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
Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-...Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-specific agricultural management and environmental modelling.We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation.The method consisted of three steps.First,environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors.Second,according to soil types of the pedon sites,the similarities were aggregated to derive similarity distribution for each soil type.Third,a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba,Canada.Based on 186 independent pedon sites,the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62.The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map,which was commonly used in practice.Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy,indicating that new environmental covariates need to be developed.We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.展开更多
As the intermittency and uncertainty of photovoltaic(PV)power generation poses considerable challenges to the power system operation,accurate PV generation estimates are critical for the distribution operation,mainten...As the intermittency and uncertainty of photovoltaic(PV)power generation poses considerable challenges to the power system operation,accurate PV generation estimates are critical for the distribution operation,maintenance,and demand response program implementation because of the increasing usage of distributed PVs.Currently,most residential PVs are installed behind the meter,with only the net load available to the utilities.Therefore,a method for disaggregating the residential PV generation from the net load data is needed to enhance the grid-edge observability.In this study,an unsupervised PV capacity estimation method based on net metering data is proposed,for estimating the PV capacity in the customer’s premise based on the distribution characteristics of nocturnal and diurnal net load extremes.Then,the PV generation disaggregation method is presented.Based on the analysis of the correlation between the nocturnal and diurnal actual loads and the correlation between the PV capacity and their actual PV generation,the PV generation of customers is estimated by applying linear fitting of multiple typical solar exemplars and then disaggregating them into hourly-resolution power profiles.Finally,the anomalies of disaggregated PV power are calibrated and corrected using the estimated capacity.Experiment results on a real-world hourly dataset involving 260 customers show that the proposed PV capacity estimation method achieves good accuracy because of the advantages of robustness and low complexity.Compared with the state-of-the-art PV disaggregation algorithm,the proposed method exhibits a reduction of over 15%for the mean absolute percentage error and over 20%for the root mean square error.展开更多
With the benefits of increased computing power and much improved software,temporal disaggregation is examined.Disaggregation,the process of obtaining high frequency data from low frequency data has been discussed for ...With the benefits of increased computing power and much improved software,temporal disaggregation is examined.Disaggregation,the process of obtaining high frequency data from low frequency data has been discussed for many years.This study examines three methods which utilize the autoregressive integrated moving average(ARIMA)model in a simulation study comparing parameter estimation,disaggregation mean square error,and forecast mean square error.Finally,the three methods are applied to a real-world time series.展开更多
Energy disaggregation is a technology that disassembles the energy consumption from the entire house into loadlevel contributions.One of the foundational tasks for this technology is to accurately ascertain the truth ...Energy disaggregation is a technology that disassembles the energy consumption from the entire house into loadlevel contributions.One of the foundational tasks for this technology is to accurately ascertain the truth electrical energy consumption of the target load.However,current energy disaggregation methods find it difficult to accurately predict the actual operating power of appliances when there are significant differences in the data distribution of appliances across various scenarios due to the diversity in manufacturers,usage times,and operating conditions.In this study,we propose a power extraction approach with load state modification to capture accurate load operating power with minimal influence from usage scenarios.To be specific,the on/off state sequence of appliances is first predicted leveraging existing energy disaggregation methods,and two state modification methods based on non-operating time and operating time of appliances are respectively proposed to modify the erroneous states in sequence.Subsequently,the power extraction approach calculates the operational power of target appliance based on the amplitude of fluctuations within the aggregated energy consumption caused by its state changes.Furthermore,a removing signal spikes method is proposed to improve the accuracy of the extracted power value.We conducted extensive experiments on a public dataset,demonstrating that the proposed method can significantly improve the accuracy of state-of-the-art solution.The average of mean ab-solute error across commonly used appliances during on state were reduced by 44.75%and 32.07%respectively in the UK-DALE and REFIT datasets.展开更多
