Artificial pure stands of masson pine (Pinus massoniana Lamb.) has a simple structure and extremely delicate ecosystem, and is easy to induce a series of ecological problems. With the changeover in function orientation, the structure of the masson pine plantation needs to be altered correspondingly, and the destructive damage to pine forests by pine wilt unquestionably intensifies the urgency of the alteration of the masson pine plantation. In this paper, the advances in pine wilt and its control, mixed stands of masson pine, volatiles of masson pine and eucalypts are reviewed, and the feasibility establishing mixed stand of masson pine and eucalypts to control pine wilt are discussed. It's deemed that it has a very strong feasibility and broad prospect of application to establish mixed stand of masson pine and eucalypts for the control of pine wilt.

In view of the defects of inefficient classification and low recall rate caused by the class imbalance of fault data of electric vehicle, an improved Bagging ensemble fault diagnosis method based on LightGBM in the fault classification process for electric vehicle is proposed in this paper. In the Bagging integrated learning model, the training set sampled by Borderline_SMOTE to improve the data imbalance in the training subset and avoid missing information on the faulty class. By embedding weight coefficients and regularization terms into the loss function of LightGBM, the misclassification cost of faulty classes in model training is increased. Through comparative experiments, the results show that the proposed model can effectively improve the recall rate, macro average F₁ measure and AUC on fault diagnosis, in which AUC reaches 0.898 4, and the recall rate is 0.808 3. The fault classification performance of this model on the unbalanced data set of electric vehicles is significantly more effective in classifying faults of electric vehicle than the single model and other comparative algorithms.

Aiming at the problems of single index and one-sided index weight in the existing distribution network node vulnerability assessment methods combining the complex network theory and starting from the distribution network structure and the system operation state, the vulnerability assessment system of distribution network nodes is constructed. The subjective weight of the index is obtained by using the analytic hierarchy process (AHP), and the objective weight is obtained by using the entropy method. Then the idea of multi-attribute decision-making is proposed, and the grey relational degree is used to improve the closeness defect of TOPSIS algorithm, and a new form of closeness is used to express the vulnerability of nodes. The calculation results based on the IEEE-33 node system show that the top 10 nodes in the system are 2, 1, 3, 6, 4, 5, 26, 7, 14, and 16 in terms of vulnerability, and their vulnerability values are 0.817 and 0.677, 0.621, 0.563, 0.515, 0.492, 0.433, 0.411, 0.408, 0.393, respectively. The results of the example are consistent with the actual situation, which verifies the rationality and feasibility of the proposed method.

Although some conventional high-sensitivity optical microfiber couplers with ultra-thin diameter have been reported, the reported optical microfiber couplers with very fragile properties can pose great challenges in real sensing applications. In order to address the above problem, an optical microfiber coupler with a broad diameter of 6.25 μm was studied experimentally and theoretically. This optical microfiber coupler can achieve a high refractive-index sensitivity of-1 753 nm/RIU at the external refractive index (RI) of 1.339 8 with good stability. Based on finite element method (FEM), and effective indices of even/odd modes and refractive index sensitivity can be calculated. By this way, the proposed optical microfiber coupler can find proper applications in fiber-optic micro-fluidic biosensor and lab-on-a-chip multifunctional sensing.

Aiming at the problems of slow processing speed and poor matching accuracy of the existing image stitching methods for UAV aerial images with high resolution and complex image information, a fast stitching algorithm for UAV aerial images is proposed. Firstly, the FM (Fourier-Mellin) algorithm is used to find out the overlapping region of the image, and obtain ROI from the overlap area of image, combining the area blocking and the image entropy to get the region of final feature extraction as the image mask. Then, the feature points are extracted in the area of feature extraction and two rounds of filtering are performed on the feature matching pairs to reduce the number of RANSAC algorithm iterations. Finally, an improved RANSAC algorithm is used to reject the wrong pairs in order to filter out the pairs with higher accuracy. Compared the operation efficiency of this algorithm with other algorithms, as well as the image stitching quality and other related indexes, the experimental results show that this algorithm reduces the stitching time by 35%, 56% and 57% compared with SURF, SIFT and area blocking algorithms respectively, meeting the requirements of today's aerial images for stitching accuracy and real-time performance.

