Reconstructing historical forest fire risk in the non-satellite era using the improved forest fire danger index and long short-term memory deep learning-a case study in Sichuan Province,southwestern China

Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches,enhancing forest fire warnings and emergency response capabilities,and accurately budgeting potential carbon emissions resulting from fires.However,due to the unavailability of spatial information technology,such databases are extremely difficult to build reliably and completely in the non-satellite era.This study presented an improved forest fire risk reconstruction framework that integrates a deep learning-based time series prediction model and spatial interpolation to address the challenge in Sichuan Province,southwestern China.First,the forest fire danger index(FFDI)was improved by supplementing slope and aspect information.We compared the performances of three time series models,namely,the autoregressive integrated moving average(ARIMA),Prophet and long short-term memory(LSTM)in predicting the modified forest fire danger index(MFFDI).The bestperforming model was used to retrace the MFFDI of individual stations from 1941 to 1970.Following this,the Anusplin spatial interpolation method was used to map the distributions of the MFFDI at five-year intervals,which were then subjected to weighted overlay with the distance-to-river layer to generate forest fire risk maps for reconstructing the forest fire danger database.The results revealed LSTM as the most accurate in fitting and predicting the historical MFFDI,with a fitting determination coefficient(R^2)of 0.709,mean square error(MSE)of0.047,and validation R^2 and MSE of 0.508 and 0.11,respectively.Independent validation of the predicted forest fire risk maps indicated that 5 out of 7 historical forest fire events were located in forest fire-prone areas,which is higher than the results determined from the original FFDI(2 out of 7).This proves the effectiveness of the improved MFFDI and indicates a high level of reliability of the historical forest fire risk reconstruction method proposed in this study.

FITTING CORRECTION METHOD OF RING ARTIFACTS FOR RECONSTRUCTING CONE-BEAM CT IMAGES

In high-resolution cone-beam computed tomography （CBCT） using the flat-panel detector, imperfect or defect detector elements cause ring artifacts due to the none-uniformity of their X-ray response. They often disturb the image quality. A dedicated fitting correction method for high-resolution micro-CT is presented. The method converts each elementary X-ray response curve to an average one, and eliminates response inconsistency among pixels. Other factors of the method are discussed, such as the correction factor variability by different sampling frames and nonlinear factors over the whole spectrum. Results show that the noise and artifacts are both reduced in reconstructed images

Stress field evolution law of mining environment reconstructing structure with change of filling height

For improving global stability of mining environment reconstructing structure,the stress field evolution law of the structure with the filling height change of low-grade backfill was studied by ADINA finite element analysis code.Three kinds of filling schemes were designed and calculated,in which the filling heights were 2,4,and 7 m,separately.The results show that there are some rules in the stress field with the increase of the filling height as follows:(1) the maximum value of tension stress of the roof decreases gradually,and stress conditions are improved gradually;(2) the tension stress status in the vertical pillar is transformed into the compressive stress status,and the carrying capacity is improved gradually;however,when the filling height is beyond 2.8 m,the carrying capacity of the vertical pillar grows very slowly,so,there is little significance to continue to fill the low-grade backfill;(3) the bottom pillar suffers the squeezing action from the vertical pillars at first and then the gravity action of the low-grade backfill,and the maximum value of tension stress of the bottom pillar firstly increases and then decreases.Considering the economic factor,security and other factors,the low-grade backfill has the most reasonable height(2.8 m) in the scope of all filling height.

The Reconstructing of Low Signal-noise Ratio Single Ion Channel Signal from Patch-clamp Recordings Sampled in the Colored Background Noise

The single ion channel signal is an ionic current that can be recorded by the patch clamp technique. Hidden Markov model (HMM) algorithm has been used to convert the low signal noise ratio (SNR) noisy recording into an idealized quantal one in the case of white background noise. The traditional HMM algorithm is extended and adapted to the colored background noise. A new algorithm called EHMM (Extended HMM) algorithm is proposed, and mainly validated by simulation. Results show that it’s effective.

