The aim of this paper is to present and analyze the factors, motivations and criteria considered in the decision-making processes of the actors belonging to the biodiesel production chain in Brazil. The biodiesel prod...The aim of this paper is to present and analyze the factors, motivations and criteria considered in the decision-making processes of the actors belonging to the biodiesel production chain in Brazil. The biodiesel production chain consists of three main agents： the farmers, the soybean processing plants and the oil refinery/distributor. For the farmers organized in cooperatives the central decision is whether to sell oil-bearing crops for the production of biodiesel. In contrast, for the soybean processing plants that convert the crops into vegetable and/or biodiesel, the decision to produce this fuel is based on the wish to expand their market portfolio. Government tax incentives strongly influence both decisions regarding which oil-bearing crop to use and the amount of vegetable oil to be transformed into biodiesel. Finally, the oil refinery/distributor is obliged by law to mix the biodiesel with the mineral diesel and perceives this as a liability. The results show the existence of different characteristics linked to the decision-making process and a significant lack of synchronicity in the aims and motivations behind the agents＇ decisions. This state of decisional misalignment leads to heightened uncertainty regarding the sustainability of the Brazilian biodiesel production program.展开更多
Community participation and community based management are topical themes in current policy and discussion revolving around decision-making processes especially those dealing with natural resources management.This rev...Community participation and community based management are topical themes in current policy and discussion revolving around decision-making processes especially those dealing with natural resources management.This review shows that while governments have accepted the need to either cede or devolve control and management of natural resources to the local communities,the communities are not part and parcel of the planning and budgeting which are crucial in decisionmaking.Communities were seen to be more involved in the implementation of natural resource management programs but lacked ownership of the projects.This causes lack of commitment to the programs and at times hostile reaction from the communities.The communities are always at the receiving end when it pertains to losses in the exchange.Community participation was shown to be effective when the local population is involved not as co-operating users but as natural resource managers or owner managers.展开更多
Decision-making is the process of deciding between two or more options in order to take the most appropriate and successful course of action in order to achieve sustainable mangrove management. However, the distinctiv...Decision-making is the process of deciding between two or more options in order to take the most appropriate and successful course of action in order to achieve sustainable mangrove management. However, the distinctiveness of mangrove as an ecosystem, and thus the attendant socio-economic and governance ramifications, causes the idea of decision making to become relatively distinct from other decision making process As a result, the purpose of this research was to evaluate the impact that community engagement plays in the decision-making process as it relates to the establishment of governance norms for sustainable mangrove management in Lamu County. In this study, a correlational research design was applied, and the researchers employed a mixed techniques approach. The target population was 296 respondents. The research used questionnaires and interviews to collect data. A descriptive statistical technique was utilized to perform an inspection and analysis on the data that was gathered. The findings indicated that having awareness about governance standards is beneficial during the process of making decisions. In addition, the findings demonstrated that respondents had the impression that the decision-making process was not done properly. On the other hand, the participants pointed out the positive aspects of the decision-making process and agreed that the participation of both gender was essential for the sustainable management of mangroves. Based on these data, it appeared that full community engagement in decision-making is necessary for sustainable management of mangrove forests.展开更多
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a fr...Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a framework for evaluating LLMs and physician decisions in challenging lung cancer cases.Methods:We curated 50 challenging lung cancer cases(25 local and 25 published)classified as complex,rare,or refractory.Blinded three-dimensional,five-point Likert evaluations(1–5 for comprehensiveness,specificity,and readability)compared standalone LLMs(DeepSeek R1,Claude 3.5,Gemini 1.5,and GPT-4o),physicians by experience level(junior,intermediate,and senior),and AI-assisted juniors;intergroup differences and augmentation effects were analyzed statistically.Results:Of 50 challenging cases(18 complex,17 rare,and 15 refractory)rated by three experts,DeepSeek R1 achieved scores of 3.95±0.33,3.71±0.53,and 4.26±0.18 for comprehensiveness,specificity,and readability,respectively,positioning it between intermediate(3.68,3.68,3.75)and senior(4.50,4.64,4.53)physicians.GPT-4o and Claude 3.5 reached intermediate physician–level comprehensiveness(3.76±0.39,3.60±0.39)but junior-to-intermediate physician–level specificity(3.39±0.39,3.39±0.49).All LLMs scored higher on rare cases than intermediate physicians but fell below junior physicians in refractory-case specificity.AIassisted junior physicians showed marked gains in rare cases,with comprehensiveness rising from 2.32 to 4.29(84.8%),specificity from 2.24 to 4.26(90.8%),and readability from 2.76 to 4.59(66.0%),while specificity declined by 3.2%(3.17 to 3.07)in refractory cases.Error analysis showed complementary strengths,with physicians demonstrating reasoning stability and LLMs excelling in knowledge updating and risk management.Conclusions:LLMs performed variably in clinical decision-making tasks depending on case type,performing better in rare cases and worse in refractory cases requiring longitudinal reasoning.Complementary strengths between LLMs and physicians support case-and task-tailored human–AI collaboration.展开更多
Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effect...Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.展开更多
With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,exist...With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,existing methods often suffer from rigid reward functions and limited adaptability to evolving adversarial strategies.Moreover,most research assumes open airspace,overlooking the influence of potential obstacles.In this paper,we address one-on-one within-visual-range ACMD in obstructed environments,and propose an improved Soft Actor-Critic(SAC)algorithm trained under a curriculum self-play framework.A maneuver strategy mirroring inference module is integrated to estimate each other's likely positions when visual obstruction occurs.By leveraging curriculum learning to guide progressive experience accumulation and self-play for adversarial evolution,our method enhances both training efficiency and tactical diversity.We further integrate an attention mechanism that dynamically adjusts the weights of sub-rewards,enabling the learned policy to adapt to rapidly changing air combat situations.Numerical simulations demonstrate that our enhanced SAC converges more quickly and achieves higher win rates than other baseline methods.An animation is available at bilibili.com/video/BV1BHVszHE98 for better illustration.展开更多
The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurode...The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurodegenerative diseases are characterized by the progressive loss of neuronal structure and function.展开更多
Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models s...Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models show some promising results,they are generally limited by non-negligible drawbacks such as interpretability issues of feature learning.To address these issues,we propose a novel concept based on the shallow-to-deep correlation network representation regression(Sh-to-De CNRR).Our approach,shallow correlation network representation regression(ShCNRR),combines neural network and canonical correlation analysis thoughts to generate explainable features via shallow correlation network representation(CNR).A twin inverse network is then derived to obtain the explicit model output,leveraging the shallow CNR.To capture deeper nonlinear information,we extend ShCNRR into a hierarchical deep correlation network representation regression(DeCNRR)model that features stacked neural networks,enabling us to learn deeper CNR from process data.The feasibility and advantages of our proposals are validated by theoretical derivations and practical IP cases,which contain one MIQ regression and three MIQ-related fault detection tasks.The results reveal that highly fused statistical and neural network models yield superior monitoring performance compared to current state-of-the-art models,while statistical tests verify the convincing feature mining.展开更多
The chemical industry plays a critical role in supporting global economic development,yet its traditional production paradigms are associated with high resource consumption,energy demand,and environmental impact.To de...The chemical industry plays a critical role in supporting global economic development,yet its traditional production paradigms are associated with high resource consumption,energy demand,and environmental impact.To deal with the growing regulatory burden,societal demands,and environmental targets,eco-friendly processes in chemicals have become one of the major approaches to ensuring industrialization with environmental safety.This review includes an overall summary of the recent developments of green chemical processes with the focus on the basic principles,facilitating structures,and technologies that form the basis of sustainable chemical production.The most important advances in sustainable feedstocks,green catalysis,environmentally benign solvents,energy-efficient and intensified process technologies are also essential introductions,and the importance of digitalization,artificial intelligence,and life cycle-informed assessment tools in environmental performance optimization is also increasing.The review also discusses some of the barriers related to industry implementation,such as scalability,economic viability,and the necessity of having strong sustainability verification.Using chosen industrial case studies of China,South Korea,and Vietnam,various ways of integrating eco-friendly processes are demonstrated,including the adoption of renewable energy and low-carbon hydrogen,the adoption of circular plastics,and refinery energy optimization.Such examples demonstrate the significance of regional settings,system-level integration,and open environmental assessment in achieving significant sustainability results.The presented insights should guide the way future research proceeds and facilitate the shift toward the low-impact chemical manufacturing systems that are resilient.展开更多
