•Mapped evolution of China’s policy mix for sustainable energy transition 1981–2020.•Over time China forms complex policy mix by layering and packaging instruments.•China has increased the diversity ...and number of policy instruments used.•Rise in charges on emissions and decrease in subsidies for renewables is observed.•Policy experimentation dominates policy instruments used to reduce carbon emissions.
Global warming and the acute domestic air pollution in China have necessitated transition to a sustainable energy system away from coal-dominated energy production. Through a systematic review of the national policy documents, this study investigates the policy mix adopted by the Chinese government to facilitate its energy transition and how that policy mix has evolved between 1981 and 2020. The chronological analysis emphasizes two dimensions of temporal changes in the policy mix: (1) changes in the policy intensity and density, and (2) the shift in policy instrument combinations. The policy mix has evolved from a few authority-based instruments to the current response that has a large density of instruments with a good diversity of instrument types. The Chinese government imposes an increasing policy intensity on air pollution abatement and a decreasing policy intensity on renewable energy support, and experiments with innovative policy instruments to reduce carbon dioxide emissions. The evolutionary trajectory features layering new policy instruments, calibrating existing ones and some degree of policy replacement and sequencing. Overall, the study shows that the Chinese government has adopted a complex mix of policy instruments to abate emissions (e.g. carbon dioxide and sulphur dioxide) in the coal-based energy system and to support renewable energy technologies. The study provides an in-depth understanding of Chinese policy design in the environment and energy fields and contributes to the public policy literature by filling a research gap – the comparative lack of empirical analyses on the temporal changes in the policy mixes.
Superhydrophobic surfaces have shown versatile applications in waterproofing, self‐cleaning, drag reduction, selective absorption, etc. The most convenient and universally applicable approach to ...forming superhydrophobic surfaces is by coating; however, currently, superhydrophobic, smart coatings with flexibility and multiple functions for wearable sensing electronics are not yet reported. Here, a highly flexible multifunctional smart coating is fabricated by spray‐coating multiwalled carbon nanotubes dispersed in a thermoplastic elastomer solution, followed by treatment with ethanol. The coatings not only endow various substrate materials with superhydrophobic surfaces, but can also respond to stretching, bending, and torsion—a property useful for flexible sensor applications. The coatings show superior sensitivity (gauge factor of 5.4–80), high resolution (1° of bending), a fast response time (<8 ms), a stable response over 5000 stretching–relaxing cycles, and wide sensing ranges (stretching: over 76%, bending: 0°–140°, torsion: 0–350 rad m−1). Moreover, multifunctional coatings with thicknesses of only 1 µm can be directly applied to clothing for full‐range and real‐time detection of human motions, which also show extreme repellency to water, acid, and alkali, which helps the sensors to work under wet and corrosive conditions.
A multifunctional, stretchable smart coating is fabricated by spray‐coating multiwalled carbon nanotubes dispersed in a thermoplastic elastomer solution, followed by treatment with ethanol. The coatings are superhydrophobic and piezoresistive, for water repellency and wearable strain‐sensor applications. The extreme repellency to water, UV, acid, and alkali characteristics of the coating endow highly sensitive and stable sensing performance under wet/corrosive conditions.
Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial ...circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved.
Vitiligo is an acquired skin disorder clinically characterized by the progressive appearance of white maculae due to a loss of functioning epidermal melanocytes. Studies have shown that microRNAs ...(miRNAs) modulate cellular differentiation, proliferation and apoptosis, including immune cell and melanocyte development and functions. The role of miRNAs in the pathogenesis of several immune-related diseases has been explored. Novel approaches to target miRNAs have recently emerged allowing modulation of miRNAs levels in diverse pathological processes, thus making them promising targets for molecular-based diagnostics and therapy. Here, we report the present status of research on miRNAs expression and functional alterations in vitiligo, in order to more fully understand the role of these molecules in vitiligo pathology.
A series of small molecular clay swelling inhibitor was prepared with tartaric acid and amines, presented as amine-tartaric salts (ATS). The inhibitor was screened based on the linear expansion rate ...of bentonite. The results show that the inhibitor prepared with tartaric acid and triethylenetetramine with the mole ratio of 1:1 (named as ATS-4) is the best inhibitor of the hydration expansion and dispersion of bentonite. The inhibitive properties of ATS-4 were evaluated by various methods, including clay linear swelling tests, anti-swelling tests, mud ball immersing tests, mud-making inhibition experiments and particle distribution measurements etc. The results show that ATS-4 has superior performance to inhibit the hydration swelling and dispersion of clays by controlling the particle size of clay. On the other hand, the bentonite linear expansion rate in 0.5% ATS-4 aqueous solution is much lower than that of others, and the hydration expansion degree of the mud ball in ATS-4 aqueous solution is appreciably weaker than the control test, and it is compatible with the conventional additives in water-based drilling fluids. Then, the inhibition mechanism of the amine-tartaric salt was well discussed based on thermogravimetric analysis (TGA), scanning electron microscope (SEM), X-ray diffraction analysis (XRD), single crystal X-ray diffraction and ion exchange tests.
•The inhibitor prepared by tartaric acid and triethylenetetramine with the mole ratio of 1:1 (ATS-4) shows the best inhibitory effect on the hydration expansion of bentonite.•ATS-4 is compatible with the modified starch and PAM in water-based drilling fluids.•The inhibition mechanism study shows ATS-4 can inhibit the swelling of bentonite and control the particle size by ion exchange.
