Accurate engine performance models are important for model-based performance evaluation of aero engine.The accuracy of the model often depends on engine component maps,so there is a need for a method that can accurate...Accurate engine performance models are important for model-based performance evaluation of aero engine.The accuracy of the model often depends on engine component maps,so there is a need for a method that can accurately correct the component maps of the model over a wide range.In this paper,a new method for modifying component maps is proposed,this method combines the correction of the scaling factors with the solution process of the off-design working point,and uses the adjustment of the variable geometric parameters of the engine to change the position of the working line,in order to obtain more correction results and guarantee high accuracy in a wider range.The method is validated by taking the main fan of the Adaptive Cycle Engine(ACE),an ideal power unit for a new generation of multi-purpose and ultra-wide working range aircraft,as an example.The results show that the maximum error between the corrected component maps and the target maps is less than 1%.New possibility for more precise component maps can be realized in this paper.展开更多
With the development of the Internet,image encryption technology has become critical for network security.Traditional methods often suffer from issues such as insufficient chaos,low randomness in key generation,and po...With the development of the Internet,image encryption technology has become critical for network security.Traditional methods often suffer from issues such as insufficient chaos,low randomness in key generation,and poor encryption efficiency.To enhance performance,this paper proposes a new encryption algorithm designed to optimize parallel processing and adapt to images of varying sizes and colors.The method begins by using SHA-384 to extract the hash value of the plaintext image,which is then processed to determine the chaotic system’s initial value and block size.The image is padded and divided into blocks for further processing.A novel two-dimensional infinite collapses hyperchaotic map(2DICHM)is employed to generate the intra-block scrambling sequence,while an improved variable Joseph traversal sequence is used for inter-block scrambling.After removing the padding,3D forward and backward shift diffusions,controlled by the 2D-ICHM sequences,are applied to the scrambled image,producing the ciphertext.Simulation results demonstrate that the proposed algorithm outperforms others in terms of entropy,anti-noise resilience,correlation coefficient,robustness,and encryption efficiency.展开更多
Efficient numerical solver for the SchrSdinger equation is very important in physics and chemistry. The finite element discrete variable representation （FE-DVR） was first proposed by Rescigno and Mc-Curdy [Phys. Rev...Efficient numerical solver for the SchrSdinger equation is very important in physics and chemistry. The finite element discrete variable representation （FE-DVR） was first proposed by Rescigno and Mc-Curdy [Phys. Rev. A 62, 032706 （2000）] for solving quantum-mechanical scattering problems. In this work, an FE-DVR method in a mapped coordinate was proposed to improve the efficiency of the original FE-DVR method. For numerical demonstration, the proposed approach is applied for solving the electronic eigenfunctions and eigenvalues of the hydrogen atom and vibrational states of the electronic state 3E＋ of the Cs2 molecule which has long-range interaction potential. The numerical results indicate that the numerical efficiency of the original FE-DVR has been improved much using our proposed mapped coordinate scheme.展开更多
Most of multimedia schemes employ variable-length codes （VLCs） like Huffman code as core components in obtaining high compression rates. However VLC methods are very sensitive to channel noise. The goal of this pape...Most of multimedia schemes employ variable-length codes （VLCs） like Huffman code as core components in obtaining high compression rates. However VLC methods are very sensitive to channel noise. The goal of this paper is to salvage as many data from the damaged packets as possible for higher audiovisual quality. This paper proposes an integrated joint source-channel decoder （I-JSCD） at a symbol-level using three-dimensional （3-D） trellis representation for first-order Markov sources encoded with VLC source code and convolutional channel code. This method combines source code and channel code state-spaces and bit-lengths to construct a two-dimensional （2-D） state-space, and then develops a 3-D trellis and a maximum a-posterior （MAP） algorithm to estimate the source sequence symbol by symbol. Experiment results demonstrate that our method results in significant improvement in decoding performance, it can salvage at least half of （50%） data in any channel error rate, and can provide additional error resilience to VLC stream like image, audio, video stream over high error rate links.展开更多
