The exponential growth of Internet ofThings(IoT)devices has created unprecedented challenges in data processing and resource management for time-critical applications.Traditional cloud computing paradigms cannot meet ...The exponential growth of Internet ofThings(IoT)devices has created unprecedented challenges in data processing and resource management for time-critical applications.Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems,while pure edge computing faces resource constraints that limit processing capabilities.This paper addresses these challenges by proposing a novel Deep Reinforcement Learning(DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments.Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency.The framework introduces three key innovations:(1)a DRL-based dynamic priority assignmentmechanism that learns fromsystem behavior,(2)a hybrid concurrency control protocol combining local edge validation with global cloud coordination,and(3)an integrated mathematical model that formalizes sensor-driven transactions across edge-cloud architectures.Extensive simulations across diverse workload scenarios demonstrate significant quantitative improvements:40%latency reduction,25%throughput increase,85%resource utilization(compared to 60%for heuristicmethods),40%reduction in energy consumption(300 vs.500 J per task),and 50%improvement in scalability factor(1.8 vs.1.2 for EDF)compared to state-of-the-art heuristic and meta-heuristic approaches.These results establish the framework as a robust solution for large-scale IoT and autonomous applications requiring real-time processing with consistency guarantees.展开更多
An improved algorithm, which solves cooperative concurrent computing tasks using the idle cycles of a number of high performance heterogeneous workstations interconnected through a high-speed network, was proposed. In...An improved algorithm, which solves cooperative concurrent computing tasks using the idle cycles of a number of high performance heterogeneous workstations interconnected through a high-speed network, was proposed. In order to get better parallel computation performance, this paper gave a model and an algorithm of task scheduling among heterogeneous workstations, in which the costs of loading data, computing, communication and collecting results are considered. Using this efficient algorithm, an optimal subset of heterogeneous workstations with the shortest parallel executing time of tasks can be selected.展开更多
The hybrid entangled state is widely discussed in quantum information processing. In this paper, we propose the first protocol to directly measure the concurrence of the hybrid entangled state. To complete the measure...The hybrid entangled state is widely discussed in quantum information processing. In this paper, we propose the first protocol to directly measure the concurrence of the hybrid entangled state. To complete the measurement, we design parity check measurements(PCMs) for both the single polarization qubit and the coherent state. In this protocol, we perform three rounds of PCMs. The results show that we can convert the concurrence into the success probability of picking up the correct states from the initial entangled states. This protocol only uses polarization beam splitters, beam splitters, and weak cross-Kerr nonlinearities, which is feasible for future experiments. This protocol may be useful in future quantum information processing.展开更多
Objective:To explore the potential of computed tomography(CT)-based delta-radiomics in predicting early shortterm responses to concurrent chemoradiotherapy for patients with non-small cell lung cancer(NSCLC),in order ...Objective:To explore the potential of computed tomography(CT)-based delta-radiomics in predicting early shortterm responses to concurrent chemoradiotherapy for patients with non-small cell lung cancer(NSCLC),in order to determine the optimal time point for the prediction.Methods:A total of 20 patients with pathologically confirmed NSCLC were prospectively enrolled in this study,who did not receive surgical treatment between February 2021 and February 2022.For each case,a total of 1,210 radiomic features(RFs)were extracted from both planning CT(pCT)images along with each of the subsequent three weeks of CT images.EffectiveΔRFs were selected using intra-class correlation coefficient(ICC)analysis,Pearson's correlation,ANOVA test(or Mann-Whitney U-test),and univariate logistic regression.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve was used to evaluate the potential to predict short-term responses of different time points.Results:Among the 1,210ΔRFs for 1-3 weeks,121 common features were retained after processing using ICC analysis and Pearson's correlation.These retained features included 54 and 58 of all time points that differed significantly between the response and non-response groups for the first and third months,respectively(P<0.05).After univariate logistic regression,11 and 44 features remained for the first and third months,respectively.Finally,eightΔRFs(P<0.05,AUC=0.77-0.91)that can discriminate short-term responses in both at 1 and 3 months with statistical accuracy were identified.Conclusion:CT-based delta-radiomics has the potential to provide reasonable biomarkers of short-term responses to concurrent chemoradiotherapy for NSCLC patients,and it can help improve clinical decisions for early treatment adaptation.