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Constraint Intensity-Driven Evolutionary Multitasking for Constrained Multi-Objective Optimization 认领 引用
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作者 Leyu Zheng Mingming Xiao +2 位作者 Yi Ren Ke Li Chang Sun 《Computers, Materials & Continua》 SCIE EI 2026年第3期1241-1261,共21页
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red... In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs. 展开更多
关键词 Constrained multi-objective optimization evolutionary algorithm evolutionary multitasking knowledge transfer
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Knowledge Classification-Assisted Evolutionary Multitasking for Two-Task Multiobjective Optimization Problems 认领 引用
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作者 Xiaoling Wang Qi Kang +3 位作者 MengChu Zhou Qi Deng Zheng Fan Haoyue Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第6期1176-1193,共18页
To realize Industry 5.0,manufacturers face various optimization problems that seldom appear in isolation.Evolutionary MultiTasking(EMT)is an effective method to solve multiple related problems by extracting and utiliz... To realize Industry 5.0,manufacturers face various optimization problems that seldom appear in isolation.Evolutionary MultiTasking(EMT)is an effective method to solve multiple related problems by extracting and utilizing common knowledge.Knowledge transfer is the key to the effectiveness of EMT.Existing EMT methods mainly focus on designing effective intertask learning methods and ignore the fact that provided knowledge's appropriateness also has a significant effect on EMT's performance.There is plentiful knowledge in assistant tasks,and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks.EMT is thus confronted with a challenge to find appropriate knowledge.This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks.During the evolution process,better-performing candidates are supposed to have advantages in exploitation.Therefore,assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge.Specifically,the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population.Considering that target and assistant sub-populations have different characteristics,we use domain adaptation to reduce their distribution discrepancies.In this way,the trained classifier can classify assistant individuals more accurately,and truly useful knowledge can be selected for target tasks.The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems. 展开更多
关键词 Artificial intelligence evolutionary multitasking intelligent optimization inter-task learning knowledge classification knowledge transfer machine learning multiobjective optimization
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Evolutionary Multitasking With Multiple Knowledge Representations and Elite Vector Guidance for Solving Large-Scale Multi-Objective Optimization Problems 认领 引用
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作者 Weijie Mai Zhifan Tang +2 位作者 Weili Liu Jinghui Zhong Hu Jin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第12期2553-2571,共19页
Evolutionary multitasking optimization(EMTO) can obtain beneficial knowledge for the target task from the auxiliary task to improve its performance, which has received extensive attention in scientific research and en... Evolutionary multitasking optimization(EMTO) can obtain beneficial knowledge for the target task from the auxiliary task to improve its performance, which has received extensive attention in scientific research and engineering problems. Nevertheless, faced with the widespread large-scale multi-objective optimization problems(LSMOPs), the existing EMTO literature barely involves the research of LSMOPs. More importantly, these EMTO algorithms often get trapped in local optima when dealing with LSMOPs, resulting in a slow convergence speed, which is worthy of our attention. To this end, this paper proposes an EMTO algorithm dedicated to solving LSMOPs. On the one hand, given the intricate nature of LSMOPs, we propose a knowledge domination-based knowledge transfer mechanism that can flexibly transfer knowledge from multiple knowledge representations, i.e., the information distribution and distribution distance of the task population. On the other hand, we design an elite vector-guided search strategy. Specifically, the generative adversarial network(GAN) model should first be trained within the divided populations. Then, the well-trained model is used to generate a high-quality individual for the target individual. After that, the high-quality individual is combined with the top-performing individual in the current population to find the elite vector corresponding to the target individual. Finally, the elite vector is applied to guide the target individual to accelerate convergence towards the global optimum in the high-dimensional decision space. We conduct comprehensive experimental investigations on two artificial LSMOPs suites and six real-world LSMOPs to validate the efficiency and robustness of the proposed algorithm,through comparative analysis with state-of-the-art peer algorithms. 展开更多
关键词 Elite vector evolutionary multitasking optimization(EMTO) large-scale multi-objective optimization multiple knowledge representations
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Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization 认领 引用 被引量：13
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作者 Kangjia Qiao Jing Liang +3 位作者 Zhongyao Liu Kunjie Yu Caitong Yue Boyang Qu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第10期1951-1964,共14页
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-obj... 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. 展开更多
关键词 Constrained multi-objective optimization evolutionary multitasking(EMT) global auxiliary task knowledge transfer local auxiliary task
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Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems 认领 引用 被引量：3
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作者 Kangjia Qiao Jing Liang +4 位作者 Kunjie Yu Xuanxuan Ban Caitong Yue Boyang Qu Ponnuthurai Nagaratnam Suganthan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第8期1819-1835,共17页
Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they prop... Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for solvers.Among all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto front.However,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single constraints.Therefore,this paper attempts to identify promising single constraints and utilize them to help solve CMOPs.To be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively.Besides,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships.Moreover,an improved tentative method is designed to find and transfer useful knowledge among tasks.Experimental results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods. 展开更多
关键词 Constrained multi-objective optimization(CMOPs) evolutionary multitasking knowledge transfer single constraint.