In order to improve the limitations of the current transient electromagnetic detection system and improve the efficiency and accuracy of ground grid defect identification, a MWOA-Elman neural network is proposed to complete the transformation process from sampling to imaging, quickly realize the apparent resistivity imaging, and accurately identify different defects of the ground grid. Firstly, the transient field parameter sample set of grounding grid is completed through theoretical calculation, and the single mapping relation of Elman neural network is constructed. Secondly, the modified Whale algorithm is improved around convergence factor, adaptive weight and threshold, and the weight and threshold of Elman neural network are optimized by modified whale optimization algorithm (MWOA). The test results show that the MWOA-Elman neural network converges at the 854 step, and the four error indexes MAE, MSE, RMSE, MAPE are 0.103 51, 0.040 09, 0.126 64 and 0.333 52%, respectively. The identification accuracy of grounding grid defects is 99.678%, and the identification efficiency and accuracy are better than that of other models. Finally, the imaging results of three typical defect locations of 3×3 grounding grid can verify the effectiveness of MWOA-Elman neural network applied to defect identification of grounding grid, and provide reference for intelligent algorithm embedded in transient electromagnetic detection system.

Target segmentation is one of the essential steps in neuronal image analysis. The accuracy of segmentation directly affects the quality of neuronal image analysis and reconstruction. In the face of fuzzy structure boundary, weak noise or weak fiber signal, the existing segmentation methods still have some problems, such as large error and inaccurate recognition signal. To solve these problems, based on the characteristics of neurons, a deep learning network based on dual coding path fusion and bidirectional ConvLSTM is proposed for neuronal image segmentation. Firstly, the network adopts dual coding paths to extract features in the encoder stage, in which the first encoding path adopts dense connection network based on dilated convolution as the fixed feature extractor, and the second encoder adopts deep residual network as the feature extraction network. Then, the densely connected ASPP network is used as a bridge to connect the encoder and decoder. Finally, bidirectional ConvLSTM is used to combine encoder and decoder in skip connection, and a fusion network is introduced in the decoder stage to fuse the features extracted by the two encoders, so as to enhance the spatial information propagation. The results of multiple comparative experiments show that the proposed network can effectively improve the segmentation accuracy of electron microscope neuron images. Sen and Dice on ISBI-2012 and SNEMI3D datasets reaches 0.952 7, 0.958 9 and 0.941 6, 0.912 7, respectively, and the average accuracy is higher than that of U-net and other models.

Household garbage classification is an effective measure to protect the ecological environment and promote green and harmonious development. Aiming at the problems such as limited computing resources and memory, and difficulty in embedding heavyweight models into mobile devices, a lightweight garbage classification detection method based on improved YOLOX-tiny is proposed in this paper. Firstly, the original IoU loss function is replaced by EIoU, which can accelerate the convergence and improve the detection accuracy. Secondly, the attention mechanism CBAM is introduced into the neck network to redistribute the weight of different channels to obtain more shallow fine-grained features and deep semantic information. Finally, the GhostBottleneck module is used to replace the CSP module in the feature picking network, which tends to retain more edge information, reduce the number of parameters, and lighten the model. Experimental results on Huawei cloud garbage dataset show that compared with YOLOX-tiny, the number of parameters of the improved algorithm is reduced to 87.97% of the original, the accuracy is increased by 0.3%, and the experimental effect on TrashNet dataset is increased by 0.36%, which proves the effectiveness of the proposed algorithm. The algorithm is conducive to the use of embedded mobile devices and has certain practical value.

With the rapid development of artificial intelligence, 5G, Internet of Things and other technologies, China has become increasingly vulnerable to attacks from outside the country in the field of cyber security. The number of ransomware attacks has increased significantly, causing huge data losses and economic losses to individuals, enterprises and countries. To effectively classify ransomware families, a ransomware classification method based on entropy image static analysis technology is proposed in this paper, which directly utilizes the entropy features extracted from ransomware binary files for classification. In addtion, a data augmentation method named Ran-GAN is proposed to solve the data imbalance problem among ransomware families. The method proposed in this paper introduces the attention mechanism into the VGG16 neural network architecture to improve the feature extraction ability of the network. Experimental results show that the proposed method achieves 97.16% accuracy and 97.12% weighted average F1-score on 14 ransomware families. Compared with the traditional visualization methods, the proposed method is obviously better than the traditional visualization methods under the four evaluation indicators. At the same time, the ransomware detection performance is significantly improved compared with other neural network methods.

In order to solve more complex optimization problem, the original algorithm of coyote is analyzed, and a kind of multi-stage coyotes optimization algorithm (MCOA)is proposed in this paper. The algorithm can be divided into before, while and post stages. Firstly, the coyote is allowed to grow normally in the early stage, and then the rotation strategy is introduced to enhance the search range of its exploration process to avoid falling into the local optimal solution prematurely. Secondly, in the middle stage, the strategy of “taking advantage of the advantages and taking advantage of the disadvantages” is proposed, and then the new regeneration method of the head wolf is adopted to update the head wolf, so as to better balance the exploration and exploitation process. Finally, in the late stage, a new type of late growth mode is proposed for coyote growth, and the convergence factor is avoided to solve the problem of late convergence. CEC2014 test function set is used for simulation. Compared with other advanced algorithms with the newly proposed Coyote algorithm, the results show that MCOA has better search efficiency, convergence accuracy, optimization performance and stability.

