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Development of a smart device Android-based decision support system for controlling non-point source nitrogen and phosphorus pollution in an agricultural catchment 认领 引用 被引量：1
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作者 Meihui Wang Wenqian Jiang +5 位作者 Yuxi Fu Yi Wang Xinliang Liu Jianlin Shen Feng Liu Yong Li 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第2期565-576,共12页
Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strateg... Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strategies often result in unsatisfactory outcomes and massive engineering costs when managing diffusive pollution in agricultural catchments.To address this issue,this paper proposes a robust,handy,catchment N&P decision support system(CNPDSS),an Android-based smartphone system integrated with a web-based geographic information system(GIS).The CNPDSS aims to provide artificial intelligence-driven decisions that minimize N&P loadings and engineering costs for mitigating pollution in agricultural catchments.It consists of four components:a general user interface(GUI),GIS,N&P pollution modeling(NPPM),and a DSS.The CNPDSS simplifies the GUI and integrates GIS modules to create a user-friendly interface,enabling non-professional users to operate the system easily through intuitive actions.The NPPM uses straightforward empirical models to predict N&P loadings,enhancing efficiency by avoiding excessive parameters.Taking into account the N&P movement pathway in the catchment,the DSS incorporates three control measures:source reduction in farmland(before migration stage),process retention by ecological ditch(midway transport stage),and down-end purification by constructed wetland(waterbody discharge stage),to formulate a comprehensive ternary controlling strategy.To optimize the cost-effectiveness of any proposed N&P control strategies for sub-catchments,a differential evolution algorithm(DEA)is employed in CNPDSS to carry out a dual-objective decision-making optimization computation.In this study,the CNPDSS is applied to a case study in an agricultural catchment in Central China to develop the most cost-effective ternary N&P control strategies that ensure the catchment water quality within Criterion Ⅲ of the Chinese Surface Water Quality Standard GB3838-2002 is met(total N concentration≤1.0 mg L-1and total P concentration≤0.2 mg L-1).Our results demonstrate that the CNPDSS is feasible and also possesses an adaptive design and flexible architecture to enable its generalization and extension to support strong hands-on applications in other catchments. 展开更多
关键词 decision support system non-point source N&P pollution a ternary controlling strategy dual-objective optimization agricultural catchment
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A review on rockburst prediction and prevention to shape an ontology-based framework for better decision-making for underground excavations 认领 引用 被引量：1
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作者 Hongchuan Yan Haijiang Li +4 位作者 Hongqiang Xie Mingli Xiao Jianliang Pei Huaizhong Liu Jiangda He 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第2期331-349,共19页
With underground engineering projects becoming deeper and more complex,the associated safety problems,especially rockburst,have increasingly increased.Despite decades of research,effective management of rockburst cont... With underground engineering projects becoming deeper and more complex,the associated safety problems,especially rockburst,have increasingly increased.Despite decades of research,effective management of rockburst continues to be a formidable challenge in underground excavations.This study presents a scientometric visualization analysis of 2449 papers and conducts a comprehensive review of 336 key studies to explore the state-of-the-art developments in rockburst research.With a primary focus on the prediction and prevention of rockburst,this review identifies existing research gaps and proposes a novel framework aimed at addressing these challenges in underground excavations.The results underscore a critical disconnect between advanced prediction methods and engineering practices,which limits the ability of engineers to carry out reliable assessments of rockburst potential.This disconnection prevents the prompt development of targeted prevention strategies,further aggravated by inadequate data sharing across large-scale projects.The review also describes the limitations of relying solely on data-driven methodologies to address the complex challenges in the lifecycle management of underground excavations.To overcome these challenges,this study proposes an innovative framework based on an ontological knowledge base.This framework is designed to integrate multisource data and diverse analysis techniques,exploring the means toward better decision-making in future digital underground projects. 展开更多
关键词 decision support system ontology rockburst scientometric analysis underground engineering
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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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Dynamic three-way decision algorithm integrating intuitionistic fuzzy information for system-of-systems in airborne laser weapon combat 认领 引用