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A deep multimodal fusion and multitasking trajectory prediction model for typhoon trajectory prediction to reduce flight scheduling cancellation 认领 引用 被引量：2
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作者 TANG Jun QIN Wanting +1 位作者 PAN Qingtao LAO Songyang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第3期666-678,共13页
Natural events have had a significant impact on overall flight activity,and the aviation industry plays a vital role in helping society cope with the impact of these events.As one of the most impactful weather typhoon... Natural events have had a significant impact on overall flight activity,and the aviation industry plays a vital role in helping society cope with the impact of these events.As one of the most impactful weather typhoon seasons appears and continues,airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms.This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation.The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules,and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction.With more dependable data accuracy,problems can be analysed rapidly and more efficiently,enabling better decision-making with a proactive versus reactive posture.When multiple modalities coexist,features can be extracted from them simultaneously to supplement each other’s information.An actual case study,the typhoon Lichma that swept China in 2019,has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather. 展开更多
关键词 flight scheduling optimization deep multimodal fusion multitasking trajectory prediction typhoon weather flight cancellation prediction reliability
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The Application of Multitasking Mechanism in Single Chip Computer System 认领 引用 被引量：2
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作者 Yu Jin Huang Jiwu Yuan Lanying 《Wuhan University Journal of Natural Sciences》 EI CAS 1999年第1期59-62,共4页
Developed a new program structure using in single chip computer system, which based on multitasking mechanism. Discussed the specific method for realization of the new structure. The applied sample is also provided.
关键词 multitasking mechanism single chip computer system interruption mechanism
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RAIENet:End-to-End Multitasking Road All Information Extractor 认领 引用
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作者 Xuemei Chen Pengfei Ren +2 位作者 Zeyuan Xu Shuyuan Xu Yaohan Jia 《Journal of Beijing Institute of Technology》 EI CAS 2024年第5期374-388,共15页
Road lanes and markings are the bases for autonomous driving environment perception.In this paper,we propose an end-to-end multi-task network,Road All Information Extractor named RAIENet,which aims to extract the full... Road lanes and markings are the bases for autonomous driving environment perception.In this paper,we propose an end-to-end multi-task network,Road All Information Extractor named RAIENet,which aims to extract the full information of the road surface including road lanes,road markings and their correspondences.Based on the prior knowledge of pavement information,we explore and use the deep progressive relationship between lane segmentation and pavement mark-ing detection.Then,different attention mechanisms are adapted for different tasks.A lane detection accuracy of 0.807 F1-score and a ground marking accuracy of 0.971 mean average precision at intersection over union(IOU)threshold 0.5 were achieved on the newly labeled see more on road plus(CeyMo+)dataset.Of course,we also validated it on two well-known datasets Berkeley Deep-Drive 100K(BDD100K)and CULane.In addition,a post-processing method for generating bird’s eye view lane(BEVLane)using lidar point cloud information is proposed,which is used for the construction of high-definition maps and subsequent decision-making planning.The code and data are available at http://gffzz188fe103f8f1460ascqnvuqw66pf96bco.ffgz.tsg.suse.edu.cn/mayberpf/RAIEnet. 展开更多
关键词 autonomous driving multitasking pavement marking detection lane segmentation pavement information
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An improved adaptive differential evolution algorithm for single unmanned aerial vehicle multitasking 认领 引用 被引量：3
