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Land surface deformation in metropolitan areas, which can cause varying degrees of hazard to both human lives and to properties, has been documented for decades in cities worldwide. Xuzhou, is one of the most important energy and industrial bases in eastern China, and has experienced significant land subsidence due to both excessive extraction of karst underground water and exploitation of mineral resources in recent decades. Furthermore, Xuzhou has recently undergone rapid urbanization in terms of urban expansion and underground construction, which could induce additional pressure on the urban land surface. However, most previous research on land surface deformation in the Xuzhou urban areas has been conducted based on traditional ground-based deformation monitoring techniques with sparse measurements. Little is known about the regional spatiotemporal behavior of land surface displacement in Xuzhou. In this study, a detailed interferometric synthetic aperture radar (InSAR) time series analysis was performed to characterize the spatial pattern and temporal evolution of land surface deformation in central areas of Xuzhou during 2015−2018. A method based on principal component analysis was adopted to correct artifacts in the InSAR signal. Results showed the correction strategy markedly reduced the discrepancy between global navigation satellite systems and InSAR measurements. Noticeable land subsidence (−5 to −41 mm/yr) was revealed widely within the Xuzhou urban areas, particularly along subway lines under construction, newly developed districts, and in old coal goafs. Remarkable consistent land uplift (up to +25 mm/yr) was found to have significantly affected two long narrow areas within the old goafs since 2015. The possible principal influencing factors contributing to the land surface displacements such as subway tunneling, building construction, mining, underground water levels and geological conditions are then discussed.

Automatic coal-rock recognition is one of the critical technologies for intelligent coal mining and processing. Most existing coal-rock recognition methods have some defects, such as unsatisfactory performance and low robustness. To solve these problems, and taking distinctive visual features of coal and rock into consideration, the multi-scale feature fusion coal-rock recognition (MFFCRR) model based on a multi-scale Completed Local Binary Pattern (CLBP) and a Convolution Neural Network (CNN) is proposed in this paper. Firstly, the multi-scale CLBP features are extracted from coal-rock image samples in the Texture Feature Extraction (TFE) sub-model, which represents texture information of the coal-rock image. Secondly, the high-level deep features are extracted from coal-rock image samples in the Deep Feature Extraction (DFE) sub-model, which represents macroscopic information of the coal-rock image. The texture information and macroscopic information are acquired based on information theory. Thirdly, the multi-scale feature vector is generated by fusing the multi-scale CLBP feature vector and deep feature vector. Finally, multi-scale feature vectors are input to the nearest neighbor classifier with the chi-square distance to realize coal-rock recognition. Experimental results show the coal-rock image recognition accuracy of the proposed MFFCRR model reaches 97.9167%, which increased by 2%−3% compared with state-of-the-art coal-rock recognition methods.

The nonspecific adsorption of proteins and bacteria on the surface of polydimethylsiloxane (PDMS) had been a serious concern in a wide range of applications, such as medical devices. In order to improve the anti-adhesive and antibacterial capability, bare silver nanoparticles (AgNPs, ~15 nm) were generated in-situ on their surface without extra reducing and stabilizing agents. The main reason for this was that the SiO2 microspheres that are covalent bonded to the bulked PDMS could not only generate AgNPs spontaneously but also insure that no AgNPs were released to the environment. Meanwhile, the thiol-group-functionalized SiO2 microspheres self-assembled on the surface of PDMS by thiol-vinyl click reaction without any impact on their biomedical applications. After the modification of SiO2 microspheres with AgNPs, the surface of PDMS showed a smaller water contact angle than before, and the adhesion and growth of E. coli and Bacillus subtilis were effectively inhibited. When the monolayer of SiO2 microspheres with AgNPs was assembled completely on the surface of PDMS, they present improved bacterial resistance performance (living bacteria, 0%). This approach offers an antibacterial and anti-adhesive surface bearing small and well-defined quantities of in-situ generated AgNPs, and it is a novel, green, simple, and low-cost technique to generate AgNPs on soft biomedical substrates.

In this paper, the de-noising problem of night vision images is studied for apple harvesting robots working at night. The wavelet threshold method is applied to the de-noising of night vision images. Due to the fact that the choice of wavelet threshold function restricts the effect of the wavelet threshold method, the fuzzy theory is introduced to construct the fuzzy threshold function. We then propose the de-noising algorithm based on the wavelet fuzzy threshold. This new method can reduce image noise interferences, which is conducive to further image segmentation and recognition. To demonstrate the performance of the proposed method, we conducted simulation experiments and compared the median filtering and the wavelet soft threshold de-noising methods. It is shown that this new method can achieve the highest relative PSNR. Compared with the original images, the median filtering de-noising method and the classical wavelet threshold de-noising method, the relative PSNR increases 24.86%, 13.95%, and 11.38% respectively. We carry out comparisons from various aspects, such as intuitive visual evaluation, objective data evaluation, edge evaluation and artificial light evaluation. The experimental results show that the proposed method has unique advantages for the de-noising of night vision images, which lay the foundation for apple harvesting robots working at night.

