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.

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.

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.

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.

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.

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.

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.

Responsive to dehydration (RD) is a class of genes that regulates dehydration in plants. They are functionally tolerant to plant dehydration, some of which are responsive to abiotic stresses such as low temperature and high salinity. However, they belong to different families, respectively, and have discrepancy in the structure and function. In this paper, the structural composition, conserved motif, regulatory mechanism, and the function in response to biotic and abiotic stress were summarized in different RDs, as well as the different cis-acting elements in the promoter region played a role in response to abiotic stress so as to provide relevant basis for future researches on RD.

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.

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.

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 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.

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.

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.

Cyclopentenones are important synthetic building blocks and as motifs appear in bioactive molecules and natural products. We applied density functional theory (DFT) calculations to gain insight into the modular strategy involved in the palladium-catalyzed synthesis of cyclopentenone from α,β-unsaturated acid chlorides and alkynes in the presence of hydrosilane. The study unveils that the transformation proceeds via the sequence:the disassembly of α,β-unsaturated acid chloride into vinyl, carbonyl, and Cl fragments with the palladium catalyst; carbon monoxide release; coupling of alkyne with vinyl group; carbon monoxide re-coordination and migratory insertion to form another C-C bond with alkyne, ring-closure via C=C bond insertion, transmetalation with hydrosilane, C, H-reductive elimination to release the product. Different from the mechanism proposed by the experimentalists, the CO group is involved in the reaction via separate liberation and re-coordination in the solvent cage, rather than persistent coordination with palladium. The transmetalation for H/Cl exchange takes place at the late stage and is a bottleneck of the transformation, instead of at early disassembly stage.

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.

Activated alumina (AA) is widely used for defluoridation. Batch experiments were carried out to investigate the influence of Cl^- (1000 mg·L^-1) and HCO₃^- (2000 mg·L^-1) on adsorption kinetics of F^- (10 mg·L^-1) on AA in coalbed methane co-produced water (CBMW). The results show that in the presence of Cl^- the adsorption rate and the equilibrium absorption capacity increase, but in the presence of HCO₃^- both the quantities decrease. These effects should be considered when one treats CBMW.

Calmodulin, the predominant calcium receptor, is one of the best-characterized calcium sensors in eukaryotes which regulates diverse cellular functions by modulating the activity of a variety of enzymes and proteins. Research workers have evidences suggesting Ca2+ and calmodulin are involved in various environment stimulation reaction. By differential screen, we isolated a positive clone in the T.caerulescens cDNA library induced in response to Cd2+. The result of cDNA sequence analysis showed that the positive clone shared more than 92 % homologies for nucleotide sequences with CaM2 gene from Arabidopsis thanliana, named TcCaM2. The TcCaM2 gene consists of 447 nucleotides and encodes 149 amino acids, and it shares more than 83 % homologies for nucleotide sequences and 95 % for encoding amino acid sequences as compared with those of calmodulin genes of other plants. A yeast-expressed plasmid with TcCaM2 was constructed and transferred into heavy metal sensitive yeast mutant cells. Further study about the growth patterns of TcCaM2 -expressed yeast indicated that over expression of TcCaM2 improved Co2+、Ni2+ resistance and increased Cd2+ hypersensitivity in the yeast cells. As an important role in the T.caerulescens’s response to heavy metal, TcCaM2 can work as a significant gene tool in the phytoremediation of heavy metal pollution.

Super-resolution technology has become an important tool for reconstructing high-resolution datasets and supplementing the shortage of high-resolution images with its characteristics of flexibility and low cost. Compared with natural images， remote sensing images of real scenes are complex and specific， which make super-resolution tasks more difficult. Meanwhile， for remote sensing images， traditional deep learning models can improve the resolution， but there is still a great deficiency of improvement for the details and textures of the ground objects. Therefore， based on the generative adversarial network model， this paper fuses channel-space attention to enhance the feature learning capability of the network and use an artifact suppression strategy to distinguish smooth regions from detail-rich regions， so that the network can focus more on detail-rich regions and suppress the generation of artifacts. Extensive experiments on GaoFen satellite data show that the quantitative metrics and visual quality of the method proposed in this paper are better than those of the current mainstream methods.

