Contemporary society is confronted with multifaceted challenges,and the intricate interplay of interconnected factors significantly complicates emergency response efforts.Current practices rely on quick decisions by d...Contemporary society is confronted with multifaceted challenges,and the intricate interplay of interconnected factors significantly complicates emergency response efforts.Current practices rely on quick decisions by domain experts;however,the limitations of individual expertise and the urgency of crises hinder both precision and standardization.To address these issues,we propose a novel approach:an intelligent method for emergency decision-making grounded in a standardized digital knowledge graph.First,our study examined the underlying theory of standardized digital transformation and event-chain evolution.This led to the construction of a knowledge graph encompassing standard emergency knowledge,as well as supplementary derivative data pertinent to event response.Second,through the application of semantic analysis and intention recognition of the decision target,coherent and interpretable query sentences for the decision system were crafted.These query sentences then served as a conduit for retrieving standard emergency knowledge relevant to the current emergency situation,as well as potential secondary disasters.The overarching goal is to provide emergency decision makers with effective support mechanisms that are both well informed and tailored to the specific demands of each situation.展开更多
Ultrasonic-Assisted Grinding(UAG)is a novel manufacturing technology that shows promising promise for use in processing Ceramic Matrix Composites(CMCs).Nevertheless,analyzing the material removal process of CMCs with ...Ultrasonic-Assisted Grinding(UAG)is a novel manufacturing technology that shows promising promise for use in processing Ceramic Matrix Composites(CMCs).Nevertheless,analyzing the material removal process of CMCs with multidirectional structure during UAG is challenging,impeding the progress and improvement of the UAG process.This work examined the impact of ultrasonic vibration on the dynamic mechanical characteristics during processing.Additionally,we experimentally elucidated the material removal mechanism of CMCs during the scratching process under the influence of vertical vibration.The results indicate that the introduction of ultrasonic vibration causes a strain rate effect,resulting in a modification of the material removal mechanism,subsequently impacting the processing quality.Ultrasonic vibration increases the dynamic strength and brittleness of the fibers in CMCs,leading to more cracks at fracture,which changes from the original bending fracture to shear fracture.In addition,ultrasonic vibration can effectively inhibit the impact of scratching depth and anisotropy on the removal mechanism of CMCs,resulting in a more uniform surface of CMCs after processing.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
Self-suspended proppants,which enable clear-water fracturing,represent a promising new class of materials for reservoir stimulation.Given the economic limitations associated with their exclusive use,this study investi...Self-suspended proppants,which enable clear-water fracturing,represent a promising new class of materials for reservoir stimulation.Given the economic limitations associated with their exclusive use,this study investigates proppant transport behavior in hybrid systems combining self-suspended proppants with conventional 40/70 mesh quartz sand at various mixing ratios.A dedicated experimental apparatus was developed to replicate field-relevant complex fracture networks,consisting of a main fracture and two branching fractures with different deflection angles.Using this system,sand bank formation and proppant distribution were examined for both conventional quartz sand fracturing and fracturing augmented with self-suspended proppants.The effects of slurry discharge volume,proppant mixing ratio,sand ratio,and injection location of the self-suspended proppant on transport and placement behavior were systematically analyzed.According to the results,the incorporation of self-suspended proppants markedly enhances the proppant-carrying capacity of the slurry and substantially modifies sand bank morphology.Increasing the discharge volume raises the inlet slope angle and promotes greater proppant penetration into branch fractures.The proportion of self-suspended proppant governs slurry viscoelasticity and,consequently,proppant settling behavior.As the fraction of self-suspended proppant decreases,the equilibrium height of the sand bank increases,while the proppant mass fraction within branch fractures exhibits a non-monotonic response,initially decreasing and then increasing.Variations in sand ratio alter both overall proppant concentration and the self-suspended proppant-to-water ratio,thereby modulating slurry rheology and influencing proppant placement.In addition,changes in injection location affect near-wellbore vortex structures,leading to distinct sand bank morphologies.展开更多
