Wetland mapping in the Balqash Lake Basin Using Multi-source Remote Sensing Data and Topographic features Synergic Retrieval

AbstractWetland plays a major role in the hydrological cycle, the carbon sink (carbon sequestration), nitrogen absorption, geochemical cycle, water conservation, biological diversity. Traditional field surveys for mapping wetlands distribution in large areas are very difficult to undertake. Remote sensing techniques offer promising solutions to this problem. But spectral confusion with other land cover classes and different types of wetlands, it is difficult to extract wetland information automatically. The overarching goal of this study was to develop a hybrid method for lake wetlands automated delineation by integrated using multi-source remote sensing data and DEM data. Firstly, it is to do radiance correction and convert image DN value to reflectance or radiance. Secondly, spectral index calculation and topographic indices derive, such as NDVI, NDWI, TVDI, slope and others topographic feature indices and etc. Thirdly, water bodies extraction through the NDWI iterative computation. Finally, it is to retrieve marsh land from image via comprehensive information of soil moisture character, topographic factors and spatial analysis. By the above steps, we got the ultimate wetlands distribution information. The methodology was evaluated by the balqash lake basin wetland extraction in Kazakhstan. Experiments result shows that the hybrid method performs well in lake wetlands delineation. The overall accuracies of wetland classes exceed 85%, which can meet the application requirements

Streptomyces tamarix sp. nov.: antagonism against Alternaria gaisen producing streptochlorin, isolated from Tamarix root soil

By the end of 2021, the pear yield in Xinjiang reached 1,795,900 tons, accounting for 1/9 of the country. Pear black spot, caused by Alternaria gaisen disease, has had a significant impact on the pear industry. A. gaisen can infect nearly all pear plants, resulting in black spots on the fruit that negatively affect both yield and quality. This study focused on the TRM76323 strain of Streptomyces, which was isolated from the soil of Tamarix chinensis in Xinjiang Province. Through a multiphase classification and identification method, the genetic classification status of the antagonistic strains was determined. The study also identified the antibacterial active components of streptochlorin using modern isolation and purification techniques. The antagonistic activity of Streptomyces against Alternaria was analyzed through in vitro and in vivo experiments. This research not only expanded the resource bank of antagonistic microorganisms in extreme environments in Xinjiang, but also identified active components that could contribute to the development of new drug lead compounds. Additionally, this study presents a novel approach for the prevention and control of pear black spot disease

Uncovering the Electron‐Phonon Interplay and Dynamical Energy‐Dissipation Mechanisms of Hot Carriers in Hybrid Lead Halide Perovskites

The discovery of slow hot carrier cooling in hybrid organic–inorganic lead halide perovskites (HOIPs) has provided exciting prospects for efficient solar cells that can overcome the Shockley–Queisser limit. Questions still loom over how electron‐phonon interactions differ from traditional polar semiconductors. Herein, the electron‐phonon coupling (EPC) strength of common perovskite films (MAPbBr3, MAPbI3, CsPbI3, and FAPbBr3) is obtained using transient absorption spectroscopy by analyzing the hot carrier cooling thermodynamics via a simplified two‐temperature model. Density function theory calculations are numerically performed at relevant electron‐temperatures to confirm experiments. Further, the variation of carrier‐temperature over a large range of carrier‐densities in HOIPs is analyzed, and an “S‐shaped” dependence of the initial carrier‐temperature to carrier‐density is reported. The phenomenon is attributed to the dominance of the large polaron screening and the destabilization effect which causes an increasing‐decreasing fluctuation in temperature at low excitation powers ; and a hot‐phonon bottleneck which effectively increases the carrier temperature at higher carrier‐densities. The turning point in the relationship is indicative of the critical Mott density related to the nonmetal‐metal transition. The EPC analysis provides a novel perspective to quantify the energy transfer in HOIPs, electron‐lattice subsystem, and the complicated screening‐bottleneck interplay is comprehensively described, resolving the existing experimental contradictions

Generative deep learning for lower limb motion prediction with a small training EMG data set

Human-Robot interaction rehabilitation systems have attracted widespread atten- tion among researchers and medical practitioners because they can help disabled people regain their mobility. Surface electromyography (sEMG) signals are ag- gregated muscle action potentials measured on the surface of the skin. Due to diversified EMG activation patterns within and across individual subjects, as well as a large number of different actions, it is often prohibitively expensive to collect massive data sets from each individual that cover each possible ac- tion class in real-world use scenarios. This dissertation uses deep convolutional generative adversarial networks (DCGANs), to design a method to learn and generate similar sEMG signals to a particular class, while the model can learn the concept of this class with others. We also analyzed the performance of this method in motion prediction and found that the discriminator in the model can identify EMG signals kicking in different directions with an accuracy of 89.31% ± 6.52. Finally, we propose to use dynamic time warping (DTW) and fast Fourier transform mean square error (FFT MSE) to evaluate the signal quality from the time domain and frequency domain respectively, and find that the signals generated by the model have a similarity of more than 95% in these two indicators. The research in this paper explores a data augmentation method applied to a small EMG dataset, which provides a broad direction for the sub- sequent research of motion-assisted robotic systems.Master of Science (Signal Processing

The Limit Theorems for Function of Markov Chains in the Environment of Single Infinite Markovian Systems

