In recent years,fine-scale gridded population data has been widely adopted for assessing and monitoring the Sustainable Development Goals(SDGs).However,the existing population disaggregation techniques struggle to gen...In recent years,fine-scale gridded population data has been widely adopted for assessing and monitoring the Sustainable Development Goals(SDGs).However,the existing population disaggregation techniques struggle to generate precise population grids for small areas with scarce data.To address this,we have introduced a novel,lightweight population gridding technique that integrates dasymetric mapping and point-based surface modeling,titled three-weight surface modeling.This method comprises three weights,each offering a unique perspective on population spatial heterogeneity.The first weight,termed building-volume weight,is equivalent to the preliminary results of assigning population based on building volume data.The second weight,termed POIcenter weight,comprises POI(Point of Interest)categories and aggregation patterns,aiming to articulate high-density population centers.It is computed using the neighborhood accumulation rule of Spearman’s correlation coefficients between POIs and population size.The third weight,termed POI-distance weight,represents varying decay rates of population with distance from high-density centers.This three-weight surface model facilitates dynamic adjustment of parameters to refine the building-volume weight according to the remaining POI-related weights,thereby generating a more precise population surface.Our analysis of the census population and the disaggregation outcomes from 544 villages in three counties of southern Guizhou Province,China(namely,Huishui,Luodian,and Pingtang)revealed that the three-weight surface model using local parameter groups outperformed individual dasymetric mapping or point-based surface modeling in terms of accuracy.Also,the 10 m population grid generated by this local parameter model(LPTW-POP)presented greater resolution and fewer errors(RMSE of 1109,MAE of 422,and MRE of 0.2630)compared to commonly use gridded population datasets like LandScan,WorldPop,and GHSPOP.展开更多
Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve ...Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.展开更多
Agricultural investment project selection is a complex multi-criteria decision-making problem,as agricultural projects are easily influenced by various risk factors,and the evaluation information provided by decisionm...Agricultural investment project selection is a complex multi-criteria decision-making problem,as agricultural projects are easily influenced by various risk factors,and the evaluation information provided by decisionmakers usually involves uncertainty and inconsistency.Existing literature primarily employed direct preference elicitation methods to address such issues,necessitating a great cognitive effort on the part of decision-makers during evaluation,specifically,determining the weights of criteria.In this study,we propose an indirect preference elicitation method,known as a preference disaggregation method,to learn decision-maker preference models fromdecision examples.To enhance evaluation ease,decision-makers merely need to compare pairs of alternatives with which they are familiar,also known as reference alternatives.Probabilistic linguistic preference relations are employed to account for the presence of incomplete and uncertain information in such pairwise comparisons.To address the inconsistency among a group of decision-makers,we develop a pair of 0-1mixed integer programming models that consider both the semantics of linguistic terms and the belief degrees of decision-makers.Finally,we conduct a case study and comparative analysis.Results reveal the effectiveness of the proposed model in solving agricultural investment project selection problems with uncertain and inconsistent decision information.展开更多
目的探究改良括约肌间瘘管结扎术(Lift-plug)在经括约肌肛瘘(TSAF)病人治疗中的应用优势。方法回顾性收集2020年3月至2022年3月首都医科大学附属北京潞河医院110例TSAF病人临床资料,根据手术方法分为两组,传统组55例行Lift,Lift-plug组5...目的探究改良括约肌间瘘管结扎术(Lift-plug)在经括约肌肛瘘(TSAF)病人治疗中的应用优势。方法回顾性收集2020年3月至2022年3月首都医科大学附属北京潞河医院110例TSAF病人临床资料,根据手术方法分为两组,传统组55例行Lift,Lift-plug组55例行Lift-plug术。比较两组围术期指标、临床疗效、并发症、复发率及神经源性炎症因子[P物质(SP)、神经生长因子(NGF)、前列腺素E_(2)(PGE_(2))]、肠道微生态[D-乳酸、二胺氧化酶(DAO)]、肛门括约肌功能(肛门功能指数评分、Wexner尿失禁评分)。结果Lift-plug组手术时间[(20.22±3.34)min比(23.89±4.11)min]、创面愈合时间[(25.50±3.38)d比(30.31±4.56)d]、住院时间[(5.88±0.82)d比(6.42±1.03)d]短于传统组,术后1 d VAS评分[(2.41±0.35)分比(3.02±0.42)分]低于传统组(P<0.05);术后1 d Lift-plug组血清SP、NGF、PGE_(2)水平低于传统组(P<0.05);术后1 d、3 d两组D-乳酸、血浆DAO水平均差异无统计学意义(P>0.05);术后1个月、3个月Lift-plug组肛门功能指数、Wexner尿失禁评分均低于传统组(P<0.05);术后3个月,Lift-plug组治疗总有效率高于传统组(P<0.05);术后3个月两组并发症发生率及复发率比较差异无统计学意义(P>0.05)。结论Lift-plug术治疗TSAF病人可优化手术,减轻神经源性炎症反应,减轻早期疼痛,改善括约肌功能,增强临床疗效,且具有较高安全性。展开更多
基金the support of the Natural Science Foundation of Shanghai Municipality(No.24ZR1420500)the Project of Yulin Science,and Technology Light(No.2024-KJZG-KXJ-005)the Project of International Research Center of Big Data for SDGs(No.CBAS2024SDG005).