Reliable and up-to-date digital soil data is crucial for achieving Sustainable Development Goal 13(Climate Action)by enabling improved monitoring of soil carbon and land degradation,thereby supporting climate-smart ag...Reliable and up-to-date digital soil data is crucial for achieving Sustainable Development Goal 13(Climate Action)by enabling improved monitoring of soil carbon and land degradation,thereby supporting climate-smart agriculture and ensuring stable crop yields in sub-Saharan Africa.This study focuses on the spatial mapping of soil organic carbon(SOC)comparing predictive models that integrate Landsat 8 variables and DEM derivatives within a Random Forest framework.Three models were evaluated:Model A,which incorporates only Landsat 8 derivatives;Model B,based solely on DEM variables;and Model C,which integrates both Landsat 8 and DEM datasets.The results indicate that Model A achieved an RMSE of 0.15(%)and an R^(2)of 0.67,while Model B achieved an RMSE of 0.19(%)and an R^(2) of 0.54.Model C(the combined model)achieved the highest explanatory power with an R^(2) of 0.69.The findings highlight the significant influence of DEM-derived variables,such as profile and plan curvature,on SOC distribution,emphasizing the role of terrain attributes in SOC mapping.This study demonstrates the potential of RF modeling for SOC prediction,reinforcing the importance of integrating spectral and topographic variables to enhance accuracy.To achieve sustainable farming and resilient crop production in sub-Saharan Africa,accurate digital soil mapping is essential.These datasets empower climate action by tracking soil health and carbon sequestration,providing the necessary evidence base for effective land management strategies.展开更多
To build any spatial soil database, a set of environmental data including digital elevation model(DEM) and satellite images beside geomorphic landscape description are essentials. Such a database, integrates field obs...To build any spatial soil database, a set of environmental data including digital elevation model(DEM) and satellite images beside geomorphic landscape description are essentials. Such a database, integrates field observations and laboratory analyses data with the results obtained from qualitative and quantitative models. So far, various techniques have been developed for soil data processing. The performance of Artificial Neural Network(ANN) and Decision Tree(DT) models was compared to map out some soil attributes in Alborz Province, Iran. Terrain attributes derived from a DEM along with Landsat 8 ETM+, geomorphology map, and the routine laboratory analyses of the studied area were used as input data. The relationships between soil properties(including sand, silt, clay, electrical conductivity, organic carbon, and carbonates) and the environmental variables were assessed using the Pearson Correlation Coefficient and Principle Components Analysis. Slope, elevation, geomforms, carbonate index, stream network, wetness index, and the band’s number 2, 3, 4, and 5 were the most significantly correlated variables. ANN and DT did not show the same accuracy in predicting all parameters. The DT model showed higher performances in estimating sand(R^2=0.73), silt(R^2=0.70), clay(R^2=0.72), organic carbon(R^2=0.71), and carbonates(R^2=0.70). While the ANN model only showed higher performance in predicting soil electrical conductivity(R^2=0.95). The results showed that determination the best model to use, is dependent upon the relation between the considered soil properties with the environmental variables. However, the DT model showed more reasonable results than the ANN model in this study. The results showed that before using a certain model to predict variability of all soil parameters, it would be better to evaluate the efficiency of all possible models for choosing the best fitted model for each property. In other words, most of the developed models are sitespecific and may not be applicable to use for predicting other soil properties or other area.展开更多
Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accura...Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy.展开更多