The latest generation of aero engines has set higher standards for thrust-to-weight ratio and energy conversion efficiency,making it imperative to address the challenge of efficiently and accurately machining film coo...The latest generation of aero engines has set higher standards for thrust-to-weight ratio and energy conversion efficiency,making it imperative to address the challenge of efficiently and accurately machining film cooling holes.It has been demonstrated that conventional long-pulse lasers are incapable of meeting the elevated quality surface finish requirements for these holes,a consequence of the severe thermal defects.The employment of backside water-assisted laser drilling technology confers a number of distinct advantages in terms of mitigating laser thermal damage,thus representing a highly promising solution to this challenge.However,significant accumulation of bubbles and machining products during the backside water-assisted laser drilling process has been demonstrated to have a detrimental effect on laser transmission and machining stability,thereby reducing machining quality.In order to surmount these challenges,a novel method has been proposed,namely an ultrasonic shock water flow-assisted picosecond laser drilling technique.Numerical models for ultrasonic acoustic streaming and particle tracking for machining product transport have been established to investigate the mechanism.The simulation results demonstrated that the majority of the machining products could rapidly move away from the machining area because of the action of acoustic streaming,thereby avoiding the accumulation of bubbles and products.Subsequent analysis,comparing the process performance in micro-hole machining,confirmed that the ultrasonic field could effectively eliminate bubble and chip accumulation,thus significantly improving micro-hole quality.Furthermore,the impact of ultrasonic and laser parameters on micro-hole quality under varying machining methods was thoroughly investigated.The findings demonstrated that the novel methodology outlined in this study yielded superior-quality micro-holes at elevated ultrasonic and laser power levels,in conjunction with reduced laser frequency and scanning velocity.The taper of the micro-holes produced by the new method was reduced by more than 25%compared with the other conventional methods.展开更多
Twinning-induced plasticity(TWIP)steel was processed using electrically assisted friction stir welding(EFSW).The microstructure,mechanical properties,and deformation behavior of the welded joints were systematically i...Twinning-induced plasticity(TWIP)steel was processed using electrically assisted friction stir welding(EFSW).The microstructure,mechanical properties,and deformation behavior of the welded joints were systematically investigated.The results show that the average grain size was refined from 3.67μm in the base material(BM)to 1.39μm in the stir zone(SZ),while it increased to 4.19μm in the heat-affected zone(HAZ).The fraction of twin boundaries(TBs)decreased from 20.7%in the BM to 6.9%in the SZ and increased to24.5%in the HAZ.The ultimate tensile strength,yield strength,and elongation of the BM were 1021 MPa,505 MPa,and 65.8%,respectively.In comparison,the EFSW joint exhibited values of 1055 MPa,561 MPa,and 60.8%,corresponding to 103.3%,111.1%,and 92.4%of those of the BM,respectively.During tensile testing,plastic deformation was primarily concentrated in the BM,although both the SZ and HAZ also exhibited notable plastic deformation.Fracture ultimately occurred in the BM.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
Based on the Bismuth-Corlette classification of hilar cholangiocarcinoma,the patients with types I,II,and III can undergo radical resection in the absence of extensive intrahepatic metastasis and vascular invasion[1]....Based on the Bismuth-Corlette classification of hilar cholangiocarcinoma,the patients with types I,II,and III can undergo radical resection in the absence of extensive intrahepatic metastasis and vascular invasion[1].Depending on the scope of tumor invasion in bile duct,a combined resection of parts of the liver,hepatic ducts,common bile ducts,regional lymph nodes,and even parts of the duodenum and pancreas is necessary,along with biliary and gastrointestinal reconstructions[2].The surgical plan is complex,involving a large resection area and significant trauma.In recent years,laparoscopic or robot assisted radical resection of hilar cholangiocarcinoma has been applied clinically[3,4].With the advanced laparoscopic equipment,many patients undergo hepatopancreatoduodenectomy successfully[5].The limitations of traditional laparoscopic techniques restrict their wide application in clinical practice.However,the Da Vinci robot has been widely applied due to its clear field of vision and flexible manipulation.However,its utilization in hepato-pancreatoduodenectomy for hilar cholangiocarcinoma is still relatively rare.Here,we report a case with hilar cholangiocarcinoma at clinical stage IIIb who underwent robot-assisted hepato-pancreatoduodenectomy.展开更多