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
Non-intrusive load monitoring(NILM)technology aims to infer the operation information of electrical appliances from the total household load signals,which is of great significance for energy conservation and planning....Non-intrusive load monitoring(NILM)technology aims to infer the operation information of electrical appliances from the total household load signals,which is of great significance for energy conservation and planning.However,existing methods are difficult to effectively capture the complex nonlinear features of the power consumption flow,which affects the energy disaggregation accuracy.To this end,this paper designs a method based on temporal convolutional network(TCN),efficient channel attention(ECA),and long short-term memory(LSTM).The method first creatively proposes a two-stage improved TCN(TSTCN),which overcomes its problems of extracting discontinuous information and poor correlation of long-distance information while enhancing the ability to extract high-level load features.Then a novel improved ECA attention mechanism(IECA)is embedded,which is also combined with the skip connection technique to pay channel-weighted attention to important feature maps and promote information fusion.Finally,the LSTM with strong temporal memory capability is introduced to learn the dependencies in the load power sequence and realize load disaggregation.Experiments on two real-world datasets,REDD and UK-DALE,show that the proposed model significantly outperforms other comparative NILM algorithms and achieves satisfactory tracking with the actual appliance operating power.The results show that the mean absolute error(MAE)of all appliances decreases by 18.67%on average,and the F1 score improves by 38.70%.展开更多
The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual elec...The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggrega-tion methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodol-ogy. The contribution of this paper is in utilizing the “k- value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.展开更多
A study of using climate sensitive deposits as a compiled climatic data to locate global climatic belt boundaries through time is developed by the present authors since the 1990s. Global latitudinal belts were present...A study of using climate sensitive deposits as a compiled climatic data to locate global climatic belt boundaries through time is developed by the present authors since the 1990s. Global latitudinal belts were presented from Cambrian to Permian as well as the in terval from the early Late Cretaceous to the present. However, during the later Permian and into the Early Cretaceous we noted that the failure of the tropical-subtropical belt to penetrate into the interior of Pangaean resulted in the merging of the two arid belts associated with the northern and southern Hadley Cells into one vast, interior arid region. A Pangaeanic paleogeography dominates and obviously affects the climatic distribution from the Late Permian to Early Cretaceous. We employ the dismission and reoccurrence of the global latitudinal climate belts to determine the aggregation and disaggregation of the Pangaean.展开更多
The deposition of highly ordered amyloid fibrils is recognized as a hallmark of amyloidosis diseases such as Alzheimer’s disease and Parkinson’s disease.Disaggregating the amyloid fibrils is considered as one of the...The deposition of highly ordered amyloid fibrils is recognized as a hallmark of amyloidosis diseases such as Alzheimer’s disease and Parkinson’s disease.Disaggregating the amyloid fibrils is considered as one of the effective strategies for the control and treatment of amyloidosis diseases.In this article,by simulating the function of natural molecular chaperones,co-assembled block copolymer micelles with coordination groups of nitrilotriacetic acid(NTA)and hydrophobic microdomains of poly(Nisopropylacrylamide)(PNIPAM)on the surface were used as nanochaperones(n Chaps)to disaggregate amyloid insulin fibrils.Zinc ions chelated by NTA can bind the histidine imidazole residues while the PNIPAM microdomains can interact with the exposed hydrophobic sites on the amyloid insulin fibrils,which synergistically perturb the stability of amyloid insulin fibrils,loosen their structure,and finally promote their disaggregation.A combination of characterizations with fluorescence spectroscopy,transmission electron microscopy(TEM),dynamic hight scattering(DLS),and quartz crystal microbalance(QCM)demonstrated that mature amyloid insulin fibrils were completely disaggregated after incubating with n Chaps for 90 h.This study may provide a promising strategy for the development of n Chaps for the treatment of amyloidosis diseases.展开更多
Comprehensive Summary Amyloid-β protein(Aβ)is a fatal cause of Alzheimer's disease,which can trigger a series of cytotoxicity by the abnormal aggregation of Aβ in human brain.The strategies for inhibition and d...Comprehensive Summary Amyloid-β protein(Aβ)is a fatal cause of Alzheimer's disease,which can trigger a series of cytotoxicity by the abnormal aggregation of Aβ in human brain.The strategies for inhibition and disaggregation of Aβ fibrillation are mostly based on the interaction between monomers,oligomers,fibrils,and materials.展开更多
