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Operational challenges and mitigation strategies in preparing malaria quality control panels 认领 引用
1
作者 Naseem Ahmed Supriya Sharma +2 位作者 Piyush Choudhary Anupkumar Anvikar Praveen Kumar Bharti 《Asian Pacific Journal of Tropical Medicine》 SCIE CAS 2026年第7期285-287,共3页
Over the past decades,rapid diagnostic tests(RDTs)have become the most widely deployed diagnostic tool,enabling timely treatment in resource-limited and remote settings[1].Their reliability,however,depends on rigorous... Over the past decades,rapid diagnostic tests(RDTs)have become the most widely deployed diagnostic tool,enabling timely treatment in resource-limited and remote settings[1].Their reliability,however,depends on rigorous quality assurance frameworks,with the World Health Organization(WHO)-endorsed quality control panels serving as the cornerstone for monitoring RDT performance and ensuring diagnostic fidelity across diverse epidemiological landscapes[2].Quality control panels are standardized,parasite-based reference materials used to evaluate antigen detection by RDTs under controlled conditions.They play an essential role in detecting lot-to-lot variations,guiding procurement decisions,and safeguarding programmatic confidence in RDTs[3].Despite this centrality,the practical and operational realities of quality control panel preparation in malaria-endemic regions remain underexplored. 展开更多
关键词 diagnostic tests rdts mitigation strategies malaria quality control panels quality assurance frameworkswith quality control panels control panels operational challenges timely treatment
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A Novel Binary Classification Neural Network Optimized by the Mosquito Mating Swarm Optimization Algorithm for Predicting Microgrid Operational Modes 认领 引用
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作者 Jesús Aguila-León Carlos Vargas-Salgado +1 位作者 Dácil Díaz-Bello Fabián Lara-Vargas 《Energy Engineering》 EI 2026年第7期150-172,共23页
Integrating renewable energy sources presents technical challenges due to their variable nature,particularly in predicting and managing microgrid operational modes.Accurate identification of grid statesinterconnected ... Integrating renewable energy sources presents technical challenges due to their variable nature,particularly in predicting and managing microgrid operational modes.Accurate identification of grid statesinterconnected or islanded—is essential for maintaining stability and optimizing performance under fluctuating environmental conditions to meet energy demand.This work proposes a bio-inspired,optimized binary classification model based on Multi-Layer Perceptron Artificial Neural Networks(MLP-ANN),with the architecture and hyperparameters tuned using the novel Mosquito Mating Swarm Optimization(MMSO)algorithm,inspired by mosquito mating behavior and swarm dynamics.The model employs an MLP-ANN with a variable number of hidden layers and neurons per layer,configured to maximize classification accuracy by dynamically adjusting parameters,including the learning rate and regularization coefficients.Training utilizes k-fold cross-validation on experimental microgrid data.The MMSO approach is benchmarked against Particle Swarm Optimization(PSO),Genetic Algorithm(GA),and Grey Wolf Optimizer(GWO)to validate its effectiveness.Results show that the MMSO-optimized MLP-ANN achieved an 86.34%recall,98.96%precision,and 92.29%accuracy,while minimizing the Mean Squared Error to 0.0206.The MMSO-optimized MLP-ANN model achieved competitive classification performance compared to the other algorithms evaluated;although no statistically significant differences in recall were observed among the optimizers(p=0.22),the MMSO achieved the lowest MSE(0.0206).The MMSO was the only algorithm capable of discovering a four-layer architecture hidden within the same search space,evidencing superior exploration of deeper architectural regions of the solution space.These findings demonstrate the model's capacity to predict microgrid operational modes under variable conditions,highlighting the potential of integrating bio-inspired algorithms with neural networks for energy management systems.This approach could enhance the efficiency and reliability of integrating renewable energy sources into dynamic energy systems. 展开更多
关键词 Microgrids binary classification deep learning artificial neural network mosquito mating swarm optimizer operational mode prediction
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Webinar“OpRa:a new transmodel-based exchange format for operational raw data” 认领 引用
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《China Standardization》 2026年第3期58-58,共1页
This one-hour webinar is a presentation of the new CEN Technical Specification for the exchange format of operational raw data in support of the“observed data”category of the MMTIS EU delegated regulation.
