Recently, researches on distributed data mining by making use of grid are in trend. This paper introduces a data mining algorithm by means of distributed decision-tree,which has taken the advantage of conveniences and...Recently, researches on distributed data mining by making use of grid are in trend. This paper introduces a data mining algorithm by means of distributed decision-tree,which has taken the advantage of conveniences and services supplied by the computing platform-grid,and can perform a data mining of distributed classification on grid.展开更多
Floods are one of the major hazards worldwide. They are the source of huge risks in rural and urban areas, resulting in severe impacts on the civil society, industry and the economy. The Elbe River has suffered from m...Floods are one of the major hazards worldwide. They are the source of huge risks in rural and urban areas, resulting in severe impacts on the civil society, industry and the economy. The Elbe River has suffered from many severe floods during recent decades. In this study, the zones flooded during 2011 were analyzed using TerraSAR-X images and a digital elevation model for the area in order to identify possible ways to mitigate flood hazards in the future, regarding sustainable land-use. Two study areas are investigated, around the Walmsburg oxbow and the Wehningen oxbow. These are located between Elbe-Kilometer (505-520) and (533-543), respectively, within the Lower Saxonian Elbe River Biosphere Reserve. Those areas are characterized by several types of land use, with agricultural land use being predominant. The study investigated the possibility of using a Decision-Tree object-based classifier for determining the major land uses and the extent of the inundation areas. The inundation areas identify for 2011 submerged some agricultural fields that must be added to existing flood risk maps, and future cultivation activities there prevented to avoid the possible economic losses. Furthermore, part of the residential area is located within the high flood zone, and must be included in risk maps to avoid the possible human and economic losses, to achieve sustainable land use for the areas studied.展开更多
Chronic hepatitis B and C together with alcoholic and non-alcoholic fatty liver diseases represent the major causes of progressive liver disease that can eventually evolve into cirrhosis and its end-stage complication...Chronic hepatitis B and C together with alcoholic and non-alcoholic fatty liver diseases represent the major causes of progressive liver disease that can eventually evolve into cirrhosis and its end-stage complications,including decompensation,bleeding and liver cancer.Formation and accumulation of fibrosis in the liver is the common pathway that leads to an evolutive liver disease.Precise definition of liver fibrosis stage is essential for management of the patient in clinical practice since the presence of bridging fibrosis represents a strong indication for antiviral therapy for chronic viral hepatitis,while cirrhosis requires a specif ic follow-up including screening for esophageal varices and hepatocellular carcinoma.Liver biopsy has always represented the standard of reference for assessment of hepatic fibrosis but it has some limitations being invasive,costly and prone to sampling errors.Recently,blood markers and instrumental methods have been proposed for the non-invasive assessment of liver fibrosis.However,there are still some doubts as to their implementation in clinical practice and a real consensus on how and when to use them is not still available.This is due to an unsatisfactory accuracy for some of them,and to an incomplete validation for others.Some studies suggest that performance of non-invasive methods for liver fibrosis assessment may increase when they are combined.Combination algorithms of non-invasive methods for assessing liver fibrosis may represent a rational and reliable approach to implement non-invasive assessment of liver fibrosis in clinical practice and to reduce rather than abolish liver biopsies.展开更多
The need for renewable energy sources has challenged most countries to comply with environmental protection actions and to handle climate change.Solar energy figures as a natural option,despite its intermittence.Brazi...The need for renewable energy sources has challenged most countries to comply with environmental protection actions and to handle climate change.Solar energy figures as a natural option,despite its intermittence.Brazil has a green energy matrix with significant expansion of solar form in recent years.To preserve the Amazon basin,the use of solar energy can help communities and cities improve their living standards without new hydroelectric units or even to burn biomass,avoiding harsh environmental consequences.The novelty of this work is using data science with machine-learning tools to predict the solar incidence(W.h/m^(2))in four cities in Amazonas state(north-west Brazil),using data from NASA satellites within the period of 2013-22.Decision-tree-based models and vector autoregressive(time-series)models were used with three time aggregations:day,week and month.The predictor model can aid in the economic assessment of solar energy in the Amazon basin and the use of satellite data was encouraged by the lack of data from ground stations.The mean absolute error was selected as the output indicator,with the lowest values obtained close to 0.20,from the adaptive boosting and light gradient boosting algorithms,in the same order of magnitude of similar references.展开更多
Preterm births have been seen to have psychological and financial implications;current surveys suggest that amongst the various methods of preterm prediction,there is yet to exist a reliable and standard means of pred...Preterm births have been seen to have psychological and financial implications;current surveys suggest that amongst the various methods of preterm prediction,there is yet to exist a reliable and standard means of predicting preterm births.This study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester,alongside information from the patients'medical health record regarding the pregnancy,towards preterm prediction and an associated delivery imminency timeline.In addition to this,the impact of both linear and non-linear dimensional embedding methods towards the preterm prediction is explored.The classification exercises were carried out using a support vector machine and decision tree,both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.展开更多
文摘Recently, researches on distributed data mining by making use of grid are in trend. This paper introduces a data mining algorithm by means of distributed decision-tree,which has taken the advantage of conveniences and services supplied by the computing platform-grid,and can perform a data mining of distributed classification on grid.