High spatial resolution land surface broadband emissivity(BBE)is not only useful for surface energy balance studies at local scales,but also can bridge the gap between existing coarser resolution BBE products and poin...High spatial resolution land surface broadband emissivity(BBE)is not only useful for surface energy balance studies at local scales,but also can bridge the gap between existing coarser resolution BBE products and point-based field measurements.This study proposes a disaggregation approach that utilizes the established BBE–reflectance relationship for estimating high spatial resolution BBE for bare soils from Landsat surface reflectance data.The disaggregated BBE is compared to the BBE calculated from spatial–temporal matched Advanced Spaceborne Thermal Emission and Reflectance Radiometer emissivity product.Comparison results show that better agreement is achieved over relative homogeneous areas,but deteriorated over heterogeneous and cloud-contaminated areas.In addition,field-measured emissivity data over large homogeneous desert were also used to validate the disaggregated BBE,and the bias is 0.005.The comparison and validation results indicated that the disaggregation approach can obtain high spatial resolution BBE with better accuracy for homogeneous area.展开更多
In the last decade, a series of severe and extensive droughts have swept across Southwest China, resulting in tremendous economic losses, deaths, and disruption to society. Consequently, this study is motivated by the...In the last decade, a series of severe and extensive droughts have swept across Southwest China, resulting in tremendous economic losses, deaths, and disruption to society. Consequently, this study is motivated by the paramount importance of as- sessing future changes in drought in Southwest China. Precipitation is likely to decrease over most parts of Southwest China around the beginning of the century, followed by widespread precipitation increases; the increase in potential evapotran- spiration （PET）, due to the joint effects of increased temperature and surface net radiation and decreased relative humidity, will overwhelm the whole region throughout the entire 21st century. In comparative terms, the enhancement of PET will outweigh that of precipitation, particularly under Representative Concentration Pathway （RCP） 8.5, resulting in intensified drought. Generally, the drying tendency will be in the southeast portion, whereas the mountainous region in the northwest will become increasingly wetter owing to abundant precipitation increases. Droughts classified as moderate/severe according to historical standards will become the norm in the 2080s under RCP4.5/RCP8.5. Future drought changes will manifest different characteristics depending on the time scale： the magnitude of change at a time scale of 48 months is nearly twice as great as that at 3 months. Furthermore, we will see that not only will incidences of severe and extreme drought increase dramatically in the future, but extremely wet events will also become more probable.展开更多
In this study, strong ground motion record （SGMR） selection based on Eta （~/） as a spectral shape indicator has been investigated as applied to steel braced flame structures. A probabilistic seismic hazard disaggr...In this study, strong ground motion record （SGMR） selection based on Eta （~/） as a spectral shape indicator has been investigated as applied to steel braced flame structures. A probabilistic seismic hazard disaggregation analysis for the definition of the target Epsilon （ε） and the target Eta （η） values at different hazard levels is presented, taking into account appropriately selected SGMR＇s. Fragility curves are developed for different limit states corresponding to three representative models of typical steel braced frames having significant irregularities in plan, by means of a weighted damage index. The results show that spectral shape indicators have an important effect on the predicted median structural capacities, and also that the parameter r/is a more robust predictor of damage than searching for records with appropriate c values.展开更多
Background： Different types of growth and yield models provide essential information for making informed decisions on how to manage forests. Whole-stand models often provide well-behaved outputs at the stand level, b...Background： Different types of growth and yield models provide essential information for making informed decisions on how to manage forests. Whole-stand models often provide well-behaved outputs at the stand level, but lack information on stand structures. Detailed information from individual-tree models and size-class models typically suffers from accumulation of errors. The disaggregation method, in assuming that predictions from a whole-stand model are reliable, partitions these outputs to individual trees. On the other hand, the combination method seeks to improve stand-level predictions from both whole-stand and individual-tree models by combining them. Methods： Data from 100 plots randomly selected from the Southwicte Seed Source Study of Ioblolly pine （Pinus taedo L） were used to evaluate the unadjusted individual-tree model against the disaggregation and combination methods. Results： Compared to the whole-stand model, the combination method did not show improvements in predicting stand attributes in this study. The combination method also did not perform as well as the disaggregation method in tree-level predictions. The disaggregation method provided the best predictions of tree- and stand-level survival and growth. Conclusions： The disaggregation approach provides a link between individual-tree models and whole-stand models, and should be considered as a better alternative to the unadjusted tree model.展开更多