The production and supply of food are core components of sustainable development. Ensuring the sustainability of global food production and supply is crucial for maintaining human survival and socioeconomic stability, and it holds significant importance in advancing the “Zero Hunger” goal within the framework of global sustainable development. This paper selects the five key cereal crops, including wheat, barley, maize, oats, and rice, as the subjects of study, focusing on the Central Asian region. It analyzes the variations in yield per hectare, total production, and cultivated area for these cereals from 1992 to 2021, investigates regional disparities in food production fluctuations within Central Asia, and employs the ARIMA model to forecast future grain production in Central Asia. The results showed that: 1) From 1992 to 2021, the grain yield, total output and sown area in Central Asia showed a trend of first decreasing and then increasing, and the three changes ranged from 0.79～1.96 t/hm², (0.14～0.37)×10⁸ t and (0.14～0.23)×10⁸ hm², respectively. Grain yield and total production reached their peaks in 2011 at 1.96 t/hm², and 0.37×10⁸ t, respectively, while the cultivated grain area peaked in 1993 at 0.23×10⁸ hm². 2) The grain volatility in Central Asia is characterized by frequent fluctuations in grain production, with a significant proportion of years experiencing fluctuations exceeding 5%. The amplitude of these fluctuations is substantial, and the average fluctuation cycle is 2-4 years, indicating a short-term cyclical pattern dominated by classical rather than growth-oriented fluctuations. 3) In the coming years, Central Asia is projected to experience an upward trend in wheat, barley, maize, and oats production, while rice production is expected to decline. Compared to the year 2021, by 2030, Central Asia’s wheat, barley, maize, and oats production is estimated to increase by (410.15, 91.6, 795.26, and 8.91)×10⁴ t, respectively, representing growth rates of 20.1%, 31%, 299.2%, and 37.1%. Conversely, rice production may decrease by 15.99×10⁴ t, with a decline of 15.5%.

Urban street view imagery, as crucial forms of spatial data, has a wide range of applications in mapping services, urban 3D reconstruction, and cartography. However, since the collected street view images often face challenges such as distracting target occlusion and privacy concerns, necessitating meticulous preprocessing. Addressing these challenges, we propose an image inpainting algorithm based on multi-scale semantic priori guided for generating more realistic and natural static street view images. Firstly, a semantic prior network is designed to learn the multi-scale semantic priors of the missing regions of the input image to enhance the contextual information. The semantic enhancement generator adaptively fuses the multi-scale semantic priors and image features and at the same time introduces a multilevel attention shifting mechanism to refine the texture information of the image. Finally, a Markov discriminator is adopted to distinguish the generated image from the real image by adversarial training, which makes the reconstructed street scene image more realistic. Experiments on the Apolloscape dataset demonstrate that the images generated by our algorithm have achieved significant improvements in semantic structural coherence and detailed texture, solving the privacy problem in street view while providing a more reliable data base for realistic city applications.

This article takes 567 national level (168) and provincial level (511) traditional villages in Shandong Province as subjects of investigation. Firstly, a spatial econometric model is used to analyze the spatial distribution characteristics of traditional villages in Shandong Province. Secondly, the Delphi method and SPSS factor analysis method are used to screen and analyze the evaluation indicators. A total of 28 indicators including village cultural resources, village ecological and natural resources, institutional management, tourism infrastructure, and village economic vitality are obtained. Finally, the AHP entropy weight method is used to combine weights to score the selected indicators. A scoring table for tourism development potential of traditional villages in Shandong Province is constructed, and a multi-objective linear weighted function model is used for scoring. The results showed that: 1) The overall spatial distribution of traditional villages in Shandong Province is uneven, mostly distributed in mountainous areas or underdeveloped areas of cities with relatively uneven development and underdeveloped infrastructure; 2) Among various indicators, the cultural resources (0.331 2) and institutional management (0.144 0) of the village play an important role, followed by tourism infrastructure (0.143 7), village ecological and natural resources (0.143 4), and village economic vitality (0.097 7); 3) The evaluation results show that there are 135 key traditional villages (S≥7.9) in Shandong Province, accounting for only 23.81% of the total number of traditional villages in Shandong Province; there are 295 traditional villages (6.2<S<7.9), accounting for 52.03% of the total number of traditional villages in Shandong Province; there are 137 traditional villages with a focus on protection (S≤6.2), accounting for 24.16% of the total number of traditional villages in Shandong Province; 4) Based on the characteristics of various traditional villages, corresponding development strategies are proposed in this paper.

