In this research, bulk graphitic carbon nitride (g‐C3N4) is exfoliated and transferred to the carbon nitride nanosheets (CNNSs), which are then coupled with MIL‐88B(Fe) to form the hybrid. From the ...results of the powder X‐ray diffraction, scanning electronic microscopy and thermogravimetric analysis, it is found that the doping of CNNSs on the surface of MIL‐88(Fe) could maintain the basic structure of MIL‐88B(Fe), and the smaller dimension of CNNSs might influence the crystallization process of metal‐organic frameworks (MOFs) compared to bulk g‐C3N4. Besides, the effects of the CNNSs incorporation on photocatalysis are also investigated. Through the photoluminescence spectra, electrochemical measurements, and photocatalytic experiments, the hybrid containing 6% CNNSs is certified to possess the highest catalytic activity to degrade methylene blue and reduce Cr(VI) under visible light. The improvement of the photocatalytic performance can be attributed to the matched energy level which favors the formation of the heterojunctions. Besides, it promotes the charge migration such that the contact between MOFs and CNNSs is more intimate, which can be inferred from the electronic microscopy images. Finally, a possible photocatalytic mechanism is put forward by the relative calculation and the employment of the scavengers to trap the active species.
A novel photocatalyst constituted of MIL‐88B(Fe) and carbon nitride nanosheets (CNNSs) is fabricated. The particiption of CNNSs enhances the photocatalytic performance. The obtained hybrids can not only degrade the dye but also dispose the heavy metal ions under visible light. Moreover, the incorporation of CNNSs influences the crystallization process of the metal‐organic framework structure.
Learning Discriminant Face Descriptor Zhen Lei; Pietikainen, Matti; Li, Stan Z.
IEEE transactions on pattern analysis and machine intelligence,
02/2014, Letnik:
36, Številka:
2
Journal Article
Recenzirano
Local feature descriptor is an important module for face recognition and those like Gabor and local binary patterns (LBP) have proven effective face descriptors. Traditionally, the form of such local ...descriptors is predefined in a handcrafted way. In this paper, we propose a method to learn a discriminant face descriptor (DFD) in a data-driven way. The idea is to learn the most discriminant local features that minimize the difference of the features between images of the same person and maximize that between images from different people. In particular, we propose to enhance the discriminative ability of face representation in three aspects. First, the discriminant image filters are learned. Second, the optimal neighborhood sampling strategy is soft determined. Third, the dominant patterns are statistically constructed. Discriminative learning is incorporated to extract effective and robust features. We further apply the proposed method to the heterogeneous (cross-modality) face recognition problem and learn DFD in a coupled way (coupled DFD or C-DFD) to reduce the gap between features of heterogeneous face images to improve the performance of this challenging problem. Extensive experiments on FERET, CAS-PEAL-R1, LFW, and HFB face databases validate the effectiveness of the proposed DFD learning on both homogeneous and heterogeneous face recognition problems. The DFD improves POEM and LQP by about 4.5 percent on LFW database and the C-DFD enhances the heterogeneous face recognition performance of LBP by over 25 percent.
An exact two-dimensional rotation–strain model describing the motion of Hookean incompressible viscoelastic materials is constructed by the polar decomposition of the deformation tensor. The global ...existence of classical solutions is proved under smallness assumptions only on the size of the initial strain tensor. The proof of global existence utilizes the weak dissipative mechanism of motion, which is revealed by passing the partial dissipation to the whole system.
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•A significant greening trend of vegetation dynamics was detected in the YRB of China during the 21st century.•The contributions to vegetation greening in the YRB under the climate ...change and increasing anthropogenic disturbances was quantitatively explored.•The extent to how much anthropogenic disturbances contributed to vegetation greening in the YRB was quantified.
Reliable detection and observation of vegetation changes are increasingly essential for investigating the dynamic balance of regional ecosystems. The Yellow River Basin (YRB) serves as an ecological barrier in northern China, where vegetation is extremely sensitive to climate change and human activities. However, whether there has been a significant change in the intensity of vegetation dynamics in the YRB during the 21st century and how much anthropogenic disturbances contribute to the change are still poorly documented so far; thus, this work may hold a far-reaching significance for maintaining the eco-balance of northern China and even all of China. In this study, based on the annually and seasonally averaged normalized deviation vegetation index (NDVI), a comprehensive analysis was conducted to detect the trends of vegetation changes derived by NDVI at six different time scales during the period 2000–2018 using Theil-Sen statistics, and the Mann-Kendall (M-K) method was employed to test the significance levels of vegetation greening and browning. Subsequently, based on Moderate Resolution Imaging Spectroradiometer land cover data, in situ climatic and meteorological data and socioeconomic and anthropogenic factors were included to explore the driving forces for vegetation change through the GeoDetector method. Our results show that the YRB experienced both significant vegetation greening and browning with great spatial heterogeneity. More than 94% of the regions in the YRB showed vegetation greening trends at average rates of 0.0040 yr−1 in the annually averaged NDVI (NDVIAN) and 0.0053 yr−1 in the growing season averaged NDVI (NDVIGS) during the 21st century; the trend was significant in more than 71% (p < 0.05) of the total YRB territory. This phenomenon was mainly detected in the middle reaches of the YRB, where effective land use activities and afforestation played vital roles. There was great seasonal disparity for greening; spring NDVI (NDVISP) and summer NDVI (NDVISU) exhibited the largest greening rates. However, only small parts of the YRB (0.9%, p < 0.05) showed browning trends at average rates of 0.0015 yr−1 in NDVIAN and 0.0021 yr−1 in NDVIGS, mainly located in the Weihe River basin and the lower reaches of the YRB, where the sharp increase in urban and built-up areas contributed substantially to the browning trends. Overall, warming, increasing population and afforestation jointly dominated the significant vegetation greening trends in the YRB.
