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
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
Magnetic resonance (MR) imaging has been increasingly used in the evaluation of prostate cancer. As studies have suggested that the majority of cancers arise from the peripheral zone (PZ), MR imaging has focused on th...Magnetic resonance (MR) imaging has been increasingly used in the evaluation of prostate cancer. As studies have suggested that the majority of cancers arise from the peripheral zone (PZ), MR imaging has focused on the PZ of the prostate gland thus far. However, a considerable number of cancers (up to 30%) originate in the transition zone (TZ), substantially contributing to morbidity and mortality. Therefore, research is needed on the TZ of the prostate gland. Recently, MR imaging and advanced MR techniques have been gaining acceptance in evaluation of the TZ. In this article, the MR imaging features of TZ prostate cancers, the role of MR imaging in TZ cancer detection and staging, and recent advanced MR techniques will be discussed in light of the literature.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
Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenario...Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.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
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
We hypothesized that a relationship existed between the mechanical properties and the magnetic resonance imaging (MRI) parameters of muscles, as already demonstrated in cartilaginous tissues. The aim was to develop an...We hypothesized that a relationship existed between the mechanical properties and the magnetic resonance imaging (MRI) parameters of muscles, as already demonstrated in cartilaginous tissues. The aim was to develop an indirect evaluation tool of the mechanical properties of degenerated muscles. Leg and arm muscles of adult rabbits were dissected, and tested 12 hours post mortem, in a state of rigor mortis, or 72 hours post mortem, in a state of post-rigor mortis. The tests consisted of a multi-parametric MRI acquisition followed by a uniaxial tensile test until failure. The statistical analysis consisted of multiple linear regressions and principal component analysis. Significant differences existed between the rigor mortis and post-rigor mortis groups for E but not for the MRI parameters. 78%, 60% or 33% of the Young’s modulus could be explained by the MRI parameters in the post-rigor mortis group, rigor mortis group or both groups respectively. These relationships were confirmed by the principal component analysis. The proposed multi-parametric MRI protocol associated to principal component analysis is a promising tool for the indirect evaluation of muscle mechanical properties and should be useful to find biomarkers and predictive factors of the evolution of the pathologies.展开更多
A process represented by nonlinear multi-parametric binary dynamic system is investigated in this work. This process is characterized by the pseudo Boolean objective functional. Since the transfer functions on the pro...A process represented by nonlinear multi-parametric binary dynamic system is investigated in this work. This process is characterized by the pseudo Boolean objective functional. Since the transfer functions on the process are Boolean functions, the optimal control problem related to the process can be solved by relating between the transfer functions and the objective functional. An analogue of Bellman function for the optimal control problem mentioned is defined and consequently suitable Bellman equation is constructed.展开更多
BACKGROUND The study focuses on the use of multi-parametric ultrasound[gray scale,color Doppler and shear wave elastography(SWE)]to differentiate stable renal allografts from acute graft dysfunction and to assess time...BACKGROUND The study focuses on the use of multi-parametric ultrasound[gray scale,color Doppler and shear wave elastography(SWE)]to differentiate stable renal allografts from acute graft dysfunction and to assess time-dependent changes in parenchymal stiffness,thereby assessing its use as an efficient monitoring tool for ongoing graft dysfunction.To date,biopsy is the gold standard for evaluation of acute graft dysfunction.However,because it is invasive,it carries certain risks and cannot be used for follow-up monitoring.SWE is a non-invasive imaging modality that identifies higher parenchymal stiffness values in cases of acute graft dysfunction compared to stable grafts.AIM To assess renal allograft parenchymal stiffness by SWE and to correlate its findings with functional status of the graft kidney.METHODS This prospective observational study included 71 renal allograft recipients.Multi-parametric ultrasound was performed on all patients,and biopsies were performed in cases of acute graft dysfunction.The study was performed for a period of 2 years at Sanjay Gandhi Postgraduate Institute of Medical Sciences,Lucknow,a tertiary care center in north India.Independent samples t-test was used to compare the means between two independent groups.Paired-samples t-test was used to test the change in mean value between baseline and follow-up obser-vations.RESULTS Thirty-one patients had experienced acute graft dysfunction at least once,followed by recovery,but none of them had a history of chronic renal allograft injury.Mean baseline parenchymal stiffness in stable grafts and acute graft dysfunction were 30.21+2.03 kPa(3.17+0.11 m/s)and 31.07+2.88 kPa(3.22+0.15 m/s),respectively;however,these differences were not statistically significant(P=0.305 and 0.252,respectively).There was a gradual decrease in SWE values during the first 3 postoperative months,followed by an increase in SWE values up to one-year post-transplantation.Patients with biopsy-confirmed graft dysfunction showed higher SWE values compared to those with a negative biopsy.However,receiver operating characteristic analysis failed to show statistically significant cut-off values to differentiate between the stable graft and acute graft dysfunction.CONCLUSION Acute graft dysfunction displays higher parenchymal stiffness values compared to stable grafts.Therefore,SWE may be useful in monitoring the functional status of allografts to predict any ongoing dysfunction.展开更多
