科研成果

We compare three countries where public policy has explicitly sought to align incentives of employers and educational institutions around closing the gap between skill formation and labor market demand. In large, heterogeneous countries such as the United States, Russia and China, collaborative arrangements such apprenticeships and other forms of public-private partnerships can be constructed at the subnational level by building on direct, face-to-face ties across educational, business, government, and civic sectors. Drawing on existing literature as well as fieldwork studying a number of specific cases in the three countries, the paper develops a typology of such arrangements and proposes an explanation for the observed variation. It emphasizes the importance of two sets of factors: those that induce cooperation on the part of firms and schools, and those that influence the character of such partnerships.

Identifying and quantifying the major sources of atmospheric particulate matter (PM) is essential for the development of pollution mitigation strategies to protect public health. However, urban PM is affected by local primary emissions, transport, and secondary formation; therefore, advanced methods are needed to elucidate the complex sources and transport patterns. Here, an improved source apportionment method was developed by incorporating the receptor model, Lagrangian simulation, and emissions inventories to quantify PM2.5 sources for an industrial city in China. PM2.5 data including ions, metals, organic carbon, and elemental carbon were obtained by analyzing 1 year of sampling results at urban and rural sites. This method identified coal combustion (30.64%), fugitive dust (13.25%), and vehicles (12.51%) as major primary sources. Secondary sources, including sulfate, nitrate, and secondary organic aerosols also contributed strongly (25.28%–30.76% in total) over urban and rural areas. Hebei Province was the major regional source contributor (43.05%–57.51%) except for fugitive dust, on which Inner Mongolia had a greater impact (43.51%). The megacities of Beijing and Tianjin exerted strong regional impacts on the secondary nitrate and secondary organic aerosols factors, contributing 11.32% and 15.65%, respectively. Pollution events were driven largely by secondary inorganic aerosols, highlighting the importance of reducing precursor emissions at the regional scale, particularly in the Beijing–Tianjin–Hebei region. Overall, our results demonstrate that this novel method offers good flexibility and efficiency for quantifying PM2.5 sources and regional contributions, and that it can be extended to other cities.

Biological aging is the gradual, progressive decline in system integrity that occurs with advancing chronological age, causing morbidity and disability. Measurements of the pace of aging are needed as surrogate endpoints in trials of therapies designed to prevent disease by slowing biological aging. We report a blood-DNA-methylation measure that is sensitive to variation in pace of biological aging among individuals born the same year. We first modeled change-over-time in 18 biomarkers tracking organ-system integrity across 12 years of follow-up in n = 954 members of the Dunedin Study born in 1972-1973. Rates of change in each biomarker over ages 26-38 years were composited to form a measure of aging-related decline, termed Pace-of-Aging. Elastic-net regression was used to develop a DNA-methylation predictor of Pace-of-Aging, called DunedinPoAm for Dunedin(P)ace(o)f(A)ging(m)ethylation. Validation analysis in cohort studies and the CALERIE trial provide proof-of-principle for DunedinPoAm as a single-time-point measure of a person's pace of biological aging. People's bodies age at different rates. Age-related biological changes that increase the risk of disease and disability progress rapidly in some people. In others, these processes occur at a slower pace, allowing those individuals to live longer, healthier lives. This observation has led scientists to try to develop therapies that slow aging. The hope is that such treatments could prevent or delay diseases like heart disease or dementia, for which older age is the leading risk factor. Studies in animals have identified treatments that extend the creatures' lives and slow age-related disease. But testing these treatments in humans is challenging. Our lives are much longer than the worms, flies or mice used in the experiments. Scientists would have to follow human study participants for decades to detect delays in disease onset or an extension of their lives. An alternative approach is to try to develop a test that measures the pace of aging, or essentially "a speedometer for aging". This would allow scientists to more quickly determine if treatments slow the aging process. Now, Belsky et al. show a blood test designed to measure the pace of aging predicts which people are at increased risk of poor health, chronic disease and an earlier death. First, data about chemical changes to an individual's DNA, called DNA methylation, were analyzed from white blood cell samples collected from 954 people in a long-term health study known as "The Dunedin Study". Using the data, Belsky et al. then developed an algorithm - named "DunedinPoAm" - that identified people with an accelerated or slowed pace of aging based on a single blood test. Next, they used the algorithm on samples from participants in three other long-term studies. This verified that those people the algorithm identified as aging faster had a greater risk of poor health, developing chronic diseases or dying earlier. Similarly, those identified as aging more slowly performed better on tests of balance, strength, walking speed and mental ability, and they also looked younger to trained raters. Additionally, Belsky et al. used the test on participants in a randomized trial testing whether restricting calories had potential to extend healthy lifespan. The results suggested that the calorie restriction could counter the effects of an accelerated pace of aging. The test developed by Belsky et al. may provide an alternate way of measuring whether age-slowing treatments work. This would allow faster testing of treatments that can extend the healthy lifespan of humans. The test may also help identify individuals with accelerated aging. This might help public health officials test whether policies or programs can help people lead longer, healthier lives. eng

