Risk analysis of Portfolio is studied ,by comparing Copula functions and the traditional VaR methods,mixing copula is made. By backtesting ,the empirical research shows that mixing Copula method makes better VaR model .

The redox property of palladium nanoparticles (NPs) is pivotal to CeO₂ supported Pd catalysts in oxidation reactions and is closely related to the structure of Pd-CeO₂ interface. Herein, we report that low-temperature CO oxidation activity of Pd/CeO₂ highly depends on the shape and crystal plane of CeO₂ supports. Pd/CeO₂ catalysts with CeO₂ nanoocthedrons (NOCs) and nanocubes (NCs) as supports were prepared by colloidal-deposition method. Results show that Pd/CeO₂ NOCs with ceria {111} facets enclosed exhibited much higher catalytic activity than Pd/CeO₂NCs with ceria {100} facets exposed. DFT calculations revealed that the redox property of surface Pd species may play important roles in determining the reducibility and activity of catalysts. The PdO_x to Pd cycle is more facile on Pd₄@CeO₂(111) than on Pd₄@CeO₂(100), which is dictated by the Pd-ceria interaction in the end. Our results show that the redox property of surface Pd is pivotal to the reducibility and activity of Pd/ceria catalysts, which could be tuned by manipulation of the Pd-CeO₂ interaction via tuning the exposed facets of ceria support.

Light field is a representation of full four-dimensional radiance of all rays with spatial and angular information in free space, and capture of light field data enables many new development potentials for computational imaging. The historical development of light field photography is summarized, and typical light field photography devices are categorized in view of capture methods for 4D light field. Based on the principles of light field camera, computational imaging theorem, refocusing theory, synthetic aperture refocusing algorithm, and light field microscopic technology are emphatically described. Finally, the promising application perspectives and existing critical issues of light field imaging are discussed.

GH3 genes belong to a primary auxin-response gene family. The 10 promoter sequences of Arabidopsis GH3 genes were analyzed using bioinformatics method. The results show that the transcription start site of these genes is generally 65~145bp away from the start codon, and the TATA boxes are located in the (-24)-(-40)bp. MDB and MatInspector analyses show that most upstream regions of these GH3 genes contain the cis-elements required for tissue and organ-specific expression responding to phytohormones and external environment, indicating that the expressions of GH3 genes are strictly controlled by multi-factors. Gene chip data show that AuxREs is very important for GH3 genes in response to IAA treatment,but it is not the unique cis-element for auxin response.

Textual document clustering is one of the effective approaches to establish a classification instance of huge textual document set. Clustering Validation or Quality Evaluation techniques can be used to assess the efficiency and effective of a clustering algorithm. This paper presents the quality evaluation criterions from outer and inner. Based on these criterions we take three typical textual document clustering algorithms for assessment with experiments. The comparison results show that STC(Suffix Tree Clustering) algorithm is better than k-Means and Ant-Based clustering algorithms. The better performance of STC algorithm comes from that it takes accounts the linguistic property when processing the documents. Ant-Based clustering algorithm’s performance variation is affected by the input variables. It is necessary to adopt linguistic properties to improve the Ant-Based text clustering’s performance.

The micron-grade PMMA microspheres with narrow size distribution were prepared by dispersion polymerization. The mechanism of the dispersion polymerization was discussed. The factors influencing both the size and size distribution of the microspheres including initial concentrations of the initiator, monomer, stabilizer, the polarity of the medium and the reaction conditions were studied. The results indicated that the size and size distribution of microspheres both increased with initial concentrations of initiator and monomer. Increasing the amount of the stabilizer resulted in decreasing size and narrowing size distribution of microspheres. Other factors such as the polarity of medium and the reaction temperature had great influences too. By controlling these factors, the desired-size monodisperse microspheres could be obtained.

DBSCAN is a classic density-based clustering algorithm. It can automatically determine the number of clusters and treat clusters of arbitrary shapes. In the clustering process of DBSCAN, two parameters, Eps and minPts,have to be specified by uses. In this paper an adaptive algorithm named SA-DBSCAN was introduced to determine the two parameters automatically via analysis of the statistical characteristics of the dataset, which enabled clustering process of DBSCAN fully automated. Experimental results indicate that SA-DBSCAN can select appropriate parameters and gain a rather high validity of clustering.

