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.

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.

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).

Enhancing Numeracy Skills of Grade 3 Students Through Authentic Performance Tasks

Numeracy is the capacity to use mathematical ideas in all facets of life.It involves activities such as adding and subtracting numbers,counting,number recognition,solving number problems involving various operations,sorting,observing,identifying,and establishing patterns.It is one of the fundamental skills that students should have mastered by the end of their primary schooling.With the notable importance of mastery of numeracy skills,low achievement and performance of the learners were observed in this aspect.This study aimed in enhancing the numeracy skills of Grade 3 learners through authentic performance tasks.The variable in numeracy skills includes the four fundamental operations and problem solving.The quasi-experimental design was utilized wherein purposive sampling or non-randomized sampling was used.In this study,33 Grade 3 learners of Rizal Elementary School were selected to participate in the tests.Pre-test and post-test crafted by the teacher were the main instrument in the study.The result revealed that in the pre-test the learners obtained a mean percentage score(MPS)of 38.20%in four fundamental operations,which implied a non-numerate level.While in terms of problem solving,the learners obtained a MPS of 20.60%which is also in the non-numerate level.It has a grand mean of 29.40%with an interpretation of non-numerate level.In the post-test,it was observed that four fundamental operations have a MPS of 81.10%which is in average numerate level,while problem solving has a MPS of 76.30%with a grand mean of 78.70%with an interpretation of average numerate level.This implied that there is a significant difference between the pre-test and post-test in the four fundamental operations and problem solving.Thus,it can be concluded that the application of authentic performance tasks was effective to bridge the gap on numeracy skills.

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.

Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

Well organized datacentres with interconnected servers constitute the cloud computing infrastructure.User requests are submitted through an interface to these servers that provide service to them in an on-demand basis.The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category.Task scheduling in cloud poses numerous challenges impacting the cloud performance.If not handled properly,user satisfaction becomes questionable.More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment.The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution.An improvised seagull optimization algorithm which combines the features of the Cuckoo search(CS)and seagull optimization algorithm(SOA)had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment.The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment.Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization(MO-ACO),ACO and Min-Min algorithms.The proposed SOA-CS technique had produced an improvement of 1.06%,4.2%,and 2.4%for makespan and had reduced the overall cost to the extent of 1.74%,3.93%and 2.77%when compared with PSO,ACO,IDEA algorithms respectively when 300 vms are considered.The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.

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.

Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

AI(Artificial Intelligence)workloads are proliferating in modernreal-time systems.As the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be reexamined.In particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline constraints.To cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two observations.First,resource planning for AI workloadsis a complicated search problem that requires much time for optimization.Second,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in advance.Based on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of resources.Instead of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of tasks.Thus,in any case,the workload isimmediately executed according to the resource plan maintained.Specifically,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload changes.The proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its effectiveness.Simulationexperiments show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.

Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

Cloud computing plays a significant role in Information Technology(IT)industry to deliver scalable resources as a service.One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling.The main advantage of this scheduling is to max-imize the performance and minimize the time loss.Various researchers examined numerous scheduling methods to achieve Quality of Service(QoS)and to reduce execution time.However,it had disadvantages in terms of low throughput and high response time.Hence,this study aimed to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the Virtual Machines(VMs).For this purpose,the research proposed novel Particle Swarm Optimization-Bandwidth Aware divisible Task(PSO-BATS)scheduling with Multi-Layered Regression Host Employment(MLRHE)to sort out the issues of task scheduling and ease the scheduling operation by load balancing.The proposed efficient sche-duling provides benefits to both cloud users and servers.The performance evalua-tion is undertaken with respect to cost,Performance Improvement Rate(PIR)and makespan which revealed the efficiency of the proposed method.Additionally,comparative analysis is undertaken which confirmed the performance of the intro-duced system than conventional system for scheduling tasks with highflexibility.

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.

Overload problem research on aperiodic real-time tasks

In this paper, a novel scheduling mechanism is proposed to handle the real-time overload problem by maximizing the cumulative values of three types of tasks： the soft, the hard and the imprecise tasks. The simulation results show that the performance of our presented mechanism in this paper is greatly improved, much better than that of the other three mechanisms： earliest deadline first （EDF）, highest value first （HVF） and highest density first （HDF）, under the same conditions of all nominal loads and task type proportions.

“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。

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.

Investigating the Use of Translation Continuation Tasks in Commercial Translation Teaching: A Study on Translating User Manuals

Based on the xu-argument, this study investigated the use of translation continuation tasks in commercial translation instruction. Forty-four second-year Business English majors at a Chinese university participated in the study. They were divided into equal-sized control and experimental groups. Members of the experimental group were asked to read Chinese-English parallel texts from a user manual, while those of the control group were asked to read the Chinese text only.Subsequently, both groups were required to translate the continued Chinese text into English. The results indicated the following: 1) the experimental group’s translated text was of significantly higher quality than that of the control group;and 2) the experimental group’s translations were well-aligned with the original text in their use of terminology, sentence structure, and stylistic features. The study concludes by suggesting that translation continuation tasks can improve commercial translation instruction and hence should be further applied in practice.