During mechanical alloying variables such as the type of mill,milling intensity,milling time,milling at- mosphere and ball-to-powder weight ratio(BPR)affect the morphology and constitution of the product. The effect o...During mechanical alloying variables such as the type of mill,milling intensity,milling time,milling at- mosphere and ball-to-powder weight ratio(BPR)affect the morphology and constitution of the product. The effect of milling time,milling atmosphere and BPR on the nature of the product formed in mechanical- ly alloyed pure Ti and blended elemental binary Ti-Al,and ternary Ti-AI-Nb alloy powders was described. Mechanical alloying of pure titanium results,after long milling times,in the formation of an fcc phase.In the binary alloy,a solid solution of aluminum in titanium,an amorphous phase,and a fcc phase form with increasing milling time.The fcc phase,which is probably a result of TiN formation,occurs more rapidly in air or nitrogen than in an inert atmosphere.Formation of the B2 phase in the ternary alloys depends both on alloy composition and the milling atmosphere,with 100％ formation in all atmospheres in Ti-25Al-25Nb but not in Ti-24Al-11Nb,and an inert atmosphere favoring formation.The times required for the formation of the different phases decrease as the BPR increases;but their sequence is unaffected.Based on this infor- mation,“milling maps”which describe phase formation as a function of the BPR and milling time are con- structed.Contamination from the milling balls increased as the BPR was increased.展开更多
Applying the extended mapping method via Riccati equation, many exact variable separation solutions for the （2＆1 ）-dimensional variable coefficient Broer-Kaup equation are obtained. Introducing multiple valued func...Applying the extended mapping method via Riccati equation, many exact variable separation solutions for the （2＆1 ）-dimensional variable coefficient Broer-Kaup equation are obtained. Introducing multiple valued function and Jacobi elliptic function in the seed solution, special types of periodic semifolded solitary waves are derived. In the long wave limit these periodic semifolded solitary wave excitations may degenerate into single semifolded localized soliton structures. The interactions of the periodic semifolded solitary waves and their degenerated single semifolded soliton structures are investigated graphically and found to be completely elastic.展开更多
Many environmental variables are frequently used to predict values of soil in locations where they are not measured. Digital soil mapping (DSM) has a long-standing convention to describe soils as a function of climate...Many environmental variables are frequently used to predict values of soil in locations where they are not measured. Digital soil mapping (DSM) has a long-standing convention to describe soils as a function of climate, organisms, topography, parent material, time and space. It is obvious that terrain, climate, parent material and organisms are used frequently in the prediction of soil properties while time and space factors are rarely used. Time is the indirect factor for the formation and development of soil. Moreover, it is very useful to explicit and implicit estimates of soil age for DSM. However, it is often difficult to obtain time factor. In the absence of explicit soil age data, geomorphologic data are commonly related to soil relative age. Consequently, this study adopts the geomorphologic types (genesis type of geomorphology) as surrogate to the time factor and analyzes its effect on DSM. To examine this idea, we selected the Ili region of northwestern China as the study area. This paper uses geomorphologic data from a new digital geomorphology map as the implicit soil age in predictive soil mapping. For this study, Soil-landscape inference model (SoLIM) was used to predict soil properties based on the individual representation of each sample. This model applies the terrain (topography), climate, parent material (geology) and time (geomorphologic type) to predict soil values in the study area where they are not measured. And the independent sample validation method was used to estimate the precision of results. The validation result shows that the use of geomorphologic data as surrogate to the time factor in the individual representation leads to a considerable and significant increase in the accuracy of results. In other words, implicit estimates of soil age by genesis type of geomorphology are very useful for DSM. This increase was due to the high purity of the geomorphologic data. This means that the geomorphologic variable, if used, can improve the quality of DSM. Predicted value through the proposed approach comes closer to the real value.展开更多
Starting from the symbolic computation system Maple and Riccati equation mapping approach and a linear variable separation approach, a new family of non-traveling wave solutions of the (1 + 1)-dimensional Burgers syst...Starting from the symbolic computation system Maple and Riccati equation mapping approach and a linear variable separation approach, a new family of non-traveling wave solutions of the (1 + 1)-dimensional Burgers system is derived.展开更多
Using a fixed point theorem of Krasnosel＇skii type, this article proves the exis- tence of asymptotically stable solutions for a Volterra-Hammerstein integral equation in two variables.
