This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating t...This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating the oil production rate of the ZH86 reservoir block under waterflood recovery.The historical input variables:reservoir pressure,reservoir pore volume containing hydrocarbons,reservoir pore volume containing water and reservoir water injection rate used as inputs for ANN models.To create the NRS-ANN hybrid models,314 data sets extracted from the NRS model,which included reservoir pressure,reservoir pore volume containing hy-drocarbons,reservoir pore volume containing water and reservoir water injection rate were used.The output of the models was the historical oil production rate(HOPR in m^(3) per day)recorded from the ZH86 reservoir block.Models were developed using MATLAB R2021a and trained with 25 models in three replicate conditions(2,4 and 6),each at 1000 epochs.A comparative analysis indicated that,for all 25 models,the ANN outperformed the NRS-ANN in terms of processing speed and prediction performance.ANN models achieved an average of R^(2) and MAE of 0.8433 and 8.0964 m^(3)/day values,respectively,while NRS-ANN hybrid models achieved an average of R^(2) and MAE of 0.7828 and 8.2484 m^(3)/day values,respectively.In addition,ANN models achieved a processing speed of 49 epochs/sec,32 epochs/sec,and 24 epochs/sec after 2,4,and 6 replicates,respectively.Whereas the NRS-ANN hybrid models achieved lower average processing speeds of 45 epochs/sec,23 epochs/sec and 20 epochs/sec.In addition,the ANN optimal model outperforms the NRS-ANN model in terms of both processing speed and accuracy.The ANN optimal model achieved a speed of 336.44 epochs/sec,compared to the NRS-ANN hybrid optimal model,which achieved a speed of 52.16 epochs/sec.The ANN optimal model achieved lower RMSE and MAE values of 7.9291 m^(3)/day and 5.3855 m^(3)/day in the validation dataset compared with the hybrid ANS optimal model,which achieved 13.6821 m^(3)/day and 9.2047 m^(3)/day,respectively.The study also showed that the ANN optimal model consistently achieved higher R^(2) values:0.9472,0.9284 and 0.9316 in the training,test and validation data sets.Whereas the NRS-ANN hybrid optimal yielded lower R^(2) values of 0.8030,0.8622 and 0.7776 for the training,testing and validation datasets.The study showed that ANN models are a more effective and reliable tool,as they balance both processing speed and accuracy in estimating the oil production rate of the ZH86 reservoir block under the waterflooding recovery method.展开更多
The purpose of the next internet of things(Io T)is that of making available myriad of services to people by high sensing intelligent devices capable of reasoning and real time acting.The convergence of Io T and multi-...The purpose of the next internet of things(Io T)is that of making available myriad of services to people by high sensing intelligent devices capable of reasoning and real time acting.The convergence of Io T and multi-agent systems(MAS)provides the opportunity to benefit from the social attitude of agents in order to perform machine-to-machine(M2 M)cooperation among smart entities.However,the selection of reliable partners for cooperation represents a hard task in a mobile and federated context,especially because the trustworthiness of devices is largely unreferenced.The issues discussed above can be synthesized by recalling the well known concept of social resilience in Io T systems,i.e.,the capability of an Io T network to resist to possible attacks by malicious agent that potentially could infect large areas of the network,spamming unreliable information and/or assuming unfair behaviors.In this sense,social resilience is devoted to face malicious activities of software agents in their social interactions,and do not deal with the correct working of the sensors and other information devices.In this setting,the use of a reputation model can be a practicable and effective solution to form local communities of agents on the basis of their social capabilities.In this paper,we propose a framework for agents operating in an Io T environment,called Res Io T,where the formation of communities for collaborative purposes is performed on the basis of agent reputation.In order to validate our approach,we performed an experimental campaign by means of a simulated framework,which allowed us to verify that,by our approach,devices have not any economic convenience to performs misleading behaviors.Moreover,further experimental results have shown that our approach is able to detect the nature of the active agents in the systems(i.e.,honest and malicious),with an accuracy of not less than 11%compared to the best competitor tested and highlighting a high resilience with respect to some malicious activities.展开更多
Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood v...Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood vessels and the herculean task involved in glaucoma detection,the exactly affected site of the optic disc of whether small or big size cup,is deemed challenging.Spatially Based Ellipse Fitting Curve Model(SBEFCM)classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup(OC)and Optic Disc(OD)boundary correspondingly.This research deploys the Ensemble Convolutional Neural Network(CNN)classification for classifying Glaucoma or Diabetes Retinopathy(DR).The detection of the boundary between the OC and the OD is performed by the SBEFCM,which is the latest weighted ellipse fitting model.The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here.There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels.The ascertaining of OCandODboundary,which characterizedmany output factors for glaucoma detection,has been developed by EnsembleCNNclassification,which includes detecting sensitivity,specificity,precision,andArea Under the receiver operating characteristic Curve(AUC)values accurately by an innovative SBEFCM.In terms of contrast,the proposed Ensemble CNNsignificantly outperformed the current methods.展开更多
The 3Φinduction motor is a broadly used electric machine in industrial applications,which plays a vital role in industries because of having plenty of beneficial impacts like low cost and easiness but the problems lik...The 3Φinduction motor is a broadly used electric machine in industrial applications,which plays a vital role in industries because of having plenty of beneficial impacts like low cost and easiness but the problems like decrease in motor speed due to load,high consumption of current and high ripple occurrence of ripples have reduced its preferences.The ultimate objective of this study is to control change in motor speed due to load variations.An improved Trans Z Source Inverter(ΓZSI)with a clamping diode is employed to maintain constant input voltage,reduce ripples and voltage overshoot.To operate induction motor at rated speed,different controllers are used.The conventional Proportional-Inte-gral(PI)controller suffers from high settling time and maximum peak overshoot.To overcome these limitations,Fractional Order Proportional Integral Derivative(FOPID)controller optimized by Gray Wolf Optimization(GWO)technique is employed to provide better performance by eliminating maximum peak overshoot pro-blems.The proposed speed controller provides good dynamic response and controls the induction motor more effectively.The complete setup is implemented in MATLAB Simulation to verify the simulation results.The proposed approach provides optimal performance with high torque and speed along with less steady state error.展开更多
An ordered set W of vertices of a graph G is called a resolving set, if all the vertices of G are uniquely determined by the vector of distances to the vertices in W. The metric dimension of G is the minimum cardinali...An ordered set W of vertices of a graph G is called a resolving set, if all the vertices of G are uniquely determined by the vector of distances to the vertices in W. The metric dimension of G is the minimum cardinality of a resolving set of G. A resolving set W for G is fault-tolerant if W\{v} is also a resolving set, for each v in W, and the fault-tolerant metric dimension of G is the minimum cardinality of such a set. In this paper we determine the metric dimension and fault-tolerant metric dimension problems for the graphs of certain crystal structures.展开更多
Small coastal pelagic fish are one of the fish families most affected by sea fishing. This man-made phenomenon leads to an imbalance in the marine and coastal ecosystem and is one of the main causes of migration north...Small coastal pelagic fish are one of the fish families most affected by sea fishing. This man-made phenomenon leads to an imbalance in the marine and coastal ecosystem and is one of the main causes of migration north and offshore of the ranges. We used the ordinary differential equations to model the interactions existing between small pelagic resources and fishermen. Modelling follows the same of the Lotka-Volterra equations with a difference in the number of variables. This study confirmed the instability of the marine ecosystem. The objective is first of all to model a system of three interacting individuals composed of two distinct types of predators and two types of prey, and then optimise this interaction with the aim of conserving biodiversity in the ecosystem under study. Determining the Jacobian matrix made it possible to calculate the reproduction rate basic (<em>R</em><sub>0</sub>). The study of the strong connectedness has made it possible to reduce the number of variables without losing the objective of the study. A computer program implemented on the language computer python facilitated the visualisation of the results.展开更多
In this study, three Danish sites having the longest (1990-2004) time-series of ozone measurements were analysed on inter-annual, monthly and diurnal cycle variability as well as elevated and lowered ozone concentrati...In this study, three Danish sites having the longest (1990-2004) time-series of ozone measurements were analysed on inter-annual, monthly and diurnal cycle variability as well as elevated and lowered ozone concentration events were identified. The atmospheric trajectory (HYSPLIT) and dispersion (HIRLAM + CAMx) models were employed to study dominating atmospheric transport patterns associated with elevated events and to evaluate spatio-temporal variability of ozone specific episode and typical seasonal patterns for Denmark. It was found that generally inter-annual variability has a