Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-scale modeling of a wind turbine wake in complex terrain

Comparing numerical simulations of wind turbine wakes in neutral atmospheric boundary layer (ABL) flow over complex terrains with full-scale experiments is not always straightforward. Pure neutral ABL conditions are rarely found in the atmosphere as the characteristics of the ABL change during the diurnal cycle. This study presents some insights into how a single wind turbine (WT) and its wake behave under near-neutral ABL conditions in complex terrain. The Perdigão Valley in Portugal was chosen as the test case as it is an excellent case study of three-dimensional flow in complex terrain to validate numerical simulations of WT wake with experimental data due to the availability of extensively deployed remote sensing equipment (e.g., German Aerospace Center (DLR) and Technical University of Denmark (DTU) multi-Doppler lidars). The Weather Research and Forecasting (WRF) [1] model is utilized in large-eddy simulation (LES) mode in order to simulate the period of interest with a multi-scale modeling approach. Five nested domains, with the finest domain having a spatial resolution of 5 m, are used to dynamically downscale mesoscale flow features to microscale. A generalized actuator line (GAL) WT parameterization is used to model the wind turbine-flow interaction. WRF-LES-GAL (hereinafter referred to as WLG) results compare quite well with the experimental data obtained from lidars and meteorological mast, with minor biases between the simulated and observed data. Due to insufficient buoyancy generation from the WRF-LES model, the simulated track wake was found to have lower vertical deflection compared to the lidar data; hence, no recirculation zone is observed in the valley. Overall, the WLG model is able to reproduce the wake characteristics observed on the first ridge top into the valley, as well as the power and thrust generation, and can be used for further analysis of other stability conditions.
RANS-AD based ANN surrogate model for wind turbine wake deficits

Inside wind farms, wake effects are the primary source of turbine interactions, and as such, they constitute one of the most important aspects of wind farm operations. The two most widespread methods for calculating the wind farm wake flow are computational fluid dynamics (CFD) methods and engineering models. Both methods have drawbacks; CFD methods can be very accurate but are computationally expensive. Vice versa, engineering models sacrifice accuracy by simplifying the physics, thereby improving computational efficiency. In cases where many evaluations of the flow are needed, this trade-off is a hindrance. One such case is wind farm layout optimization problems. It has been shown that the estimation of wake flows can be improved by surrogate modeling. Recently, Artificial Neural Networks (ANN) have been demonstrated to predict accurate wakes over a mesh. In this work, a new mesh-free ANN-based wake model is proposed. This new model can predict the flow everywhere in the domain, and as it employs smooth activation functions, it is suitable for gradient-based optimization. Two ANNs were trained with data generated by Reynolds-Averaged Navier-Stokes with Actuator Disc simulations for several yaw angles. The first ANN predicts streamwise wake velocity induction/deficit, the second ANN predicts added wake turbulence intensity. Both ANNs predict with a low error.
Investigations of Farm-to-Farm Interactions and Blockage Effects from AWAKEN Using Large-Scale Numerical Simulations

A large-scale numerical computation of five wind farms was performed as a part of the American WAKE experimeNt (AWAKEN). This high-fidelity computation used the ExaWind/AMR-Wind LES solver to simulate a 100 km × 100 km domain containing 541 turbines under unstable atmospheric conditions matching previous measurements. The turbines were represented by Joukowski and OpenFAST coupled actuator disk models. Results of this qualitative comparison illustrate the interactions of wind farms with large-scale ABL structures in the flow, as well as the extent of downstream wake penetration in the flow and blockage effects around wind farms.
Comparison of three DWM-based wake models at above-rated wind speeds

In this study we investigate three mid-fidelity wind turbine wake models based on the dynamic wake meandering (DWM) model principle, and compare their performance with a reference dataset, produced with large-eddy simulations using the actuator line model. The models are compared with respect to flow field, power, and loads on a row of four 5MW reference turbines experiencing above-rated wind conditions. In general, the DWM models show fairly good agreement with large-eddy simulation for the time-averaged flow fields, blade forces and power, with increasing differences along the turbine row. Also when comparing fatigue loads of blade root moments, the differences between the models increase further into the row, with deviations up to 25 % of the reference case. However, while the development in blade root moment fatigue along the turbine row is predominantly driven by the energy content at the frequency corresponding to the turbine’s rotational period (1P) for the DWM models, the large-eddy simulation results suggest that the key drivers for the blade root and tower loads are the increase in meandering and energy at higher frequencies (> 1P) deeper into the turbine row. For the tower loads, the DWM models highly underestimate the fatigue for the waked turbines. From these results, we suggest priorities for future model developments so that robust model implementations can be used in wind farm design and operation.
RANS simulation of a wind turbine wake in the neutral atmospheric pressure-driven boundary layer

