Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in oper...Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.展开更多
Performance and energy consumption of a solid state disk(SSD) highly depend on file systems and I/O schedulers in operating systems. To find an optimal combination of a file system and an I/O scheduler for SSDs, we us...Performance and energy consumption of a solid state disk(SSD) highly depend on file systems and I/O schedulers in operating systems. To find an optimal combination of a file system and an I/O scheduler for SSDs, we use a metric called the aggregative indicator(AI), which is the ratio of SSD performance value(e.g., data transfer rate in MB/s or throughput in IOPS) to that of energy consumption for an SSD. This metric aims to evaluate SSD performance per energy consumption and to study the SSD which delivers high performance at low energy consumption in a combination of a file system and an I/O scheduler. We also propose a metric called Cemp to study the changes of energy consumption and mean performance for an Intel SSD(SSD-I) when it provides the largest AI, lowest power, and highest performance, respectively. Using Cemp, we attempt to find the combination of a file system and an I/O scheduler to make SSD-I deliver a smooth change in energy consumption. We employ Filebench as a workload generator to simulate a wide range of workloads(i.e., varmail, fileserver, and webserver), and explore optimal combinations of file systems and I/O schedulers(i.e., optimal values of AI) for tested SSDs under different workloads. Experimental results reveal that the proposed aggregative indicator is comprehensive for exploring the optimal combination of a file system and an I/O scheduler for SSDs, compared with an individual metric.展开更多
Emerging non-volatile memory technologies,especially flash-based solid state drives(SSDs),have increasingly been adopted in the storage stack.They provide numerous advantages over traditional mechanically rotating har...Emerging non-volatile memory technologies,especially flash-based solid state drives(SSDs),have increasingly been adopted in the storage stack.They provide numerous advantages over traditional mechanically rotating hard disk drives(HDDs)and have a tendency to replace HDDs.Due to the long existence of HDDs as primary building blocks for storage systems,however,much of the system software has been specially designed for HDD and may not be optimal for non-volatile memory media.Therefore,in order to realistically leverage its superior raw performance to the maximum,the existing upper layer software has to be re-evaluated or re-designed.To this end,in this paper,we propose PASS,an optimized I/O scheduler at the Linux block layer to accommodate the changing trend of underlying storage devices toward flash-based SSDs.PASS takes the rich internal parallelism in SSDs into account when dispatching requests to the device driver in order to achieve high performance.Specifically,it partitions the logical storage space into fixed-size regions(preferably the component package sizes)as scheduling units.These scheduling units are serviced in a round-robin manner and for every chance that the chosen dispatching unit issues only a batch of either read or write requests to suppress the excessive mutual interference.Additionally,the requests are sorted according to their visiting addresses while waiting in the dispatching queues to exploit high sequential performance of SSD.The experimental results with a variety of workloads have shown that PASS outperforms the four Linux off-the-shelf I/O schedulers by a degree of 3%up to41%,while at the same time it improves the lifetime significantly,due to reducing the internal write amplification.展开更多