positive trend, and events with low ozone concentration (≤10 μg/m3) continued to diminish. On a monthly scale, the highest and lowest mean concentrations are observed in May and November-December, respectively. The elevated concentrations (≥120 μg/m3) are observed during March-September. On a diurnal cycle, it is observed mostly during 13-16 of local time, and more frequent (ten-fold) compared with nighttime-early morning hours. For ozone elevated events, several sectors (or pathways of atmospheric transport) were identified depending on the sites’ positions, showing the largest (39%) number of such events associated with the north-western sector, and lowest (13% each)—southwestern and northern sectors. For each site, less than 60 events showed very high concentrations (≥180 μg/m3). Among 12 episodes, one longest elevated episode (19-21 Jun 2000) simultaneously registered at all sites and characterized by dominating transport from the south-southwestern sector, low wind speed, clear-sky, and multiple inversions was studied using modelling tools. For this episode, both measurements and modeling (trajectory and dispersion) results showed a relatively good agreement.展开更多
Recently,an innovative trend like cloud computing has progressed quickly in InformationTechnology.For a background of distributed networks,the extensive sprawl of internet resources on the Web and the increasing numbe...Recently,an innovative trend like cloud computing has progressed quickly in InformationTechnology.For a background of distributed networks,the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service model.The cloud computing environment extracts the execution details of services and systems from end-users and developers.Additionally,through the system’s virtualization accomplished using resource pooling,cloud computing resources become more accessible.The attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this paper.With the help of multi-agents,we attempt to represent Open-Identity(ID)design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the cloud.This research aims to determine how authentication and authorization services were provided in an agreeable and preventive manner.Based on attack-oriented threat model security,the evaluation works.By considering security for both authentication and authorization systems,possible security threats are analyzed by the proposed security systems.展开更多
In the current study,an artificial neural network(ANN)and a numerical reservoir simulation(NRS)technique are used to analyse reservoir performance under waterflooding in the ZH86 block of the Zhaozhouqiao oilfield,Chi...In the current study,an artificial neural network(ANN)and a numerical reservoir simulation(NRS)technique are used to analyse reservoir performance under waterflooding in the ZH86 block of the Zhaozhouqiao oilfield,China.Using five input datasets extracted from the history-matched NRS model,an NRS-ANN hybrid is trained using a trial-and-error approach.NRS-ANN hybrid model#46(which has 5,10,10,6,6,and 1 neurons in the input layer,four hidden layers,and output layer,respectively)is found to produce the minimal root mean square error on the test dataset.On the validation data,the prediction performance of the selected NRS-ANN hybrid model achieves a minimal root mean square error of 0.0274 m^(3)/day and maximal coefficient of determination and coefficient of correlation values of about 0.9999.The correlation between the block liquid production rate(BLPR,m^(3)/day),block water production rate(BWPR,m^(3)/day),block water cut(BWCT,%),block water injection rate(BWIR,m^(3)/day),and block reservoir pressure(BRP,bar)as input variables and the simulated oil production rate(SOPRH)as the output variable is investigated.There is a positive correlation between SOPRH and BLPR,BWIR,and BWCT,and a negative correlation between SOPRH and BRP and BWPR.Segment B of ZH86 block experiences a 3.8%increase in BLPR,while segments A and C show declines of 1.3%and 1.6%,respectively.These variations in the liquid production rate correspond to changes in SOPRH of 4.3%,1.9%,and 9.7%for segments A,B,and C,respectively.The prediction performance of the NRS-ANN hybrid model is compared with that of a simple NRS model.The accuracy of the NRS-ANN hybrid model in predicting oil production is found to be 1125 times that of the NRS model.Based on these results,it is concluded that the proposed NRS-ANN hybrid provides an accurate and useful tool for analysing reservoir performance under the waterflooding oil recovery technique.展开更多
Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced b...Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced by the existing system. This research concentrates on adopting a graphbasedfeature learning process to reduce feature dimensionality. The incoming samples arecorrectly classified and optimised using an Adaboost classifier with an improved grey wolfoptimiser (g-AGWO). The proposed IGWO optimisation approach is adopted to fulfil the multiconstraintissues related to bot detection and provide better local and global solutions (to satisfyexploration and exploitation). The extensive results show that the proposed g-AGWO model outperformsexisting approaches to reduce feature dimensionality, under-fitting/over-fitting andexecution time. The error rate prediction shows the feasibility of the given model to work over thechallenging environment. This model also works efficiently towards the unseen data to achievebetter generalization.展开更多