The palm-leaf manuscript is a treasure of human civilization， which is the scripture written or engraved on the processed leaves of palm trees. Originating in ancient India， it was gradually introduced into China in the historical period， and reached a period of great prosperity in the Tang Dynasty. Chinese ancestors had a clear understanding of the materials used to make the palm-leaf manuscript. According to our study， the “Doro” （多罗） tree in ancient books should be talipot（Corypha umbraculifera）， while the “Beiduo” （贝多） tree should be palmyra（Borassus flabellifer）. The word “Beiduo”， which appeared many times in Chinese classical poetry， has become a synonym of Buddhist classics. Material evidence of ancient palm-leaf manuscript has been unearthed in many places in Xinjiang， among which Powell’s manuscript as well as Spitzer’s manuscript unearthed in Kuqa were of great significance， with the latter being considered to be the earliest physical evidence of palm-leaf manuscript all over the world. In addition， ancient palm-leaf manuscripts were also unearthed in the Mogao Caves in Dunhuang， which was also of great importance. To date， palm-leaf manuscripts are mainly preserved in Xizang， as well as Xishuangbanna in Yunnan Province， where they have been well organized and protected.​ The characters on the palm-leaf manuscripts were carved and written， respectively， which should be adapted to their preservation conditions.

Based on the perspective of human-earth relationships, firstly, this article analyzes the theoretical connotation of “Lucid waters and lush mountains are invaluable assets” from the perspectives of philosophical meaning, geographical logic, and economic thinking. Secondly, from the three aspects of serving the rural revitalization, building a beautiful China, and building a community of human destiny, the era value of “Lucid waters and lush mountains are invaluable assets”. Subsequently, from the perspective of coordinated development of human-earth relations, from the four aspects of highlighting the participation of people, the value of excavation land, maintaining human-land balance, and the standardized human and ground behavior, it proposed the development of the transformation from “lucid waters and lush mountains” to “gold and silver mountains”. Finally, according to the key issues that have not been resolved in the practical path, it is pointed out that future research should be strengthened on the selection of transformation path and model, the valuation of ecological products, and the evaluation of transformation efficiency of the “Two Mountains”.

This paper proposes a multi-spectral remote sensing image sharpening method based on a deep convolutional neural network and residual network. The method addresses the problems of spectral distortion in traditional remote sensing image sharpening methods and insufficient information utilization between network layers in current deep learning-based methods. The proposed method uses the depth convolution and residual network to design the depth residual module to extract the spatial and spectral features of the deep image. Additionally, residual connections between sub-blocks are established to transmit gradient information to deeper networks and avoid gradient explosion problems, making the network more efficient. Experiments are conducted on simulated and real-world multi-spectral images from WorldView-2, and the results are compared with traditional and existing deep learning-based methods. The proposed method improves the spectral distortion phenomenon and learns deeper image features to better preserve the spatial and spectral information of the image. The proposed method outperforms the deep convolutional sharpening network method in terms of various evaluation metrics, including ERGAS, SAM, SCC, UIQI, and the global fusion quality evaluation index. The proposed method improves these metrics by 24.4%, 26.7%,6.2%,4.7%, and 6.3%, respectively. Subjective and objective evaluations and spectral curve also indicate that the proposed method significantly improves the spatial and spectral resolution of remote sensing images, especially under complex environmental conditions.

Regime shifts and their underlying mechanisms in ecosystems are a critical issue in ecological research， with profound implications for predicting ecological risks under global change. This review systematically synthesizes the theoretical advances in alternative stable state （ASS） of ecosystem multi-stability， focusing on three key dimensions： micro-macro process coupling， mechanisms of threshold response， and the role of regulatory nodes in ecosystem resilience. By integrating methodologies such as ASS theory， potential landscape modeling， and bifurcation analysis， we highlight how climate change and anthropogenic activities are driving critical ecosystems （e.g.， coral reefs， Amazon rainforest， Arctic permafrost） toward tipping points， while hysteresis effects and irreversible potentials exacerbate recovery challenges. Emerging approaches combining network theory and energy （carbon） flux analysis offer novel insights for cross-scale early warning， yet bridging micro-scale mechanisms with macro-scale patterns remains a critical challenge. This review provides a theoretical framework for ecological threshold management and underscores the urgent need for interdisciplinary approaches to address planetary-scale regime shift risks.

The convergence of broadband LEO satellite communication systems with 5G is the development trend of satellite communication. To meet the demand for high-speed data transmission, multibeam technology based on large-scale digital phased arrays is indispensable. The peak-to-average ratio problem of multibeam signals induces the non-linear distortion of onboard power amplifiers, while the non-linear distortion of power amplifiers leads to serious interference between multibeams. A digital pre-distortion structure for multibeam is proposed for low-orbit satellite communication systems using large-scale phased arrays. A pre-distortion model is developed to solve the problems of non-linearity, intermodulation distortion and multibeam interference. Through simulation experiments, it is found that the proposed structure is more advantageous than the traditional pre-distortion structure in terms of performance and complexity balance, providing a feasible solution for the implementation of multibeam and overcoming non-linearity on board.

