Goal Attainments and the Role of Metacognitive Self in Task Accomplishment

Based on metacognition theories we present a construct of metacognitive self(MCS)as self-awareness of biases.Contrary to counterintuitive idea metacognitive self fosters self-regulation in the area of goal’s attainment.Study 1(N=118)showed that high metacognitive self individuals created more clear mental picture of their plans than low metacognitive self counter partners.Moreover participants high in metacognitive self undertook more actions to fulfill their goals then low MCS colleagues.Study 2(N=201)revealed that high metacognitive self individuals strive for autonomy and they work much better to attain their goals in the no load settings,while low metacognitive students work much better under supervision.

An Analysis of Li's Prose The Great Goal with Gee's "the Seven Building Tasks"

This paper,by using the theory of Seven Building Tasks to analyze the Prose The Great Goal2 which was ever analyzed from the perspective of theme and rheme structure,aims to demonstrate the application of the new theory Severn Building Tasks to analyzing an essay,and thus provide a fresh theory and approach for English learners to analyze prose and learn the English language.

GOAL问卷和Epworth嗜睡量表联合筛查阻塞性睡眠呼吸暂停的效能

目的检验GOAL问卷和Epworth嗜睡量表(Epworth sleeping scale,ESS)在筛查阻塞性睡眠呼吸暂停(obstructive sleep apnea,OSA)中联合应用的效能。方法从睡眠医学中心招募2958例参与者,完成夜间多导睡眠图监测和筛查问卷,包括GOAL、ESS、STOP-Bang问卷(SBQ)和NoSAS评分。评估每个量表的敏感度、特异度、阳性预测值、阴性预测值、诊断优势比(diagnostic odds ratio,DOR)和受试者工作特征(ROC)曲线下面积(area under the curve,AUC)。结果GOAL问卷在筛选OSA方面具有更高的敏感度和DOR(敏感度为0.831,DOR为3.72),优于STOP-Bang问卷和NoSAS评分。当GOAL问卷和ESS量表相结合时,特异度和DOR分别显著上升至0.894和4.22。GOAL问卷得分为3且ESS量表≥11分的参与者极有可能患有OSA,概率为0.96。结论GOAL问卷和ESS量表相结合具有优秀的诊断能力,可有效筛查OSA。对疑似OSA患者进行GOAL问卷后的第二阶段进行ESS量表筛查,可以提高预测准确性和早期诊断。

MADDPG-D2: An Intelligent Dynamic Task Allocation Algorithm Based on Multi-Agent Architecture Driven by Prior Knowledge

Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinforce-ment learning(DRL)theory and an improved Multi-Agent Deep Deterministic Policy Gradient(MADDPG-D2)algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA.The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm,and considers the introduction of a double noise mechanism to increase the action exploration space in the early stage of the algorithm,and the introduction of a double experience pool to improve the data utilization rate;at the same time,in order to accelerate the training speed and efficiency of the agents,and to solve the cold-start problem of the training,the a priori knowledge technology is applied to the training of the algorithm.Finally,the MADDPG-D2 algorithm is compared and analyzed based on the digital battlefield of ground and air confrontation.The experimental results show that the agents trained by the MADDPG-D2 algorithm have higher win rates and average rewards,can utilize the resources more reasonably,and better solve the problem of the traditional single agent algorithms facing the difficulty of solving the problem in the high-dimensional decision space.The MADDPG-D2 algorithm based on multi-agent architecture proposed in this paper has certain superiority and rationality in DTA.

The Importance of Setting Treatment Goals for Cardiovascular Diseases

Background: Guidelines are issued by most major organizations that focus on a specific disease entity. Guidelines should be a significant help to the practicing physician who may not be up-to-date with the recent medical literature. Unfortunately, when conflicting guidelines for a specific disease are published, confusion results. Purpose: This article provides a suggested guideline outcome measure that would benefit the physician and patient. Methods: A review of 19 different guidelines for cardiovascular disease treatment is one example of the lack of specific outcomes that currently exist. The basic problem with most guidelines is that they do not state the expected end result (i.e., the benefit to the patient) if that guideline is followed. When guidelines use cardiovascular disease risk factors to dictate therapy, the end benefit is never stated so that the patient can make an appropriate choice of which (if any) guideline to follow. Results: A good example is guidelines published by the American Heart Association for reducing cardiovascular disease. These guidelines are risk factor based and only indicate that cardiovascular disease would be reduced if followed. No specific percentage in the reduction of the incidence of disease is given. In contrast, when elimination of the disease is the stated goal of the guideline, the end result is clear. To date, this goal has been stated by only one organization devoted to eliminating cardiovascular disease. Conclusion: Guidelines need to be written to provide the physician and the patient with a specific end point that is expected when the guideline is followed. Patient acceptance and compliance will be much improved if the patient knows the risk/benefit of following the guideline’s recommendations.