Blockchain software development is becoming more and more important for any modern software developer and IT startup.Nonetheless,blockchain software production still lacks a disciplined,organized and mature developmen...Blockchain software development is becoming more and more important for any modern software developer and IT startup.Nonetheless,blockchain software production still lacks a disciplined,organized and mature development process,as demonstrated by the many and(in)famous failures and frauds occurred in recent years.In this paper we present ABCDE,a complete method addressing blockchain software development.The method considers the software integration among the blockchain components—smart contracts,libraries,data structures—and the out-of-chain components,such as web or mobile applications,which all together constitute a complete DApp system.We advocate for ABCDE the use of agile practices,because these are suited to develop systems whose requirements are not completely understood since the beginning,or tend to change,as it is the case of most blockchain-based applications.ABCDE is based on Scrum,and is therefore iterative and incremental.From Scrum,we kept the requirement gathering with user stories,the iterative-incremental approach,the key roles,and the meetings.The main difference with Scrum is the separation of development activities in two flows—one for smart contracts and the other for out-of-chain software interacting with the blockchain—each performed iteratively,with integration activities every 2–3 iterations.ABCDE makes explicit the activities that must be performed to design,develop,test and integrate smart contracts and out-of-chain software,and documents the smart contracts using formal diagrams to help development,security assessment,and maintenance.A diagram derived from UML class diagram helps to effectively model the data structure of smart contracts,whereas the exchange of messages between the entities of the system is modeled using a modified UML sequence diagram.The proposed method has also specific activities for security assessment and gas optimization,through systematic use of patterns and checklists.ABCDE focuses on Ethereum blockchain and its Solidity language,but preserves generality and with proper modifications might be applied to any blockchain software project.ABCDE method is described in detail,and an example is given to show how to concretely implement the various development steps.展开更多
基金National Natural Science Foundation of China grants no.41972326 and 51774258.
文摘This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating the oil production rate of the ZH86 reservoir block under waterflood recovery.The historical input variables:reservoir pressure,reservoir pore volume containing hydrocarbons,reservoir pore volume containing water and reservoir water injection rate used as inputs for ANN models.To create the NRS-ANN hybrid models,314 data sets extracted from the NRS model,which included reservoir pressure,reservoir pore volume containing hy-drocarbons,reservoir pore volume containing water and reservoir water injection rate were used.The output of the models was the historical oil production rate(HOPR in m^(3) per day)recorded from the ZH86 reservoir block.Models were developed using MATLAB R2021a and trained with 25 models in three replicate conditions(2,4 and 6),each at 1000 epochs.A comparative analysis indicated that,for all 25 models,the ANN outperformed the NRS-ANN in terms of processing speed and prediction performance.ANN models achieved an average of R^(2) and MAE of 0.8433 and 8.0964 m^(3)/day values,respectively,while NRS-ANN hybrid models achieved an average of R^(2) and MAE of 0.7828 and 8.2484 m^(3)/day values,respectively.In addition,ANN models achieved a processing speed of 49 epochs/sec,32 epochs/sec,and 24 epochs/sec after 2,4,and 6 replicates,respectively.Whereas the NRS-ANN hybrid models achieved lower average processing speeds of 45 epochs/sec,23 epochs/sec and 20 epochs/sec.In addition,the ANN optimal model outperforms the NRS-ANN model in terms of both processing speed and accuracy.The ANN optimal model achieved a speed of 336.44 epochs/sec,compared to the NRS-ANN hybrid optimal model,which achieved a speed of 52.16 epochs/sec.The ANN optimal model achieved lower RMSE and MAE values of 7.9291 m^(3)/day and 5.3855 m^(3)/day in the validation dataset compared with the hybrid ANS optimal model,which achieved 13.6821 m^(3)/day and 9.2047 m^(3)/day,respectively.The study also showed that the ANN optimal model consistently achieved higher R^(2) values:0.9472,0.9284 and 0.9316 in the training,test and validation data sets.Whereas the NRS-ANN hybrid optimal yielded lower R^(2) values of 0.8030,0.8622 and 0.7776 for the training,testing and validation datasets.The study showed that ANN models are a more effective and reliable tool,as they balance both processing speed and accuracy in estimating the oil production rate of the ZH86 reservoir block under the waterflooding recovery method.