The Front Variable Area Bypass Injector(FVABI)is a key to bypass ratio adjustment for a Variable Cycle Engine(VCE).In order to study the role of the FVABI with the Core Driven Fan Stage(CDFS)duct,firstly,the engine by...The Front Variable Area Bypass Injector(FVABI)is a key to bypass ratio adjustment for a Variable Cycle Engine(VCE).In order to study the role of the FVABI with the Core Driven Fan Stage(CDFS)duct,firstly,the engine bypass with the CDFS duct model and the equivalent engine bypass without the CDFS duct model are designed using the concept of a jet boundary line.By comparing the difference between airflow driving forces in the two engine bypass models,the quantitative effects of the injection from the CDFS duct on the mass flow rate of the engine bypass airflow are obtained under different combinations of pressure difference and area ratios.Then,the CDFS duct injection characteristic map is obtained through the typical experiment of the FVABI.Based on this map,the performance model of the FVABI is developed.Finally,the turbofan engine model with the Variable Inlet Guide Vane(VIGV),the First Variable Cycle Engine model(VCE1)with the CDFS duct and without the VIGV,and the Second Variable Cycle Engine model(VCE2)with the CDFS duct and VIGV are built.The gain on the engine bypass ratio adjustment range caused by the injection from the CDFS duct is clarified by comparing the three engine models.It is concluded that the bypass ratio adjustment range of the variable cycle engine with the FVABI is about twice that of the traditional turbofan engine.展开更多
Feature Model (FM) became an important role in Software Product Line Engineering (SPLE) field. Many approaches have been introduced since the original FM came up with Feature Oriented Domain Analysis (FODA) introduced...Feature Model (FM) became an important role in Software Product Line Engineering (SPLE) field. Many approaches have been introduced since the original FM came up with Feature Oriented Domain Analysis (FODA) introduced by Kang in 1990. The main purpose of FM is used for commonality and variability analysis in domain engineering, to optimize the reusable aspect of software features or components. Cardinality-based Feature Model (CBFM) is one extension of original FM, which integrates several notations of other extensions. In CBFM, feature model defined as hierarchy of feature, with each of feature has a cardinality. The other notation to express variability within SPLE is Orthogonal Variability Model (OVM). At the other hand, OMG as standard organization makes an effort to build standard generic language to express the commonality and variability in SPL field, by initiate Common Variability Language (CVL). This paper reports the comparison and mapping of FODA, CBFM and OVM to CVL where need to be explored first to define meta model mapping of these several approaches. Furthermore, the comparison and mapping of those approaches are discussed in term of R3ST (read as “REST”) software feature model as the case study.展开更多
Soil quality is one of the most important factors in sustaining the global biosphere and developing sustainable agricultural practices. Land use and management practices greatly impact the direction and degree of soil...Soil quality is one of the most important factors in sustaining the global biosphere and developing sustainable agricultural practices. Land use and management practices greatly impact the direction and degree of soil quality changes in time and space. Understanding the effects of land use and management practices on soil quality and its indicators has been identified as one of the most important goals for modern soil science. Soil quality mapping study represents a method for assessing and mapping soil quality changes in time and space in small units. For the present study, changes in the physical, chemical parameters and nematode density of the soils in the rural and urban areas of Thiruvananthapuram district, Kerala, were determined. The soil samples were collected from seven different categories of contaminated soils namely coastal area, sewage disposal area, industrial area, road-side area, agricultural area, market area and gasoline station area, and also from two control stations in rural and urban areas. The soil physico-chemical parameters and nematode density were determined. Geostatistics combined with GIS was applied to analyze the spatial variability of soil physico-chemical characteristics and nematode density. This soil quality mapping study provides a basis for identifying tension zones and serves as a triggering mechanism for implementation of soil contamination mitigating strategies.展开更多
With the aid of symbolic computation system Maple, some families of new rational variable separation solutions of the （2＋1）-dimensional dispersive long wave equations are constructed by means of a function transfor...With the aid of symbolic computation system Maple, some families of new rational variable separation solutions of the （2＋1）-dimensional dispersive long wave equations are constructed by means of a function transformation, improved mapping approach, and variable separation approach, among which there are rational solitary wave solutions, periodic wave solutions and rational wave solutions.展开更多
Open-source and free tools are readily available to the public to process data and assist producers in making management decisions related to agricultural landscapes. On-the-go soil sensors are being used as a proxy t...Open-source and free tools are readily available to the public to process data and assist producers in making management decisions related to agricultural landscapes. On-the-go soil sensors are being used as a proxy to develop digital soil maps because of the data they can collect and their ability to cover a large area quickly. Machine learning, a subcomponent of artificial intelligence, makes predictions from data. Intermixing open-source tools, on-the-go sensor technologies, and machine learning may improve Mississippi soil mapping and crop production. This study aimed to evaluate machine learning for mapping apparent soil electrical conductivity (EC<sub>a</sub>) collected with an on-the-go sensor system at two sites (i.e., MF2, MF9) on a research farm in Mississippi. Machine learning tools (support vector machine) incorporated in Smart-Map, an open-source application, were used to evaluate the sites and derive the apparent electrical conductivity maps. Autocorrelation of the shallow (EC<sub>as</sub>) and deep (EC<sub>ad</sub>) readings was statistically significant at both locations (Moran’s I, p 0.001);however, the spatial correlation was greater at MF2. According to the leave-one-out cross-validation results, the best models were developed for EC<sub>as</sub> versus EC<sub>ad</sub>. Spatial patterns were observed for the EC<sub>as</sub> and EC<sub>ad</sub> readings in both fields. The patterns observed for the EC<sub>ad</sub> readings were more distinct than the EC<sub>as</sub> measurements. The research results indicated that machine learning was valuable for deriving apparent electrical conductivity maps in two Mississippi fields. Location and depth played a role in the machine learner’s ability to develop maps.展开更多
