approach currently implemented for yaw-misalignment wake deflection, the wake is shifted to match the unsteady wake centreline. The developed model is validated against Large Eddy Simulation for unsteady inflow with variable wind turbine control and wake interactions. The new model is applied to a simulation of the Horns Rev wind farm with a sinusoidally time-varying wind inflow direction to show an hysteresis in the wind farm power extraction curve.
Analysis of wake properties and meandering under different cases of atmospheric stability: a large eddy simulation study
Authors: Jézéquel Erwan , Blondel Frédéric , Masson Valéry 
Published: June 2022

Abstract
Large eddy simulations (LES) with the solver Meso-NH are performed to analyse a single wind turbine wake. In the first part of this work, four algorithms to determine the instantaneous wake centre (wake tracking) from the literature are compared. A data-processing method is also proposed to improve the results of these algorithms. In the second part, three different atmospheric conditions are simulated to assess the effect of inflow conditions on the wake. The focus is on the 2-dimensional wake width, the maximum deficit, the maximum added turbulence and the amount of wake meandering in the lateral and vertical directions. The three formers are computed in both the Moving and Fixed Frames of Reference (MFOR and FFOR). Results in the MFOR are shown to be sensitive to the wake tracking algorithm, in particular for turbulence. The flowfield in the MFOR is found to be independent of atmospheric conditions for the two cases with similar operating conditions and length scales larger than two diameters of the rotor. The third case with lower length scales and turbulence intensity behaves differently.
Optimum gust detection by nacelle-based lidar: A study on the Vestas V52
Authors: Hannesdóttir Ásta , Meseguer Urbán Albert , Spasov Filip , Larvol Antoine 
Published: June 2022

Abstract
In this study we use lidar measurements from a four-beam nacelle mounted lidar, together with wind turbine load measurements, to analyse events that cause extreme blade flapwise loading on the Vestas V52 at Risø campus. We define a simple gust detection algorithm that is based on fitting a linear model to the line of sight velocity measurements in order to detect changes and fluctuations in the incoming wind field. The detection algorithm is run on a whole year of measurements and we perform an iterative parameter sweep using different values for two parameters of the gust detection method. These parameters are a slope threshold and slope duration of the linear fit. The blade loads that are associated with the detected gusts are analysed to find the optimum settings of the gust detection parameters. It was found that the most extreme observed blade load was detected for a range of parameters and by setting the slope threshold to 1.2 m/s2 and duration to 4 s, we could achieve the highest average load within the parameter range. On the other hand, the highest average blade load was found by setting the slope threshold to 1.4 m/s2 and duration to 8.5 s, though the most extreme load was not detected with those settings. The gust detection method shows potential to be used as an add-on solution to a traditional wind turbine controller to mitigate extreme loading during operation.
Improved wind speed estimation and rain quantification with continuous-wave wind lidar
Authors: Jin Liqin , Angelou Nikolas , Mann Jakob , Larsen Gunner Chr. 
Published: June 2022

Abstract
The presence of raindrops has an adverse impact on the line-of-sight wind speed measurement of Doppler lidars. Here, we propose a method to improve the accuracy of wind speed estimation through a filtering process on rapidly sampled (3000 Hz) lidar data. For this purpose, we conducted a field study at the Risø campus of the Technical University of Denmark using a ground-based, continuous-wave Doppler lidar. Data was acquired during a three-hour period with rain. We propose that we can differentiate between the rain and aerosol back-scattering signals by assessing the maximum of the noise-normalized Doppler spectra. To reduce the influence of rain of the velocity signal, we filter away the Doppler spectra where the maximum is larger than a given threshold. The comparison between the raw and the filtered lidar data with sonic anemometer measurements acquired at the same location, shows that we can effectively remove rain signals and improve the measurement accuracy of a Doppler lidar. However, this method is not applicable when the back-scattering of aerosols and rain are characterized by the same statistics.