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作者 Jian-li Su Hua Wang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2021年第6期1967-1975,共9页
Single unmanned aerial vehicle(UAV)multitasking plays an important role in multiple UAVs cooperative control,which is as well as the most complicated and hardest part.This paper establishes a threedimensional topograp... Single unmanned aerial vehicle(UAV)multitasking plays an important role in multiple UAVs cooperative control,which is as well as the most complicated and hardest part.This paper establishes a threedimensional topographical map,and an improved adaptive differential evolution(IADE)algorithm is proposed for single UAV multitasking.As an optimized problem,the efficiency of using standard differential evolution to obtain the global optimal solution is very low to avoid this problem.Therefore,the algorithm adopts the mutation factor and crossover factor into dynamic adaptive functions,which makes the crossover factor and variation factor can be adjusted with the number of population iteration and individual fitness value,letting the algorithm exploration and development more reasonable.The experimental results implicate that the IADE algorithm has better performance,higher convergence and efficiency to solve the multitasking problem compared with other algorithms. 展开更多
关键词 Unmanned aerial vehicle Multitasking Adaptive differential evolution Mutation factor Crossover factor
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Optimal Design of a Ship Multitasking Cabin Layout Based on the Interval Optimization Method 认领 引用 被引量：1
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作者 Haonan Li Yuanhang Hou +3 位作者 Wei Chen Tu Yu Yulong Hu Yeping Xiong 《Journal of Marine Science and Application》 CSCD 2021年第4期723-734,共12页
Searching for the optimal cabin layout plan is an efective way to improve the efciency of the overall design and reduce a ship’s operation costs.The multitasking states of a ship involve several statuses when facing ... Searching for the optimal cabin layout plan is an efective way to improve the efciency of the overall design and reduce a ship’s operation costs.The multitasking states of a ship involve several statuses when facing diferent missions during a voyage,such as the status of the marine supply and emergency escape.The human fow and logistics between cabins will change as the state changes.An ideal cabin layout plan,which is directly impacted by the above-mentioned factors,can meet the diferent requirements of several statuses to a higher degree.Inevitable deviations exist in the quantifcation of human fow and logistics.Moreover,uncontrollability is present in the fow situation during actual operations.The coupling of these deviations and uncontrollability shows typical uncertainties,which must be considered in the design process.Thus,it is important to integrate the demands of the human fow and logistics in multiple states into an uncertainty parameter scheme.This research considers the uncertainties of adjacent and circulating strengths obtained after quantifying the human fow and logistics.Interval numbers are used to integrate them,a two-layer nested system of interval optimization is introduced,and diferent optimization algorithms are substituted for solving calculations.The comparison and analysis of the calculation results with deterministic optimization show that the conclusions obtained can provide feasible guidance for cabin layout scheme. 展开更多
关键词 Cabin layout Multitasking states Uncertainty parameters Interval optimization Human fow and logistics
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Multitasking Behavior and Perceptions of Academic Performance in University Business Students in Mexico during the COVID-19 Pandemic 认领 引用
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作者 Victoria Gonzáles-Gutierrez Aldo Alvarez-Risco +4 位作者 Alfredo Estrada-Merino María de las Mercedes Anderson-Seminario Sabina Mlodzianowska Shyla Del-Aguila-Arcentales Jaime A.Yáñez 《International Journal of Mental Health Promotion》 2022年第4期565-581,共17页
