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Domain-Specific Large Language Model for Maintenance Decision-Making on Wind Farms by Labeled-Data-Supervised Fine-Tuning 认领 引用 被引量：1
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作者 Dongming Fan Meng Liu +5 位作者 Yi Shao Linchao Yang Yiliu Liu Yue Zhang Yi Ren Zili Wang 《Engineering》 SCIE EI CSCD 2026年第5期343-361,共19页
Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs.However,conventional maintenance decision-making approaches a... Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs.However,conventional maintenance decision-making approaches are time-consuming and have poor flexibility and adaptability to various scenarios.This study addressed these challenges by using a large language model(LLM)to understand,generate,and plan maintenance strategies for wind farms characterized by various failure modes and maintenance costs.A labelled-data-supervised fine-tuning LLM for maintenance,named LLM4M,is proposed.The proposed LLM4M model is trained on an extensive dataset of mathematical programs for maintenance to generate optimal strategies for wind farms.Compared with other large parameter LLMs,the fine-tuned LLM4M model demonstrates remarkable accuracy,with an error of approximately 2%from the optimal strategy.In addition,the generalization of the proposed LLM4M model has achieved remarkable results.If the LLM4M model correctly generates the maintenance strategy,the maintenance cost deviates from the optimal solution by only approximately 5%.Furthermore,phase transition behavior is observed,which provides considerable guidance for the development of domain-specific LLMs for the maintenance domain. 展开更多
关键词 Large language model Maintenance decision-making Wind farms Fine-tuning
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Auditable LLM Autonomy for Operational Decision-Making: Big Data Evidence and Decision Traces 认领 引用
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作者 Leonidas Theodorakopoulos Alexandra Theodoropoulou 《Computers, Materials & Continua》 SCIE EI 2026年第8期336-381,共46页
Auditable autonomy is becoming a practical requirement for deploying large language model(LLM)agents in operational workflows where recommendations can trigger consequential actions.Many autonomy claims remain hard to... Auditable autonomy is becoming a practical requirement for deploying large language model(LLM)agents in operational workflows where recommendations can trigger consequential actions.Many autonomy claims remain hard to evaluate because studies emphasize task completion or fluent explanations while underreporting tool privileges,verification conditions,rollback feasibility,and trace completeness.This review develops a decision-making–centered framework that treats autonomy as an auditable engineering property.It introduces a three-plane big data foundation:an evidence plane with provenance and freshness constraints;a decision-trace plane that records retrieval identifiers,tool invocations,intermediate checks,and policy evaluations;and an outcomes plane that captures post-decision effects such as KPI shifts,rollback incidence,recurrence,and operator overrides.On this basis,we present an assurance stack taxonomy covering grounding controls,verification and precondition checks,action gating for risk bounding,and accountability mechanisms.We then propose an evaluation ladder that moves beyond text metrics toward evidence fidelity,action validity,and longitudinal stability,with reporting requirements that improve cross-domain comparability.Finally,we synthesize accidental failures and adversarial threats,including staleness,drift,prompt injection,partial execution,and trace tampering,and map trace-based detection signals and mitigations to the assurance stack. 展开更多
关键词 LLM agents big data analytics artificial intelligence autonomous agents decision support systems auditability automation data governance
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Multimodal artificial intelligence integrates imaging,endoscopic,and omics data for intelligent decision-making in individualized gastrointestinal tumor treatment 认领 引用
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作者 Hui Nian Yi-Bin Wu +5 位作者 Yu Bai Zhi-Long Zhang Xiao-Huang Tu Qi-Zhi Liu De-Hua Zhou Qian-Cheng Du 《Artificial Intelligence in Gastroenterology》 2026年第1期1-19,共19页
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ... Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies. 展开更多
关键词 Multimodal artificial intelligence Gastrointestinal tumors Individualized therapy Intelligent diagnosis Treatment optimization Prognostic prediction Data fusion Deep learning Precision medicine
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Dynamic decision-making of UAV swarm based on constrained multi-objective optimization under incomplete interference information 认领 引用 被引量：1
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作者 Kuixian LI Jinjie LIU +5 位作者 Xin GU Yandie YANG Cheng CHANG Haipeng CHEN Liangtian WAN Yun LIN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第7期64-77,共14页