To facilitate the operation of distribution networks with a large scale of household photovoltaic systems integrated,the availability of community-level behind-the-meter(BTM)PV power generation is crucial.Yet,due to t...To facilitate the operation of distribution networks with a large scale of household photovoltaic systems integrated,the availability of community-level behind-the-meter(BTM)PV power generation is crucial.Yet,due to the scarcity of smart meters installed,it is challenging to obtain such information via directly aggregating measured power outputs of individual PV systems,and an effective estimation method needs to be developed.Considering the similarity between household-level and community-level data within the same geographical area,this paper develops a synchronization and model-transfer fused LSTM framework(SAM-LSTM).The core technical contribution lies in the development of the Synchronized Long Short-Term Memory(Syn-LSTM),which separately models the synchronized factors and disaggregated BTM data to capture more generalized representations.The learned household-level representations are then transferred to the community-level.Finally,by explicitly leveraging the complementarity between PV generation and consumption,a dual time-series modeling architecture is developed to refine the initial community-level PV power generation estimates,thereby alleviating potential biases introduced during the model-transfer process.Extensive computational studies are conducted to demonstrate the effectiveness of SAM-LSTM in community-level BTM PV power generation disaggregation in real data from Hebei,China.Compared with the best-performing benchmarks,SAM-LSTM achieves up to 56%lower MSE,significantly demonstrating its strong generalization and robustness capabilities.展开更多
The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)h...The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.展开更多
The de-manufacturing stage is an overlooked component of most current LCA （life cycle assessment） methodologies. Most of the current LCA techniques do not fully account for the usage of the product and end of life a...The de-manufacturing stage is an overlooked component of most current LCA （life cycle assessment） methodologies. Most of the current LCA techniques do not fully account for the usage of the product and end of life aspects. This paper introduces a comprehensive methodology that takes strong consideration of the inventory costs of use and end of life of the functional unit by combining manufacturing and de-manufacturing into the centerpiece of the hybrid analysis. In order to obtain this goal, a new disaggregated model was developed by enhancing current LCA hybrid methods related to life cycle inventory compilations. The new methodology is also compared to existing methodologies.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.
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.
文摘Magnetic resonance (MR) imaging has been increasingly used in the evaluation of prostate cancer. As studies have suggested that the majority of cancers arise from the peripheral zone (PZ), MR imaging has focused on the PZ of the prostate gland thus far. However, a considerable number of cancers (up to 30%) originate in the transition zone (TZ), substantially contributing to morbidity and mortality. Therefore, research is needed on the TZ of the prostate gland. Recently, MR imaging and advanced MR techniques have been gaining acceptance in evaluation of the TZ. In this article, the MR imaging features of TZ prostate cancers, the role of MR imaging in TZ cancer detection and staging, and recent advanced MR techniques will be discussed in light of the literature.
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.
基金the Science and Technology Project of State Grid Corporation of China,Grant Number 5108-202304065A-1-1-ZN.
文摘Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.
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.
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We hypothesized that a relationship existed between the mechanical properties and the magnetic resonance imaging (MRI) parameters of muscles, as already demonstrated in cartilaginous tissues. The aim was to develop an indirect evaluation tool of the mechanical properties of degenerated muscles. Leg and arm muscles of adult rabbits were dissected, and tested 12 hours post mortem, in a state of rigor mortis, or 72 hours post mortem, in a state of post-rigor mortis. The tests consisted of a multi-parametric MRI acquisition followed by a uniaxial tensile test until failure. The statistical analysis consisted of multiple linear regressions and principal component analysis. Significant differences existed between the rigor mortis and post-rigor mortis groups for E but not for the MRI parameters. 78%, 60% or 33% of the Young’s modulus could be explained by the MRI parameters in the post-rigor mortis group, rigor mortis group or both groups respectively. These relationships were confirmed by the principal component analysis. The proposed multi-parametric MRI protocol associated to principal component analysis is a promising tool for the indirect evaluation of muscle mechanical properties and should be useful to find biomarkers and predictive factors of the evolution of the pathologies.
文摘A process represented by nonlinear multi-parametric binary dynamic system is investigated in this work. This process is characterized by the pseudo Boolean objective functional. Since the transfer functions on the process are Boolean functions, the optimal control problem related to the process can be solved by relating between the transfer functions and the objective functional. An analogue of Bellman function for the optimal control problem mentioned is defined and consequently suitable Bellman equation is constructed.
