With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that consid...With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.展开更多
Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal ...Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.展开更多
This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagno...This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state cells.These models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases.Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed decisions.Our goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic support.We propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies effectively.Additionally,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN models.We further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and imbalance.Comprehensive validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’reliability.Moreover,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model interpretability.Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.展开更多
Across four studies,we explore the impact of solitude on consumers’reliance on feelings versus reasons in decision making,along with the underlying mechanism and boundary conditions.The results indicate that solitude...Across four studies,we explore the impact of solitude on consumers’reliance on feelings versus reasons in decision making,along with the underlying mechanism and boundary conditions.The results indicate that solitude individuals(vs.non-solitude)would prefer feeling-based strategy in decision-making,resulting in a higher intention of choosing the affectively superior option over the cognitively superior option(Study 1).Self-focus plays the underlying mechanism in the solitude effect(Study 2).Moreover,we also examine two boundary conditions:motivation(Study 3)and temporal orientation(Study 4),which indicates that involuntary motivation and future orientation can mitigate the solitude effect on affective processing.These findings provide insights into consumers’judgments of product attributes and selection of decision-making strategies according to their situations.展开更多
Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefit...Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefits of two calibrations of the Nutrient Expert(NE)tool for rice in Sri Lanka’s Alfisols:the basic calibration(Nutrient Expert Sri Lanka 1,NESL1)and the comprehensive calibration(Nutrient Expert Sri Lanka 2,NESL2).NESL1 was developed by adapting the South Indian version of NE to local conditions,while NESL2 was an updated version,using three years of data from 71 farmer fields.展开更多
People have been engaged in sports activities both individually and collectively for years.Sports consumption,which refers to the process that covers many issues related to sports in the form of playing,watching,liste...People have been engaged in sports activities both individually and collectively for years.Sports consumption,which refers to the process that covers many issues related to sports in the form of playing,watching,listening or reading,is a form of human behavior.The satisfaction of the four marketing components of product,price,distribution and promotion by using the leisure time of the sports consumer effectively and ensuring its continuity in the future process can be ensured by effective utilization of facilities and quality recreation activities.Consumer behaviors,which have a very complex structure,are seen in the form of choosing,buying,using and obtaining.With this study,it is aimed to determine the mediating role of consumer decision-making styles in determining the effect of marketing components in the consumption of sports activities on the satisfaction of sports consumers.In this direction,data were collected in the province of Istanbul,which was determined as the sample.Data were obtained with a questionnaire form created on Google Form.These data were analyzed in line with the model and hypotheses created with these data and it was determined that the marketing components of sports consumption have an impact on the sports consumer and it was concluded that consumer decision-making styles have a positive mediating effect in this regard.展开更多
BACKGROUND Cancer care faces challenges due to tumor heterogeneity and rapidly evolving therapies,necessitating artificial intelligence(AI)-driven clinical decision support.While general-purpose models like ChatGPT of...BACKGROUND Cancer care faces challenges due to tumor heterogeneity and rapidly evolving therapies,necessitating artificial intelligence(AI)-driven clinical decision support.While general-purpose models like ChatGPT offer adaptability,domain-specific systems(e.g.,DeepSeek)may better align with clinical guidelines.However,their comparative efficacy in oncology remains underexplored.This study hypothesizes that domain-specific AI will outperform general-purpose models in technical accuracy,while the latter will excel in patient-centered communication.AIMS To compare ChatGPT and DeepSeek in oncology decision support for diagnosis,treatment,and patient communication.METHODS A retrospective analysis was conducted using 1200 anonymized oncology cases(2018–2023)from The Cancer Genome Atlas and institutional databases,covering six cancer types.Each case included histopathology,imaging,genomic profiles,and treatment histories.Both models generated diagnostic interpretations,staging assessments,and therapy recommendations.Performance was evaluated against NCCN/ESMO guidelines and expert oncologist panels using F1-scores,Cohen'sκ,Likert-scale