RECONSTRUCTING THE EARLY HISTORY OF THE UR Ⅲ STATE:SOME METHODOLOGICAL CONSIDERATIONS OF THE USE OF YEAR FORMULAE

Ⅰ.INTRODUCTION AND BACKGROUNDIn the introduction of her recent book on Ur-Namma in the Sumerian literarytradition,E. Flckiger-Hawker offers a comprehensive account and summary ofthe previous scholarship of the complicated political history between the fall ofthe Akkadian empire and the beginning of the Ur Ⅲ period.From this account,itbecomes clear that this scholarship primarily relies on various interpretations

Reconstructing Prior Beliefs through Teacher Education Programs——Review of Research into Changes in Language Teacher Beliefs

This review of research into changes in language teacher beliefs reveals that carefully designed language teacher education courses are effective in reconstructing teachers'prior beliefs. When teaching practices are added to the teacher education programs, when adjustments are made to the structure of teacher education courses, and when research practices are incorporated in teacher education programs, significant changes are seen in teachers'beliefs.

Algorithm for reconstructing compressed sensing color imaging using the quaternion total variation

A new method for reconstructing the compressed sensing color image by solving an optimization problem based on total variation in the quaternion field is proposed, which can effectively improve the reconstructing ability of the color image. First, the color image is converted from RGB （red, green, blue） space to CMYK （cyan, magenta, yellow, black） space, which is assigned to a quaternion matrix. Meanwhile, the quaternion matrix is converted into the information of the phase and amplitude by the Euler form of the quatemion. Secondly, the phase and amplitude of the quatemion matrix are used as the smoothness constraints for the compressed sensing （CS） problem to make the reconstructing results more accurate. Finally, an iterative method based on gradient is used to solve the CS problem. Experimental results show that by considering the information of the phase and amplitude, the proposed method can achieve better performance than the existing method that treats the three components of the color image as independent parts.

RECONSTRUCTING TRIANGLES INSCRIBED IN CONVEX BODIES FROM X-RAY FUNCTIONS

This paper provides a method using fixed-point theory for the reconstruction of the triangle inscribed in convex bodies from X-ray functions in three arbitrary mutual directions.

Reconstructing Spatial Distribution of Historical Cropland in China′s Traditional Cultivated Region: Methods and Case Study

As an important part of land use/cover change(LUCC), historical LUCC in long time series attracts much more attention from scholars. Currently, based on the view of combining the overall control of cropland area and ′top-down′ decision-making behaviors, here are two global historical land-use datasets, generally referred as the Sustainability and the Global Environment datasets(SAGE datasets) and History Database of the Global Environment datasets(HYDE datasets). However, at the regional level, these global datasets have coarse resolutions and inevitable errors. Considering various factors that influenced cropland distribution, including cropland connectivity and the limitation of natural and human factors, this study developed a reconstruction model of historical cropland based on constrained Cellular Automaton(CA) of ′bottom-up′. Then, an available labor force index is used as a proxy for the amount of cropland to inspect and calibrate these spatial patterns. Applied the reconstruction model to Shandong Province, we reconstructed its spatial distribution of cropland during 8 periods. The reconstructed results show that: 1) it is properly suitable for constrained CA to simulate and reconstruct the spatial distribution of cropland in traditional cultivated region of China; 2) compared with ′SAGE datasets′ and ′HYDE datasets′, this study have formed higher-resolution Boolean spatial distribution datasets of historical cropland with a more definitive concept of spatial pattern in terms of fractional format.

Reconstructing ancestral ranges in historical biogeography:properties and prospects

Recent years have witnessed a proliferation of quantitative methods for biogeographic inference. In particular, novel parametric approaches represent exciting new opportunities for the study of range evolution. Here, we review a selection of current methods for biogeographic analysis and discuss their respective properties. These methods include generalized parsimony approaches, weighted ancestral area analysis, dispersal-vicariance analysis, the dispersal--extinction--cladogenesis model and other maximum likelihood approaches, and Bayesian stochastic mapping of ancestral ranges, including a novel approach to inferring range evolution in the context of island biogeography. Some of these methods were developed specifically for problems of ancestral range reconstruction, whereas others were designed for more general problems of character state reconstruction and subsequently applied to the study of ancestral ranges. Methods for reconstructing ancestral history on a phylogenetic tree differ not only in the types of ancestral range states that are allowed, but also in the various historical events that may change the ancestral ranges. We explore how the form of allowed ancestral ranges and allowed transitions can both affect the outcome of ancestral range estimation. Finally, we mention some promising avenues for future work in the development of model-based approaches to biogeographic analysis.