Gram-Schmidt algorithm is one of the fundamental methods in linear algebra, which is mainly used to compute QR decomposition. The classical and modified Gram-Schmidt are both based on level 1 or level 2 BLAS operations which have low cache reuse. In this paper, a new block Gram-Schmidt algorithm is proposed. The new algorithm ensures the orthogonality of resulting matrix Q is close to machine precision and improves performance because of using level 3 BLAS. Numerical experiments confirm the favorable numerical stability of the new algorithm and its effectiveness on modern computers.

In this paper, we present a new method based on self-modification mechanism to protect softwares against illegal acts of hacking. The key idea is to converse key codes into data in the original program so as to make programs harder to analyze correctly. Then, we translate data to executable codes by enabling the virtual memory page which stores the hidden code to be executable at run-time. Our experiments demonstrate that the method is practical and efficient.

A design of high resolution camera system based on DALSA’ s CCD evaluation kit EKxxxx was presented. It was composed of a pulse pattern generator (SAA8103), a vertical line driver (TDA9991), four analog-to-digital interfaces (TDA9965) and a system controller (P89LV51RD2). Camera link with medium configuration was adopted to transfer digital images. The software for controlling and debugging the camera was developed. To correct the non-uniformity of 4 outputs, a method based on two-point correction was described. The system can acquire ultra high resolution pictures at a high frame rate thus it is suitable for aero photography.

Poly (vinyl alcohol) (PVA) and its derivatives,the excellent water-soluble polymers,have attracted more and more attention,as they show the usability in many processes,as well as possess the promise of degradation in the presence of some specific microbials.In the present paper,the recent advances in the biodegradation of PVA and its derivatives,including the mechanism,influential factors,evaluation method and degradation environment etc,have been reviewed.Moreover,the latest development of PVA-based blendsPcomposites and their biodegradation is also introduced in the present article.

In recent years, the heavy metal pollution incidents in China were frequently reported. However, studies on pollution, sources, and control strategies of atmospheric heavy metals in China are rare. We summarize the research results reported in recent years. The features of pollution level, seasonal variation, regional differences, size distribution of the atmospheric heavy metal elements including Pb, V, As, Mn, Ni, Cr, and Cd in China are analyzed. The main sources, current control status, and control technologies of atmospheric heavy metals are discussed. Comprehensive suggestions for China's heavy metal pollution control are put forward based on the summarization of the progress and experience of the atmospheric heavy metal pollution control in other countries and regions.

Uranium dioxide is a kind of steady nuclear fuel that has the characteristic of high melting point and steady property. The thermal conductivity of uranium dioxide can directly influence the temperature distribution of nuclear fuel and the max temperature of the center of nuclear fuel. The experimental results and expression of thermal conductivity have been compared in the paper. The deviation between the experiment results has decreased. The non-equilibrium molecular dynamics simulation results are in good agreement with the experiment results in medium temperature region. In low temperature region, it is necessary to add the quantum correction to the kinetic energy computation of phonon. In high temperature region, it is needed to use the accurate potential model and build up the electron gas energy transport model and photon radiation energy transport to study the thermal conductivity well and truly for the nuclear reactor safety design and uranium dioxide engineering application.

A high performance CMOS sample/hold circuit is presented, which achieves the precision of 10-bit over Nyquist band in 50-MHz sampling frequency at 3.3-V supply. This circuit uses full differential circuits, bottom-plate sampling, bootstrap circuits and high performance gain-boost operational amplifier. Simulation in 0.35-μm CMOS process shows the circuit consumes 18-mW of power.

Malicious applications pose tremendous threats to Android platform. More than 90% of malicious codes are introduced in the form of Android apps. Hence, behavior monitoring scheme for Android applications are required in order to resolve the problem. However, most of the schemes are based on system customization and hard to deploy on devices for Android's fragmentation problem. In this paper, an easy-to-deploy Android application monitoring method on the basis of process hijacking is proposed after analysis of Android process model and code execution details. The method depends on Dalvik interpreter entry point and system call interception. The authors created a fully usable prototype of the system, and the evaluation results show that the system is easy to deploy, provides a whole-scale behavior of Android applications, and incurs little performance overhead.

