Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization

In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often overlooked.It is frequently assumed that vehicles can be accurately modeled during actual motion processes.However,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network scenarios.Taking into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading process.The optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming problem.Due to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and transmission.Finally,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization problem.Simulation results show that the algorithm proposed in this paper is able to achieve lower latency task computation offloading.Meanwhile,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).

Prevalence and Tasks Associated with Respiratory Symptoms among Waste Electrical and Electronic Equipment Handlers in Ouagadougou, Burkina Faso in 2019

Introduction: The uncontrolled management of waste electrical and electronic equipment (W3E) causes respiratory problems in the handlers of this waste. The objective was to study the stains associated with respiratory symptoms in W3E handlers. Methods: The study was cross-sectional with an analytical focus on W3E handlers in the informal sector in Ouagadougou. A peer-validated questionnaire collected data on a sample of 161 manipulators. Results: the most common W3E processing tasks were the purchase or sale of W3E (67.70%), its repair (39.75%) and its collection (31.06%). The prevalence of cough was 21.74%, that of wheezing 14.91%, phlegm 12.50% and dyspnea at rest 10.56%. In bivariate analysis, there were significant associations at the 5% level between W3E repair and phlegm (p-value = 0.044), between W3E burning and wheezing (p-value = 0.011) and between W3E and cough (p-value = 0.01). The final logistic regression models suggested that the burning of W3E and the melting of lead batteries represented risk factors for the occurrence of cough with respective prevalence ratios of 4.57 and 4.63. Conclusion: raising awareness on the wearing of personal protective equipment, in particular masks adapted by W3E handlers, favoring those who are dedicated to the burning of electronic waste and the melting of lead could make it possible to reduce the risk of occurrence of respiratory symptoms.

Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

云环境中Bag-of-tasks应用的多核虚拟计算资源分配机制研究

云计算环境为用户提供了大量弹性可扩展的基础设施资源,用户可以按需购买和支付服务.如果云平台能够自动分配给一批任务合理规模的计算资源,将会方便用户使用并且较大节省用户服务成本.本文在瀚海星云云计算平台上构建了一个软件应用服务层,提供Bag-of-tasks(BoT)应用公共服务.根据历史信息通过回归方法和BP神经网络方法对BoT中的子任务进行执行时间预测.然后使用最大并发度概念,在虚拟机内存是否满足任务情况下,提出了VMA,NP-IO和NP-DP三种算法.最后,使用图片分割软件作为BoT应用,从资源分配情况、任务完成率和算法时间复杂度方面验证了算法的有效性.

Making Writing Tasks Easy For College Students

The aim of teaching writing isn’t quite clear in the daily classroom teaching. This article emphasises that the aim of teaching writing isn’ t only to test, but to train. The writer analyses the problems in students’ writing and suggests some possible ways for teachers to help students with their writing.

“Tasks”的多元设计在英语教学中的使用

任务型教学(Task-Based Learning,简称TBL)是20世纪80年代外语教学研究者经过大量研究和实践提出的一个具有重要影响的语言教学模式。一、理论基础任务型语言教学法的理论基础是:输入与互动假设。Krashen(1982)区分出两个语言学习概念:学习和习得。学习是指通过教学有意识地学得语言,而习得只是指通过交际无意识地接触语言系统而掌握语言。Krashen强调掌握语言大多数是在交际活动中使用语言的结果。

Earth observation satellite scheduling for emergency tasks

The earth observation satellites(EOSs)scheduling problem for emergency tasks often presents many challenges.For example,the scheduling calculation should be completed in seconds,the scheduled task rate is supposed to be as high as possible,the disturbance measure of the scheme should be as low as possible,which may lead to the loss of important observation opportunities and data transmission delays.Existing scheduling algorithms are not designed for these requirements.Consequently,we propose a rolling horizon strategy(RHS)based on event triggering as well as a heuristic algorithm based on direct insertion,shifting,backtracking,deletion,and reinsertion(ISBDR).In the RHS,the driven scheduling mode based on the emergency task arrival and control station time window events are designed to transform the long-term,large-scale problem into a short-term,small-scale problem,which can improve the schedulability of the original scheduling scheme and emergency response sensitivity.In the ISBDR algorithm,the shifting rule with breadth search capability and backtracking rule with depth search capability are established to realize the rapid adjustment of the original plan and improve the overall benefit of the plan and early completion of emergency tasks.Simultaneously,two heuristic factors,namely the emergency task urgency degree and task conflict degree,are constructed to improve the emergency task scheduling guidance and algorithm efficiency.Finally,we conduct extensive experiments by means of simulations to compare the algorithms based on ISBDR and direct insertion,shifting,deletion,and reinsertion(ISDR).The results demonstrate that the proposed algorithm can improve the timeliness of emergency tasks and scheduling performance,and decrease the disturbance measure of the scheme,therefore,it is more suitable for emergency task scheduling.