背景:骨代谢紊乱会引起骨相关疾病的发生,而叉头框转录因子O3可以通过调节氧化应激、自噬水平等来影响骨组织细胞增殖、分化与凋亡,调控骨代谢过程。目的:系统性分析叉头框转录因子O3调控骨代谢及其在骨科疾病中作用机制的相关研究文献...背景:骨代谢紊乱会引起骨相关疾病的发生,而叉头框转录因子O3可以通过调节氧化应激、自噬水平等来影响骨组织细胞增殖、分化与凋亡,调控骨代谢过程。目的:系统性分析叉头框转录因子O3调控骨代谢及其在骨科疾病中作用机制的相关研究文献,为后续以叉头框转录因子O3为靶点治疗骨疾病的研究提供参考。方法:以“(SU=FoxO3a OR SU=Foxo3 OR SU=Forkhead box O3 OR SU=叉头框转录因子O3)AND SU=骨”为检索句在中国知网进行检索,以“主题:(“FoxO3a”)OR主题:(“Foxo3”)OR主题:(“Forkhead box O3”)OR主题:(“叉头框转录因子O3”)AND主题:(“骨”)”为检索句在万方医学数据库进行检索;以“((FoxO3a)OR(Foxo3)OR(Forkhead box O3))AND((bone)OR(Skeleton))”为检索句在PubMed数据库进行检索,排除陈旧、重复、质量较差以及不相关的文献,最终纳入56篇文献进行综述分析。结果与结论:①叉头框转录因子O3与骨髓间充质干细胞:叉头框转录因子O3能够促进成骨谱系的形成,还可通过激活自噬促进早期成骨分化。同时,叉头框转录因子O3在骨髓间充质干细胞中体现抗氧化特性,保护细胞免受氧化应激诱导的衰老。②叉头框转录因子O3与成骨细胞:叉头框转录因子O3在成骨细胞中能通过干扰Wnt/β-连环蛋白通路抑制成骨,同时能激活抗氧化酶保护成熟成骨细胞。叉头框转录因子O3能促进成骨祖细胞的增殖,并通过激活自噬促进成骨分化。③叉头框转录因子O3与破骨细胞:叉头框转录因子O3表达可抵抗氧化应激和激活自噬抑制破骨细胞生成。④叉头框转录因子O3与骨细胞:叉头框转录因子O3可通过抗氧化作用保护骨细胞,还可通过抑制p16和p53信号通路和抑制衰老相关分泌表型来减少骨流失。⑤叉头框转录因子O3与软骨细胞:叉头框转录因子O3在骨关节炎中对软骨细胞起到保护作用,抑制软骨细胞分解或凋亡,促进软骨细胞外基质合成,可抑制软骨细胞肥大;然而,叉头框转录因子O3与Runt相关转录因子1在软骨细胞中高度共表达却会促进软骨祖细胞的早期软骨形成和终末肥大。⑥叉头框转录因子O3通过参与氧化应激抵抗与调控自噬等过程影响骨代谢,参与多类骨相关疾病的病理进程。展开更多
Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e....Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.展开更多
Ship outfitting is a key process in shipbuilding.Efficient and high-quality ship outfitting is a top priority for modern shipyards.These activities are conducted at different stations of shipyards.The outfitting plan ...Ship outfitting is a key process in shipbuilding.Efficient and high-quality ship outfitting is a top priority for modern shipyards.These activities are conducted at different stations of shipyards.The outfitting plan is one of the crucial issues in shipbuilding.In this paper,production scheduling and material ordering with endogenous uncertainty of the outfitting process are investigated.The uncertain factors in outfitting equipment production are usually decision-related,which leads to difficulties in addressing uncertainties in the outfitting production workshops before production is conducted according to plan.This uncertainty is regarded as endogenous uncertainty and can be treated as non-anticipativity constraints in the model.To address this problem,a stochastic two-stage programming model with endogenous uncertainty is established to optimize the outfitting job scheduling and raw material ordering process.A practical case of the shipyard of China Merchants Heavy Industry Co.,Ltd.is used to evaluate the performance of the proposed method.Satisfactory results are achieved at the lowest expected total cost as the complete kit rate of outfitting equipment is improved and emergency replenishment is reduced.展开更多
Operation in multiple frequency bands simultaneously is an important enabler for future wireless communication systems. This article presents a new concept for scheduling transmissions in a wireless radio system opera...Operation in multiple frequency bands simultaneously is an important enabler for future wireless communication systems. This article presents a new concept for scheduling transmissions in a wireless radio system operating in multiple frequency bands: the Multiband Scheduler (MBS). The MBS ensures that the operation in multiple bands is transparent to higher network layers. Special attention is paid to achieving low delay and latency when operating the system in the multiband mode. In particular, we propose additions to the ARQ procedures in order to achieve this. Deployment details and assessment results are presented for two multiband deployment scenarios. The first scenario is operation in a spectrum sharing context where multiple bands are used: one dedicated band for basic service and one shared extension band for extended services. In the second scenario we consider multiband operation in a relay environment, where the two bands have different propagation properties and relays provide extra coverage and capacity in the whole cell.展开更多