文摘BACKGROUND The study focuses on the use of multi-parametric ultrasound[gray scale,color Doppler and shear wave elastography(SWE)]to differentiate stable renal allografts from acute graft dysfunction and to assess time-dependent changes in parenchymal stiffness,thereby assessing its use as an efficient monitoring tool for ongoing graft dysfunction.To date,biopsy is the gold standard for evaluation of acute graft dysfunction.However,because it is invasive,it carries certain risks and cannot be used for follow-up monitoring.SWE is a non-invasive imaging modality that identifies higher parenchymal stiffness values in cases of acute graft dysfunction compared to stable grafts.AIM To assess renal allograft parenchymal stiffness by SWE and to correlate its findings with functional status of the graft kidney.METHODS This prospective observational study included 71 renal allograft recipients.Multi-parametric ultrasound was performed on all patients,and biopsies were performed in cases of acute graft dysfunction.The study was performed for a period of 2 years at Sanjay Gandhi Postgraduate Institute of Medical Sciences,Lucknow,a tertiary care center in north India.Independent samples t-test was used to compare the means between two independent groups.Paired-samples t-test was used to test the change in mean value between baseline and follow-up obser-vations.RESULTS Thirty-one patients had experienced acute graft dysfunction at least once,followed by recovery,but none of them had a history of chronic renal allograft injury.Mean baseline parenchymal stiffness in stable grafts and acute graft dysfunction were 30.21+2.03 kPa(3.17+0.11 m/s)and 31.07+2.88 kPa(3.22+0.15 m/s),respectively;however,these differences were not statistically significant(P=0.305 and 0.252,respectively).There was a gradual decrease in SWE values during the first 3 postoperative months,followed by an increase in SWE values up to one-year post-transplantation.Patients with biopsy-confirmed graft dysfunction showed higher SWE values compared to those with a negative biopsy.However,receiver operating characteristic analysis failed to show statistically significant cut-off values to differentiate between the stable graft and acute graft dysfunction.CONCLUSION Acute graft dysfunction displays higher parenchymal stiffness values compared to stable grafts.Therefore,SWE may be useful in monitoring the functional status of allografts to predict any ongoing dysfunction.
基金National Natural Science Foun-dation of China under Grant(No.62572116)Hong Kong RGC General Research Fund Project under Grant(No.11213124)+5 种基金Guangdong Provincial Basic and Applied Basic Re-search-Offshore Wind Power Joint Fund Project under Grant(No.2022A1515240066)Hong Kong RGC Collaborative Research Fund Project under Grant(No.C1049-24GF)Shenzhen-Hong Kong-Macao Science and Technology Category C Project under Grant(No.SGDX20220530111205037)Hong Kong ITC Innovation and Technology Fund Project under Grant(No.ITS/034/22MS)Young Elite Scientists Sponsorship Program by CAST under Grant(No.2023ONRC001)National Natural Science Foundation of China under Grant(No.62576098).
文摘To facilitate the operation of distribution networks with a large scale of household photovoltaic systems integrated,the availability of community-level behind-the-meter(BTM)PV power generation is crucial.Yet,due to the scarcity of smart meters installed,it is challenging to obtain such information via directly aggregating measured power outputs of individual PV systems,and an effective estimation method needs to be developed.Considering the similarity between household-level and community-level data within the same geographical area,this paper develops a synchronization and model-transfer fused LSTM framework(SAM-LSTM).The core technical contribution lies in the development of the Synchronized Long Short-Term Memory(Syn-LSTM),which separately models the synchronized factors and disaggregated BTM data to capture more generalized representations.The learned household-level representations are then transferred to the community-level.Finally,by explicitly leveraging the complementarity between PV generation and consumption,a dual time-series modeling architecture is developed to refine the initial community-level PV power generation estimates,thereby alleviating potential biases introduced during the model-transfer process.Extensive computational studies are conducted to demonstrate the effectiveness of SAM-LSTM in community-level BTM PV power generation disaggregation in real data from Hebei,China.Compared with the best-performing benchmarks,SAM-LSTM achieves up to 56%lower MSE,significantly demonstrating its strong generalization and robustness capabilities.
文摘The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.
文摘The de-manufacturing stage is an overlooked component of most current LCA （life cycle assessment） methodologies. Most of the current LCA techniques do not fully account for the usage of the product and end of life aspects. This paper introduces a comprehensive methodology that takes strong consideration of the inventory costs of use and end of life of the functional unit by combining manufacturing and de-manufacturing into the centerpiece of the hybrid analysis. In order to obtain this goal, a new disaggregated model was developed by enhancing current LCA hybrid methods related to life cycle inventory compilations. The new methodology is also compared to existing methodologies.
