•An indicator system was established to measure urban-rural transformation from the perspective of population-land-industry.•Urban-rural transformation development was divided into four ...categories.•Spatio-temporal variations and internal mechanisms of BTH’s urban-rural coordinated transformation were captured.
Since the turn of the new millennium, urban-rural transformation has entered a critical period. Ever-widening gaps between urban and rural areas are common in most parts of China. The Beijing-Tianjin-Hebei (BTH) region, as the capital circle of China, has experienced significant urban-rural transformation with accelerated urbanization. On the basis of space analytic geometry, this study measures BTH’s urban-rural transformation by establishing an indicator system from the population-land-industry perspective to capture spatio-temporal variations and explore the internal mechanisms of BTH's urban-rural transformation. The results indicate that the urban-rural system in the BTH region has undergone an intensive and dramatic transformation during 2000–2015. Evolving from basic to advanced and from disorder to order, urban districts in Beijing and Tianjin have always had levels of coordinated transformation degrees (CTD) higher than those of the surrounding high-level counties while the overall coordinated deviation degree (CDD) has exhibited a decreasing trend over time. Four functional zones, which are dominant transition area, key transition area, potential transition area and restricted transition area, of urban-rural transformation were established by adopting the trajectory computing method. This research could track the development process of regional urban-rural change from the essence of an urban-rural system itself. When this indicator system that measures urban-rural transformation is applied in other countries, national differences such as differences in urban-rural structures, as well as the limitations, should be noted.
Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data. Current works like the well-acknowledged Cycle GAN provide a general ...solution for any two domains through modeling injective mappings with a symmetric structure. While in situations where two domains are asymmetric in complexity, i.e., the amount of information between two domains is different, these approaches pose problems of poor generation quality, mapping ambiguity, and model sensitivity. To address these issues, we propose Asymmetric GAN (AsymGAN) to adapt the asymmetric domains by introducing an auxiliary variable (aux) to learn the extra information for transferring from the information-poor domain to the information-rich domain, which improves the performance of state-of-the-art approaches in the following ways. First, aux better balances the information between two domains which benefits the quality of generation. Second, the imbalance of information commonly leads to mapping ambiguity, where we are able to model one-to-many mappings by tuning aux, and furthermore, our aux is controllable. Third, the training of Cycle GAN can easily make the generator pair sensitive to small disturbances and variations while our model decouples the ill-conditioned relevance of generators by injecting aux during training. We verify the effectiveness of our proposed method both qualitatively and quantitatively on asymmetric situation, label-photo task, on Cityscapes and Helen datasets, and show many applications of asymmetric image translations. In conclusion, our AsymGAN provides a better solution for unpaired image-to-image translation in asymmetric domains.
•An analytical model is developed to predict the impact of a bullet on UHMWPE laminate.•The analytical model is based on four failure modes and stress wave’s propagation.•The analytical model can ...accurately predict ballistic limit of the composite laminate.•The analytical model can well predict bullet’s acceleration and velocity versus time.•The analytical model can properly predict laminate’s energy absorption from different damage modes.
High-performance fiber reinforced polymers are widely used in military and aerospace applications and easily subjected to high-velocity bullet impacts. Despite the abundantly available experimental and numerical studies related to energy absorption mechanisms of laminates under impact loading, measuring levels of the absorbed energy from different failure modes of laminates is a challenging task. Acquiring this important information using simplified analytical models is vital to evaluate the bulletproof ability of laminates and effectively optimize its structures. The present work develops an analytical model to predict the ballistic limit of a laminate and the energy absorption mechanism is considered from matrix crush, laminate shear, laminate compressive, fiber stretch, fiber break and delamination. The penetration process of a bullet striking laminates is divided into four sequential stages, including crush stage, compression-shear stage, stretch-shear stage, and fiber fracture and delamination stage. The analytical model predicts the ballistic limit, energy absorbed by each failure mode, and time histories of velocity and acceleration. The results are validated with experimental data and computational results, and good correlations are found for ballistic limits and thickness of the laminate, diameters of the deformation cone and thickness, ballistic limits, and non-dimension masses as well as energy absorptions and thicknesses.