study of human-earth system is the most important content in geography; regional sustainable development relies on the reasonable structure optimal model and effective regulation of regional human-earth system. County region is a complex and opening human-earth system, taking Huangling county as an example, the environment, resources exploitation, economic development and structure evolvement of industry were systematically analyzed. And the systematical dynamic model was established and multi-projects were simulated with the theory and method of system dynamic. Optimized regulation models of human-earth system evolvement were educed based on three projects: (1) Traditional evolvement method. The intensity of resource exploitation and environment pollution is the least, but the speed of its economy development is the lowest, which restrict social sustainable development and economic reproduction. (2) Economy development method. The intensity of resource exploitation and environment pollution is the worst, economic development mostly depends on higher investment and pollution, which is a traditional mode of unsustainable development. (3) Harmonious development mode. The mode considers not only economic sustainable development and natural recourses utilizing reasonably, but also gives more attention to environment protection and harmonious development of industry, agriculture, tourism and so on. It is the optimized mode of the human-earth system evolvement.

K-means is an important clustering algorithm. It is widely used in Internet information processing technologies. Because the procedure terminates at a local optimum, K-means is sensitive to initial starting condition. An improved algorithm is proposed, which searches for the relative density parts of the database and then generates initial points based on them. The method can achieve higher clustering accuracies by well excluding the effects of edge points and outliers, as well as adapt to databases which have very skewed density distributions.

The total polyphenols(TP)content of Quercus mongolicus Fisch leaves extracts was analyzed by Folin-ciocalteu colorimetry,with gallic acid as standard.The method was improved and verified in the aspects of stability, linearity,precision and accuracy.The results showed that the total polyphenols content of Quercus mongolicus Fisch leaves extracts could be well calculated according to their colorimetric absorption at 760nm by applying Folin-ciocalteu reagent (1mol/L) 0.15mL and 10% volume fraction of Na₂CO₃ 0.15mL at 25℃ for 80min. The (TP)content in Quercus mongolicus Fisch leaves determinated by the method was 6.39% and RSD was 1.90%.

Gallium nitride is a novel kind of semiconductor,whose direct band gap is 3139eV at the room temperature. It has been proved to be a promising material for electronic and photoelectric devices. A good many of its growth methods have been discovered, and some of them had been implemented in production practice with monitoring systems. Some comparisons were made between different methods. The structure-performance dependence of GaN itself, GaN-based family and multinitrides have been summarized. The main fields of GaN-based material were presented. GaN-based material is being considered to be the excellent candidate of electronic device potentially used in high temperature,high-power and worst environment surroundings.

The estimation of forest biomass at individual tree scale is the basis for the estimation of forest biomass at the regional scale. This paper aims at developing a reliable and effective allometric equation for Eucalyptus in Fujian in order to improve the estimation accuracy of Eucalyptus biomass in this area and to provide basic supporting data for the sustainable forestry development of Eucalyptus. This study takes Eucalyptus, a major fast-growing and productive tree species in Southern China, as the research object. Using 90 Eucalyptus woods harvested in the field, the partitioning of Eucalyptus biomass among organs are studied, the optimal allometric equations are constructed, and the Eucalyptus root/shoot ratios are calculated and applied to estimate Eucalyptus root biomass. Results show Eucalyptus has the following biomass allocation strategies: the biomass proportion of trunks increases with increasing stand age, while that of branches, leaves, and roots decreases. The most feasible and effective way to estimate Eucalyptus root biomass is to use root/shoot ratio data with stand age. In the construction of the allometric equation for Eucalyptus biomass, the multiplicative power equation is better than the linear equation, and the optimal independent variable varied by organ type. This paper provides data and theoretical support for the accurate estimation of Eucalyptus plantation biomass, and has implications for species growth patterns, survival strategies, and even forest ecological management.