The decision-making and resource allocation of UAV swarms play a crucial role in dynamic,uncertain environments.In such complex scenarios,UAV swarms need to effectively collaborate and communicate in frequently changi... The decision-making and resource allocation of UAV swarms play a crucial role in dynamic,uncertain environments.In such complex scenarios,UAV swarms need to effectively collaborate and communicate in frequently changing interference conditions.However,existing resource allocation methods typically assume complete interference information or are suitable only for static environments,leading to significant performance degradation in the face of external uncertainties and incomplete information.To address these challenges,this paper employs fuzzy set theory to dynamically model the uncertainty of external interference and defuzzify its impact on the available frequency bands during iterative diagnostics.Additionally,a dynamic constrained multi-objective optimization model is developed,and a novel Dynamic Constrained MultiObjective Evolutionary Algorithm based on Transfer Search(TrS-DCMOEA)is proposed.By integrating transfer learning and dynamic adjustment strategies,the algorithm quickly adapts to environmental changes,ensuring communication performance while maintaining the security of UAV swarm communications.Simulation results show that the proposed algorithm achieves superior decision-making and resource allocation efficiency in most time slots,with TrS-DCMOEA particularly excelling in tracking the Pareto front in dynamic environments. 展开更多
关键词 UAV swarm Resource allocation Dynamic constraints Incomplete information Dynamic decision-making Multi-objective optimization
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A self-adaptive intelligent decision-making method with multi-objective optimization for shield tunnel construction risk control 认领 引用
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作者 Tao TIAN Mengyan SONG +4 位作者 Fanchao KONG Xin HE Wei YIN Dechun LU Xiuli DU 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第2期279-296,共18页
Tunnel engineering faces significant challenges due to the complexity and variability of geological conditions.In such contexts,the timely and appropriate adjustment of shield construction parameters(SCP)to match curr... Tunnel engineering faces significant challenges due to the complexity and variability of geological conditions.In such contexts,the timely and appropriate adjustment of shield construction parameters(SCP)to match current geological conditions is crucial for ensuring safe and efficient construction.This paper proposes a self-adaptive intelligent decision-making method for risk control in shield tunnel construction,which integrates a multilayer perceptron(MLP)with a multi-objective optimization(MOO)algorithm.Specifically,an MLP combined with particle swarm optimization(PSO)is employed to predict the optimal SCP.These predicted parameters,along with stratum mechanical properties and tunnel depth,serve as inputs for forecasting two critical performance indicators:maximum surface settlement(MSS)and driving speed(DS).The predictive model,coupled with the MOO algorithm,is then utilized to enable dynamic feedback control of the SCP during construction.To demonstrate the applicability of the proposed method,a case study of Qingdao Metro Line 6 is presented.The results indicated that based on the proposed PSO-MLP-non-dominated sorting genetic algorithm II(NSGA-II),both MSS and DS are effectively improved.The adaptive decision-making between SCP and geological conditions can be realized. 展开更多
关键词 shield tunnel construction intelligent decision-making MOO performance parameters
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ZhongdaChat-ED:a medical large language model for personalized erectile dysfunction health consultation and professional clinical decision-making using retrieval-augmented generation 认领 引用
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作者 Yi Xia Yong-Kun Zhu +6 位作者 Chun-Hui Liu Xiang Hong Rui-Xin Zhang Nie-Ke Zhang Ming Chen Guang-Yuan Zhang Chao Sun 《Asian Journal of Andrology》 SCIE CAS CSCD 2026年第1期71-79,共9页
Artificial intelligence(AI)-driven large language models(LLMs)hold potential for medical applications but face challenges,such as inaccurate or outdated training data.In this study,ZhongdaChat-ED,a personalized medica... Artificial intelligence(AI)-driven large language models(LLMs)hold potential for medical applications but face challenges,such as inaccurate or outdated training data.In this study,ZhongdaChat-ED,a personalized medical LLM integrating retrieval-augmented generation(RAG)technology,was developed to enhance erectile dysfunction(ED)counseling and clinical decision-making.The model was built using the open-source Deepseek-r1:32b framework,augmented with two specialized databases:a patient health consultation database and a clinical decision support database updated with real-time medical advancements.Two versions of ZhongdaChat-ED were developed:a