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作者 Xiaochen LI Yuxue GE +3 位作者 Luguang CAO Zibing DU Aiming SHI Yang PEI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期221-237,共17页
Dynamic threat assessment and decision-making are crucial in airborne laser weapon combat,particularly in highly interactive System-of-Systems(So S)-oriented scenarios.This paper proposes a novel So S-oriented Dynamic... Dynamic threat assessment and decision-making are crucial in airborne laser weapon combat,particularly in highly interactive System-of-Systems(So S)-oriented scenarios.This paper proposes a novel So S-oriented Dynamic Three-Way Decision(So S-DTWD)algorithm developed through hierarchical agent-based modeling for airborne laser weapons against incoming missiles in sea combat.The algorithm aggregates dynamic intuitionistic fuzzy threat assessment with the VIKOR method(Multi-criteria Optimization and Compromise Solution)to derive conditional probabilities.A time-based loss function matrix and dynamic decision-making rules are constructed by integrating time series with three-way decision theory.Experimental results demonstrate that the So S-DTWD algorithm effectively transforms two-way ranking results into dynamic three-way classification,significantly improving combat effectiveness.Specifically,it increases aircraft survival rates by 12.05%-28.99%,enhances missile interception by 17.54%-44.39%,and improves laser weapon kill rates by 3%-5%.Moreover,the findings indicate that improving weapon and aircraft performance and enhancing cooperative tactics can contribute to higher survivability.The optimal results are achieved under low Radar Cross Section(RCS)and appropriate formation distance.This prototype system can evolve into a future real-time decision-making tool for cooperative laser weapon combat. 展开更多
关键词 Dynamic three-way decisions Fuzzy set theory Laser weapon combat System-of-systems(SoS) VIKOR
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Cooperative perception,decision,and control of intelligent group systems 认领 引用
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作者 Fang DENG Zhengtao DING Wei DONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第7期1-2,共2页
Intelligent Group Systems(IGSs),including Unmanned Aerial Vehicles(UAVs),Unmanned Ground Vehicles(UGVs),and space-based platforms,have fundamentally transformed modern aerospace engineering.These multi-agent systems a... Intelligent Group Systems(IGSs),including Unmanned Aerial Vehicles(UAVs),Unmanned Ground Vehicles(UGVs),and space-based platforms,have fundamentally transformed modern aerospace engineering.These multi-agent systems are highly valued for their operational flexibility and distributed robustness.Traditional single-agent design paradigms are no longer sufficient for such systems,especially in dynamic,highly adversarial,or resource-constrained environments. 展开更多
关键词 Control Unmanned Ground Vehicles Space Based Platforms intelligent group systems igss including unmanned aerial vehicles uavs unmanned ground vehicles ugvs Cooperative Perception Unmanned Aerial Vehicles Decision
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Mesolimbic Dopaminergic Encoding of Decision Value:Linking Phenotype-Specific Signals to Strategic Adaptation 认领 引用 被引量：1
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作者 Zhengyi Xu Dadao An +2 位作者 Jingjia Liang Lingyan Zheng Zhong Chen 《Neuroscience Bulletin》 SCIE CAS CSCD 2026年第4期937-940,共4页
Numerous neuropsychiatric disorders are characterized by significant impairments in decision-making function.These include impulsive decision-making in attention-deficit hyperactivity disorder(ADHD)[1],excessive risk-... Numerous neuropsychiatric disorders are characterized by significant impairments in decision-making function.These include impulsive decision-making in attention-deficit hyperactivity disorder(ADHD)[1],excessive risk-taking during manic episodes in bipolar disorder,and the distorted prioritization observed in substance use disorders.Decisionmaking involves reflecting on the outcomes of past actions and weighing the potential consequences of future actions.In this complex balancing process,mesolimbic dopamine influences reward value assessment,the strength of motivation,and the initiation of action[2]. 展开更多
关键词 phenotype specific signals bipolar disorderand strategic adaptation decision making reflecting outcomes past actions mesolimbic dopaminergic encoding distorted prioritization balancing processmesolimbic dopamine
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Decision regret after robot-assisted radical prostatectomy:A systematic review and meta-analysis 认领 引用 被引量：1
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作者 Mattia Longoni Paolo Zaurito +12 位作者 Pietro Scilipoti Leonardo Quarta Francesco Barletta Simone Scuderi Armando Stabile Luigi Nocera Pawel Rajwa Fabio Zattoni Giancarlo Marra Pierre IKarakiewicz Francesco Montorsi Alberto Briganti Giorgio Gandaglia 《Asian Journal of Urology》 CAS CSCD 2026年第1期1-10,共10页