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R909),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of Internet ofThings(IoT)devices has created unprecedented challenges in data processing and resource management for time-critical applications.Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems,while pure edge computing faces resource constraints that limit processing capabilities.This paper addresses these challenges by proposing a novel Deep Reinforcement Learning(DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments.Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency.The framework introduces three key innovations:(1)a DRL-based dynamic priority assignmentmechanism that learns fromsystem behavior,(2)a hybrid concurrency control protocol combining local edge validation with global cloud coordination,and(3)an integrated mathematical model that formalizes sensor-driven transactions across edge-cloud architectures.Extensive simulations across diverse workload scenarios demonstrate significant quantitative improvements:40%latency reduction,25%throughput increase,85%resource utilization(compared to 60%for heuristicmethods),40%reduction in energy consumption(300 vs.500 J per task),and 50%improvement in scalability factor(1.8 vs.1.2 for EDF)compared to state-of-the-art heuristic and meta-heuristic approaches.These results establish the framework as a robust solution for large-scale IoT and autonomous applications requiring real-time processing with consistency guarantees.
文摘An improved algorithm, which solves cooperative concurrent computing tasks using the idle cycles of a number of high performance heterogeneous workstations interconnected through a high-speed network, was proposed. In order to get better parallel computation performance, this paper gave a model and an algorithm of task scheduling among heterogeneous workstations, in which the costs of loading data, computing, communication and collecting results are considered. Using this efficient algorithm, an optimal subset of heterogeneous workstations with the shortest parallel executing time of tasks can be selected.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11474168 and 11747161)a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘The hybrid entangled state is widely discussed in quantum information processing. In this paper, we propose the first protocol to directly measure the concurrence of the hybrid entangled state. To complete the measurement, we design parity check measurements(PCMs) for both the single polarization qubit and the coherent state. In this protocol, we perform three rounds of PCMs. The results show that we can convert the concurrence into the success probability of picking up the correct states from the initial entangled states. This protocol only uses polarization beam splitters, beam splitters, and weak cross-Kerr nonlinearities, which is feasible for future experiments. This protocol may be useful in future quantum information processing.
基金supported by the Climbing Program from the National Cancer Center(NCC201917B03)Bethune Research Foundation of China(flzh202121)the key project of the Health Commission of Hubei Province,China(No:WJ2019Z015).
文摘Objective:To explore the potential of computed tomography(CT)-based delta-radiomics in predicting early shortterm responses to concurrent chemoradiotherapy for patients with non-small cell lung cancer(NSCLC),in order to determine the optimal time point for the prediction.Methods:A total of 20 patients with pathologically confirmed NSCLC were prospectively enrolled in this study,who did not receive surgical treatment between February 2021 and February 2022.For each case,a total of 1,210 radiomic features(RFs)were extracted from both planning CT(pCT)images along with each of the subsequent three weeks of CT images.EffectiveΔRFs were selected using intra-class correlation coefficient(ICC)analysis,Pearson's correlation,ANOVA test(or Mann-Whitney U-test),and univariate logistic regression.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve was used to evaluate the potential to predict short-term responses of different time points.Results:Among the 1,210ΔRFs for 1-3 weeks,121 common features were retained after processing using ICC analysis and Pearson's correlation.These retained features included 54 and 58 of all time points that differed significantly between the response and non-response groups for the first and third months,respectively(P<0.05).After univariate logistic regression,11 and 44 features remained for the first and third months,respectively.Finally,eightΔRFs(P<0.05,AUC=0.77-0.91)that can discriminate short-term responses in both at 1 and 3 months with statistical accuracy were identified.Conclusion:CT-based delta-radiomics has the potential to provide reasonable biomarkers of short-term responses to concurrent chemoradiotherapy for NSCLC patients,and it can help improve clinical decisions for early treatment adaptation.