The quadratic assignment problem (QAP) is one of the NP-hard combinatorial optimization problems and is known for its diverse applications in real life. The current relatively mature heuristic algorithms are usually problem-oriented to design customized algorithms and lack the ability to transfer and generalize. In order to provide a unified QAP solution strategy, this paper abstracts the flow matrix and distance matrix of QAP problem into two undirected complete graphs and constructs corresponding correlation graphs, thus transforming the assignment task of facilities and locations into node selection task on the association graph. Based on actor-critic framework, this paper proposes a new algorithm ACQAP(actor-critic for QAP). Firstly, the model uses a multi-headed attention mechanism to construct a policy network to process the node representation vectors from the graph convolutional neural network; Then, the actor-critic algorithm is used to predict the probability of each node being output as the optimal node. Finally, the model outputs an action decision sequence that satisfies the objective reward function within a feasible time. The algorithm is free from manual design and is more flexible and reliable as it is applicable to different sizes of inputs. The experimental results show that on QAPLIB instances, the algorithm has stronger transfer and generalization ability under the premise that the accuracy is comparable to the traditional heuristic algorithm, while the assignment cost for solving is less compared to the latest learning-based algorithms such as NGM, and the deviation is less than 20% in most instances.

The fine particulate matter PM_2.5 could be harmful to human health and the atmospheric environment. Beijing-Tianjin-Hebei is one of the most serious regions in China in terms of atmospheric PM_2.5 pollution. Based on PM_2.5 concentrations data, natural factors data, and human activity factors data, this study used kriging interpolation and statistical analysis to explore the spatial and temporal distribution characteristics of atmospheric PM_2.5 pollution in 13 cities of Beijing-Tianjin-Hebei in 2017 and then used correlation analysis models and factor analysis models to explore its influential factors. The results show that in Beijing-Tianjin-Hebei, 1) PM_2.5 concentrations are low in the north and high in the south. The gradient of annual average concentrations between the southern and northern cities can reach up to 64μg/m³. 2) PM_2.5 concentrations are high in winter and low in summer, high in the morning and evening, and low in the afternoon. PM_2.5 concentration in winter is 1.3-2.8 times higher than in summer, and the daily differences in PM_2.5 concentrations in all seasons are between 11-29μg/m³. 3) Atmospheric PM_2.5 pollution is closely related to natural factors. Terrain and topography affect the processes of PM_2.5 aggregation, transport, and dispersion. Wind speed, sunshine hours, and relative humidity are the dominant meteorological factors affecting atmospheric PM_2.5 pollution, and PM_2.5 concentrations have the strongest correlation with meteorological factors in winter. 4) Atmospheric PM_2.5 pollution is closely related to human activities, which can be summarized into social economy factor, industrial pollutant discharge factor, and urban construction factor. The results of this study will help fill the gaps in air pollution prevention and control in Beijing-Tianjin-Hebei.

Image completion is widely used in unwanted object removal and media editing, which aims to find a semantically consistent way to recover corrupted images. This paper is based on generative adversarial network (GAN) inversion, which leverages a pre-trained GAN model as an effective prior to filling in the missing regions with photo-realistic textures. However, existing GAN inversion methods ignore that image completion is a generative task with hard constraints, making final images have noticeable color and semantic discontinuity issues. This paper designs a novel bi-directional perceptual generator and pre-modulation network to seamlessly fill in the images. The bi-directional perceptual generator uses extended latent space to help the model perceive the non-missing regions of the input images in terms of data representations. The pre-modulated networks utilize a multiscale structure further providing more discriminative semantics for the style vectors. In this paper, experiments are conducted on Places2 and CelebA-HQ datasets to verify that the proposed method builds a bridge between GAN inversion and image completion and outperforms current mainstream algorithms, especially in FID metrics up to 49.2% enhancement at most.

With the rapid development of broadband low-orbit satellite systems, communication frequency bands such as Ku and Ka tend to be saturated gradually, and non-geostationary orbit (NGSO) satellites will inevitably cause interference to geostationary orbit (GSO) satellites operating at the same frequency. At present, a spatial isolation strategy is often adopted to avoid interference. NGSO satellites always produce the strongest interference to the collinear area. Increasing the isolation angle can reduce the interference, but it will greatly lose the coverage of the LEO satellite. This paper proposes an interference avoidance strategy based on sidelobe nulling of the transmit beam. The antenna array is divided into row and column elements by establishing the LEO satellite coordinate system. In the dimension of column elements, the robust LCMV algorithm is used to realize wide nulling. In the dimension of row elements, it is expanded in combination with beam direction, and finally forms a “null band” in the direction of the collinear area. Through simulation analysis, the proposed strategy can effectively reduce the interference avoidance isolation area of LEO satellites while avoiding collinear interference. The algorithm has low complexity and is easy to implement on satellites.