基金partially supported by the University of Catania,Piano per la Ricerca 2016-2018-Linea di intervento 1(Chance),prot.2019-UNCTCLE-0343614the Italian MIUR,PRIN 2017 Project“Fluidware”(CUP H24I17000070001)。
文摘The purpose of the next internet of things(Io T)is that of making available myriad of services to people by high sensing intelligent devices capable of reasoning and real time acting.The convergence of Io T and multi-agent systems(MAS)provides the opportunity to benefit from the social attitude of agents in order to perform machine-to-machine(M2 M)cooperation among smart entities.However,the selection of reliable partners for cooperation represents a hard task in a mobile and federated context,especially because the trustworthiness of devices is largely unreferenced.The issues discussed above can be synthesized by recalling the well known concept of social resilience in Io T systems,i.e.,the capability of an Io T network to resist to possible attacks by malicious agent that potentially could infect large areas of the network,spamming unreliable information and/or assuming unfair behaviors.In this sense,social resilience is devoted to face malicious activities of software agents in their social interactions,and do not deal with the correct working of the sensors and other information devices.In this setting,the use of a reputation model can be a practicable and effective solution to form local communities of agents on the basis of their social capabilities.In this paper,we propose a framework for agents operating in an Io T environment,called Res Io T,where the formation of communities for collaborative purposes is performed on the basis of agent reputation.In order to validate our approach,we performed an experimental campaign by means of a simulated framework,which allowed us to verify that,by our approach,devices have not any economic convenience to performs misleading behaviors.Moreover,further experimental results have shown that our approach is able to detect the nature of the active agents in the systems(i.e.,honest and malicious),with an accuracy of not less than 11%compared to the best competitor tested and highlighting a high resilience with respect to some malicious activities.
文摘Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood vessels and the herculean task involved in glaucoma detection,the exactly affected site of the optic disc of whether small or big size cup,is deemed challenging.Spatially Based Ellipse Fitting Curve Model(SBEFCM)classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup(OC)and Optic Disc(OD)boundary correspondingly.This research deploys the Ensemble Convolutional Neural Network(CNN)classification for classifying Glaucoma or Diabetes Retinopathy(DR).The detection of the boundary between the OC and the OD is performed by the SBEFCM,which is the latest weighted ellipse fitting model.The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here.There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels.The ascertaining of OCandODboundary,which characterizedmany output factors for glaucoma detection,has been developed by EnsembleCNNclassification,which includes detecting sensitivity,specificity,precision,andArea Under the receiver operating characteristic Curve(AUC)values accurately by an innovative SBEFCM.In terms of contrast,the proposed Ensemble CNNsignificantly outperformed the current methods.
文摘The 3Φinduction motor is a broadly used electric machine in industrial applications,which plays a vital role in industries because of having plenty of beneficial impacts like low cost and easiness but the problems like decrease in motor speed due to load,high consumption of current and high ripple occurrence of ripples have reduced its preferences.The ultimate objective of this study is to control change in motor speed due to load variations.An improved Trans Z Source Inverter(ΓZSI)with a clamping diode is employed to maintain constant input voltage,reduce ripples and voltage overshoot.To operate induction motor at rated speed,different controllers are used.The conventional Proportional-Inte-gral(PI)controller suffers from high settling time and maximum peak overshoot.To overcome these limitations,Fractional Order Proportional Integral Derivative(FOPID)controller optimized by Gray Wolf Optimization(GWO)technique is employed to provide better performance by eliminating maximum peak overshoot pro-blems.The proposed speed controller provides good dynamic response and controls the induction motor more effectively.The complete setup is implemented in MATLAB Simulation to verify the simulation results.The proposed approach provides optimal performance with high torque and speed along with less steady state error.
