This paper presents a novel CFD-driven machine learning framework to develop Reynolds-averaged Navier-Stokes (RANS) models. The CFD-driven training is an extension of the gene expression programming method Weatheritt and Sandberg (2016) [8], but crucially the fitness of candidate models is now evaluated by running RANS calculations in an integrated way, rather than using an algebraic function. Unlike other data-driven methods that fit the Reynolds stresses of trained models to high-fidelity data, the cost function for the CFD-driven training can be defined based on any flow feature from the CFD results. This extends the applicability of the method especially when the training data is limited. Furthermore, the resulting model, which is the one providing the most accurate CFD results at the end of the training, inherently shows good performance in RANS calculations. To demonstrate the potential of this new method, the CFD-driven machine learning approach is applied to model development for wake mixing in turbomachines. A new model is trained based on a high-pressure turbine case and then tested for three additional cases, all representative of modern turbine nozzles. Despite the geometric configurations and operating conditions being different among the cases, the predicted wake mixing profiles are significantly improved in all of these a posteriori tests. Moreover, the model equation is explicitly given and available for analysis, thus it could be deduced that the enhanced wake prediction is predominantly due to the extra diffusion introduced by the CFD-driven model.
Neural coding is one of the central questions in systems neuroscience for understanding how the brain processes stimulus from the environment, moreover, it is also a cornerstone for designing algorithms of brain–machine interface, where decoding incoming stimulus is highly demanded for better performance of physical devices. Traditionally researchers have focused on functional magnetic resonance imaging (fMRI) data as the neural signals of interest for decoding visual scenes. However, our visual perception operates in a fast time scale of millisecond in terms of an event termed neural spike. There are few studies of decoding by using spikes. Here we fulfill this aim by developing a novel decoding framework based on deep neural networks, named spike-image decoder (SID), for reconstructing natural visual scenes, including static images and dynamic videos, from experimentally recorded spikes of a population of retinal ganglion cells. The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion. Our SID also outperforms on the reconstruction of visual stimulus compared to existing fMRI decoding models. In addition, with the aid of a spike encoder, we show that SID can be generalized to arbitrary visual scenes by using the image datasets of MNIST, CIFAR10, and CIFAR100. Furthermore, with a pre-trained SID, one can decode any dynamic videos to achieve real-time encoding and decoding of visual scenes by spikes. Altogether, our results shed new light on neuromorphic computing for artificial visual systems, such as event-based visual cameras and visual neuroprostheses.
Uranium is one of the most commonly detected radionuclides in the environment. Of the two most predominant oxidation states, U(VI) is much more soluble, mobile and toxic than U(IV). Consequently, converting U(VI) to U(IV) can facilitate the removal of U from water and reduce its mobility and biological exposure. In this work, stabilized zero-valent iron (ZVI) nanoparticles were prepared using starch or carboxymethyl cellulose (CMC) as stabilizers and then tested for reductive removal of U(VI) from simulated groundwater. Nearly 100% removal of U(VI) (initial U = 25 mg/L) was achieved using CMC-stabilized ZVI (Fe = 35 mg/L) at pH 6. In pH range of 6–9, the lower pH favored the reaction. CMC-ZVI nanoparticles presented better deliverability than starch-ZVI, while bare ZVI nanoparticles was almost trapped in the soil column. CMC-ZVI worked effectively in the presence of a model humic acid (up to 10 mg/L as TOC) and bicarbonate (1 mM), though higher dosages of the ligands inhibited U(VI) removal. After treatment, no re-mobilization of U was detected when aged for 6 months under anoxic conditions and the addition of strong ligands only remobilized U(VI). When exposed to oxic conditions, the immobilized U will be partially oxidized and remobilized due to the ingress of atmospheric O2 and CO2. In terms of toxicity reduction, the ZVI treated U had almost no inhibition for natural bacteria activity, while dissolved U(VI) showed significant inhibitive effects. The CMC-ZVI nanoparticles may serve as effective reactive materials to facilitate immobilization of U(VI) in groundwater, which in turn can greatly mitigate the human exposure and toxic effects of U on biota.
