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The growing number and growing size of wind energy projects coupled with the rapid growth in high-performance computing technology are driving researchers toward conducting large-scale simulations of the flow field surrounding entire wind farms. This requires highly parallel-efficient tools, given the large number of degrees of freedom involved in such simulations, and yields valuable insights into farm-scale physical phenomena, such as gravity wave interaction with the wind farm and farm–farm wake interactions. In the current study, we introduce the open-source, finite-volume, large-eddy simulation (LES) code TOSCA (Toolbox fOr Stratified Convective Atmospheres) and demonstrate its capabilities by simulating the flow around a finite-size wind farm immersed in a shallow, conventionally neutral boundary layer (CNBL), ultimately assessing gravity-wave-induced blockage effects. Turbulent inflow conditions are generated using a new hybrid off-line–concurrent-precursor method. Velocity is forced with a novel pressure controller that allows us to prescribe a desired average hub-height wind speed while avoiding inertial oscillations above the atmospheric boundary layer (ABL) caused by the Coriolis force, a known problem in wind farm LES studies. Moreover, to eliminate the dependency of the potential-temperature profile evolution on the code architecture observed in previous studies, we introduce a method that allows us to maintain the mean potential-temperature profile constant throughout the precursor simulation. Furthermore, we highlight that different codes do not predict the same velocity inside the boundary layer under geostrophic forcing owing to their intrinsically different numerical dissipation. The proposed methodology allows us to reduce such spread by ensuring that inflow conditions produced from different codes feature the same hub wind and thermal stratification, regardless of the adopted precursor run time. Finally, validation of actuator line and disk models, CNBL evolution, and velocity profiles inside a periodic wind farm is also presented to assess TOSCA’s ability to model large-scale wind farm flows accurately and with high parallel efficiency.

This study introduces the actuator farm model (AFM), a novel parameterization for simulating wind turbines within large eddy simulations (LESs) of wind farms. Unlike conventional models like the actuator disk (AD) or actuator line (AL), the AFM utilizes a single actuator point at the rotor center and only requires two to three mesh cells across the rotor diameter. Turbine force is distributed to the surrounding cells using a new projection function characterized by an axisymmetric spatial support in the rotor plane and Gaussian decay in the streamwise direction. The spatial support's size is controlled by three parameters: the half-decay radius r1/2, smoothness s, and streamwise standard deviation σ. Numerical experiments on an isolated National Renewable Energy Laboratory (NREL) 5MW wind turbine demonstrate that selecting r1/2=R (where R is the turbine radius), s between 6 and 10, and σ≈Δx/1.6 (where Δx is the grid size in the streamwise direction) yields wake deficit profiles, turbine thrust, and power predictions similar to those obtained using the actuator disk model (ADM), irrespective of horizontal grid spacing down to the order of the rotor radius.Using these parameters, LESs of a small cluster of 25 turbines in both staggered and aligned layouts are conducted at different horizontal grid resolutions using the AFM. Results are compared against ADM simulations employing a spatial resolution that places at least 10 grid points across the rotor diameter. The wind farm is placed in a neutral atmospheric boundary layer (ABL) with turbulent inflow conditions interpolated from a previous simulation without turbines. At horizontal resolutions finer than or equal to R/2, the AFM yields similar velocity, shear stress, turbine thrust, and power as the ADM. Coarser resolutions reveal the AFM's ability to accurately capture power at the non-waked wind farm rows, although it underestimates the power of waked turbines. However, the far wake of the cluster can be predicted well even when the cell size is of the order of the turbine radius.Finally, combining the AFM with a domain nesting method allows us to conduct simulations of two aligned wind farms in a fully neutral ABL and of wind-farm-induced atmospheric gravity waves under a conventionally neutral ABL, obtaining excellent agreement with ADM simulations but with much lower computational cost. The simulations highlight the AFM's ability to investigate the mutual interactions between large turbine arrays and the thermally stratified atmosphere.

The growth in the number and size of wind energy projects in the last decade has revealed structural limitations in the current approach adopted by the wind industry to assess potential wind farm sites. These limitations are the result of neglecting the mutual interaction of large wind farms and the thermally stratified atmospheric boundary layer. While currently available analytical models are sufficiently accurate to conduct site assessments for isolated rotors or small wind turbine clusters, the wind farm's interaction with the atmosphere cannot be neglected for large-size arrays. Specifically, the wind farm displaces the boundary layer vertically, triggering atmospheric gravity waves that induce large-scale horizontal pressure gradients. These perturbations in pressure alter the velocity field at the turbine locations, ultimately affecting global wind farm power production. The implication of such dynamics can also produce an extended blockage region upstream of the first turbines and a favorable pressure gradient inside the wind farm. In this paper, we present the multi-scale coupled (MSC) model, a novel approach that allows the simultaneous prediction of micro-scale effects occurring at the wind turbine scale, such as individual wake interactions and rotor induction, and meso-scale phenomena occurring at the wind farm scale and larger, such as atmospheric gravity waves. This is achieved by evaluating wake models on a spatially heterogeneous background velocity field obtained from a reduced-order meso-scale model. Verification of the MSC model is performed against two large-eddy simulations (LESs) with similar average inflow velocity profiles and a different capping inversion strength, so that two distinct interfacial gravity wave regimes are produced, i.e. subcritical and supercritical. Interfacial waves can produce high blockage in the first case, as they are allowed to propagate upstream. On the other hand, in the supercritical regime their propagation speed is less than their advection velocity, and upstream blockage is only operated by internal waves. The MSC model not only proves to successfully capture both local induction and global blockage effects in the two analyzed regimes, but also captures the interaction between the wind farm and gravity waves, underestimating wind farm power by about only 2 % compared with the LES results. Conversely, wake models alone cannot distinguish between differences in thermal stratification, even if combined with a local induction model. Specifically, they are affected by a first-row overprediction bias that leads to an overestimation of the wind farm power by 13 % to 20 % for the modeled regimes.