Generating Factual Text via Entailment Recognition Task

Generating diverse and factual text is challenging and is receiving increasing attention.By sampling from the latent space,variational autoencoder-based models have recently enhanced the diversity of generated text.However,existing research predominantly depends on summarizationmodels to offer paragraph-level semantic information for enhancing factual correctness.The challenge lies in effectively generating factual text using sentence-level variational autoencoder-based models.In this paper,a novel model called fact-aware conditional variational autoencoder is proposed to balance the factual correctness and diversity of generated text.Specifically,our model encodes the input sentences and uses them as facts to build a conditional variational autoencoder network.By training a conditional variational autoencoder network,the model is enabled to generate text based on input facts.Building upon this foundation,the input text is passed to the discriminator along with the generated text.By employing adversarial training,the model is encouraged to generate text that is indistinguishable to the discriminator,thereby enhancing the quality of the generated text.To further improve the factual correctness,inspired by the natural language inference system,the entailment recognition task is introduced to be trained together with the discriminator via multi-task learning.Moreover,based on the entailment recognition results,a penalty term is further proposed to reconstruct the loss of our model,forcing the generator to generate text consistent with the facts.Experimental results demonstrate that compared with competitivemodels,ourmodel has achieved substantial improvements in both the quality and factual correctness of the text,despite only sacrificing a small amount of diversity.Furthermore,when considering a comprehensive evaluation of diversity and quality metrics,our model has also demonstrated the best performance.

Role of self-help groups on socioeconomic development and the achievement of Sustainable Development Goals(SDGs)among rural women in Cooch Behar District,India

This study examines the transformative role of self-help groups(SHGs)in the socioeconomic development of rural women in Cooch Behar District,India,and their contribution toward achieving Sustainable Development Goals(SDGs)of the United Nations.In this study,we explored the effect of SHGs on rural women by specifically addressing SDGs,such as no poverty(SDG 1),zero hunger(SDG 2),good health and well-being(SDG 3),quality education(SDG 4),and gender equality(SDG 5).Given this issue,a cross-sectional survey and comparison analyses are needed to assess the socioeconomic development of rural women and their awareness level before and after the participation of rural women in SHGs.The survey conducted as part of this study was divided into three sections,namely,demographic characteristics,socioeconomic development,and awareness level,with each focusing on different aspects.A group of 400 individuals who were part of SHGs completed the questionnaire survey form.The results showed that the participation of rural women in SHGs significantly improved their socioeconomic development and awareness level,as supported by both mean values and t test results.Memberships in SHGs and microcredit programs were the major elements that boosted the socioeconomic development of rural women,which also achieves SDGs 1,2,3,4,and 5.This study revealed that participation in SHGs and related financial services significantly aided rural women in economically disadvantaged communities in accumulating savings and initiating entrepreneurial ventures.Moreover,participation in SHGs was instrumental in enhancing the self-confidence,self-efficacy,and overall self-esteem of rural women.Finally,doing so enabled them to move more freely for work and other activities and to make family and common decisions.

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.

Spatial differences of Sustainable Development Goals(SDGs)among counties(cities)on the northern slope of the Kunlun Mountains

The county(city)located on the northern slope of the Kunlun Mountains is the primary area to solidify and extend the success of Xinjiang Uygur Autonomous Region,China in poverty alleviation.Its Sustainable Development Goals(SDGs)are intertwined with the concerted economic and social development of Xinjiang and the objective of achieving shared prosperity within the region.This study established a sustainable development evaluation framework by selecting 15 SDGs and 20 secondary indicators from the United Nations’SDGs.The aim of this study is to quantitatively assess the progress of SDGs at the county(city)level on the northern slope of the Kunlun Mountains.The results indicate that there are substantial variations in the scores of SDGs among the nine counties and one city located on the northern slope of the Kunlun Mountains.Notable high scores of SDGs are observed in the central and eastern regions,whereas lower scores are prevalent in the western areas.The scores of SDGs,in descending order,are as follows:62.22 for Minfeng County,54.22 for Hotan City,50.21 for Qiemo County,42.54 for Moyu County,41.56 for Ruoqiang County,41.39 for Qira County,39.86 for Lop County,38.25 for Yutian County,38.10 for Pishan County,and 36.87 for Hotan County.The performances of SDGs reveal that Hotan City,Lop County,Minfeng County,and Ruoqiang County have significant sustainable development capacity because they have three or more SDGs ranked as green color.However,Hotan County,Moyu County,Qira County,and Yutian County show the poorest performance,as they lack SDGs with green color.It is important to establish and enhance mechanisms that can ensure sustained income growth among poverty alleviation beneficiaries,sustained improvement in the capacity of rural governance,and the gradual improvement of social security system.These measures will facilitate the effective implementation of SDGs.Finally,this study offers a valuable support for governmental authorities and relevant departments in their decision-making processes.In addition,these results hold significant reference value for assessing SDGs at the county(city)level,particularly in areas characterized by low levels of economic development.

Strategies for Cultivating Public Funded Targeted Normal Students Based on Goal Management Theory : A Case Study of East, West and North Guangdong

Public funded targeted normal students are an important component of China s teacher team construction.Since its implementation in 2007,a large number of outstanding rural teachers who have been striving on the front line of education have been trained.Based on the theory of goal management,this paper explores the problems and countermeasures in the training of public funded targeted normal students.It strives to solve the problems of low willingness to teach and high default rates among public funded normal students,and hopes that the suggestions proposed in this paper can further promote the effective implementation of policies for public funded normal students.

Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation.While traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and efficiency.To address these issues,this study proposes a novel two-part invalid detection task allocation framework.In the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data.Compared to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public dataset.In the second step of the framework,task allocation modeling is performed using a twopart graph matching method.This phase introduces a P-queue KM algorithm that implements a more efficient optimization strategy.The allocation efficiency is improved by approximately 23.83%compared to the baseline method.Empirical results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.

Policy Network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud Networks

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

Online task planning method of anti-ship missile based on rolling optimization

Based on the wave attack task planning method in static complex environment and the rolling optimization framework, an online task planning method in dynamic complex environment based on rolling optimization is proposed. In the process of online task planning in dynamic complex environment,online task planning is based on event triggering including target information update event, new target addition event, target failure event, weapon failure event, etc., and the methods include defense area reanalysis, parameter space update, and mission re-planning. Simulation is conducted for different events and the result shows that the index value of the attack scenario after re-planning is better than that before re-planning and according to the probability distribution of statistical simulation method, the index value distribution after re-planning is obviously in the region of high index value, and the index value gap before and after re-planning is related to the degree of posture change.

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.

Dynamic access task scheduling of LEO constellation based on space-based distributed computing

A dynamic multi-beam resource allocation algorithm for large low Earth orbit(LEO)constellation based on on-board distributed computing is proposed in this paper.The allocation is a combinatorial optimization process under a series of complex constraints,which is important for enhancing the matching between resources and requirements.A complex algorithm is not available because that the LEO on-board resources is limi-ted.The proposed genetic algorithm(GA)based on two-dimen-sional individual model and uncorrelated single paternal inheri-tance method is designed to support distributed computation to enhance the feasibility of on-board application.A distributed system composed of eight embedded devices is built to verify the algorithm.A typical scenario is built in the system to evalu-ate the resource allocation process,algorithm mathematical model,trigger strategy,and distributed computation architec-ture.According to the simulation and measurement results,the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91%in a typical scene.The response time is decreased by 40%com-pared with the conditional GA.

Multi-Agent Deep Deterministic Policy Gradien-Based Task Offloading Resource Allocation Joint Offloading

With the advancement of technology and the continuous innovation of applications, low-latency applications such as drones, online games and virtual reality are gradually becoming popular demands in modern society. However, these applications pose a great challenge to the traditional centralized mobile cloud computing paradigm, and it is obvious that the traditional cloud computing model is already struggling to meet such demands. To address the shortcomings of cloud computing, mobile edge computing has emerged. Mobile edge computing provides users with computing and storage resources by offloading computing tasks to servers at the edge of the network. However, most existing work only considers single-objective performance optimization in terms of latency or energy consumption, but not balanced optimization in terms of latency and energy consumption. To reduce task latency and device energy consumption, the problem of joint optimization of computation offloading and resource allocation in multi-cell, multi-user, multi-server MEC environments is investigated. In this paper, a dynamic computation offloading algorithm based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is proposed to obtain the optimal policy. The experimental results show that the algorithm proposed in this paper reduces the delay by 5 ms compared to PPO, 1.5 ms compared to DDPG and 10.7 ms compared to DQN, and reduces the energy consumption by 300 compared to PPO, 760 compared to DDPG and 380 compared to DQN. This fully proves that the algorithm proposed in this paper has excellent performance.

Optimizing Spatial Crowdsourcing:A Quality-Aware Task Assignment Approach for Mobile Communication

The widespread use of advanced electronic devices has led to the emergence of spatial crowdsourcing,a method that taps into collective efforts to perform real-world tasks like environmental monitoring and traffic surveillance.Our research focuses on a specific type of spatial crowdsourcing that involves ongoing,collaborative efforts for continuous spatial data acquisition.However,due to limited budgets and workforce availability,the collected data often lacks completeness,posing a data deficiency problem.To address this,we propose a reciprocal framework to optimize task assignments by leveraging the mutual benefits of spatiotemporal subtask execution.We introduce an entropy-based quality metric to capture the combined effects of incomplete data acquisition and interpolation imprecision.Building on this,we explore a quality-aware task assignment method,corresponding to spatiotemporal assignment strategies.Since the assignment problem is NP-hard,we develop a polynomial-time algorithm with the guaranteed approximation ratio.Novel indexing and pruning techniques are proposed to further enhance performance.Extensive experiments conducted on datasets validate the effectiveness of our methods.

QoS-Constrained,Reliable and Energy-Efficient Task Deployment in Cloud Computing

Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns.However,these three factors have intrinsic trade-off relationships.The existing studies show that load concentration can reduce the number of servers and hence save energy.In this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure.We propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and re-execution.For each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task replica.Each main task runs on an individual server.The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure.Different from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas.Specifically,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task failure.We conduct experiments through simulations.The experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.