Genomic disorders affecting the central nervous system(CNS)are among the most complex and devastating conditions in human health.Moreover,these disorders,such as Rett syndrome,spinal muscular atrophy,and Fragile X syn...Genomic disorders affecting the central nervous system(CNS)are among the most complex and devastating conditions in human health.Moreover,these disorders,such as Rett syndrome,spinal muscular atrophy,and Fragile X syndrome,are typically caused by mutations in genes essential for neural development,synaptic function,or cellular homeostasis.Despite the genetic diversity involved,these diseases share key pathological features,including progressive neurodegeneration,disruption of neural circuits,and loss of cognitive or motor function.Meanwhile,one of the significant clinical challenges in treating CNS disorders is the limited regenerative capacity of the adult nervous system,which makes reversing disease progression extremely difficult once symptoms appear.In addition,the blood-brain barrier(BBB)restricts the passage of most systemically administered therapeutics,further complicating effective intervention.Consequently,current treatment options remain largely palliative,and effective cures remain elusive.展开更多
Cases of widespread bone hydatid infection are relatively rare in clinical practice.In this study,we reported for the first time a validated integrated repair therapy for multiple bone tissues,including the hip,femur,...Cases of widespread bone hydatid infection are relatively rare in clinical practice.In this study,we reported for the first time a validated integrated repair therapy for multiple bone tissues,including the hip,femur,and knee,caused by echinococ cosis.Artificial intelligence(AI)was used to develop a targeted surgical plan and to design a personalized prosthesis.Finite element analysis(FEA)was used to optimize the mechanical effectiveness of a customized integrated replacement prosthesis and to model stress distribution in the surrounding bone.Three-dimensional(3 D)printing was used to fabricate a customized prosthesis.With the assistance of AI,FEA,and 3 D printing technology,a personalized surgical plan and customized prosthesis were successfully constructed based on the patient’s disease.This approach achieved a successful therapeutic effect,demonstrating that AI-assisted personalized medicine holds great promise for the future.展开更多
In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to ...In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.展开更多
The critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific research.PROMs encapsulate a pati...The critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific research.PROMs encapsulate a patient's health status directly from their perspective,encompassing various domains such as symptom severity,functional status,and overall quality of life.By integrating PROMs into routine clinical practice and research,healthcare providers can achieve a more nuanced understanding of patient experiences and tailor treatments accordingly.The deployment of PROMs supports dynamic patient-provider interactions,fostering better patient engagement and adherence to tre-atment plans.Moreover,PROMs are pivotal in clinical settings for monitoring disease progression and treatment efficacy,particularly in chronic and mental health conditions.However,challenges in implementing PROMs include data collection and management,integration into existing health systems,and acceptance by patients and providers.Overcoming these barriers necessitates technological advancements,policy development,and continuous education to enhance the acceptability and effectiveness of PROMs.The paper concludes with recommendations for future research and policy-making aimed at optimizing the use and impact of PROMs across healthcare settings.展开更多
1.Introduction.Cold Spray(CS)is a highly advanced solid-state metal depo-sition process that was first developed in the 1980s.This innovative technique involves the high-speed(300-1200 m/s)impact deposition of micron-...1.Introduction.Cold Spray(CS)is a highly advanced solid-state metal depo-sition process that was first developed in the 1980s.This innovative technique involves the high-speed(300-1200 m/s)impact deposition of micron-sized particles(5-50μm)to fabricate coatings[1-3].CS has been extensively used in a variety of coating applications,such as aerospace,automotive,energy,medical,marine,and others,to provide protection against high temperatures,corrosion,erosion,oxidation,and chemicals[4,5].Nowadays,the technical interest in CS is twofold:(i)as a repair process for damaged components,and(ii)as a solid-state additive manufacturing process.Compared to other fusion-based additive manufacturing(AM)technologies,Cold Spray Additive Manufacturing(CSAM)is a new member of the AM family that can enable the fabrication of deposits without undergoing melting.The chemical composition has been largely preserved from the powder to the deposit due to the minimal oxidation.The significant advantages of CSAM over other additive manufacturing processes include a high production rate,unlimited deposition size,high flexibility,and suitability for repairing damaged parts.展开更多