The coordinated development of regional economy-resource-environment system (ERE) is a crucial aspect of achieving the sustainable development goals. Taking Binzhou City in Shandong Province, a typical city in the Yellow River Delta, as an example, this paper constructs a coupling coordination degree model and examines the coupled and coordinated development of its ERE system from 2000 to 2020. Furthermore, a system dynamics (SD) model of Binzhou City’s ERE is constructed based on 71 indicators, through which causal feedback relationships and flows among subsystems are illustrated. Four scenarios are set for simulation as follows: current trend continuing mode, secondary industry leading mode, resource and environment prioritizing mode, and system coordinated mode. The results show that: 1) Over the past 20 years, the level of coupled and coordinated development in Binzhou City has been increasing year by year, and the type of coupled and coordinated development has gradually transitioned from an unbalanced state to a well-coordinated state, but there is still instability in the ERE; 2) Through SD parameter calculation and multi-scenario simulation analysis of Binzhou City’s ERE, it is pointed out that continuing the current development model cannot achieve high-quality sustainable development in Binzhou City. Therefore, a coordinated and stable development model, which takes economic, resource, and environmental benefits into account, is suggested as the relatively optimal solution for Binzhou City to achieve sustainable development in the long run.

The identification of paleo-cropping layers is an essential part of the study of the origins of agriculture and the exploration of ancient human land use. It is a challenge that archaeologists and palaeoenvironmentalists are facing together. The existing methods of identifying ancient cultivation layers are costly and limited, and there is an urgent need to develop new, simple, convenient, and reliable methods to identify ancient cultivation layers. Farming without irrigation, using plows to turn the land, once occurred in Beijing’s Dongling Mountain and continued for at least 300 years before retiring. There are also natural meadows and broadleaf forests in the area with no tillage history. In this study, four types of soil profiles, namely natural meadow, natural broadleaf forest, abandoned farming meadow, and abandoned farming broadleaf forest, were compared in terms of their soluble salt content, pH, magnetic susceptibility, and color characteristics in order to establish a method for identifying ancient cultivation layers. It was found that the soluble salt content in the abandoned farming soil profile was significantly lower than that in the natural profile, and the coefficient of variation was also significantly lower than that in the natural profile. The soluble salt content and its coefficient of variation can be used to identify ancient tillage layers without irrigation. Soil magnetic susceptibility and pH also have potential value in identifying ancient cultivation layers, and further in-depth studies are needed.

The potential of light detection and ranging (LiDAR) technology in the application of individual tree segmentation and parameter estimation in desert Haloxylon ammodendron forests has not been explored. This study uses UAV LiDAR data to extract canopy height models (CHM) at spatial resolutions of 0.1, 0.25, 0.5, and 1 m on different interpolation methods, and applies the CHM seed point segmentation algorithm to segment individual trees in three types of Haloxylon ammodendron plots with different growth conditions. This study evaluates the impact of spatial resolution and growth conditions on segmentation accuracy, and verifies the extraction accuracy of tree height and crown width with field measurement data. The results show that the inverse distance weighting interpolation has a higher segmentation accuracy in this study. Spatial resolution is a key factor affecting the results of individual tree segmentation, with the best segmentation results obtained at a resolution of 0.25 m.Class III plots had the highest segmentation accuracy, which was 27% higher than that of Class II plots and 44% higher than that of Class I sample plots. The overlapping crowns of Haloxylon ammodendron in plot I make it difficult to distinguish the crown boundaries, while the independent crowns in plot III make it easier to achieve accurate segmentation. The R² of the tree height fitting model for all three types of plots is around 0.80, with RMSE less than 0.31 m. The R² of the canopy extraction fit for the Class I and II plots is around 0.70, with a slightly higher RMSE error, and the branches in a half dead state of Haloxylon ammodendron in plot III affect the extraction accuracy of crown width. This study demonstrates that LiDAR data has great potential for individual tree segmentation in desert Haloxylon ammodendron forests, which can provide data support for desert forests carbon sink estimation in Xinjiang.

