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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Being closely related to human socioeconomic activity and its footprints, nighttime light (NTL) data shows great advantages in urbanization and socioeconomic development research, especially in densely populated cities. Based on CiteSpace software and the core databases of CNKI (China National Knowledge Infrastructure) and WOS (Web of Science), this paper tracked the hot spots of NTL data in the study of urbanization and related resource consumption and environmental effects from 2000 to 2019. The main results are as follows. 1) Urbanization was the main focus of the application of NTL data, but the researches on the resource consumption and environmental effects caused by urbanization were slightly weak, which was more obvious in Chinese literature. 2) Researches of urban expansion and urban form evolution focused on process of land expansion based on different features of NTL datasets, while in researches of population, socioeconomic development, electricity consumption and carbon emissions, NTL data usually played the role as a supporting tool to explore spatiotemporal characteristics and mechanism. 3) In regards of air pollution and urban heat island induced by urbanization, NTL datasets were usually used to represent factors related to human activities and their impacts. 4) Urbanization process and its impacts on resource and environment are complex, the improved spatial resolution and integrated multi-source data, along with new methods as machine learning, will make the urbanization related research be more precise and scientific. Finally, the paper summarizes the possible new directions of the application of NTL data in urban geography.

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 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 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%.

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”.

The area of volcanic and plutonic rocks in the Greater Khingan Range accounts for about 75%, characterized by the development of copper, molybdenum, tin, lead zinc, gold and silver, and rare metal minerals related to magmatic and hydrothermal activities. The middle section of the Greater Khingan Range, where the Potoushan Kaolin Deposit is located, mainly develops a compound mineralization system consisting of porphyry type mineralization, epithermal type mineralization, cryptoexplosive breccia type mineralization, and skarn type mineralization, but its overall exploration level is relatively low. Samples from the mining pit and shallow drill cores of the Potoushan kaolin mine have been identified minerals such as kaolinite, dikaite, alunite, pyrophyllite, microveined quartz, chalcedony quartz, sericite, chlorite, boehmite, gypsum, barite, pyrite, sphalerite, tellurite, pyrargyrite, cinnabar, limonite, etc., with the characteristic of developing low-temperature advanced argillation such as kaolinization, dickitization, alunitization, clayification, and silicification (chalcedony quartz), belonging to the top lithocap of deep porphyry-epithermal system. Based on the identified typical altered minerals, trace metal minerals, and four types of hydrothermal breccia within the mining area, it is speculated that there may be a potential ore bearing magma-hydrothermal system in the deep of Potoushan lithocaps. For the Greater Khingan Range, the extensive magmatic activity from the Paleozoic to Mesozoic and the moist and thick forest cover provided sufficient conditions for the development and preservation of lithocaps. It is suggested that future geological research and exploration work should pay more attention to lithocap for discovering more lithocaps, identifying their altered minerals, and researching their genetic mechanisms, to provide theoretical supports for further exploration of potential ore bearing magma-hydrothermal systems in the region.

The space exploration missions for precise attitude control and orbit adjustment require spacecraft propulsion systems with micro-thrust, high precision, and wide-range continuous adjustment. The cusp-type Hall thruster has the characteristics of simple structure, large thrust range, low power consumption and long working life. This paper proposes a micro-newton cusp Hall thruster with narrower channel and stronger magnetic field. Under micro-flow rate conditions, the narrower channel increases the density of the propellant in the discharge chamber, and the stronger magnetic field improves the confinement efficiency of electrons. This promotes the collision ionization process between electrons and propellant atoms, and the stable thrust of the thruster output is realized. The experimental results show that the flow range(propellant is Xe) is 0.5-1.0 sccm, the voltage range is 0-300 V, the thrust range reaches 5.4-518.9 μN. The response time is better than 150 ms, and the thrust noise reaches 0.1 μN/Hz^1/2 at 0.05-1 Hz. When the flow rate of propellant Xe is 0.5 sccm and the voltage is 500 V, the thrust output reaches 50 μN and the specific impulse reaches 104 s. By optimizing the magnetic field design, the performance of this type of micro-newton cusp Hall thruster can be further improved, which can meet the requirements of space exploration missions.