With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, curr...With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential replacement (SR) method. Through Monte Carlo simulation and comparing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.展开更多
基金funded by National Nature Science Foundation of China(NSFC)(Nos.51776010,and 91860205)the support from Collaborative Innovation Center of Advanced Aero-Engine,china。
文摘Accurate engine performance models are important for model-based performance evaluation of aero engine.The accuracy of the model often depends on engine component maps,so there is a need for a method that can accurately correct the component maps of the model over a wide range.In this paper,a new method for modifying component maps is proposed,this method combines the correction of the scaling factors with the solution process of the off-design working point,and uses the adjustment of the variable geometric parameters of the engine to change the position of the working line,in order to obtain more correction results and guarantee high accuracy in a wider range.The method is validated by taking the main fan of the Adaptive Cycle Engine(ACE),an ideal power unit for a new generation of multi-purpose and ultra-wide working range aircraft,as an example.The results show that the maximum error between the corrected component maps and the target maps is less than 1%.New possibility for more precise component maps can be realized in this paper.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62105004 and 52174141)the College Student Innovation and Entrepreneurship Fund Project(Grant No.202210361053)+4 种基金Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center,Anhui University of Science&Technology(Grant No.KSJD202304)the Anhui Province Digital Agricultural Engineering Technology Research Center Open Project(Grant No.AHSZNYGC-ZXKF021)the Talent Recruitment Special Fund of Anhui University of Science and Technology(Grant No.2024yjrc175)the Graduate Innovation Fund Project of Anhui University of Science and Technology(Grant Nos.2024cx2067,2024cx2107,and 2024cx2064)Seed Support Project for Postgraduate Innovation,Entrepreneurship and Practice at Anhui University of Science and Technology(Grant No.2024cxcysj084).
文摘With the development of the Internet,image encryption technology has become critical for network security.Traditional methods often suffer from issues such as insufficient chaos,low randomness in key generation,and poor encryption efficiency.To enhance performance,this paper proposes a new encryption algorithm designed to optimize parallel processing and adapt to images of varying sizes and colors.The method begins by using SHA-384 to extract the hash value of the plaintext image,which is then processed to determine the chaotic system’s initial value and block size.The image is padded and divided into blocks for further processing.A novel two-dimensional infinite collapses hyperchaotic map(2DICHM)is employed to generate the intra-block scrambling sequence,while an improved variable Joseph traversal sequence is used for inter-block scrambling.After removing the padding,3D forward and backward shift diffusions,controlled by the 2D-ICHM sequences,are applied to the scrambled image,producing the ciphertext.Simulation results demonstrate that the proposed algorithm outperforms others in terms of entropy,anti-noise resilience,correlation coefficient,robustness,and encryption efficiency.
文摘Efficient numerical solver for the SchrSdinger equation is very important in physics and chemistry. The finite element discrete variable representation （FE-DVR） was first proposed by Rescigno and Mc-Curdy [Phys. Rev. A 62, 032706 （2000）] for solving quantum-mechanical scattering problems. In this work, an FE-DVR method in a mapped coordinate was proposed to improve the efficiency of the original FE-DVR method. For numerical demonstration, the proposed approach is applied for solving the electronic eigenfunctions and eigenvalues of the hydrogen atom and vibrational states of the electronic state 3E＋ of the Cs2 molecule which has long-range interaction potential. The numerical results indicate that the numerical efficiency of the original FE-DVR has been improved much using our proposed mapped coordinate scheme.
基金Supported by the Foundation of Ministry of Education of China (211CERS10)
文摘Most of multimedia schemes employ variable-length codes （VLCs） like Huffman code as core components in obtaining high compression rates. However VLC methods are very sensitive to channel noise. The goal of this paper is to salvage as many data from the damaged packets as possible for higher audiovisual quality. This paper proposes an integrated joint source-channel decoder （I-JSCD） at a symbol-level using three-dimensional （3-D） trellis representation for first-order Markov sources encoded with VLC source code and convolutional channel code. This method combines source code and channel code state-spaces and bit-lengths to construct a two-dimensional （2-D） state-space, and then develops a 3-D trellis and a maximum a-posterior （MAP） algorithm to estimate the source sequence symbol by symbol. Experiment results demonstrate that our method results in significant improvement in decoding performance, it can salvage at least half of （50%） data in any channel error rate, and can provide additional error resilience to VLC stream like image, audio, video stream over high error rate links.