Reynolds-averaged Navier-Stokes (RANS) simulations of a single wind turbine wake in the neutral atmospheric pressure-driven boundary layer (PDBL) are conducted and compared to RANS simulations with inflow based on the more traditional log-law. The latter is valid in the neutral atmospheric surface layer (ASL), while the PDBL is a better representation of the whole atmospheric boundary layer (ABL). It is found that the wake results of the two types of simulations become more similar for increasing ABL height to rotor diameter ratio. In fact, the ASL is shown to be a special asymptotic case of the PDBL. The RANS simulations are also compared to a large-eddy simulation (LES) PDBL case, where it is found that both the ASL and PDBL RANS simulations compare well with the reference LES data in the wake region, while the RANS PDBL compares better with the data in the upper region of the domain.
A cross-circulatory platform for monitoring innate allo-responses in lung grafts

Lung transplantation is the only curative option for end-stage chronic respiratory diseases. However the survival rate is only about 50% at 5 years. Although experimental evidences have shown that innate allo-responses impact on the clinical outcome, the knowledge of the involved mechanisms involved is limited. We established a cross-circulatory platform to monitor the early recruitment and activation of immune cells in an extracorporeal donor lung by coupling blood perfusion to cell mapping with a fluorescent marker in the pig, a commonly-used species for lung transplantation. The perfusing pig cells were easily detectable in lung cell suspensions, in broncho-alveolar lavages and in different areas of lung sections, indicating infiltration of the organ. Myeloid cells (granulocytes and monocytic cells) were the dominant recruited subsets. Between 6 and 10 h of perfusion, recruited monocytic cells presented a strong upregulation of MHC class II and CD80/86 expression, whereas alveolar macrophages and donor monocytic cells showed no significant modulation of expression. This cross-circulation model allowed us to monitor the initial encounter between perfusing cells and the lung graft, in an easy, rapid, and controllable manner, to generate robust information on innate response and test targeted therapies for improvement of lung transplantation outcome.
A Generalized Description of Discrete Event System Rolling Optimal Control Theory

The principles of predictive control was applied in discrete event system (DES) control theory. A general method of DES control was achieved. The systems are modeled according to the behavior theory presented by J.C. Willems. Some important concepts, such as model set for prediction, model subset for prediction were also presented based on this model, and a general algorithm was given.
Retinal transcriptome of neonatal mice after optic nerve injury

Background The axonal growth capacity of retinal ganglion cells decreases dramatically within the first day of birth, and the axonal regeneration after injury in mature mammals is very limited. Here, this study aimed to delineate the transcriptomic changes associated with altered axonal growth capacity and to identify the key genes associated with axonal regeneration by the RNA sequencing (RNA-Seq) analysis. Methods The whole retinas from the mice of embryonic day (E) 20, postnatal day (P) 1 and P3 were collected at 6 hours after optic nerve crush (ONC). Differentially expressed genes (DEGs) for ONC or ages were identified by the RNA-Seq analysis. K-means analysis was conducted for the clustering of DEGs based on expression patterns. Enrichment of functions and signaling pathways analysis were performed based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and Gene Set Enrichment analysis (GSEA). Quantitative real time polymerase chain reaction (qRT-PCR) was used to validate the DEGs selected from the RNA-Seq analysis. Results In total, 5,408 DEGs were identified for ages, and 2,639 DEGs in neonatal mouse retina after ONC. K-means analysis revealed 7 clusters in age-DEGs and 11 clusters in ONC-DEGs. The GO, KEGG and GSEA pathway analyses identified significantly enrichment of DEGs in the visual perception and phototransduction for the age effect, and the break repair, neuron projection guidance, and immune system pathway for the ONC. PPI analysis identified hub genes in the axon-related gene cluster. The expressions of Mlc1, Zfp296, Atoh7, Ecel1, Creb5, Fosb, and Lcn2, thought to be involved in RGC death and axonal growth were validated by qRT-PCR. Conclusions This study, for the first time, delineated the gene expression changes following ON injury in embryonic and neonatal mice, providing a new resource of age- and injury-driven data on axonal growth capacity.
Genetic testing in children with Brugada syndrome: results from a large prospective registry