文摘An ordered set W of vertices of a graph G is called a resolving set, if all the vertices of G are uniquely determined by the vector of distances to the vertices in W. The metric dimension of G is the minimum cardinality of a resolving set of G. A resolving set W for G is fault-tolerant if W\{v} is also a resolving set, for each v in W, and the fault-tolerant metric dimension of G is the minimum cardinality of such a set. In this paper we determine the metric dimension and fault-tolerant metric dimension problems for the graphs of certain crystal structures.
文摘Small coastal pelagic fish are one of the fish families most affected by sea fishing. This man-made phenomenon leads to an imbalance in the marine and coastal ecosystem and is one of the main causes of migration north and offshore of the ranges. We used the ordinary differential equations to model the interactions existing between small pelagic resources and fishermen. Modelling follows the same of the Lotka-Volterra equations with a difference in the number of variables. This study confirmed the instability of the marine ecosystem. The objective is first of all to model a system of three interacting individuals composed of two distinct types of predators and two types of prey, and then optimise this interaction with the aim of conserving biodiversity in the ecosystem under study. Determining the Jacobian matrix made it possible to calculate the reproduction rate basic (<em>R</em><sub>0</sub>). The study of the strong connectedness has made it possible to reduce the number of variables without losing the objective of the study. A computer program implemented on the language computer python facilitated the visualisation of the results.
文摘In this study, three Danish sites having the longest (1990-2004) time-series of ozone measurements were analysed on inter-annual, monthly and diurnal cycle variability as well as elevated and lowered ozone concentration events were identified. The atmospheric trajectory (HYSPLIT) and dispersion (HIRLAM + CAMx) models were employed to study dominating atmospheric transport patterns associated with elevated events and to evaluate spatio-temporal variability of ozone specific episode and typical seasonal patterns for Denmark. It was found that generally inter-annual variability has a positive trend, and events with low ozone concentration (≤10 μg/m3) continued to diminish. On a monthly scale, the highest and lowest mean concentrations are observed in May and November-December, respectively. The elevated concentrations (≥120 μg/m3) are observed during March-September. On a diurnal cycle, it is observed mostly during 13-16 of local time, and more frequent (ten-fold) compared with nighttime-early morning hours. For ozone elevated events, several sectors (or pathways of atmospheric transport) were identified depending on the sites’ positions, showing the largest (39%) number of such events associated with the north-western sector, and lowest (13% each)—southwestern and northern sectors. For each site, less than 60 events showed very high concentrations (≥180 μg/m3). Among 12 episodes, one longest elevated episode (19-21 Jun 2000) simultaneously registered at all sites and characterized by dominating transport from the south-southwestern sector, low wind speed, clear-sky, and multiple inversions was studied using modelling tools. For this episode, both measurements and modeling (trajectory and dispersion) results showed a relatively good agreement.
文摘Recently,an innovative trend like cloud computing has progressed quickly in InformationTechnology.For a background of distributed networks,the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service model.The cloud computing environment extracts the execution details of services and systems from end-users and developers.Additionally,through the system’s virtualization accomplished using resource pooling,cloud computing resources become more accessible.The attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this paper.With the help of multi-agents,we attempt to represent Open-Identity(ID)design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the cloud.This research aims to determine how authentication and authorization services were provided in an agreeable and preventive manner.Based on attack-oriented threat model security,the evaluation works.By considering security for both authentication and authorization systems,possible security threats are analyzed by the proposed security systems.
基金Funding for this research was provided by the Department of Petroleum Engineering,China University of Geosciences,under the sponsorship of the China Scholarship Council (CSC)the National Natural Science Foundation of China through grant nos.41972326 and 51774258.