Based on the moderate resolution imaging spectroradiometer products and global land data assimilation system meteorological data from 2001 to 2020, soil moisture in summer in the Yellow River basin was retrieved based on the vegetation index/land surface temperature trapezoid feature spatial model. The spatial-temporal pattern and driving factors of soil moisture in the Yellow River basin were analyzed using the Sen slope method, Mann-Kendall method, and geographical detector. The results showed that soil moisture in the Yellow River basin had apparent spatial heterogeneity. The source and lower reaches of the Yellow River are humid, while the middle reaches are relatively dry. From 2001 to 2020, soil moisture in the Yellow River basin showed an insignificant increase and an insignificant decrease in space, accounting for 39.54% and 58.01% of the regional area, respectively. The growth rate of soil moisture in the upper reaches was the fastest. Precipitation is the dominant factor of temporal variation of soil moisture in the Yellow River basin. Temperature and elevation are the main factors affecting the spatial variation of soil moisture in the upper reaches, and normalized difference vegetation index and precipitation are the main driving factors influencing soil moisture change in the middle reaches of the Yellow River.

The phenomenon of sample imbalance refers to the excessive number of background easy samples in the dataset but too few foreground hard samples, which means the sample suffers from inter-class imbalance and hard-easy imbalance. Most of the existing object detection methods are two-stage detectors based on proposed regions or one-stage detectors based on regression. When applied to imbalanced samples, it is impossible to avoid the over-dependence of the prediction bounding box generated in training on a large number of negative samples, which leads to overfitting of the model and low detection accuracy, poor accuracy and generalization. In order to achieve efficient and accurate object detection under imbalanced samples, a new SA-YOLO self-adaptive loss object detection method is proposed in the paper. 1) To address the sample imbalance problem, we propose the SA-Focal Loss function, which adjusts the loss adaptively for different datasets and training stages to balance inter-class samples and hard-easy samples. 2) In this paper, we construct the CSPDarknet53-SP network architecture based on the multi-scale feature prediction mechanism, which enhances the extraction ability of global features of difficult small target samples and improves the detection accuracy of difficult samples. To verify the performance of the SA-YOLO method, extensive simulation experiments are conducted on the sample imbalance dataset and the COCO dataset respectively. The results show that compared with the optimal metrics of YOLO series method, SA-YOLO reaches 91.46% of mAP in the imbalance dataset, which improves 10.87%, and the enhancement of AP₅₀ for all kinds of objects is more than 2%, with excellent specialization; mAP₅₀ in the COCO dataset is upgraded by 1.58%, and all indexes are not lower than the optimal value, with good effectiveness.

Stochastic variance reduction methods have been successful in solving large scale machine learning problems, and researchers cooperate them with adaptive stepsize schemes to further alleviate the burden of parameter-tuning. In this article, we propose that there exists a trade-off between progress and effectiveness of adaptive stepsize arising in the SVRG-BB algorithm. To enhance the practical performance of SVRG-BB, we introduce the Katyusha momentum to handle the aforementioned trade-off. The linear convergence rate of the resulting SVRG-BB-Katyusha algorithm is proven under strong convexity condition. Moreover, we propose SVRG-BB-Katyusha-SPARSE algorithm which uses Katyusha momentum sparsely in the inner iterations. Numerical experiments are given to illustrate that the proposed algorithms have promising advantages over SVRG-BB, in the sense that the optimality gaps of the proposed algorithms are smaller than the optimality gap of SVRG-BB by orders of magnitude.