Objective:Robot-assisted radical prostatectomy(RARP)is the most commonly performed surgical treatment for prostate cancer.However,decision regret(DR)represents a concern for both patients undergoing the procedure and ... Objective:Robot-assisted radical prostatectomy(RARP)is the most commonly performed surgical treatment for prostate cancer.However,decision regret(DR)represents a concern for both patients undergoing the procedure and clinicians involved in therapeutic management.To address this need,we performed a systematic review exploring DR severity and its associations after RARP.Methods:A comprehensive search in scientific literature databases(PubMed,Embase,Scopus,and Web of Science)identified studies on DR in RARP-treated patients.All studies objectively evaluating DR were included.Within studies using the validated 5-item DR scale(range 0-100),the pooled estimate was calculated using fixedand random-effects models accounting for different follow-ups.A qualitative synthesis analyzed the impact of multiple baseline,perioperative,and postoperative factors on DR.Results:We retrieved 493 articles using our search strategy,with 15 meeting inclusion criteria.A total of 3480 prostate cancer patients with objective DR assessment after RARP were identified.The median follow-up ranged from 4.8 months to 6.3 years while response rates varied between 45% and 100%.Among the included studies,10 used the Decision Regret Scale,with a pooled mean estimate of 15.22(95%confidence interval 11.52-18.93)under the random-effects model.In the remaining five studies,DR was generally low(65%-75%)and even absent in some(12%-49%).Functional outcomes,such as continence and potency,were the most frequently reported factors significantly associated with DR.However,variability in assessing DR and other outcomes limits the ability to draw definitive conclusions.Conclusion:Most patients report low DR after RARP.Functional outcomes correlate with DR,but the heterogeneity in assessments and reporting methods warrants the need for more standardized evaluation. 展开更多
关键词 Prostate cancer Robot-assisted radical prostatectomy Decision regret Surgical regret Oncological outcomes Functional outcomes
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Multi-Agent Reinforcement Learning Based Context-Aware Heterogeneous Decision Support System 认领 引用 被引量：1
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作者 Taimoor Hassan Ibrar Hussain +4 位作者 Hafiz Mahfooz Ul Haque Hamid Turab Mirza Muhammad Nadeem Ali Byung-Seo Kim Changheun Oh 《Computers, Materials & Continua》 SCIE EI 2026年第6期1764-1780,共17页
The expeditious proliferation of the smart computing paradigm has a remarkable upsurge towards Artificial Intelligence(AI)assistive reasoning with the incorporation of context-awareness.Context-awareness plays a signi... The expeditious proliferation of the smart computing paradigm has a remarkable upsurge towards Artificial Intelligence(AI)assistive reasoning with the incorporation of context-awareness.Context-awareness plays a significant role in fulfilling users’needs whenever and wherever needed.Context-aware systems acquire contextual information from sensors/embedded sensors using smart gadgets and/or systems,perform reasoning using reinforcement learning(RL)or other reasoning techniques,and then adapt behavior.The core intention of using an RL-based reasoning strategy is to train agents to take the right actions at the right time and in the right place.Generally,agents are rewarded for the correct actions and punished for incorrect actions.In an RL deployment setting,agents intend to get cumulative maximal rewards through the continuous learning process.These systems often operate in a highly decentralized environment and exhibit complex adaptive behavior.However,the agent’s actions on the imperfect nature of context may cause inconsistent reasoning behavior in terms of the agent’s reward policies.In this paper,we present a semantic knowledge-based Multi-agent Reinforcement Learning(MARL)formalism for a context-aware heterogeneous decision support system.This is a four-layered architecture to schedule user’s routine tasks where user’s data is acquired with limited or no human intervention and perform operations autonomously based on agent’s reward/punishment policies.For this,we develop a comprehensive case study considering three different domains’ontologies;namely,Smart Home,Smart Shopping,and Smart Fridge Systems,with the prototypal implementation of the system and show the valid execution dynamics,correctness behavior,and verify the agent’s optimal reward policies. 展开更多
关键词 Multi-agent reinforcement learning Markov decision process heterogeneous systems ontologies reinforcement learning context-awareness
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Validation of the Traditional Chinese version of Mothers on Respect Index(MORi)and Mother’s Autonomy in Decision Making(MADM)Scale 认领 引用
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作者 Junyan Li Nitya Nagesh +2 位作者 Heidi SL。Fan Daniel YT.Fong Kris Yuet Wan Lok 《International Journal of Nursing Sciences》 CSCD 2026年第4期435-441,I0003,共7页