In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical propert...In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical properties of rocks,the cracking processes of pre-cracked rocks have been extensively studied using numerical modeling methods.The peridynamics(PD)exhibits advantages over other numerical methods due to the absence of the requirements for remeshing and external crack growth criterion.However,for modeling pre-cracked rock cracking processes under impact,current PD implementations lack generally applicable rock constitutive models and impact contact models,which leads to difficulties in determining rock material parameters and efficiently calculating impact loads.This paper proposes a non-ordinary state-based peridynamics(NOSBPD)modeling method integrating the Drucker-Prager(DP)plasticity model and an efficient contact model to address the above problems.In the proposed method,the Drucker-Prager plasticity model is integrated into the NOSBPD,thereby equipping NOSBPD with the capability to accurately characterize the nonlinear stress-strain relationship inherent in rocks.An efficient contact model between particles and meshes is designed to calculate the impact loads,which is essentially a coupling method of PD with the finite element method(FEM).The effectiveness of the proposed NOSBPD modeling method is verified by comparison with other numerical methods and experiments.Experimental results indicate that the proposed method can effectively and accurately predict the 3D cracking processes of pre-cracked cracks under impact loading,and the maximum principal stress is the key driver behind wing crack formation in pre-cracked rocks.展开更多
To address the persistent challenge of dynamic mismatch between wellbore lifting capacity and reservoir fluid supply,and to establish a robust optimization framework for drainage operations in high-water-cut tight san...To address the persistent challenge of dynamic mismatch between wellbore lifting capacity and reservoir fluid supply,and to establish a robust optimization framework for drainage operations in high-water-cut tight sandstone gas reservoirs,this study systematically investigates the graded optimization and dynamic adaptation of drainage gas recovery technologies.Production data from a representative tight gas field were first employed to forecast reservoir performance.The predictive reliability was rigorously validated through high-precision history matching,thereby providing a quantitatively consistent foundation for subsequent wellbore optimization.Building on this characterization,a coupled simulation framework was developed that integrates wellbore multiphase flow modeling with nodal analysis based on the Inflow Performance Relationship,IPR,and the Vertical Lift Performance,VLP.This coordinated approach enables comprehensive evaluation of process adaptability and dynamic optimization of foam-assisted drainage,mechanical pumping,and jet pumping systems under evolving water-gas ratio,WGR conditions.The results reveal that a progressively increasing water-gas ratio is the dominant factor driving the transition from chemically assisted drainage methods to mechanically enhanced lifting technologies.A distinct quantitative threshold is identified at WGR≈0.002,beyond which mechanical intervention becomes more effective and economically justified.For mechanical pumping and jet pumping systems,a parameter inversion optimization strategy constrained by the target bottomhole flowing pressure,Pwf,is proposed to ensure stable production while maintaining reservoir drawdown control.In particular,the nozzle-to-throat area ratio of the jet pump is identified as the key governing parameter influencing entrainment capacity and lifting efficiency.Moreover,a configuration characterized by small pump diameter,long stroke length,and low operating speed is demonstrated to satisfy drainage requirements while mitigating torque fluctuations,enhancing volumetric efficiency,and improving pump fillage stability.展开更多
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
Electrocatalytic oxidation is a promising technology for wastewater treatment,but poor mass transfer and low current efficiency impaded its engineering applications.To address these issues,researchers have developed f...Electrocatalytic oxidation is a promising technology for wastewater treatment,but poor mass transfer and low current efficiency impaded its engineering applications.To address these issues,researchers have developed flow-through electrochemical reactors(FERs)primarily based on porous electrodes,where the pore structure significantly impacts the electrochemical reaction.Therefore,this study systematically investigated the impact of different pore sizes on the fluid dynamics,current potential distribution,mass transfer processes,and degradation performance of FERs.Computational Fluid Dynamics(CFD)results indicated that smaller pore sizes(10μm,30μm,and 60μm)significantly enhanced convective effects within the fluid,reduced short fluid paths and dead volume regions within the microchannels,and facilitated mass transfer processes.Additionally,smaller pore sizes were conducive to a uniform distribution of current density.Furthermore,Fe(CN)_(6)^(4−)oxidation experiments revealed that the current density at a pore size of 160μm was notably lower than that at 10μm,indicating slower mass transfer of Fe(CN)_(6)^(4−)within larger channels.Calculations based on experimental results demonstrated that the mass transfer rate at a pore size of 10μm was six times than that at 160μm,further confirming the enhancing effect of smaller pore sizes on the mass transfer process.Lastly,experiments on tetracycline degradation showed that at a residence time of 90 s,the removal efficiencies of tetracycline were 80%and 39.1%for porous electrodes with pore sizes of 10μm and 160μm,respectively,demonstrating the superior removal efficiency of smaller pore sizes for tetracycline degradation.展开更多
