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
Precisely forecasting the performance of Deep Learning(DL)models,particularly in critical areas such as Uniform Resource Locator(URL)-based threat detection,aids in improving systems developed for difficult tasks.In c...Precisely forecasting the performance of Deep Learning(DL)models,particularly in critical areas such as Uniform Resource Locator(URL)-based threat detection,aids in improving systems developed for difficult tasks.In cybersecurity,recognizing harmful URLs is vital to lowering risks associated with phishing,malware,and other online-based attacks.Since it directly affects the model’s capacity to differentiate between benign and harmful URLs,finding the optimum mix of hyperparameters in DL models is a significant difficulty.Two commonly used architectures for sequential and spatial data processing,Long Short-Term Memory(LSTM)/Gated Recurrent Unit(GRU)and Convolutional Neural Network(CNN)/Long Short-Term Memory(LSTM)models are targeted in this study to have higher predictive capacity by modifying crucial hyperparameters such as learning rate,batch size,and dropout rate using cloud capability.Research finds the best settings for the models by testing 50 dropout rates(between 0.1 and 0.5)with different learning rates and batch sizes.Performances were measured in the form of accuracy,precision,recall,F1-score,and errors such as Mean Absolute Error(MAE),Mean Squared Error(MSE),Root Mean Squared Error(RMSE)and Mean Absolute Percent Error(MAPE).In our results,CNN/LSTM performed better often than LSTM/GRU,with up to 10%better F1-score and much lower MAPE when the learning rate was 0.001 and the dropout rate was 0.2.These results show the value of fine-tuning hyperparameters to increase model performance and reduce errors.Higher on many of the parameters,CNN/LSTM architecture became obvious as the more trustworthy one.It also discussed the importance of DL in enhancing URL attack detection mechanisms to provide increased accuracy and precision for real-world cybersecurity.展开更多
The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive...The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive connectivity,5G is poised to revolutionize real-time communication and coordination in manufacturing environments.This paper explores the prospects and challenges of applying 5G technology in industrial robots,focusing on cloud-based control systems that enable scalable,flexible,and efficient operations.Key advantages of 5G,including improved communication speed,enhanced real-time control,scalability,and predictive maintenance capabilities,are discussed.However,the transition to 5G also presents challenges,such as network reliability,security concerns,integration with legacy systems,and high implementation costs.The paper also examines case studies in the automotive,electronics,and aerospace industries,providing real-world examples of 5G adoption in industrial automation.The conclusion highlights key insights and outlines potential research directions for overcoming existing barriers and fully realizing the potential of 5G technology in industrial robot control.展开更多
近年来,各医学类院校为了适应基层医学人才培养需求,在各门课程教学中积极探索新的教学方法,以促进学生综合素质的提升。其中,以问题为导向的教学方法(Problem Based Learning,PBL)和以病例为基础的教学方法(Case Based Learning,CBL)...近年来,各医学类院校为了适应基层医学人才培养需求,在各门课程教学中积极探索新的教学方法,以促进学生综合素质的提升。其中,以问题为导向的教学方法(Problem Based Learning,PBL)和以病例为基础的教学方法(Case Based Learning,CBL)得到广泛应用。本文以安徽中医药高等专科学校临床医学专业“妇产科学”课程为例,在校内对PBL+CBL教学法进行实践,选取2020—2023级四个年级的学生作为研究对象,其中两个年级采取PBL+CBL教学法,另外两个年级沿用传统教学法。通过对四个年级学生的成绩进行对比分析发现,PBL+CBL教学法可以明显提升学生成绩,同时后续随访结果表明,该模式下学生的临床思维能力得到显著提升。展开更多
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Precisely forecasting the performance of Deep Learning(DL)models,particularly in critical areas such as Uniform Resource Locator(URL)-based threat detection,aids in improving systems developed for difficult tasks.In cybersecurity,recognizing harmful URLs is vital to lowering risks associated with phishing,malware,and other online-based attacks.Since it directly affects the model’s capacity to differentiate between benign and harmful URLs,finding the optimum mix of hyperparameters in DL models is a significant difficulty.Two commonly used architectures for sequential and spatial data processing,Long Short-Term Memory(LSTM)/Gated Recurrent Unit(GRU)and Convolutional Neural Network(CNN)/Long Short-Term Memory(LSTM)models are targeted in this study to have higher predictive capacity by modifying crucial hyperparameters such as learning rate,batch size,and dropout rate using cloud capability.Research finds the best settings for the models by testing 50 dropout rates(between 0.1 and 0.5)with different learning rates and batch sizes.Performances were measured in the form of accuracy,precision,recall,F1-score,and errors such as Mean Absolute Error(MAE),Mean Squared Error(MSE),Root Mean Squared Error(RMSE)and Mean Absolute Percent Error(MAPE).In our results,CNN/LSTM performed better often than LSTM/GRU,with up to 10%better F1-score and much lower MAPE when the learning rate was 0.001 and the dropout rate was 0.2.These results show the value of fine-tuning hyperparameters to increase model performance and reduce errors.Higher on many of the parameters,CNN/LSTM architecture became obvious as the more trustworthy one.It also discussed the importance of DL in enhancing URL attack detection mechanisms to provide increased accuracy and precision for real-world cybersecurity.
文摘The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive connectivity,5G is poised to revolutionize real-time communication and coordination in manufacturing environments.This paper explores the prospects and challenges of applying 5G technology in industrial robots,focusing on cloud-based control systems that enable scalable,flexible,and efficient operations.Key advantages of 5G,including improved communication speed,enhanced real-time control,scalability,and predictive maintenance capabilities,are discussed.However,the transition to 5G also presents challenges,such as network reliability,security concerns,integration with legacy systems,and high implementation costs.The paper also examines case studies in the automotive,electronics,and aerospace industries,providing real-world examples of 5G adoption in industrial automation.The conclusion highlights key insights and outlines potential research directions for overcoming existing barriers and fully realizing the potential of 5G technology in industrial robot control.
文摘近年来,各医学类院校为了适应基层医学人才培养需求,在各门课程教学中积极探索新的教学方法,以促进学生综合素质的提升。其中,以问题为导向的教学方法(Problem Based Learning,PBL)和以病例为基础的教学方法(Case Based Learning,CBL)得到广泛应用。本文以安徽中医药高等专科学校临床医学专业“妇产科学”课程为例,在校内对PBL+CBL教学法进行实践,选取2020—2023级四个年级的学生作为研究对象,其中两个年级采取PBL+CBL教学法,另外两个年级沿用传统教学法。通过对四个年级学生的成绩进行对比分析发现,PBL+CBL教学法可以明显提升学生成绩,同时后续随访结果表明,该模式下学生的临床思维能力得到显著提升。