To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforce...To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.展开更多
Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pP...Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.展开更多
We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We...We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We systematically analyze the technical pathways for deploying LLMs in disaster scenarios,emphasizing three breakthrough directions:(1)knowledge graph-driven dynamic risk modeling,(2)reinforcement learning-optimized emergency decision systems,and(3)secure local deployment architectures.The DeepSeek model demonstrates unique advantages through its hybrid reasoning mechanism combining semantic analysis with geospatial pattern recognition,enabling cost-effective processing of multi-source data spanning historical disaster records,real-time IoT sensor feeds,and socio-environmental parameters.A modular system architecture is designed to achieve three critical objectives:(a)automated construction of domain-specific knowledge graphs through unsupervised learning of disaster physics relationships,(b)scenario-adaptive resource allocation using risk simulations,and(c)preserving emergency coordination via federated learning across distributed response nodes.The proposed local deployment paradigm addresses critical data security concerns in cross-border disaster management while complying with the FAIR principles(Findable,Accessible,Interoperable,Reusable)for geoscientific data governance.This work establishes a methodological foundation for next-generation AI-earth science convergence in disaster mitigation.展开更多
基金funded by the National Natural Science Foundation of China(Grant No 52304273)the Opening Fund of Key Laboratory of Civil Aviation Emergency Science&Technology(CAAC)(NJ2022022)the Fundamental Research Funds for the Central Universities(2023ZKPYAQ07).
文摘Contemporary society is confronted with multifaceted challenges,and the intricate interplay of interconnected factors significantly complicates emergency response efforts.Current practices rely on quick decisions by domain experts;however,the limitations of individual expertise and the urgency of crises hinder both precision and standardization.To address these issues,we propose a novel approach:an intelligent method for emergency decision-making grounded in a standardized digital knowledge graph.First,our study examined the underlying theory of standardized digital transformation and event-chain evolution.This led to the construction of a knowledge graph encompassing standard emergency knowledge,as well as supplementary derivative data pertinent to event response.Second,through the application of semantic analysis and intention recognition of the decision target,coherent and interpretable query sentences for the decision system were crafted.These query sentences then served as a conduit for retrieving standard emergency knowledge relevant to the current emergency situation,as well as potential secondary disasters.The overarching goal is to provide emergency decision makers with effective support mechanisms that are both well informed and tailored to the specific demands of each situation.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(No.52325506)the Fundamental Research Funds for the Central Universities(No.DUT22LAB501)。
文摘Ultrasonic-Assisted Grinding(UAG)is a novel manufacturing technology that shows promising promise for use in processing Ceramic Matrix Composites(CMCs).Nevertheless,analyzing the material removal process of CMCs with multidirectional structure during UAG is challenging,impeding the progress and improvement of the UAG process.This work examined the impact of ultrasonic vibration on the dynamic mechanical characteristics during processing.Additionally,we experimentally elucidated the material removal mechanism of CMCs during the scratching process under the influence of vertical vibration.The results indicate that the introduction of ultrasonic vibration causes a strain rate effect,resulting in a modification of the material removal mechanism,subsequently impacting the processing quality.Ultrasonic vibration increases the dynamic strength and brittleness of the fibers in CMCs,leading to more cracks at fracture,which changes from the original bending fracture to shear fracture.In addition,ultrasonic vibration can effectively inhibit the impact of scratching depth and anisotropy on the removal mechanism of CMCs,resulting in a more uniform surface of CMCs after processing.
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.
基金the China National Petroleum Corporation’s Forward-Looking Fundamental Technology Breakthrough Project(2021DJ2305).