任务型教学中Tasks的选择与设计

任务型教学越来越受到人们的重视。全文通过分析拼图玩具型、信息差型、解决问题型、做出决定型、意见交流型任务类型 ,讨论任务主题及任务设计原则 ,强调教师要根据大学生的实际情况 ,设计出适合于自己学生的任务 ,从而全面提高大学生英语运用能力。

Scheduling algorithm based on critical tasks in heterogeneous environments

Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of task scheduling algorithms for homogeneous environments have been proposed, whereas, a few for heterogeneous environments can be found in the literature. A novel task scheduling algorithm for heterogeneous environments, called the heterogeneous critical task （HCT） scheduling algorithm is presented. By means of the directed acyclic graph and the gantt graph, the HCT algorithm defines the critical task and the idle time slot. After determining the critical tasks of a given task, the HCT algorithm tentatively duplicates the critical tasks onto the processor that has the given task in the idle time slot, to reduce the start time of the given task. To compare the performance of the HCT algorithm with several recently proposed algorithms, a large set of randomly generated applications and the Gaussian elimination application are randomly generated. The experimental result has shown that the HCT algorithm outperforms the other algorithm.

Multi-satellite observation integrated scheduling method oriented to emergency tasks and common tasks

Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.

Distributed tasks-platforms scheduling method to holonic-C2 organization

To solve the problem of distributed tasks-platforms scheduling in holonic command and control(C2) organization,the basic elements of the organization are analyzed firstly and the formal description of organizational elements and structure is provided. Based on the improvement of task execution quality,a single task resource scheduling model is established and the solving method based on the m-best algorithm is proposed. For the problem of tactical decision-holon cannot handle tasks with low priority effectively, a distributed resource scheduling collaboration mechanism based on platform pricing and a platform exchange mechanism based on resource capacities are designed. Finally,a series of experiments are designed to prove the effectiveness of these methods. The results show that the proposed distributed scheduling methods can realize the effective balance of platform resources.

The Application of Surface Electromyography in the Assessment of Ergonomic Risk Factors Associated with Manual Lifting Tasks

Summary: The purpose of this study was to evaluate the ergonomic risk factors associated with manual lifting tasks using surface electromyography (EMG). 13 volunteers lifted loads of 6 and 13 kg at two speeds and at two horizontal distances in 3 different postures and three boxes of different sizes, from floor to knuckle height, performing 72 lifting tasks. For each lift, the surface electromyography signals from the erector spinae muscles, bilaterally at T_~10 and L_3, was recorded. The ergonomic risk factors associated with manual lifting tasks were evaluated by comparing the average amplitude of EMG signals from the erector spinae muscles. The EMG average amplitude for lifting the load of 13 kg was 14.3 % greater than that for lifting the load of 6 kg (t=-10.93, P<0.01). The EMG average amplitude at the site of L_3 was 10.3 % greater than that at the site of T_~10 (t=-7.98, P<0.01). The EMG average amplitude when performing “fast” lift was 5.9 %greater than the “slow” lift (t=-4.63, P<0.01). The posture of lifting affected the EMG average amplitude. It was lowest with semi-squat posture and greatest with squat posture (F=27.76, P<0.01). The result of multiple stepwise regression analysis showed that the loads of lifting, the size of box, horizontal distance, posture of lifting, the site of the spine subjected to force, lifting speed were the factors affecting the EMG average amplitude. The most significant factor was the loads of lifting, followed by the site of the spine subjected to force and the lifting speed in terms of risk. The ergonomic risk factors associated with manual lifting tasks includes the loads, posture, lifting speed, horizontal distance, the site of the spine subjected to force etc. The results of signal amplitude of EMG from the erector spinae muscles showed that semi-squat posture is the best posture for lifting tasks.

Sustainablc agriculture progress and tasks inrecent studies in China

In the paper the sustainable agricultural development is detailed. China, Which is amongthe eariest countries that published its State Agenda 21 in the worid, is facing a lot of challenges andproblems in the developmen of sustainable agriculture. Although great attention has been paid to andeffective actions taken to agricultural development, there are still a lot of tasks awaiting to be done.Furthermore, the strategies and measures of sustainable agriculture that must be correctly selectedaccording to the element combinative theory in the future are put forward.

A Training Program for Visual Discrimination Tasks in Monkeys

Go/NoGo tasks are a useful behavioral model in the study of cognitive neurosciences. The present developmental study is aimed at establishing a developmental protocol of Go/NoGo visual-discrimination tasks to investigate more cognitive process. We used two rhesus monkeys to test our procedures. Our results suggested that the monkeys quickly learned Go/NoGo visual-discrimination tasks, and performed NoGo tasks better and easier than Go tasks. Using this visual-discrimination task, we can easily study related cognitive neurosciences.

Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization

Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.