The Cauchy problem is extremely ill-posed and requires effective numerical algorithms to solve it. A B-spline method is proposed to solve the problem in this paper. Firstly, the approximate solution of the problem is given in the shift invariant space generated by cubic B-spline functions. Then, based on the properties of equation and B-spline basis function whose derivative can be expressed by low-order spline basis function, the variational form of the problem is obtained. Furthermore, Tikhonov regularization method is proposed to obtain stable numerical solutions in order to reduce the influence of noise. Finally, numerical experiments on rectangular region and the region with non-smooth boundary show that the proposed method is effective.

Based on the sixth kind of Chebyshev wavelet collocation method, a numerical method for solving fractional differential equations is proposed. By using the shifted sixth kind of Chebyshev polynomials and under the definition of Riemann-Liouville fractional order, the exact expressions of the fractional order integral formulas of the sixth kind of Chebyshev wavelet functions are obtained. Using the integral formulas together with the effective collocation method, the problem of solving fractional differential equations is transformed into algebraic equations. The uniform convergence analysis of the expansion of the sixth kind of Chebyshev wavelet functions and error estimation in the sense of L² norm are given. Numerical examples show the applicability and effectiveness of the algorithm.

In this paper, the quasi maximum likelihood estimation method is used to obtain the estimation equation of the linear model with MA errors. In order to use the empirical likelihood method, the quadratic form in the estimation equation is transformed into a linear form of a martingale difference array, and then the empirical likelihood ratio statistics of the model parameters are constructed. Under certain assumptions, it is proved that the statistics asymptotically obey the chi-square distribution. Finally, the simulation resucts show that the empirical likehood method is closer to the confidence level than the parametric likehood method in terms of the coverage of confidence region.

To investigate the expression and source of interleukin-10 (IL-10) in lung cancer tumor tissues, and to investigate the effect of IL-10 on tumors. The levels of IL-10 in the serum of Lewis lung carcinoma mouse model were detected by MSD hypersensitive electrochemiluminescence assay, the main source of IL-10 in tumor tissues was analyzed using flow cytometry, and the effects of IL-10 recombinant protein on proliferation, apoptosis and migration of lung cancer cells were examined by CCK-8 assay, annexin V-propidium iodide (annexin V-PI) apoptosis assay and scratch wound assay. The results showed that the serum levels of IL-10 were significantly increased in Lewis lung carcinoma mice compared with normal mice, and IL-10 in tumor tissues was mainly produced by immune cells, including B cells and myeloid-derived suppressor cells (MDSCs), which were the main source of IL-10, and the recombinant protein of IL-10 could significantly inhibit the proliferation and migration and promote the apoptosis of LLC (mouse Lewis lung carcinoma line). IL-10 level was closely related to lung cancer, B cells and MDSCs were the main source of IL-10 production, and IL-10 may play an antitumor role by activating CD8 T cells or directly acting on tumor cells.

Mutations in LMNA (LaminA/C) gene trigger abnormal expression of nucleofibrillar proteins and their linked or interacting proteins, causing a series of physiological responses in the body and leading to the development of nucleofibrillar protein disease, however, the specific pathogenic mechanism of the disease is still unclear. In this paper, a cell line carrying LMNA mutation Q517X is successfully constructed by using CRISPR/Cas9 technology to select pathogenic mutation loci in accordance with the sgRNA screening principle, and examined the RNA expression levels and protein expression levels of LaminB1, PCNA, P53, PKCα and other genes linked to or interacting with LaminA/C in the mutant cell line and the nuclear membrane of the mutant cells. It was found that Q517X mutant cells were not mutated, and the mRNA expression of LaminB1, P53 and PCNA genes were significantly reduced by about 70% in Q517X mutant cells, and the expression of LaminA/C, PKCα, LaminB1 and P53 proteins were significantly reduced by 75%, 60% and 80% respectively in Q517X mutant cells. The results showed that the Q517X mutation altered the expression of LMNA-related genes in human cardiomyocytes and thus affected the normal cytoskeletal structure and function of the nucleus.