ratings,and readability metrics.Statistical significance was assessed via analysis of variance and post-hoc Tukey tests.RESULTS DeepSeek demonstrated superior performance in diagnostic accuracy(F1-score:89.2%vs ChatGPT's 76.5%,P<0.001)and treatment alignment with guidelines(κ=0.82 vs 0.67,P=0.003).ChatGPT exhibited strengths in patient communi-cation,generating layman-friendly explanations(readability score:8.2/10 vs DeepSeek's 6.5/10,P=0.012).Both models showed limitations in rare cancer subtypes(e.g.,cholangiocarcinoma),with accuracy dropping below 60%.Clinicians rated DeepSeek's outputs as more actionable(4.3/5 vs 3.7/5,P=0.021)but highlighted ChatGPT's utility in palliative care discussions.CONCLUSION Domain-specific AI(DeepSeek)excels in technical precision,while general-purpose models(ChatGPT)enhance patient engagement.A hybrid system integrating both approaches may optimize oncology workflows,contingent on expanded training for rare cancers and real-time guideline updates.展开更多
This study focuses on the construction and application of intelligent financial decision-making models driven by generative artificial intelligence(AI).It analyzes the mechanisms by which generative AI empowers financ...This study focuses on the construction and application of intelligent financial decision-making models driven by generative artificial intelligence(AI).It analyzes the mechanisms by which generative AI empowers financial decision-making within a dual framework of dynamic knowledge evolution and risk control.The research reveals that generative AI,with its superior data processing,pattern recognition,and autonomous learning capabilities,can transcend the limitations of traditional decision-making models,facilitating a significant shift from causal inference to probabilistic creation in decision-making paradigms.By systematically constructing an intelligent financial decision-making model that includes data governance,core engine,and decision output layers,the study clarifies the functional roles and collaborative mechanisms of each layer.Additionally,it addresses key challenges in technology application,institutional adaptation,and organizational transformation by proposing systematic strategies for technical risk management,institutional innovation,and organizational capability enhancement,aiming to provide robust theoretical support and practical guidance for the intelligent transformation of corporate financial decision-making.展开更多
The high maneuverability of modern fighters in close air combat imposes significant cognitive demands on pilots,making rapid,accurate decision-making challenging.While reinforcement learning(RL)has shown promise in th...The high maneuverability of modern fighters in close air combat imposes significant cognitive demands on pilots,making rapid,accurate decision-making challenging.While reinforcement learning(RL)has shown promise in this domain,the existing methods often lack strategic depth and generalization in complex,high-dimensional environments.To address these limitations,this paper proposes an optimized self-play method enhanced by advancements in fighter modeling,neural network design,and algorithmic frameworks.This study employs a six-degree-of-freedom(6-DOF)F-16 fighter model based on open-source aerodynamic data,featuring airborne equipment and a realistic visual simulation platform,unlike traditional 3-DOF models.To capture temporal dynamics,Long Short-Term Memory(LSTM)layers are integrated into the neural network,complemented by delayed input stacking.The RL environment incorporates expert strategies,curiositydriven rewards,and curriculum learning to improve adaptability and strategic decision-making.Experimental results demonstrate that the proposed approach achieves a winning rate exceeding90%against classical single-agent methods.Additionally,through enhanced 3D visual platforms,we conducted human-agent confrontation experiments,where the agent attained an average winning rate of over 75%.The agent's maneuver trajectories closely align with human pilot strategies,showcasing its potential in decision-making and pilot training applications.This study highlights the effectiveness of integrating advanced modeling and self-play techniques in developing robust air combat decision-making systems.展开更多
To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on ...To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.展开更多
Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of ca...Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.展开更多
System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose sign...System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose significant challenges for efficient decision-making,necessitating a modular multiagent control system.Deep Reinforcement Learning(DRL)and Decision Tree(DT)have been utilized for these complex decision-making tasks,but each has its limitations:DRL is highly adaptive but lacks interpretability,while DT is inherently interpretable but has limited adaptability.To overcome these challenges,we propose the Adaptive Interpretable Decision Tree(AIDT),an evolutionary-based algorithm that is both adaptable to diverse environmental settings and highly interpretable in its decision-making processes.We first construct a Markov decision process(MDP)-based simulation environment using the Cooperative Submarine Search task as a representative scenario for training and testing the proposed method.Specifically,we use the heat map as a state variable to address the issue of multi-agent input state proliferation.Next,we introduce the curiosity-guiding intrinsic reward to encourage comprehensive exploration and enhance algorithm performance.Additionally,we incorporate decision tree size as an influence factor in the adaptation process to balance task completion with computational efficiency.To further improve the generalization capability of the decision tree,we apply a normalization method to ensure consistent processing of input states.Finally,we validate the proposed algorithm in different environmental settings,and the results demonstrate both its adaptability and interpretability.展开更多