文摘Reliable and up-to-date digital soil data is crucial for achieving Sustainable Development Goal 13(Climate Action)by enabling improved monitoring of soil carbon and land degradation,thereby supporting climate-smart agriculture and ensuring stable crop yields in sub-Saharan Africa.This study focuses on the spatial mapping of soil organic carbon(SOC)comparing predictive models that integrate Landsat 8 variables and DEM derivatives within a Random Forest framework.Three models were evaluated:Model A,which incorporates only Landsat 8 derivatives;Model B,based solely on DEM variables;and Model C,which integrates both Landsat 8 and DEM datasets.The results indicate that Model A achieved an RMSE of 0.15(%)and an R^(2)of 0.67,while Model B achieved an RMSE of 0.19(%)and an R^(2) of 0.54.Model C(the combined model)achieved the highest explanatory power with an R^(2) of 0.69.The findings highlight the significant influence of DEM-derived variables,such as profile and plan curvature,on SOC distribution,emphasizing the role of terrain attributes in SOC mapping.This study demonstrates the potential of RF modeling for SOC prediction,reinforcing the importance of integrating spectral and topographic variables to enhance accuracy.To achieve sustainable farming and resilient crop production in sub-Saharan Africa,accurate digital soil mapping is essential.These datasets empower climate action by tracking soil health and carbon sequestration,providing the necessary evidence base for effective land management strategies.
基金College of Agriculture and Natural Resources,University of Tehran for financial support of the study(Grant No.7104017/6/24 and 28)
文摘To build any spatial soil database, a set of environmental data including digital elevation model(DEM) and satellite images beside geomorphic landscape description are essentials. Such a database, integrates field observations and laboratory analyses data with the results obtained from qualitative and quantitative models. So far, various techniques have been developed for soil data processing. The performance of Artificial Neural Network(ANN) and Decision Tree(DT) models was compared to map out some soil attributes in Alborz Province, Iran. Terrain attributes derived from a DEM along with Landsat 8 ETM+, geomorphology map, and the routine laboratory analyses of the studied area were used as input data. The relationships between soil properties(including sand, silt, clay, electrical conductivity, organic carbon, and carbonates) and the environmental variables were assessed using the Pearson Correlation Coefficient and Principle Components Analysis. Slope, elevation, geomforms, carbonate index, stream network, wetness index, and the band’s number 2, 3, 4, and 5 were the most significantly correlated variables. ANN and DT did not show the same accuracy in predicting all parameters. The DT model showed higher performances in estimating sand(R^2=0.73), silt(R^2=0.70), clay(R^2=0.72), organic carbon(R^2=0.71), and carbonates(R^2=0.70). While the ANN model only showed higher performance in predicting soil electrical conductivity(R^2=0.95). The results showed that determination the best model to use, is dependent upon the relation between the considered soil properties with the environmental variables. However, the DT model showed more reasonable results than the ANN model in this study. The results showed that before using a certain model to predict variability of all soil parameters, it would be better to evaluate the efficiency of all possible models for choosing the best fitted model for each property. In other words, most of the developed models are sitespecific and may not be applicable to use for predicting other soil properties or other area.
基金Under the auspices of the National Natural Science Foundation of China(No.41571217)the National Key Research and Development Program of China(No.2016YFD0300801)
文摘Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy.
文摘During mechanical alloying variables such as the type of mill,milling intensity,milling time,milling at- mosphere and ball-to-powder weight ratio(BPR)affect the morphology and constitution of the product. The effect of milling time,milling atmosphere and BPR on the nature of the product formed in mechanical- ly alloyed pure Ti and blended elemental binary Ti-Al,and ternary Ti-AI-Nb alloy powders was described. Mechanical alloying of pure titanium results,after long milling times,in the formation of an fcc phase.In the binary alloy,a solid solution of aluminum in titanium,an amorphous phase,and a fcc phase form with increasing milling time.The fcc phase,which is probably a result of TiN formation,occurs more rapidly in air or nitrogen than in an inert atmosphere.Formation of the B2 phase in the ternary alloys depends both on alloy composition and the milling atmosphere,with 100％ formation in all atmospheres in Ti-25Al-25Nb but not in Ti-24Al-11Nb,and an inert atmosphere favoring formation.The times required for the formation of the different phases decrease as the BPR increases;but their sequence is unaffected.Based on this infor- mation,“milling maps”which describe phase formation as a function of the BPR and milling time are con- structed.Contamination from the milling balls increased as the BPR was increased.
