The diffusion of e-commerce has played a significant role in recent rural economic development in China. E-commerce is also considered as an efficient channel to alleviate poverty in rural China. ...Voluminous studies have investigated the contribution of e-commerce to agricultural development, yet it is lacking empirical evidence as to the effects of e-commerce on rural poverty alleviation. Since the year of 2014, in order to develop rural e-commerce, Chinese government launched the National Rural E-commerce Comprehensive Demonstration Project. This gradual involvement policy offered a natural experiment for evaluation of e-commerce. Based on village-level survey data from rural China and Heckit method, our study finds that rural e-commerce has a significantly positive effect on rural income. Moreover, the effect is inverted U-shaped for the relative-poverty villages. The estimation of the propensity scores matching model confirms that the results are robust. The following policy recommendations are proposed: (1) policy support to rural e-commerce should prioritize the poverty-stricken villages. By doing so, the marginal income effects of e-commerce will be maximized. (2) Investment in internet infrastructure and establishment of human resources for e-commerce in rural areas will have spillover effects, increasing rural income through the “digital dividend”.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Patterning of liquid metal (LM) is usually an integral step toward its practical applications. However, the high surface tension along with surface oxide makes direct patterning of LM very ...challenging. Existing LM patterning techniques are designed for limited types of planar substrates, which require multiple‐step operation, delicate molds and masks, and expensive equipment. In this work, a simple, versatile, and equipment‐free approach for direct patterning of LM on various substrates using magnetic field is reported. To achieve this, magnetic microparticles are dispersed into LM by stirring. When a moving magnetic field is applied to the LM droplet, the aggregated magnetic microparticles deform the droplet to a continuous line. In addition, this approach is also applicable to supermetallophobic substrates since the applied magnetic field significantly enhances the contact between LM and substrate. Moreover, remote manipulation of the magnetic microparticles allows direct patterning of LM on nonplanar surfaces, even in a narrow and near closed space, which is impossible for the existing techniques. A few applications are also demonstrated using the proposed technique for flexible electronics and wearable sensors.
A novel liquid metal patterning approach is presented using a magnetic field. This is achieved by applying a moving magnetic field to a liquid metal droplet dispersed with magnetic microparticles. This approach is compatible with different substrates including supermetallophobic and curved surfaces. A few applications of the versatile patterning approach for flexible electronics and wearable sensors are also demonstrated.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic ...detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496 × 496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder-decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it's proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from -11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.
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Elevated temperature as a result of global climate warming, either in form of sudden heatwave (heat shock) or prolonged warming, has profound effects on the growth and development of plants. However, ...how plants differentially respond to these two forms of elevated temperatures is largely unknown. Here we have therefore performed a comprehensive comparison of multi-level responses of Arabidopsis leaves to heat shock and prolonged warming.
The plant responded to prolonged warming through decreased stomatal conductance, and to heat shock by increased transpiration. In carbon metabolism, the glycolysis pathway was enhanced while the tricarboxylic acid (TCA) cycle was inhibited under prolonged warming, and heat shock significantly limited the conversion of pyruvate into acetyl coenzyme A. The cellular concentration of hydrogen peroxide (H
O
) and the activities of antioxidant enzymes were increased under both conditions but exhibited a higher induction under heat shock. Interestingly, the transcription factors, class A1 heat shock factors (HSFA1s) and dehydration responsive element-binding proteins (DREBs), were up-regulated under heat shock, whereas with prolonged warming, other abiotic stress response pathways, especially basic leucine zipper factors (bZIPs) were up-regulated instead.
Our findings reveal that Arabidopsis exhibits different response patterns under heat shock versus prolonged warming, and plants employ distinctly different response strategies to combat these two types of thermal stress.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Lateral organ boundaries (LOB) domain (
) genes, a gene family encoding plant-specific transcription factors, play important roles in plant growth and development. At present, though there have been ...a number of genome-wide analyses on
gene families and functional studies on individual LBD proteins, the diverse functions of LBD family members still confuse researchers and an effective strategy is required to summarize their functional diversity. To further integrate and improve our understanding of the phylogenetic classification, functional characteristics and regulatory mechanisms of LBD proteins, we review and discuss the functional characteristics of LBD proteins according to their classifications under a phylogenetic framework. It is proved that this strategy is effective in the anatomy of diverse functions of LBD family members. Additionally, by phylogenetic analysis, one monocot-specific and one eudicot-specific subclade of LBD proteins were found and their biological significance in monocot and eudicot development were also discussed separately. The review will help us better understand the functional diversity of LBD proteins and facilitate further studies on this plant-specific transcription factor family.
