With the development of 3D data acquisition technology, point cloud wins the favor of researchers for it's simple but effective representation and it is widely used in the fields of remote sensing, scene reconstruction, 3D modeling, etc. Considering that the data acquisition process is easily disturbed by many factors such as equipment, environment and material, raw point cloud is often corrupted with noise and so it is of great significance to explore robust and efficient denoising algorithms. This paper firstly investigates the relevant research works of point cloud denoising and divides them into traditional algorithms based on the optimization idea and denoising algorithms based on the deep learning idea according to the implementation principles. Secondly, the research progress of each kind of algorithm is discussed and a detailed analysis of representative algorithms is presented. Thirdly, the data sets, the evaluation metrics and experimental results are summarized with an in-depth comparison. Finally, the problems and possible development directions and trends of point cloud denoising are prospected.

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.

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

Youjiang basin (Nanpanjiang basin) experienced a complex tectonic evolution of Paleozoic prototype basin-superimposed basin, and finally, it appears as a residual basin. According to the tectonic setting, sedimentary series and magmatic rocks, the evolution of Youjiang basin after Caledonian movement can be divided into six stages:intracontinental extensional basin (early rift valley) evolution stage (D²₁-D¹₂), oceanic extensional basin (rift ocean basin) evolution stage (D₂-T₁), ocean basin extinction and foreland flexure basin evolution stage (T²₁-T¹₃), fold orogeny and post collisional extension stage (T¹₃-J₁), NW trending compression orogeny stage (J₂-K²₁), and local extension stage (K³₁-E). There are a large number of Carlin-type gold deposits in the basin, and most ore bodies occur in thrust-fold belts. The Carlin-type gold deposit has multi-stage metallogenic characteristics, large-scale mineralization mainly began in the compressive background and continued to the post orogenic extension stage, and there are two concentrated metallogenic periods. The first stage was formed in the evolution stage of foreland flexure basin related to collision orogeny to post collisional extension (235-193 Ma, Carlin-type gold deposit in the central and southern part of the basin). The mineralization in this stage was controlled by metamorphic hydrothermal fluids or hydrothermal fluids relate to magmatic melting which induced by the superposition of collision orogeny of the Yangtze Block and the Indochina Block and the subduction of the Paleo-Pacific plate to the Eurasian continent. The second stage was formed in the stage of NW trending compression orogeny to local extension (148-103 Ma, Carlin-type gold deposit in the whole Youjiang basin). The mineralization in this stage was mainly affected by the magmatic hydrothermal activities during the superposition and transformation of the pre-existing structures by the NW trending compression orogeny. Magmatic or metamorphic hydrothermal activities under extensional background before the Early Triassic has the effect of initial enrichment, and it has the effect of superimposed and reformation post mineralization after the Early Cretaceous. The ore-forming fluid of Carlin-type gold deposit has the characteristics of mixed sources. It is mainly metamorphic hydrothermal solution in the central and southern part of the basin, and mostly mixed source hydrothermal solution in the central part of the basin. While, the ore-forming fluid is mainly magmatic hydrothermal solution in the northern part of Youjiang basin. Through comparative analysis, it is considered that the ages obtained by different dating methods can represent the metallogenic age to a certain extent. And the combined application of multiple methods should be used to limit the metallogenic age of Carlin-type gold deposits.

Recently, a major feature of global warming is asymmetrical temperature increase during day and night, i.e., the rate of warming at night is greater than that during the day. However, effects of asymmetrical warming have not yet attracted widespread attention. In this study, by collecting historical observation data of 838 meteorological stations in China, and using methods such as trend estimation, the characteristics of the temporal and spatial variation of diurnal temperature range (DTR) and the long-term trends of DTR from 1952 to 2018 were analyzed. The results indicated that:spatially, the regions with the highest average annual DTR in the country were distributed in the northwest and southwest, followed by the northeast and north China, and the areas with lowest DTR located in central, eastern, and southern China. The average monthly DTR in the year was generally multimodal. Among four seasons, DTR in spring and autumn was higher than in summer. From 1952 to 2018, the annual average DTR decreased significantly (τ=-0.396; p<0.01). Long-term changes in DTR were positively correlated with evaporation, sunshine hours, latitude and altitude, and negatively correlated with wind speed, precipitation, relative humidity, station pressure, total cloudiness, and longitude.