基金National Natural Science Foundation of China under Grant Nos.10472063 and 10672096
文摘Applying the extended mapping method via Riccati equation, many exact variable separation solutions for the （2＆1 ）-dimensional variable coefficient Broer-Kaup equation are obtained. Introducing multiple valued function and Jacobi elliptic function in the seed solution, special types of periodic semifolded solitary waves are derived. In the long wave limit these periodic semifolded solitary wave excitations may degenerate into single semifolded localized soliton structures. The interactions of the periodic semifolded solitary waves and their degenerated single semifolded soliton structures are investigated graphically and found to be completely elastic.
文摘Many environmental variables are frequently used to predict values of soil in locations where they are not measured. Digital soil mapping (DSM) has a long-standing convention to describe soils as a function of climate, organisms, topography, parent material, time and space. It is obvious that terrain, climate, parent material and organisms are used frequently in the prediction of soil properties while time and space factors are rarely used. Time is the indirect factor for the formation and development of soil. Moreover, it is very useful to explicit and implicit estimates of soil age for DSM. However, it is often difficult to obtain time factor. In the absence of explicit soil age data, geomorphologic data are commonly related to soil relative age. Consequently, this study adopts the geomorphologic types (genesis type of geomorphology) as surrogate to the time factor and analyzes its effect on DSM. To examine this idea, we selected the Ili region of northwestern China as the study area. This paper uses geomorphologic data from a new digital geomorphology map as the implicit soil age in predictive soil mapping. For this study, Soil-landscape inference model (SoLIM) was used to predict soil properties based on the individual representation of each sample. This model applies the terrain (topography), climate, parent material (geology) and time (geomorphologic type) to predict soil values in the study area where they are not measured. And the independent sample validation method was used to estimate the precision of results. The validation result shows that the use of geomorphologic data as surrogate to the time factor in the individual representation leads to a considerable and significant increase in the accuracy of results. In other words, implicit estimates of soil age by genesis type of geomorphology are very useful for DSM. This increase was due to the high purity of the geomorphologic data. This means that the geomorphologic variable, if used, can improve the quality of DSM. Predicted value through the proposed approach comes closer to the real value.
文摘Starting from the symbolic computation system Maple and Riccati equation mapping approach and a linear variable separation approach, a new family of non-traveling wave solutions of the (1 + 1)-dimensional Burgers system is derived.
基金the support given by Vietnam’s National Foundation for Science and Technology Development (NAFOSTED) under Project 101.01-2012.12
文摘Using a fixed point theorem of Krasnosel＇skii type, this article proves the exis- tence of asymptotically stable solutions for a Volterra-Hammerstein integral equation in two variables.
基金supported by the National Science and Technology Major Project of China (No. J2019-II-00070027)the China Academy of Launch Vehicle Technology Funding (No. CALT2023-07)
文摘The Front Variable Area Bypass Injector(FVABI)is a key to bypass ratio adjustment for a Variable Cycle Engine(VCE).In order to study the role of the FVABI with the Core Driven Fan Stage(CDFS)duct,firstly,the engine bypass with the CDFS duct model and the equivalent engine bypass without the CDFS duct model are designed using the concept of a jet boundary line.By comparing the difference between airflow driving forces in the two engine bypass models,the quantitative effects of the injection from the CDFS duct on the mass flow rate of the engine bypass airflow are obtained under different combinations of pressure difference and area ratios.Then,the CDFS duct injection characteristic map is obtained through the typical experiment of the FVABI.Based on this map,the performance model of the FVABI is developed.Finally,the turbofan engine model with the Variable Inlet Guide Vane(VIGV),the First Variable Cycle Engine model(VCE1)with the CDFS duct and without the VIGV,and the Second Variable Cycle Engine model(VCE2)with the CDFS duct and VIGV are built.The gain on the engine bypass ratio adjustment range caused by the injection from the CDFS duct is clarified by comparing the three engine models.It is concluded that the bypass ratio adjustment range of the variable cycle engine with the FVABI is about twice that of the traditional turbofan engine.