Objective:This study aimed to translate and culturally adapt the Mothers on Respect Index(MORi)and the Mothers’Autonomy in Decision Making(MADM)scale into Chinese,and to assess their reliability and validity among po... Objective:This study aimed to translate and culturally adapt the Mothers on Respect Index(MORi)and the Mothers’Autonomy in Decision Making(MADM)scale into Chinese,and to assess their reliability and validity among postpartum women in Hong Kong,China.Methods:The MORi and MADM scales were translated into Chinese following a rigorous forward-backward translation procedure.Between December 2023 and February 2024,Chinesespeaking women who had given birth in Hong Kong within the past five years were recruited through social media platforms.The instruments’validity was examined through content validity,construct validity,convergent validity,discriminant validity,and known-groups validity.Reliability was assessed using internal consistency.Results:A total of 1,395 mothers participated.The Mothers on Respect Index-Revised(MORi-R)and MADM showed perfect content validity(S-CVI=1.00).Confirmatory factor analysis identified a revised three-factor model MORi-R with satisfactory fit indices(CFI=0.953;TLI=0.94;SRMR=0.07;RMSEA=0.08).The MORi-R consisted of 13 items after Item 4 was removed.The MADM scale,comprising seven items within a single factor,remained unchanged and demonstrated good model fit(CFI=0.995;TLI=0.99;SRMR=0.01;RMSEA=0.05).The Cronbach's α was 0.86 for the MORi-R and 0.91 for the MADM scale.The MORi-R and MADM scores were moderate and showed a positive correlation(r=0.57).Conclusions:The MORi-R and MADM scales were used to assess perceived disrespectful care experiences and decision-making autonomy among postpartum women in Hong Kong,China.Both instruments demonstrated desirable psychometric properties,with satisfactory reliability and validity,and may serve as valuable tools to inform clinical nursing practice and improve the quality of maternity care. 展开更多
关键词 Autonomy China Decision making Mothers Psychometrics Respect
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Effects of wind and temperature on the migration decisions of a short-distance migratory shorebird across the annual cycle 认领 引用
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作者 Xinjie Zhao Qingquan Bai +5 位作者 Shoudong Zhang David S.Melville Ningning Liu Yihao Fang Donglai Li Zhijun Ma 《Avian Research》 SCIE CAS CSCD 2026年第2期396-404,共9页
Elucidating the spatial-temporal characteristics and influencing factors of migration activities is the basis for understanding the life cycle of migratory birds and for making conservation measures.Short-distance mig... Elucidating the spatial-temporal characteristics and influencing factors of migration activities is the basis for understanding the life cycle of migratory birds and for making conservation measures.Short-distance migratory birds experience less time pressure during migration;they tend to migrate under suitable environmental conditions and thus the migration decision might be strongly influenced by external environmental conditions.To test this hypothesis,we deployed GPS tags on Far Eastern Oystercatchers(Haematopus ostralegus osculans)at the Yalu Jiang estuary in the northern Yellow Sea and tracked their annual migration.The tagged oystercatchers,including five adults and seven immatures(secondhird-year birds),mainly wintered along the west coast of the Yellow Sea and bred inland in northeastern China.The migration distance between breeding and wintering sites was 1640.7±260.4 km in spring and 1668.7±306.1 km in autumn.Compared with adults,immatures departed from the wintering sites and arrived at the breeding sites later,while there were no significant differences between adults and immatures in the dates of departure from breeding sites and arrival at wintering sites.Migration initiation generally occurred around sunset.Wind conditions consistently provided tailwinds on departure days from wintering,breeding,and stopover sites during both seasons,with wind support exceeding the 5-or 10-day pre-departure average.During autumn migration,the temperature on departure days at stopover sites was lower than the mean temperature over the preceding 5 or 10 days.The Yalu Jiang estuary was the main stopover site for the tagged birds during both spring and autumn migrations.The stopover duration in autumn(adults:118.3±8.4 days;immatures:130.4±5.1 days)was the longest among the studied shorebirds,likely due to moulting of flight feathers at the stopover site.Our results indicate that the migratory decisions of oystercatchers were strongly influenced by wind conditions during the whole annual cycle,whereas they were also affected by temperature when departing from stopover sites in autumn.The extended stopover at the Yalu Jiang estuary in autumn highlights its importance for the species. 展开更多
关键词 Far Eastern Oystercatcher GPS tracking Migration decision Stopover Temperature Wind support
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A methodology for constructing the system-of-systems environment to evaluate UAV decision systems 认领 引用
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作者 Zhiqi Liu Mingqiang Luo +4 位作者 Yulu Ma Chenguang Xing Ruo Wang Daheng Chen Xiaolu Wang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第2期337-351,共15页