With the growth of the construction industry,risk management in construction projects has garnered significant attention from the academic community.Effective risk management during the decision-making stage can great...With the growth of the construction industry,risk management in construction projects has garnered significant attention from the academic community.Effective risk management during the decision-making stage can greatly enhance project management efficiency.This paper integrates the AHP-entropy value method and constructs a risk management model based on the DPSIR framework for construction projects.The model is applied to evaluate and analyze the risk level of the decision-making stage in a navigation and electricity hub project in Chongqing Municipality.The results demonstrate the scientific validity and effectiveness of the proposed model.展开更多
The Internet of Things(IoT)is a smart infrastructure where devices share captured data with the respective server or edge modules.However,secure and reliable communication is among the challenging tasks in these netwo...The Internet of Things(IoT)is a smart infrastructure where devices share captured data with the respective server or edge modules.However,secure and reliable communication is among the challenging tasks in these networks,as shared channels are used to transmit packets.In this paper,a decision tree is integrated with other metrics to form a secure distributed communication strategy for IoT.Initially,every device works collaboratively to form a distributed network.In this model,if a device is deployed outside the coverage area of the nearest server,it communicates indirectly through the neighboring devices.For this purpose,every device collects data from the respective neighboring devices,such as hop count,average packet transmission delay,criticality factor,link reliability,and RSSI value,etc.These parameters are used to find an optimal route from the source to the destination.Secondly,the proposed approach has enabled devices to learn from the environment and adjust the optimal route-finding formula accordingly.Moreover,these devices and server modules must ensure that every packet is transmitted securely,which is possible only if it is encrypted with an encryption algorithm.For this purpose,a decision tree-enabled device-to-server authentication algorithm is presented where every device and server must take part in the offline phase.Simulation results have verified that the proposed distributed communication approach has the potential to ensure the integrity and confidentiality of data during transmission.Moreover,the proposed approach has outperformed the existing approaches in terms of communication cost,processing overhead,end-to-end delay,packet loss ratio,and throughput.Finally,the proposed approach is adoptable in different networking infrastructures.展开更多
Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs...Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.展开更多
Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique f...Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.展开更多
The rapid increase in the number of Internet of Things(IoT)devices,coupled with a rise in sophisticated cyberattacks,demands robust intrusion detection systems.This study presents a holistic,intelligent intrusion dete...The rapid increase in the number of Internet of Things(IoT)devices,coupled with a rise in sophisticated cyberattacks,demands robust intrusion detection systems.This study presents a holistic,intelligent intrusion detection system.It uses a combined method that integrates machine learning(ML)and deep learning(DL)techniques to improve the protection of contemporary information technology(IT)systems.Unlike traditional signature-based or singlemodel methods,this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification.This combination provides a more nuanced and adaptable defense.The research utilizes the NF-UQ-NIDS-v2 dataset,a recent,comprehensive benchmark for evaluating network intrusion detection systems(NIDS).Our methodological framework employs advanced artificial intelligence techniques.Specifically,we use ensemble learning algorithms(Random Forest,Gradient Boosting,AdaBoost,and XGBoost)for binary classification.Deep learning architectures are also employed to address the complexities of multi-class classification,allowing for fine-grained identification of intrusion types.To mitigate class imbalance,a common problem in multi-class intrusion detection that biases model performance,we use oversampling and data augmentation.These techniques ensure equitable class representation.The results demonstrate the efficacy of the proposed hybrid ML-DL system.It achieves significant improvements in intrusion detection accuracy and reliability.This research contributes substantively to cybersecurity by providing a more robust and adaptable intrusion detection solution.展开更多