Coal-fired power plants are important contributors to CO₂ emissions in China. Due to the low timeliness of statistical data and inaccurate emission factors, the existing emission inventories gradually fail to reflect the CO₂ emissions of power plants. This study provides a method to estimate CO₂ emissions from power plants based on Orbiting Carbon Observatory 2/3 (OCO-2/3) satellite data and Gaussian plume model, retrieving the images of super-large coal-fired power plants (≥5 000 MW) in China from the OCO-2 (September 6,2014-October 1, 2021) and OCO-3 (August 6, 2019-October 1, 2021) dataset, and identifying a total of seven plumes near Tuoketuo, Waigaoqiao, and Jiaxing power plants. Using a combination of three atmospheric background value determination methods, the CO₂ emissions estimated by the Gaussian plume model range from 43 to 77 kt/d, with correlation coefficients ranging from 0.50 to 0.87. The uncertainties of individual plumes varied from 8% to 32% (1σ), with wind speed being the largest uncertainty (6%-31%), followed by background values (5%-18%), enhanced values (1%-21%), and plume rise (1%-8%). The estimates are verified to be in high agreement with Carbon Monitoring for Action, Carbon Brief, and the Global Power Emissions Database (Tuoketuo: (76.48±15.75), Waigaoqiao: (55.98±6.90), Jiaxing: (64.55±15.89) kt/d). This study helps monitor and estimate important point source carbon emissions, which is not only a prerequisite for the power industry to carry out carbon reduction efforts but also helps develop specific regional carbon reduction policies, thereby reducing anthropogenic carbon emissions.

Automatic extraction of building information from high-resolution remote sensing images is of great significance in the fields of environmental monitoring, earthquake mitigation， and land use, making it a research hotspot in the field of high-resolution remote sensing applications. In order to improve the accuracy of building extraction from high-resolution remote sensing images, a building extraction method based on MFF-Deeplabv3+(multiscale feature fusion-Deeplabv3+) network for high-resolution remote sensing images is proposed in this paper. First, the multi-scale feature enhancement module is designed to enable the network to capture more scale context information; then, the feature fusion module is designed to effectively fuse deep features with shallow features to reduce the loss of detail information; finally, the attention mechanism module is introduced to select accurate features adaptively. In the comparison experiments of the Inria building dataset, MFF-Deeplabv3+ achieved the highest accuracy in PA, MPA, FWIoU, and MIoU metrics with 95.75%, 91.22%, 92.12%, and 85.01%, respectively, while the generalization experiments of the WHU building dataset achieved good results. The results show that this method extracts building information from high-resolution remote sensing images with high accuracy and strong generalization.

In this paper, we express the Mahler measure of a two-variable polynomial P(x,y)=(x²+1)y²+2(x²+x)y+x(x²+1) as a linear sum of some Bloch-Wigner Dilogarithm functions. and prove that the Mahler measure of P(x,y) is rationally proportional to L'(χ_-3,-1):m(P(x,y))=5/2L'(χ_-3,-1).

In breeding, single nucleotide polymorphisms (SNPs) in the genome are often used to predict quantitative phenotypes to assist breeding, thereby improving breeding efficiency. The traditional statistical analysis method is limited by many factors including missing data, and its performance sometimes can not meet the requirements. In this paper, we proposed a multi-scale feature convolutional neural network model (MSF-CNN) to predict plant traits. The model extracted SNP features at three different scales through convolution and analyzed the significance of SNP sites through the weight of the SNPs input into the model. The test results showed that MSF-CNN model performed with higher accuracy than the known methods and other deep learning models in phenotype prediction on the datasets with missing genotypic data. This paper also studied the contribution of genotype to traits through saliency map, and discovered several significant SNP loci. These results showed that, compared with other known methods available at present, the deep learning model proposed in this paper can obtain more accurate prediction results of quantitative phenotypes, and can also effectively and efficiently identify SNPs associated with genome-wide association research.

There might be great differences in spatiotemporal variation in leaf area index (LAI) of different grassland types; the responses of LAI with different annual variations to climate change are probably distinct. To explore these differences, from the long-term LAI data and meteorological data and the grassland type data, we obtained the temporal and spatial varying characteristics of the maximum LAI of different types of temperate grassland in northern China from 1981 to 2017, and explored the responses of LAI with different annual variations to the changes of air temperature and precipitation. The results showed that the average annual maximum LAI of temperate grassland was (0.76±1.07) m²/m², the higher in the east and the lower in the west. The meadow steppe had the highest LAI ((2.73±1.20) m²/m²) and the steppe desert had the lowest LAI ((0.13±0.17) m²/m²). From 1981 to 2017, the average annual maximum LAI showed a significantly increasing trend. The maximum LAI increased significantly for 32.52% of the area and decreased significantly for 6.31% of the area. The areas with a significantly increasing LAI were greater than those with a significantly decreasing LAI for all the grassland types. The annual average maximum LAI was positively correlated with the annual total precipitation from January to August and the annual mean air temperature for July and August. Both the significant decrease and increase of the maximum LAI was mainly affected by the significant increase of annual mean air temperature for July and August. There existed a critical threshold of the rising rate of air temperature for July and August that influenced the variation in LAI; for the grassland types that accounted for the larger areas, this threshold was 0.042-0.043 ℃/a. LAI increased with the rising air temperature as the rising rate of air temperature was lower than this threshold; conversely, LAI decreased. These results are expected to provide important scientific basis for grassland utilization, protection and restoration in the context of climate change.