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The wellbore of a sucker-rod pumping well experiences a multi-phase flow consisting of oil, gas, and water. The flow pattern and pump discharge pressure are greatly impacted by oil well production, ...which in turn significantly affects the simulation results of longitudinal vibration in the sucker-rod string. When calculating the discharge pressure in a hydrostatic column containing both oil and water (HC), the pressure is not affected by the oil well’s production. This thereby avoids interference between vibrations in the sucker-rod string’s longitudinal direction and the flow from the wellbore. Considering the coupling characteristics between the longitudinal vibration of the sucker-rod string and the wellbore flow, a mathematical model of the sucker-rod pumping system (CMSRS) and a mathematical model of the downhole energy efficiency parameters were established. In detail, the CMSRS comprises two parts: the discharge pressure mathematical models of multi-phase flow dynamics (MD) and the wave equation of the longitudinal vibration of the sucker-rod string. A numerical simulation model of the sucker-rod pumping system was constructed based on a mathematical model. We compared the experimental results, the simulation results of the CMSRS and the simulation results of the sucker-rod string based on the oil-water two–phase hydrostatic column (SMSRS) and found good agreement, indicating the feasibility of the CMSRS. The simulation details show the following: (1) The HC model’s discharge pressure exceeds that of the MD model by more than 33.52%. The polished rod load for the CMSRS is 18.01% lower than that of the SMSRS, and the pump input power for the CMSRS is 36.23% lower than that of the SMSRS. (2) The effective power simulation model based on the energy balance relationship is essentially the same as the effective power calculated by the model based on multi-phase flow effective power. This validates the accuracy of the multi-phase flow effective power model. (3) The limitations of the industry standard effective power model are that (i) the effective head is the net lift height of the fluid in the wellbore reduced to the oil and water phases rather than the effective lift height based on the energy balance relationship and (ii) the power of the gas phase delivered by the pumping pump is disregarded, and only the effective power of the pump delivering the oil-water mixture is considered. (4) The influence of the wellbore parameters on the wellbore efficiency and sub–efficiency is systematically analyzed. The analysis results have an important significance in the guidance of energy saving in pumping wells.
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Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is ...needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.
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Ethylene as a gas phytohormone plays significant roles in the whole life cycle of plants, ranging from growth and development to stress responses. A linear ethylene signaling pathway has been ...established in the dicotyledonous model plant Arabidopsis. However, the ethylene signaling mechanism in monocotyledonous plants such as rice is largely unclear. In this review, We compare the ethylene response phenotypes of dark-grown seedlings of Arabidopsis, rice, and other monocotyledonous plants （maize, wheat, sorghum, and Brachypodium distachyon） and pinpoint that rice has a distinct phenotype of root inhibition but coleoptile promotion in etiolated seedlings upon ethylene treatment. We further summarize the homologous genes of Arabidopsis ethylene signaling components in these monocotyledonous plants and discuss recent progress. Although conserved in most aspects, ethylene signaling in rice has evolved new features compared with that in Arabidopsis. These analyses provide novel insights into the understanding of ethylene signaling in the dicotyledonous Arabidopsis and monocotyledonous plants, particularly rice. Further characterization of rice ethylene-responsive mutants and their corresponding genes will help us better understand the whole picture of ethylene signaling mechanisms in plants.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Cracks and exposed steel bars are the main factors that affect the service life of bridges. It is necessary to detect the surface damage during regular bridge inspections. Due to the complex ...structure of bridges, automatically detecting bridge damage is a challenging task. In the field of crack classification and segmentation, convolutional neural networks have offer advantages, but ordinary networks cannot completely solve the environmental impact problems in reality. To further overcome these problems, in this paper a new algorithm to detect surface damage called EMA-DenseNet is proposed. The main contribution of this article is to redesign the structure of the densely connected convolutional networks (DenseNet) and add the expected maximum attention (EMA) module after the last pooling layer. The EMA module is obviously helpful to the bridge damage feature extraction. Besides, we use a new loss function which considers the connectivity of pixels, it has been proved to be effective in reducing the break point of fracture prediction and improving the accuracy. To train and test the model, we captured many images from multiple bridges located in Zhejiang (China), and then built a dataset of bridge damage images. First, experiments were carried out on an open concrete crack dataset. The mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and frames per second (FPS) of the EMA-DenseNet are 87.42%, 92.59%, 81.97% and 25.4, respectively. Then we also conducted experiments on a more challenging bridge damage dataset, the MIoU, where MPA, precision and FPS were 79.87%, 86.35%, 74.70% and 14.6, respectively. Compared with the current state-of-the-art algorithms, the proposed algorithm is more accurate and robust in bridge damage detection.
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