Evaluating Unmanned Aerial Vehicle(UAV)systems within a System-of-Systems(SoS)environment helps clarify their contribution to the overall combat capability and supports effectiveness-oriented system optimization.When ... Evaluating Unmanned Aerial Vehicle(UAV)systems within a System-of-Systems(SoS)environment helps clarify their contribution to the overall combat capability and supports effectiveness-oriented system optimization.When assessing decision systems in such an environment,cross-level modeling and simulation are required,which often face a trade-off between low modeling cost and high simulation accuracy,while the credibility of results remains challenging to ensure.To address these issues,this study proposes a hybrid-granularity Hardware-In-the-Loop(HIL)SoS environment construction method based on Graphical Evaluation and Review Technique(GERT).The method employs GERT to analyze the relationships between simulation systems,the System Under Test(SUT),and mission outcomes,thereby determining the required model precision for different systems.A dynamic resource allocation algorithm is applied to adjust model granularity on demand,ensuring high-fidelity simulation under constrained total cost.Additionally,GERT estimates the computational frequency and communication bandwidth requirements of the SUT,guiding hardware selection to enhance simulation credibility.A UAV maritime combat case study was conducted for validation.The results demonstrate that,compared to the flat modeling approach,the hybrid-granularity scenario based on GERT analysis achieves higher simulation accuracy with lower overall model complexity.The coefficient of variation in evaluation results significantly decreases in HIL simulations compared to virtual simulations,confirming improved credibility.Under the hybrid-granularity HIL scenario,the decision system was evaluated from an effectiveness perspective,identifying the most sensitive performance parameter.Subsequent targeted optimization led to an 11.90%improvement in effectiveness,validating the method's practical utility. 展开更多
关键词 System-of-systems Unmanned aerial vehicle Decision system Hardware-in-the-loop Hybrid-granularity simulation
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Optimising Wave Energy Plant Location Through Neutrosophic Multi-Criteria Group Decision-Making 认领 引用
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作者 Hafiz Muhammad Athar Farid Ayesha Razzaq +2 位作者 Muhammad Riaz Tapan Senapati Sarbast Moslem 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第1期167-189,共23页
The global shift towards sustainable energy has intensified research into renewable sources,particularly wave energy.Pakistan,with its long coastline,holds significant potential for wave energy development.However,ide... The global shift towards sustainable energy has intensified research into renewable sources,particularly wave energy.Pakistan,with its long coastline,holds significant potential for wave energy development.However,identifying optimal locations for wave energy plants involves evaluating complex,multi-faceted criteria.This study employs a multi-criteria group decisionmaking(MCGDM)approach using single-valued neutrosophic numbers(SVNNs)to address both qualitative and quantitative uncertainties inherent in real-world scenarios.To enhance decision quality,we introduce two novel operators:the singlevalued neutrosophic prioritised averaging(SVNPAd)operator and the single-valued neutrosophic prioritised geometric(SVNPGd)operator,both incorporating priority degrees.These tools allow decision-makers to express preferences better and handle ambiguous data.The proposed model is validated through comparative analysis with prior studies and demonstrates improved robustness in site selection.Furthermore,we analyse how variations in priority degrees influence decision outcomes,enabling a more dynamic and tailored decision-making process.Our method contributes a more holistic and adaptive framework for selecting locations for wave energy projects,ultimately supporting informed investments in renewable energy infrastructure and improving energy access in underserved coastal regions. 展开更多
关键词 aggregation operators decision making fuzzy set priority degrees sustainable energy
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Large Language Models for analyzing project stakeholders’environmental opinions to support decision‑making 认领 引用
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作者 Xinyi XU Yuxuan MU +1 位作者 Jin XUE Petr MATOUS 《ENGINEERING Management》 CSCD 2026年第1期105-123,共19页
