With a goal of improving health system quality and efficiency, reforms of China's health system over the past decade have sought to strengthen primary healthcare in lower-level clinics and health centers. Despite these wide-ranging reforms and initiatives, population-based studies have documented dramatic declines in patients' use of primary care facilities during this period. In this paper, we explore the determinants of this trend in China's rural areas using detailed longitudinal data following a nationally-representative sample of rural households and village clinics from 2011 to 2018. We estimate that between 2011 and 2018, the probability that individuals sought care at village clinics when ill dropped by 44%. At the same time, the utilization of outpatient services in county hospitals increased by 56% and patient self-treatment increased by 20%. Detailed Kitagawa-Oaxaca-Blinder decompositions suggest four primary drivers of this trend: the shifting burden of disease in rural areas, changes in how patients choose to seek care given different disease conditions, declining drug inventory in village clinics, and the decreasing importance of remoteness as a determinant of healthcare seeking behavior. Our results highlight the deteriorating role of village clinics in the rural healthcare system and the increasing importance of self-treatment and higher-tier primary care services.
Agricultural systems are already major forces of ammonia pollution and environmental degradation. How agricultural ammonia emissions affect the spatio-temporal patterns of nitrogen deposition and where to target future mitigation efforts, remains poorly understood. We develop a substantially complete and coherent agricultural ammonia emissions dataset in nearly recent four decades, and evaluate the relative role of reduced nitrogen in total nitrogen deposition in a spatially explicit way. Global reduced nitrogen deposition has grown rapidly, and will occupy a greater dominant position in total nitrogen deposition without future ammonia regulations. Recognition of agricultural ammonia emissions on nitrogen deposition is critical to formulate effective policies to address ammonia related environmental challenges and protect ecosystems from excessive nitrogen inputs. Global gains in food production over the past decades have been associated with substantial agricultural nitrogen overuse and ammonia emissions, which have caused excessive nitrogen deposition and subsequent damage to the ecosystem health. However, it is unclear which crops or animals have high ammonia emission potential, how these emissions affect the temporal and spatial patterns of nitrogen deposition, and where to target future abatement. Here, we develop a long-term agricultural ammonia emission dataset in nearly recent four decades (1980–2018) and link it with a chemical transport model for an integrated assessment of global nitrogen deposition patterns. We found global agricultural ammonia emissions increased by 78% from 1980 and 2018, in which cropland ammonia emissions increased by 128%, and livestock ammonia emissions increased by 45%. Our analyses demonstrated that three crops (wheat, maize, and rice) and four animals (cattle, chicken, goats, and pigs) accounted for over 70% total ammonia emissions. Global reduced nitrogen deposition increased by 72% between 1980 and 2018 and would account for a larger part of total nitrogen deposition due to the lack of ammonia regulations. Three countries (China, India, and the United States) accounted for 47% of global ammonia emissions, and had substantial nitrogen fertilizer overuse. Nitrogen deposition caused by nitrogen overuse accounted for 10 to 20% of total nitrogen deposition in hotspot regions including China, India, and the United States. Future progress toward reducing nitrogen deposition will be increasingly difficult without reducing agricultural ammonia emissions.
In the present study, an innovative, environmentally benign recyclable, and magnetically mediated surface washing fluid based on water-dispersible magnetite nanoparticles has been designed and investigated for the cleanup of oiled beach sand. The characterization results showed that the as-prepared magnetite nanoparticles had a spherical morphology with an average diameter of around 250 nm and the particle surface was successfully functionalized with carboxyl groups. The magnetite nanoparticles could be easily re-dispersed by lightly shaking the dispersion after withdrawing the magnet. In addition, prolonging the magnetic field strength and response time promoted the oil recovery from the washing effluent. Thermodynamic modeling was applied to theoretically elucidate the mechanism and the results were in alignment with the experimental findings. Four mechanisms were identified to likely affect surface washing performance. The magnetic fluid had a relatively low operation cost and good reusability for a number of multiple cycles. In terms of other operational limitations, it was noted that washing performance declined as clay (kaolinite) concentrations and salinity values increased. Based on these findings, the proposed stable, low-cost magnetite fluid formulation warrants further investigation as the basis for an operational system for the cleanup of sand beaches contaminated by oil spills.