文摘Floods are one of the major hazards worldwide. They are the source of huge risks in rural and urban areas, resulting in severe impacts on the civil society, industry and the economy. The Elbe River has suffered from many severe floods during recent decades. In this study, the zones flooded during 2011 were analyzed using TerraSAR-X images and a digital elevation model for the area in order to identify possible ways to mitigate flood hazards in the future, regarding sustainable land-use. Two study areas are investigated, around the Walmsburg oxbow and the Wehningen oxbow. These are located between Elbe-Kilometer (505-520) and (533-543), respectively, within the Lower Saxonian Elbe River Biosphere Reserve. Those areas are characterized by several types of land use, with agricultural land use being predominant. The study investigated the possibility of using a Decision-Tree object-based classifier for determining the major land uses and the extent of the inundation areas. The inundation areas identify for 2011 submerged some agricultural fields that must be added to existing flood risk maps, and future cultivation activities there prevented to avoid the possible economic losses. Furthermore, part of the residential area is located within the high flood zone, and must be included in risk maps to avoid the possible human and economic losses, to achieve sustainable land use for the areas studied.
基金Supported by An unrestricted grant from Roche-Italia
文摘Chronic hepatitis B and C together with alcoholic and non-alcoholic fatty liver diseases represent the major causes of progressive liver disease that can eventually evolve into cirrhosis and its end-stage complications,including decompensation,bleeding and liver cancer.Formation and accumulation of fibrosis in the liver is the common pathway that leads to an evolutive liver disease.Precise definition of liver fibrosis stage is essential for management of the patient in clinical practice since the presence of bridging fibrosis represents a strong indication for antiviral therapy for chronic viral hepatitis,while cirrhosis requires a specif ic follow-up including screening for esophageal varices and hepatocellular carcinoma.Liver biopsy has always represented the standard of reference for assessment of hepatic fibrosis but it has some limitations being invasive,costly and prone to sampling errors.Recently,blood markers and instrumental methods have been proposed for the non-invasive assessment of liver fibrosis.However,there are still some doubts as to their implementation in clinical practice and a real consensus on how and when to use them is not still available.This is due to an unsatisfactory accuracy for some of them,and to an incomplete validation for others.Some studies suggest that performance of non-invasive methods for liver fibrosis assessment may increase when they are combined.Combination algorithms of non-invasive methods for assessing liver fibrosis may represent a rational and reliable approach to implement non-invasive assessment of liver fibrosis in clinical practice and to reduce rather than abolish liver biopsies.
基金The authors acknowledge the support of the Research Centre for Greenhouse Gas Innovation(RCGI),hosted by University of Sao Paulo(USP)and sponsored by FAPESP(grants#2014/50279-4 and#2020/15230-5,#2022/07974-0)Shell Brasil,and the strategic importance of the support given by Brazil’s National Oil,Natural Gas and Biofuels Agency(ANP)through the R&D levy regulation.Equally importantly,Felipe Almeida is sponsored by the National Council for Scientific and Technological Development(CNPq),grant#140253/2021-1.
文摘The need for renewable energy sources has challenged most countries to comply with environmental protection actions and to handle climate change.Solar energy figures as a natural option,despite its intermittence.Brazil has a green energy matrix with significant expansion of solar form in recent years.To preserve the Amazon basin,the use of solar energy can help communities and cities improve their living standards without new hydroelectric units or even to burn biomass,avoiding harsh environmental consequences.The novelty of this work is using data science with machine-learning tools to predict the solar incidence(W.h/m^(2))in four cities in Amazonas state(north-west Brazil),using data from NASA satellites within the period of 2013-22.Decision-tree-based models and vector autoregressive(time-series)models were used with three time aggregations:day,week and month.The predictor model can aid in the economic assessment of solar energy in the Amazon basin and the use of satellite data was encouraged by the lack of data from ground stations.The mean absolute error was selected as the output indicator,with the lowest values obtained close to 0.20,from the adaptive boosting and light gradient boosting algorithms,in the same order of magnitude of similar references.
文摘Preterm births have been seen to have psychological and financial implications;current surveys suggest that amongst the various methods of preterm prediction,there is yet to exist a reliable and standard means of predicting preterm births.This study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester,alongside information from the patients'medical health record regarding the pregnancy,towards preterm prediction and an associated delivery imminency timeline.In addition to this,the impact of both linear and non-linear dimensional embedding methods towards the preterm prediction is explored.The classification exercises were carried out using a support vector machine and decision tree,both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.