Selecting fires safety measures for road tunnels relies mainly on strict regulatory requirements. However, the choice should also be based on many different criteria and ranking of alternatives should take place. Exis...Selecting fires safety measures for road tunnels relies mainly on strict regulatory requirements. However, the choice should also be based on many different criteria and ranking of alternatives should take place. Existing methods exhibit lack in dealing rigorously with measures’ selection amongst different alternatives. This paper contributes to the body of knowledge by proposing a novel method, named EVADE, which aims to incorporate diverse stakeholders’ views and provide a meaningful ranking of alternatives.To do so, it estimates the tunnel level of safety taking into account only any standard measures existing. Subsequently, the performance of additional measures is examined.Then, a list of the most significant criteria that are valuable to judge the appropriateness of selected measures is introduced. The relative importance amongst the decision criteria is calculated through the Analytic Hierarchy Process, based on the expert opinion. Sensitivity analysis through Monte Carlo simulation is embedded to allow for a meaningful prioritization of the decision criteria. Thus, the alternatives’ ranking comes as a distribution instead of a single number, providing the decision-maker richer information for selecting the most suitable measure(s) according to the specific tunnel situation. At last, a typical tunnel is examined to showcase the utilization of the method.展开更多
Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and oper...Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.展开更多
In the coming decades, agricultural systems will have to adapt to tremendous challenges. Behavioral models have important potential to better understand and steer changes toward sustainability brought about by this co...In the coming decades, agricultural systems will have to adapt to tremendous challenges. Behavioral models have important potential to better understand and steer changes toward sustainability brought about by this context. Relying on a literature review, we distinguish incremental changes (extensions of what is already done) and transformational changes, which involve the reorientation of a considerable amount of farming activities. Transformational changes are particularly important in the context of global change. Existing integrated modelling frameworks based on behavioral theories are suited for incremental changes, but remain limited for transformational changes. Qualitative studies provide important insights on two key aspects of transformational changes, learning and social relations, but they have not been explicitly oriented toward computer modelling yet. Based on this literature and three seminal decision-making approaches, we propose a description of transformational change processes in farm decision-making, as a first step toward an implementation in agent-based models.展开更多
Based on analyzing the influences of a slicing scheme on stair-stepping effect, supporting structure, efficiency and deformation, etc. , analytical hierarchical process （AHP） combining with fuzzy synthetic evaluatio...Based on analyzing the influences of a slicing scheme on stair-stepping effect, supporting structure, efficiency and deformation, etc. , analytical hierarchical process （AHP） combining with fuzzy synthetic evaluation is introduced to make decision in slicing schemes for a processing part. The application in determining the slicing scheme for a computer mouse during prototyping shows that the method increases the rationality during decision- making and improves quality and efficiency for the prototyping part.展开更多
一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家...一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。展开更多
Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making i...Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making in real life driving, delphi approach and mathematical statistics method are introduced to construct pair-wise comparison judgment matrix of collision avoidance decision choices to each collision situation. Analytic hierarchy process （AHP） is adopted to establish the agents＇ collision avoidance decision-making model. To simulate drivers＇ characteristics, driver factors are added to categorize driving modes into impatient mode, normal mode, and the cautious mode. The results show that this model can simulate human＇s thinking process, and the agents in the virtual environment can deal with collision situations and make decisions to avoid collisions without intervention. The model can also reflect diversity and uncertainly of real life driving behaviors, and solves the multi-objective, multi-choice ranking priority problem in multi-vehicle collision scenarios. This collision avoidance model of multi-agents model is feasible and effective, and can provide richer and closer-to-life virtual scene for driving simulator, reflecting real-life traffic environment more truly, this model can also promote the practicality of driving simulator.展开更多
文摘The aim of this paper is to present and analyze the factors, motivations and criteria considered in the decision-making processes of the actors belonging to the biodiesel production chain in Brazil. The biodiesel production chain consists of three main agents： the farmers, the soybean processing plants and the oil refinery/distributor. For the farmers organized in cooperatives the central decision is whether to sell oil-bearing crops for the production of biodiesel. In contrast, for the soybean processing plants that convert the crops into vegetable and/or biodiesel, the decision to produce this fuel is based on the wish to expand their market portfolio. Government tax incentives strongly influence both decisions regarding which oil-bearing crop to use and the amount of vegetable oil to be transformed into biodiesel. Finally, the oil refinery/distributor is obliged by law to mix the biodiesel with the mineral diesel and perceives this as a liability. The results show the existence of different characteristics linked to the decision-making process and a significant lack of synchronicity in the aims and motivations behind the agents＇ decisions. This state of decisional misalignment leads to heightened uncertainty regarding the sustainability of the Brazilian biodiesel production program.
