This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of th...Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.展开更多
The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic.Cloud environments pose significant challenges in maintaining privacy and security.Global approach...The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic.Cloud environments pose significant challenges in maintaining privacy and security.Global approaches,such as IDS,have been developed to tackle these issues.However,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional data.In fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within it.The traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of features.The selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)module.In this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious traffic.The classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable decisions.With the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive measures.Extensive experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different types.Theproposedmodel outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%F1-score.Such results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.展开更多
With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud...With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various doma...Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.展开更多
The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tas...The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.展开更多
The impact of aerosols on clouds,which remains one of the largest aspects of uncertainty in current weather forecasting and climate change research,can be influenced by various factors,such as the underlying surface t...The impact of aerosols on clouds,which remains one of the largest aspects of uncertainty in current weather forecasting and climate change research,can be influenced by various factors,such as the underlying surface type,cloud type,cloud phase,and aerosol type.To explore the impact of different underlying surfaces on the effect of aerosols on cloud development,this study focused on the Yangtze River Delta(YRD)and its offshore regions(YRD sea)for a comparative analysis based on multi-source satellite data,while also considering the variations in cloud type and cloud phase.The results show lower cloud-top height and depth of single-layer clouds over the ocean than land,and higher liquid cloud in spring over the ocean.Aerosols are found to enhance the cumulus cloud depth through microphysical effects,which is particularly evident over the ocean.Aerosols are also found to decrease the cloud droplet effective radius in the ocean region and during the mature stage of cloud development in the land region,while opposite results are found during the early stage of cloud development in the land region.The quantitative results indicate that the indirect effect is positive(0.05)in the land region at relatively high cloud water path,which is smaller than that in the ocean region(0.11).The findings deepen our understanding of the influence aerosols on cloud development and the mechanisms involved,which could then be applied to improve the ability to simulate cloud-associated weather processes.展开更多
Cloud storage,a core component of cloud computing,plays a vital role in the storage and management of data.Electronic Health Records(EHRs),which document users’health information,are typically stored on cloud servers...Cloud storage,a core component of cloud computing,plays a vital role in the storage and management of data.Electronic Health Records(EHRs),which document users’health information,are typically stored on cloud servers.However,users’sensitive data would then become unregulated.In the event of data loss,cloud storage providers might conceal the fact that data has been compromised to protect their reputation and mitigate losses.Ensuring the integrity of data stored in the cloud remains a pressing issue that urgently needs to be addressed.In this paper,we propose a data auditing scheme for cloud-based EHRs that incorporates recoverability and batch auditing,alongside a thorough security and performance evaluation.Our scheme builds upon the indistinguishability-based privacy-preserving auditing approach proposed by Zhou et al.We identify that this scheme is insecure and vulnerable to forgery attacks on data storage proofs.To address these vulnerabilities,we enhanced the auditing process using masking techniques and designed new algorithms to strengthen security.We also provide formal proof of the security of the signature algorithm and the auditing scheme.Furthermore,our results show that our scheme effectively protects user privacy and is resilient against malicious attacks.Experimental results indicate that our scheme is not only secure and efficient but also supports batch auditing of cloud data.Specifically,when auditing 10,000 users,batch auditing reduces computational overhead by 101 s compared to normal auditing.展开更多
Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in we...Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in weather forecasting and climate prediction models.Hence,the latest activation and triple-moment condensation schemes were combined to simulate and analyze the evolution characteristics of a cloud droplet spectrum from activation to condensation and compared with a high-resolution Lagrangian bin model and the current double-moment condensation schemes,in which the spectral shape parameter is fixed or diagnosed by an empirical formula.The results demonstrate that the latest schemes effectively capture the evolution characteristics of the cloud droplet spectrum during activation and condensation,which is in line with the performance of the bin model.The simulation of the latest activation and condensation schemes in a parcel model shows that the cloud droplet spectrum gradually widens and exhibits a multimodal distribution during the activation process,accompanied by a decrease in the spectral shape and slope parameters over time.Conversely,during the condensation process,the cloud droplet spectrum gradually narrows,resulting in increases in the spectral shape and slope parameters.However,these double-moment schemes fail to accurately replicate the evolution of the cloud droplet spectrum and its multimodal distribution characteristics.Furthermore,the latest schemes were coupled into a 1.5D cumulus model,and an observation case was simulated.The simulations confirm that the cloud droplet spectrum appears wider at the supersaturated cloud base and cloud top due to activation,while it becomes narrower at the middle altitudes of the cloud due to condensation growth.展开更多