Water index is one of the most effective methods to extract water bodies from remote sensing images. There are many kinds of water index, each with its own characteristics. It is, therefore, necessary to select the index with best classification accuracy. Taking Shijiazhuang City as the research area, 11 common water indices were used to extract water bodies from Landsat 8 OLI images. The accuracy of the water index extraction results is validated by using the visual interpretation (VI) result as the standard classification map from Sentinel-2 MSI based on the area test method in combination with transition matrix and sampling test method. Results show little difference in the extraction of large water bodies among different water indices. Small ponds and rivers can better check the extraction ability of water index. It is proved that Water Index 2019 (WI2019) has the best water classification. WI2019 is then used to find out the recent expansion of water bodies after the start of South-to-North Water Diversion Project for water transfer. It was found that the area of surface water body in Shijiazhuang excluding large reservoirs increased significantly, from 42 km² in 2014 to 62 km² in 2020, an increase of 20 km². In view of the canal seepage control treatment at the bottom of most newly added water bodies, with poor groundwater recharge function, and more ineffective evaporation, it is recommended to properly control the scale of water bodies in order to effectively reduce the waste of water transferred from outside.

Subgroup analysis of heterogeneous groups is a crucial step in the development of individualized treatment and personalized marketing strategies. Regression-based approaches are one of the main schools of subgroup analysis, a paradigm that divides predictor variables into two parts with heterogeneous and homogeneous effects and divides the sample into subgroups based on the heterogeneous effects. However, most of the existing regression-based subgroup analysis methods have two major limitations: First, they still consider the sample homogeneous within subgroups and do not fully consider individual effects; Second, the common contamination phenomenon of homogeneous effect variables is not taken into account, which will lead to large bias in the model results. To address these challenges, we propose a robust individualized subgroup analysis. We use a multidirectional separation penalty function to achieve individualized effects analysis for the heterogeneous part of the model and use γ-divergence to obtain robust estimates for the contaminated homogeneous part. We also propose an efficient alternating iterative two-step algorithm, combining coordinate descent and alternating direction method of multipliers (ADMM) techniques to implement this process. Our proposed method is further illustrated by simulation studies and analysis of a skin cutaneous melanoma dataset.

The agricultural greenhouse is a kind of agricultural facility, which is divided into transparent and non-transparent according to the surface transmittance. The large-scale statistics of agricultural greenhouses are of great significance to the survey of agricultural facilities, the formulation of agricultural policies, and the planning of county economic development. Aiming at the problem that manual statistics are time-consuming and laborious, this paper utilizes the convolutional neural network to extract agricultural greenhouses information from high-resolution remote sensing images. To solve the problems of insufficient semantic information extraction in remote sensing images and insufficient utilization of edge information of the U-Net model, this paper proposes the following improvements: 1) The semantic segmentation task is optimized, and ConvNeXt and attention mechanism is utilized to extract deep semantic information of agricultural greenhouses in remote sensing images. 2) The edge detection task is introduced, and the gated convolution layer and concate operation are used to fuse the semantic features of the encoder and the image gradient output by the decoder, and then the edge information is combined to optimize the segmentation results. After testing, the improved model can extract both transparent and non-transparent agricultural greenhouses information at the same time and the recognition effect is good, which is greatly improved compared with the traditional method.

