[ Objective ] The study aimed to explore the release conditions for the conidia of Botryosphaena berengeriana and understand the release dynamic of conidia. [Method] The systematical survey on the release conditions f...[ Objective ] The study aimed to explore the release conditions for the conidia of Botryosphaena berengeriana and understand the release dynamic of conidia. [Method] The systematical survey on the release conditions for the conidia of B. berengeriana were conducted in two growing seasons in 2008 and 2009, combined with the collection of meteorological data around conidia release period, the weather conditions causing large amount release of B. berengedana were analyzed. [ Result] During a growing season, the conidia of pathogen appeared several large release peaks. Under the suitable temperature, when the precipitation lasted for 4 h, the conidia of B. berengeriana began to release with large amount, the amount of conidia reached the peak after release and trended to be stable during 4 - 12 h, which significantly reduced after 24 h, tended to dis- appear after 36 h, and completely disappeared after 72 h. [Conclusion] The dominant factor affecting B. berengeriana conidia release in large a- mount was precipitation, while the lasting time of precipitation played a decisive role.展开更多
We propose using the concept of decisive moment in order to deconstruct the obvious ideological effects found in discourse. The subject is constituted in enunciation, and its polysemic discourse clashes with the trans...We propose using the concept of decisive moment in order to deconstruct the obvious ideological effects found in discourse. The subject is constituted in enunciation, and its polysemic discourse clashes with the transparency of meaning. According to Pêcheux （1988）, the contradictions in discourse simultaneously establish regularity and instability of meanings, leading it to misunderstanding, to the event. Photography destabilizes that which is already formulated and brings out that which is new, the unexpected meaning, the decisive moment. We analyze this process in the picture by Sebastiao Salgado---＂The cradle of inequality lies in the inequality of the cradle＂. （CAPES-BEX 4394/10-0, FAPESP 09/54417-4, CNPq.）展开更多
The p eriod of"The 13th Five-Year Plan"for national socioeconomic development(2016-2020)is a decisive stage of completion of all-round construction of a moderately prosperous society in China,and the year of...The p eriod of"The 13th Five-Year Plan"for national socioeconomic development(2016-2020)is a decisive stage of completion of all-round construction of a moderately prosperous society in China,and the year of 2016 is the first annum of this decisive stage.The Central Committee of the Communist Party of China(CPC)headed by General Secretary Xi Jinping has,with a new stance and new ideas,set strategic tasks展开更多
The fourth plenary session of the 20th Central Committee of the Communist Party of China(CPC)was successfully held in Beijing,amidst worldwide attention,from October 20 to 23,2025.This session holds great importance a...The fourth plenary session of the 20th Central Committee of the Communist Party of China(CPC)was successfully held in Beijing,amidst worldwide attention,from October 20 to 23,2025.This session holds great importance as it takes place at this historic juncture when the Chinese people embark on a new journey of building our country into a modern socialist country and advance toward the Second Centenary Goal.展开更多
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
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
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
Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspect...Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspects of the driving decisions(strategic decision,tactical decision and operation decision)to analyze the economy of vehicle energy.The analytic hierarchy process(AHP)is used to assign the weight of the internal evaluation indexes,so as to form a complete assessment for drivers'eco-driving behaviors.The research result can not only quantitatively describe the energy-saving effect of drivers'decisions,but also put forward targeted driving suggestions to optimize drivers'eco-driving behaviors.This assessment model helps to clarify the potential of eco-driving on energy economy of transportation in a hierarchical way,and provides a valuable theoretical basis for the further promotion and application of eco-driving education.展开更多
The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang...The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang for multiple years via a decision tree method based on a classification and regression tree(CART)algorithm using Landsat time series images.Spatiotemporal transform and fragmentation patterns of mangrove distribution were separately assessed with a transfer matrix of land cover types and a landscape pattern index.The classification method combined with multi-band images showed good accuracy,with overall accuracy higher than 90%.Mangrove areas in 1988,1999,2009,and 2019 were 2050,1875,1818,and 1750 ha,respectively,with decreases mainly due to conversion to aquaculture ponds and farmland.A mangrove growth index(MGI)was proposed,reflecting the water-mangrove relationship,showing positive mangrove growth from 1988–2009 and negative growth from 2009–2019.Study results indicated anthropogenic factors play a leading role in the extent and scale of mangrove effects over the past 30 years.According to the analysis results,corresponding management and protection measures are proposed to provide reference for the sustainable development of Dongzhaigang Mangrove Wetland ecosystem.展开更多