关键词 transmodel OPRA mmtis eu delegated regulation operational raw data exchange format operational raw data cen technical specification webinar exchange format
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Operational Characteristics,Challenges,and Countermeasures for Gas-fired Power Plants in Jiangsu Province 认领 引用
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作者 Wang Peng Chen Shuangying +2 位作者 Hao Yingpeng Chi Lixun Lin Jian 《China Oil & Gas》 CAS 2026年第2期59-65,共7页
Gas-fired power plants in Jiangsu Province are characterized by large installed capacity,concentrated geographic distribution,and prominent peak-shaving functions,making them a critical source of flexible support for ... Gas-fired power plants in Jiangsu Province are characterized by large installed capacity,concentrated geographic distribution,and prominent peak-shaving functions,making them a critical source of flexible support for the regional power system.Currently,these plants face significant operational pressure due to a combination of factors,including high volatility in power generation output,elevated gas prices and operating costs,inadequate price transmission between gas and electricity markets,and an underdeveloped electricity pricing mechanism.This paper analyzes the operational characteristics and practical challenges of gas-fired power plants in Jiangsu Province,and proposes countermeasures in terms of policy optimization,mechanism innovation,and upstream-downstream coordination,with a view to supporting power security and advancing the low-carbon energy transition. 展开更多
关键词 Jiangsu Province gas-fired power plants operational characteristics operational challenges development countermeasures
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Design of interface structure to enhance the operational temperature range of CuO/Cu-SSZ-13 for concurrent NOxselective catalytic reduction and CO oxidation 认领 引用
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作者 Zheguan Lin Shuyi He +3 位作者 Tiesen Li Qingyan Cui Wenfu Yan Yuanyuan Yue 《Chinese Journal of Catalysis》 SCIE EI CAS CSCD 2026年第6期286-297,共12页
The non-selective oxidation of NH3at CO oxidation sites is a major limitation for bifunctional catalysts used in NH3-selective catalytic reduction and CO oxidation.This issue restricts these catalysts from achie... The non-selective oxidation of NH3at CO oxidation sites is a major limitation for bifunctional catalysts used in NH3-selective catalytic reduction and CO oxidation.This issue restricts these catalysts from achieving a wide operational temperature window,where both NOxand CO conversions exceed 90%,thus hindering their industrial application.Herein,we propose a novel strategy to expand the temperature window of bifunctional catalysts.By exploiting the synergistic effects of interfacial electron regulation and spatial decoupling of acid sites,we demonstrate that the CuO/Cu-SSZ-13 catalyst achieves an unprecedented operational window(200–425℃),surpassing previously reported results.Our investigation reveals a new mechanism of bifunctional synergy,driven by Cu–O bond reconstruction at the interface and the preferential anchoring of Brönsted acid sites on NH3.This mechanism mitigates the non-selective oxidation of NH3,thereby extending the catalyst’s temperature window.This work provides a new design paradigm for bifunctional catalysts,facilitating broader operational temperature windows and advancing the field. 展开更多
关键词 NOxselective catalytic reduction CO oxidation Bifunctional catalyst Synergistic effect Wide operational window
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Deep reinforcement learning-based adaptive collision avoidance method for UAV in joint operational airspace 认领 引用
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作者 Yan Shen Xuejun Zhang +1 位作者 Yan Li Weidong Zhang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第2期142-159,共18页
As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,t... As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios. 展开更多
关键词 Unmanned aerial vehicle Collision avoidance Deep reinforcement learning Joint operational airspace Hierarchical prioritized experience replay
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Female-female Amplexus and Extremely Female-skewed Operational Sex Ratio in the Toad Duttaphrynus cyphosus During Breeding Seasons 认领 引用
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作者 Hongzhou CHEN Jinyu XIA +2 位作者 Tuo LI Buddhi DAYANANDA Wei CHEN 《Asian Herpetological Research》 SCIE CAS CSCD 2026年第1期45-46,共2页
Dear Editor,Amplexus,a reproductive behavior in which the male clasps the female dorsally,is a characteristic of amphibians(Wells,2007).Most species exhibit either inguinal or axillary amplexus,although some reproduce... Dear Editor,Amplexus,a reproductive behavior in which the male clasps the female dorsally,is a characteristic of amphibians(Wells,2007).Most species exhibit either inguinal or axillary amplexus,although some reproduce without it(Wells,2007).While male-female amplexus is predominant,other forms such as male-male and multiple-male amplexus have also been documented(Soni et al.,2025).Release calls are typically produced by amplexed males to signal mating mismatches and mitigate the costs of inappropriate clasping(Kelehear and Shine,2019).Female-female amplexus,however,is rare and considered maladaptive,as it does not lead to fertilization(Chuang et al.,2019). 展开更多