In this paper， we study the Cauchy problem for the 4-th order nonlinear equation {\partial }_{t}h+{\partial }_x^{\mathrm{4}}h+{\partial }_x^{\mathrm{2}}\left({\left|{\partial }_{x}h\right|}^{\alpha }\right)=\mathrm{0} in one dimension for the initial data h_{\mathrm{0}} where \alpha \ge \mathrm{5} and \alpha \in \mathbb{R}. Making use of some subtle estimates of the corresponding linear equation and the nonlinear term， Littlewood-Paley theory， two-norm method， and contraction mapping principle， we obtain the local well-posedness result in nonhomogeneous Besov spaces.

This paper addresses the research challenges in unmanned aerial vehicle communication networks, including significant interference among multiple users, high complexity of dynamic models, and the difficulty in designing real-time optimal control strategies. For the first time, the multi-antenna technology is introduced into the quadcopter unmanned aerial vehicle (UAV) communication network, proposing a complex communication network model under the 3D multi-user multiple-input single-output scenario. This model takes into account the small-scale variations of UAV attitudes and their impact on channel quality. Additionally, the space division multiple access technology is employed to effectively mitigate inter-user interference model predictive control. In this context, an online UAV attitude control and resource allocation strategy based on model predictive control(MPC) algorithm is designed. By iteratively solving the open-loop control problem with a rolling window of limited time duration, the average spectral efficiency of the UAV communication system is maximized. Simulation results demonstrate that the proposed coupled communication model significantly improves system gains, and the control strategy efficiently optimizes UAV flight trajectories and allocates communication resources in dynamic environments.

The use of excavated soil from highway construction sites to produce foamed polymeric soil， employed as a thermal insulation layer for seasonally frozen subgrades， not only achieves comprehensive resource utilization but also mitigates frost damage to the subgrades. Based on the excavated soil along the Urumqi Ring Expressway， combined with cement and foam， the foamed polymeric soil with porous insulation properties was developed. The influences of wet density， soil admixture， and water-cement ratio on the thermal conductivity， unconfined compressive strength， and stiffness of foamed polymeric soil were systematically investigated， establishing an intrinsic connection between pore structure and macroscopic performance. The research findings indicate that when the density of foamed polymeric soil increases from 600 kg/m³ to 1 200 kg/m³， its thermal conductivity approximately doubles， strength increases by about 3.95 times， and modulus increases by approximately 10.5 times. Compared to traditional subgrade soil， the thermal conductivity of foamed polymeric soil is significantly reduced by 52%-96.4%. Further analysis of the pore structure reveals that as the pore size of foamed polymeric soil within a unit volume decreases， the proportion of the skeleton increases correspondingly， while the volume of air pores decreases. This microstructural change manifests as improved thermal insulation performance （i.e.， reduced thermal conductivity） and significant enhancement in mechanical properties （including strength and stiffness） at the macroscopic level. Foamed polymeric soil not only demonstrates better thermal insulation performance but also exhibits strong mechanical characteristics， providing a suitable solution for the thermal insulation layer of seasonally frozen subgrades.

Multitemporal polarimetric synthetic aperture radar (SAR) provides abundant polarimetric scattering information, which is of great value to the long-term monitoring of various crop lands. To make full use of the time correlation and polarimetric information of multitemporal polarimetric SAR, this paper proposed a multitemporal polarimetric SAR crop classification method, which is based on the complete polarimetric covariance matrix. The method can maintain the complex matrix structure of covariance matrix and realize the independent representation of time dimension in tensor space, so that it can be applied to both full- and compact-polarimetric SAR. The method adopted the object-level classification strategy. Firstly, the superpixel segmentation of multitemporal SAR data was achieved by the simple linear iterative clustering (SLIC) method. Then, the covariance matrices of multitemporal SAR were expressed as tensors, and the multilinear principal component analysis (MPCA) method was used to reduce the feature dimension. Finally, the crop classification is achieved by decision tree. In this research, four multitemporal RADARSAT-2 Fine Quad SAR images covered Wuqing District，Tianjin, were used for the crop classification experiments. Compared with methods proposed in other references, the method proposed in this paper achieved the highest overall classification accuracy. Besides, the proposed method was applied to the π/4 mode and the CTLR mode compact-polarimetric SAR to discuss the capability of different kinds of polarimetric SAR in crop classification. Compared with the full-polarimetric SAR, the compact-polarimetric SAR could achieve comparable classification accuracies, but the full-polarimetric SAR performed better at the classes with small sample size, such as rice and lotus.