文摘Self-suspended proppants,which enable clear-water fracturing,represent a promising new class of materials for reservoir stimulation.Given the economic limitations associated with their exclusive use,this study investigates proppant transport behavior in hybrid systems combining self-suspended proppants with conventional 40/70 mesh quartz sand at various mixing ratios.A dedicated experimental apparatus was developed to replicate field-relevant complex fracture networks,consisting of a main fracture and two branching fractures with different deflection angles.Using this system,sand bank formation and proppant distribution were examined for both conventional quartz sand fracturing and fracturing augmented with self-suspended proppants.The effects of slurry discharge volume,proppant mixing ratio,sand ratio,and injection location of the self-suspended proppant on transport and placement behavior were systematically analyzed.According to the results,the incorporation of self-suspended proppants markedly enhances the proppant-carrying capacity of the slurry and substantially modifies sand bank morphology.Increasing the discharge volume raises the inlet slope angle and promotes greater proppant penetration into branch fractures.The proportion of self-suspended proppant governs slurry viscoelasticity and,consequently,proppant settling behavior.As the fraction of self-suspended proppant decreases,the equilibrium height of the sand bank increases,while the proppant mass fraction within branch fractures exhibits a non-monotonic response,initially decreasing and then increasing.Variations in sand ratio alter both overall proppant concentration and the self-suspended proppant-to-water ratio,thereby modulating slurry rheology and influencing proppant placement.In addition,changes in injection location affect near-wellbore vortex structures,leading to distinct sand bank morphologies.
基金supported by the National Natural Science Foundation of China(No.52205468,No.52275431,No.52375186)China Postdoctoral Science Foundation(No.2025M771349)Zhejiang Province Natural Science Foundation(No.LD22E050001)。
文摘The latest generation of aero engines has set higher standards for thrust-to-weight ratio and energy conversion efficiency,making it imperative to address the challenge of efficiently and accurately machining film cooling holes.It has been demonstrated that conventional long-pulse lasers are incapable of meeting the elevated quality surface finish requirements for these holes,a consequence of the severe thermal defects.The employment of backside water-assisted laser drilling technology confers a number of distinct advantages in terms of mitigating laser thermal damage,thus representing a highly promising solution to this challenge.However,significant accumulation of bubbles and machining products during the backside water-assisted laser drilling process has been demonstrated to have a detrimental effect on laser transmission and machining stability,thereby reducing machining quality.In order to surmount these challenges,a novel method has been proposed,namely an ultrasonic shock water flow-assisted picosecond laser drilling technique.Numerical models for ultrasonic acoustic streaming and particle tracking for machining product transport have been established to investigate the mechanism.The simulation results demonstrated that the majority of the machining products could rapidly move away from the machining area because of the action of acoustic streaming,thereby avoiding the accumulation of bubbles and products.Subsequent analysis,comparing the process performance in micro-hole machining,confirmed that the ultrasonic field could effectively eliminate bubble and chip accumulation,thus significantly improving micro-hole quality.Furthermore,the impact of ultrasonic and laser parameters on micro-hole quality under varying machining methods was thoroughly investigated.The findings demonstrated that the novel methodology outlined in this study yielded superior-quality micro-holes at elevated ultrasonic and laser power levels,in conjunction with reduced laser frequency and scanning velocity.The taper of the micro-holes produced by the new method was reduced by more than 25%compared with the other conventional methods.
基金financially supported by the National Natural Science Foundation of China(Nos.52034005,52227807,52104383,and 52222410)the Shaanxi Province National Science Fund for Distinguished Young Scholars,China(No.2022JC-24)+1 种基金the Key Research and Development Program of Shaanxi Province,China(No.2022JBGS2-01)the Central Guidance on Local Science and TechnologyDevelopment Fund of Shaanxi Province,China(No.2024ZY-JCYJ-04-09).