文摘Feature Model (FM) became an important role in Software Product Line Engineering (SPLE) field. Many approaches have been introduced since the original FM came up with Feature Oriented Domain Analysis (FODA) introduced by Kang in 1990. The main purpose of FM is used for commonality and variability analysis in domain engineering, to optimize the reusable aspect of software features or components. Cardinality-based Feature Model (CBFM) is one extension of original FM, which integrates several notations of other extensions. In CBFM, feature model defined as hierarchy of feature, with each of feature has a cardinality. The other notation to express variability within SPLE is Orthogonal Variability Model (OVM). At the other hand, OMG as standard organization makes an effort to build standard generic language to express the commonality and variability in SPL field, by initiate Common Variability Language (CVL). This paper reports the comparison and mapping of FODA, CBFM and OVM to CVL where need to be explored first to define meta model mapping of these several approaches. Furthermore, the comparison and mapping of those approaches are discussed in term of R3ST (read as “REST”) software feature model as the case study.
文摘Soil quality is one of the most important factors in sustaining the global biosphere and developing sustainable agricultural practices. Land use and management practices greatly impact the direction and degree of soil quality changes in time and space. Understanding the effects of land use and management practices on soil quality and its indicators has been identified as one of the most important goals for modern soil science. Soil quality mapping study represents a method for assessing and mapping soil quality changes in time and space in small units. For the present study, changes in the physical, chemical parameters and nematode density of the soils in the rural and urban areas of Thiruvananthapuram district, Kerala, were determined. The soil samples were collected from seven different categories of contaminated soils namely coastal area, sewage disposal area, industrial area, road-side area, agricultural area, market area and gasoline station area, and also from two control stations in rural and urban areas. The soil physico-chemical parameters and nematode density were determined. Geostatistics combined with GIS was applied to analyze the spatial variability of soil physico-chemical characteristics and nematode density. This soil quality mapping study provides a basis for identifying tension zones and serves as a triggering mechanism for implementation of soil contamination mitigating strategies.
基金supported by the Scientific Research Foundation of Beijing Information Science and Technology UniversityScientific Creative Platform Foundation of Beijing Municipal Commission of Education
文摘With the aid of symbolic computation system Maple, some families of new rational variable separation solutions of the （2＋1）-dimensional dispersive long wave equations are constructed by means of a function transformation, improved mapping approach, and variable separation approach, among which there are rational solitary wave solutions, periodic wave solutions and rational wave solutions.
文摘Open-source and free tools are readily available to the public to process data and assist producers in making management decisions related to agricultural landscapes. On-the-go soil sensors are being used as a proxy to develop digital soil maps because of the data they can collect and their ability to cover a large area quickly. Machine learning, a subcomponent of artificial intelligence, makes predictions from data. Intermixing open-source tools, on-the-go sensor technologies, and machine learning may improve Mississippi soil mapping and crop production. This study aimed to evaluate machine learning for mapping apparent soil electrical conductivity (EC<sub>a</sub>) collected with an on-the-go sensor system at two sites (i.e., MF2, MF9) on a research farm in Mississippi. Machine learning tools (support vector machine) incorporated in Smart-Map, an open-source application, were used to evaluate the sites and derive the apparent electrical conductivity maps. Autocorrelation of the shallow (EC<sub>as</sub>) and deep (EC<sub>ad</sub>) readings was statistically significant at both locations (Moran’s I, p 0.001);however, the spatial correlation was greater at MF2. According to the leave-one-out cross-validation results, the best models were developed for EC<sub>as</sub> versus EC<sub>ad</sub>. Spatial patterns were observed for the EC<sub>as</sub> and EC<sub>ad</sub> readings in both fields. The patterns observed for the EC<sub>ad</sub> readings were more distinct than the EC<sub>as</sub> measurements. The research results indicated that machine learning was valuable for deriving apparent electrical conductivity maps in two Mississippi fields. Location and depth played a role in the machine learner’s ability to develop maps.
文摘With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential replacement (SR) method. Through Monte Carlo simulation and comparing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.