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

Status on remediation of contaminated sites in developed countries and regions, such as the United States, Canada, the United Kingdom, the Netherlands, Japan, and Taiwan, were reviewed in this paper, including legislations, pollutants of concern, technologies, source of remediation funds, verification of remediation. Then, the similarities and differences of the five aspects in these five countries and Taiwan with mainland China were compared. The results showed that China had made special legislation for soil pollution prevention and control, the number of contaminated sites is expected to be initially figured out after the detailed soil survey, and the gap with developed countries and regions has been significantly reduced. However, it is still facing the fact that government is mainly responsible for the remediation of historical sites, extensive technology prevails and lone time-consuming verification of remediation. The implementation of the "Soil Pollution Prevention Law" should be taken as an opportunity, combined with the implementation of accountability, effectiveness evaluation, damage compensation and other systems, to properly solve the problem of remediation funds, strictly control historical pollution risks, effectively prevent and control new pollution, build a long-term management mechanism for risk management and restoration of contaminated sites, and maximize environmental, economic and social benefits.

There exist various vortical structures in the turbulent flow field of confined impinging jet reactors, which have important effects on the flow characteristic and mixing performance. A numerical study was conducted on the flow field of a confined impinging jet reactor using the IDDES method based on SST k-ω turbulent model. Various large-scale vortical structures were identified, and their generation and evolution mechanisms were discussed. The effects of vortices on the phase-averaged temperature and heat flux fields were discussed to reveal the mixing mechanism. Results indicated that the alternating deflection of two jets leaded to unequal momentum between them, and the self-sustained oscillation of two jet shear layers caused the periodic motion of vortices, hence the stagnation point deviated from the mid-point periodically. Large-scale spanwise vortices were generated in the downstream region due to the development of K-H instability. The streamwise vortices in the impinging region only enhanced the thermal mixing near the channel axis, while the spanwise vortices could stimulate a large-scale heat transport and turbulent mixing in the downstream region.

Mobile ad hoc network (MANET) is widely used in emergency rescue scenarios because of its dynamic network reconfiguration. However, the dynamic topology of MANET causes routes to be easily broken and route reconstruction will occupy more network resources. To solve this problem, this paper proposed a new multipath routing protocol FLMP based on flooding limiting and multiple measurement functions. It uses the flooding limiting mechanism based on node mobility, combines local and global optimal ideas to construct routs, and uses multiple route measurement functions to select the optimal main route and backup route for data transmission. The simulation results show that the FLMP routing protocol can effectively reduce the number of routing breaks, ensure reliable data transmission, and significantly reduce overhead.

On the basis of the previous work, considering the solar heat absorbed by the lunar surface inward and the lunar thermal radiation heat released outward, as well as the nonlinearities of the thermodynamic parameters of the lunar soil related to the temperature and depth, we have developed a thermo-elastic coupled finite element parallel program suitable for the study of the temporal and spatial evolutions of the temperature, deformation, and thermal stress of the lunar soil, and have utilized the four sets of finite element models to investigate the effects of the characteristic thicknesses of the lunar soil on the temporal and spatial evolutions of the temperature, deformation and thermal stresses of the lunar surface. The computational results show that the temperature of the lunar surface varies periodically over one lunar day (29.5 Earth days), and the temperature of the equatorial lunar surface varies from 100 to 385 K, with the variation decaying exponentially with the increase of the depth, and the depth of influence reaches to about 50 cm. The temperature cyclic changes also cause the vertical displacement of the lunar surface to rise and fall, and the horizontal normal stress of the lunar surface in the form of compression and tension. In general, the horizontal stresses are compressed during the day and tensile during the night, with the fastest increase in tensile stress at 18:00 and the highest tensile stress at 06:00. The characteristic thickness of the lunar soil has a strong influence on the temporal and spatial distributions of the temperature and the horizontal positive stresses. The magnitude of thermal stresses may reach the tensile strength of the lunar surface. The fastest growth of tensile stress and the period of maximum amplitude coincide with the observed high frequency of thermal events on the lunar surface in the morning and evening.

The physical unclonable function (PUF) implemented on SRAM and other schemes exists inherent demerit of poor reproducibility for environmental factors such as voltage changes and thermal noise. This disadvantage greatly restricts functional application in cryptography, communication and other fields. In this paper, a fuzzy extractor with large error correction capacity is designed to reconstruct the original data of SRAM by means of BCH codes (Bose-Chaudhuri-Hocquenghem Codes). The SRAM PUF chip applying this design is manufactured on the Hua Hong Grace 0.11 μm CMOS platform with area of 306 267 μm². The original BCH code has a code length of 127 bits and an error correction capability of 27 bits, which achieves the practical requirements of PUF applications.