BACKGROUND Eyelid reconstruction is an intricate process,addressing both aesthetic and functional aspects post-trauma or oncological surgery.Aesthetic concerns and oncological radicality guide personalized approaches....BACKGROUND Eyelid reconstruction is an intricate process,addressing both aesthetic and functional aspects post-trauma or oncological surgery.Aesthetic concerns and oncological radicality guide personalized approaches.The complex anatomy,involving anterior and posterior lamellae,requires tailored reconstruction for optimal functionality.AIM To formulate an eyelid reconstruction algorithm through an extensive literature review and to validate it by juxtaposing surgical outcomes from Cattinara Hos-in dry eye and tears,which may lead to long-term consequences such as chronic conjunctivitis,discomfort,or photo-phobia.To prevent this issue,scars should be oriented vertically or perpendicularly to the free eyelid margin when the size of the tumor allows.In employing a malar flap to repair a lower eyelid defect,the malar incision must ascend diagonally;this facilitates enhanced flap advancement and mitigates ectropion by restricting vertical traction.Conse-quently,it is imperative to maintain that the generated tension remains consistently horizontal and never vertical[9].Lagophthalmos is a disorder characterized by the inability to completely close the eyelids,leading to corneal exposure and an increased risk of keratitis or ulceration;it may arise following upper eyelid surgery.To avert this issue,it is essential to preserve a minimum of 1 cm of skin between the superior edge of the excision and the inferior boundary of the eyebrow.Epiphora may occur in cancers involving the lacrimal puncta,requiring their removal.As previously stated,when employing a glabellar flap to rectify medial canthal abnormalities,it is essential to prevent a trapdoor effect or thickening of the flap relative to the eyelid skin to which it is affixed.Constraints about our proposed algorithm enco-mpass limited sample sizes and possible publication biases in existing studies.Subsequent investigations ought to examine long-term results to further refine the algorithm.Future research should evaluate the algorithm across varied populations and examine the impact of novel graft materials on enhancing reconstructive outcomes.CONCLUSION Eyelid reconstruction remains one of the most intriguing challenges for a plastic surgeon today.The most fascinating aspect of this discipline is the need to restore the functionality of such an essential structure while maintaining its aesthetics.In our opinion,creating decision-making algorithms can facilitate reaching this goal by allowing for the individualization of the reconstructive path while minimizing the incidence of complications.The fact that we have decreased the incidence of severe complications is a sign that the work is moving in the right direction.The fact that there has been no need for reintervention,neither for reconstructive issues nor for inadequate oncological radicality,overall signifies greater patient satisfaction as they do not have to undergo the stress of new surgeries.Even the minor complic-ations recorded are in line with those reported in the literature,and,even more importantly for patients,they are of limited duration.In our experience,after a year of application,we can say that the objective has been achieved,but much more can still be done.Behind every work,a scientific basis must be continually renewed and refreshed to maintain high-quality standards.Therefore,searching for possible alternative solutions to be included in one’s surgical armamentarium is fundamental to providing the patient with a fully personalized option.展开更多
Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy ...Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy logic,and multi-criteria decision-making(MCDM)in transportation research.A comprehensive literature search was conducted in the Scopus database,utilizing carefully selected AI,fuzzy,and MCDM keywords.Studies were rigorously screened according to explicit inclusion and exclusion criteria,resulting in 73 eligible publications spanning 2006-2025.The review protocol included transparent data extraction on methodological approaches,application domains,and geographic distribution.Key findings highlight the prevalence of hybrid fuzzyAHPand TOPSIS methods,the widespread integration of machine learning for prediction and optimization,and a predominant focus on logistics and infrastructure planning within the transportation sector.Geographic analysis underscores a marked concentration of research activity in Asia,while other regions remain underrepresented,signaling the need for broader international collaboration.The review also addresses persistent challenges such asmethodological complexity,data limitations,and model interpretability.Future research directions are proposed,including the integration of reinforcement learning,real-time analytics,and big data-driven adaptive solutions.This study offers a comprehensive synthesis and critical perspective,serving as a valuable reference for researchers,practitioners,and policymakers seeking to enhance the efficiency,resilience,and sustainability of transportation systems through intelligent decision-making frameworks.展开更多
This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with...This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.展开更多
基金The work was supported by Humanities and Social Sciences Fund of the Ministry of Education(No.22YJA630119)the National Natural Science Foundation of China(No.71971051)Natural Science Foundation of Hebei Province(No.G2021501004).