The current study measures the influence of multitasking behavior and self-efficacy for self-regulated learning(SESRL)on perceptions of academic performance and views in university students during the COVID-19 pan-demic... The current study measures the influence of multitasking behavior and self-efficacy for self-regulated learning(SESRL)on perceptions of academic performance and views in university students during the COVID-19 pan-demic in Mexico.264 university students fulfilled an online questionnaire.It was observed that multitasking beha-vior negatively influences SESRL(-0.203),while SESRL showed a positive influence of 0.537 on perceptions of academic performance,and multitasking behavior had an influence of-0.097 on the perception of academic per-formance.Cronbach’s alpha and Average Variance Extracted values were 0.809 and 0.577(multitasking behavior),0.819 and 0.626(SESRL),0.873 and 0.725(perceptions of academic performance),respectively.The results of the bootstrapping test showed that the path coefficients were significant.The study outcomes can support new plans in universities to ensure the best academic outcomes.Our study showed evidence of the COVID-19 impact on education behavior.This study’s novelty is based on using the partial least square structural equation modeling(PLS-SEM)technique to evaluate these variables. 展开更多
关键词 Multitasking behavior COVID-19 Mexico self-efficacy for self-regulated learning academic performance online class pandemic Peru
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Performance Analysis of Robotic Arm Manipulators Control System under Multitasking Environment 认领 引用 被引量：2
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作者 Adnan Al Moshi Salwa Salam Cynthia Eftakhairul Islam Rumana Rahman Akm Abdul Malek Azad 《Journal of Mechanics Engineering and Automation》 2012年第5期327-331,共5页
This work is to observe the performance of PC based robot manipulator under general purpose （Windows）, Soft （Linux） and Hard （RT Linux） Real Time Operating Systems （OS）. The same open loop control system is ob... This work is to observe the performance of PC based robot manipulator under general purpose （Windows）, Soft （Linux） and Hard （RT Linux） Real Time Operating Systems （OS）. The same open loop control system is observed in different operating systems with and without multitasking environment. The Data Acquisition （DAQ, PLC-812PG） card is used as a hardware interface. From the experiment, it could be seen that in the non real time operating system （Windows）, the delay of the control system is larger than the Soft Real Time OS （Linux）. Further, the authors observed the same control system under Hard Real Time OS （RT-Linux）. At this point, the experiment showed that the real time error （jitter） is minimum in RT-Linux OS than the both of the previous OS. It is because the RT-Linux OS kernel can set the priority level and the control system was given the highest priority. The same experiment was observed under multitasking environment and the comparison of delay was similar to the preceding evaluation. 展开更多
关键词 Control system DAQ （data acquisition） card jitter multitasking RT-Linux.
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From Rubbish to a Large Scale Industry: A Simple Fabrication of Superfiber with Multitasking Applications 认领 引用
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作者 Hendry Izaac Elim Ronaldo Talapessy +2 位作者 Rafael Martinus Osok Sawia Eliyas Andreas 《Journal of Environmental Science and Engineering(B)》 CAS 2015年第11期620-623,共4页
In the whole earth, people increased dramatically from generation to generation which had created a large scale of broken environment so that people are facing more various types of garbage. Most of garbages are not u... In the whole earth, people increased dramatically from generation to generation which had created a large scale of broken environment so that people are facing more various types of garbage. Most of garbages are not useful and as a matter of fact, they are used to be neglected. Furthermore, many efforts have been conducted to change it by many types of recycled methods. Here, a simple technique is proposed with and without using fires to transform the useless natural or man-made rubbish things to be a superfiber as well as thin film with multitasking applications in human daily life. Since most of earth environment is covered by oceans, here the authors show how the ocean related garbage such as the crab skins, broken coral reefs and beach stones were changed to be superfiber and a multitasking device prototype. 展开更多
关键词 Rubbish fabrication superfiber multitasking marine environment.