In the global context of sustainable development,stakeholder concerns about the environmental impacts of infrastructure projects have become increasingly prominent,which can significantly influence the progress of pro... In the global context of sustainable development,stakeholder concerns about the environmental impacts of infrastructure projects have become increasingly prominent,which can significantly influence the progress of projects.However,integrating changing environmental opinions into project decision‑making remains a challenge due to the complexity,highly dynamic nature and volume of data.Large Language Models(LLMs)have emerged as transformative tools for efficiently and rapidly analyzing this type of data,offering new opportunities for enhancing decision‑making processes.This research proposes a framework utilizing LLM for three major approaches in opinion analysis among stakeholders:sentiment analysis,stance analysis,and topic modeling.The framework has been applied to the case of the Scarborough Gas Project in Western Australia.A set of smaller models,including Neural Networks(NNs),Support Vector Machines(SVMs),Random Forest,Logistic Regression,and BERT,were fine‑tuned using GPT‑3.5 as a base and compared for performance in sentiment and stance analysis,with SVM achieving the highest accuracy rates of 83.90%and 87.55%,respectively.Integrating LLMs into topic modeling also significantly enhanced the interpretation of stakeholder environmental opinions by transforming keyword lists generated by traditional LDA methods into coherent narratives,reducing reliance on human interpretation,refining themes,and enabling a more comprehensive understanding of environmental,political,and legal issues.This study presents the first unified framework that integrates LLM embeddings with external classifiers to simultaneously analyze all three analytical tasks,to our knowledge.Central to the framework is the theoretically grounded Sentiment‑Stance‑Topic Matrix and Decision‑Making Map,which systematically translate unstructured stakeholder input into prioritized engagement actions.By categorizing sentiment,stance,and topic configurations into targeted strategies,the framework offers structured,data‑driven guidance for project decision‑makers.This approach bridges gaps in traditional stakeholder analysis and provides a transferable decision‑support tool,enabling more inclusive,responsive project governance aligned with global sustainable development goals. 展开更多
关键词 online stakeholder engagement large language models decision‑making sentiment analysis topic modeling environmental discourse
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Energy-Efficient Data Dissemination Approach Using Multiple-Criteria Decision Modeling for Internet of Things Environments 认领 引用
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作者 Ambreen Memon Aaron Bere +1 位作者 Muhammad Nadeem Ali Byung-Seo Kim 《Computers, Materials & Continua》 SCIE EI 2026年第8期459-473,共15页
The modern internet infrastructure has enabled numerous applications by providing a seamless connectivity experience across each mode of connectivity.Infrastructure-based connectivity and device-to-device(D2D)are well... The modern internet infrastructure has enabled numerous applications by providing a seamless connectivity experience across each mode of connectivity.Infrastructure-based connectivity and device-to-device(D2D)are well-known connectivity modes for internet-based applications.The selection of the underlying communication medium significantly affects energy consumption during data transfer.This study proposes an Energy-Efficient Data Dissemination Approach(EEDDA)that integrates encounter prediction with a multi-criteria decision-making(MCDM)framework to reduce infrastructure-based energy consumption in IoT mobility environments.Unlike traditional optimization approaches that focus on single-objective routing or static network models,the proposed framework dynamically selects between Device-to-Device(D2D)and Internet-based transmission based on delay tolerance,encounter probability,data size,and energy consumption metrics.Real mobility traces from the publicly available University of Southern California(USC)dataset were used for validation.Simulation results demonstrate that under high delay tolerance scenarios,the proposed approach achieves up to 70%–80%reduction in energy consumption compared to conventional Internet-based transmission while maintaining Quality of Service(QoS). 展开更多
关键词 Internet of things device-to-device communication energy efficiency multi-criteria decision modeling delay tolerant network
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Clinical decision and prescription generation for diarrhea in traditional Chinese medicine based on large language model 认领 引用
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作者 Jiaze Wu Hao Liang +2 位作者 Haoran Dai Hongliang Rui Baoli Liu 《Digital Chinese Medicine》 CAS CSCD 2026年第1期13-30,共18页