文摘With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.
基金supported by National Key Research and Development Program of China(2018YFC1504502).
文摘Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.
基金supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and granted financial resources from the Ministry of Trade,Industry,and Energy,Korea(No.20204010600090).
文摘This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state cells.These models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases.Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed decisions.Our goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic support.We propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies effectively.Additionally,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN models.We further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and imbalance.Comprehensive validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’reliability.Moreover,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model interpretability.Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.
文摘Across four studies,we explore the impact of solitude on consumers’reliance on feelings versus reasons in decision making,along with the underlying mechanism and boundary conditions.The results indicate that solitude individuals(vs.non-solitude)would prefer feeling-based strategy in decision-making,resulting in a higher intention of choosing the affectively superior option over the cognitively superior option(Study 1).Self-focus plays the underlying mechanism in the solitude effect(Study 2).Moreover,we also examine two boundary conditions:motivation(Study 3)and temporal orientation(Study 4),which indicates that involuntary motivation and future orientation can mitigate the solitude effect on affective processing.These findings provide insights into consumers’judgments of product attributes and selection of decision-making strategies according to their situations.
基金supported by the National Research Council of Sri Lanka(Grant No.NRC TO 16-07).
文摘Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefits of two calibrations of the Nutrient Expert(NE)tool for rice in Sri Lanka’s Alfisols:the basic calibration(Nutrient Expert Sri Lanka 1,NESL1)and the comprehensive calibration(Nutrient Expert Sri Lanka 2,NESL2).NESL1 was developed by adapting the South Indian version of NE to local conditions,while NESL2 was an updated version,using three years of data from 71 farmer fields.
文摘People have been engaged in sports activities both individually and collectively for years.Sports consumption,which refers to the process that covers many issues related to sports in the form of playing,watching,listening or reading,is a form of human behavior.The satisfaction of the four marketing components of product,price,distribution and promotion by using the leisure time of the sports consumer effectively and ensuring its continuity in the future process can be ensured by effective utilization of facilities and quality recreation activities.Consumer behaviors,which have a very complex structure,are seen in the form of choosing,buying,using and obtaining.With this study,it is aimed to determine the mediating role of consumer decision-making styles in determining the effect of marketing components in the consumption of sports activities on the satisfaction of sports consumers.In this direction,data were collected in the province of Istanbul,which was determined as the sample.Data were obtained with a questionnaire form created on Google Form.These data were analyzed in line with the model and hypotheses created with these data and it was determined that the marketing components of sports consumption have an impact on the sports consumer and it was concluded that consumer decision-making styles have a positive mediating effect in this regard.