A mixed system that includes poly（ethylene oxide） （PEO） and silica （SiO2） nanoparticles is prepared using two mixing methods. The interaction between PEO and the SiO2 nanoparticles in the dilute basic solution i...A mixed system that includes poly（ethylene oxide） （PEO） and silica （SiO2） nanoparticles is prepared using two mixing methods. The interaction between PEO and the SiO2 nanoparticles in the dilute basic solution is investigated using the dynamic tight scattering （DLS） and isothermal titration calorimetry （ITC） techniques. The DLS results show qualitatively that SiO2 nanoparticles interact with both random coils and aggregates of PEO through hydrogen bonding, and PEO-SiO2 complexes are formed. The degree of disaggregation of aggregates of PEO is readily adjusted by changing the concentration of SiO2 nanoparticle suspensions. Moreover, the ITC results also certify quantitatively the interaction between PEO and SiO2 nanoparticle, and give the evidence of formation of PEO-SiO2 complex.展开更多
Occurrence of neurofibrillary tangles of the tau protein is a hallmark of tau-related neurodegenerative diseases, i.e. Alzheimer's disease(AD) and frontotemporal dementia. The pathological mechanism underlying AD ...Occurrence of neurofibrillary tangles of the tau protein is a hallmark of tau-related neurodegenerative diseases, i.e. Alzheimer's disease(AD) and frontotemporal dementia. The pathological mechanism underlying AD remains poorly understood, and effective treatments are still unavailable to mitigate the disease.Inhibiting of tau aggregation and disrupting the existing fibrils are key targets in drug discovery towards preventing or curing AD. In this study, grape seed proanthocyanidins(GSPs) was found to effectively inhibit the repeat domain of tau(tau-RD) aggregation and disaggregate tau-RD fibrils in a concentrationdependent manner by inhibiting β-sheet formation of tau-RD. In cells, GSPs relieved cytotoxicity induced by tau-RD aggregates. Molecular dynamics simulations indicated that strong hydrogen bonding,hydrophobic interaction and π-π stacking between GSPs and tau-RD protein were major reasons why GSPs had high inhibitory activity on tau-RD fibrillogenesis. These results provide preliminary data to develop GSPs into medicines, foodstuffs or nutritional supplements for AD patients, suggesting that GSPs could be a candidate molecule in the drug design for AD therapeutics.展开更多
Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this ...Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this study,we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles(MMEs)from the Coupled Model Intercomparison Project Phase 6(CMIP6)under four Shared Socioeconomic Pathway and Representative Concentration Pathway(SSP-RCP)scenarios(SSP126(SSP1-RCP2.6),SSP245(SSP2-RCP4.5),SSP460(SSP4-RCP6.0),and SSP585(SSP5-RCP8.5))during 2015-2100.The bias correction and spatial disaggregation,water-thermal product index,and sensitivity analysis were used in this study.The results showed that the hydrothermal condition is mismatched in the central and southern deserts,whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition.Compared with the historical period,the matched degree of hydrothermal condition improves during 2046-2075,but degenerates during 2015-2044 and 2076-2100.The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions.The result suggests that the optimal scenario in Central Asia is SSP126 scenario,while SSP585 scenario brings further hydrothermal contradictions.This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.展开更多
Islet amyloid polypeptide(IAPP),or amylin,has been identifi ed as a key factor in the development of type 2 diabetes(T2D).IAPP aggregates,which form amyloid fi brils,contribute to cytotoxicity of the pancreatic β-cel...Islet amyloid polypeptide(IAPP),or amylin,has been identifi ed as a key factor in the development of type 2 diabetes(T2D).IAPP aggregates,which form amyloid fi brils,contribute to cytotoxicity of the pancreatic β-cells,resulting in loss of function and subsequent reduction in insulin production.As a result,surviving β-cells overcompensate for this reduction of insulin production,further contributing to the loss of function because of increased stress,thus leading to insulin resistance.Endogenously,IAPP monomers function in a variety of roles;however,aggregation renders them non-functional.The use of naturally occurring compounds,including peptides and phytochemicals,has been explored as a way to mitigate or inhibit IAPP fi bril formation.This review discusses the structure,endogenous roles and mechanism of IAPP fi bril formation,recent advances on inhibitors of IAPP fi bril formation,and new insights on the future development and application of foodderived inhibitors towards T2D management.展开更多
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.