Consumer Version for patient-facing health consultations and a Professional Version for clinician support.Performance was evaluated against four commonly used LLMs(ChatGPT4,Copilot,Claude,and Gemini)through simulated clinical consultations and case analyses.Three urologists and three patients assessed responses across various dimensions,including accuracy,human caring,ease of understanding,clinical significance,and informational frontier.The Consumer Version outperformed commonly used LLMs in accuracy(4.77/5),human caring(4.86/5),and ease of understanding(4.88/5)with all P85.2%case score rate)and informational frontier scores(4.52/5)than those of other models(P<0.001).ZhongdaChat-ED effectively addresses limitations of conventional LLMs by leveraging RAG to integrate real-time,domain-specific data.ZhongdaChat-ED shows promise in enhancing patient health consultation and clinician decision-making for ED,underscoring the value of tailored AI systems in bridging gaps between generalized AI and specialized medical needs.Future work should expand multimodal capabilities and cross-disciplinary integration to broaden clinical utility. 展开更多
关键词 artificial intelligence clinical decision-making erectile dysfunction large language models medical consultation ZhongdaChat-ED
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Multi-Ship Collision Avoidance Decision-Making Considering Dynamic Risk and Interaction Strategies 认领 引用
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作者 ZHAO Cong-cong YIP Tsz Leung +2 位作者 LU Yu-xu LI Yi-teng WU Bing 《China Ocean Engineering》 SCIE EI CSCD 2026年第1期106-119,共14页
Maritime collision accidents occur frequently and result in severe consequences,constituting approximately 60%of maritime incidents.Human error accounts for 75%−96%of these collisions,emphasizing the necessity for int... Maritime collision accidents occur frequently and result in severe consequences,constituting approximately 60%of maritime incidents.Human error accounts for 75%−96%of these collisions,emphasizing the necessity for intelligent decision-making systems.This study proposes a fuzzy inference system for multi-ship collision avoidance decisionmaking based on dynamic collision risk assessment incorporating multiple ship interactions.The proposed method enhances collision avoidance through two primary innovations:a finite state mechanism for dynamic multi-ship interaction analysis and game-theoretic collision-avoidance strategies that integrate real-time behavioural responses with Convention on the International Regulations for Preventing Collisions at Sea(COLREGs)compliance.The IFTHEN rule incorporates action strategies,ship behaviour,and navigation rules,with collision avoidance decisions derived through the fuzzy inference system.This framework uniquely addresses collision risk evaluation by integrating dynamic risk assessment and ship interaction strategies,enabling proactive adjustments that maintain safety margins while adhering to maritime regulations.Simulation experiments assess multi-ship collision-avoidance maneuvers under various interaction scenarios,demonstrating the model’s ability to execute timely adjustments and effectively mitigate collision risks among multiple target vessels,thereby ensuring minimal exposure during encounters. 展开更多
关键词 fuzzy logic collision risk assessment ship interactive action analysis decision-making
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Hybrid Pythagorean Fuzzy Decision-Making Framework for Sustainable Urban Planning under Uncertainty 认领 引用
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作者 Sana Shahab Vladimir Simic +2 位作者 Ashit Kumar Dutta Mohd Anjum Dragan Pamucar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期892-925,共34页
Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effect... Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses. 展开更多
关键词 Sustainable urban planning criterion importance assessment two-step normalization environmental impact decision-making
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Decision-making performance of large language models vs.human physicians in challenging lung cancer cases:A real-world case-based study 认领 引用
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作者 Ning Yang Kailai Li +19 位作者 Baiyang Liu Xiting Chen Aimin Jiang Chang Qi Wenyi Gan Lingxuan Zhu Weiming Mou Dongqiang Zeng Mingjia Xiao Guangdi Chu Shengkun Peng Hank ZHWong Lin Zhang Hengguo Zhang Xinpei Deng Quan Cheng Bufu Tang Anqi Lin Juan Zhou Peng Luo 《Intelligent Oncology》 CAS 2026年第1期15-24,共10页
Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a fr... Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a framework for evaluating LLMs and physician decisions in challenging lung cancer cases.Methods:We curated 50 challenging lung cancer cases(25 local and 25 published)classified as complex,rare,or refractory.Blinded three-dimensional,five-point Likert evaluations(1–5 for comprehensiveness,specificity,and readability)compared standalone LLMs(DeepSeek R1,Claude 3.5,Gemini 1.5,and GPT-4o),physicians by experience level(junior,intermediate,and senior),and AI-assisted juniors;intergroup differences and augmentation effects were analyzed statistically.Results:Of 50 challenging cases(18 complex,17 rare,and 15 refractory)rated by three experts,DeepSeek R1 achieved scores of 3.95±0.33,3.71±0.53,and 4.26±0.18 for comprehensiveness,specificity,and readability,respectively,positioning it between intermediate(3.68,3.68,3.75)and senior(4.50,4.64,4.53)physicians.GPT-4o and Claude 3.5 reached intermediate physician–level comprehensiveness(3.76±0.39,3.60±0.39)but junior-to-intermediate physician–level specificity(3.39±0.39,3.39±0.49).All LLMs scored higher on rare cases than intermediate physicians but fell below junior physicians in refractory-case specificity.AIassisted junior physicians showed marked gains in rare cases,with comprehensiveness rising from 2.32 to 4.29(84.8%),specificity from 2.24 to 4.26(90.8%),and readability from 2.76 to 4.59(66.0%),while specificity declined by 3.2%(3.17 to 3.07)in refractory cases.Error analysis showed complementary strengths,with physicians demonstrating reasoning stability and LLMs excelling in knowledge updating and risk management.Conclusions:LLMs performed variably in clinical decision-making tasks depending on case type,performing better in rare cases and worse in refractory cases requiring longitudinal reasoning.Complementary strengths between LLMs and physicians support case-and task-tailored human–AI collaboration. 展开更多
关键词 Large language models Clinical evaluation Decision-making Lung cancer
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An Integrated Approach to Condition-Based Maintenance Decision-Making of Planetary Gearboxes: Combining Temporal Convolutional Network Auto Encoders with Wiener Process 认领 引用 被引量：1
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作者 Bo Zhu Enzhi Dong +3 位作者 Zhonghua Cheng Xianbiao Zhan Kexin Jiang Rongcai Wang 《Computers, Materials & Continua》 SCIE EI 2026年第1期661-686,共26页
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s... With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes. 展开更多
关键词 Temporal convolutional network autoencoder full lifecycle degradation experiment nonlinear Wiener process condition-based maintenance decision-making fault monitoring
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Within-visual-range air combat maneuver decision-making in obstructed environments via a curriculum self-play soft actor-critic with an attention mechanism 认领 引用
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作者 Longjie Zheng Xin Li +6 位作者 Xichao Su Bai Li Lei Wang Junlin Zhou Haijun Peng Wei Tian Xinwei Wang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第3期122-137,共16页
With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,exist... With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,existing methods often suffer from rigid reward functions and limited adaptability to evolving adversarial strategies.Moreover,most research assumes open airspace,overlooking the influence of potential obstacles.In this paper,we address one-on-one within-visual-range ACMD in obstructed environments,and propose an improved Soft Actor-Critic(SAC)algorithm trained under a curriculum self-play framework.A maneuver strategy mirroring inference module is integrated to estimate each other's likely positions when visual obstruction occurs.By leveraging curriculum learning to guide progressive experience accumulation and self-play for adversarial evolution,our method enhances both training efficiency and tactical diversity.We further integrate an attention mechanism that dynamically adjusts the weights of sub-rewards,enabling the learned policy to adapt to rapidly changing air combat situations.Numerical simulations demonstrate that our enhanced SAC converges more quickly and achieves higher win rates than other baseline methods.An animation is available at bilibili.com/video/BV1BHVszHE98 for better illustration. 展开更多
关键词 Air combat maneuver decision-making Soft actor-critic Curriculum self-play training Attention mechanism Obstructed environment
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Assessment of the value of complication risk prediction models following interventional therapy for hepatocellular carcinoma in nursing decision-making 认领 引用
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作者 Hui Wang Hao-Tian Guo +3 位作者 Jing Shen Fei Zhang Yue-Jiao Jiang Zhen-Xing Liu 《World Journal of Gastroenterology》 SCIE CAS 2026年第24期44-53,共10页