关键词 amplexed males female female amplexus reproductive behavior signal mating mismatches inguinal axillary amplexusalthough operational sex ratio breeding season Duttaphrynus cyphosus
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How leadership and digital capability drive operational performance in railway supply chains:the mediating role of agility 认领 引用
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作者 Meisam Karami 《Railway Sciences》 2026年第4期453-472,共20页
Purpose-This study examines how leadership and digital capability influence supply chain agility and operational performance within railway supply chains in North America,with a focus on both direct and indirect pathw... Purpose-This study examines how leadership and digital capability influence supply chain agility and operational performance within railway supply chains in North America,with a focus on both direct and indirect pathways.Design/methodology/approach-A quantitative research design was employed using survey data collected from 214 organizations operating within railway-centered supply chain networks across Canada,the United States and Mexico.The proposed model was tested using partial least squares structural equation modeling(PLS-SEM).Findings-The results indicate that leadership plays a dual role in railway supply chain networks by directly enhancing supply chain agility and indirectly influencing agility through digital capability.Digital capability significantly improves both operational agility and performance within railway supply chain networks,while supply chain agility partially mediates the relationship between digital capability and performance.Practical implications-The findings suggest that railway organizations should align leadership,digital investments and agile operational processes to improve responsiveness,coordination and operational performance in infrastructure-intensive environments.Originality/value-This study extends existing literature by integrating leadership into capability-based models and demonstrating its direct and indirect influence on agility and performance in railway supply chains.This study highlights how performance in railway systems depends on the alignment of leadership,digital capability and agility within structurally constrained operational environments. 展开更多
关键词 Leadership Digital capability Supply chain agility Operational performance Railway supply chains Dynamic capabilities
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Psychological well-being and multimodal predictors of operational safety in heavy-haul railway drivers 认领 引用
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作者 Jian-Hua Wang Shu-Yi Jia +4 位作者 Kai-Gong Zhao Ting Meng Yan-Ming Ren Xin Gao Pei Sun 《World Journal of Psychiatry》 SCIE 2026年第8期401-410,共10页
BACKGROUND Operational safety in heavy-haul railway systems is influenced not only by technical competence,but also by drivers’psychological and physiological functioning.However,the relative contributions of psychol... BACKGROUND Operational safety in heavy-haul railway systems is influenced not only by technical competence,but also by drivers’psychological and physiological functioning.However,the relative contributions of psychological well-being,cognitive performance,and physiological indicators to real-world operational safety remain unclear.AIM To examine the associations of psychological well-being,cognitive task performance,and physiological indicators with operational safety among heavy-haul railway drivers.METHODS This observational study included 1117 operational records from 203 heavy-haul railway drivers.Psychological well-being was assessed using a multidimensional questionnaire covering mental fatigue,workload,self-efficacy,stress level,and emotional state.Cognitive performance was assessed using a rotating battery of computerized tasks,including Stroop,target tracking,ligature test,digit memory,BallSport,and Balloon tasks.A composite physiological indicator was derived from routine multimodal monitoring data.Operational safety was defined as full-score vs non-full-score performance.The overall psychological well-being score and other analytic predictors were entered into Poisson event-rate models with offset terms to estimate the associations of psychological well-being,cognitive task.RESULTS Higher overall psychological well-being was associated with a lower rate of non-full-score operational events at the driver level[incidence rate ratio(IRR)=0.80,95%confidence interval(CI):0.65-0.99,P=0.040].In contrast,cognitive task indicators did not show stable independent associations with operational risk across task-specific models.The physiological indicator was not significantly associated with event rates overall(IRR=0.90,95%CI:0.65-1.22,P=0.510),but showed a significant protective association in the digit memory subsample(IRR=0.26,95%CI:0.09-0.68,P=0.012).Overall,operational safety appeared to be more consistently related to general psychological well-being than to isolated cognitive task performance,whereas the effect of physiological indicators may vary across cognitive load conditions.CONCLUSION Psychological well-being was a relatively stable protective correlate of operational safety among heavy-haul railway drivers,whereas individual cognitive task indicators showed limited independent explanatory value.Physiological indicators may have context-dependent relevance under specific cognitive load conditions.These findings support the value of multimodal safety assessment frameworks that prioritize psychological well-being while integrating cognitive and physiological information in a context-sensitive manner. 展开更多