In the field of SAR image ship detection based on deep learning, traditional models are usually complex in structure and require a large amount of calculation, making them unsuitable for low computing power platforms and real-time detection. And convolutional neural networks that rely on preset anchor boxes will lead to a lot of computational redundancy due to the difficulty of setting a reasonable anchor box. To solve these problems, an end-to-end lightweight convolutional neural network based on anchor-free design is proposed, and a lightweight channel attention module (EESE) is designed and applied to the detection head (ED-head), to resolve the conflict between classification and localization tasks. In addition, an optimized EIOU loss function is proposed, which enables the model to effectively improve the network performance without increasing the inference time. The proposed method is tested on the SSDD dataset, and the experimental results show that compared to YOLOX-nano, AP₅₀ and AP are increased by 2.1 and 7.4 percentage points, respectively, with the CPU latency being only 5.33 ms, much less than 13.13 ms of YOLOX-nano. The proposed method achieves a balance between accuracy and efficiency.

The power performance measurement (PPM) of wind turbine is an objective method to evaluate the performance of wind turbine, which is the basis for the optimization of the wind turbine, the calculation of annual energy production and other related topics. Traditional testing method usually installs the ground-based equipment, such as the meteorological mast or ground-based lidar, which are more constrained by the on-site conditions. The nacelle mounted Lidar (NML), which can be installed on the top roof on wind turbine nacelle, could overcome the constraints of the on-site conditions. In recent years, NML starts to be used in PPMs, especially for offshore wind turbines. In this paper, the PPM had been conducted by using NML, and the differences were analyzed. The results showed that the wind speed accuracy of NML was consistent with the meteorological mast, the correlation coefficient of the two devices was 0.994, and the slope and offset by linear regression were 0.979 and 0.084, respectively; the scattering point of PPM by NML was more concentrated, and NML was always measuring the wind speed exactly in front of the wind turbine with the yawing of the wind turbine nacelle, which was better representative of the wind speed. The AEP assessment results showed that the NML was 1.73% overestimated relative to the met mast, and the overall evaluation error range was smaller due to the lower dispersion of NML. The NML had a better representation of wind speed than met mast, with less uncertainty, and had value for the application of PPM of wind turbine.

Since the late Cenozoic, the geodynamic mechanism of the transformation of Qinghai Lake from the external lake to the endorheic lake is still an open problem. The Daotang River is an important channel for transforming from external-flow to the endorheic-flow type of Qinghai Lake. Its evolution records the prominent landform transition event. Based on the newly developed numerical calculation program for geomorphic evolution with finite volume method, in this work, we conduct a series of the landscape evolution models of the Daotang River under the combined influence of mountain uplift and river downcutting, and have a quantitative analysis of the effects of uplift rate and river undercutting coefficient on river backflow patterns. We attempt to explore the mechanism of landform transition events that could provide evidence for the formation process of Daotang River basin and the origin of Qinghai Lake. Our modeling results indicate that the reorganization of the river system and backward flow of the river is jointly controlled by the rapid mountain uplift from the Riyue Mountain active fault and the river undercutting coefficient. The mountain uplift rate is the controlling factor that affects the reorganization of the river system in the Daotang River basin. When rising mountains block the river, the river incision coefficient is the factor that controls the rate of reorganization that occurs in the basin. The result recognizes they have enlightening significance for further understanding the dynamic mechanism of river evolution under the regional tectonic deformation and the coupled surface processes.

In recent years, multi-agent cooperation under partially observable conditions has attracted extensive attention. As a general paradigm to deal with such tasks, centralized training with decentralized execution faces the core problem of credit assignment. Value decomposition is a representative method within this paradigm. Through the mixing network, the joint state action-value function is decomposed into multiple local observation action-value functions to realize credit assignment, which performs well in many problems. However, the single point estimation of the mixing network parameters maintained by these methods lacks the representation of uncertainty and is thus difficult to effectively deal with the random factors in the environment, resulting in convergence to the suboptimal strategy. To alleviate this problem, this paper performs Bayesian analysis on the mixing network and proposes a method based on uncertainty for multi-agent credit assignment, which guides the credit assignment by explicitly quantifying the uncertainty of parameters. Considering the complex interactions among agents, this paper utilizes the Bayesian hypernetwork to implicitly model the arbitrary complex posterior distribution of the mixing network parameters, to avoid falling into the local optima by specifying the distribution type a priori. This paper compares and analyzes the performance of representative algorithms on multiple maps in StarCraft multi-agent challenge (SMAC) and verifies the effectiveness of the proposed algorithm.