CLDASSD:Reconstructing Fine Textures of the Temperature Field Using Super-Resolution Technology

Before 2008,the number of surface observation stations in China was small.Thus,the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration’s Land Assimilation System(HRCLDAS)which ultimately inhibited the output of high-resolution and high-quality gridded products.This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem.Specifically,we take temperature as an example.The model is used to downscale the 0.0625°×0.0625°,2-m temperature data from the China Meteorological Administration’s Land Data Assimilation System(CLDAS)to 0.01°×0.01°,named CLDASSD.We performed quality control on the paired data from CLDAS and HRCLDAS,using data from 2018 and 2019.CLDASSD was trained on the data from 31 March 2018 to 28 February 2019,and then tested with the remaining data.Finally,extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology.Taking the HRCLDAS product and surface observation data as the"true values"and comparing them with the results of bilinear interpolation,especially in complex terrain such as mountains,the root mean square error(RMSE)of the CLDASSD output can be reduced by approximately 0.1℃,and its structural similarity(SSIM)was approximately 0.2 higher.CLDASSD can estimate detailed textures,in terms of spatial distribution,with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks.

Reconstructing bubble profiles from gas-liquid two-phase flow data using agglomerative hierarchical clustering method

The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved computational fluid dynamics (CFD) simulations. To obtain this information, an efficient bubble profile reconstruction method based on an improved agglomerative hierarchical clustering (AHC) algorithm is proposed in this paper. The reconstruction method is featured by the implementations of a binary space division preprocessing, which aims to reduce the computational complexity, an adaptive linkage criterion, which guarantees the applicability of the AHC algorithm when dealing with datasets involving either non-uniform or distorted grids, and a stepwise execution strategy, which enables the separation of attached bubbles. To illustrate and verify this method, it was applied to dealing with 3 datasets, 2 of them with pre-specified spherical bubbles and the other obtained by a surface-resolved CFD simulation. Application results indicate that the proposed method is effective even when the data include some non-uniform and distortion.

Reconstructing the portal vein through a posterior pancreatic tunnel:New choice for portal vein thrombosis during liver transplantation

BACKGROUND Thrombectomy and anatomical anastomosis(TAA)has long been considered the optimal approach to portal vein thrombosis(PVT)in liver transplantation(LT).However,TAA and the current approach for non-physiological portal reconstructions are associated with a higher rate of complications and mortality in some cases.AIM To describe a new choice for reconstructing the portal vein through a posterior pancreatic tunnel(RPVPPT)to address cases of unresectable PVT.METHODS Between August 2019 and August 2021,245 adult LTs were performed.Forty-five(18.4%)patients were confirmed to have PVT before surgery,among which seven underwent PV reconstruction via the RPVPPT approach.We retrospectively analyzed the surgical procedure and postoperative complications of these seven recipients that underwent PV reconstruction due to PVT.RESULTS During the procedure,PVT was found in all the seven cases with significant adhesion to the vascular wall and could not be dissected.The portal vein proximal to the superior mesenteric vein was damaged in one case when attempting thrombolectomy,resulting in massive bleeding.LT was successfully performed in all patients with a mean duration of 585 min(range 491-756 min)and mean intraoperative blood loss of 800 mL(range 500-3000 mL).Postoperative complications consisted of chylous leakage(n=3),insufficient portal venous flow to the graft(n=1),intra-abdominal hemorrhage(n=1),pulmonary infection(n=1),and perioperative death(n=1).The remaining six patients survived at 12-17 mo follow-up.CONCLUSION The RPVPPT technique might be a safe and effective surgical procedure during LT for complex PVT.However,follow-up studies with large samples are still warranted due to the relatively small number of cases.

Hamstring autograft utilization in reconstructing anterior cruciate ligament:Review of harvesting techniques,graft preparation,and different fixation methods

Rupture of the anterior cruciate ligament(ACL)is a common orthopedic injury.Various graft options are available for the reconstruction of ruptured ACL.Using the hamstring muscle as an autograft was first described in 1934,and it remains a commonly harvested graft for ACL reconstruction.Hamstring autografts can be harvested using the traditional anteromedial approach or the newer posteromedial technique.An isolated semitendinosus tendon can be used or combined with the gracilis tendon.There are numerous methods for graft fixation,such as intra-tunnel or extra-tunnel fixation.This comprehensive review discusses the different hamstring muscle harvesting techniques and graft preparation options and fixation methods.It provides a comprehensive overview for choosing the optimal surgical technique when treating patients.