基金supported by the National Natural Science Foundation of China (41130530,91325301,41431177,41571212,41401237)the Project of "One-Three-Five" Strategic Planning & Frontier Sciences of the Institute of Soil Science,Chinese Academy of Sciences (ISSASIP1622)+1 种基金the Government Interest Related Program between Canadian Space Agency and Agriculture and Agri-Food,Canada (13MOA01002)the Natural Science Research Program of Jiangsu Province (14KJA170001)
文摘Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-specific agricultural management and environmental modelling.We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation.The method consisted of three steps.First,environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors.Second,according to soil types of the pedon sites,the similarities were aggregated to derive similarity distribution for each soil type.Third,a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba,Canada.Based on 186 independent pedon sites,the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62.The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map,which was commonly used in practice.Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy,indicating that new environmental covariates need to be developed.We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.
基金supported by the Science and Technology Project of State Grid Corporation of China(No.5400-202112507A-0-5-ZN)the National Nature Science Foundation for Young Scholars of China(No.52107120).
文摘As the intermittency and uncertainty of photovoltaic(PV)power generation poses considerable challenges to the power system operation,accurate PV generation estimates are critical for the distribution operation,maintenance,and demand response program implementation because of the increasing usage of distributed PVs.Currently,most residential PVs are installed behind the meter,with only the net load available to the utilities.Therefore,a method for disaggregating the residential PV generation from the net load data is needed to enhance the grid-edge observability.In this study,an unsupervised PV capacity estimation method based on net metering data is proposed,for estimating the PV capacity in the customer’s premise based on the distribution characteristics of nocturnal and diurnal net load extremes.Then,the PV generation disaggregation method is presented.Based on the analysis of the correlation between the nocturnal and diurnal actual loads and the correlation between the PV capacity and their actual PV generation,the PV generation of customers is estimated by applying linear fitting of multiple typical solar exemplars and then disaggregating them into hourly-resolution power profiles.Finally,the anomalies of disaggregated PV power are calibrated and corrected using the estimated capacity.Experiment results on a real-world hourly dataset involving 260 customers show that the proposed PV capacity estimation method achieves good accuracy because of the advantages of robustness and low complexity.Compared with the state-of-the-art PV disaggregation algorithm,the proposed method exhibits a reduction of over 15%for the mean absolute percentage error and over 20%for the root mean square error.
文摘With the benefits of increased computing power and much improved software,temporal disaggregation is examined.Disaggregation,the process of obtaining high frequency data from low frequency data has been discussed for many years.This study examines three methods which utilize the autoregressive integrated moving average(ARIMA)model in a simulation study comparing parameter estimation,disaggregation mean square error,and forecast mean square error.Finally,the three methods are applied to a real-world time series.
基金supported by the National Natural Science Foundation of China(Grant No.52225705).
文摘Energy disaggregation is a technology that disassembles the energy consumption from the entire house into loadlevel contributions.One of the foundational tasks for this technology is to accurately ascertain the truth electrical energy consumption of the target load.However,current energy disaggregation methods find it difficult to accurately predict the actual operating power of appliances when there are significant differences in the data distribution of appliances across various scenarios due to the diversity in manufacturers,usage times,and operating conditions.In this study,we propose a power extraction approach with load state modification to capture accurate load operating power with minimal influence from usage scenarios.To be specific,the on/off state sequence of appliances is first predicted leveraging existing energy disaggregation methods,and two state modification methods based on non-operating time and operating time of appliances are respectively proposed to modify the erroneous states in sequence.Subsequently,the power extraction approach calculates the operational power of target appliance based on the amplitude of fluctuations within the aggregated energy consumption caused by its state changes.Furthermore,a removing signal spikes method is proposed to improve the accuracy of the extracted power value.We conducted extensive experiments on a public dataset,demonstrating that the proposed method can significantly improve the accuracy of state-of-the-art solution.The average of mean ab-solute error across commonly used appliances during on state were reduced by 44.75%and 32.07%respectively in the UK-DALE and REFIT datasets.
