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.

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.

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.

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.

This paper overviews the significance, advances and future direction in the glycobiology field. Special emphasis is given to cell cell adhesion which is mediated by the interaction between carbohydrates and carbohydrates binding proteins (CBP). The roles of carbohydrates in the folding of nascent polypeptides, immune system, and cellular signal transductions are also reviewed. The scope also covers carbohydrates in infections, carbohydrates in diseases,and chemical synthesis/structural analysis of carbohydrates. Finally,the features and future directions of glycobiological research are pointed out by the author.

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.

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.

This review aims to guide the future development of related research in the field of learning path planning through the analysis of the current research status of learning path planning. Specifically, this review first introduces the definition of learning path planning and the commonly used parameters in learning path planning methods; then, it classifies in detail according to the algorithms used to generate learning path planning and summarizes the advantages and disadvantages of various learning path planning methods. In addition, the data set and evaluation method used by the learning path planning method is introduced. Finally, the challenges faced by the learning path planning method are summarized and the future development trend is predicted.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Removing counties (cities) into districts is one of the main modes of administrative division adjustment, which has an impact on the urban spatial structure. Using multi-temporal remote sensing images of Changchun, administrative division vector graphics and statistical survey data of social and economic conditions, this paper analyzes the evolution of construction land and economic development in Changchun City before and after Jiutai was withdrawn from the city and divided into districts. Utilizing ENVI software, GIS technology, and other means, this paper revealed the impact of Jiutai’s withdrawal from the city and the establishment as a district on the spatial expansion of Changchun City at the two-level spatial scale of Changchun City and Jiutai District. The research found that: 1) Dismantling the city into districts accelerated the expansion process of Jiutai District, which further affected the direction and extent of urban spatial expansion in Changchun City. 2) The decommissioning of the city into districts caused Jiutai and the main urban area of Changchun to converge towards each other, and the spatial distance between the two districts gradually narrowed. 3) The “polarization effect” of the Jiutai District became more pronounced after the city was withdrawn and divided into districts, and high-density clusters appeared in the main urban area of Changchun. 4) The division of counties (cities) into districts is an important driving force for urban spatial expansion, primarily reflected in three aspects: policy, industry, and land use.

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.

Semantic segmentation of remote sensing images is an important task in remote sensing image processing and analysis, especially in multi-category semantic segmentation. Current methods mainly revolve around convolutional neural networks, but convolution only focuses on the local information of the image while ignoring the global information. Therefore, inspired by high resolution network (HRNet) and relational graph convolutional network (R-GCN), this paper proposes a high-resolution relational graph convolutional network (HRGCN) for multi-category semantic segmentation. Firstly, simple linear iterative clustering (SLIC) is done on the original image, and the result is used to segment the feature map output from HRNet to obtain superpixel blocks with high homogeneity and containing multi-resolution information; then graph nodes and edges are constructed based on the superpixel blocks, and R-GCN is used to classify the graph nodes, so as to learn the long-distance dependency between different features and complete the extraction and classification of remote sensing images. The HRGCN model designed in this paper is experimented on Potsdam and Vaihingen datasets, and the results are compared with the existing methods, and the $\bar{F}_1$ values and MIoU values are improved to certain degrees, which proves that the method has good advancement.

The ramp event of wind speed is a large increase or decrease in wind speed within a short period, causing a significant change in wind farm power, affecting the safe operation of the grid and even triggering accidents such as frequency reduction and voltage collapse. This paper selects the simultaneous data of wind turbines and meteorological towers in wind farms, identifies the ramp events by the swinging door algorithm (SDA), analyzes the duration, magnitude and change rate of the ramp events, and discusses the influence of mountainous terrain on them. In this paper, the recognition algorithm of the ramp event of wind speed and power is designed based on the SDA, and the algorithm parameters are set as follows: the time threshold 4 h, wind speed threshold 6 m·s^-1, and power threshold 1 000 kW. For the recognition of ramp events in other wind turbines, this paper suggests using 2/3 value of the difference between rated wind speed and cut-in wind speed as the wind speed threshold parameter, and 2/3 value of rated power as the power threshold parameter. The terrain influence on the ramp event is significant, and the ramp event is more related to the altitude and average wind speed at the turbine, and the time proportion of the ramp event under different terrain ranges from 6.5% to 9.8%, with the average value of 7.8%.

In this paper, the physical modeling of frill-neck lizard, a reptile with special heat dissipation structure, was carried out, and the natural convection heat transfer properties of it were numerically studied. It was found that there was only one maximum heat transfer coefficient in the range of 45°-85°, which was obtained at around 65° for the simulation of different field angle adjustments of frill-neck lizard neck fold. At the same time, by changing the size of frill-neck lizard neck fold, it was also observed that there was a unique maximum convection heat transfer coefficient in the range of 6.3≤ L₀/d ≤6.6. In addition, the field angle and area corresponding to the maximum heat transfer rate were close to the natural state of Australian subspecies. Therefore, it can be inferred that the evolution direction of frill-neck lizard neck fold may be beneficial to improving the natural convection heat transfer rate. Hence, the natural convection between frill-neck lizard and external environment may be considered as an important reason affecting the evolution direction of Chlamydosaurus kingii neck fold.

This paper focuses on the continuity of the truncated Hardy-Littlewood maximal function. We first show that the truncated Hardy-Littlewood maximal function is lower semi-continuous. Then by investigating the behavior of the truncated Hardy-Littlewood maximal function when the truncated parameter γ changes, we obtain an equivalent condition of the continuity of the truncated Hardy-Littlewood maximal function.