文摘BACKGROUND Cancer care faces challenges due to tumor heterogeneity and rapidly evolving therapies,necessitating artificial intelligence(AI)-driven clinical decision support.While general-purpose models like ChatGPT offer adaptability,domain-specific systems(e.g.,DeepSeek)may better align with clinical guidelines.However,their comparative efficacy in oncology remains underexplored.This study hypothesizes that domain-specific AI will outperform general-purpose models in technical accuracy,while the latter will excel in patient-centered communication.AIMS To compare ChatGPT and DeepSeek in oncology decision support for diagnosis,treatment,and patient communication.METHODS A retrospective analysis was conducted using 1200 anonymized oncology cases(2018–2023)from The Cancer Genome Atlas and institutional databases,covering six cancer types.Each case included histopathology,imaging,genomic profiles,and treatment histories.Both models generated diagnostic interpretations,staging assessments,and therapy recommendations.Performance was evaluated against NCCN/ESMO guidelines and expert oncologist panels using F1-scores,Cohen'sκ,Likert-scale ratings,and readability metrics.Statistical significance was assessed via analysis of variance and post-hoc Tukey tests.RESULTS DeepSeek demonstrated superior performance in diagnostic accuracy(F1-score:89.2%vs ChatGPT's 76.5%,P<0.001)and treatment alignment with guidelines(κ=0.82 vs 0.67,P=0.003).ChatGPT exhibited strengths in patient communi-cation,generating layman-friendly explanations(readability score:8.2/10 vs DeepSeek's 6.5/10,P=0.012).Both models showed limitations in rare cancer subtypes(e.g.,cholangiocarcinoma),with accuracy dropping below 60%.Clinicians rated DeepSeek's outputs as more actionable(4.3/5 vs 3.7/5,P=0.021)but highlighted ChatGPT's utility in palliative care discussions.CONCLUSION Domain-specific AI(DeepSeek)excels in technical precision,while general-purpose models(ChatGPT)enhance patient engagement.A hybrid system integrating both approaches may optimize oncology workflows,contingent on expanded training for rare cancers and real-time guideline updates.
文摘This study focuses on the construction and application of intelligent financial decision-making models driven by generative artificial intelligence(AI).It analyzes the mechanisms by which generative AI empowers financial decision-making within a dual framework of dynamic knowledge evolution and risk control.The research reveals that generative AI,with its superior data processing,pattern recognition,and autonomous learning capabilities,can transcend the limitations of traditional decision-making models,facilitating a significant shift from causal inference to probabilistic creation in decision-making paradigms.By systematically constructing an intelligent financial decision-making model that includes data governance,core engine,and decision output layers,the study clarifies the functional roles and collaborative mechanisms of each layer.Additionally,it addresses key challenges in technology application,institutional adaptation,and organizational transformation by proposing systematic strategies for technical risk management,institutional innovation,and organizational capability enhancement,aiming to provide robust theoretical support and practical guidance for the intelligent transformation of corporate financial decision-making.
基金co-supported by the National Natural Science Foundation of China(No.91852115)。
文摘The high maneuverability of modern fighters in close air combat imposes significant cognitive demands on pilots,making rapid,accurate decision-making challenging.While reinforcement learning(RL)has shown promise in this domain,the existing methods often lack strategic depth and generalization in complex,high-dimensional environments.To address these limitations,this paper proposes an optimized self-play method enhanced by advancements in fighter modeling,neural network design,and algorithmic frameworks.This study employs a six-degree-of-freedom(6-DOF)F-16 fighter model based on open-source aerodynamic data,featuring airborne equipment and a realistic visual simulation platform,unlike traditional 3-DOF models.To capture temporal dynamics,Long Short-Term Memory(LSTM)layers are integrated into the neural network,complemented by delayed input stacking.The RL environment incorporates expert strategies,curiositydriven rewards,and curriculum learning to improve adaptability and strategic decision-making.Experimental results demonstrate that the proposed approach achieves a winning rate exceeding90%against classical single-agent methods.Additionally,through enhanced 3D visual platforms,we conducted human-agent confrontation experiments,where the agent attained an average winning rate of over 75%.The agent's maneuver trajectories closely align with human pilot strategies,showcasing its potential in decision-making and pilot training applications.This study highlights the effectiveness of integrating advanced modeling and self-play techniques in developing robust air combat decision-making systems.
基金supported by the Major Projects for Science and Technology Innovation 2030(2018AAA0100805).
文摘To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.
文摘Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.