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 National Natural Science Foundation Project(Grant No.52374177)Anhui Provincial Natural Science Foundation Energy Internet Joint Fund Key Project(Grant No.2008085UD06)State Grid Anhui Electric Power Co.,Ltd.Fuyang Power Supply Company Science and Technology Project(Grant No.SGAHFY00TKJS2310510)。
文摘Non-intrusive load monitoring(NILM)technology aims to infer the operation information of electrical appliances from the total household load signals,which is of great significance for energy conservation and planning.However,existing methods are difficult to effectively capture the complex nonlinear features of the power consumption flow,which affects the energy disaggregation accuracy.To this end,this paper designs a method based on temporal convolutional network(TCN),efficient channel attention(ECA),and long short-term memory(LSTM).The method first creatively proposes a two-stage improved TCN(TSTCN),which overcomes its problems of extracting discontinuous information and poor correlation of long-distance information while enhancing the ability to extract high-level load features.Then a novel improved ECA attention mechanism(IECA)is embedded,which is also combined with the skip connection technique to pay channel-weighted attention to important feature maps and promote information fusion.Finally,the LSTM with strong temporal memory capability is introduced to learn the dependencies in the load power sequence and realize load disaggregation.Experiments on two real-world datasets,REDD and UK-DALE,show that the proposed model significantly outperforms other comparative NILM algorithms and achieves satisfactory tracking with the actual appliance operating power.The results show that the mean absolute error(MAE)of all appliances decreases by 18.67%on average,and the F1 score improves by 38.70%.
文摘The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggrega-tion methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodol-ogy. The contribution of this paper is in utilizing the “k- value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.
基金supported by the State Key Laboratory project on the Kwangsian Orogeny of South Chinaa contribution to the IGCP project 591
文摘A study of using climate sensitive deposits as a compiled climatic data to locate global climatic belt boundaries through time is developed by the present authors since the 1990s. Global latitudinal belts were presented from Cambrian to Permian as well as the in terval from the early Late Cretaceous to the present. However, during the later Permian and into the Early Cretaceous we noted that the failure of the tropical-subtropical belt to penetrate into the interior of Pangaean resulted in the merging of the two arid belts associated with the northern and southern Hadley Cells into one vast, interior arid region. A Pangaeanic paleogeography dominates and obviously affects the climatic distribution from the Late Permian to Early Cretaceous. We employ the dismission and reoccurrence of the global latitudinal climate belts to determine the aggregation and disaggregation of the Pangaean.
基金supported by the National Natural Science Foundation of China(51773099,51933006)。
文摘The deposition of highly ordered amyloid fibrils is recognized as a hallmark of amyloidosis diseases such as Alzheimer’s disease and Parkinson’s disease.Disaggregating the amyloid fibrils is considered as one of the effective strategies for the control and treatment of amyloidosis diseases.In this article,by simulating the function of natural molecular chaperones,co-assembled block copolymer micelles with coordination groups of nitrilotriacetic acid(NTA)and hydrophobic microdomains of poly(Nisopropylacrylamide)(PNIPAM)on the surface were used as nanochaperones(n Chaps)to disaggregate amyloid insulin fibrils.Zinc ions chelated by NTA can bind the histidine imidazole residues while the PNIPAM microdomains can interact with the exposed hydrophobic sites on the amyloid insulin fibrils,which synergistically perturb the stability of amyloid insulin fibrils,loosen their structure,and finally promote their disaggregation.A combination of characterizations with fluorescence spectroscopy,transmission electron microscopy(TEM),dynamic hight scattering(DLS),and quartz crystal microbalance(QCM)demonstrated that mature amyloid insulin fibrils were completely disaggregated after incubating with n Chaps for 90 h.This study may provide a promising strategy for the development of n Chaps for the treatment of amyloidosis diseases.
基金the financial support from the National Natural Science Foundation of China(Nos.21773054,21905072 and 22077025)the Natural Science Foundation of Hebei Province(Nos.B2020202062 and B2020202086).
文摘Comprehensive Summary Amyloid-β protein(Aβ)is a fatal cause of Alzheimer's disease,which can trigger a series of cytotoxicity by the abnormal aggregation of Aβ in human brain.The strategies for inhibition and disaggregation of Aβ fibrillation are mostly based on the interaction between monomers,oligomers,fibrils,and materials.