Chronic diseases can be controlled through effective self-management. The purpose of this study is to explore the regularity of clinical visits and medication adherence of patients with hypertension or diabetes (PWHD), and its association with the first experience with care and individual factors in rural Southwestern China. This cross-sectional study was carried out in Yunnan province in 2018 and recruited 292 PWHD and 122 village clinics from 122 villages in 10 counties. Participants were interviewed using a structured questionnaire. Results show around 39% of hypertensive and 25% of diabetic patients neither visited physicians nor took medicine regularly during the preceding three months of the interview date. The regression results further indicated that individual characteristics of the PWHD, including patient age, health status, and economic level, as well as their first experience with care, were significantly associated with their regular healthcare behavior. In addition to providing medical services, on average each sample village clinic, with around two physicians, simultaneously managed 180 hypertensive and 45 diabetic patients. This study revealed the need for further reforms in terms of improving self-management and thus recommends an increase in the quantity and the quality of human resources in the primary healthcare realm in rural China.
High concentrations of ultrafine particles (UFPs), approaching 1 million/cm3, are frequently produced from new particle formation under urban environments, but the fundamental mechanisms regulating nucleation and growth for UFPs are poorly understood. From simultaneous ambient and environmental chamber measurements, we demonstrate remarkable formation of UFPs from urban traffic emissions. By replicating ambient conditions using an environmental chamber method, we elucidate the roles of existing particles, photochemistry, and synergy of multipollutant photooxidation in nucleation and growth of UFPs. Our results reveal that synergetic oxidation of vehicular exhaust leads to efficient formation of UFPs under urban conditions. Recognition of this large urban source for UFPs is essential to accurately assessing their impacts and to effectively developing mitigation policies.High levels of ultrafine particles (UFPs; diameter of less than 50 nm) are frequently produced from new particle formation under urban conditions, with profound implications on human health, weather, and climate. However, the fundamental mechanisms of new particle formation remain elusive, and few experimental studies have realistically replicated the relevant atmospheric conditions. Previous experimental studies simulated oxidation of one compound or a mixture of a few compounds, and extrapolation of the laboratory results to chemically complex air was uncertain. Here, we show striking formation of UFPs in urban air from combining ambient and chamber measurements. By capturing the ambient conditions (i.e., temperature, relative humidity, sunlight, and the types and abundances of chemical species), we elucidate the roles of existing particles, photochemistry, and synergy of multipollutants in new particle formation. Aerosol nucleation in urban air is limited by existing particles but negligibly by nitrogen oxides. Photooxidation of vehicular exhaust yields abundant precursors, and organics, rather than sulfuric acid or base species, dominate formation of UFPs under urban conditions. Recognition of this source of UFPs is essential to assessing their impacts and developing mitigation policies. Our results imply that reduction of primary particles or removal of existing particles without simultaneously limiting organics from automobile emissions is ineffective and can even exacerbate this problem.
A low-cost composite of activated charcoal supported titanate nanotubes (TNTs@AC) was developed via the facile hydrothermal method to remove the 17β-estradiol (E2, a model of pharmaceutical and personal care products) in water matrix by initial adsorption and subsequent photo-degradation. Characterizations indicated that the modification occurred, i.e., the titanate nanotubes would be grafted onto the activated charcoal (AC) surface, and the micro-carbon could modify the tubular structure of TNTs. E2 was rapidly adsorbed onto TNTs@AC, and the uptake reached 1.87 mg/g from the dual-mode model fitting. Subsequently, the adsorbed E2 could be degraded 99.8% within 2 h under ultraviolet (UV) light irradiation. TNTs@AC was attributed with a unique hybrid structure, providing the hydrophobic effect, π−π interaction, and capillary condensation for E2 adsorption, and facilitating the electron transfer and then enhancing photocatalytic ability for E2-degradation. In addition, the removal mechanism of E2 was elucidated through the density functional theory calculation. Our study is expected to provide a promising material for environmental application.
A Wigner distributionlike function based on the improved strong-field approximation theory is proposed to calculate the rescattering time-energy distribution (RTED) of high-energy photoelectrons of atomic above-threshold ionization process in few-cycle laser fields with different frequencies. The RTED shows bell-like stripes and the outermost stripe is compared with semiclassical results given by the simple-man model with consideration of different positions of tunnel exit and different initial longitudinal momenta. Analysis indicates the existence of the tunnel exit. However, though it shifts farther away from the core with decreasing frequency, the position of the tunnel exit is significantly less than the prediction by adiabatic theory even for the low-frequency case which is well in the tunneling regime. Our results also imply that the effect of the tunnel exit is more important than that of the initial longitudinal momentum at the tunnel exit for the backward-scattering electrons. Moreover, the inner stripe structures in the RTED are attributed to interference between electrons with the same final energy emitted at different ionization times.