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Two-Stage IoT Computational Task Offloading Decision-Making in MEC with Request Holding and Dynamic Eviction
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作者 Dayong Wang Kamalrulnizam Bin Abu Bakar Babangida Isyaku 《Computers, Materials & Continua》 SCIE EI 2024年第8期2065-2080,共16页
The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support ... The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted.In addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time slots.It is resulting in TD missing potential offloading opportunities in the future.To fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic eviction.Long Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot.The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology.Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method. 展开更多
关键词 Decision making internet of things load prediction task offloading multi-access edge computing
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Representation and Decomposition of Complex Decision-Making Tasks in AOBDIDSS
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作者 YANG Shanlin HU Xiaojian FANG Fang 《Tsinghua Science and Technology》 SCIE EI CAS 2005年第z1期811-816,共6页
Representation and decomposition of complex decision-making tasks are bottleneck problem of complex task decision. This paper uses multi-agent technology to construct an agent organization-based distributed intelligen... Representation and decomposition of complex decision-making tasks are bottleneck problem of complex task decision. This paper uses multi-agent technology to construct an agent organization-based distributed intelligence decision support system (AOBDIDSS) structure model,applies generalized decision function (GDF) to the decomposition of decision task specifications, and determines decomposition criteria and properties of decision task specifications based on GDF. Because the task decomposition based on GDF is equivalent to the decomposition of Bayesian network, we present the representation and decomposition methods of decision tasks and properties based on Bayesian network. On these bases, the decision task decomposition problems can be entailed basically to construct a multi-sectioned Bayesian network and sub-Bayesian networks related to decision task specifications. The method is used to analyze the representation and decomposition of decision tasks in medical diagnosis. The results show that the model and method is not only feasible, but also effective and novel. 展开更多
关键词 agent organization-based distributed INTELLIGENCE DECISION support system (AOBDIDSS) DECISION tasks generalized DECISION function (GDF) BAYESIAN network
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Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation 被引量:2
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作者 Kang Yuan Yanjun Huang +4 位作者 Shuo Yang Zewei Zhou Yulei Wang Dongpu Cao Hong Chen 《Engineering》 SCIE EI CAS CSCD 2024年第2期108-120,共13页
Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning frame... Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment. 展开更多
关键词 Autonomous driving decision-making Motion planning Deep reinforcement learning Model predictive control
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Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees
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作者 Xiangkun He Wenhui Huang Chen Lv 《Engineering》 SCIE EI CAS CSCD 2024年第2期77-89,共13页
While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present... While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies. 展开更多
关键词 Autonomous vehicle decision-making Reinforcement learning Adversarial attack Safety guarantee
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Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review
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作者 Zhiqiang Pu Yi Pan +4 位作者 Shijie Wang Boyin Liu Min Chen Hao Ma Yixiong Cui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期37-57,共21页
Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professio... Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making. 展开更多
关键词 Artificial intelligence(AI) decision-making FOOTBALL review SOCCER sports analytics
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Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts
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作者 Xuemin Wang Qiaochen Li Xuguang Bao 《Computers, Materials & Continua》 SCIE EI 2024年第3期3619-3643,共25页
Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values... Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems. 展开更多
关键词 Ethical decision-making inductive logic programming incremental learning conflicts
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Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization
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作者 Yu Zhou Yun Zhang +4 位作者 Guowei Li Hang Yang Wei Zhang Ting Lyu Yueqiang Xu 《Computers, Materials & Continua》 SCIE EI 2024年第8期1809-1829,共21页
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 ... 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). 展开更多
关键词 Component vehicular DYNAMIC task offloading resource scheduling
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Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost
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作者 Congjun Rao Mengxi Li +1 位作者 Tingting Huang Feiyu Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期699-724,共26页
Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to ob... Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk. 展开更多
关键词 STROKE risk assessment decision-making CatBoost feature selection borderline SMOTE Logistic-AB
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Prevalence and Tasks Associated with Respiratory Symptoms among Waste Electrical and Electronic Equipment Handlers in Ouagadougou, Burkina Faso in 2019
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作者 Marthe Sandrine Sanon Lompo Sombenewindé Bienvenu Alexandre Nikiéma +3 位作者 Issa Traoré Marius Kédoté Jules Owona Manga Nicolas Méda 《Occupational Diseases and Environmental Medicine》 2024年第3期199-210,共12页
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... 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. 展开更多
关键词 Respiratory Symptoms W3E Associated tasks OUAGADOUGOU
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Determination of the best materials for development and designing product using a multi-criteria decision-making
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作者 Rabia Hassan Zeeshan Ahmad Arfeen +2 位作者 Mehreen Kausar Azam Zain ul Abiden Akhtar Abubakar Siddique 《Railway Sciences》 2024年第5期541-557,共17页
Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are eff... Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design. 展开更多
关键词 TOPSIS Multi-criteria decision-making Entropy method Material selection
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The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure
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作者 Huzaira Razzaque Shahzaib Ashraf +1 位作者 Muhammad Naeem Yu-Ming Chu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1925-1950,共26页
Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is a... Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done. 展开更多
关键词 Multi-criteria group decision-making spherical q-linear Diophantine fuzzy sets differencemeasures photovoltaic cells medical diagnosis
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A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment
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作者 Salma Khan Muhammad Gulistan +2 位作者 NasreenKausar Seifedine Kadry Jungeun Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1951-1979,共29页
Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathema... Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology. 展开更多
关键词 q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator
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UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience
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作者 ZHAN Guang ZHANG Kun +1 位作者 LI Ke PIAO Haiyin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第3期644-665,共22页
Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devo... Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy. 展开更多
关键词 unmanned aerial vehicle(UAV) maneuvering decision-making autonomous air-delivery deep reinforcement learning reward shaping expert experience
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Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning
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作者 Jiang Fan Qin Junwei +1 位作者 Liu Lei Tian Hui 《China Communications》 SCIE CSCD 2024年第4期38-52,共15页
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-rel... 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. 展开更多
关键词 associative tasks cache-aided procedure double deep Q-network Internet of Medical Things(IoMT) multi-access edge computing(MEC)
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Influence of social media on maternal decision-making and breastfeeding practices
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作者 Gowda Parameshwara Prashanth 《World Journal of Clinical Pediatrics》 2024年第4期109-111,共3页
Breastfeeding practices are influenced by multifactorial determinants including individual characteristics,external support systems,and media influences.This commentary emphasizes such complex factors influencing brea... Breastfeeding practices are influenced by multifactorial determinants including individual characteristics,external support systems,and media influences.This commentary emphasizes such complex factors influencing breastfeeding practices.Potential methodological limitations and the need for diverse sampling in studying breastfeeding practices are highlighted.Further research must explore the interplay between social influences,cultural norms,government policies,and individual factors in shaping maternal breastfeeding decisions. 展开更多
关键词 Exclusive breastfeeding Breastfeeding promotion Mass communication Maternal decision-making Social media
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Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study
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作者 Paul E. Plonski Jasmin Vassileva +5 位作者 Ryan Shahidi Paul B. Perrin William Carter Lance L. Goetz Amber Brochetti James M. Bjork 《Journal of Behavioral and Brain Science》 2024年第2期47-63,共17页
Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobeha... Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions. 展开更多
关键词 Spinal Cord Injury SELF-CARE decision-making PARAPLEGIA Impulsive Behavior Health Care
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Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information
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作者 Huajun Liu Zengqiang Wang 《Journal of Architectural Research and Development》 2024年第1期32-37,共6页
To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select... To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example. 展开更多
关键词 Public engineering EMERGENCY Multi-granularity language decision-making
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Effects of Emotion on Decision-Making ofMethamphetamine Users: Based on theEmotional Iowa Gambling Task
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作者 Xiaoqing Zeng Song Tu Ting Liu 《International Journal of Mental Health Promotion》 2023年第11期1229-1236,共8页
The relapse of methamphetamine (meth) is associated with decision-making dysfunction. The present study aims to investigate theimpact of different emotions on the decision-making behavior of meth users. We used 2 (gen... The relapse of methamphetamine (meth) is associated with decision-making dysfunction. The present study aims to investigate theimpact of different emotions on the decision-making behavior of meth users. We used 2 (gender: male, female) × 3 (emotion:positive, negative, neutral) × 5 (block: 1, 2, 3, 4, 5) mixed experiment design. The study involved 168 meth users who weredivided into three groups: positive emotion, negative emotion and neutral emotion group, and tested by the emotional IowaGambling Task (IGT). The IGT performance of male users exhibited a decreasing trend from Block 1 to Block 3. Female methusers in positive emotion had the best performance in IGT than females in the other two groups. In positive emotion, the IGTperformance of female meth users was significantly better than that of men. Female meth users in positive emotion had betterdecision-making than those in negative or neutral emotion. Female meth users in positive emotion had better decision-makingperformance than males in positive emotion. In negative and neutral emotions, there was no significant gender difference indecision-making. 展开更多
关键词 Methamphetamine user EMOTION gender difference Iowa gambling task decision-making
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A Blind Spot in the Reframing of a Universe of Possibles: Towards a Suitable Model for Decision-Making Theory and A.I.
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作者 Gilbert Giacomoni 《Journal of Applied Mathematics and Physics》 2024年第6期2172-2189,共18页
Bayesian inference model is an optimal processing of incomplete information that, more than other models, better captures the way in which any decision-maker learns and updates his degree of rational beliefs about pos... Bayesian inference model is an optimal processing of incomplete information that, more than other models, better captures the way in which any decision-maker learns and updates his degree of rational beliefs about possible states of nature, in order to make a better judgment while taking new evidence into account. Such a scientific model proposed for the general theory of decision-making, like all others in general, whether in statistics, economics, operations research, A.I., data science or applied mathematics, regardless of whether they are time-dependent, have in common a theoretical basis that is axiomatized by relying on related concepts of a universe of possibles, especially the so-called universe (or the world), the state of nature (or the state of the world), when formulated explicitly. The issue of where to stand as an observer or a decision-maker to reframe such a universe of possibles together with a partition structure of knowledge (i.e. semantic formalisms), including a copy of itself as it was initially while generalizing it, is not addressed. Memory being the substratum, whether human or artificial, wherein everything stands, to date, even the theoretical possibility of such an operation of self-inclusion is prohibited by pure mathematics. We make this blind spot come to light through a counter-example (namely Archimedes’ Eureka experiment) and explore novel theoretical foundations, fitting better with a quantum form than with fuzzy modeling, to deal with more than a reference universe of possibles. This could open up a new path of investigation for the general theory of decision-making, as well as for Artificial Intelligence, often considered as the science of the imitation of human abilities, while being also the science of knowledge representation and the science of concept formation and reasoning. 展开更多
关键词 decision-making INNOVATION Universe of Possibles A.I. Quantum Form Fuzzy Modeling
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Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making
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作者 Kexin Chen 《Journal of Electronic Research and Application》 2024年第1期69-72,共4页
In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to ... In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system. 展开更多
关键词 Artificial intelligence Automated decision-making Algorithmic law system Due process Algorithmic justice
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