Parallel Test Tasks Scheduling and Resources Configuration Based on GA-ACA

A Genetic Algorithm-Ant Colony Algorithm(GA-ACA),which can be used to optimize multi-Unit Under Test(UUT)parallel test tasks sequences and resources configuration quickly and accurately,is proposed in the paper.With the establishment of the mathematic model of multi-UUT parallel test tasks and resources,the condition of multi-UUT resources mergence is analyzed to obtain minimum resource requirement under minimum test time.The definition of cost efficiency is put forward,followed by the design of gene coding and path selection project,which can satisfy multi-UUT parallel test tasks scheduling.At the threshold of the algorithm,GA is adopted to provide initial pheromone for ACA,and then dual-convergence pheromone feedback mode is applied in ACA to avoid local optimization and parameters dependence.The practical application proves that the algorithm has a remarkable effect on solving the problems of multi-UUT parallel test tasks scheduling and resources configuration.

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.

Comparing the Effects of Different Post-Listening Output Tasks on Second Language Incidental Vocabulary Acquisition:Revisiting the Involvement Load Hypothesis

Based on the Involvement Load Hypothesis, the present study investigated the differential effects of three post-listening output tasks(gap-filling, translation, and sentence-making) on immediate acquisition and retention of such vocabulary dimensions as productive knowledge of orthography,receptive recall of meaning and form, and productive knowledge of grammatical functions. Ninety second-year English majors were divided into three groups to finish listening plus one of the postlistening tasks. The results showed that the post-listening output tasks had positive effects on immediate acquisition of productive vocabulary knowledge, partially in agreement with the Involvement Load Hypothesis. However, the effects on vocabulary knowledge retention were found to be largely inconsistent with the Involvement Load Hypothesis. The finding thus challenges this hypothesis in that involvement load is not the only determining factor and suggests that the theoretical construct of involvement load should be constructed with more caution.

Streamlining the Writing and Tutoring Process of English Major Theses With Stringent Tasks and Procedures

English thesis writing and tutoring process is traditionally loose and open-ended. Both students and teachers enjoy much liberty in the maneuver of different steps in the whole process, which, as it were, may cause a series of problems and dissatisfactions, consequently undermining the quality and significance. This paper is intended to explore a streamlined and systematic procedure to ensure both the students and teachers are doing their bit and make due contribution to the thesis perfection.

Functional magnetic resonance imaging of cerebellar activation and lateralization during verbal and visuospatial tasks

BACKGROUND： Previous studies have analyzed cerebral activation and lateralization of cognitive functions, as well as cerebellar function with reference to high-level cognitive processing. However, there has been very little research on systematization and diversification. In particular, there are no reports on cerebellar lateralization, although reliable results have been reported on cerebral lateralization. OBJECTIVE： This study analyzed cerebellar activation and lateralization in relation to verbal and visuospatial tasks using functional magnetic resonance imaging （fMRI）. DESIGN, TIME AND SETTING： A block design for fMRI observation was performed at the fMRI Laboratory, Brain Science Research Center, Korea Advanced Institute of Science and Technology from May 2006 to September 2008. PARTICIPANTS： Sixteen healthy, male, college students, aged （23.3 ± 0.5） years, and 16 healthy, male, college students, aged （21.5 ± 2.3） years, participated in the study, respectively. METHODS： Verbal and visuospatial tasks were presented while functional brain images were acquired using a 3T fMRI system. Verbal analogy testing required the subject to select the word with the same relationship as one of the given words. Verbal antonym testing required the subject to select the word with a different meaning among four words. Visuospatial tasks involved selecting a shape that corresponded to a given figure with four examples, as well as selecting a development figure of a diagram. MAIN OUTCOME MEASURES： Changes in cerebellar activation and lateralization between two cognition tasks. RESULTS： Bilateral hemisphere Iobules VI and IX, right hemisphere Iobule VIII, bilateral hemisphere Iobules Crus I, and vermis Iobule IV, V, and Vl were closely related to verbal tasks in comparison to visuospatial tasks. Conversely, bilateral hemisphere Iobules IV and V, as well as the right hemisphere Iobule VI, were closely related to visuospatial tasks compared to verbal tasks. There was no great difference between the number of activated voxels in the cerebellums during the tasks, and cerebellar lateralization was not observed. CONCLUSION： In the cerebellum, the activation region, but not lateralization, was different between verbal and visuospatial tasks.

Residual force enhancement due to active muscle lengthening allows similar reductions in neuromuscular activation during position-and force-control tasks

Background:Residual torque enhancement(rTE)is the increase in torque observed during the isometric steady state following active muscle lengthening when compared with a fixed-end isometric contraction at the same muscle length and level of neuromuscular activation.In the rTE state,owing to an elevated contribution of passive force to total force production,less active force is required,and there is a subsequent reduction in activation.In vivo studies of rTE reporting an activation reduction are often performed using a dynamometer,where participants contract against a rigid restraint,resisting a torque motor.rTE has yet to be investigated during a position task,which involves the displacement of an inertial load with positional control.Methods:A total of 12 participants(6 males,6 females;age=22.8±1.1 years,height=174.7±8.6 cm,mass=82.1±37.7 kg;mean±SD)completed torque-and position-matching tasks at 60%maximum voluntary contraction for a fixed-end isometric contraction and an isometric contraction following active lengthening of the ankle dorsiflexors.Results:There were no significant differences in activation between torque-and position-matching tasks(p=0.743),with^27%activation reduction following active lengthening for both task types(p<0.001).Conclusion:These results indicate that rTE is a feature of voluntary,position-controlled contractions.These findings support and extend previous findings of isometric torque-control conditions to position-controlled contractions that represent different tasks of daily living.