This paper explores earthquake prediction using neural networks, focusing mainly on using long-short-time memory (LSTM) neural networks to construct an earthquake prediction model. Based on the Southern California earthquake catalog data from 1932 to 2021, the earthquake catalog from January 1932 to March 2002 was used as the training set(80% of the entire earthquake catalog), and the earthquake catalog from March 2002 to September 2021 was used as the test set (the remaining 20%). The LSTM neural network was selected, and 11 earthquake prediction factors reflecting the spatiotemporal intensity distribution characteristics of the earthquake time series data were calculated from the training set. The maximum magnitude label corresponding to these factors was used to construct the model. The test set was then used for retrospective prediction testing. The model’s prediction performance was evaluated using metrics such as accuracy, precision, and R-value, which were calculated based on the values in the confusion matrix. The results show that the prediction has achieved certain results, predicting the M7.2 earthquake in April 2010. The R-value of some models is significantly higher than China’s current medium-term prediction level. However, the value of the evaluation model is still not ideal, and further exploration is needed.

The study uses factor analysis, coupled coordination degree model, the Theil index, vector autoregression model, and other methods, and combines geographic spatial expressions to make a specific analysis of the coordinated benefits of China’s regional innovation capacity and economic development level as well as the dynamic evolution process of the two from 2002 to 2020. The results found that: 1) From 2002 to 2020, China’s regional innovation capacity and economic development level showed a synchronous growth trend, and the coupling coordination changed from weak to strong, roughly going through a temporal evolution process of “lagging coordination (2002-2006) → coordinated transition (2008-2010) → coordinated development (2012-2020) ”, showing the stage characteristics of tending to high-quality coordination transition. 2) Spatially, the degree of coupling coordination decreases from the coast to the interior, showing obvious characteristics of a step-like distribution. The Theil index fluctuates between 0.033 6 and 0.071 2, with small regional spatial differences and a decreasing trend. The disparity between groups is more significant than the disparity within groups. The intra-group gap and the contribution rate of the eastern and western regions are significantly higher than those of the central and northeastern regions, and both have a stronger influence on the overall geographical differences across the country. 3) There is a long-term stable equilibrium relationship between regional innovation capacity and economic development level, and regional innovation capacity is not the Granger cause of economic development level, but regional innovation capacity has a more significant contribution to China’s economic development level, and its contribution rate to economic development level is over 50%.

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.

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.

Aiming at the rapid changes and complex relations in the stock market, this paper proposes a stock price prediction method based on individual stock news. First, through dynamic time warping algorithm，the benchmark sequence with the highest similarity to the target individual stock sequence is found, and then we can extract the length and time of news impact through smooth-and-abrupt change point model, which is converted into time series data. We introduce the relationship between stocks into time series forecasting through statistical models, examine the relationship between news influence and historical stock price data, and combine news influence with individual stock data for price forecasting by using long-and-short-term memory network. The results show that the stock sector’s influence of news in the technology sector is the most obvious. Compared to existing stock prediction methods, the prediction performance of the fusion model has been improved, and the prediction accuracy has decreased slightly over time. The fusion model can more accurately describe the changes in stock prices, achieving an average return of 14.50% under the conditions of simulating investment strategies.

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.

Remote sensing image-text cross-modal retrieval technology can quickly obtain valuable information from massive remote sensing data. However, existing remote sensing image-text retrieval methods have limitations in utilizing multi-scale information within remote sensing images, and the weak recognition of target information leads to relatively low retrieval accuracy. To address these issues, this paper proposes a new method for remote sensing image-text cross-modal retrieval. This method mainly comprises a multi-scale feature fusion module and a salient feature enhancement module, which are designed to integrate multi-scale information of remote sensing images and enhance the expression of target information in remote sensing images, so as to improve the precision of remote sensing image-text cross-modal retrieval. Experimental validation was conducted on two publicly available remote sensing image-text datasets. The results demonstrate that the proposed method outperforms other methods across most evaluation metrics in the remote sensing image-text cross-modal retrieval task and exhibits the best overall retrieval performance.