With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ...With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.展开更多
Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power sta...Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.展开更多
Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and ...Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.展开更多
Due to the complex,uncertainty and dynamics in the modern manufacturing environment,a flexible and robust shop floor scheduler is essential to achieve the production goals.A design framework of a shop floor dynamical ...Due to the complex,uncertainty and dynamics in the modern manufacturing environment,a flexible and robust shop floor scheduler is essential to achieve the production goals.A design framework of a shop floor dynamical scheduler is presented in this paper.The workflow and function modules of the scheduler are discussed in detail.A multi-step adaptive scheduling strategy and a process specification language,which is an ontology-based representation of process plan,are utilized in the proposed scheduler.The scheduler acquires the dispatching rule from the knowledge base and uses the build-in on-line simulator to evaluate the obtained rule.These technologies enable the scheduler to improve its fine-tune ability and effectively transfer process information into other heterogeneous information systems in a shop floor.The effectiveness of the suggested structure will be demonstrated via its application in the scheduling system of a manufacturing enterprise.展开更多
The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because o...The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.展开更多
Energy consumption has become a key metric for evaluating how good an embedded system is,alongside more performance metrics like respecting operation deadlines and speed of execution.Schedulability improvement is no l...Energy consumption has become a key metric for evaluating how good an embedded system is,alongside more performance metrics like respecting operation deadlines and speed of execution.Schedulability improvement is no longer the only metric by which optimality is judged.In fact,energy efficiency is becoming a preferred choice with a fundamental objective to optimize the system's lifetime.In this work,we propose an optimal energy efficient scheduling algorithm for aperiodic real-time jobs to reduce CPU energy consumption.Specifically,we apply the concept of real-time process scheduling to a dynamic voltage and frequency scaling(DVFS)technique.We address a variant of earliest deadline first(EDF)scheduling algorithm called energy saving-dynamic voltage and frequency scaling(ES-DVFS)algorithm that is suited to unpredictable future energy production and irregular job arrivals.We prove that ES-DVFS cannot attain a total value greater than C/ˆSα,whereˆS is the minimum speed of any job and C is the available energy capacity.We also investigate the implications of having in advance,information about the largest job size and the minimum speed used for the competitive factor of ES-DVFS.We show that such advance knowledge makes possible the design of semi-on-line algorithm,ES-DVFS∗∗,that achieved a constant competitive factor of 0.5 which is proved as an optimal competitive factor.The experimental study demonstrates that substantial energy savings and highest percentage of feasible job sets can be obtained through our solution that combines EDF and DVFS optimally under the given aperiodic jobs and energy models.展开更多