BACKGROUND Transcatheter arterial chemoembolization(TACE)is a primary interventional modality for intermediate-to-advanced hepatocellular carcinoma(HCC).However,the high incidence of post-procedural complications sign... BACKGROUND Transcatheter arterial chemoembolization(TACE)is a primary interventional modality for intermediate-to-advanced hepatocellular carcinoma(HCC).However,the high incidence of post-procedural complications significantly compromises patient recovery and prognosis.Currently,clinical nursing assessment lacks a multifaceted risk prediction tool that integrates multiple risk factors.AIM To establish a predictive model for postoperative complications in patients with HCC undergoing TACE.METHODS A retrospective analysis was conducted on 386 patients with HCC who underwent interventional therapy at our hospital from January 2023 to December 2024.Patients were divided into a complication group(n=104)and a control group(n=282)based on postoperative complication occurrence.General clinical data and nursing-related indicators were collected and compared between groups.Multivariate logistic regression analysis was used to identify independent risk factors,construct a risk prediction model,and validate its discriminatory power and calibration by using receiver operating characteristic curve and Hosmer-Lemeshow test.RESULTS The incidence of complications following interventional therapy for HCC in this study was 26.94%.Factors associated with complications included age≥60 years,liver cirrhosis,vascular invasion,procedure duration≥2 hours,TACE sessions>2,Child-Pugh grade B,tumor diameter≥5 cm,nutritional risk(Nutritional Risk Screening 2002≥3),anxiety(Self-Rating Anxiety Scale≥50),depression(Self-Rating Depression Scale≥53),caregiver burden(high Zarit Burden Interview Score),functional independence(low Exercise of Self-Care Agency Scale Score),quality of life(low Quality of Life Instruments for Cancer Patients-General Module Score),and low compliance with early postoperative activity and exercise were all independent risk factors(P<0.05).The predictive model demonstrated a C-index of 0.773,an area under the curve of 0.936,sensitivity of 93.25%,specificity of 84.96%,and good calibration(Hosmer-Lemeshow test,P=0.382).CONCLUSION The TACE postoperative complication prediction model derived during the research combines multidimensional clinical and nursing predictors,which proves a high predictive quality and clinical usefulness.It provides healthcare professionals with a scientifically grounded assessment tool to facilitate risk stratification management and precision nursing. 展开更多
关键词 Hepatocellular carcinoma Interventional therapy Transcatheter arterial chemoembolization Complication risk Risk prediction model Nursing decision-making
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Research on the evaluation of dynamic decision-making effectiveness of UAV’s air combat 认领 引用
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作者 DING Shulin WANG Yuhui ZHANG Haodi 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期534-547,共14页
The evaluation of air combat decision-making has garnered significant attention due to its potential to effectively mitigate losses resulting from erroneous decisions.However,existing research primarily focuses on sta... The evaluation of air combat decision-making has garnered significant attention due to its potential to effectively mitigate losses resulting from erroneous decisions.However,existing research primarily focuses on static evaluation methods.Therefore,this paper proposes a dynamic multi-round decision evaluation method based on the characteristics of multi-round unmanned aerial vehicle air combat under opponent’s optimal strategy.In order to determine objective weights,an improved multi-attribute decision making method is proposed,which incorporates the proximity as a correction coefficient for evaluation indicators,utilizing the cosine similarity instead of Euclidean distance,and incorporating both actual and theoretical objective weights to prevent data mutations.Subsequently,the game theory is employed to reasonably adjust subjective and objective weights to obtain comprehensive weights.To address the issues related to the ambiguity and randomness during the evaluation process,a reverse cloud generator is utilized to determine the center of gravity of the cloud model using comprehensive weights while employing the weighted deviation degree for evaluating air combat decision-making effectiveness.By activating the cloud generator through the cloud model,the optimal strategies for each round of air combat are determined,thereby completing the dynamic evaluations for multi-round sequential decision-making processes.Finally,the feasibility and effectiveness of the proposed method are verified through simulations. 展开更多
关键词 dynamic decision-making effectiveness evaluation improved multi-attribute decision making game theory cloud model
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Data-Driven Component-Level Decision-Making for Online Remanufacturing of Gas-Insulated Switchgear 认领 引用
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作者 Hansam Cho Seokho Moon +2 位作者 Sunhyeok Hwang Seoung Bum Kim Younghoon Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第11期1941-1967,共27页
Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment.However,existing approaches face three significant limitations:(1)reliance on predefined ma... Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment.However,existing approaches face three significant limitations:(1)reliance on predefined mathematical models that often fail to capture equipment-specific degradation,(2)offline optimization methods that assume access to future data,and(3)the absence of component-level guidance.To address these challenges,we propose a data-driven framework for component-level decision-making.The framework leverages streaming sensor data to predict the remaining useful life(RUL)without relying on mathematical models,employs an online optimization algorithm suitable for practical settings,and,through remanufacturing simulations,provides guidance on which components should be replaced.In a case study on gas-insulated switchgear,the proposed framework achieved RUL prediction performance comparable to an oracle model in an online setting without relying on predefined mathematical models.Furthermore,by employing online optimization,it determined a remanufacturing timing close to the global optimum using only past and current data.In addition,unlike previous studies,the framework enables component-level decision-making,allowing for more detailed and actionable remanufacturing guidance in practical applications. 展开更多
关键词 Remanufacturing online decision-making gas-insulated switchgear online learning
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A Scoping Review of Shared Decision-Making and Patient Decision Aids for Patients with Peripheral Arterial Disease 认领 引用
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作者 Jiabei Lu Yuexian Tao +3 位作者 Qiuying Lou Yating Liu Yeqin Xu Siying Qiu 《Journal of Clinical and Nursing Research》 2026年第7期210-222,共13页
Objective:To review the current evidence on shared decision-making(SDM)and patient decision aids(PDAs)for patients with peripheral arterial disease(PAD),focusing on application characteristics,intervention effects,and... Objective:To review the current evidence on shared decision-making(SDM)and patient decision aids(PDAs)for patients with peripheral arterial disease(PAD),focusing on application characteristics,intervention effects,and implementation factors to inform clinical decision support models.Methods:A scoping review was conducted following the Joanna Briggs Institute(JBI)framework and PRISMA-ScR guidelines.A systematic search was performed in eight Chinese and English databases,including PubMed,Embase,the Cochrane Library,CINAHL,CNKI,Wanfang,VIP,and the China Biomedical Literature Database,up to 2025.Eligible studies included randomized controlled trials,crosssectional studies,qualitative studies,and mixed-methods studies.Data were extracted and descriptively synthesized.Results:Fifteen studies published between 2011 and 2025 were included,mainly from Europe,North America,and Asia.Studies explored SDM preferences,the development and application of PDAs,decision support tools,SDM training,multimedia decision-making interventions,and patients’experiences.SDM and PDAs improved patient knowledge,participation,decision quality,and satisfaction,while reducing decisional conflict and negative emotions.Implementation was affected by time constraints,information complexity,provider–patient communication,and healthcare system factors.Conclusion:SDM and PDA research in PAD is increasing;however,tool standardization and clinical integration remain limited.Future research should develop patient-centered decision-support tools and promote nurse-led SDM models to improve long-term PAD management and patient decision-making experiences. 展开更多
关键词 Peripheral arterial disease Shared decision-making Patient decision-making aids Scoping review Evidence-based nursing
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Ultrasound-guided thyroid nodule biopsy in esophageal cancer patients with thyroid nodules for surgical decision-making clinical value 认领 引用
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作者 Han Han Wei Geng +1 位作者 Fang Zheng Juan-Juan Huang 《World Journal of Gastrointestinal Surgery》 SCIE 2026年第5期147-155,共9页
BACKGROUND Esophageal cancer is a highly malignant digestive tract tumor with severe consequences,and radical surgery remains its primary treatment modality.Precise preoperative assessment is crucial for identifying s... BACKGROUND Esophageal cancer is a highly malignant digestive tract tumor with severe consequences,and radical surgery remains its primary treatment modality.Precise preoperative assessment is crucial for identifying suitable candidates for surgery.Some esophageal cancer patients present with concomitant thyroid masses,whose nature directly determines treatment strategy selection.Current conventional imaging methods offer limited specificity for the differential diagnosis of thyroid masses.Ultrasound-guided fine-needle aspiration biopsy(US-FNAB),as a minimally invasive diagnostic technique,can provide cytological evidence;however,its decision-guidance value in this specific patient population remains unclear.AIM To investigate the clinical use of US-FNAB in the esophagectomy decision-making process for patients with thyroid lesions and esophageal cancer.METHODS This retrospective cohort study included 120 patients with thyroid nodules