With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Tradition...With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints.However,these methods often overlook the correlation between pilot control quality and UAV flight dynamics,limiting their adaptability in complex scenarios.In this paper,we propose an intelligent flight evaluation model specifically designed to enhancemulti-waypoint trajectory optimization algorithms.Our model leverages a decision tree to integrate attitude parameters and trajectory matching metrics,establishing a quantitative link between pilot control quality and UAV flight states.Experimental results demonstrate that the proposed model not only accurately assesses pilot performance across diverse skill levels but also improves the optimality of generated trajectories.When integrated with our path planning algorithm,it efficiently produces optimal trajectories while strictly adhering to UAV flight constraints.This integrated framework highlights significant potential for real-time UAV training,performance assessment,and adaptive mission planning applications.展开更多
The Double Take column looks at a single topic from an African and Chinese perspective.This month,we discuss how to understand the growing emphasis on emotional returns among young people.Emotional value has emerged a...The Double Take column looks at a single topic from an African and Chinese perspective.This month,we discuss how to understand the growing emphasis on emotional returns among young people.Emotional value has emerged as a central force shaping youth decision-making across work,consumption,relationships and lifestyle choices.Unlike traditional economic rationality that prioritises income and material security,emotional value focuses on how choices make individuals feel and how they align with personal meaning.This shift is particularly evident in rapidly transforming societies such as China and Ghana,where economic restructuring,globalisation and social change have reshaped pathways to adulthood.展开更多
Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the ...Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.展开更多
The exploitation of quarries represents a strategic component of Morocco’s construction-materials sector,especially amid rapid urbanization and infrastructure expansion.To ensure that extractive activities remain env...The exploitation of quarries represents a strategic component of Morocco’s construction-materials sector,especially amid rapid urbanization and infrastructure expansion.To ensure that extractive activities remain environmentally sustainable and compliant with national regulations,this study applies a spatial suitability analysis based on Geographic Information Systems(GIS)and Multi-Criteria Decision Analysis(MCDA)within the ArcGIS Pro environment.The methodology integrates six key criteria:lithology,slope gradient,hydrographic buffers,land-use/land-cover patterns,accessibility to transport networks,and exclusion of urbanized or ecologically sensitive zones.Each parameter was weighted using the Analytical Hierarchy Process(AHP)to generate a composite suitability map for quarry site selection in north-western Morocco.The resulting classification shows that 18%of the total area is highly suitable,34%moderately suitable,and 48%unsuitable for sustainable quarrying.Priority zones occur mainly within carbonate formations in the Tangier–Assilah Province and,to a lesser extent,within Numidian flysch units in the Fahs-Anjra Province.These findings demonstrate that GIS–MCDA methods offer a robust and transparent framework for optimizing quarry site selection,reducing ecological risk,and improving decision-making for land-use planning and resource management in Morocco’s extractive sector.展开更多
Hesitation analysis plays a crucial role in decision-making processes by capturing the intermediary position between supportive and opposing information.This study introduces a refined approach to addressing uncertain...Hesitation analysis plays a crucial role in decision-making processes by capturing the intermediary position between supportive and opposing information.This study introduces a refined approach to addressing uncertainty in decision-making,employing existing measures used in decision problems.Building on information theory,the Kullback–Leibler(KL)divergence is extended to incorporate additional insights,specifically by applying temporal data,as illustrated by time series data fromtwo datasets(e.g.,affirmative and dissent information).Cumulative hesitation provides quantifiable insights into the decision-making process.Accordingly,a modified KL divergence,which incorporates historical trends,is proposed,enabling dynamic updates using conditional probability.The efficacy of this enhanced KL divergence is validated through a case study predicting Korean election outcomes.Immediate and historical data are processed using direct hesitation calculations and accumulated temporal information.The computational example demonstrates that the proposed KL divergence yields favorable results compared to existing methods.展开更多
Seismic resilience(SR)has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure,recover from,and adapt to seismic events.This study presents an entropy-based multicriter...Seismic resilience(SR)has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure,recover from,and adapt to seismic events.This study presents an entropy-based multicriteria approach for selecting optimal intensity measures(IMs)to assess SR of structures.Eight representative IMs,derived from time histories and response spectrum are evaluated.Incremental dynamic analysis is con-ducted on a reinforced concrete structure,using engineering demand parameters such as the maximum interstory drift and floor acceleration to generate fragility curves via a probabilistic seismic demand model.The optimal IMs are identified through a multi-criteria decision-making process,with scores calculated using the entropy weight method to incorporate factors such as efficiency,proficiency,and uncertainty based on infor-mation entropy.An effective SR framework is derived from fragility results.The findings indicate that peak ground velocity and spectral IMs are the most effective,while energy-related IMs underestimate SR.The study highlights the importance of optimizing IMs for more accurate seismic resilience assessments.The proposed entropy-based multi-criteria approach is shown to be both reliable and effective for selecting optimal IMs in this context.展开更多
Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudi...Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudinal or sensor-based data,which are not always available in public health contexts.In this article,we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset.This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System,including both COVID-19 and non-COVID-19 patients.Four classification models—extreme gradient boosting(XGBoost),logistic regression,random forest,and a deep neural network(DNN)—are trained using cost-sensitive learning to address class imbalance.The models are evaluated using accuracy,precision,recall,F1-score,and area under the curve(AUC)related to the receiver operating characteristic(ROC).The framework supports simulated interventions by modifying selected inputs and recalculating predicted mortality.Additionally,we incorporate multiple correspondence analysis and K-means clustering to explore model sensitivity.A Python library has been developed to ensure reproducibility.All models achieve AUC-ROC values near or above 0.85.XGBoost yields the highest accuracy(0.84),while the DNN achieves the highest recall(0.81).Scenario-based simulations reveal how key clinical factors,such as intensive care unit admission and oxygen support,affect predicted outcomes.The proposed proto-DT framework demonstrates the feasibility of mortality prediction and intervention simulation using categorical data alone.This framework provides a foundation for data-driven explainable DTs in public health,even in the absence of time-series data.展开更多
This study examines the methods to plan the development of offshore oilfields over the years,which are used to support the decision-making on the development of offshore oilfields.About 100 papers are analysed and cat...This study examines the methods to plan the development of offshore oilfields over the years,which are used to support the decision-making on the development of offshore oilfields.About 100 papers are analysed and categorised into different groups of main early-stage decisions.The present study stands in contrast to the contributions of the operations research and system engineering review articles,on the one hand,and the petroleum engineering review articles,on the other.This is because it does not focus on one methodological approach,nor does it limit the literature analysis by offshore oilfield characteristics.Consequently,the present analysis may offer valuable insights,for instance,by identifying environmental planning decisions as a recent yet highly significant concern that is currently being imposed on decision-making process.Thus,it is evident that the incorporation of safety criteria within the technical-economic decision-making process for the design of production systems would be a crucial requirement at development phase.展开更多
Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When opera...Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.展开更多
This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg gam...This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg game is utilized to model the P2P energy trading process.Stackelberg equilibrium(SE)is regarded as a P2P optimal trading strategy.A two-stage privacy protection solution technique combining data-driven and model-driven is developed to obtain the SE.Specifically,energy storage scheduling problem in MGs is formulated as a Markov decision process with discrete periods,and a multi-action single-observation deep deterministic policy gradient(MASO-DDPG)algorithm is proposed to tackle optimal scheduling of energy storage in the first stage.According to optimal scheduling of energy storage,the closed-form expression for SE based on model-driven is derived,and distributed SE solution technique(DSET)is developed to obtain SE in the second stage.Case studies involving a 4-Microgrid demonstrate the P2P electricity price obtained by the two-stage method,as a novel pricing mechanism,can reasonably regulate microgrid operation mode and improve microgrid income participating in the P2P market,which verifies effectiveness and superiority of the proposed P2P energy trading model and two-stage solution method.展开更多
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^(-1)and 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
基金Supported by State Apple Industry Technology System Project(nybcytx-08-04-01)~~
文摘[ Objective ] The study aimed to explore the release conditions for the conidia of Botryosphaena berengeriana and understand the release dynamic of conidia. [Method] The systematical survey on the release conditions for the conidia of B. berengeriana were conducted in two growing seasons in 2008 and 2009, combined with the collection of meteorological data around conidia release period, the weather conditions causing large amount release of B. berengedana were analyzed. [ Result] During a growing season, the conidia of pathogen appeared several large release peaks. Under the suitable temperature, when the precipitation lasted for 4 h, the conidia of B. berengeriana began to release with large amount, the amount of conidia reached the peak after release and trended to be stable during 4 - 12 h, which significantly reduced after 24 h, tended to dis- appear after 36 h, and completely disappeared after 72 h. [Conclusion] The dominant factor affecting B. berengeriana conidia release in large a- mount was precipitation, while the lasting time of precipitation played a decisive role.