Forest cover information extraction is one of the essential tasks in forest remote sensing applications, which is of great significance for forest resource management, ecological environment protection, and climate change research. Traditional convolutional neural network-based methods can effectively extract local features, but struggle to capture long-range dependencies and global context information. To address this issue, we propose a method for forest cover information extraction that fuses Transformer and UNet, referred to as DiUNet. This approach embeds Transformer modules into the UNet network to enhance its perception of long-range dependencies and global context information. Meanwhile, considering the fragmentation, irregularity, and inconsistent scale of forest cover information, our method enhances the model’s ability to capture spatial information by using relative position encoding to increase the positional information, enabling the model to capture features at different levels and scales. We constructed a forest cover information dataset based on Landsat 8 and CDL data layers and conducted in-depth experimental analyses on this dataset. In the comparative experiments, DiUNet achieved the best results in accuracy, recall, F₁ score, intersection-over-union, and frequency-weighted intersection-over-union indices, which were 91.22%, 92.66%, 91.94%, 85.08%, and 81.65%, respectively. The model also performed well in generalization experiments. The experimental results show that the DiUNet method outperforms existing methods in forest cover information extraction and has high robustness and generalization capabilities.

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.

Low earth orbit (LEO) communication satellites can break through terrain constraints and work with 6G to build an integrated space-ground information network. In terms of the beam scheduling problem of satellites for fixed terminals on the ground, a beam scheduling strategy that can achieve dual optimization of interference and delay is proposed, considering that the uneven distribution of global user demands exists. The model with the optimization goal of minimizing the queuing delay and co-channel interference is constructed, combining with constraints such as transmit power as well as carrier-to-noise ratio. By means of step-by-step optimization, a beam-hopping scheme including demand clustering, time slot allocation and beam position matching is designed. When it comes to the interference optimization problem in the beam position matching process, a genetic algorithm-based chromosome crossover mechanism of “beam position self-crossover within a cluster” is proposed. The simulation results show that the improved genetic algorithm can reduce the co-channel interference by 32% to 58% compared with the other algorithms. Besides, the proposed strategy can schedule the beam within the resource allocation period while achieving dual optimization of delay and interference.

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.

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.

The magnetic penetration depth of the external magnetic field into the superconductor varies with the temperature, especially near the superconducting transition temperature, the penetration depth changes dramatically. Detecting the change of the penetration depth can achieve high-resolution measurement of temperature changes, which is an important principle of deep and low temperature measurement. Based on this basic principle, this work studies a temperature measurement scheme using the change of superconductor penetration depth, the quantization of closed superconducting loop magnetic flux and the superconducting quantum interference device (SQUID), carries out theoretical analysis and simulation, which is expected to achieve a temperature resolution of nK/$\sqrt{\mathrm{Hz}}$ in the liquid helium temperature region, and gives the specific experimental device design. This method offers high resolution, does not introduce additional heat flow, and does not require continuous current excitation. It can greatly reduce the adverse effects of the traditional temperature measurement caused by thermometer self-heating effect and contact thermal resistance. By plating superconducting film on the surface of the object，which is beneficial to temperature measurement experiments and applications below 10 K.

MEC (mobile edge computing) is the supporting technology for the 6G mobile communication network to connect communication and service and realize the smart connection of everything. For the computational delay optimization of the MEC system, a horizontal multi-host architecture is proposed and a complete signaling flow is designed. For the transmission delay optimization of the MEC system and the straggler problem of multi-host parallel computation, a master-slave architecture of multi-connectivity is proposed and a complete signaling flow is designed. For the evaluation of MEC system performance, a multi-host MEC simulation platform based on the open-source libraries is built. The experiments show that the horizontal multi-host MEC architecture effectively improves computational latency performance; the proposed master-slave MEC architecture of multi-connectivity effectively alleviates the straggler problem and improves transmission latency performance; the built MEC simulation platform can effectively evaluate the key performance indicators of the multi-host architecture.