关键词 Heavy-haul railway Operational safety Psychological health Cognitive tasks Multimodal assessment
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Operational Practices and Management Strategies for Enhancing the Quality and Efficiency of Municipal Wastewater Treatment Plants 认领 引用
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作者 GAN Zhiming 《外文科技期刊数据库（文摘版）工程技术》 2026年第7期102-107,共6页
As environmental protection requirements become increasingly stringent and operating costs continue to rise, enhancing treatment quality and improving efficiency have become paramount challenges for urban wastewater t... As environmental protection requirements become increasingly stringent and operating costs continue to rise, enhancing treatment quality and improving efficiency have become paramount challenges for urban wastewater treatment plants across the industry. This article examines practical operational and management approaches in municipal wastewater treatment systems, aiming to identify actionable strategies that effectively improve treatment performance while significantly reducing operational expenses. It first analyzes common challenges faced by these facilities—including significant fluctuations in treatment loads, highly variable wastewater quality entering treatment systems, excessive energy and chemical consumption, and suboptimal performance of aging equipment. The paper highlights two primary improvement pathways: First, implementing advanced biological and advanced treatment technologies to optimize operational parameters at every stage, ensuring discharged wastewater consistently meets regulatory standards;Second, accelerating energy-efficient retrofits and smart upgrades for critical equipment to fully unlock hardware-based potential for cost reduction and energy savings. To ensure sustained progress in both areas, the study proposes comprehensive management measures including establishing integrated monitoring systems, conducting regular performance evaluations, and developing organizational frameworks and policies aligned with future smart infrastructure development needs. All of the above content represents valuable experiences and lessons that can serve as references for the future daily and refined management of similar wastewater treatment plants. 展开更多
关键词 Municipal wastewater treatment Quality improvement and efficiency enhancement Process optimization Intelligent transformation Operational management
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Design methodologies and operational strategies for air conditioning intelligent manufacturing production lines 认领 引用
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作者 Xiaohan Sun Qingfeng Bie +11 位作者 Zhigang Zhou Xiangguo Chen Yuewen Feng Shouhai Chen Jun Wang Guanqun Li Yanbin Zhang Benkai Li Xiao Ma Dewei Liu Xu Yan Changhe Li 《ENGINEERING Mechanical Engineering》 SCIE CAS CSCD 2026年第1期153-178,共26页
The air conditioning manufacturing industry is characterized by discrete manufacturing features including multiple processes,a wide variety of products,small batch sizes and rapid production cycles.Traditional product... The air conditioning manufacturing industry is characterized by discrete manufacturing features including multiple processes,a wide variety of products,small batch sizes and rapid production cycles.Traditional production lines have been rendered insufficient to meet the rapidly evolving market demands concerning flexibility,efficiency,quality and resource management.To address this challenge,an intelligent production line and operational model has been proposed and validated for air conditioning manufacturing,based on the concept of data-driven,system-integrated,and intelligently-scheduled operations.First,three core hypotheses were formulated based on theoretical considerations.An integrated technical framework was subsequently established,incorporating a cyber-physical system architecture,core assembly processes,four sub-production line systems and an intelligent maintenance platform.Key innovations were implemented in technologies including radio frequency identification traceability,artificial intelligence visual inspection,automated equipment integration,Internet of Things sensing networks,as well as an integrated air-ground coordinated transportation system.Through comparative studies with traditional air conditioner production lines,the intelligent production line was shown to significantly outperform traditional systems in production capacity:daily output increased by 57.6%,cycle time was reduced by 57.6%,workforce requirements decreased by 57.4%and unit per person per hour improved to 3.8 times the original level.Additionally,lighting energy consumption was reduced by an average of 60%and the system achieved substantial improvements in efficiency across six dimensions.The established intelligent air conditioner production line model not only effectively validated the research hypotheses and addressed critical limitations of traditional production lines but also provided theoretical support and technical pathways for the intelligent transformation of the discrete manufacturing industry,demonstrating considerable engineering application value and promotion potential. 展开更多
关键词 intelligent production line Internet of Things data-driven intelligent operation and maintenance sustainable manufacturing air conditioner manufacturing