Correlation between LIFG and Autonomic Activation during Stressful Tasks:A Functional Near-infrared Spectroscopy (fNIRS) Study

It remains unclear whether language tasks in one＇s first （L1） or second （L2） language can cause stress responses and whether frontal, autonomic and behavioral responses to stressful tasks are correlated. In this study, we studied 22 Chinese subjects whose L2 was English and measured the cerebral blood oxygenation in their frontal lobe by using functional near-infrared spectroscopy （fNIRS） as par- ticipants engaged in a mental arithmetic task （MAT） and verbal fluency tasks （VFTs） in L1 （Chinese） and L2 （English）. To examine the activated cortical areas, we estimated the channel location based on Montreal Neurological Institute （MNI） standard brain space by using a-probabilistic estimation method. We evaluated heart rate （HR） changes to analyze autonomic nervous system （ANS） functioning. We found that the MAT and VFTs induced greater increases in HR than did the control （Ctrl） task. Further- more, subjects developed greater increases in HR in the MAT and VFTt~ than they did in the VFTL1. Compared with the Ctrl task, the MAT and both VFTLland VFTL2 produced robust and widespread bi- lateral activation of the frontal cortex. Interestingly, partial correlation analysis indicated that the activity in the left inferior frontal gyrus （LIFG） [Brodmarm＇s area （BA） 47] was consistently correlated with the increases in HR across the three tasks （MAT, VFTL2, and VFTL1）, after controlling for the performance data. The present results suggested that a VFT in L2 may be more stressful than in L1. The LIFG may affect the activation of the sympathetic system induced by stressful tasks, includin~ MATs and VFTs.

Emergency Tasks Planning Based on Formal Modeling of Emergency Plan and HTN Planning System SHOP2

A theoretical approach of ordered emergency tasks generation is proposed for dealing with a specific emergency event rapidly, exactly and effectively. According to the general principles of an emergency plan developed to response to an emergency management, a workflow model is employed to complete the formal modeling of concrete emergency plan firstly. Then the HTN planning system SHOP2 is introduced, the transformation method of domain knowledge from emergency domain to SHOP2 domain is studied. At last, the general procedure to solve the emergency decision prob-lems and to generate executive emergency tasks is set up drawing support from SHOP2 planning system, which will combine the principles (or knowledge) of emergency plan and the real emergency situations.

Method to Evaluate Earliest Release Time of New Real-time Tasks

In a periodic real-time system scheduled with the Earliest Deadline First (EDF) algorithm,it is necessary to compress some current tasks to avoid overloading if new task requests to run. Compressing a task means that its period is prolonged while its computation time keeps unchanged. An interesting problem is to find the earliest time to release new tasks without any deadline missing,that is,the earliest smooth insertion time. In this paper,a general frame to calculate the earliest time with multiple rounds of deadline checking is given,which shows that the checking can be done from the request time of the new tasks. A smart way is provided and proved,which takes the value of theΔchecking of the current round as the time step to the next. These techniques potentially reduce the amount of the calculation and the number of the rounds of the checking to get the earliest time. Simulation results are also given to support the conclusion.

Coordinated Planning Transmission Tasks in Heterogeneous Space Networks:A Semi-Distributed Approach

This paper studies the coordinated planning of transmission tasks in the heterogeneous space networks to enable efficient sharing of ground stations cross satellite systems.Specifically,we first formulate the coordinated planning problem into a mixed integer liner programming(MILP)problem based on time expanded graph.Then,the problem is transferred and reformulated into a consensus optimization framework which can be solved by satellite systems parallelly.With alternating direction method of multipliers(ADMM),a semi-distributed coordinated transmission task planning algorithm is proposed,in which each satellite system plans its own tasks based on local information and limited communication with the coordination center.Simulation results demonstrate that compared with the centralized and fully-distributed methods,the proposed semi-distributed coordinated method can strike a better balance among task complete rate,complexity,and the amount of information required to be exchanged.

Real-time online rescheduling for multiple agile satellites with emergent tasks

The emergent task is a kind of uncertain event that satellite systems often encounter in the application process.In this paper,the multi-satellite distributed coordinating and scheduling problem considering emergent tasks is studied.Due to the limitation of onboard computational resources and time,common online onboard rescheduling methods for such problems usually adopt simple greedy methods,sacrificing the solution quality to deliver timely solutions.To better solve the problem,a new multi-satellite onboard scheduling and coordinating framework based on multi-solution integration is proposed.This method uses high computational power on the ground and generates multiple solutions,changing the complex onboard rescheduling problem to a solution selection problem.With this method,it is possible that little time is used to generate a solution that is as good as the solutions on the ground.We further propose several multi-satellite coordination methods based on the multi-agent Markov decision process(MMDP)and mixed-integer programming(MIP).These methods enable the satellite to make independent decisions and produce high-quality solutions.Compared with the traditional centralized scheduling method,the proposed distributed method reduces the cost of satellite communication and increases the response speed for emergent tasks.Extensive experiments show that the proposed multi-solution integration framework and the distributed coordinating strategies are efficient and effective for onboard scheduling considering emergent tasks.