Recent years have witnessed the significant advance in fine-grained visual categorization, which targets to classify the objects belonging to the same species. To capture enough subtle visual ...differences and build discriminative visual description, most of the existing methods heavily rely on the artificial part annotations, which are expensive to collect in real applications. Motivated to conquer this issue, this paper proposes a multi-level coarse-to-fine object description. This novel description only requires the original image as input, but could automatically generate visual descriptions discriminative enough for fine-grained visual categorization. This description is extracted from five sources representing coarse-to-fine visual clues: 1) original image is used as the source of global visual clue; 2) object bounding boxes are generated using convolutional neural network (CNN); 3) with the generated bounding box, foreground is segmented using the proposed k nearest neighbour-based co-segmentation algorithm; and 4) two types of part segmentations are generated by dividing the foreground with an unsupervised part learning strategy. The final description is generated by feeding these sources into CNN models and concatenating their outputs. Experiments on two public benchmark data sets show the impressive performance of this coarse-to-fine description, i.e., classification accuracy achieves 82.5% on CUB-200-2011, and 86.9% on fine-grained visual categorization-Aircraft, respectively, which outperform many recent works.
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Asymmetrical and lower heat transport caused by the cladding strip during twin-roll casting (TRC) of clad sheet makes the process window narrow compared with traditional TRC. The ...effect of pouring temperature on process stability is more remarkable when it is relatively high under high casting speed and casting speed becomes a leading factor when its value is low. The inter-diffusion zone width at the Al/Fe interface depends on the solid/semi-solid contacting time. The longer the time is, the wider the inter-diffusion zone. A widest zone of 3.8μm is attained under condition of casting speed of 1.25m/min and pouring temperature of 720°C. Bonding strength of clad sheet by TRC depends on inter-diffusion zone width and rolling reduction. Proper combinations of inter-diffusion zone width and rolling reduction can result in same high bonding strength. The highest bonding strength of 16N/mm is attained under a combination of rolling reduction of 29% and inter-diffusion zone of 3.7μm. The wetting of aluminum melt on steel surface is uneven and insufficient as a result of short contacting time in TRC. As a result, same rolling reduction in TRC induces lower bonding strength than in roll bonding process. Bonding strength of clad sheet by TRC decreases when the surface of the steel strip is brushed, as a result of decrement of actual contacting area.
The clustered regularly interspaced short palindromic repeats (CRISPR) system is a natural adaptive immune system of prokaryotes. The CRISPR-Cas system is currently divided into two classes and six ...types: types I, III, and IV in class 1 systems and types II, V, and VI in class 2 systems. Among the CRISPR-Cas type VI systems, the CRISPR/Cas13a system has been the most widely characterized for its application in molecular diagnostics, gene therapy, gene editing, and RNA imaging. Moreover, because of the trans-cleavage activity of Cas13a and the high specificity of its CRISPR RNA, the CRISPR/Cas13a system has enormous potential in the field of molecular diagnostics. Herein, we summarize the applications of the CRISPR/Cas13a system in the detection of pathogens, including viruses, bacteria, parasites, chlamydia, and fungus; biomarkers, such as microRNAs, lncRNAs, and circRNAs; and some non-nucleic acid targets, including proteins, ions, and methyl groups. Meanwhile, we highlight the working principles of some novel Cas13a-based detection methods, including the Specific High-Sensitivity Enzymatic Reporter UnLOCKing (SHERLOCK) and its improved versions, Cas13a-based nucleic acid amplification-free biosensors, and Cas13a-based biosensors for non-nucleic acid target detection. Finally, we focus on some issues that need to be solved and the development prospects of the CRISPR/Cas13a system.
•Integrating ESV into the evaluation of sustainable land use is applicable.•Six sustainability indicators based on ESV from two dimensions are valid.•Land use changes in fast-growing cities ...profoundly influence ESV.•ESV-based SLU evaluation results provide guidance for land use management.
Integrating ecosystem service value (ESV) into the evaluation system for sustainable land use (SLU) in fast-growing cities is an effective way to implement Sustainable Development Goals (SDGs) 15 and 11 of the United Nations' 2030 Agenda for Sustainable Development. Taking ESV as the intermediate parameter, six sustainability indicators were developed to evaluate SLU from two dimensions: sustainable land ecosystem services and economic development. These indicators are the total supply and demand index (RT) of ecosystem services, the potential supply and demand index (RP) of ecosystem services, the ecological cost index per unit of GDP (RC), the eco-economic coordination index (RD), the eco-economic equilibrium index (RE), and the ESV-based sustainable land use index (RS). Empirical research was conducted in Qingdao, a fast-growing city in eastern China, to verify the validity of the indicators. Five sets of Landsat TM/ETM/OLI remote sensing images from 1996, 2005, 2010, 2015, and 2017 were used to analyze the land use and ESV changes in Qingdao. The results indicate that land use changes in fast-growing cities significantly affect the ESV. The decrease in the area of ecological land and the expansion of construction land caused by rapid urbanization reduced ESV. The RC and RD of Qingdao were sustainable, while the RT and RP were unsustainable, and the RE showed volatility, which reduced the evaluation level of SLU in Qingdao from extremely strongly sustainable (L1) to moderately sustainable (L3). Thus, it is necessary to improve the ESV of Qingdao. Optimization strategies for promoting SLU in Qingdao are proposed based on the results, confirming that the evaluation provides guidance for land use management.