Community assembly is a central issue in ecology research. The research methods of functional traits and phylogeny can improve the role of niche theory in community assembly. In this study, the plant communities on the sunny slope and shady slope of Guilin Karst Hills were taken as the research subjects, based on 82 species appearing in the habitat, the phylogenetic trees were constructed, soil water content, total nitrogen, organic matter and the plant functional traits of leaf dry matter, chlorophyll, leaf thickness and wood density on the sunny slope and shady slope were measured, and the phylogenetic signals of the functional traits were examined. The results showed that: 1) Soil water content, total nitrogen and organic matter content on sunny slope were significantly higher than those on shady slope (P<0.05). 2) The chlorophyll, leaf thickness and wood density on the shady slope were significantly higher than those on the sunny slope (P<0.05). 3) The four functional traits showed weak phylogenetic signals. 4) The phylogenetic structures of communities on both shady slope and sunny slope showed divergent distributions, suggesting that habitat filtration was the main driving force for the community assembly on both shady slope and sunny slope.

With the large-scale reduction of the natural forest of tungmian provenance of Pinus massoniana, as well as the increase in the degree of selection and the reduction in genetic diversity. It is necessary to carry out research on the genetic variation of the growth traits of the progeny of P. massoniana from tungmian provenance, and to provide reference for the protection and genetic improvement of the natural population of P. massoniana from Tungmian provenance. Using 74 half-sib families of tungmian provenance of P. massoniana as materials, the growth traits such as diameter at breast height and tree height were determined and genetic variation analysis was carried out, and the principal coordinate dimensionality reduction analysis method was combined with genetic gain analysis to screen excellent families and individual plants. The results showed that the growth traits (diameter at breast height, tree height and volume per plant) were significantly differentiated and significantly different among families of tungmian provenance of P. massoniana (P<0.05). The order of family heritability was volume per plant (0.224 5) > diameter at breast height (0.107 5) > Tree height (0.091 0). Principal coordinate analysis showed that 8 excellent families were screened out and the genetic gains of each trait were 1.42%, 0.71%, and 6.68%. Twenty-three excellent individual plants were screened from 8 excellent families and their genetic gains of each trait were 5.50%, 1.88% and 29.15%, and the selection effect was significant. In conclusion, there was great breeding potential among families of superior growth traits of the progeny tungmian Provenance of P. massoniana. The selected excellent families and individual plants could provide excellent germplasm data for the protection and genetic improvement of the natural population of tungmian provenance of P. massoniana.

The visual software of CiteSpace 5.8 R3 was used to analyze 5 281 literatures on the field of heavy metal hyperaccumulators (HHMs) extracted from the database of Web of Science Core collection. The results showed as follows: 1)According to the number of publications, the whole study period of HHMs could be divided into three periods, i.e., germination period, accumulation period, and prosperity period. 2)The top contributing countries/regions, institutions, and authors were China, USA, and France; the Chinese Academy of Sciences, Zhejiang University, and the University of Florida; Guillaume Echevarria, Lena Q Ma, and Alan J M Baker, respectively. 3)The hot topics in these three periods were varied from the topics of hyperaccumlators, especially the Ni-hyperaccumlators, distribution and screening, HMs tolerance, and toxic stress of hyperaccumulators in germination period; to the discovery of new hyperaccumulators and the mechanisms of HHMs, the HMs accumulation compares of hyperaccumlators and crops; and then to the new hyperaccumulators report, including the multi-HMs hyperaccumulators, the studies of As-hyperaccumulators and Cd-hyperaccumulators, the combine remediation of hyperaccumulators and microbe, the remediation mechanisms, the strengthen remediation as well as the hyperaccumulators application. 4)Nine, eight, and seven representative knowledge domains were obtained in the three periods based on knowledge domains analysis. 5)Keywords burst detection analysis showed that the last five years of research topics mainly related to the quantitative variation in HHMs and its genetic mechanism, Cd-hyperaccumulators, chemical forms and distributions of HMs, antioxidant mechanism and remediation enhancement techniques of hyperaccumulators, and tailings remediation, etc., which is expected to be focused on in the future continously.

CuO/MIL(Cr, Cu) heterogeneous Fenton-like catalysts were synthesized by hydrothermal calcination. The CuO/MIL(Cr, Cu) composite was characterized by XRD, FT-IR, SEM, XPS, BET, etc. The results showed that CuO was uniformly dispersed on the surface of the metal-organic framework MIL(Cr, Cu). In addition, the performance of different catalysts for the catalytic degradation of phenol were compared. The results of degradation experiments showed that when the catalyst dosage was 0.3 g/L, the H₂O₂ concentration was 50 mmol/L, pH=6, and the initial substrate concentration was 30 mg/L, the CuO/MIL(Cr, Cu) composite had the best degradation efficiency as high as almost 100%, better than that of CuO and MIL-101(Cr). Finally, the radical quenching experimentand EPR test proved that ·OH was the main active radical in the catalytic system, and the possible catalytic mechanism was proposed. CuO/MIL(Cr, Cu) composite can degrade phenolic pollutants in the near-neutral range and have good application prospects.