Because of the high BER (Bit Error Rate), low time delay and low channel transmission efficiency of HAPS (High Altitude Platform Station) in the near space. The link budget of HAPS and channel model are proposed in this paper. According to the channel characteristic, the channel model is set up, combined with different CNR (Carrier Noise Ratio), elevation angle, coding situations of wireless communication link by using Hamming code, PSK (Pulse Shift Keying) and Golay code respectively, then the situations of link quality and BER are analyzed. The simulation results show that the established model of the link budget and channel are suitable for the theoretical analysis results. The elevation of the HAPS communication link is smaller while the BER is increasing. The case of channel in the coding is better than in the un-coded situation. When every bit power and thermal noise power spectral density is larger, the BER of the HAPS communication link is becoming smaller.

Wind shear is a dangerous atmospheric phenomenon in aviation. Wind shear is defined as a sudden change of speed or direction of the wind. In order to analyze the influence of wind shear on the efficiency of the airplane, this paper proposes a mathematical model of point target rain echo and weather target signal echo based on Doppler effect. The wind field model is developed in this paper, and the antenna model is also studied by using Bessel function. The spectrum distribution of symmetric and asymmetric wind fields is researched by using the mathematical model proposed in this paper. The simulation results are in accordance with radial velocity component, and the simulation results also confirm the correctness of the established model of antenna.

Fe2+-H2O2 Fenton system is widely applied in water treatment nowadays, but the acidification and sludge generation are crucial problems to be solved. Herein, we report that Fe3O4/TiO2/C nanocomposites (FTCNCs) were able to catalyze the decomposition of H2O2 at neutral pH and can be applied in dye decoloration. FTCNCs were prepared by precipitating TiO2 on Fe3O4 cores via the hydrolysis of tetrabutyl titanate followed by the hydrothermal dehydrogenization of glucose to deposit carbon on Fe3O4/TiO2. The decoloration of methylene blue (MB) in the FTCNC-H2O2 Fenton-like system was monitored to reflect the catalytic activity of FTCNC. The radical generation capability was analyzed by electron spin resonance. Our results indicated that FTCNC-H2O2 Fenton-like system was efficient in decolorizing MB, and the radicals led to the near complete oxidation of MB. The FTCNC-H2O2 Fenton-like system could be used in a wide pH range of 4–9. A greater catalyst amount, a higher H2O2 concentration, and a higher temperature accelerated the decoloration kinetics. FTCNCs showed good activity after the regeneration of 8 cycles. The implication to the practical applications of FTCNCs in water treatment is discussed.

This paper is concerned with the finite-time stabilization for a class of stochastic neural networks (SNNs) with noise perturbations. The purpose of the addressed problem is to design a nonlinear stabilizator which can stabilize the states of neural networks in finite time. Compared with the previous references, a continuous stabilizator is designed to realize such stabilization objective. Based on the recent finite-time stability theorem of stochastic nonlinear systems, sufficient conditions are established for ensuring the finite-time stability of the dynamics of SNNs in probability. Then, the gain parameters of the finite-time controller could be obtained by solving a linear matrix inequality and the robust finite-time stabilization could also be guaranteed for SNNs with uncertain parameters. Finally, two numerical examples are given to illustrate the effectiveness of the proposed design method.

This paper is concerned with the regular dynamic loop-like subdivision scheme based on the extension of Box-Spline method. The purpose of the addressed problem is to prove the convergence of the subdivision scheme and verify the C0 and C1 continuity by calculating the eigenvalues of the 2-neighbor subdivision matrix. Compared with the loop subdivision scheme in previous references, the designed scheme can generate exact revolving surfaces and hold a stronger modeling capability because the subdivision matrix can update regularly with the iteration procedure. Finally, some examples are given to illustrate the effectiveness of the proposed method.

Molten salts play an important role in various energy-related applications such as high-temperature heat transfer fluids and reaction media. However, the extreme molten salt environment causes the degradation of materials, raising safety and sustainability challenges. A fundamental understanding of material-molten salt interfacial evolution is needed. This work studies the transformation of metallic Cr in molten 50/50 mol% KCl-MgCl2 via multi-modal in situ synchrotron X-ray nano-tomography, diffraction and spectroscopy combined with density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. Notably, in addition to the dissolution of Cr in the molten salt to form porous structures, a d-A15 Cr phase was found to gradually form as a result of the metal-salt interaction. This phase change of Cr is associated with a change in the coordination environment of Cr at the interface. DFT and AIMD simulations provide a basis for understanding the enhanced stability of d-A15 Cr vs. bcc Cr, by revealing their competitive phase thermodynamics at elevated temperatures and probing the interfacial behavior of the molten salt at relevant facets. This study provides critical insights into the morphological and chemical evolution of metal-molten salt interfaces. The combination of multimodal synchrotron analysis and atomic simulation also offers an opportunity to explore a broader range of systems critical to energy applications. [ABSTRACT FROM AUTHOR]