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Surface and Content Validity of an Advanced Beginner Nurse’s Self-Monitoring Scale While Multitasking 认领 引用
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作者 Chihiro Mizuhiki Yasuko Hosoda 《Open Journal of Nursing》 2024年第10期491-502,共12页
Background: Self-monitoring is important for recognizing the situations one is facing and assessing one’s own competence to respond appropriately to situations that require multitasking. Purpose: This study aimed to ... Background: Self-monitoring is important for recognizing the situations one is facing and assessing one’s own competence to respond appropriately to situations that require multitasking. Purpose: This study aimed to examine the surface and content validity of the Advanced Beginner Nurses’ Self-Monitoring Scale While Multitasking and refine the scale items accordingly. It is expected that the development of such scale will allow for reflection on advanced beginner nurses’ response to multitasking, leading to further capacity building. Methods: The surface validity of 96 items of the Advanced Beginner Nurses’ Self-Monitoring Scale While Multitasking was examined at a meeting with five expert researchers. Five researchers and five nurses examined the items’ content using an item-level content validity index through a questionnaire survey. Results and Conclusion: The Advanced Beginner Nurses’ Self-Monitoring Scale While Multitasking was organized into 73 items that were refined into scales with surface and content validity. Consequently, five sub-concepts were identified: recognizing the situation one’s facing, seeing one’s self from multiple perspectives, devising concrete strategies depending on the situation, considering a predictable time schedule, and being aware of the situation surrounding one’s self. In the future, it will be necessary to examine the reliability and validity of the scale. 展开更多
关键词 Advanced Beginner Nurses Multitasking Self-Monitoring Refining the Scale Items
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The Impact of Multitasking With Digital Devices on Classroom Learning: A Critical Review on the Future of Digital Distraction in Education 认领 引用
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作者 Adrianus Nabung 《US-China Education Review(A)》 2024年第6期369-383,共15页
The integration of digital devices into educational environments has sparked a concerning trend,namely the widespread practice of multitasking among students during instructional sessions.Despite the plethora of empir... The integration of digital devices into educational environments has sparked a concerning trend,namely the widespread practice of multitasking among students during instructional sessions.Despite the plethora of empirical studies and theoretical frameworks addressing this phenomenon,there remains a need for comprehensive research elucidating the nuanced interplay between multitasking behaviors and academic performance.This study seeks to fill this gap by offering a synthesized examination of existing literature and theoretical perspectives.To unravel the complex dynamics underlying multitasking with digital devices and its impact on classroom learning outcomes,this study employs a multifaceted research approach.Drawing upon both literature studies and library research methodologies,the research endeavors to capture the breadth and depth of students’multitasking behaviors.Observation will be administered to gauge the frequency and nature of digital distractions,while qualitative systematic analysis will provide rich insights into students’motivations and cognitive processes during multitasking episodes.Preliminary analyses reveal a concerning correlation between multitasking behaviors and diminished academic performance.Students who engage in concurrent digital activities during instructional sessions exhibit lower grades,reduced comprehension,and impaired information retention compared to their focused counterparts.Moreover,qualitative data shed light on the underlying motivations driving multitasking tendencies,including perceived task efficiency,social connectivity,and habitual smartphone use patterns.Thus,this study underscores the urgent need for proactive interventions to address the detrimental effects of multitasking on classroom learning outcomes.By synthesizing empirical evidence and theoretical frameworks,the research provides valuable insights into the intricate dynamics of multitasking behaviors and their ramifications for academic performance.Moving forward,educators and policymakers must prioritize strategies that promote self-regulated learning and mitigate the adverse impact of digital distractions in educational settings. 展开更多
关键词 multitasking digital devices classroom learning academic performance digital distraction
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Executive Functioning as Mediator in the Longitudinal Relationship Between Media Multitasking and Divergent Thinking in Adolescents 认领 引用