Reconstructing the Nonlinear Dynamical Systems by Evolutionary Computation Techniques

We introduce a new dynamical evolutionary algorithm（DEA） based on the theory of statistical mechanics and investigate the reconstruction problem for the nonlinear dynamical systems using observation data. The convergence of the algorithm is discussed. We make the numerical experiments and test our model using the two famous chaotic systems （mainly the Lorenz and Chen systems）. The results show the relatively accurate reconstruction of these chaotic systems based on observational data can be obtained. Therefore we may conclude that there are broad prospects using our method to model the nonlinear dynamical systems.

An efficient inverse approach for reconstructing time-and space-dependent heat flux of participating medium

The decentralized fuzzy inference method(DFIM)is employed as an optimization technique to reconstruct time-and space-dependent heat flux of two-dimensional(2D)participating medium.The forward coupled radiative and conductive heat transfer problem is solved by a combination of finite volume method and discrete ordinate method.The reconstruction task is formulated as an inverse problem,and the DFIM is used to reconstruct the unknown heat flux.No prior information on the heat flux distribution is required for the inverse analysis.All retrieval results illustrate that the time-and spacedependent heat flux of participating medium can be exactly recovered by the DFIM.The present method is proved to be more efficient and accurate than other optimization techniques.The effects of heat flux form,initial guess,medium property,and measurement error on reconstruction results are investigated.Simulated results indicate that the DFIM is robust to reconstruct different kinds of heat fluxes even with noisy data.

A Scheme for Reconstructing Free-form Surface from Shading with Cellular Automata

The reconstruction of free-form surface is a fundamental problem in digital manufacture.This article presents a novel solution to reconstructing free-form surface from an intensity image under the Lambertian reflection model,that is a method called shape from shading (SFS).Our approach is based on the two-dimensional cellular automata (CA),and fully uses the local information of both image and the reconstructed surface.After several iterations,the free-form surface corresponding to the input image is obtained.The encouraging results on both synthetic and real-world images are provided in this paper,and the perfor- mance of our algorithm is analyzed on synthetic images using mean and standard deviation of depth (Z) errors.

Experiments of Reconstructing Discrete Atmospheric Dynamic Models from Data (I)

In this paper, we give some experimental results of our study in reconstructing discrete atmospheric dynamic models from data. After a great deal of numerical experiments, we found that the logistic map, xn +1= 1-uxn2 could be used in monthly mean temperature prediction when it was approaching the chaotic region, and its predictive results were in reverse states to the practical data. This means that the nonlinear developing behavior of the monthly mean temperature system is bifurcating back into the critical chaotic states from the chaotic ones.

A method of reconstructing complex stratigraphic surfaces with multitype fault constraints

The construction of complex stratigraphic surfaces is widely employed in many fields, such as petroleum exploration, geological modeling, and geological structure analysis. It also serves as an important foundation for data visualization and visual analysis in these fields. The existing surface construction methods have several deficiencies and face various difficulties, such as the presence of multitype faults and roughness of resulting surfaces. In this paper, a surface modeling method that uses geometric partial differential equations （PDEs） is introduced for the construction of stratigraphic surfaces. It effectively solves the problem of surface roughness caused by the irregularity of stratigraphic data distribution. To cope with the presence of multitype complex faults, a two-way projection algorithm between three- dimensional space and a two-dimensional plane is proposed. Using this algorithm, a unified method based on geometric PDEs is developed for dealing with multitype faults. Moreover, the corresponding geometric PDE is derived, and an algorithm based on an evolutionary solution is developed. The algorithm proposed for constructing spatial surfaces with real data verifies its computational efficiency and its ability to handle irregular data distribution. In particular, it can reconstruct faulty surfaces, especially those with overthrust faults.

Reconstructing Concept Lattices Using nth-Order Context Kernels

To be different from traditional algorithms for concept lattice constructing, a method based on nth-order context kernel is suggested in this paper. The context kernels support generating small lattices for sub-contexts split by a given context. The final concept lattice is reconstructed by combining these small lattices. All relevant algorithms are implemented in a system IsoFCA. Test shows that the method yields concept lattices in lower time complexity than Godin algorithm in practical case.