This paper is intended to study the limit theorem of Markov chain function in the environment of single infinite Markovian systems. Moreover, the problem of the strong law of large numbers in the infinite environment is presented by means of constructing martingale differential sequence for the measurement under some different sufficient conditions. If the sequence of even functions gnx,n≥0 satisfies different conditions when the value ranges of x are different, we have obtained SLLN for function of Markov chain in the environment of single infinite Markovian systems. In addition, the paper studies the strong convergence of the weighted sums of function for finite state Markov Chains in single infinitely Markovian environments. Although the similar conclusions have been carried out, the difference results performed by previous scholars are that we give weaker different sufficient conditions of the strong convergence of weighted sums compared with the previous conclusions

Study of Flexural Response in Strain Hardening Cementitious Composites Based on Proposed Parametric Model

Strain hardening cementitious composites (SHCCs) are widely used in projects due to their excellent deformation resistance and large energy absorption capacity. However, determining tensile strain capacity is still a challenge for engineers. The current popular approach is to use inverse methods to predict the tensile behavior of SHCCs, such as the UM method (Qian and Li) and the JCI (Japan Concrete Institute) method. The key to these two approaches is to acquire the exact relationship between the bending and the uniaxial response. In this paper, a reasonable linear constitutive model of the SHCCs is modified. Initially, the moment-curvature diagrams are discussed by material parameters. The results reveal that the moment-curvature response is quite sensitive to the variations in the parameter of transition strain α, post-cracking tensile stiffness η, and strain softening stiffness μ, however, for the compressive parameters, the moment-curvature responses influence on flexural behavior is insignificant. Moreover, the load-deflection curve in the mid-span of SHCC, based on the consideration of shear effect, is simulated under a four-point bending test (FPBT). The results show a remarkable consistency with the experimental data when compared to the previous simulations. It is expected that this modified method can enhance an accurate program in order to obtain the tensile capacity

Surface-mediated selective photocatalytic aerobic oxidation reactions on TiO2 nanofibres

N-doped TiO2 nanofibres were observed to possess lower aerobic oxidation activity than undoped TiO2 nanofibres in the selective photocatalytic aerobic oxidation of enzylamine and 4-methoxybenzyl alcohol. This was attributed to the reduction free energy of O2 adsorption in the vicinity of nitrogen dopant sites, as indicated by density functional theory (DFT) calculations when three-coordinated oxygen atoms are substituted by nitrogen atoms. It was found that the activity recovered following a controlled calcination of the N-doped NFs in air. The dependence of the conversion of benzylamine and 4-methoxybenzyl alcohol on the intensity of light irradiation confirmed that these reactions were driven by light. Action spectra showed that the two oxidation reactions are responsive to light from the UV region through to the visible light irradiation range. The extended light absorption wavelength range in these systems compared to pure TiO2 materials was found to result from the formation of surface complex species following adsorption of reactants onto the catalysts' surface, evidenced by the in situ IR experiment. Both catalytic and in situ IR results reveal that benzaldehyde is the intermediate in the aerobic oxidation of benzylamine to N-benzylidenebenzylamine process

IMCSA: Providing Better Sequence Alignment Space for Industrial Control Protocol Reverse Engineering

Nowadays, with the wide application of industrial control facilities, industrial control protocol reverse engineering has significant security implications. The reverse method of industrial protocol based on sequence alignment is the current mainstream method because of its high accuracy. However, this method will incur a huge time overhead due to unnecessary alignments during the sequence alignment process. In this paper, we optimize the traditional sequence alignment method by combining the characteristics of industrial control protocols. We improve the frequent sequence mining algorithm, Apriori, to propose a more efficient Bag-of-Words generation algorithm for finding keywords. Then, we precluster the messages based on the generated Bag-of-Words to improve the similarity of the message within a cluster. Finally, we propose an industrial control protocol message preclustering model for sequence alignment, namely, IMCSA. We evaluate it over five industrial control protocols, and the results show that IMCSA can generate clusters with higher message similarity, which will greatly reduce the invalid alignments existing in the sequence alignment stage and ultimately improve the overall efficiency

Modified alumina nanofiber membranes for protein separation

Large-scale purification/separation of bio-substances is a key technology required for rapid production of biological substances in bioengineering. Membrane filtration is a new separation process and has potential to be used for concentration (removal of solvent), desalting (removal of low molecular weight compounds), clarification (removal of particles), and fractionation (protein-protein separation). In this study, we developed an efficient membrane for protein separation based on ceramic nanofibers. Alumina nanofibers were prepared on a porous support and formed large flow passages. The radical changes in membrane structure provided new ceramic membranes with a large porosity (more than 70%) due to the replacement of bulk particles with fine fibers as building components. The pore size had an average of 11 nm and pure water flux was approximately 360 L•h-1•m-2•bar-1. Further surface modification with a self-assembled monolayer of (3-aminopropyl) triethoxysilane enhanced the membrane filtration properties. Characterization with SEM, FTIR, contact angle, and proteins separation tests indicated that the fibril layers uniformly spread on the surface of the porous support. Moreover, the membrane surface was changed from hydrophilic to hydrophobic after silane groups were grafted. It demonstrated that the silane-grafted alumina fiber membrane can reject 100% BSA protein and 92% cellulase protein. It was also able to retain 75% trypsin protein while maintaining a permeation flux of 48 L•h-1•m-2•bar-1