How does the capacity removal policy affect China’s economy? To quantify the policy outcomes and costs, a four-sector model with vertical market structures is built. The calibrated model shows that, to achieve the policy goal, 10% of equipment operation in the high energy-consuming sectors must be shut down. This policy leads to an improved energy structure in which total energy consumption drops by 4.75% at the cost of a contraction in economic growth, where the total output declines by 12.31%. The numerical experiments find that the optimal policy is to limit the production scale in both the iron/steel industry and the fossil energy industry, closing 9% and 7% of the production, respectively, since doing so minimizes output loss and improves the energy structure. This paper quantifies the impact of the current capacity removal policy and provides policy alternatives to reach the same policy target with a lower output loss.

Abstract Soluble and miscible states are two important thermodynamic states in academic research and practical applications but their quantitative distinctions are still fuzzy. In this study, for the first time, the mathematical formulations of the quantitative criteria for distinguishing the thermodynamic soluble and miscible states are analytically developed by means of the Flory–Huggins solution theory and solubility parameter. The quantitative bottom and upper solubility limits for a total of 13 binary and ternary mixtures are calculated at different conditions. Moreover, the composition, temperature and pore radius are specifically studied to evaluate their effects on the soluble and miscible states. On the basis of the work from this study, the insoluble, soluble but immiscible, and miscible states are definitively quantified and clearly distinguished for the first time.

In this letter, we report a quasi-vertical GaN Schottky barrier diode (SBD) fabricated on a hetero-epitaxial layer on silicon with low dislocation density and high carrier mobility. The reduction of dislocation is realized by inserting a thin layer with high density of Ga vacancies to promote the dislocation bending. The dislocation density is $1.6\times 10^8$ cm?2 with a GaN drift layer thickness of $4.5 μ \textm$ . The fabricated prototype GaN SBD delivers a high on/off current ratio of $10^10$ , a high forward current density of 1.6 kA/cm2@3 V, a low specific on-resistance of 1.1 $\textmØmega \cdot \text cm^2$ , and a low ideality factor of 1.23.

Degradation of phenols with different substituent groups (including –OCH3, –CHO, –NHCOCH3, –NO2, and −Cl) at boron-doped diamond (BDD) anodes has been studied previously based on the removal efficiency and •OH detection. Innovatively, formations of CO2 gas and various inorganic ions were examined to probe the mineralization process combined with quantitative structure-activity relationship (QSAR) analysis. As results, all phenols were efficiently degraded within 8 h with high COD removal efficiency. Three primary intermediates (hydroquinone, 1,4-benzoquinone and catechol) were identified during electrochemical oxidation and degradation pathway was proposed. More importantly, CO2 transformation efficiency ranked as: no N or Cl contained phenols (p-CHO, p-OCH3 and Ph) > N-contained phenols (p-NHCOCH3 and p-NO2) > Cl-contained phenols (p-Cl and o,p-Cl). Carbon mass balance study suggested formation of inorganic carbon (H2CO3, CO32− and HCO3−) and CO2 after organic carbon elimination. Inorganic nitrogen species (NH4+, NO3− and NO2−) and chlorine species (Cl−, ClO3− and ClO4−) were also formed after N- and Cl-contained phenols mineralization, while no volatile nitrogen species were detected. The phenols with electron-withdrawing substituents were easier to be oxidized than those with electron-donating substituents. QSAR analysis indicated that the reaction rate constant (k1) for phenols degradation was highly related to Hammett constant (∑σo,m,p) and energy gap (ELUMO - EHOMO) of the compound (R2 = 0.908), which were key parameters on evaluating the effect of structural moieties on electronic character and the chemical stability upon radical attack for a specific compound. This study presents clear evidence on mineralization mechanisms of phenols degradation at BDD anodes.

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.