and esophageal cancer treated between May 2023 and May 2025.They were divided into surgical(n=85)and non-surgical(n=35)groups based on esophagectomy status.Clinical data,ultrasound features,fine-needle aspiration biopsy,and pathology results were compared.Binary logistic regression identified independent factors influencing surgical decisions.RESULTS No statistically significant differences were found between the two groups in baseline characteristics including age,sex,body mass index,and comorbidities(P>0.05).However,the non-surgical group had a significantly higher proportion of stage IV esophageal cancer(51.43%vs surgical group,P<0.05)and thyroid nodules with malignant ultrasound features(e.g.,solid composition,hypoechoicity;P<0.05).Suspected or confirmed thyroid malignancy was also more frequent in the non-surgical group(48.57%vs 7.06%,P<0.05).Multivariate analysis identified USFNAB results and esophageal cancer stage as factors influencing non-operative management.Using pathology as the gold standard,US-FNAB showed 90.00%sensitivity,95.00%specificity,and 94.20%accuracy for diagnosing thyroid malignancy(P<0.05).CONCLUSION US-FNAB guides treatment decisions in esophageal cancer patients with thyroid lesions,avoiding unnecessary surgery for metastases and adjusting plans for combination therapy. 展开更多
关键词 Esophageal cancer Thyroid nodules Surgical decision-making Ultrasound-guided thyroid nodule biopsy Diagnostic value
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Predictive Hegemony and the Interpretive Gap:On the Philosophical Premises and Ethical Boundaries of AI-Assisted Clinical Decision-Making 认领 引用
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作者 Yubo Wang 《Journal of Electronic Research and Application》 2026年第1期124-129,共6页
When debating the application boundaries of artificial intelligence(AI)predictive models in clinical medicine,it is clear that high predictive accuracy is desirable,but on its own,does not provide a sufficient conditi... When debating the application boundaries of artificial intelligence(AI)predictive models in clinical medicine,it is clear that high predictive accuracy is desirable,but on its own,does not provide a sufficient condition for clinical application.Drawing on three example,AlphaFold’s prediction of protein structure,radiomics’prediction of disease diagnosis and prognosis,and clinical risk scoring models’prediction of morbidity,and engaging with David Hume’s empiricist skepticism towards causality,argue that interpretability is an indispensable condition in a discipline that values mechanistic explanation.In order for AI to evolve from a capable recommender to a decision-making machine that begins to develop a sense of individual self,several preconditions need to be fulfilled.Predictions must be falsifiable,minimally grounded in mechanistic knowledge,accompanied by partially explicable decision logics,designed to be fair to populations,and embedded in an error-tolerant architecture that enables correction and rollback.The utility of AI today lies in its ability to dramatically reduce the costs of human trial and error,but should not diminish the doctor’s right to make,learn from,and reflect on mistakes as the final accountable link in the chain. 展开更多
关键词 AI interpretability Clinical decision-making Hume Machine learning Predictive models
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Employing Interval Grey Relational Analysis in ESG Investment Decision-Making Based on Ratings 认领 引用
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作者 Zhaoyi Zheng 《Proceedings of Business and Economic Studies》 2026年第6期137-143,共7页
ESG ratings have gradually become an important reference basis in investment decisions.Currently,different rating agencies often give significant differences in ESG scores to the same enterprise based on their own ass... ESG ratings have gradually become an important reference basis in investment decisions.Currently,different rating agencies often give significant differences in ESG scores to the same enterprise based on their own assessment criteria,data sources,and weight settings.The inconsistent scores caused by such multi-source heterogeneous data increase the cognitive uncertainty and decision-making complexity of investors when utilizing ESG information,affecting the accuracy and reliability of investment judgments.In this paper,by introducing the Interval Number Grey Relational Analysis(IGRA)method,an enterprise investment ranking model based on multi-source ESG scores is constructed.The scores from different rating agencies are integrated into the form of interval numbers,effectively reflecting the fluctuation range of the scores.And with the help of the grey system theory,the similarity degree between enterprises and ideal reference objects is measured.Realize the comprehensive processing and scientific ranking of multi-dimensional uncertain information.An empirical analysis was conducted based on the ESG rating data to verify the effectiveness of the method.This research provides methods for ESG investment practices and also offers theoretical references for dealing with uncertain investment issues. 展开更多