The research on the relationship between public participation, environmental regulation and residents’ well-being is of great significance for the scientific formulation of environmental regulation policies and the optimization of the governance environment. Using the data of journal papers collected by Web of Science and CNKI from 2006 to 2021, using CiteSpace bibliometric analysis software and combining with traditional review methods, this paper draws the following conclusions: 1) Chinese literature research hotspots have gone through three stages: the western experience discussion of public participation in environmental governance and the initial stage in China, the theoretical model analysis of public participation in environmental governance and the empirical research stage of influencing factors, the evaluation of residents’ well-being effect of environmental regulation and the specific case study of public participation in environmental regulation. English literature research initially focused on the participation of residents at the community level in environmental regulation, and then focused on the exploration of problems and influencing factors in practice. At present, it focuses on the impact of environmental regulation on residents’ well-being and environmental health inequity and big data analysis applications； 2) The academic community has not yet reached a consistent conclusion on the impact of public participation in environmental regulation and residents’ well-being. The study confirms that environmental regulation has a positive impact on residents’ health and enhances individual subjective well-being, but at the same time it exacerbates the income gap between residents and between regions； 3) The research trend shows that the research perspective changes from macro to micro, the research method changes from statistical model to spatial analysis and quasi-natural experiment method, and the variable measurement changes from single index to comprehensive index； 4) Future research needs to focus on the analysis and optimization of government response mechanism, the research on the equity of environmental regulations on residents’ well-being, and the improvement and perfection of research methods, data, and variable measurement methods.

Aiming at pose estimation for non-cooperative targets in on-orbit maintenance operations of space robots, we propose a pose estimation algorithm based on point cloud registration. Firstly, the hybrid filtering algorithm preserves the shape of the point cloud to the greatest extent while reducing its density. Then, the principal component analysis algorithm is used to establish the eigenvector transformation. The RANSAC algorithm is employed for coarse registration, followed by the improved ICP algorithm for fine registration, which results in the estimation of the rotation matrix, translation matrix, and attitude. Experiments are carried out to evaluate the performance of the algorithm. Simulated satellite point cloud models processed by rotation-translation transformations and Gaussian noise are used to verify the point cloud registration performance. The satellite model scene collected by a TOF camera point cloud is used to validate the pose estimation algorithm. The results show that the proposed algorithm has improved anti-noise performance, and showed higher robustness compared to traditional registration algorithms, with rotation attitude angle error less than 0.4° and displacement error less than 3 mm.

Helical gears are widely used in high-speed and heavy-duty situations due to their smooth transmission and many other advantages. When helical gear pair meshing, it is inevitablely affected by friction, so it is necessary to take tooth surface friction into account when calculating the time-varying meshing stiffness(TVMS) of helical gear pair. Firstly, the helical gear is cut into micro-element slices of spur gear by the slicing method, and then the meshing stiffness of micro-element slices is calculated by the energy method. Finally, the accurate TVMS of helical gear pair is obtained by integral accumulation along the tooth width direction. The change rule of TVMS of helical gear pair under zero friction, constant friction, and time-varying friction is studied. The action mechanism of tooth surface roughness, torque, speed, tooth width, and other parameters on TVMS of helical gear pair under time-varying friction are revealed. The results show that the TVMS of helical gear pair will change under the action of friction; Under constant friction, the TVMS of helical gear pair will decrease, and the greater the constant friction coefficient, the smaller TVMS; Under the action of time-varying friction, the TVMS of helical gear pair will decrease with the increase of tooth surface roughness, increase with the increase of input torque, speed, and tooth width, respectively.

The administrative center is a core element in the framework of administrative division research. The location, adjustment, and change of a city’s administrative center can significantly impact the spatial form and structure of the city. This paper takes Qingdao City as a case study and utilizes the CLCD dataset, along with GIS spatial analysis methods, to quantitatively measure the spatial and temporal patterns of urban construction land expansion after the relocation of the administrative center. It analyzes the differences in urban built-up area spatial expansion before and after the relocation and explores the mechanisms of how the relocation of the administrative center influences urban spatial expansion. The results show that：1) After relocating the administrative center, Qingdao’s construction land area exhibits higher average values for the number, rate, and intensity of expansion compared to before relocation, indicating that the relocation of the administrative center accelerates urban construction land expansion in Qingdao; 2) The relocation of the administrative center has led to an agglomeration trend in Qingdao’s urban spatial expansion, with a shift towards polycentric agglomeration, and the primary hotspot of spatial expansion lies along the Jiaozhou Bay Ring; 3) Land finance is the direct cause of urban spatial expansion following the relocation of Qingdao’s administrative center. The strengthening of land finance enhances the spatial governance capacity of the government, further promoting the reconfiguration of production factors, urban planning, and urban functions in Qingdao. As a result, this process indirectly influences the city’s spatial expansion. The findings of this study emphasize the significance of incorporating the spatial impact of administrative center relocation into urban planning and development strategies. These findings can serve as a reference for the strategic relocation of urban administrative centers and the planning of future urban development directions.

3D point clouds have extensive applications in the auto-drive, 3D real scene, and other fields. But complex scene requires massive point clouds to represent which brings great challenges to storage space, data processing and transmission bandwidth. A multi-scale attention point cloud geometry compression (MSA-GPCC) is proposed to compress point cloud data based on multi-scale features, attention mechanism, and variational auto-encoders (VAE). Experiments and analysis are carried out based on MPEG data sets. The results show that MSA-GPCC performs better than those of the traditional G-PCC and deep-learning-based D-PCC algorithms, D₁ BD-PSNR is improved by 7.72 and 4.91 dB respectively, and D₂ BD-PSNR is improved by 5.56 and 3.09 dB respectively.