文摘System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose significant challenges for efficient decision-making,necessitating a modular multiagent control system.Deep Reinforcement Learning(DRL)and Decision Tree(DT)have been utilized for these complex decision-making tasks,but each has its limitations:DRL is highly adaptive but lacks interpretability,while DT is inherently interpretable but has limited adaptability.To overcome these challenges,we propose the Adaptive Interpretable Decision Tree(AIDT),an evolutionary-based algorithm that is both adaptable to diverse environmental settings and highly interpretable in its decision-making processes.We first construct a Markov decision process(MDP)-based simulation environment using the Cooperative Submarine Search task as a representative scenario for training and testing the proposed method.Specifically,we use the heat map as a state variable to address the issue of multi-agent input state proliferation.Next,we introduce the curiosity-guiding intrinsic reward to encourage comprehensive exploration and enhance algorithm performance.Additionally,we incorporate decision tree size as an influence factor in the adaptation process to balance task completion with computational efficiency.To further improve the generalization capability of the decision tree,we apply a normalization method to ensure consistent processing of input states.Finally,we validate the proposed algorithm in different environmental settings,and the results demonstrate both its adaptability and interpretability.
文摘With the growth of the construction industry,risk management in construction projects has garnered significant attention from the academic community.Effective risk management during the decision-making stage can greatly enhance project management efficiency.This paper integrates the AHP-entropy value method and constructs a risk management model based on the DPSIR framework for construction projects.The model is applied to evaluate and analyze the risk level of the decision-making stage in a navigation and electricity hub project in Chongqing Municipality.The results demonstrate the scientific validity and effectiveness of the proposed model.
基金supported by the Princess Nourah bint Abdulrahman University Riyadh,Saudi Arabia,through Project number(PNURSP2025R235).
文摘The Internet of Things(IoT)is a smart infrastructure where devices share captured data with the respective server or edge modules.However,secure and reliable communication is among the challenging tasks in these networks,as shared channels are used to transmit packets.In this paper,a decision tree is integrated with other metrics to form a secure distributed communication strategy for IoT.Initially,every device works collaboratively to form a distributed network.In this model,if a device is deployed outside the coverage area of the nearest server,it communicates indirectly through the neighboring devices.For this purpose,every device collects data from the respective neighboring devices,such as hop count,average packet transmission delay,criticality factor,link reliability,and RSSI value,etc.These parameters are used to find an optimal route from the source to the destination.Secondly,the proposed approach has enabled devices to learn from the environment and adjust the optimal route-finding formula accordingly.Moreover,these devices and server modules must ensure that every packet is transmitted securely,which is possible only if it is encrypted with an encryption algorithm.For this purpose,a decision tree-enabled device-to-server authentication algorithm is presented where every device and server must take part in the offline phase.Simulation results have verified that the proposed distributed communication approach has the potential to ensure the integrity and confidentiality of data during transmission.Moreover,the proposed approach has outperformed the existing approaches in terms of communication cost,processing overhead,end-to-end delay,packet loss ratio,and throughput.Finally,the proposed approach is adoptable in different networking infrastructures.
文摘Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.
文摘Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.
文摘The rapid increase in the number of Internet of Things(IoT)devices,coupled with a rise in sophisticated cyberattacks,demands robust intrusion detection systems.This study presents a holistic,intelligent intrusion detection system.It uses a combined method that integrates machine learning(ML)and deep learning(DL)techniques to improve the protection of contemporary information technology(IT)systems.Unlike traditional signature-based or singlemodel methods,this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification.This combination provides a more nuanced and adaptable defense.The research utilizes the NF-UQ-NIDS-v2 dataset,a recent,comprehensive benchmark for evaluating network intrusion detection systems(NIDS).Our methodological framework employs advanced artificial intelligence techniques.Specifically,we use ensemble learning algorithms(Random Forest,Gradient Boosting,AdaBoost,and XGBoost)for binary classification.Deep learning architectures are also employed to address the complexities of multi-class classification,allowing for fine-grained identification of intrusion types.To mitigate class imbalance,a common problem in multi-class intrusion detection that biases model performance,we use oversampling and data augmentation.These techniques ensure equitable class representation.The results demonstrate the efficacy of the proposed hybrid ML-DL system.It achieves significant improvements in intrusion detection accuracy and reliability.This research contributes substantively to cybersecurity by providing a more robust and adaptable intrusion detection solution.