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作者 Shuyu Shan Ziying Li +3 位作者 Yuxin Fan Xinru Zhao Xiuya Lei Yidi Chen 《PsyCh Journal》 CSCD 2025年第6期901-911,共11页
Junior high school students frequently multitask with media because of the rapid development of media tools.It is vital to investi-gate the relationship between junior high school students'individual cognitive abi... Junior high school students frequently multitask with media because of the rapid development of media tools.It is vital to investi-gate the relationship between junior high school students'individual cognitive abilities and media multitasking to better support their educational and developmental needs.Using a longitudinal design,this study investigated the relationship between media multitasking and divergent thinking,and the mediating role of executive function.Creativity and media were measured using the Development of Adolescent Executive Function Scale,the Alternative Uses Test(AUT),and the Media Multitasking Scale(MMS).Six hundred and nine junior high school students were assessed twice within a six-month period(at T1 and T2).After controlling for grade,gender,and place of origin,T1 media multitasking was negatively correlated with T2 divergent think-ing and T2 executive function.Moreover,T2 executive function was negatively correlated with T2 divergent thinking.Middle schoolers'T1 media multitasking significantly negatively predicted their T2 divergent thinkingβ=−0.1.Vertically,T2 executive function partially mediates the relationship between T1 media multitasking and T2 divergent thinking.High media multitasking reduces individual executive function,whereas low executive function can improve individual divergent thinking.This study reveals the relationship between media multitasking and divergent thinking,as well as the longitudinal mediating mechanism of executive function.Media multitasking can negatively predict divergent thinking,and T2 executive function had a significant longitudinal mediating effect on the relationship between T1 media multitasking and T2 divergent thinking. 展开更多
关键词 divergent thinking executive function media multitasking mediating effect middle school students
Robust Multitask Diffusion Bias-Compensated LMS Algorithm for Distributed Estimation with Noisy Link and Input 认领 引用
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作者 Xuzhao Chai Linru Zhang +2 位作者 Pengwei Wen Sheng Zhang Haiquan Zhao 《Journal of Beijing Institute of Technology》 EI CAS 2026年第2期137-149,共13页
This paper presents a robust multitask diffusion average bias compensation least mean square(RM-DABC-LMS)algorithm for distributed estimation in noisy input and communication link noise.The algorithm utilizes a robust... This paper presents a robust multitask diffusion average bias compensation least mean square(RM-DABC-LMS)algorithm for distributed estimation in noisy input and communication link noise.The algorithm utilizes a robust cost function based on the maximum Versoria criterion,incorporates bias compensation,and applies adaptive combination coefficients to reduce noise impacts.Theoretical analysis demonstrates the stability of the algorithm,providing closed-form expressions for the steady-state mean square deviation(MSD).A compression diffusion strategy is introduced to reduce communication cost of the RM-DABC-LMS algorithm,ensuring fast convergence and accurate estimation.Simulation results indicate that the proposed algorithm outperforms existing methods in noisy environments,achieving faster convergence and lower steady-state error. 展开更多
关键词 multitask networks robust bias compensation average estimate
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Bridging machine learning and CosMo-SAC for accurate prediction of infinite dilute activity coefficients of binary mixtures 认领 引用
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作者 Yuxin Qiu Guzhong Chen +3 位作者 Qian Liu Zhiwen Qi Kake Zhu Zhen Song 《ENGINEERING Chemical Engineering》 SCIE EI CAS CSCD 2026年第1期55-65,共11页
Infinite dilution activity coefficient(γ∞)is a key thermodynamic parameter in solvent design for chemical processes.Although conductor-like screening model for segment activity coefficient(COSMO-SAC)exhibits stro... Infinite dilution activity coefficient(γ∞)is a key thermodynamic parameter in solvent design for chemical processes.Although conductor-like screening model for segment activity coefficient(COSMO-SAC)exhibits strong prior predictive capabilities,its estimations are sometimes only qualitative rather than quantitative.Another limitation of COSMO-SAC arises from the reliance on time-intensive quantum chemistry calculations,which restricts its scalability for large-scale solvent screening.To overcome these issues,this study integrates COSMO-SAC with machine learning for accurate γ∞ prediction of binary mixtures.By bypassing the necessity for quantum chemistry calculations,the multi-task machine learning model could rapidly predict the surface charge density distribution(o-profiles)and molecular cavity volume(Vcosmo)of molecules and ions,while accurately distinguishing isomers.Four adjustable parameters of COSMO-SAC are optimized using more than 20000 experimental data points of γ,and residual systematic errors are further corrected with the boosting ensemble strategy to improve the model performance.The resulting hybrid model reduces the mean absolute error from 0.944 to 0.102(R2=0.969),representing an 89%improvement,while preserving the physicochemical interpretability of model.This accurate and efficient approach broadens the practical applicability of o-profiles and Vcosmo prediction,as well as γ∞ calculations based on COSMO-SAC,facilitating the high-throughput solvent screening for diverse chemical engineering applications. 展开更多