为探究稻茬小麦深施肥“一基一追”机艺融合技术的增产增效减排机制,2021—2024年在长江下游南通稻茬麦区开展大田试验。试验采用缓释掺混肥料(SRF,N∶P_(2)O_(5)∶K_(2)O=26∶12∶12)和普通尿素(U,46%N),结合自主研发的2BFGK-12(6)260...为探究稻茬小麦深施肥“一基一追”机艺融合技术的增产增效减排机制,2021—2024年在长江下游南通稻茬麦区开展大田试验。试验采用缓释掺混肥料(SRF,N∶P_(2)O_(5)∶K_(2)O=26∶12∶12)和普通尿素(U,46%N),结合自主研发的2BFGK-12(6)260全秸秆茬地洁区旋耕智能施肥播种机和3ZF-4(200)中耕追肥机,设置7种施肥模式(30 cm+15 cm宽窄行种植):以尿素4次分施(N 240 kg hm^(-2),基肥∶分蘖肥∶拔节肥∶孕穗肥=5∶1∶2∶2,窄行基施,追肥全田撒施)为对照(CK);减氮15%(N 204 kg hm^(-2))条件下设置6种处理:M_(1)(100%SRF窄行基施);M_(2)(60%SRF窄行基施+40%U拔节期窄行撒施);M_(3)(60%SRF窄行基施+40%U返青期宽行条施);M_(4)(60%SRF窄行基施+40%SRF返青期窄行撒施);M_(5)(60%SRF窄行基施+40%SRF返青期宽行条施);M_(4+5)(60%SRF窄行基施+20%SRF返青期宽行条施+20%SRF返青期窄行撒施)。研究比较不同施肥模式对小麦产量效益、根系形态生理、氮素利用效率及N_(2)O排放的影响。结果表明,与CK相比,M_(2)~M_(5)处理提高了小麦产量(4.0%~19.0%)和经济效益(13.7%~35.7%),其中M_(4)和M_(5)处理表现最优,分别增产14.1%和19.0%,经济效益提升34.5%和35.7%。这些处理明显改善了根系特性(根干重密度增加9.7%~111.8%,根系活力和氧化力分别提高6.8%~52.0%和4.2%~44.2%),降低N_(2)O累积排放量22.6%~34.5%,提高0~20 cm土层硝态氮含量11.2%~40.0%。在氮素利用方面,M_(2)~M_(5)处理均提高了籽粒氮素积累量、花后氮素积累量及其对籽粒氮素的贡献率,氮肥利用效率指标(包括偏生产力、农学效率和表观利用率)分别显著提升了22.4%~40.0%、29.7%~74.3%和9.41~18.77个百分点。值得注意的是,M_(4)和M_(5)处理表现出最优的综合效益:N_(2)O累积排放量降幅最大(分别达27.0%和34.5%),氮肥表观利用率2季均维持在43.0%以上(均值分别为43.5%和46.8%),同时在生育后期保持较高的根系活性和耕层无机氮含量。相比之下,M_(1)处理虽然实现了最大的N_(2)O减排效果(降幅35.9%),但导致减产10.4%和经济效益下降10.8%,且氮肥利用效率呈现不稳定的年际变化特征。而优化处理M_(4+5)进一步改善了根系形态生理特性,并提高氮肥表观利用率和籽粒氮素积累量。综上,减氮15%条件下(N 204 kg hm^(-2)),缓混肥2次施用处理(M_(4)和M_(5))能实现产量、经济效益、氮肥利用效率和N_(2)O减排的协同提高,并以追肥深施处理(M_(5))效应更强。本研究为稻茬小麦缓释肥减氮优化高效应用提供重要理论依据。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52475543)Natural Science Foundation of Henan(Grant No.252300421101)+1 种基金Henan Province University Science and Technology Innovation Talent Support Plan(Grant No.24HASTIT048)Science and Technology Innovation Team Project of Zhengzhou University of Light Industry(Grant No.23XNKJTD0101).
文摘Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.
基金supported by the National Basic Research Program(973)of China(No.2011CB302303)the National Natural Science Foundation of China(No.60933002)+1 种基金the National High-Tech R&D Program(863)of China(No.2013AA013203)the U.S. National Science Foundation under Grants CCF0845257(CAREER),CNS-0917137(CSR),CNS-0757778(CSR),CCF-0742187(CPA),CNS-0831502(CyberTrust),CNS-0855251(CRI),OCI-0753305(CI-TEAM),DUE-0837341(CCLI),and DUE-0830831(SFS)
文摘Performance and energy consumption of a solid state disk(SSD) highly depend on file systems and I/O schedulers in operating systems. To find an optimal combination of a file system and an I/O scheduler for SSDs, we use a metric called the aggregative indicator(AI), which is the ratio of SSD performance value(e.g., data transfer rate in MB/s or throughput in IOPS) to that of energy consumption for an SSD. This metric aims to evaluate SSD performance per energy consumption and to study the SSD which delivers high performance at low energy consumption in a combination of a file system and an I/O scheduler. We also propose a metric called Cemp to study the changes of energy consumption and mean performance for an Intel SSD(SSD-I) when it provides the largest AI, lowest power, and highest performance, respectively. Using Cemp, we attempt to find the combination of a file system and an I/O scheduler to make SSD-I deliver a smooth change in energy consumption. We employ Filebench as a workload generator to simulate a wide range of workloads(i.e., varmail, fileserver, and webserver), and explore optimal combinations of file systems and I/O schedulers(i.e., optimal values of AI) for tested SSDs under different workloads. Experimental results reveal that the proposed aggregative indicator is comprehensive for exploring the optimal combination of a file system and an I/O scheduler for SSDs, compared with an individual metric.