文摘BACKGROUND Eyelid reconstruction is an intricate process,addressing both aesthetic and functional aspects post-trauma or oncological surgery.Aesthetic concerns and oncological radicality guide personalized approaches.The complex anatomy,involving anterior and posterior lamellae,requires tailored reconstruction for optimal functionality.AIM To formulate an eyelid reconstruction algorithm through an extensive literature review and to validate it by juxtaposing surgical outcomes from Cattinara Hos-in dry eye and tears,which may lead to long-term consequences such as chronic conjunctivitis,discomfort,or photo-phobia.To prevent this issue,scars should be oriented vertically or perpendicularly to the free eyelid margin when the size of the tumor allows.In employing a malar flap to repair a lower eyelid defect,the malar incision must ascend diagonally;this facilitates enhanced flap advancement and mitigates ectropion by restricting vertical traction.Conse-quently,it is imperative to maintain that the generated tension remains consistently horizontal and never vertical[9].Lagophthalmos is a disorder characterized by the inability to completely close the eyelids,leading to corneal exposure and an increased risk of keratitis or ulceration;it may arise following upper eyelid surgery.To avert this issue,it is essential to preserve a minimum of 1 cm of skin between the superior edge of the excision and the inferior boundary of the eyebrow.Epiphora may occur in cancers involving the lacrimal puncta,requiring their removal.As previously stated,when employing a glabellar flap to rectify medial canthal abnormalities,it is essential to prevent a trapdoor effect or thickening of the flap relative to the eyelid skin to which it is affixed.Constraints about our proposed algorithm enco-mpass limited sample sizes and possible publication biases in existing studies.Subsequent investigations ought to examine long-term results to further refine the algorithm.Future research should evaluate the algorithm across varied populations and examine the impact of novel graft materials on enhancing reconstructive outcomes.CONCLUSION Eyelid reconstruction remains one of the most intriguing challenges for a plastic surgeon today.The most fascinating aspect of this discipline is the need to restore the functionality of such an essential structure while maintaining its aesthetics.In our opinion,creating decision-making algorithms can facilitate reaching this goal by allowing for the individualization of the reconstructive path while minimizing the incidence of complications.The fact that we have decreased the incidence of severe complications is a sign that the work is moving in the right direction.The fact that there has been no need for reintervention,neither for reconstructive issues nor for inadequate oncological radicality,overall signifies greater patient satisfaction as they do not have to undergo the stress of new surgeries.Even the minor complic-ations recorded are in line with those reported in the literature,and,even more importantly for patients,they are of limited duration.In our experience,after a year of application,we can say that the objective has been achieved,but much more can still be done.Behind every work,a scientific basis must be continually renewed and refreshed to maintain high-quality standards.Therefore,searching for possible alternative solutions to be included in one’s surgical armamentarium is fundamental to providing the patient with a fully personalized option.
文摘Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy logic,and multi-criteria decision-making(MCDM)in transportation research.A comprehensive literature search was conducted in the Scopus database,utilizing carefully selected AI,fuzzy,and MCDM keywords.Studies were rigorously screened according to explicit inclusion and exclusion criteria,resulting in 73 eligible publications spanning 2006-2025.The review protocol included transparent data extraction on methodological approaches,application domains,and geographic distribution.Key findings highlight the prevalence of hybrid fuzzyAHPand TOPSIS methods,the widespread integration of machine learning for prediction and optimization,and a predominant focus on logistics and infrastructure planning within the transportation sector.Geographic analysis underscores a marked concentration of research activity in Asia,while other regions remain underrepresented,signaling the need for broader international collaboration.The review also addresses persistent challenges such asmethodological complexity,data limitations,and model interpretability.Future research directions are proposed,including the integration of reinforcement learning,real-time analytics,and big data-driven adaptive solutions.This study offers a comprehensive synthesis and critical perspective,serving as a valuable reference for researchers,practitioners,and policymakers seeking to enhance the efficiency,resilience,and sustainability of transportation systems through intelligent decision-making frameworks.
基金supported by the National Natural Science Foundation of China(72101025,72271049),the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities,FRF-IDRY-24-024)the Hebei Natural Science Foundation(F2023501011)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-20-073A1)the R&D Program of Beijing Municipal Education Commission(KM202411232015).
文摘This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.