In recent years, multi-agent reinforcement learning has received a lot of attention from researchers. In the study of multi-agent reinforcement learning, the question of how to perform ad-hoc cooperation, i.e., how to adapt to a changing variety and number of teammates, is a key problem. Existing methods either have strong prior knowledge assumptions or use hard-coded protocols for cooperation, which lack generality and can not be generalized to more general ad-hoc cooperation scenarios. To address this problem, this paper proposes a local observation reconstruction algorithm for ad-hoc cooperation, which uses attention mechanisms and sampling networks to reconstruct local observations, enabling the algorithm to recognize and make full use of high-dimensional state representations in different situations and achieve zero-shot generalization in ad-hoc cooperation scenarios. In this paper, the performance of the algorithm is compared and analyzed with representative algorithms on the StarCraft micromanagement environment and ad-hoc cooperation scenarios to verify the effectiveness of the algorithm.

Contact detection takes the most time in the calculation process of discontinuous deformation analysis method. Contact detection consists of two steps:contact coarse detection and contact precise detection; contact coarse detection searches all possible block pairs in the calculation space; the contact precise detection determines the specific contact position for the block pairs from the contact coarse detection results for subsequent mechanical treatment. In this paper, an efficient contact coarse detection algorithm based on voxel meshing is proposed. The algorithm divides the complex blocks that meet the specific conditions into sub grids, which effectively reduces the number of generated pre-detected blocks. The algorithm has been integrated into the discontinuous deformation analysis program and tested by classical examples. The results show that the proposed algorithm has obvious advantages over the existing algorithms.

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

Online social networks have become the disaster areas where rumors grow. It is of great significance to identify core rumor spreaders for rumor prevention and control. The traditional rumor control model is mainly based on the dynamics of rumor propagation, and it is mainly focused on in-event or post-event control. In view of the timeliness of rumor control, this paper proposes a multi-stage graph convolutional network based on multi-dimensional features (MSF-GCN) deep learning model to accurately locate core rumor spreaders as early as possible and block rumor diffusion from the source. This work compares the MSF-GCN method with other three baseline methods on rumor data set, and the experimental results verify that our method is more efficient.

With the development of remote sensing satellite technology, the temporal and spatial resolution of remote sensing data has been continuously improved, showing the trend of big data and massive quantification, which has brought challenges to the screening of remote sensing data. Traditional remote sensing data retrieval often has the problem of large amount of query results and high overlap. It requires manual data selection, which is inefficient and low in accuracy. Therefore, how to quickly and accurately find the required data from a large number of remote sensing images is a problem that needs to be solved urgently. In this paper, a remote sensing data screening algorithm based on area coverage is used to divide the target area into non-overlapping fragments using the effective range of images. A normalized mathematical calculation model is established based on the number of fragments contained in the image, imaging time, and cloud cover. The model obtains a comprehensive cost. An optimal image combination is selected according to the cost, which completely covers the target area. This paper confirms the effectiveness of the method through Landsat8 data, and improves the screening efficiency through parallel computing.

For the unsteady process of step-type landslide displacement, a method combining ensemble empirical mode decomposition (EEMD), Prophet, and long short time memory network (LSTM) to predict landslide displacement is proposed. The displacement data of Baishuihe landslide was taken as examples. The displacement time series was decomposed into residual(RES) and several intrinsic mode functions(IMF) by EEMD. The superimposition of IMFS which included periodic factors and random factors was considered as a volatility item, and the RES was regarded as a trend term. The trending term was fitted by the Prophet and the the volatility term was predicted by LSTM. The addition of the two prediction results was the predictied value of the landslide displacement. The results show that the coefficient of determination(R²) of the EEMD-Prophet-LSTM model is above 0.98 for Baishuihe landslide displacement prediction, which is better than traditional machine learning methods such as support vector machine and artificial neural network. Moreover, the prediction accuracy R² of this method for each monitoring point of the Bazimen landslide is also above 0.96, which proves the applicability of this method.

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

Traditional synthetic aperture radar (SAR) image change detection methods have some problems, such as big impact by speckle noise, difficult to use deep information of the image, and low detection accuracy. To solve above problems, this paper presents an SAR amplitude image change detection algorithm based on convolution neural network and fuzzy clustering. Firstly, a hierarchical FLICM algorithm based on Gabor texture is used to pre-classify the difference images, and reliable training samples are automatically selected based on the pre-classification results without manual labeling. Then, a multiscale channel attention mechanism is introduced, and a MSCA_WCNN is used to complete the second classification, and the result of change detection is obtained. This algorithm extracts the different scale features of SAR images while suppressing the irrelevant feature channels to effectively utilize the image features. The wavelet convolution neural network achieves the denoising function while preserving the useful information of the image and enhances the robustness of the algorithm. The comparison experiments using real spaceborne SAR image data show that the algorithm has high detection accuracy and the effectiveness of the algorithm is verified.

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