The crystalline violet was isolated from wastewater by using Bacillus sp. LM-24, a strain selected from deep-sea microorganisms with a dominant adsorption capacity for crystalline violet. The reaction conditions were optimized by single-factor experiments, and the inorganic carrier silica gel and organic carrier resin were selected for its immobilization. The effects of inorganic salts on the adsorption and its adsorption kinetics and adsorption isotherms were analyzed. The results showed that the adsorption of 30 mg/L crystalline violet was 86% removed by the free strain at the optimum adsorbent concentration of 1.216 g/L, optimum pH 9.0 and 30 ℃ for 10 min, the adsorption of immobilized microorganisms was more stable than that of free microorganisms at different pH and concentration. Kinetic measurements showed that all three adsorbents were physically and chemically adsorbed, and isotherm measurements showed that the free and silica immobilized strains fit the Langmuir model better, while the macroporous resin immobilized strains fit the Freundlich model better. The immobilization technique could improve its stability to the external environment, and it could be a potential strain for the decolorization of crystalline violet dye.

In this study, a new hot oxygen stream modification and regeneration method was used to modify sepiolite (H/sepiolite) and remove the organic dye auramine O. It was found that the unit adsorption capacity of modified sepiolite exceeded 200 mg/g at the modification temperature and time of 550 ℃ and 1 h and an oxygen flow of 6 L/min. The characteristics of static adsorption of auramine O over H/sepiolite were studied and fitted by relative models, and the results showed that the Langmuir isotherm and pseudo-first-order kinetic could describe the adsorption behavior and kinetics of H/sepiolite. The effects height-diameter ratio (the ratio of packing height to inner diameter of adsorption column), flow rate and concentration of dye solution on dynamic adsorption were studied, the results displayed that about 55 BV(bed volume), 42 BV and 45 BV of wastewater ware treated on the condition of height-diameter ratio = 2.5, the dye flow rate = 6 BV/h and the dye initial concentration = 200 mg/L, respectively. The hot oxygen stream was used to regenerate the saturated H/sepiolite, the regeneration rate could reach nearly 100% at the gas flow rate of 6 L/min, regeneration temperature of 550℃, and regeneration time of 20 min.

Heavy metal pollution in water environment cannot be ignored, especially the pollution phenomenon of hexavalent chromium (Cr(Ⅵ)). In order to explore a new idea for efficient treatment of Cr(Ⅵ)-containing wastewater, a microbial fuel cell (MFC)-granular sludge coupling system was established. The effects of the Cr(Ⅵ) concentration on the performance and microecology of MFC-granular sludge coupling system were investigated. When the Cr(Ⅵ) content reached 80 mg/L in the influent, the chemical oxygen demand (COD) degradation and biological reduction efficiency of Cr(Ⅵ) by the system were 92.71% and 99.70%, respectively. In the electrochemical performance analysis, cyclic voltammetry (CV) showed an obvious reduction peak. Meanwhile, the electrochemical impedance spectroscopy (EIS) illustrated that with increasing Cr(Ⅵ) concentrations, the charge transfer resistance gradually decreased. When the influent Cr(Ⅵ) concentration increased to 100 mg/L, the lowest activity of coenzyme F₄₂₀ was 0.026 2±0.000 2 mmol/g; in addition, the activity of INT-ETS sharply decreased to 5.20±0.23 μg/(mg·h). The morphology of Cr(Ⅵ) after biological reduction of chromate was determined by X-ray photoelectron spectroscopy (XPS), the binding energy of the characteristic peaks demonstrated that was mainly represented by Cr₂O₃ and Cr(OH)₃. High-throughput sequencing analysis illustrated that the dominant microbial flora were Chloroflexi and Proteobacteria. When the concentration of Cr(Ⅵ) increased to 100 mg/L, the dominant microbial community structure were transformed from Methanothrix and Methanobacterium to Geobacter and Syntrophobacter at genus level, the maximum relative abundance of each bacteria were 21.24%, 4.28%, 5.60% and 6.22%, respectively. It was indicated that increasing the concentration of Cr(Ⅵ) would change the microbial community structure of the MFC-granular sludge coupling system, and Cr(Ⅵ) was reduced through the association mechanism between microbial communities.