基金the National Natural Science Foundation of China[grant number 41371323]the National Key Research and Development Program of China[grant number 2016YFA0600101].
文摘High spatial resolution land surface broadband emissivity(BBE)is not only useful for surface energy balance studies at local scales,but also can bridge the gap between existing coarser resolution BBE products and point-based field measurements.This study proposes a disaggregation approach that utilizes the established BBE–reflectance relationship for estimating high spatial resolution BBE for bare soils from Landsat surface reflectance data.The disaggregated BBE is compared to the BBE calculated from spatial–temporal matched Advanced Spaceborne Thermal Emission and Reflectance Radiometer emissivity product.Comparison results show that better agreement is achieved over relative homogeneous areas,but deteriorated over heterogeneous and cloud-contaminated areas.In addition,field-measured emissivity data over large homogeneous desert were also used to validate the disaggregated BBE,and the bias is 0.005.The comparison and validation results indicated that the disaggregation approach can obtain high spatial resolution BBE with better accuracy for homogeneous area.
基金supported by the National Natural Science Foundation of China (Grant Nos.41230527, 41175079, and 41025017)the Jiangsu Collaborative Innovation Center for Climate Change
文摘In the last decade, a series of severe and extensive droughts have swept across Southwest China, resulting in tremendous economic losses, deaths, and disruption to society. Consequently, this study is motivated by the paramount importance of as- sessing future changes in drought in Southwest China. Precipitation is likely to decrease over most parts of Southwest China around the beginning of the century, followed by widespread precipitation increases; the increase in potential evapotran- spiration （PET）, due to the joint effects of increased temperature and surface net radiation and decreased relative humidity, will overwhelm the whole region throughout the entire 21st century. In comparative terms, the enhancement of PET will outweigh that of precipitation, particularly under Representative Concentration Pathway （RCP） 8.5, resulting in intensified drought. Generally, the drying tendency will be in the southeast portion, whereas the mountainous region in the northwest will become increasingly wetter owing to abundant precipitation increases. Droughts classified as moderate/severe according to historical standards will become the norm in the 2080s under RCP4.5/RCP8.5. Future drought changes will manifest different characteristics depending on the time scale： the magnitude of change at a time scale of 48 months is nearly twice as great as that at 3 months. Furthermore, we will see that not only will incidences of severe and extreme drought increase dramatically in the future, but extremely wet events will also become more probable.
文摘In this study, strong ground motion record （SGMR） selection based on Eta （~/） as a spectral shape indicator has been investigated as applied to steel braced flame structures. A probabilistic seismic hazard disaggregation analysis for the definition of the target Epsilon （ε） and the target Eta （η） values at different hazard levels is presented, taking into account appropriately selected SGMR＇s. Fragility curves are developed for different limit states corresponding to three representative models of typical steel braced frames having significant irregularities in plan, by means of a weighted damage index. The results show that spectral shape indicators have an important effect on the predicted median structural capacities, and also that the parameter r/is a more robust predictor of damage than searching for records with appropriate c values.
基金Funding for this project was provided in part by the Mclntire-Stennis funds
文摘Background： Different types of growth and yield models provide essential information for making informed decisions on how to manage forests. Whole-stand models often provide well-behaved outputs at the stand level, but lack information on stand structures. Detailed information from individual-tree models and size-class models typically suffers from accumulation of errors. The disaggregation method, in assuming that predictions from a whole-stand model are reliable, partitions these outputs to individual trees. On the other hand, the combination method seeks to improve stand-level predictions from both whole-stand and individual-tree models by combining them. Methods： Data from 100 plots randomly selected from the Southwicte Seed Source Study of Ioblolly pine （Pinus taedo L） were used to evaluate the unadjusted individual-tree model against the disaggregation and combination methods. Results： Compared to the whole-stand model, the combination method did not show improvements in predicting stand attributes in this study. The combination method also did not perform as well as the disaggregation method in tree-level predictions. The disaggregation method provided the best predictions of tree- and stand-level survival and growth. Conclusions： The disaggregation approach provides a link between individual-tree models and whole-stand models, and should be considered as a better alternative to the unadjusted tree model.
