In rice systems under continuous flooding(CF)irrigation,rice grains with high arsenic(As)concentration can be produced.In Argentina,these areas are located in the south of Corrientes Province and the north of Entre R&...In rice systems under continuous flooding(CF)irrigation,rice grains with high arsenic(As)concentration can be produced.In Argentina,these areas are located in the south of Corrientes Province and the north of Entre Ríos Province.The combination of agronomic management,genetic variability of rice varieties,and the characteristics of soil and irrigation water determines the concentration and proportion of grain As species.In this study,we evaluated two factors affecting grain As accumulation:irrigation management,CF and interrupted flooding(IF),and rice variety,rice with medium,long,and double long/wide grains.The experiments were conducted during four cropping cycles(2015–2016,2016–2017,2017–2018,and 2020–2021)on a farm in the north of Entre Ríos Province.Total As concentration in husked grains showed a wide range and was mostly above 0.30 mg kg^(-1),even after the polishing process.Fortunately,organic As was the predominant species.In polished rice,inorganic As concentration ranged between 0.02 and 0.28 mg kg^(-1).Significant differences were observed in grain As concentration between four rice varieties,with the highest inorganic and total As concentrations in grains of the medium-grain variety.The interaction of rice variety by irrigation management did not affect grain yield,but significantly reduced total As concentration in grains.Soil drainage under IF explained 43%–46%of the reduction of total As concentration in grains.The management practices of irrigation and rice variety had slight effects on inorganic As concentration in grains.In conclusion,a single soil drying period combined with proper rice varieties can be an effective management practice for mitigating As accumulation in rice grains.展开更多
Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such atta...Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.展开更多
Based on monthly precipitation data during 1961-2008 in 50 stations in Fushun,drought and flood indicators of three counties were calculated with Z index method. The geographical and seasonal distribution characterist...Based on monthly precipitation data during 1961-2008 in 50 stations in Fushun,drought and flood indicators of three counties were calculated with Z index method. The geographical and seasonal distribution characteristics of Fushun were analyzed,and so was the impact of droughts and floods on food production. It shows that,since 1961,there are 7 poor harvest years in Fushun,with quadrennial caused by continuous seasonal floods or droughts,two years by year drought,one year by summer flood.展开更多
基金the National Agency for the Promotion of Research,Technological Development and Innovation(Argentina)the National University of Entre Ríos(Argentina)+1 种基金the PROARROZ Foundation(Argentina)the company EBRO(Argentina)for the financial support of this research。
文摘In rice systems under continuous flooding(CF)irrigation,rice grains with high arsenic(As)concentration can be produced.In Argentina,these areas are located in the south of Corrientes Province and the north of Entre Ríos Province.The combination of agronomic management,genetic variability of rice varieties,and the characteristics of soil and irrigation water determines the concentration and proportion of grain As species.In this study,we evaluated two factors affecting grain As accumulation:irrigation management,CF and interrupted flooding(IF),and rice variety,rice with medium,long,and double long/wide grains.The experiments were conducted during four cropping cycles(2015–2016,2016–2017,2017–2018,and 2020–2021)on a farm in the north of Entre Ríos Province.Total As concentration in husked grains showed a wide range and was mostly above 0.30 mg kg^(-1),even after the polishing process.Fortunately,organic As was the predominant species.In polished rice,inorganic As concentration ranged between 0.02 and 0.28 mg kg^(-1).Significant differences were observed in grain As concentration between four rice varieties,with the highest inorganic and total As concentrations in grains of the medium-grain variety.The interaction of rice variety by irrigation management did not affect grain yield,but significantly reduced total As concentration in grains.Soil drainage under IF explained 43%–46%of the reduction of total As concentration in grains.The management practices of irrigation and rice variety had slight effects on inorganic As concentration in grains.In conclusion,a single soil drying period combined with proper rice varieties can be an effective management practice for mitigating As accumulation in rice grains.
文摘Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.
基金Supported by Fushun Government Financed Subject(20071209)
文摘Based on monthly precipitation data during 1961-2008 in 50 stations in Fushun,drought and flood indicators of three counties were calculated with Z index method. The geographical and seasonal distribution characteristics of Fushun were analyzed,and so was the impact of droughts and floods on food production. It shows that,since 1961,there are 7 poor harvest years in Fushun,with quadrennial caused by continuous seasonal floods or droughts,two years by year drought,one year by summer flood.