文摘We propose using the concept of decisive moment in order to deconstruct the obvious ideological effects found in discourse. The subject is constituted in enunciation, and its polysemic discourse clashes with the transparency of meaning. According to Pêcheux （1988）, the contradictions in discourse simultaneously establish regularity and instability of meanings, leading it to misunderstanding, to the event. Photography destabilizes that which is already formulated and brings out that which is new, the unexpected meaning, the decisive moment. We analyze this process in the picture by Sebastiao Salgado---＂The cradle of inequality lies in the inequality of the cradle＂. （CAPES-BEX 4394/10-0, FAPESP 09/54417-4, CNPq.）
文摘The p eriod of"The 13th Five-Year Plan"for national socioeconomic development(2016-2020)is a decisive stage of completion of all-round construction of a moderately prosperous society in China,and the year of 2016 is the first annum of this decisive stage.The Central Committee of the Communist Party of China(CPC)headed by General Secretary Xi Jinping has,with a new stance and new ideas,set strategic tasks
文摘The fourth plenary session of the 20th Central Committee of the Communist Party of China(CPC)was successfully held in Beijing,amidst worldwide attention,from October 20 to 23,2025.This session holds great importance as it takes place at this historic juncture when the Chinese people embark on a new journey of building our country into a modern socialist country and advance toward the Second Centenary Goal.
基金funded by Henan Key Laboratory of General Aviation Technology,grant number ZHKF-240202。
文摘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.
基金supported by Science foundation Ireland(22/NCF/DR/11309).
文摘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.
文摘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.
文摘Eco-driving behaviors have been recommended around the world because the transport is a key factor of energy use and pollution emissions.Therefore,based on the driving decision model,this paper introduces three aspects of the driving decisions(strategic decision,tactical decision and operation decision)to analyze the economy of vehicle energy.The analytic hierarchy process(AHP)is used to assign the weight of the internal evaluation indexes,so as to form a complete assessment for drivers'eco-driving behaviors.The research result can not only quantitatively describe the energy-saving effect of drivers'decisions,but also put forward targeted driving suggestions to optimize drivers'eco-driving behaviors.This assessment model helps to clarify the potential of eco-driving on energy economy of transportation in a hierarchical way,and provides a valuable theoretical basis for the further promotion and application of eco-driving education.
基金financially supported by the National Natural Science Foundation of China(Nos.U2244225 and 42020104005)the Ministry of Education of China(111 Project)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)and China Geological Survey(No.DD20211391)。
文摘The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution and fragmentation patterns from 1988 through 2019 in Dongzhaigang.Land cover datasets were generated for Dongzhaigang for multiple years via a decision tree method based on a classification and regression tree(CART)algorithm using Landsat time series images.Spatiotemporal transform and fragmentation patterns of mangrove distribution were separately assessed with a transfer matrix of land cover types and a landscape pattern index.The classification method combined with multi-band images showed good accuracy,with overall accuracy higher than 90%.Mangrove areas in 1988,1999,2009,and 2019 were 2050,1875,1818,and 1750 ha,respectively,with decreases mainly due to conversion to aquaculture ponds and farmland.A mangrove growth index(MGI)was proposed,reflecting the water-mangrove relationship,showing positive mangrove growth from 1988–2009 and negative growth from 2009–2019.Study results indicated anthropogenic factors play a leading role in the extent and scale of mangrove effects over the past 30 years.According to the analysis results,corresponding management and protection measures are proposed to provide reference for the sustainable development of Dongzhaigang Mangrove Wetland ecosystem.
基金funded in part by the Fundamental Research Funds for the Central Universities under Grant NS2023052in part by the Natural Science Foundation of Jiangsu Province of China under Grants No.BK20231439 and No.BK20222012.