关键词 Interval number Grey relational analysis ESG rating Uncertainty decision-making
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Application Research of Collaborative Decision-Making Nursing Intervention Based on Information Asymmetry Theory in Postoperative Limb Functional Rehabilitation of Patients with Lower Limb Fractures 认领 引用
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作者 Jianglin Hu 《Journal of Clinical and Nursing Research》 2026年第3期160-166,共7页
Lower limb fractures are a prevalent clinical orthopedic condition,primarily caused by factors such as trauma and osteoporosis.Surgical treatment serves as the main intervention method;however,the postoperative rehabi... Lower limb fractures are a prevalent clinical orthopedic condition,primarily caused by factors such as trauma and osteoporosis.Surgical treatment serves as the main intervention method;however,the postoperative rehabilitation of limb function is characterized by a lengthy and challenging process.Patients often experience suboptimal rehabilitation outcomes due to a lack of rehabilitation knowledge and insufficient compliance.The information asymmetry theory elucidates the information imbalance between doctors and patients resulting from disparities in professional knowledge.Collaborative decision-making nursing intervention effectively alleviates information asymmetry by establishing a mechanism for information sharing between doctors and patients,thereby fully mobilizing patients’initiative in rehabilitation.This paper synthesizes relevant domestic and international research in recent years to summarize the application methods and implementation effects of collaborative decision-making nursing intervention based on information asymmetry theory in the postoperative limb functional rehabilitation of patients with lower limb fractures.It aims to provide theoretical references and practical evidence for optimizing clinical rehabilitation nursing models and enhancing rehabilitation quality following lower limb fracture surgery. 展开更多
关键词 Information asymmetry theory Collaborative decision-making Lower limb fractures Postoperative rehabilitation Limb function
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Analysis of the Efficacy of Quantitative Parameters from Prenatal Magnetic Resonance Imaging in Intrauterine Intervention Decision-making for Fetal Congenital Developmental Disorders 认领 引用
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作者 WAN Honge MENG Lingqian +2 位作者 CAO Xue XIN Hui WANG Lei 《外文科技期刊数据库（文摘版）医药卫生》 2026年第7期097-101,共5页
Objective: To explore the clinical application value of prenatal magnetic resonance scanning quantitative indicators in assisting in the determination of whether fetal congenital abnormalities require intrauterine sur... Objective: To explore the clinical application value of prenatal magnetic resonance scanning quantitative indicators in assisting in the determination of whether fetal congenital abnormalities require intrauterine surgery or drainage treatment. Methods: Eighty singleton pregnant women who had prenatal ultrasound suspected of fetal structural abnormalities and agreed to undergo magnetic resonance examination were selected and divided into the intrauterine intervention group (32 cases) and the follow-up observation group (48 cases). All fetal magnetic resonance images were quantitatively measured, and the differences in measurement results between the two groups were compared. The receiver operating characteristic curve was used to evaluate the discrimination ability of each parameter for treatment decisions. Results: The lateral ventricle size, brain white matter T2 value, lung-liver signal ratio, and renal cortex-medulla T2 difference of the fetuses in the intrauterine intervention group were significantly higher than those in the observation group (all P values < 0.01). The area under the curve for the lateral ventricle width in predicting the need for ventricular shunt was 0.92, with a cutoff value of 17.8 mm;the area under the curve for the lung-liver signal ratio in predicting thoracic drainage was 0.88, with the optimal cutoff value of 2.45;the area under the curve for the renal cortex-medulla T2 difference in predicting bladder drainage was 0.85, with the critical point of 125 milliseconds. The predictive model constructed by combining multiple quantitative indicators had an accuracy of 89.7%, significantly superior to the judgment method relying on a single indicator. Conclusion: The quantitative measurement indicators of prenatal magnetic resonance provide objective and repeatable criteria for the selection of intrauterine treatment strategies for severe fetal congenital malformations. and it improves the accuracy of clinical decision-making. 展开更多
关键词 prenatal magnetic resonance imaging quantitative parameters fetal congenital malformations intrauterine interventions decision-making value
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