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Multiscale track-seabed dynamic interaction during deep-sea seabed mining across operational modes 认领 引用 被引量：2
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作者 Bin Zhu Xianhao Xiu +5 位作者 Ying Lai Yunmin Chen Viroon Kamchoom Anthony Gunawan Ruishi Zhang Shusen Xiong 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2025年第11期2055-2071,共17页
Deep-sea mining has emerged as a critical solution to address global resource shortages;however,the mechanical interaction between tracked mining vehicles(TMVs)and soft seabed sediments presents fundamental engineerin... Deep-sea mining has emerged as a critical solution to address global resource shortages;however,the mechanical interaction between tracked mining vehicles(TMVs)and soft seabed sediments presents fundamental engineering challenges.This study establishes a multiscale modelling framework coupling the discrete element method(DEM)with multi-body dynamics(MBD)to investigate track-seabed dynamic interactions across three operational modes:flat terrain,slope climbing,and ditch surmounting.The simulation framework,validated against laboratory experiments,systematically evaluates the influence of grouser geometry(involute,triangular,and pin-type)and traveling speed(0.2–1.0 m/s)on traction performance,slip rate,and ground pressure distribution.Results reveal rate-dependent traction mechanisms governed by soil microstructural responses:higher speeds enhance peak traction but exacerbate slip instability on complex terrain.Critical operational thresholds are established—0.7 m/s for flat terrain,≤0.5 m/s for slopes and ditches—with distinct grouser optimization strategies:involute grousers achieve 35%–40%slip reduction on slopes through progressive soil engagement,while triangular grousers provide optimal impact resistance during ditch crossing with 30%–35%performance improvement.These findings provide quantitative design criteria and operational guidelines for optimizing TMV structural parameters and control strategies,offering a robust theoretical foundation for enhancing the performance,safety,and reliability of deep-sea mining equipment in complex submarine environments. 展开更多
关键词 Deep-sea mining Tracked mining vehicle Track-soil interaction DEM-MBD coupling Soft seabed sediment Operational performance
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How Do Deep Learning Forecasting Models Perform for Surface Variables in the South China Sea Compared to Operational Oceanography Forecasting Systems? 认领 引用 被引量：2
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作者 Ziqing ZU Jiangjiang XIA +6 位作者 Xueming ZHU Marie DREVILLON Huier MO Xiao LOU Qian ZHOU Yunfei ZHANG Qing YANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第1期178-189,共12页
It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using... It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical reference.In this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China Sea.The DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 dataset.Results show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the former.Therefore,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and OFSs.Against the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other variables.The error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the DLMs.For extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current direction.This study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs. 展开更多
关键词 forecast error deep learning forecasting model operational oceanography forecasting system validation intercomparison
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Functional evidential reasoning model(FERM)-A new systematic approach for exploring hazardous chemical operational accidents under uncertainty 认领 引用 被引量：1
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作者 Qianlin Wang Jiaqi Han +6 位作者 Lei Cheng Feng Wang Yiming Chen Zhan Dou Bing Zhang Feng Chen Guoan Yang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第5期255-269,共15页
This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal... This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective. 展开更多
关键词 Functional evidential reasoning model (FERM) Accident causation analysis Operational accidents Hazardous chemical Uncertainty
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A methodology for aircraft mission effectiveness evaluation and design space exploration based on virtual operational test 认领 引用 被引量：1
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作者 Hu LIU Qiang GUO +2 位作者 Yongliang TIAN Kangwen SUN Chenxuan ZHAO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第2期92-110,共19页
The early involvement of test and evaluation can significantly reduce the cost of modifying issues and errors found in the later stages of aircraft development and design process.This paper presents a methodology for ... The early involvement of test and evaluation can significantly reduce the cost of modifying issues and errors found in the later stages of aircraft development and design process.This paper presents a methodology for aircraft mission effectiveness evaluation and design space exploration based on Virtual Operational Test(VOT),incorporating Virtual Open Scenario(VOS)and User in Scenarios(UIS)concepts.By employing modeling and simulation technologies in the early stages of aircraft development and design,a virtual environment can be constructed,allowing aircraft users to participate more closely and conveniently in the design process.Virtual tests conducted by users within the mission context provide data on mission effectiveness and critical user feedback.This paper outlines the main components of the virtual operational test process and related conceptual methods,and discusses an open support system framework that supports VOT.The effectiveness and adaptability of the method are demonstrated through two case studies:a beyond-visual-range air combat scenario and a helicopter ground attack scenario.These case studies demonstrate the evaluation of aircraft mission effectiveness and the sensitivity analysis and optimization of design and operational parameters based on VOT. 展开更多