Single image super-resolution （SISR） can improve the resolution of remote sensing images （RSIs）， thereby improving the application value of data. At present， the number of pixels of RSIs generally reaches hundreds of millions， and it is usually necessary to divide the image into patches when performing SISR. However， there is a lack of relevant research on how to effectively determine the patch size and whether different sizes affect the results. In this paper， taking a large-scale high-resolution RSIs as the experiment data， 3 typical SISR models are selected， 9 groups of SR experiments under different patch sizes are carried out， and the super-resolution （SR） results for the whole of the large-scaled RSI are analyzed comprehensively both qualitatively and quantitatively. The results show that： 1） Cutting of the patches results in stitching seams at the stitching place. In particular， when the patch size is small， a large number of stitching seams show a block effect and the inconsistency is more obvious. 2） With the increase of the patch size， the SR accuracy of the three models is improved， and the overall computational efficiency is also improved. When the test patches are larger than the training patches， the elapsed time and accuracy stabilize. 3） The feasibility and accuracy of the whole RSI input are closely related to the model. The ESPCN model has the best accuracy when inputting the whole RSI， the RDBPN model may cause the accuracy to decrease due to the non-square matrix of the RSI， and the HSENET model has high requirements for computing power and cannot calculate the whole RSI. In conclusion， this paper provides an experimental basis for the selection of patch size for RSI SR engineering applications.

The integration of low earth orbit （LEO） satellite with 5G in the mobile communication system presents extensive application prospects. To ensure effective establishment of communication links between users and base stations， as well as reliable data transmission， time-frequency synchronization technology plays a crucial role. However， traditional timing and frequency synchronization algorithms face limitations when dealing with millimeter wave frequency bands， large bandwidths， high Doppler frequency shifts and change rates， and low signal-to-noise ratios in low-orbit satellite channels. These limitations result in decreased estimation accuracy. For the uplink service data channel of a 5G-based low orbit satellite communication system， a joint timing and frequency synchronization algorithm based on weighted embedded synchronization sequences is proposed to improve the accuracy of timing and frequency offset estimation. Simulation results demonstrate that the proposed timing and frequency synchronization algorithm outperforms traditional algorithms with comparable complexity in terms of synchronization performance.

Huanjing-1A （HJ-1A） CCD1 has four reflected solar bands in visible and near-infrared bands. The on-orbit absolute radiometric calibration frequency of HJ-1A/CCD1 is limited to the annual site calibration. Cross calibration， as a supplement means of site calibration， can achieve high-frequency on-orbit absolute radiometric calibration. In this paper， we select Terra/MODIS as the reference sensor to perform long time series cross calibration on HJ-1A/CCD1 based on Dunhuang calibration site. By limiting conditions such as the imaging angle difference （less than 20°）， the imaging time difference （less than 2 hours）， cloud cover， and imaging quality between HJ-1A/CCD1 and Terra/MODIS， a total of 147 effective cross calibration image pairs were screened out from September 2008 to December 2021. Then the 6SV v2.1 radiative transfer model was used to calculate the spectral band adjustment factor， and ultimately achieving long time series cross calibration of HJ-1A/CCD1. The results show that： 1） The cross-calibration coefficients calculated in this article are highly consistent with the officially published calibration coefficients， with an average relative difference of less than 2.25%. The calibration uncertainty is within 5.34%. 2） The long time series cross calibration results showed that after one year on-orbit operation， the gain status of HJ-1A/CCD1 was adjusted on October 20， 2009， resulting in a sudden change in the cross-calibration coefficients on the 409th day after launch. After the adjustment， the overall radiometric performance was relatively stable. 3） The radiometric performance of HJ-1A/CCD1 showed a slow and fluctuating downward trend from October 2009 to December 2021， with an annual attenuation rate of less than 3.10%. The method proposed in this paper can effectively improve the radiometric calibration frequency and accuracy， and can be used for radiometric performance monitoring over the whole life cycle of HJ-1A/CCD1.

As the initial step of multimodal medical image registration, the accuracy and speed of registration will largely affect the effect of medical image fusion. Due to the large difference in grayscale and texture structure of multimodal medical images, it is difficult to extract correlating features, resulting in low registration accuracy. This paper proposes a multi-layer feature fusion registration network, parallel extraction of features of the fix image and moving image, and the multimodal feature is gradually fused by using the dual-input spatial attention module in the multi-layer structure, obtaining their correlation and mapping such correlation to image registration transformation. At the same time, the structural information loss term guidance network based on dense symmetric scale invariant feature transform is introduced for iterative optimization to achieve accurate unsupervised registration.