•3D modeling of existing trees from incomplete point cloud.•Complete, accurate and detailed extraction of tree skeleton.•Details preserved tree branch points whorl-shaped over-segmentation.
Laser ...point cloud provides a high-precision data source for the 3D model reconstruction of existing trees. However, due to the complexity of tree structure and the data missing of point cloud, it is difficult to reconstruct complete, detailed and accurate 3D tree models from discrete points, especially the points are incomplete due to single-view scanning. To this end, a new point cloud driven method based on skeleton refined extraction is proposed in this paper. First, as the focus of refined modeling, the main (thick) branches are separated from the raw tree points. Then, the main branch points are over-segmented into some whorl-segments, and the corresponding raw points on the branch surfaces of each whorl-segment are shrunk to generate smooth, continuous and local detail preserved potential skeleton nodes of the main branches. Next, a new shortest path metric meeting the natural growth characteristics of trees is adopted to extract the complete, detailed and correctly connected tree skeleton from both of the shrunk main branch points and the non-main-branch points. After that, to ensure the skeleton centralization, especially that extracted from single-view scanned data, the extracted skeleton is translated by a combination of cylinder fitting and allometric theory. Finally, the complete, detailed and accurate 3D tree model is expressed by a series of cylinders based on the extracted refined skeleton. The proposed method is tested with 7 different tree point clouds, some of which are obtained by single-view scanning and suffer from serious data missing. Both qualitative and quantitative evaluations prove the effectiveness of this method for the refined reconstruction of trees from point clouds. Overall, the average modeling accuracy and its standard deviation of the used different trees are 2.58 cm and 3.20 cm respectively.
When packing for a journey, have you ever asked "what clothes should I take with me?" Wearing appropriate and aesthetically pleasing clothing when traveling is a concern for many of us. Our data ...observation of photos from several popular travel websites reveals that people's choice of clothing items and their color combinations have strong correlations with the weather, the season, and the main type of attraction at the destination. This leads to an interesting and novel problem: can the correlation between clothing and locations be automatically learned from social photos and leveraged for location-oriented clothing recommendations? In this paper, we systematically study this problem and propose a hybrid multilabel convolutional neural network combined with the support vector machine (mCNN-SVM) approach to capture the intrinsic and complex correlations between clothing attributes and location attributes. Specifically, we adapt the CNN architecture to multilabel learning and fine-tune it using each fine-grained clothing item. Then, the recognized items are fed to the SVM to learn the correlations. Experiments on three fashion datasets and a benchmark journey outfit dataset show that our proposed approach outperforms several baselines by over 10.52-16.38% in terms of the mAP for clothing item recognition and outperforms several alternative methods by over 9.59-29.41% in terms of the mAP when ranking clothing by appropriateness for travel destinations. Finally, an interesting case study demonstrates the effectiveness of our method by answering what items to wear, how to match them, and how to dress in an aesthetically pleasing manner for a journey.
Acetaminophen (APAP) is a widely used analgesic and antipyretic over-the-counter medicine worldwide. Hepatotoxicity caused by APAP overdose is one of the leading causes of acute liver failure (ALF) ...in the US and in some parts of Europe, limiting its clinical application. Excessive APAP metabolism depletes glutathione and increases N-acetyl-p-benzoquinoneimide (NAPQI) levels, leading to oxidative stress, DNA damage, and cell necrosis in the liver, which in turn leads to liver damage. Studies have shown that natural products such as polyphenols, terpenes, anthraquinones, and sulforaphane can activate the hepatocyte antioxidant defense system with Nrf2 as the core player, reduce oxidative stress damage, and protect the liver. As the key enzyme metabolizing APAP into NAPQI, cytochrome P450 enzymes are also considered to be intriguing target for the treatment of APAP-induced liver injury. Here, we systematically review the hepatoprotective activity and molecular mechanisms of the natural products that are found to counteract the hepatotoxicity caused by APAP, providing reference information for future preclinical and clinical trials of such natural products.
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