Abstract Aims A pathogenic/likely pathogenic (P/LP) variant in SCN5A is found in 20–25% of patients with Brugada syndrome (BrS). However, the diagnostic yield and prognosis of gene panel testing in paediatric BrS is unclear. The aim of this study is to define the diagnostic yield and outcomes of SCN5A gene testing with ACMG variant classification in paediatric BrS patients compared with adults. Methods and results All consecutive patients diagnosed with BrS, between 1992 and 2022, were prospectively enrolled in the UZ Brussel BrS registry. Inclusion criteria were: (i) BrS diagnosis; (ii) genetic analysis performed with a large gene panel; and (iii) classification of gene variants following ACMG guidelines. Paediatric patients were defined as ≤16 years of age. The primary endpoint was ventricular arrhythmias (VAs). A total of 500 BrS patients were included, with 63 paediatric patients and 437 adult patients. Among children with BrS, 29 patients (46%) had a P/LP variant (P+) in SCN5A and no variants were found in 34 (54%) patients (P−). After a mean follow-up of 125.9 months, 8 children (12.7%) experienced a VA, treated with implanted cardioverter defibrillator shock. At survival analysis, P− paediatric patients had higher VA-free survival during the follow-up, compared with P+ paediatric patients. P+ status was an independent predictor of VA. There was no difference in VA-free survival between paediatric and adult BrS patients for both P− and P+. Conclusion In a large BrS cohort, the diagnostic yield for P/LP variants in the paediatric population is 46%. P+ children with BrS have a worse arrhythmic prognosis.
Solution composition dependent Soret coefficient using commercial MicroScale Thermophoresis instrument

Thermal diffusion of particles in dilute aqueous suspensions is driven by the interactions between the dispersing medium and the particle, which are largely influenced by the properties of the medium. Using a commercial instrument to generate thermophoresis, we developed a method to quantify the migration of colloids in a temperature gradient and further studied how it varies based on the composition and pH of the dispersing medium and with an anionic surfactant, at different salt concentrations. Thermophoretic migration of aqueous suspensions of carboxylate-modified polystyrene particles with different compositions is measured as MicroScale Thermophoresis (MST) traces and a mathematical model is developed to extract the Soret coefficient (ST). Soret coefficient measurements obtained using the developed method are in-line with previous theories and scientific findings from other literature, indicating a dependence of the ST on the Debye length and surface charge density of the suspended particles, both of which are controlled by the composition of the dispersing medium. The thermophobic/thermophilic behavior of particles is also found to be strongly influenced by the thermoelectric effect of the buffer ions. In this paper, a new analytical model is introduced and applied to complex systems to understand their thermophoretic behavior as a function of solvent properties.
Model-Based Vehicle-Miles Traveled and Emission Evaluation of On-Demand Food Delivery Considering the Impact of COVID-19 Pandemic

The market for on-demand food delivery (ODFD) has increased considerably, especially during the COVID-19 pandemic. It is crucial for transportation and environmental agencies to understand how ODFD has reshaped the travel patterns of people, affecting vehicle-miles traveled (VMT) as well as pollutant emissions in the transportation system. However, the lack of public data from food delivery companies makes it challenging to quantify the impact of on-demand delivery on the real-world transportation network. In this research, we propose a comprehensive framework to quantify the VMT and emissions incurred by ODFD with three main components: (i) a daily activity generation tool, Comprehensive Econometric Micro-simulator for Daily Activity-travel Patterns, to create a simulation scenario of ODFD behaviors based on a real-world roadway network and population demographics in the City of Riverside, California; (ii) an efficient order dispatching and routing algorithm, adaptive large neighborhood search, to obtain a high quality order dispatching and routing plan; (iii) an emission evaluation model, emission factor (EMFAC), to evaluate pollutant emissions from all dining-related trips. Both short-term and long-term impacts of the COVID-19 pandemic are evaluated. Experimental results show that ODFD has great potential to reduce the dining-related VMT and emissions. The total dining-related VMT in the during-pandemic case decreased by 38% and in the after-pandemic case reduced by 6% to 9%, and the corresponding environmental impacts were reduced accordingly. Meanwhile, emissions reduced significantly with more electric vehicles involved in food delivery. With 100% electric delivery fleet, the ODFD service can save 14% to 22% of emissions after the COVID-19 pandemic.
The composite effect reveals that human (but not other primate) faces are special to humans

Recognising faces is widely believed to be achieved using “special” neural and cognitive mechanisms that depend on “holistic” processing, which are not used when recognising other kinds of objects. An important, but largely unaddressed, question is how much like a Human face a stimulus needs to be to engage this “special” mechanism(s). In the current study, we attempted to answer this question in 3 ways. In Experiments 1 and 2 we examined the extent to which the disproportionate inversion effect for human faces extends to the faces of other species (including a range of other primates). Results suggested that the faces of other primates engage the mechanism responsible for the inversion effect approximately as well as that mechanism is engaged by Human faces, but that non-primate faces engage the mechanism less well. And so primate faces, in general, seem to produce a disproportionate inversion effect. In Experiment 3 we examined the extent to which the Composite effect extends to the faces of a range of other primates, and found no compelling evidence of a composite effect for the faces of any other primate. The composite effect was exclusive to Human faces. Because these data differ so dramatically from a previously reported study asking similar questions Taubert (2009), we also (in Experiment 4) ran an exact replication of Taubert’s Experiment 2, which reported on both Inversion and Composite effects in a range of species. We were unable to reproduce the pattern of data reported by Taubert. Overall, the results suggest that the disproportionate inversion effect extends to all of the faces of the non-human primates tested, but that the composite effect is exclusive to Human faces.
A theoretical approach for a new design of an ultrasensitive angular plasmonic chemical sensor using black phosphorus and aluminum oxide architecture

