Due to the anonymity of blockchain,frequent security incidents and attacks occur through it,among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses.Machine learningbase...Due to the anonymity of blockchain,frequent security incidents and attacks occur through it,among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses.Machine learningbased methods are believed to be promising for detecting ethereum Ponzi schemes.However,there are still some flaws in current research,e.g.,insufficient feature extraction of Ponzi scheme smart contracts,without considering class imbalance.In addition,there is room for improvement in detection precision.Aiming at the above problems,this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting(AdaBoost)algorithm.Firstly,this paper uses the n-gram algorithm to extract more comprehensive contract opcode features and combine them with contract account features,which helps to improve the feature extraction effect.Meanwhile,adaptive synthetic sampling(ADASYN)is introduced to deal with class imbalanced data,and integrated with the Adaboost classifier.Finally,this paper uses the improved AdaBoost classifier for the identification of Ponzi scheme contracts.Experimentally,this paper tests our model in real-world smart contracts and compares it with representative methods in the aspect of F1-score and precision.Moreover,this article compares and discusses the state of art methods with our method in four aspects:data acquisition,data preprocessing,feature extraction,and classifier design.Both experiment and discussion validate the effectiveness of our model.展开更多
Pulp and paper industry is responsible for large discharge of highly polluted effluents, which often be treated by biological treatment process. For biological treatment system, pH is an important environmental factor...Pulp and paper industry is responsible for large discharge of highly polluted effluents, which often be treated by biological treatment process. For biological treatment system, pH is an important environmental factor that can influence the activity of microorganisms. In general, the optimal pH for aerobic processes is around neutral pH (7_7.8) and for the anaerobic process is between 6.8_7.2. The control of pH is a difficult link in the biological treatment system due to its nonlinearity and large time-delay. Aiming at the difficult point in the pH control of the biological wastewater treatment system, a mathematical model of pH control is estab-lished in the essay. On this basis, a traditional PID control and a cascade control are adopted to carry out simulation and comparison with MATLAB. The results show that the cascade control has better comprehen-sive effect in terms of response speed, stability and disturbance resistance.展开更多
The change of extreme precipitation with temperature has regional characteristics in the context of global warming.In this study, radiosonde data, co-located rain gauge(RG) observations, and Tropical Rainfall Measurin...The change of extreme precipitation with temperature has regional characteristics in the context of global warming.In this study, radiosonde data, co-located rain gauge(RG) observations, and Tropical Rainfall Measuring Mission(TRMM) precipitation radar(PR) products are used to explore the relationship between extreme precipitation intensity and near-surface temperature in Middle–East China(MEC) and the eastern Tibetan Plateau(TP) during1998–2012. The results show that extreme precipitation intensity increases with increasing temperature at an approximate Clausius–Clapeyron(C–C) rate(i.e., water vapor increases by 7% as temperature increases by 1°C based on the C–C equation) in MEC and TP, but the rate of increase is larger in TP than in MEC. This is probably because TP(MEC) is featured with deep convective(stratiform) precipitation, which releases more(less) latent heat and strengthens the convection intensity on a shorter(longer) timescale. It is also found that when temperature is higher than 25°C(15°C) in MEC(TP), the extreme precipitation intensity decreases with rise of temperature, suggesting that the precipitation intensity does not always increase with warming. In this case, the limited atmospheric humidity and precipitable water could be the primary factors for the decrease in extreme precipitation intensity at higher temperatures.展开更多
基金This work was supported by National Key R&D Program of China(Grant Numbers 2020YFB1005900,2022YFB3305802).
文摘Due to the anonymity of blockchain,frequent security incidents and attacks occur through it,among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses.Machine learningbased methods are believed to be promising for detecting ethereum Ponzi schemes.However,there are still some flaws in current research,e.g.,insufficient feature extraction of Ponzi scheme smart contracts,without considering class imbalance.In addition,there is room for improvement in detection precision.Aiming at the above problems,this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting(AdaBoost)algorithm.Firstly,this paper uses the n-gram algorithm to extract more comprehensive contract opcode features and combine them with contract account features,which helps to improve the feature extraction effect.Meanwhile,adaptive synthetic sampling(ADASYN)is introduced to deal with class imbalanced data,and integrated with the Adaboost classifier.Finally,this paper uses the improved AdaBoost classifier for the identification of Ponzi scheme contracts.Experimentally,this paper tests our model in real-world smart contracts and compares it with representative methods in the aspect of F1-score and precision.Moreover,this article compares and discusses the state of art methods with our method in four aspects:data acquisition,data preprocessing,feature extraction,and classifier design.Both experiment and discussion validate the effectiveness of our model.
文摘Pulp and paper industry is responsible for large discharge of highly polluted effluents, which often be treated by biological treatment process. For biological treatment system, pH is an important environmental factor that can influence the activity of microorganisms. In general, the optimal pH for aerobic processes is around neutral pH (7_7.8) and for the anaerobic process is between 6.8_7.2. The control of pH is a difficult link in the biological treatment system due to its nonlinearity and large time-delay. Aiming at the difficult point in the pH control of the biological wastewater treatment system, a mathematical model of pH control is estab-lished in the essay. On this basis, a traditional PID control and a cascade control are adopted to carry out simulation and comparison with MATLAB. The results show that the cascade control has better comprehen-sive effect in terms of response speed, stability and disturbance resistance.
基金supported by the Science Foundation for Excellent Youth of Henan Province(202300410166)the Science and Technology Project of Henan Province(212102210201 and 212102310015)+3 种基金China Postdoctoral Science Foundation(2020M672179)the Key Project of Science and Technology Research of Henan Provincial Department of Education(21A430017)the Training Program for Young Backbone Teachers in the University of Henan Province(2020GGJS052)the Major Project of WIUCAS(WIUCASQD2021004 and WIUCASQD2021035)。
基金Supported by the National Natural Science Foundation of China(91837310)National Key Research and Development Program of China(2017YFC1501402 and 2018YFC1507200)
文摘The change of extreme precipitation with temperature has regional characteristics in the context of global warming.In this study, radiosonde data, co-located rain gauge(RG) observations, and Tropical Rainfall Measuring Mission(TRMM) precipitation radar(PR) products are used to explore the relationship between extreme precipitation intensity and near-surface temperature in Middle–East China(MEC) and the eastern Tibetan Plateau(TP) during1998–2012. The results show that extreme precipitation intensity increases with increasing temperature at an approximate Clausius–Clapeyron(C–C) rate(i.e., water vapor increases by 7% as temperature increases by 1°C based on the C–C equation) in MEC and TP, but the rate of increase is larger in TP than in MEC. This is probably because TP(MEC) is featured with deep convective(stratiform) precipitation, which releases more(less) latent heat and strengthens the convection intensity on a shorter(longer) timescale. It is also found that when temperature is higher than 25°C(15°C) in MEC(TP), the extreme precipitation intensity decreases with rise of temperature, suggesting that the precipitation intensity does not always increase with warming. In this case, the limited atmospheric humidity and precipitable water could be the primary factors for the decrease in extreme precipitation intensity at higher temperatures.