文摘Community participation and community based management are topical themes in current policy and discussion revolving around decision-making processes especially those dealing with natural resources management.This review shows that while governments have accepted the need to either cede or devolve control and management of natural resources to the local communities,the communities are not part and parcel of the planning and budgeting which are crucial in decisionmaking.Communities were seen to be more involved in the implementation of natural resource management programs but lacked ownership of the projects.This causes lack of commitment to the programs and at times hostile reaction from the communities.The communities are always at the receiving end when it pertains to losses in the exchange.Community participation was shown to be effective when the local population is involved not as co-operating users but as natural resource managers or owner managers.
文摘Decision-making is the process of deciding between two or more options in order to take the most appropriate and successful course of action in order to achieve sustainable mangrove management. However, the distinctiveness of mangrove as an ecosystem, and thus the attendant socio-economic and governance ramifications, causes the idea of decision making to become relatively distinct from other decision making process As a result, the purpose of this research was to evaluate the impact that community engagement plays in the decision-making process as it relates to the establishment of governance norms for sustainable mangrove management in Lamu County. In this study, a correlational research design was applied, and the researchers employed a mixed techniques approach. The target population was 296 respondents. The research used questionnaires and interviews to collect data. A descriptive statistical technique was utilized to perform an inspection and analysis on the data that was gathered. The findings indicated that having awareness about governance standards is beneficial during the process of making decisions. In addition, the findings demonstrated that respondents had the impression that the decision-making process was not done properly. On the other hand, the participants pointed out the positive aspects of the decision-making process and agreed that the participation of both gender was essential for the sustainable management of mangroves. Based on these data, it appeared that full community engagement in decision-making is necessary for sustainable management of mangrove forests.
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
文摘Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a framework for evaluating LLMs and physician decisions in challenging lung cancer cases.Methods:We curated 50 challenging lung cancer cases(25 local and 25 published)classified as complex,rare,or refractory.Blinded three-dimensional,five-point Likert evaluations(1–5 for comprehensiveness,specificity,and readability)compared standalone LLMs(DeepSeek R1,Claude 3.5,Gemini 1.5,and GPT-4o),physicians by experience level(junior,intermediate,and senior),and AI-assisted juniors;intergroup differences and augmentation effects were analyzed statistically.Results:Of 50 challenging cases(18 complex,17 rare,and 15 refractory)rated by three experts,DeepSeek R1 achieved scores of 3.95±0.33,3.71±0.53,and 4.26±0.18 for comprehensiveness,specificity,and readability,respectively,positioning it between intermediate(3.68,3.68,3.75)and senior(4.50,4.64,4.53)physicians.GPT-4o and Claude 3.5 reached intermediate physician–level comprehensiveness(3.76±0.39,3.60±0.39)but junior-to-intermediate physician–level specificity(3.39±0.39,3.39±0.49).All LLMs scored higher on rare cases than intermediate physicians but fell below junior physicians in refractory-case specificity.AIassisted junior physicians showed marked gains in rare cases,with comprehensiveness rising from 2.32 to 4.29(84.8%),specificity from 2.24 to 4.26(90.8%),and readability from 2.76 to 4.59(66.0%),while specificity declined by 3.2%(3.17 to 3.07)in refractory cases.Error analysis showed complementary strengths,with physicians demonstrating reasoning stability and LLMs excelling in knowledge updating and risk management.Conclusions:LLMs performed variably in clinical decision-making tasks depending on case type,performing better in rare cases and worse in refractory cases requiring longitudinal reasoning.Complementary strengths between LLMs and physicians support case-and task-tailored human–AI collaboration.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R259)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.Ashit Kumar Dutta would like to thank AlMaarefa University for supporting this research under project number MHIRSP2025017.
文摘Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.
基金support of the National Key Research and Development Plan(No.2021YFB3302501)the financial support of the National Science Foundation of China(No.12161076)the financial support of the Fundamental Research Funds for the Central Universities(No.DUT25GF207).