This study quantitatively analyzes the effects of cloud seeding on precipitation and seasonal variations over the Boryeong Dam region,which has the lowest dam storage in South Korea,based on a one-year numerical simul...This study quantitatively analyzes the effects of cloud seeding on precipitation and seasonal variations over the Boryeong Dam region,which has the lowest dam storage in South Korea,based on a one-year numerical simulation for2021.The Morrison microphysics scheme in the WRF(Weather Research and Forecasting)model was modified to estimate differences in precipitation between simulations with seeding materials(Ag I and Ca Cl2;SEED)and without them(UNSD).The effect of cloud seeding on increasing precipitation or artificial rainfall(AR)between the two simulations was highest in August(average:0.21 mm;31%of the SEED-simulated monthly mean)and lowest in January(average:0.003 mm;30%).This large AR may be attributable to a combination of abundant moisture from the summer monsoon climate and enhanced cloud droplet growth resulting from cloud seeding.In the analysis of seasonal representative cases,cloud seeding demonstrated more pronounced effects in spring and summer,with mean 180-min accumulated AR values of 0.46 and 0.43 mm,respectively,within the study area.In the spring,where an actual flight experiment was conducted,the simulated mean180-min accumulated AR(1.41 mm)in the flight experiment area was close to the observed value(1.61 mm)for the same area.Additionally,cloud seeding promoted the hygroscopic growth of water vapor,thereby reducing the cloud water mixing ratio and increasing the rain water mixing ratio.Seasonal cross-sectional analysis further highlighted the impact of cloud seeding on changes in these two mixing ratios,with the most pronounced effects observed in spring and summer.展开更多
Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate...Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate the daily mean(CFR)and diurnal variation(CDV)of cloud fraction across high-,middle-,low-level,and total clouds in the FGOALS-f3-L general circulation model.The bias of total CDV is decomposed into the model biases in CFRs and CDVs of clouds at all three levels.Results indicate that the model generally underestimates low-level cloud fraction during the daytime and high-/middle-level cloud fraction at nighttime.The simulation biases of low clouds,especially their CDV biases,dominate the bias of total CDV.Compensation effects exist among the bias decompositions,where the negative contributions of underestimated daytime low-level cloud fraction are partially offset by the opposing contributions from biases in high-/middle-level clouds.Meanwhile,the bias contributions have notable land–ocean differences and region-dependent characteristics,consistent with the model biases in these variables.Additionally,the study estimates the influences of CFR and CDV biases on the bias of shortwave cloud radiative effects.It reveals that the impacts of CDV biases can reach half of those from CFR biases,highlighting the importance of accurate CDV representation in climate models.展开更多
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
基金supported By Grant (PLN2022-14) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University)。
文摘Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.
文摘The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic.Cloud environments pose significant challenges in maintaining privacy and security.Global approaches,such as IDS,have been developed to tackle these issues.However,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional data.In fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within it.The traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of features.The selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)module.In this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious traffic.The classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable decisions.With the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive measures.Extensive experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different types.Theproposedmodel outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%F1-score.Such results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.
基金supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00399401,Development of Quantum-Safe Infrastructure Migration and Quantum Security Verification Technologies).
文摘With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
文摘Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.
文摘The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.
基金supported by the National Natural Science Foundation of China(Grant No.42230601).
文摘The impact of aerosols on clouds,which remains one of the largest aspects of uncertainty in current weather forecasting and climate change research,can be influenced by various factors,such as the underlying surface type,cloud type,cloud phase,and aerosol type.To explore the impact of different underlying surfaces on the effect of aerosols on cloud development,this study focused on the Yangtze River Delta(YRD)and its offshore regions(YRD sea)for a comparative analysis based on multi-source satellite data,while also considering the variations in cloud type and cloud phase.The results show lower cloud-top height and depth of single-layer clouds over the ocean than land,and higher liquid cloud in spring over the ocean.Aerosols are found to enhance the cumulus cloud depth through microphysical effects,which is particularly evident over the ocean.Aerosols are also found to decrease the cloud droplet effective radius in the ocean region and during the mature stage of cloud development in the land region,while opposite results are found during the early stage of cloud development in the land region.The quantitative results indicate that the indirect effect is positive(0.05)in the land region at relatively high cloud water path,which is smaller than that in the ocean region(0.11).The findings deepen our understanding of the influence aerosols on cloud development and the mechanisms involved,which could then be applied to improve the ability to simulate cloud-associated weather processes.
基金supported by National Natural Science Foundation of China(No.62172436)Additionally,it is supported by Natural Science Foundation of Shaanxi Province(No.2023-JC-YB-584)Engineering University of PAP’s Funding for Scientific Research Innovation Team and Key Researcher(No.KYGG202011).
文摘Cloud storage,a core component of cloud computing,plays a vital role in the storage and management of data.Electronic Health Records(EHRs),which document users’health information,are typically stored on cloud servers.However,users’sensitive data would then become unregulated.In the event of data loss,cloud storage providers might conceal the fact that data has been compromised to protect their reputation and mitigate losses.Ensuring the integrity of data stored in the cloud remains a pressing issue that urgently needs to be addressed.In this paper,we propose a data auditing scheme for cloud-based EHRs that incorporates recoverability and batch auditing,alongside a thorough security and performance evaluation.Our scheme builds upon the indistinguishability-based privacy-preserving auditing approach proposed by Zhou et al.We identify that this scheme is insecure and vulnerable to forgery attacks on data storage proofs.To address these vulnerabilities,we enhanced the auditing process using masking techniques and designed new algorithms to strengthen security.We also provide formal proof of the security of the signature algorithm and the auditing scheme.Furthermore,our results show that our scheme effectively protects user privacy and is resilient against malicious attacks.Experimental results indicate that our scheme is not only secure and efficient but also supports batch auditing of cloud data.Specifically,when auditing 10,000 users,batch auditing reduces computational overhead by 101 s compared to normal auditing.