文摘Twinning-induced plasticity(TWIP)steel was processed using electrically assisted friction stir welding(EFSW).The microstructure,mechanical properties,and deformation behavior of the welded joints were systematically investigated.The results show that the average grain size was refined from 3.67μm in the base material(BM)to 1.39μm in the stir zone(SZ),while it increased to 4.19μm in the heat-affected zone(HAZ).The fraction of twin boundaries(TBs)decreased from 20.7%in the BM to 6.9%in the SZ and increased to24.5%in the HAZ.The ultimate tensile strength,yield strength,and elongation of the BM were 1021 MPa,505 MPa,and 65.8%,respectively.In comparison,the EFSW joint exhibited values of 1055 MPa,561 MPa,and 60.8%,corresponding to 103.3%,111.1%,and 92.4%of those of the BM,respectively.During tensile testing,plastic deformation was primarily concentrated in the BM,although both the SZ and HAZ also exhibited notable plastic deformation.Fracture ultimately occurred in the BM.
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Based on the Bismuth-Corlette classification of hilar cholangiocarcinoma,the patients with types I,II,and III can undergo radical resection in the absence of extensive intrahepatic metastasis and vascular invasion[1].Depending on the scope of tumor invasion in bile duct,a combined resection of parts of the liver,hepatic ducts,common bile ducts,regional lymph nodes,and even parts of the duodenum and pancreas is necessary,along with biliary and gastrointestinal reconstructions[2].The surgical plan is complex,involving a large resection area and significant trauma.In recent years,laparoscopic or robot assisted radical resection of hilar cholangiocarcinoma has been applied clinically[3,4].With the advanced laparoscopic equipment,many patients undergo hepatopancreatoduodenectomy successfully[5].The limitations of traditional laparoscopic techniques restrict their wide application in clinical practice.However,the Da Vinci robot has been widely applied due to its clear field of vision and flexible manipulation.However,its utilization in hepato-pancreatoduodenectomy for hilar cholangiocarcinoma is still relatively rare.Here,we report a case with hilar cholangiocarcinoma at clinical stage IIIb who underwent robot-assisted hepato-pancreatoduodenectomy.
基金the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2024-00344633)HYC acknowledges the financial support from the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2023-00211360)Biomaterials Specialized Graduate Program through the Korea Environmental Industry&Technology Institute(KEITI)funded by the Ministry of Environment(MOE).
文摘Genomic disorders affecting the central nervous system(CNS)are among the most complex and devastating conditions in human health.Moreover,these disorders,such as Rett syndrome,spinal muscular atrophy,and Fragile X syndrome,are typically caused by mutations in genes essential for neural development,synaptic function,or cellular homeostasis.Despite the genetic diversity involved,these diseases share key pathological features,including progressive neurodegeneration,disruption of neural circuits,and loss of cognitive or motor function.Meanwhile,one of the significant clinical challenges in treating CNS disorders is the limited regenerative capacity of the adult nervous system,which makes reversing disease progression extremely difficult once symptoms appear.In addition,the blood-brain barrier(BBB)restricts the passage of most systemically administered therapeutics,further complicating effective intervention.Consequently,current treatment options remain largely palliative,and effective cures remain elusive.
基金partially supported by the National Natural Science Foundation of China(Nos.32471474 and 82102574)the Precision Medicine Project of People’s Hospital of Xinjiang Uygur Autonomous Region(No.20220305)+4 种基金Chengdu Advanced Metal Materials Industry Technology Research Institute Co.,Ltd.Support Project(No.24H0802)Sichuan Science and Technology Program(Nos.2025YFHZ0086,2023YFS0053,2024YFHZ0125,and 2025ZNSFSC0381)Project of Tianfu Jincheng Laboratory(No.2025ZH009)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515220102)Xinjiang Autonomous Region Science and Technology Support Project Plan(Directive)Project(No.2024E02049)。
文摘Cases of widespread bone hydatid infection are relatively rare in clinical practice.In this study,we reported for the first time a validated integrated repair therapy for multiple bone tissues,including the hip,femur,and knee,caused by echinococ cosis.Artificial intelligence(AI)was used to develop a targeted surgical plan and to design a personalized prosthesis.Finite element analysis(FEA)was used to optimize the mechanical effectiveness of a customized integrated replacement prosthesis and to model stress distribution in the surrounding bone.Three-dimensional(3 D)printing was used to fabricate a customized prosthesis.With the assistance of AI,FEA,and 3 D printing technology,a personalized surgical plan and customized prosthesis were successfully constructed based on the patient’s disease.This approach achieved a successful therapeutic effect,demonstrating that AI-assisted personalized medicine holds great promise for the future.