文摘In recent years,fine-scale gridded population data has been widely adopted for assessing and monitoring the Sustainable Development Goals(SDGs).However,the existing population disaggregation techniques struggle to generate precise population grids for small areas with scarce data.To address this,we have introduced a novel,lightweight population gridding technique that integrates dasymetric mapping and point-based surface modeling,titled three-weight surface modeling.This method comprises three weights,each offering a unique perspective on population spatial heterogeneity.The first weight,termed building-volume weight,is equivalent to the preliminary results of assigning population based on building volume data.The second weight,termed POIcenter weight,comprises POI(Point of Interest)categories and aggregation patterns,aiming to articulate high-density population centers.It is computed using the neighborhood accumulation rule of Spearman’s correlation coefficients between POIs and population size.The third weight,termed POI-distance weight,represents varying decay rates of population with distance from high-density centers.This three-weight surface model facilitates dynamic adjustment of parameters to refine the building-volume weight according to the remaining POI-related weights,thereby generating a more precise population surface.Our analysis of the census population and the disaggregation outcomes from 544 villages in three counties of southern Guizhou Province,China(namely,Huishui,Luodian,and Pingtang)revealed that the three-weight surface model using local parameter groups outperformed individual dasymetric mapping or point-based surface modeling in terms of accuracy.Also,the 10 m population grid generated by this local parameter model(LPTW-POP)presented greater resolution and fewer errors(RMSE of 1109,MAE of 422,and MRE of 0.2630)compared to commonly use gridded population datasets like LandScan,WorldPop,and GHSPOP.
基金the Ontario Ministry of Agriculture,Food and Rural Affairs,Canada,who supported this project by providing updated soil information on Ontario and Middlesex Countysupported by the Natural Science and Engineering Research Council of Canada(No.RGPIN-2014-4100)。
文摘Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.
文摘Agricultural investment project selection is a complex multi-criteria decision-making problem,as agricultural projects are easily influenced by various risk factors,and the evaluation information provided by decisionmakers usually involves uncertainty and inconsistency.Existing literature primarily employed direct preference elicitation methods to address such issues,necessitating a great cognitive effort on the part of decision-makers during evaluation,specifically,determining the weights of criteria.In this study,we propose an indirect preference elicitation method,known as a preference disaggregation method,to learn decision-maker preference models fromdecision examples.To enhance evaluation ease,decision-makers merely need to compare pairs of alternatives with which they are familiar,also known as reference alternatives.Probabilistic linguistic preference relations are employed to account for the presence of incomplete and uncertain information in such pairwise comparisons.To address the inconsistency among a group of decision-makers,we develop a pair of 0-1mixed integer programming models that consider both the semantics of linguistic terms and the belief degrees of decision-makers.Finally,we conduct a case study and comparative analysis.Results reveal the effectiveness of the proposed model in solving agricultural investment project selection problems with uncertain and inconsistent decision information.
文摘目的探究改良括约肌间瘘管结扎术(Lift-plug)在经括约肌肛瘘(TSAF)病人治疗中的应用优势。方法回顾性收集2020年3月至2022年3月首都医科大学附属北京潞河医院110例TSAF病人临床资料,根据手术方法分为两组,传统组55例行Lift,Lift-plug组55例行Lift-plug术。比较两组围术期指标、临床疗效、并发症、复发率及神经源性炎症因子[P物质(SP)、神经生长因子(NGF)、前列腺素E_(2)(PGE_(2))]、肠道微生态[D-乳酸、二胺氧化酶(DAO)]、肛门括约肌功能(肛门功能指数评分、Wexner尿失禁评分)。结果Lift-plug组手术时间[(20.22±3.34)min比(23.89±4.11)min]、创面愈合时间[(25.50±3.38)d比(30.31±4.56)d]、住院时间[(5.88±0.82)d比(6.42±1.03)d]短于传统组,术后1 d VAS评分[(2.41±0.35)分比(3.02±0.42)分]低于传统组(P<0.05);术后1 d Lift-plug组血清SP、NGF、PGE_(2)水平低于传统组(P<0.05);术后1 d、3 d两组D-乳酸、血浆DAO水平均差异无统计学意义(P>0.05);术后1个月、3个月Lift-plug组肛门功能指数、Wexner尿失禁评分均低于传统组(P<0.05);术后3个月,Lift-plug组治疗总有效率高于传统组(P<0.05);术后3个月两组并发症发生率及复发率比较差异无统计学意义(P>0.05)。结论Lift-plug术治疗TSAF病人可优化手术,减轻神经源性炎症反应,减轻早期疼痛,改善括约肌功能,增强临床疗效,且具有较高安全性。