基金supported by the National Natural Science Foundations of China(Grant Nos.42305163 and U22A20577)the Construction Project of Weather Modification Ability in Central China(Grant No.ZQC-H22256)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0760300)the Projects of the Earth System Numerical Simulation Facility(Grant Nos.2024-EL-PT-000707,2023-ELPT-000482,2023-EL-ZD-00026,and 2022-EL-PT-00083)the STS Program of the Inner Mongolia Meteorological Service,Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,and Institute of Atmospheric Physics,Chinese Academy of Sciences(Grant No.2021CG0047)。
文摘Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in weather forecasting and climate prediction models.Hence,the latest activation and triple-moment condensation schemes were combined to simulate and analyze the evolution characteristics of a cloud droplet spectrum from activation to condensation and compared with a high-resolution Lagrangian bin model and the current double-moment condensation schemes,in which the spectral shape parameter is fixed or diagnosed by an empirical formula.The results demonstrate that the latest schemes effectively capture the evolution characteristics of the cloud droplet spectrum during activation and condensation,which is in line with the performance of the bin model.The simulation of the latest activation and condensation schemes in a parcel model shows that the cloud droplet spectrum gradually widens and exhibits a multimodal distribution during the activation process,accompanied by a decrease in the spectral shape and slope parameters over time.Conversely,during the condensation process,the cloud droplet spectrum gradually narrows,resulting in increases in the spectral shape and slope parameters.However,these double-moment schemes fail to accurately replicate the evolution of the cloud droplet spectrum and its multimodal distribution characteristics.Furthermore,the latest schemes were coupled into a 1.5D cumulus model,and an observation case was simulated.The simulations confirm that the cloud droplet spectrum appears wider at the supersaturated cloud base and cloud top due to activation,while it becomes narrower at the middle altitudes of the cloud due to condensation growth.
基金funded by the Korea Meteorological Administration Research and Development Program“Research on Weather Modification and Cloud Physics”(Grant No.KMA2018-00224)supported by Korea Institute of Marine Science&Technology Promotion(KIMST)funded by the Ministry of Oceans and Fisheries,Korea(RS-202502217872)supported by an NRF grant funded by the Korean government(MSIT)(Grant No.NRF2023R1A2C1002367)。
文摘This study quantitatively analyzes the effects of cloud seeding on precipitation and seasonal variations over the Boryeong Dam region,which has the lowest dam storage in South Korea,based on a one-year numerical simulation for2021.The Morrison microphysics scheme in the WRF(Weather Research and Forecasting)model was modified to estimate differences in precipitation between simulations with seeding materials(Ag I and Ca Cl2;SEED)and without them(UNSD).The effect of cloud seeding on increasing precipitation or artificial rainfall(AR)between the two simulations was highest in August(average:0.21 mm;31%of the SEED-simulated monthly mean)and lowest in January(average:0.003 mm;30%).This large AR may be attributable to a combination of abundant moisture from the summer monsoon climate and enhanced cloud droplet growth resulting from cloud seeding.In the analysis of seasonal representative cases,cloud seeding demonstrated more pronounced effects in spring and summer,with mean 180-min accumulated AR values of 0.46 and 0.43 mm,respectively,within the study area.In the spring,where an actual flight experiment was conducted,the simulated mean180-min accumulated AR(1.41 mm)in the flight experiment area was close to the observed value(1.61 mm)for the same area.Additionally,cloud seeding promoted the hygroscopic growth of water vapor,thereby reducing the cloud water mixing ratio and increasing the rain water mixing ratio.Seasonal cross-sectional analysis further highlighted the impact of cloud seeding on changes in these two mixing ratios,with the most pronounced effects observed in spring and summer.
基金supported by the National Natural Science Foundation of China[grant number 42275074].
文摘Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate the daily mean(CFR)and diurnal variation(CDV)of cloud fraction across high-,middle-,low-level,and total clouds in the FGOALS-f3-L general circulation model.The bias of total CDV is decomposed into the model biases in CFRs and CDVs of clouds at all three levels.Results indicate that the model generally underestimates low-level cloud fraction during the daytime and high-/middle-level cloud fraction at nighttime.The simulation biases of low clouds,especially their CDV biases,dominate the bias of total CDV.Compensation effects exist among the bias decompositions,where the negative contributions of underestimated daytime low-level cloud fraction are partially offset by the opposing contributions from biases in high-/middle-level clouds.Meanwhile,the bias contributions have notable land–ocean differences and region-dependent characteristics,consistent with the model biases in these variables.Additionally,the study estimates the influences of CFR and CDV biases on the bias of shortwave cloud radiative effects.It reveals that the impacts of CDV biases can reach half of those from CFR biases,highlighting the importance of accurate CDV representation in climate models.