文摘In the current study,an artificial neural network(ANN)and a numerical reservoir simulation(NRS)technique are used to analyse reservoir performance under waterflooding in the ZH86 block of the Zhaozhouqiao oilfield,China.Using five input datasets extracted from the history-matched NRS model,an NRS-ANN hybrid is trained using a trial-and-error approach.NRS-ANN hybrid model#46(which has 5,10,10,6,6,and 1 neurons in the input layer,four hidden layers,and output layer,respectively)is found to produce the minimal root mean square error on the test dataset.On the validation data,the prediction performance of the selected NRS-ANN hybrid model achieves a minimal root mean square error of 0.0274 m^(3)/day and maximal coefficient of determination and coefficient of correlation values of about 0.9999.The correlation between the block liquid production rate(BLPR,m^(3)/day),block water production rate(BWPR,m^(3)/day),block water cut(BWCT,%),block water injection rate(BWIR,m^(3)/day),and block reservoir pressure(BRP,bar)as input variables and the simulated oil production rate(SOPRH)as the output variable is investigated.There is a positive correlation between SOPRH and BLPR,BWIR,and BWCT,and a negative correlation between SOPRH and BRP and BWPR.Segment B of ZH86 block experiences a 3.8%increase in BLPR,while segments A and C show declines of 1.3%and 1.6%,respectively.These variations in the liquid production rate correspond to changes in SOPRH of 4.3%,1.9%,and 9.7%for segments A,B,and C,respectively.The prediction performance of the NRS-ANN hybrid model is compared with that of a simple NRS model.The accuracy of the NRS-ANN hybrid model in predicting oil production is found to be 1125 times that of the NRS model.Based on these results,it is concluded that the proposed NRS-ANN hybrid provides an accurate and useful tool for analysing reservoir performance under the waterflooding oil recovery technique.
文摘Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced by the existing system. This research concentrates on adopting a graphbasedfeature learning process to reduce feature dimensionality. The incoming samples arecorrectly classified and optimised using an Adaboost classifier with an improved grey wolfoptimiser (g-AGWO). The proposed IGWO optimisation approach is adopted to fulfil the multiconstraintissues related to bot detection and provide better local and global solutions (to satisfyexploration and exploitation). The extensive results show that the proposed g-AGWO model outperformsexisting approaches to reduce feature dimensionality, under-fitting/over-fitting andexecution time. The error rate prediction shows the feasibility of the given model to work over thechallenging environment. This model also works efficiently towards the unseen data to achievebetter generalization.
基金funded by the CRYPTOVOTING project,funded by Sardinia Region,call POR FESR Sardegna 2014–2020,Prot.0010083,no.1361 REA,August 01,2018,and by the ABATA project(Application of Blockchain to Authenticity and Traceability of Aliments)funded by Italian Ministry for Economic Development,National Operational Program“Enterprises and Competitiveness”,project No.F/200130/01–02/X45.
文摘Blockchain software development is becoming more and more important for any modern software developer and IT startup.Nonetheless,blockchain software production still lacks a disciplined,organized and mature development process,as demonstrated by the many and(in)famous failures and frauds occurred in recent years.In this paper we present ABCDE,a complete method addressing blockchain software development.The method considers the software integration among the blockchain components—smart contracts,libraries,data structures—and the out-of-chain components,such as web or mobile applications,which all together constitute a complete DApp system.We advocate for ABCDE the use of agile practices,because these are suited to develop systems whose requirements are not completely understood since the beginning,or tend to change,as it is the case of most blockchain-based applications.ABCDE is based on Scrum,and is therefore iterative and incremental.From Scrum,we kept the requirement gathering with user stories,the iterative-incremental approach,the key roles,and the meetings.The main difference with Scrum is the separation of development activities in two flows—one for smart contracts and the other for out-of-chain software interacting with the blockchain—each performed iteratively,with integration activities every 2–3 iterations.ABCDE makes explicit the activities that must be performed to design,develop,test and integrate smart contracts and out-of-chain software,and documents the smart contracts using formal diagrams to help development,security assessment,and maintenance.A diagram derived from UML class diagram helps to effectively model the data structure of smart contracts,whereas the exchange of messages between the entities of the system is modeled using a modified UML sequence diagram.The proposed method has also specific activities for security assessment and gas optimization,through systematic use of patterns and checklists.ABCDE focuses on Ethereum blockchain and its Solidity language,but preserves generality and with proper modifications might be applied to any blockchain software project.ABCDE method is described in detail,and an example is given to show how to concretely implement the various development steps.