The perceived position of a moving object in vision entails an accumulation of neural signals over space and time. Due to neural signal transmission delays, the visual system cannot acquire immediate information about the moving object's position. Although physiological and psychophysical studies on the flash-lag effect (FLE), a moving object is perceived ahead of a flash even when they are aligned at the same location, have shown that the visual system develops the mechanisms of predicting the object's location to compensate for the neural delays, the neural mechanisms of motion-induced location prediction are not still understood well. Here, we investigated the role of neural activity changes in areas MT+ (specialized for motion processing) and the potential contralateral processing preference of MT+ in modulating the FLE. Using transcranial direct current stimulations (tDCS) over the left and right MT+ between pre- and posttests of the FLE in different motion directions, we measured the effects of tDCS on the FLE. The results found that anodal and cathodal tDCS enhanced and reduced the FLE with the moving object heading to but not deviating from the side of the brain stimulated, respectively, compared with sham tDCS. These findings suggest a causal role of area MT+ in motion-induced location prediction, which may involve the integration of position information.NEW & NOTEWORTHY Perceived positions of moving objects are related to neural activities in areas MT+. We demonstrate that tDCS over areas MT+ can modulate the FLE, and further anodal and cathodal tDCS facilitated and inhibited the FLE with a moving object heading to but not deviating from the side of the brain stimulated, respectively. These findings suggest a causal role of area MT+ in motion-induced location prediction and contribute to understanding the neural mechanism of the FLE.
Fe2O3, as an earth-abundant photocatalyst for water purification, has attracted great attention. However, the high-spin FeIII in traditional Fe2O3 restricts its catalytic performance. In this work, based on the nanocrystal size alteration strategy, cubic Fe2O3 nanoclusters (3–4 nm) with low-spin FeIII were successfully anchored on six-fold cavities of the supramolecular condensed g-C3N4 rod (FCN) through the impregnation-coprecipitation method. FCN showed high photocatalytic activity, as the d band center of Fe 3d orbital (−1.79 eV) in low-spin FeIII shifted closer to Femi level, generating a weaker antibonding state. Then, the enhanced bonding state strengthened the interaction between Fe and O, further accelerating the charge carrier separation and enhancing its ability to capture OH−. Thus, low-spin FeIII enhanced the production of dominant reactive oxygen species (•OH/•O2−), promoting diclofenac photocatalytic degradation under solar light, with a kinetic rate constant (0.206 min−1) of 5 times compared with that of pristine g-C3N4.
In face of the critical endurance issue, for the first time we take a holistic perspective to co-optimize the ferroelectric materials and interlayer in FeFET. Compared to the common HZO based gate stack, the novel combination of Hf0.95 Al0.05 O2+Al2 O3 enhances the endurance to $\gt 5 \times 10 ^9$ cycles while maintaining a retention > 10 years. In-depth analysis based on DFT and DQSCV reveal the reduction of interlayer electric field and interface charge trapping as the mechanism of optimization. We also develop a distributed interface trap model to correlate different trapping dynamics with the interlayer property in each device. This work pushes forward the understanding and development of high endurance strategy for FeFET.
This paper introduces a novel meta-learning algorithm for time series forecast model performance prediction. We model the forecast error as a function of time series features calculated from historical time series with an efficient Bayesian multivariate surface regression approach. The minimum predicted forecast error is then used to identify an individual model or a combination of models to produce the final forecasts. It is well known that the performance of most meta-learning models depends on the representativeness of the reference dataset used for training. In such circumstances, we augment the reference dataset with a feature-based time series simulation approach, namely GRATIS, to generate a rich and representative time series collection. The proposed framework is tested using the M4 competition data and is compared against commonly used forecasting approaches. Our approach provides comparable performance to other model selection and combination approaches but at a lower computational cost and a higher degree of interpretability, which is important for supporting decisions. We also provide useful insights regarding which forecasting models are expected to work better for particular types of time series, the intrinsic mechanisms of the meta-learners, and how the forecasting performance is affected by various factors.
This study uses the data of a nationally representative survey in China to investigate the role of financial literacy overconfidence in investment fraud victimization. The study finds that male, wealthy, and educated respondents tend to be more confident about their financial knowledge. Moreover, overconfident respondents are more likely to believe that the abnormally high returns claimed in two hypothetical investment opportunities are attainable.