In this study, the biosensing capabilities of conventional and hybrid multilayer structures were theoretically examined based on surface plasmon resonance (SPR). The transfer matrix method is adopted to obtain the reflectance spectra of the hybrid multilayer structure in the visible region. In this regard, the considered SPR sensor is configured as, [prism (CaF2)/Al2O3/Ag/Al2O3/2D material/Al2O3/Sensing medium]. Interestingly, many optimization steps were conducted to obtain the highest sensitivity of the new SPR biosensor from the hybrid structure. Firstly, the thickness of an Al2O3 layer with a 2D material (Blue P/WS2) is optimized to obtain an upgraded sensitivity of 360° RIU−1. Secondly, the method to find the most appropriate 2D material for the proposed design is investigated to obtain an ultra-high sensitivity. Meanwhile, the inclusion of black phosphorus (BP) increases the sensor's sensitivity to 466° RIU−1. Thus, black phosphorus (BP) was obtained as the most suitable 2D material for the proposed design. In this regard, the proposed hybrid SPR biosensing design may pave the way for further opportunities for the development of various SPR sensors to be utilized in chemical and biomedical engineering fields.
Saliency Cards: A Framework to Characterize and Compare Saliency Methods

Saliency methods are a common class of machine learning interpretability techniques that calculate how important each input feature is to a model’s output. We find that, with the rapid pace of development, users struggle to stay informed of the strengths and limitations of new methods and, thus, choose methods for unprincipled reasons (e.g., popularity). Moreover, despite a corresponding rise in evaluation metrics, existing approaches assume universal desiderata for saliency methods (e.g., faithfulness) that do not account for diverse user needs. In response, we introduce saliency cards: structured documentation of how saliency methods operate and their performance across a battery of evaluative metrics. Through a review of 25 saliency method papers and 33 method evaluations, we identify 10 attributes that users should account for when choosing a method. We group these attributes into three categories that span the process of computing and interpreting saliency: methodology, or how the saliency is calculated; sensitivity, or the relationship between the saliency and the underlying model and data; and, perceptibility, or how an end user ultimately interprets the result. By collating this information, saliency cards allow users to more holistically assess and compare the implications of different methods. Through nine semi-structured interviews with users from various backgrounds, including researchers, radiologists, and computational biologists, we find that saliency cards provide a detailed vocabulary for discussing individual methods and allow for a more systematic selection of task-appropriate methods. Moreover, with saliency cards, we are able to analyze the research landscape in a more structured fashion to identify opportunities for new methods and evaluation metrics for unmet user needs.
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties

Constrained Reinforcement Learning has been employed to enforce safety constraints on policy through the use of expected cost constraints. The key challenge is in handling expected cost accumulated using the policy and not just in a single step. Existing methods have developed innovative ways of converting this cost constraint over entire policy to constraints over local decisions (at each time step). While such approaches have provided good solutions with regards to objective, they can either be overly aggressive or conservative with respect to costs. This is owing to use of estimates for"future"or"backward"costs in local cost constraints. To that end, we provide an equivalent unconstrained formulation to constrained RL that has an augmented state space and reward penalties. This intuitive formulation is general and has interesting theoretical properties. More importantly, this provides a new paradigm for solving constrained RL problems effectively. As we show in our experimental results, we are able to outperform leading approaches on multiple benchmark problems from literature.
Crowdsourcing Subjective Annotations Using Pairwise Comparisons Reduces Bias and Error Compared to the Majority-vote Method

How to better reduce measurement variability and bias introduced by subjectivity in crowdsourced labelling remains an open question. We introduce a theoretical framework for understanding how random error and measurement bias enter into crowdsourced annotations of subjective constructs. We then propose a pipeline that combines pairwise comparison labelling with Elo scoring, and demonstrate that it outperforms the ubiquitous majority-voting method in reducing both types of measurement error. To assess the performance of the labelling approaches, we constructed an agent-based model of crowdsourced labelling that lets us introduce different types of subjectivity into the tasks. We find that under most conditions with task subjectivity, the comparison approach produced higher f1 scores. Further, the comparison approach is less susceptible to inflating bias, which majority voting tends to do. To facilitate applications, we show with simulated and real-world data that the number of required random comparisons for the same classification accuracy scales log-linearly O(N log N) with the number of labelled items. We also implemented the Elo system as an open-source Python package.