Objective To develop a clinical decision and prescription generation system(CDPGS)specifically for diarrhea in traditional Chinese medicine(TCM),utilizing a specialized large language model(LLM),Qwen-TCM-Dia,to standa... Objective To develop a clinical decision and prescription generation system(CDPGS)specifically for diarrhea in traditional Chinese medicine(TCM),utilizing a specialized large language model(LLM),Qwen-TCM-Dia,to standardize diagnostic processes and prescription generation.Methods Two primary datasets were constructed:an evaluation benchmark and a fine-tuning dataset consisting of fundamental diarrhea knowledge,medical records,and chain-ofthought(CoT)reasoning datasets.After an initial evaluation of 16 open-source LLMs across inference time,accuracy,and output quality,Qwen2.5 was selected as the base model due to its superior overall performance.We then employed a two-stage low-rank adaptation(LoRA)fine-tuning strategy,integrating continued pre-training on domain-specific knowledge with instruction fine-tuning using CoT-enriched medical records.This approach was designed to embed the clinical logic(symptoms→pathogenesis→therapeutic principles→prescriptions)into the model’s reasoning capabilities.The resulting fine-tuned model,specialized for TCM diarrhea,was designated as Qwen-TCM-Dia.Model performance was evaluated for disease diagnosis and syndrome type differentiation using accuracy,precision,recall,and F1-score.Furthermore,the quality of the generated prescriptions was compared with that of established open-source TCM LLMs.Results Qwen-TCM-Dia achieved peak performance compared to both the base Qwen2.5 model and five other open-source TCM LLMs.It achieved 97.05%accuracy and 91.48%F1-score in disease diagnosis,and 74.54%accuracy and 74.21%F1-score in syndrome type differentiation.Compared with existing open-source TCM LLMs(BianCang,HuangDi,LingDan,TCMLLM-PR,and ZhongJing),Qwen-TCM-Dia exhibited higher fidelity in reconstructing the“symptoms→pathogenesis→therapeutic principles→prescriptions”logic chain.It provided complete prescriptions,whereas other models often omitted dosages or generated mismatched prescriptions.Conclusion By integrating continued pre-training,CoT reasoning,and a two-stage fine-tuning strategy,this study establishes a CDPGS for diarrhea in TCM.The results demonstrate the synergistic effect of strengthening domain representation through pre-training and activating logical reasoning via CoT.This research not only provides critical technical support for the standardized diagnosis and treatment of diarrhea but also offers a scalable paradigm for the digital inheritance of expert TCM experience and the intelligent transformation of TCM. 展开更多
关键词 Diarrhea Traditional Chinese medicine Large language model Clinical decision and prescription generation Natural language processing
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Spatio-Temporal Earthquake Analysis via Data Warehousing for Big Data-Driven Decision Systems 认领 引用
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作者 Georgia Garani George Pramantiotis Francisco Javier Moreno Arboleda 《Computers, Materials & Continua》 SCIE EI 2026年第3期1963-1988,共26页
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei... Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management. 展开更多
关键词 Data warehouse data analysis big data decision systems seismology data visualization
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Adaptive Pareto-Based Multi-Agent Decision Model for Resource Management in Cloud Business Intelligence Systems 认领 引用
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作者 Khamza Eshankulov Bahodir Muminov +4 位作者 Robiya Farmonova Dilnavoz Sodikova Bakhriddin Bozorov Zavqiddin Temirov Rashid Nasimov 《Computers, Materials & Continua》 SCIE EI 2026年第9期2146-2166,共21页
Cloud-based Business Intelligence(BI)systems operate under highly dynamic analytical workloads,including bursty OLAP queries,concurrent aggregations,and real-time microservice interactions,where static resource alloca... Cloud-based Business Intelligence(BI)systems operate under highly dynamic analytical workloads,including bursty OLAP queries,concurrent aggregations,and real-time microservice interactions,where static resource allocation leads to latency spikes and inefficient resource utilization.This paper proposes a decentralized adaptive Pareto-based multi-agent decision model for real-time resource coordination in cloud BI microservice environments.The agent placement problem is formulated as a multi-criteria decision process that minimizes service response latency,improves computational resource utilization,and preserves Quality-of-Service(QoS)stability.Instead of constructing a centralized global optimization policy,the proposed framework relies on decentralized locally Pareto-efficient decisions combined with adaptive priority regulation driven by QoS deviation.The approach is evaluated through large-scale controlled simulation and validated in a Kubernetes-based pilot cloud environment.Experimental results demonstrate up to 54%latency reduction compared to static allocation and 22%improvement over GA-based optimization,with enhanced CPU utilization balance under dynamic workloads.Statistical analysis confirms the significance of improvements(p<0.05).The proposed model ensures bounded monotonic decision transitions without centralized orchestration or predictive training,making it suitable for real-time cloud-native BI service ecosystems. 展开更多
关键词 Cloud resource allocation pareto decision making decentralized coordination business intelligence microservices adaptive multi-agent systems
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Peer review for funding decisions 认领 引用
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作者 Yuxian Liu Sisi Li Ronald Rousseau 《Journal of Data and Information Science》 CSCD 2026年第1期1-22,共22页