文摘With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints.However,these methods often overlook the correlation between pilot control quality and UAV flight dynamics,limiting their adaptability in complex scenarios.In this paper,we propose an intelligent flight evaluation model specifically designed to enhancemulti-waypoint trajectory optimization algorithms.Our model leverages a decision tree to integrate attitude parameters and trajectory matching metrics,establishing a quantitative link between pilot control quality and UAV flight states.Experimental results demonstrate that the proposed model not only accurately assesses pilot performance across diverse skill levels but also improves the optimality of generated trajectories.When integrated with our path planning algorithm,it efficiently produces optimal trajectories while strictly adhering to UAV flight constraints.This integrated framework highlights significant potential for real-time UAV training,performance assessment,and adaptive mission planning applications.
文摘The Double Take column looks at a single topic from an African and Chinese perspective.This month,we discuss how to understand the growing emphasis on emotional returns among young people.Emotional value has emerged as a central force shaping youth decision-making across work,consumption,relationships and lifestyle choices.Unlike traditional economic rationality that prioritises income and material security,emotional value focuses on how choices make individuals feel and how they align with personal meaning.This shift is particularly evident in rapidly transforming societies such as China and Ghana,where economic restructuring,globalisation and social change have reshaped pathways to adulthood.
文摘Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.
文摘The exploitation of quarries represents a strategic component of Morocco’s construction-materials sector,especially amid rapid urbanization and infrastructure expansion.To ensure that extractive activities remain environmentally sustainable and compliant with national regulations,this study applies a spatial suitability analysis based on Geographic Information Systems(GIS)and Multi-Criteria Decision Analysis(MCDA)within the ArcGIS Pro environment.The methodology integrates six key criteria:lithology,slope gradient,hydrographic buffers,land-use/land-cover patterns,accessibility to transport networks,and exclusion of urbanized or ecologically sensitive zones.Each parameter was weighted using the Analytical Hierarchy Process(AHP)to generate a composite suitability map for quarry site selection in north-western Morocco.The resulting classification shows that 18%of the total area is highly suitable,34%moderately suitable,and 48%unsuitable for sustainable quarrying.Priority zones occur mainly within carbonate formations in the Tangier–Assilah Province and,to a lesser extent,within Numidian flysch units in the Fahs-Anjra Province.These findings demonstrate that GIS–MCDA methods offer a robust and transparent framework for optimizing quarry site selection,reducing ecological risk,and improving decision-making for land-use planning and resource management in Morocco’s extractive sector.
基金Uzbekistan to China International Science and Technology Innovation Cooperation:IL-8724053120-R11National Research Foundation of Korea:NRF-2025S1A5A2A01011466.
文摘Hesitation analysis plays a crucial role in decision-making processes by capturing the intermediary position between supportive and opposing information.This study introduces a refined approach to addressing uncertainty in decision-making,employing existing measures used in decision problems.Building on information theory,the Kullback–Leibler(KL)divergence is extended to incorporate additional insights,specifically by applying temporal data,as illustrated by time series data fromtwo datasets(e.g.,affirmative and dissent information).Cumulative hesitation provides quantifiable insights into the decision-making process.Accordingly,a modified KL divergence,which incorporates historical trends,is proposed,enabling dynamic updates using conditional probability.The efficacy of this enhanced KL divergence is validated through a case study predicting Korean election outcomes.Immediate and historical data are processed using direct hesitation calculations and accumulated temporal information.The computational example demonstrates that the proposed KL divergence yields favorable results compared to existing methods.
基金partly supported by Engineering Partners Inter-national,LLC,Richfield,MN 55423(PC13803,482842-58309).
文摘Seismic resilience(SR)has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure,recover from,and adapt to seismic events.This study presents an entropy-based multicriteria approach for selecting optimal intensity measures(IMs)to assess SR of structures.Eight representative IMs,derived from time histories and response spectrum are evaluated.Incremental dynamic analysis is con-ducted on a reinforced concrete structure,using engineering demand parameters such as the maximum interstory drift and floor acceleration to generate fragility curves via a probabilistic seismic demand model.The optimal IMs are identified through a multi-criteria decision-making process,with scores calculated using the entropy weight method to incorporate factors such as efficiency,proficiency,and uncertainty based on infor-mation entropy.An effective SR framework is derived from fragility results.The findings indicate that peak ground velocity and spectral IMs are the most effective,while energy-related IMs underestimate SR.The study highlights the importance of optimizing IMs for more accurate seismic resilience assessments.The proposed entropy-based multi-criteria approach is shown to be both reliable and effective for selecting optimal IMs in this context.