Most governments have enacted physical or social distancing measures to control COVID-19 transmission. Yet little is known about the socio-economic trade-offs of these measures, especially for ...vulnerable populations, who are exposed to increased risks and are susceptible to adverse health outcomes. To examine the impacts of physical distancing measures on the most vulnerable in society, this scoping review screened 39,816 records and synthesised results from 265 studies worldwide documenting the negative impacts of physical distancing on older people, children/students, low-income populations, migrant workers, people in prison, people with disabilities, sex workers, victims of domestic violence, refugees, ethnic minorities, and people from sexual and gender minorities. We show that prolonged loneliness, mental distress, unemployment, income loss, food insecurity, widened inequality and disruption of access to social support and health services were unintended consequences of physical distancing that impacted these vulnerable groups and highlight that physical distancing measures exacerbated the vulnerabilities of different vulnerable populations.
The speciation of doped-nitrogen (pyridinic N, pyrrolic N, graphitic N and amino N) was controlled by ferric ion catalysis for improving fluorescence of nitrogen-doped carbon dots (NCDs). Comparing ...to the synthesis of NCDs without ferric ion catalysis, the present strategy induces two following effects: (1) an increase of pyrrolic N and a decrease of pyridinic N; (2) a more well-organized arrangement of graphitic core and more N moieties exposed on the surface of NCDs. Consequently, absolute fluorescence quantum yield is increased from 6.2% to 27.0% and fluorescence can be further enhanced in acidity-media. Such phenomena provide solid evidence that pyrrolic N enhances the fluorescence of NCDs while pyridinic N inhibits it. Besides, NCDs achieve selective response to ferric ions. The present work provides a deeper insight into the understanding of luminescence mechanism and causes broader interest in manipulating CDs chemical structure.
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The PCL/CS nanofibers are electrospun by a surfactant-free emulsion system, and CS fibers are obtained by removing PCL shell.
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Electrospun core-shell structured nanofibers have got ...wide attention due to their excellent properties and potential applications. Encapsulated CS in PCL to form the core-shell structured fibers would be a festival way to expanding the applications of biopolymers. In this work, the electrospun core-shell polycaprolacone/chitosan (PCL/CS) composite nanofibers were prepared by a stable emulsion system. Different from other reported works, the surfactant-free and low toxic water-in-oil emulsion was selected to form the electrospinning precursor. The high stability of the emulsion was achieved by adjusting the volume ratio of the solvents and the concentration of the polymers. PCL/CS fibers with different core to shell ratios were obtained via the adjustment of the concentration of CS and PCL in emulsion system. The distribution of the composition in the fibers was analyzed by FT-IR, XRD, EDS, XPS and WCA. Further, intact ultrafine CS fibers with a mean diameter of 143 ± 49 nm could be obtained after the removal of PCL shell, indicating the low diffusivity between core and shell solutions during emulsion electrospinning process. This study provided a promising method to fabricate natural polymer fibers which were difficult to electrospin.
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•Analysis of the battery electric vehicle (BEV) energy consumption distribution.•A simplified analytical BEV energy efficiency model to understand the driving pattern ...impact.•Parameter variation analysis of rolling coefficient, drag area, battery energy density and grid carbon intensity.•Evaluation of vehicle design optimization potentials.•Suggestions of appropriate BEV battery ranges in China.
To meet increasingly stringent emission legislation, electric vehicles are expected to offer promising sustainable mobility in the future. However, the driving range of battery electric vehicles (BEVs) is limited as compared with hybrid electric vehicles (HEVs). Additionally, the grid power supply in China is highly dependent on coal-based thermal power generation, which leads to high grid-carbon intensity and increased well-to-tank (WTT) emission for BEVs. Therefore, the tradeoff between electric vehicle driving range and environmental impact has become a critical problem in BEV development in China.
In this study, a BEV model is built and validated. The energy consumption and well-to-wheel (WTW) CO2 emission rates of different driving ranges and test cycles are simulated. To determine the impact of driving patterns on BEV energy consumption, the distribution of vehicle energy consumption is analyzed and an analytical model is proposed to generalize the energy consumption of BEVs in standardized driving cycles to real-world driving with only two statistical characteristics: the average and the variance of the speeds. It is found that BEVs have a great advantage in terms of energy saving only at driving cycles with low average speeds and frequent stops. While driving at highway speeds, the energy consumption of BEVs can be very high. With an understanding of driving pattern impact, parameter variation analysis of the BEV WTW CO2 emission rates for different driving ranges is simulated. Simulation results show that the rolling coefficient and battery energy density have a significant impact on driving range, followed by the drag area. However, grid-carbon intensity is more efficient for reducing WTW CO2 emissions. Currently, optimization of the rolling coefficient and drag area is the most viable option for increasing the battery range and decreasing the WTW CO2 emission rate.
Finally, to reduce the energy and environmental impact of BEVs in China, short driving ranges (<250km) and low driving speeds (<80km/h) are suggested for current BEVs, and optimization of the vehicle design and reduction of grid-carbon intensity are considered to be the most critical issues for the future application of BEVs.
The intelligent identification and classification of plant diseases is an important research objective in agriculture. In this study, in order to realize the rapid and accurate identification of ...apple leaf disease, a new lightweight convolutional neural network RegNet was proposed. A series of comparative experiments had been conducted based on 2141 images of 5 apple leaf diseases (rust, scab, ring rot, panonychus ulmi, and healthy leaves) in the field environment. To assess the effectiveness of the RegNet model, a series of comparison experiments were conducted with state-of-the-art convolutional neural networks (CNN) such as ShuffleNet, EfficientNet-B0, MobileNetV3, and Vision Transformer. The results show that RegNet-Adam with a learning rate of 0.0001 obtained an average accuracy of 99.8% on the validation set and an overall accuracy of 99.23% on the test set, outperforming all other pre-trained models. In other words, the proposed method based on transfer learning established in this research can realize the rapid and accurate identification of apple leaf disease.
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