基金supported by the National Natural Science Foundation of China (Nos: 50621302, 50921062)
文摘A mixed system that includes poly（ethylene oxide） （PEO） and silica （SiO2） nanoparticles is prepared using two mixing methods. The interaction between PEO and the SiO2 nanoparticles in the dilute basic solution is investigated using the dynamic tight scattering （DLS） and isothermal titration calorimetry （ITC） techniques. The DLS results show qualitatively that SiO2 nanoparticles interact with both random coils and aggregates of PEO through hydrogen bonding, and PEO-SiO2 complexes are formed. The degree of disaggregation of aggregates of PEO is readily adjusted by changing the concentration of SiO2 nanoparticle suspensions. Moreover, the ITC results also certify quantitatively the interaction between PEO and SiO2 nanoparticle, and give the evidence of formation of PEO-SiO2 complex.
基金supported by the National Natural Science Foundation of China (21878262)。
文摘Occurrence of neurofibrillary tangles of the tau protein is a hallmark of tau-related neurodegenerative diseases, i.e. Alzheimer's disease(AD) and frontotemporal dementia. The pathological mechanism underlying AD remains poorly understood, and effective treatments are still unavailable to mitigate the disease.Inhibiting of tau aggregation and disrupting the existing fibrils are key targets in drug discovery towards preventing or curing AD. In this study, grape seed proanthocyanidins(GSPs) was found to effectively inhibit the repeat domain of tau(tau-RD) aggregation and disaggregate tau-RD fibrils in a concentrationdependent manner by inhibiting β-sheet formation of tau-RD. In cells, GSPs relieved cytotoxicity induced by tau-RD aggregates. Molecular dynamics simulations indicated that strong hydrogen bonding,hydrophobic interaction and π-π stacking between GSPs and tau-RD protein were major reasons why GSPs had high inhibitory activity on tau-RD fibrillogenesis. These results provide preliminary data to develop GSPs into medicines, foodstuffs or nutritional supplements for AD patients, suggesting that GSPs could be a candidate molecule in the drug design for AD therapeutics.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences,Pan-Third Pole Environment Study for a Green Silk Road(Pan-TPE)of China(XDA2004030202)Shanghai Cooperation and the Organization Science and Technology Partnership of China(2021E01019)。
文摘Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this study,we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles(MMEs)from the Coupled Model Intercomparison Project Phase 6(CMIP6)under four Shared Socioeconomic Pathway and Representative Concentration Pathway(SSP-RCP)scenarios(SSP126(SSP1-RCP2.6),SSP245(SSP2-RCP4.5),SSP460(SSP4-RCP6.0),and SSP585(SSP5-RCP8.5))during 2015-2100.The bias correction and spatial disaggregation,water-thermal product index,and sensitivity analysis were used in this study.The results showed that the hydrothermal condition is mismatched in the central and southern deserts,whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition.Compared with the historical period,the matched degree of hydrothermal condition improves during 2046-2075,but degenerates during 2015-2044 and 2076-2100.The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions.The result suggests that the optimal scenario in Central Asia is SSP126 scenario,while SSP585 scenario brings further hydrothermal contradictions.This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.
基金Authors acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada(NSERC)(grant number RGPIN-2018-06839)the University Research Chairs Program of the University of Ottawa,Canada.
文摘Islet amyloid polypeptide(IAPP),or amylin,has been identifi ed as a key factor in the development of type 2 diabetes(T2D).IAPP aggregates,which form amyloid fi brils,contribute to cytotoxicity of the pancreatic β-cells,resulting in loss of function and subsequent reduction in insulin production.As a result,surviving β-cells overcompensate for this reduction of insulin production,further contributing to the loss of function because of increased stress,thus leading to insulin resistance.Endogenously,IAPP monomers function in a variety of roles;however,aggregation renders them non-functional.The use of naturally occurring compounds,including peptides and phytochemicals,has been explored as a way to mitigate or inhibit IAPP fi bril formation.This review discusses the structure,endogenous roles and mechanism of IAPP fi bril formation,recent advances on inhibitors of IAPP fi bril formation,and new insights on the future development and application of foodderived inhibitors towards T2D management.