文摘Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudinal or sensor-based data,which are not always available in public health contexts.In this article,we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset.This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System,including both COVID-19 and non-COVID-19 patients.Four classification models—extreme gradient boosting(XGBoost),logistic regression,random forest,and a deep neural network(DNN)—are trained using cost-sensitive learning to address class imbalance.The models are evaluated using accuracy,precision,recall,F1-score,and area under the curve(AUC)related to the receiver operating characteristic(ROC).The framework supports simulated interventions by modifying selected inputs and recalculating predicted mortality.Additionally,we incorporate multiple correspondence analysis and K-means clustering to explore model sensitivity.A Python library has been developed to ensure reproducibility.All models achieve AUC-ROC values near or above 0.85.XGBoost yields the highest accuracy(0.84),while the DNN achieves the highest recall(0.81).Scenario-based simulations reveal how key clinical factors,such as intensive care unit admission and oxygen support,affect predicted outcomes.The proposed proto-DT framework demonstrates the feasibility of mortality prediction and intervention simulation using categorical data alone.This framework provides a foundation for data-driven explainable DTs in public health,even in the absence of time-series data.
基金the Strategic Research Plan of the Centre for Marine Technology and Ocean Engineering(CENTEC),which is financed by the Portuguese Foundation for Science and Technology(Fundação para a Ciência e a Tecnologia FCT)under contract UIDB/UIDP/00134/2020.
文摘This study examines the methods to plan the development of offshore oilfields over the years,which are used to support the decision-making on the development of offshore oilfields.About 100 papers are analysed and categorised into different groups of main early-stage decisions.The present study stands in contrast to the contributions of the operations research and system engineering review articles,on the one hand,and the petroleum engineering review articles,on the other.This is because it does not focus on one methodological approach,nor does it limit the literature analysis by offshore oilfield characteristics.Consequently,the present analysis may offer valuable insights,for instance,by identifying environmental planning decisions as a recent yet highly significant concern that is currently being imposed on decision-making process.Thus,it is evident that the incorporation of safety criteria within the technical-economic decision-making process for the design of production systems would be a crucial requirement at development phase.
文摘Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.
基金supported in part by the Fundamental Research Funds for the Central Universities(No.2020YJS162).
文摘This paper proposes a novel framework based on the Stackelberg game and deep reinforcement learning for multi-microgrids(MGs)in achieving peer-to-peer(P2P)energy trading.A multi-leaders,multi-followers Stackelberg game is utilized to model the P2P energy trading process.Stackelberg equilibrium(SE)is regarded as a P2P optimal trading strategy.A two-stage privacy protection solution technique combining data-driven and model-driven is developed to obtain the SE.Specifically,energy storage scheduling problem in MGs is formulated as a Markov decision process with discrete periods,and a multi-action single-observation deep deterministic policy gradient(MASO-DDPG)algorithm is proposed to tackle optimal scheduling of energy storage in the first stage.According to optimal scheduling of energy storage,the closed-form expression for SE based on model-driven is derived,and distributed SE solution technique(DSET)is developed to obtain SE in the second stage.Case studies involving a 4-Microgrid demonstrate the P2P electricity price obtained by the two-stage method,as a novel pricing mechanism,can reasonably regulate microgrid operation mode and improve microgrid income participating in the P2P market,which verifies effectiveness and superiority of the proposed P2P energy trading model and two-stage solution method.
基金financially supported by the National Key Research and Development Program of China(2024YFD1700104 and 2022YFE0209200-03)the National Natural Science Foundation of China(42161144002 and 41977156)+3 种基金the Guangxi Natural Science Foundation,China(2022GXNSFBA035625)the Guangxi Technology Base and Talent Subject,China(Guike AD22035927)the Shandong Key Research and Development Project,China(2022TZXD0045)the State Key Laboratory of Earth System Numerical Modeling and Application,Institute of Atmospheric Physics,Chinese Academy of Sciences。
文摘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^(-1)and 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.