基金supported by the National Basic Research Program(973)of China(No.2004CB318203) the National High-Tech R&D Program(863)of China(No.2009AA01A402)+1 种基金the Natural Science Foundation of Hubei Province,China(No.2013CFB035)the Key Science Research Project of Hubei Education Office in China(No.D20141301)
文摘Emerging non-volatile memory technologies,especially flash-based solid state drives(SSDs),have increasingly been adopted in the storage stack.They provide numerous advantages over traditional mechanically rotating hard disk drives(HDDs)and have a tendency to replace HDDs.Due to the long existence of HDDs as primary building blocks for storage systems,however,much of the system software has been specially designed for HDD and may not be optimal for non-volatile memory media.Therefore,in order to realistically leverage its superior raw performance to the maximum,the existing upper layer software has to be re-evaluated or re-designed.To this end,in this paper,we propose PASS,an optimized I/O scheduler at the Linux block layer to accommodate the changing trend of underlying storage devices toward flash-based SSDs.PASS takes the rich internal parallelism in SSDs into account when dispatching requests to the device driver in order to achieve high performance.Specifically,it partitions the logical storage space into fixed-size regions(preferably the component package sizes)as scheduling units.These scheduling units are serviced in a round-robin manner and for every chance that the chosen dispatching unit issues only a batch of either read or write requests to suppress the excessive mutual interference.Additionally,the requests are sorted according to their visiting addresses while waiting in the dispatching queues to exploit high sequential performance of SSD.The experimental results with a variety of workloads have shown that PASS outperforms the four Linux off-the-shelf I/O schedulers by a degree of 3%up to41%,while at the same time it improves the lifetime significantly,due to reducing the internal write amplification.
文摘背景:骨代谢紊乱会引起骨相关疾病的发生,而叉头框转录因子O3可以通过调节氧化应激、自噬水平等来影响骨组织细胞增殖、分化与凋亡,调控骨代谢过程。目的:系统性分析叉头框转录因子O3调控骨代谢及其在骨科疾病中作用机制的相关研究文献,为后续以叉头框转录因子O3为靶点治疗骨疾病的研究提供参考。方法:以“(SU=FoxO3a OR SU=Foxo3 OR SU=Forkhead box O3 OR SU=叉头框转录因子O3)AND SU=骨”为检索句在中国知网进行检索,以“主题:(“FoxO3a”)OR主题:(“Foxo3”)OR主题:(“Forkhead box O3”)OR主题:(“叉头框转录因子O3”)AND主题:(“骨”)”为检索句在万方医学数据库进行检索;以“((FoxO3a)OR(Foxo3)OR(Forkhead box O3))AND((bone)OR(Skeleton))”为检索句在PubMed数据库进行检索,排除陈旧、重复、质量较差以及不相关的文献,最终纳入56篇文献进行综述分析。结果与结论:①叉头框转录因子O3与骨髓间充质干细胞:叉头框转录因子O3能够促进成骨谱系的形成,还可通过激活自噬促进早期成骨分化。同时,叉头框转录因子O3在骨髓间充质干细胞中体现抗氧化特性,保护细胞免受氧化应激诱导的衰老。②叉头框转录因子O3与成骨细胞:叉头框转录因子O3在成骨细胞中能通过干扰Wnt/β-连环蛋白通路抑制成骨,同时能激活抗氧化酶保护成熟成骨细胞。叉头框转录因子O3能促进成骨祖细胞的增殖,并通过激活自噬促进成骨分化。③叉头框转录因子O3与破骨细胞:叉头框转录因子O3表达可抵抗氧化应激和激活自噬抑制破骨细胞生成。④叉头框转录因子O3与骨细胞:叉头框转录因子O3可通过抗氧化作用保护骨细胞,还可通过抑制p16和p53信号通路和抑制衰老相关分泌表型来减少骨流失。⑤叉头框转录因子O3与软骨细胞:叉头框转录因子O3在骨关节炎中对软骨细胞起到保护作用,抑制软骨细胞分解或凋亡,促进软骨细胞外基质合成,可抑制软骨细胞肥大;然而,叉头框转录因子O3与Runt相关转录因子1在软骨细胞中高度共表达却会促进软骨祖细胞的早期软骨形成和终末肥大。⑥叉头框转录因子O3通过参与氧化应激抵抗与调控自噬等过程影响骨代谢,参与多类骨相关疾病的病理进程。
基金the financial support of the National Key Research and Development Plan(2021YFB3302501)the financial support of the National Natural Science Foundation of China(12102077)。
文摘Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.