关键词 Virtual operational test Effectiveness evaluation User in scenarios System of systems Virtual simulation Design space exploration
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Intelligent Operational Decision-Making in Industrial Process:Development and Prospects 认领 引用 被引量：1
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作者 Tianyou Chai Siyu Cheng 《Engineering》 SCIE EI CSCD 2025年第9期40-52,共13页
Based on an analysis of the role of industrial control and optimization technologies in the Industrial Revolution,as well as the current situation and existing problems of operational decision-making(ODM)for industria... Based on an analysis of the role of industrial control and optimization technologies in the Industrial Revolution,as well as the current situation and existing problems of operational decision-making(ODM)for industrial process,this paper introduces the concept of intelligent ODM in industrial process,shapes its future directions,and highlights key technical challenges.By the tight conjoining of and coordination between industrial artificial intelligence(AI)with industrial control and optimization technologies,as well as the Industrial Internet with industrial computer management and control systems,an intelligent operational optimization decision-making methodology is proposed for complex industrial process.The intelligent ODM methodology and its successful application demonstrate that the tight conjoining of and coordination between next-generation information technologies with industrial control and optimization technologies will promote the development of industrial intelligent ODM.Finally,main research directions and ideas are outlined for realizing intelligent ODM in industrial process. 展开更多
关键词 Operational decision-making Intelligence Industrial artificial intelligence Industrial Internet Complex industrial process
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Lessons from the development and operational experiences of international carbon markets for the construction of China’s carbon market 认领 引用
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作者 Qingkai Sun Li Ma +3 位作者 Menghua Fan Xiaojun Wang Zheng Zhao Yiru Shi 《Global Energy Interconnection》 EI CSCD 2025年第2期188-204,共17页
With the intensifying global climate crisis,carbon emissions trading has emerged as a crucial market-based instrument for emissions reduction,attracting significant attention from government agencies and academia worl... With the intensifying global climate crisis,carbon emissions trading has emerged as a crucial market-based instrument for emissions reduction,attracting significant attention from government agencies and academia worldwide.As of January 2024,28 carbon trading markets have been established globally,encompassing approximately 17%of global greenhouse gas emissions and serving approximately 1/3 of the global population.With various nations setting carbon neutrality targets and delineating carbon reduction pathways,the con-struction,operation,and regulatory frameworks of carbon markets are becoming increasingly refined and comprehensive.This study elucidates the importance and necessity of establishing carbon markets from the perspective of energy system transformation and sus-tainable economic development.Second,it provides a comparative analysis of the operational mechanisms,trading scales,and emission reduction outcomes of major carbon markets in the European Union,United States,and New Zealand,systematically summarizing their development processes and recent advancements.Finally,this study addresses issues and challenges in the construction of China’s carbon market.Drawing on the successful experiences of leading global carbon markets in institutional design and market operations,we pro-pose development strategies and recommendations for a carbon market with Chinese characteristics.These strategies are intended to align with international standards while meeting China’s national conditions,thereby contributing insights into the global carbon market trading system. 展开更多
关键词 International carbon market Development history Operational experience Carbon market with Chinese characteristics
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Functional cartography of heterogeneous combat networks using operational chain-based label propagation algorithm 认领 引用
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作者 CHEN Kebin JIANG Xuping +2 位作者 ZENG Guangjun YANG Wenjing ZHENG Xue 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第5期1202-1215,共14页