关键词 infinite dilution activity coefficient COSMO-SAC machine learning solvent design multitask learning
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Robust Graph Diffusion for Multi-Task Learning in Ultra-High Voltage Direct Current Monitoring Systems 认领 引用
19
作者 Shaojie Wang Chuliang Xue +3 位作者 Hui Li Rui Zhu Xiaoping Ren Limei Hu 《Journal of Beijing Institute of Technology》 EI CAS 2026年第2期150-158,共9页
In this work,we investigate the problem of multi-task learning(MTL)in ultra-high voltage direct current(UHVDC)monitoring systems.Considering the measurements are affected by wireless channel impairments,typically char... In this work,we investigate the problem of multi-task learning(MTL)in ultra-high voltage direct current(UHVDC)monitoring systems.Considering the measurements are affected by wireless channel impairments,typically characterized by block fading and link noise.Such channel imperfections significantly degrade the performance of distributed estimation in real-world power system environments.Based on the graph signal processing method,we propose the multi-task robust decoupled diffusion least mean square algorithm(MT-RDDLMS).Specifically,a decoupled adapt-then-combine strategy is introduced to reduce the influence of wireless channels on data exchange among measurement units.Moreover,an average estimation method with an adaptive smoothing factor is developed to further suppress link noise and enhance estimation accuracy.Simulation results confirm the robustness and effectiveness of the proposed algorithm under realistic wireless channel conditions. 展开更多
关键词 graph signal processing distributed multitask learning ultra-high voltage direct cur-rent(UHVDC)systems average estimation
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Research on Crop Leaf Disease Identification and Severity Assessment Based on Lightweight Multitask Deep Networks 认领 引用
20
作者 Jiali Du 《Journal of Electronic Research and Application》 2026年第5期88-98,共11页
In response to model 1,we first cleaned and standardized 61 crop disease image categories by removing duplicates through comparing image filenames with label files using provided path information.Valid samples were re... In response to model 1,we first cleaned and standardized 61 crop disease image categories by removing duplicates through comparing image filenames with label files using provided path information.Valid samples were resized and augmented to construct a multi-disease classification model based on the lightweight MobileNetV3-Large,with category IDs mapped to disease names.The model was trained and validated with cross-entropy loss,AdamW optimizer,and cosine annealing learning rate,with epochdependent loss and accuracy curves recorded.For model 2,a few-shot recognition solution was developed based on model 1,retaining 10 training samples per category.Using pre-trained MobileNetV3-Large as the feature backbone(parameters<20M),only upper convolutional and classification layers were finetuned.Enhanced augmentation,label smoothing,and cosine annealing mitigated overfitting and class imbalance,achieving~73%validation accuracy for 61 categories;Grad-CAM confirmed the model focuses on leaf lesions.Regarding model 3,severity-graded prediction was implemented by mapping 61 diseases to 3 severity levels via appendix JSON annotations and disease description tables.Images were regrouped to build a three-classification dataset,and a severity prediction model with MobileNetV3-Large(transfer learning,augmentation)was trained,outputting overall accuracy,macro-F1,recall,and a confusion matrix;Grad-CAM visualized key lesions for high-confidence correct predictions.For model 4,a lightweight integrated multi-task model was developed for simultaneous disease identification and severity assessment,using MobileNetV3-Large as the shared feature backbone with 61-category disease and 3-category severity classification heads.Joint optimization via multi-task loss enabled feature sharing and fine-grained assessment,with joint accuracy,confusion matrices,and Grad-CAM analyzing synergy and lesion focus,supporting interpretable diagnosis reports. 展开更多
关键词 MobileNetV3-large convolutional neural network Transfer learning,Cross-entropy loss function Multitask learning
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