Patients presenting to the emergency department (ED) with dizziness may be imaged via CTA head and neck to detect acute vascular pathology including large vessel occlusion. We identify commonly documented clinical variables which could delineate dizzy patients with near zero risk of acute vascular abnormality on CTA. We performed a cross-sectional analysis of adult ED encounters with chief complaint of dizziness and CTA head and neck imaging at three EDs between 1/1/2014-12/31/2017. A decision rule was derived to exclude acute vascular pathology tested on a separate validation cohort; sensitivity analysis was performed using dizzy \"stroke code\" presentations. Testing, validation, and sensitivity analysis cohorts were composed of 1072, 357, and 81 cases with 41, 6, and 12 instances of acute vascular pathology respectively. The decision rule had the following features: no past medical history of stroke, arterial dissection, or transient ischemic attack (including unexplained aphasia, incoordination, or ataxia); no history of coronary artery disease, diabetes, migraines, current/long-term smoker, and current/long-term anti-coagulation or anti-platelet medication use. In the derivation phase, the rule had a sensitivity of 100% (95% CI: 0.91-1.00), specificity of 59% (95% CI: 0.56-0.62), and negative predictive value of 100% (95% CI: 0.99-1.00). In the validation phase, the rule had a sensitivity of 100% (95% CI: 0.61-1.00), specificity of 53% (95% CI: 0.48-0.58), and negative predictive value of 100% (95% CI: 0.98-1.00). The rule performed similarly on dizzy stroke codes and was more sensitive/predictive than all NIHSS cut-offs. CTAs for dizziness might be avoidable in 52% (95% CI: 0.47-0.57) of cases. A collection of clinical factors may be able to \"exclude\" acute vascular pathology in up to half of patients imaged by CTA for dizziness. These findings require further development and prospective validation, though could improve the evaluation of dizzy patients in the ED.

Jwarahara Kwatha Choornam (JKC) is a polyherbal coded Ayurvedic formulation developed by the Central Council for Research in Ayurvedic Sciences (CCRAS), New Delhi, India. Traditionally used for managing chronic fever, cold, and malaria, JKC has gained recognition for its therapeutic benefits, such as enhancing digestion, stimulating appetite, detoxifying blood, modulating the immune response, and offering protection against common bacterial infections. The medicinal plant used in JKC is widely utilized by Ayurvedic practitioners and the general population in the Kerala region, where it holds a longstanding place in traditional health practices. Notably, during the COVID-19 pandemic, both practitioners and users have reported the formulation’s supportive role in treatment, further highlighting its therapeutic relevance. To ensure the quality, safety, and efficacy of this important Ayurvedic preparation, CCRAS has undertaken standardization efforts, including the development of a novel High-Performance Thin-Layer Chromatography (HPTLC) method for the simultaneous estimation of five key bioactive marker compounds. The study establishes a robust High-Performance Thin-Layer Chromatography (HPTLC) method for the simultaneous estimation of five key bioactive markers—Andrographolide (AG), Piperine (PP), Picroside-I (P-I), Picroside-II (P-II), and α-Cyperone (AC) present in the plants Andrographis paniculata, Cyperus rotundus, Piper longum, Piper nigrum, Zingiber officinale, Hedyotis corymbosa, and Picrorhiza kurroa. Used in the Jwarahara Kwatha Choornam (JKC) formulation. Effective separation of these compounds was achieved using a carefully optimized mobile phase comprising Toluene, Ethyl Acetate, Methanol, and Formic Acid in a 4:4:1:1 (v/v/v/v) ratio. The developed HPTLC method, resolved the five targeted bioactive markers— Andrographolide (AG) , Piperine (PP) , Picroside-I (P-I) , Picroside-II (P-II) , and α-Cyperone (AC) —with distinct R f values of 0.563 ± 0.005, 0.706 ± 0.015, 0.280 ± 0.0173, 0.180 ± 0.0115, and 0.803 ± 0.005, respectively, using a mobile phase of Toluene: Ethyl Acetate: Methanol: Formic Acid (4:4:1:1, v/v/v/v ). The method was rigorously validated, demonstrating excellent linearity (r² = 0.97–0.99), precision, accuracy (RSD < 2%), robustness, and ruggedness under optimized analytical conditions. Quantitative analysis of JKC revealed the presence of AG (3.638 ± 0.0234 mg/g), PP (3.360 ± 0.0792 mg/g), P-I (0.1426 ± 0.0031 mg/g), P-II (0.6025 ± 0.0198 mg/g), and AC (0.2102 ± 0.0023 mg/g). This study demonstrates that the developed HPTLC method is a rapid, precise, and reliable analytical tool for simultaneously quantifying five key bioactive markers in individual plant materials and polyherbal formulations. Owing to its robustness and reproducibility, this method offers a practical and efficient approach for routine quality control and standardization of JKC formulations.

Efficiency and substrate specificity of proteases in the Potyviridae family have not been comprehensively profiled. Here we develop a model that learns co-evolutionary features to accurately predict and experimentally validate protease performance at single amino-acid resolution. We identify and engineer several proteases that perform better than the commercially available tobacco etch virus protease. To demonstrate the resolving power of our methods, we engineer protease crosstalk to selectively trigger a synthetic cell-death program in human cells. How proteases recognize specific substrate sequences is difficult to predict. To enable rational protease-substrate design, the authors introduce ProSSpeC, an interpretable probabilistic model that predicts specificity at single-residue resolution using evolutionary information.