Purpose:Since peer review for funding decisions is crucial to the scientific system,we direct the reader towards new ideas related to research funding and the associated peer review process.Design/methodology/approach... Purpose:Since peer review for funding decisions is crucial to the scientific system,we direct the reader towards new ideas related to research funding and the associated peer review process.Design/methodology/approach:We describe the overall structure of the funding review system and explore the expectations of its various key stakeholders.An examination of testing across the review processes of different funding agencies revealed several issues in the current system.We then summarize the efforts to explore potential solutions.Before concluding,we also discuss recent initiatives,including partial lottery mechanisms,distributed peer review,and methods for identifying originality in proposals by examining areas of non-consensus among reviewers and applicants.Findings:It is difficult to test whether the funding peer review system functions as expected.Moreover,when the peer-review process was replicated across different review groups,the inter-rater problem,where two or more well-intentioned reviewers reached divergent conclusions,was found to be widespread in funding evaluations.At its core,this issue stems from substantive disagreements among reviewers,which can introduce bias into the process.As a result,organizing a peer-review system that is fair,valid,and reliable for funding decisions is particularly challenging.The contemporary organization of the funding review system does not guarantee that it can fulfill its purpose.Consequently,scientists are looking to substantiate funding decisions with more scientific evidence.Some new initiatives have been proposed,which are either more interactive with a strictly organized procedure or are more random(or stochastic),leading to less bias.Research limitations:For practical reasons,we were not able to discuss all,or at least the main,funders in the world.Practical implications:Considering the various steps in peer review procedures for funding decisions may inspire the readers to suggest improvements to the existing system,resulting in reduced bias and greater equality among scientists.Originality/value:Our work contributes to understanding peer review in funding contexts and to exploring possible reforms aimed at improving the existing system. 展开更多
关键词 Peer review procedure Funding decisions Testing the review process Inter-rater problem Non-consensus Reliability Validity Fairness Random selection
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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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Multi-Agent Large Language Model-Based Decision Tree Analysis for Explainable Electric Vehicle Drive Motor Fault Diagnosis 认领 引用
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作者 Jaeseung Lee Jehyeok Rew 《Computers, Materials & Continua》 SCIE EI 2026年第6期2437-2489,共53页
The accelerating transition toward electrified mobility has positioned electric vehicles(EVs)as a primary technology in modern transportation systems.In this context,ensuring the reliability of EV drive motors(EVDMs)b... The accelerating transition toward electrified mobility has positioned electric vehicles(EVs)as a primary technology in modern transportation systems.In this context,ensuring the reliability of EV drive motors(EVDMs)becomes increasingly critical,given their central role in propulsion performance and operational safety.Accurate and interpretable fault diagnosis of EVDMs is therefore essential for enabling effective maintenance and supporting the broader sustainability and resilience of EVs.This study presents a novel framework that combines decision tree-based fault classification with a multi-agent large language model(LLM)interpretation architecture to deliver transparent and human-readable diagnostic explanations.The proposed framework integrates domain-specific decision rules derived from sensor measurements and utilizes specialized LLM agents to translate tree-based decision logic into coherent narratives.The multi-agent architecture decomposes complex diagnostic reasoning into modular subtasks,allowing for enhanced interpretability and facilitating practical understanding for vehicle engineers.Experimental results on a publicly available EVDM dataset demonstrate that the proposed framework maintains high classification accuracy while significantly improving explanation quality and trustworthiness relative to conventional rule-based and single-agent approaches.By coupling symbolic decision models with LLM-driven reasoning,this work contributes to the advancement of trustworthy artificial intelligence for energy and mobility systems,particularly in predictive maintenance and explainable fault diagnosis.The findings highlight the value of integrating classical machine learning with multi-agent LLMs to support reliable,transparent,and human-centered EV infrastructures. 展开更多
关键词 Electric vehicle drive motor large language model decision tree explainable artificial intelligence AI agent fault diagnosis
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