文摘With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,existing methods often suffer from rigid reward functions and limited adaptability to evolving adversarial strategies.Moreover,most research assumes open airspace,overlooking the influence of potential obstacles.In this paper,we address one-on-one within-visual-range ACMD in obstructed environments,and propose an improved Soft Actor-Critic(SAC)algorithm trained under a curriculum self-play framework.A maneuver strategy mirroring inference module is integrated to estimate each other's likely positions when visual obstruction occurs.By leveraging curriculum learning to guide progressive experience accumulation and self-play for adversarial evolution,our method enhances both training efficiency and tactical diversity.We further integrate an attention mechanism that dynamically adjusts the weights of sub-rewards,enabling the learned policy to adapt to rapidly changing air combat situations.Numerical simulations demonstrate that our enhanced SAC converges more quickly and achieves higher win rates than other baseline methods.An animation is available at bilibili.com/video/BV1BHVszHE98 for better illustration.
文摘The aging process is an inexorable fact throughout our lives and is considered a major factor in develo ping neurological dysfunctions associated with cognitive,emotional,and motor impairments.Aging-associated neurodegenerative diseases are characterized by the progressive loss of neuronal structure and function.
基金supported in part by the Pioneer Research and Development Program of Zhejiang(2025C01021)Zhejiang Province Postdoctoral Research Project Selection Fund(ZJ2025061)+3 种基金the National Science and Technology Major Project-Intelligent Manufacturing Systems and Robotics of China(2025ZD1602000,2025ZD1601800)the National Natural Science Foundation of China(61933015,62273030,62573387)the Natural Science Foundation of Zhejiang province,China(LY24F030004)the Fundamental Research Funds of Zhejiang Sci-Tech University(25222139-Y)。
文摘Ironmaking process(IP)is indispensable to modern iron and steel industry,where real-time monitoring is crucial for achieving high molten iron quality(MIQ)with low energy consumption.While neural network-based models show some promising results,they are generally limited by non-negligible drawbacks such as interpretability issues of feature learning.To address these issues,we propose a novel concept based on the shallow-to-deep correlation network representation regression(Sh-to-De CNRR).Our approach,shallow correlation network representation regression(ShCNRR),combines neural network and canonical correlation analysis thoughts to generate explainable features via shallow correlation network representation(CNR).A twin inverse network is then derived to obtain the explicit model output,leveraging the shallow CNR.To capture deeper nonlinear information,we extend ShCNRR into a hierarchical deep correlation network representation regression(DeCNRR)model that features stacked neural networks,enabling us to learn deeper CNR from process data.The feasibility and advantages of our proposals are validated by theoretical derivations and practical IP cases,which contain one MIQ regression and three MIQ-related fault detection tasks.The results reveal that highly fused statistical and neural network models yield superior monitoring performance compared to current state-of-the-art models,while statistical tests verify the convincing feature mining.
文摘The chemical industry plays a critical role in supporting global economic development,yet its traditional production paradigms are associated with high resource consumption,energy demand,and environmental impact.To deal with the growing regulatory burden,societal demands,and environmental targets,eco-friendly processes in chemicals have become one of the major approaches to ensuring industrialization with environmental safety.This review includes an overall summary of the recent developments of green chemical processes with the focus on the basic principles,facilitating structures,and technologies that form the basis of sustainable chemical production.The most important advances in sustainable feedstocks,green catalysis,environmentally benign solvents,energy-efficient and intensified process technologies are also essential introductions,and the importance of digitalization,artificial intelligence,and life cycle-informed assessment tools in environmental performance optimization is also increasing.The review also discusses some of the barriers related to industry implementation,such as scalability,economic viability,and the necessity of having strong sustainability verification.Using chosen industrial case studies of China,South Korea,and Vietnam,various ways of integrating eco-friendly processes are demonstrated,including the adoption of renewable energy and low-carbon hydrogen,the adoption of circular plastics,and refinery energy optimization.Such examples demonstrate the significance of regional settings,system-level integration,and open environmental assessment in achieving significant sustainability results.The presented insights should guide the way future research proceeds and facilitate the shift toward the low-impact chemical manufacturing systems that are resilient.
基金support from the National Natural Science Foundation of China(Grant Nos.42277161 and 42230709).
文摘In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical properties of rocks,the cracking processes of pre-cracked rocks have been extensively studied using numerical modeling methods.The peridynamics(PD)exhibits advantages over other numerical methods due to the absence of the requirements for remeshing and external crack growth criterion.However,for modeling pre-cracked rock cracking processes under impact,current PD implementations lack generally applicable rock constitutive models and impact contact models,which leads to difficulties in determining rock material parameters and efficiently calculating impact loads.This paper proposes a non-ordinary state-based peridynamics(NOSBPD)modeling method integrating the Drucker-Prager(DP)plasticity model and an efficient contact model to address the above problems.In the proposed method,the Drucker-Prager plasticity model is integrated into the NOSBPD,thereby equipping NOSBPD with the capability to accurately characterize the nonlinear stress-strain relationship inherent in rocks.An efficient contact model between particles and meshes is designed to calculate the impact loads,which is essentially a coupling method of PD with the finite element method(FEM).The effectiveness of the proposed NOSBPD modeling method is verified by comparison with other numerical methods and experiments.Experimental results indicate that the proposed method can effectively and accurately predict the 3D cracking processes of pre-cracked cracks under impact loading,and the maximum principal stress is the key driver behind wing crack formation in pre-cracked rocks.