Pressure retarded osmosis (PRO) is an emerging technology to harvest the renewable salinity-gradient energy. However, its performance can be significantly limited by severe membrane fouling if a real ...waste effluent is used as the feed solution. To tackle this, we have experimentally demonstrated that forward osmosis (FO) can be used as a pretreatment means to extract water from the wastewater stream to the inter-loop solution. Subsequently, the diluted inter-loop solution could serve as a clean feed to the PRO unit at negligible fouling tendency. As a result, the newly developed FO-PRO hybrid system is capable of sustaining osmotic power generation with the advantages of FO such as (1) low fouling propensity, (2) easy membrane cleaning, and (3) minimal external energy required. Model simulations of full-scale analyses reveal that, by choosing the salinity of the inter-loop solution to PRO as 0.1 M, it is possible to reach a power density greater than 5 W/m2 set by Statkraft as the commercially viable benchmark. Meanwhile, the concentrated brine draw solution of PRO can be diluted back to the seawater level for easy disposal or reuse. Under this condition, FO possesses a comparable water flux with conventional pretreatment methods using pressure driven membrane processes. A simple design strategy is also provided in detail for the integration of FO and PRO units.
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•A novel FO-PRO hybrid system is developed for sustainable osmotic power generation.•A real waste effluent from a water recycling plant serves as the feed solution.•The FO unit shows superior fouling resistance with a water flux of 10–15 LMH.•The PRO unit can reach a power density greater than 5 W/m2 at 50% dilution of SWBr.•A simple design strategy is provided for the integration of FO and PRO units.
Radiation pattern analysis is important in reflector antenna design. Mesh model based physical optics method is widely used in such circumstance. There are always geometric errors between mesh model ...and CAD model, and the analysis will be subjected to geometric noise, which will ultimately deteriorate its accuracy. In this paper, such analysis is implemented directly with the CAD model. The reflector expressed by Non-Uniform Rational B-Spline (NURBS) is firstly refined by NURBS operation. It does not change the original shape, but divides it into small parametric areas, named as elements. The physical optics integration performs in each element with Gauss quadrature, and finally superimposes to obtain the radiation pattern of the antenna. Several examples show that this method is effective and efficient, and it will lay a foundation for antenna shape optimization with CAD models.
In order to resist the adverse effect of viewpoint variations, we design quadruple directional deep learning networks to extract quadruple directional deep learning features (QD-DLF) of vehicle ...images for improving vehicle re-identification performance. The quadruple directional deep learning networks are of similar overall architecture, including the same basic deep learning architecture but different directional feature pooling layers. Specifically, the same basic deep learning architecture that is a shortly and densely connected convolutional neural network is utilized to extract the basic feature maps of an input square vehicle image in the first stage. Then, the quadruple directional deep learning networks utilize different directional pooling layers, i.e., horizontal average pooling layer, vertical average pooling layer, diagonal average pooling layer, and anti-diagonal average pooling layer, to compress the basic feature maps into horizontal, vertical, diagonal, and anti-diagonal directional feature maps, respectively. Finally, these directional feature maps are spatially normalized and concatenated together as a quadruple directional deep learning feature for vehicle re-identification. The extensive experiments on both VeRi and VehicleID databases show that the proposed QD-DLF approach outperforms multiple state-of-the-art vehicle re-identification methods.
The well-known Constantin–Lax–Majda (CLM) equation, an important toy model of the 3D Euler equations without convection, can develop finite time singularities (Constantin et al. in Commun Pure Appl ...Math 38:715–724, 1985). De Gregorio modified the CLM model by adding a convective term (De Gregorio in Math Methods Appl Sci 19(15):1233–1255, 1996), which is known important for fluid dynamics (Hou and Lei in Commun Pure Appl Math 62(4):501–564, 2009; Okamoto in J Math Soc Jpn 65(4):1079–1099, 2013). Presented are two results on the De Gregorio model. The first one is the global well-posedness of such a model for general initial data with non-negative (or non-positive) vorticity which is based on a newly discovered conserved quantity. This verifies the numerical observations for such class of initial data. The second one is an exponential stability result of ground states, which is similar to the recent significant work of Jia et al. (Ration Mech Anal, 231:1269–1304, 2019), with the zero mean constraint on the initial data being removable. The novelty of the method is the introduction of the new solution space
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