To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartogra... To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartography of heterogeneous combat networks based on the operational chain”(FCBOC).In this framework,a functional module detection algorithm named operational chain-based label propagation algorithm(OCLPA),which considers the cooperation and interactions among combat entities and can thus naturally tackle network heterogeneity,is proposed to identify the functional modules of the network.Then,the nodes and their modules are classified into different roles according to their properties.A case study shows that FCBOC can provide a simplified description of disorderly information of combat networks and enable us to identify their functional and structural network characteristics.The results provide useful information to help commanders make precise and accurate decisions regarding the protection,disintegration or optimization of combat networks.Three algorithms are also compared with OCLPA to show that FCBOC can most effectively find functional modules with practical meaning. 展开更多
关键词 functional cartography heterogeneous combat network functional module label propagation algorithm operational chain
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Vibration signal predictions of damaged sensors on rotor blades based on operational modal analysis and virtual sensing 认领 引用
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作者 Yuhan SUN Zhiguang SONG +2 位作者 Jie LI Guochen CAI Zefeng WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第6期462-486,共25页
Rotor blade is one of the most significant components of helicopters. But due to its highspeed rotation characteristics, it is difficult to collect the vibration signals during the flight stage.Moreover, sensors are h... Rotor blade is one of the most significant components of helicopters. But due to its highspeed rotation characteristics, it is difficult to collect the vibration signals during the flight stage.Moreover, sensors are highly susceptible to damage resulting in the failure of the measurement.In order to make signal predictions for the damaged sensors, an operational modal analysis(OMA) together with the virtual sensing(VS) technology is proposed in this paper. This paper discusses two situations, i.e., mode shapes measured by all sensors(both normal and damaged) can be obtained using OMA, and mode shapes measured by some sensors(only including normal) can be obtained using OMA. For the second situation, it is necessary to use finite element(FE) analysis to supplement the missing mode shapes of damaged sensor. In order to improve the correlation between the FE model and the real structure, the FE mode shapes are corrected using the local correspondence(LC) principle and mode shapes measured by some sensors(only including normal).Then, based on the VS technology, the vibration signals of the damaged sensors during the flight stage can be accurately predicted using the identified mode shapes(obtained based on OMA and FE analysis) and the normal sensors signals. Given the high degrees of freedom(DOFs) in the FE mode shapes, this approach can also be used to predict vibration data at locations without sensors. The effectiveness and robustness of the proposed method is verified through finite element simulation, experiment as well as the actual flight test. The present work can be further used in the fault diagnosis and damage identification for rotor blade of helicopters. 展开更多
关键词 Composite helicopter rotor blades Operational modal analysis Virtual sensing Vibration prediction Model updating Finite element method
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A two-stage framework for automated operational modal identification using OPTICS-KNN-based clustering 认领 引用
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作者 Yi CHEN Wenwei FU +3 位作者 Yaozhi LUO Yanbin SHEN Hui YANG Shiying WANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第11期1052-1069,共18页
Modal analysis,which provides modal parameters including frequencies,damping ratios,and mode shapes,is essential for assessing structural safety in structural health monitoring.Automated operational modal analysis(AOM... Modal analysis,which provides modal parameters including frequencies,damping ratios,and mode shapes,is essential for assessing structural safety in structural health monitoring.Automated operational modal analysis(AOMA)offers a promising alternative to traditional methods that depend heavily on human intervention and engineering judgment.However,estimating structural dynamic properties and managing spurious modes remain challenging due to uncertainties in practical application conditions.To address this issue,we propose an automated modal identification approach comprising three key aspects:(1)identification of modal parameters using covariance-driven stochastic subspace identification;(2)automated interpretation of the stabilization diagram;(3)an improved self-adaptive algorithm for grouping physical modes based on ordering points to identify the clustering structure(OPTICS)combined with k-nearest neighbors(KNN).The proposed approach can play a crucial role in enabling real-time structural health monitoring without human intervention.A simulated 10-story shear frame was used to verify the methodology.Identification results from a cable-stayed bridge demonstrate the practicality of the proposed method for conducting AOMA in engineering practice.The proposed approach can automatically identify modal parameters with high accuracy,making it suitable for a real-time structural health monitoring framework. 展开更多
关键词 Structural health monitoring Covariance-driven stochastic subspace identification Automated operational modal analysis(AOMA) Ordering points to identify the clustering structure(OPTICS) k-nearest neighbors(KNN)
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