基金supported by the Major Science and Technology Project of PetroChina Company Limited“Research on Key Technologies for Enhancing Recovery in Tight Sandstone Gas Reservoirs”,specifically under its third sub-project:“Research on Integrated Fracturing,Drainage,and Production Technology to Enhance Single-Well Production in Water-Bearing Gas Reservoirs”(Grant number:2023ZZ25YJ03).
文摘To address the persistent challenge of dynamic mismatch between wellbore lifting capacity and reservoir fluid supply,and to establish a robust optimization framework for drainage operations in high-water-cut tight sandstone gas reservoirs,this study systematically investigates the graded optimization and dynamic adaptation of drainage gas recovery technologies.Production data from a representative tight gas field were first employed to forecast reservoir performance.The predictive reliability was rigorously validated through high-precision history matching,thereby providing a quantitatively consistent foundation for subsequent wellbore optimization.Building on this characterization,a coupled simulation framework was developed that integrates wellbore multiphase flow modeling with nodal analysis based on the Inflow Performance Relationship,IPR,and the Vertical Lift Performance,VLP.This coordinated approach enables comprehensive evaluation of process adaptability and dynamic optimization of foam-assisted drainage,mechanical pumping,and jet pumping systems under evolving water-gas ratio,WGR conditions.The results reveal that a progressively increasing water-gas ratio is the dominant factor driving the transition from chemically assisted drainage methods to mechanically enhanced lifting technologies.A distinct quantitative threshold is identified at WGR≈0.002,beyond which mechanical intervention becomes more effective and economically justified.For mechanical pumping and jet pumping systems,a parameter inversion optimization strategy constrained by the target bottomhole flowing pressure,Pwf,is proposed to ensure stable production while maintaining reservoir drawdown control.In particular,the nozzle-to-throat area ratio of the jet pump is identified as the key governing parameter influencing entrainment capacity and lifting efficiency.Moreover,a configuration characterized by small pump diameter,long stroke length,and low operating speed is demonstrated to satisfy drainage requirements while mitigating torque fluctuations,enhancing volumetric efficiency,and improving pump fillage stability.
基金supported by the Research Program of the Liaoning Liaohe Laboratory(LLL23ZZ-05-012)China Academy of Engineering Institute of Land Cooperation Consulting Project(2023-DFZD-60-02)+3 种基金the Key Research and Development Program of Liaoning Province(2023JH26/10200011)the National Natural Science Foundation of China(61991404)the National Key Research and Development Program of China(2024YFB3309700)the Science and Technology Major Project 2024 of Liaoning Province(2024JH1/11700048).
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.U22A20241 and 21876105)Shaanxi“Scientist&Engineer”Team(No.2023KXJ-131)Xianyang Key S&T Special Projects(No.L2023-ZDKJ-QCY-SXGG-GY-007).
文摘Electrocatalytic oxidation is a promising technology for wastewater treatment,but poor mass transfer and low current efficiency impaded its engineering applications.To address these issues,researchers have developed flow-through electrochemical reactors(FERs)primarily based on porous electrodes,where the pore structure significantly impacts the electrochemical reaction.Therefore,this study systematically investigated the impact of different pore sizes on the fluid dynamics,current potential distribution,mass transfer processes,and degradation performance of FERs.Computational Fluid Dynamics(CFD)results indicated that smaller pore sizes(10μm,30μm,and 60μm)significantly enhanced convective effects within the fluid,reduced short fluid paths and dead volume regions within the microchannels,and facilitated mass transfer processes.Additionally,smaller pore sizes were conducive to a uniform distribution of current density.Furthermore,Fe(CN)_(6)^(4−)oxidation experiments revealed that the current density at a pore size of 160μm was notably lower than that at 10μm,indicating slower mass transfer of Fe(CN)_(6)^(4−)within larger channels.Calculations based on experimental results demonstrated that the mass transfer rate at a pore size of 10μm was six times than that at 160μm,further confirming the enhancing effect of smaller pore sizes on the mass transfer process.Lastly,experiments on tetracycline degradation showed that at a residence time of 90 s,the removal efficiencies of tetracycline were 80%and 39.1%for porous electrodes with pore sizes of 10μm and 160μm,respectively,demonstrating the superior removal efficiency of smaller pore sizes for tetracycline degradation.