文摘In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.
文摘The critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific research.PROMs encapsulate a patient's health status directly from their perspective,encompassing various domains such as symptom severity,functional status,and overall quality of life.By integrating PROMs into routine clinical practice and research,healthcare providers can achieve a more nuanced understanding of patient experiences and tailor treatments accordingly.The deployment of PROMs supports dynamic patient-provider interactions,fostering better patient engagement and adherence to tre-atment plans.Moreover,PROMs are pivotal in clinical settings for monitoring disease progression and treatment efficacy,particularly in chronic and mental health conditions.However,challenges in implementing PROMs include data collection and management,integration into existing health systems,and acceptance by patients and providers.Overcoming these barriers necessitates technological advancements,policy development,and continuous education to enhance the acceptability and effectiveness of PROMs.The paper concludes with recommendations for future research and policy-making aimed at optimizing the use and impact of PROMs across healthcare settings.
基金supported by the National Natural Science Foundation of China(No.52061135101 and 52001078)the German Research Foundation(DFG,No.448318292)+3 种基金the Technology Innovation Guidance Special Foundation of Shaanxi Province(No.2023GXLH-085)the Fundamental Research Funds for the Central Universities(No.D5000240161)the Project of Key areas of innovation team in Shaanxi Province(No.2024RS-CXTD-20)The author Yingchun Xie thanks the support from the National Key R&D Program(No.2023YFE0108000).
文摘1.Introduction.Cold Spray(CS)is a highly advanced solid-state metal depo-sition process that was first developed in the 1980s.This innovative technique involves the high-speed(300-1200 m/s)impact deposition of micron-sized particles(5-50μm)to fabricate coatings[1-3].CS has been extensively used in a variety of coating applications,such as aerospace,automotive,energy,medical,marine,and others,to provide protection against high temperatures,corrosion,erosion,oxidation,and chemicals[4,5].Nowadays,the technical interest in CS is twofold:(i)as a repair process for damaged components,and(ii)as a solid-state additive manufacturing process.Compared to other fusion-based additive manufacturing(AM)technologies,Cold Spray Additive Manufacturing(CSAM)is a new member of the AM family that can enable the fabrication of deposits without undergoing melting.The chemical composition has been largely preserved from the powder to the deposit due to the minimal oxidation.The significant advantages of CSAM over other additive manufacturing processes include a high production rate,unlimited deposition size,high flexibility,and suitability for repairing damaged parts.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFE0117100)National Science Foundation of China(Grant No.52102468,52325212)Fundamental Research Funds for the Central Universities。
文摘To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.
基金supported by the Deanship of Graduate Studies and Scientific Research at Qassim University(QU-APC-2024-9/1).
文摘Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.
基金funded by the Chongqing Water Resources Bureau,China(Project No.CQS24C00836).
文摘We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We systematically analyze the technical pathways for deploying LLMs in disaster scenarios,emphasizing three breakthrough directions:(1)knowledge graph-driven dynamic risk modeling,(2)reinforcement learning-optimized emergency decision systems,and(3)secure local deployment architectures.The DeepSeek model demonstrates unique advantages through its hybrid reasoning mechanism combining semantic analysis with geospatial pattern recognition,enabling cost-effective processing of multi-source data spanning historical disaster records,real-time IoT sensor feeds,and socio-environmental parameters.A modular system architecture is designed to achieve three critical objectives:(a)automated construction of domain-specific knowledge graphs through unsupervised learning of disaster physics relationships,(b)scenario-adaptive resource allocation using risk simulations,and(c)preserving emergency coordination via federated learning across distributed response nodes.The proposed local deployment paradigm addresses critical data security concerns in cross-border disaster management while complying with the FAIR principles(Findable,Accessible,Interoperable,Reusable)for geoscientific data governance.This work establishes a methodological foundation for next-generation AI-earth science convergence in disaster mitigation.