基金supported in part by the High-tech ship scientific research project of the Ministry of Industry and Information Technology of the People’s Republic of China,and the National Nature Science Foundation of China(Grant No.71671113)the Science and Technology Department of Shaanxi Province(No.2020GY-219)the Ministry of Education Collaborative Project of Production,Learning and Research(No.201901024016).
文摘Ship outfitting is a key process in shipbuilding.Efficient and high-quality ship outfitting is a top priority for modern shipyards.These activities are conducted at different stations of shipyards.The outfitting plan is one of the crucial issues in shipbuilding.In this paper,production scheduling and material ordering with endogenous uncertainty of the outfitting process are investigated.The uncertain factors in outfitting equipment production are usually decision-related,which leads to difficulties in addressing uncertainties in the outfitting production workshops before production is conducted according to plan.This uncertainty is regarded as endogenous uncertainty and can be treated as non-anticipativity constraints in the model.To address this problem,a stochastic two-stage programming model with endogenous uncertainty is established to optimize the outfitting job scheduling and raw material ordering process.A practical case of the shipyard of China Merchants Heavy Industry Co.,Ltd.is used to evaluate the performance of the proposed method.Satisfactory results are achieved at the lowest expected total cost as the complete kit rate of outfitting equipment is improved and emergency replenishment is reduced.
文摘Operation in multiple frequency bands simultaneously is an important enabler for future wireless communication systems. This article presents a new concept for scheduling transmissions in a wireless radio system operating in multiple frequency bands: the Multiband Scheduler (MBS). The MBS ensures that the operation in multiple bands is transparent to higher network layers. Special attention is paid to achieving low delay and latency when operating the system in the multiband mode. In particular, we propose additions to the ARQ procedures in order to achieve this. Deployment details and assessment results are presented for two multiband deployment scenarios. The first scenario is operation in a spectrum sharing context where multiple bands are used: one dedicated band for basic service and one shared extension band for extended services. In the second scenario we consider multiband operation in a relay environment, where the two bands have different propagation properties and relays provide extra coverage and capacity in the whole cell.
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.
基金supported in part by the National Natural Science Foundation of China under Grant No.61473066in part by the Natural Science Foundation of Hebei Province under Grant No.F2021501020+2 种基金in part by the S&T Program of Qinhuangdao under Grant No.202401A195in part by the Science Research Project of Hebei Education Department under Grant No.QN2025008in part by the Innovation Capability Improvement Plan Project of Hebei Province under Grant No.22567637H
文摘Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.
基金supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/93/45).
文摘Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.
基金National Defense Fund(No.20030119)NSFC(No.60775060)the Foundation Research Fund of Harbin Engineering University(No.HEUFT07027)
文摘Due to the complex,uncertainty and dynamics in the modern manufacturing environment,a flexible and robust shop floor scheduler is essential to achieve the production goals.A design framework of a shop floor dynamical scheduler is presented in this paper.The workflow and function modules of the scheduler are discussed in detail.A multi-step adaptive scheduling strategy and a process specification language,which is an ontology-based representation of process plan,are utilized in the proposed scheduler.The scheduler acquires the dispatching rule from the knowledge base and uses the build-in on-line simulator to evaluate the obtained rule.These technologies enable the scheduler to improve its fine-tune ability and effectively transfer process information into other heterogeneous information systems in a shop floor.The effectiveness of the suggested structure will be demonstrated via its application in the scheduling system of a manufacturing enterprise.
基金supported in part by the National Key Research and Development Program of China under Grant No.2021YFF0901300in part by the National Natural Science Foundation of China under Grant Nos.62173076 and 72271048.
文摘The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.