文摘Selecting fires safety measures for road tunnels relies mainly on strict regulatory requirements. However, the choice should also be based on many different criteria and ranking of alternatives should take place. Existing methods exhibit lack in dealing rigorously with measures’ selection amongst different alternatives. This paper contributes to the body of knowledge by proposing a novel method, named EVADE, which aims to incorporate diverse stakeholders’ views and provide a meaningful ranking of alternatives.To do so, it estimates the tunnel level of safety taking into account only any standard measures existing. Subsequently, the performance of additional measures is examined.Then, a list of the most significant criteria that are valuable to judge the appropriateness of selected measures is introduced. The relative importance amongst the decision criteria is calculated through the Analytic Hierarchy Process, based on the expert opinion. Sensitivity analysis through Monte Carlo simulation is embedded to allow for a meaningful prioritization of the decision criteria. Thus, the alternatives’ ranking comes as a distribution instead of a single number, providing the decision-maker richer information for selecting the most suitable measure(s) according to the specific tunnel situation. At last, a typical tunnel is examined to showcase the utilization of the method.
基金supported by the National Key Research and Development Program of China (2020YFB1713800)the National Natural Science Foundation of China (92267205)+1 种基金the Hunan Provincial Innovation Foundation for Postgraduate (CX2022 0267)the Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0181)。
文摘Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.
文摘In the coming decades, agricultural systems will have to adapt to tremendous challenges. Behavioral models have important potential to better understand and steer changes toward sustainability brought about by this context. Relying on a literature review, we distinguish incremental changes (extensions of what is already done) and transformational changes, which involve the reorientation of a considerable amount of farming activities. Transformational changes are particularly important in the context of global change. Existing integrated modelling frameworks based on behavioral theories are suited for incremental changes, but remain limited for transformational changes. Qualitative studies provide important insights on two key aspects of transformational changes, learning and social relations, but they have not been explicitly oriented toward computer modelling yet. Based on this literature and three seminal decision-making approaches, we propose a description of transformational change processes in farm decision-making, as a first step toward an implementation in agent-based models.
基金Supported by the Science and Technology Support Key Project of Jiangsu Province (DE2008365)~~
文摘Based on analyzing the influences of a slicing scheme on stair-stepping effect, supporting structure, efficiency and deformation, etc. , analytical hierarchical process （AHP） combining with fuzzy synthetic evaluation is introduced to make decision in slicing schemes for a processing part. The application in determining the slicing scheme for a computer mouse during prototyping shows that the method increases the rationality during decision- making and improves quality and efficiency for the prototyping part.
文摘一、作为哲学的AI for Process(一)郭为的哲学思想1.郭为是谁郭为是谁?他是一位哲学家。顺便说,他同时还领导着神州数码。为什么说郭为是哲学家呢?因为他在著作中谈到高深的哲学,如“数据如水,奔流不息,无界融合”。他引述古希腊哲学家赫拉克利特所说的“万物流转”,又说“你不能两次踏进同一条河流,因为新的水不断地流过你的身旁”,他所表达的意思是“世界上唯一不变的就是变化”。
基金supported by National Basic Research Program (973 Program,No.2004CB719402)National Natural Science Foundation of China (No.60736019)Natural Science Foundation of Zhejiang Province, China(No.Y105430).
文摘Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making in real life driving, delphi approach and mathematical statistics method are introduced to construct pair-wise comparison judgment matrix of collision avoidance decision choices to each collision situation. Analytic hierarchy process （AHP） is adopted to establish the agents＇ collision avoidance decision-making model. To simulate drivers＇ characteristics, driver factors are added to categorize driving modes into impatient mode, normal mode, and the cautious mode. The results show that this model can simulate human＇s thinking process, and the agents in the virtual environment can deal with collision situations and make decisions to avoid collisions without intervention. The model can also reflect diversity and uncertainly of real life driving behaviors, and solves the multi-objective, multi-choice ranking priority problem in multi-vehicle collision scenarios. This collision avoidance model of multi-agents model is feasible and effective, and can provide richer and closer-to-life virtual scene for driving simulator, reflecting real-life traffic environment more truly, this model can also promote the practicality of driving simulator.