文摘Energy consumption has become a key metric for evaluating how good an embedded system is,alongside more performance metrics like respecting operation deadlines and speed of execution.Schedulability improvement is no longer the only metric by which optimality is judged.In fact,energy efficiency is becoming a preferred choice with a fundamental objective to optimize the system's lifetime.In this work,we propose an optimal energy efficient scheduling algorithm for aperiodic real-time jobs to reduce CPU energy consumption.Specifically,we apply the concept of real-time process scheduling to a dynamic voltage and frequency scaling(DVFS)technique.We address a variant of earliest deadline first(EDF)scheduling algorithm called energy saving-dynamic voltage and frequency scaling(ES-DVFS)algorithm that is suited to unpredictable future energy production and irregular job arrivals.We prove that ES-DVFS cannot attain a total value greater than C/ˆSα,whereˆS is the minimum speed of any job and C is the available energy capacity.We also investigate the implications of having in advance,information about the largest job size and the minimum speed used for the competitive factor of ES-DVFS.We show that such advance knowledge makes possible the design of semi-on-line algorithm,ES-DVFS∗∗,that achieved a constant competitive factor of 0.5 which is proved as an optimal competitive factor.The experimental study demonstrates that substantial energy savings and highest percentage of feasible job sets can be obtained through our solution that combines EDF and DVFS optimally under the given aperiodic jobs and energy models.
文摘为探究稻茬小麦深施肥“一基一追”机艺融合技术的增产增效减排机制,2021—2024年在长江下游南通稻茬麦区开展大田试验。试验采用缓释掺混肥料(SRF,N∶P_(2)O_(5)∶K_(2)O=26∶12∶12)和普通尿素(U,46%N),结合自主研发的2BFGK-12(6)260全秸秆茬地洁区旋耕智能施肥播种机和3ZF-4(200)中耕追肥机,设置7种施肥模式(30 cm+15 cm宽窄行种植):以尿素4次分施(N 240 kg hm^(-2),基肥∶分蘖肥∶拔节肥∶孕穗肥=5∶1∶2∶2,窄行基施,追肥全田撒施)为对照(CK);减氮15%(N 204 kg hm^(-2))条件下设置6种处理:M_(1)(100%SRF窄行基施);M_(2)(60%SRF窄行基施+40%U拔节期窄行撒施);M_(3)(60%SRF窄行基施+40%U返青期宽行条施);M_(4)(60%SRF窄行基施+40%SRF返青期窄行撒施);M_(5)(60%SRF窄行基施+40%SRF返青期宽行条施);M_(4+5)(60%SRF窄行基施+20%SRF返青期宽行条施+20%SRF返青期窄行撒施)。研究比较不同施肥模式对小麦产量效益、根系形态生理、氮素利用效率及N_(2)O排放的影响。结果表明,与CK相比,M_(2)~M_(5)处理提高了小麦产量(4.0%~19.0%)和经济效益(13.7%~35.7%),其中M_(4)和M_(5)处理表现最优,分别增产14.1%和19.0%,经济效益提升34.5%和35.7%。这些处理明显改善了根系特性(根干重密度增加9.7%~111.8%,根系活力和氧化力分别提高6.8%~52.0%和4.2%~44.2%),降低N_(2)O累积排放量22.6%~34.5%,提高0~20 cm土层硝态氮含量11.2%~40.0%。在氮素利用方面,M_(2)~M_(5)处理均提高了籽粒氮素积累量、花后氮素积累量及其对籽粒氮素的贡献率,氮肥利用效率指标(包括偏生产力、农学效率和表观利用率)分别显著提升了22.4%~40.0%、29.7%~74.3%和9.41~18.77个百分点。值得注意的是,M_(4)和M_(5)处理表现出最优的综合效益:N_(2)O累积排放量降幅最大(分别达27.0%和34.5%),氮肥表观利用率2季均维持在43.0%以上(均值分别为43.5%和46.8%),同时在生育后期保持较高的根系活性和耕层无机氮含量。相比之下,M_(1)处理虽然实现了最大的N_(2)O减排效果(降幅35.9%),但导致减产10.4%和经济效益下降10.8%,且氮肥利用效率呈现不稳定的年际变化特征。而优化处理M_(4+5)进一步改善了根系形态生理特性,并提高氮肥表观利用率和籽粒氮素积累量。综上,减氮15%条件下(N 204 kg hm^(-2)),缓混肥2次施用处理(M_(4)和M_(5))能实现产量、经济效益、氮肥利用效率和N_(2)O减排的协同提高,并以追肥深施处理(M_(5))效应更强。本研究为稻茬小麦缓释肥减氮优化高效应用提供重要理论依据。
