Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to t...Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to tasks involving medical images.We provide an experimental insight into this phenomenon:CLIP is insensitive to the class names of medical images.For instance,replacing the class name“medulloblastoma”(a type of brain tumor)with“dog”in prompts has minimal impact on performance,a phenomenon not observed with natural images.To realign prompt learning with medical image recognition,we propose a novel prompt learning strategy,termed prompt reverse learning(PeLen).Different from the existing methods that adapt CLIP’s representations to downstream tasks,PeLen adapts task-specific representations to CLIP’s representations.Built upon the insensitivity to the class names of medical images,PeLen designates natural images and their class names to represent a specific class of medical images and class names,e.g.,allowing the image and text of a dog to correspond to the image and text of medulloblastoma.Consequently,PeLen learns prompts to align the representations between the medical images and the visual and textual representations of natural images.Our experiments demonstrate the efficacy of PeLen for medical image recognition.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
In this work,100 nm gate-length InP-based high electron mobility transistors(HEMTs)with a composite InGaAs/InAs/InGaAs channel are fabricated.DC measurements indicate that the InAs channel enhances transconductance bu...In this work,100 nm gate-length InP-based high electron mobility transistors(HEMTs)with a composite InGaAs/InAs/InGaAs channel are fabricated.DC measurements indicate that the InAs channel enhances transconductance but shifts the peak point toward lower V_(gs) under high V_(ds) bias.Peak separation analysis reveals the DC transconductance curve is composed of two components:the gate-controlled transconductance and the impact-ionization-induced additional transconductance.Further analysis demonstrates that the anomalous shift originates from the channel impact ionization intensity variation,which is caused by changes in the gate-drain electric field rather than the carrier density in the channel.Two additional current sources are introduced in the small-signal model to characterize the impact-ionization-induced transconductance,and the numerical variation trends of their parameters are consistent with the peak separation results,which validate the mechanism's correctness.RF measurements confirm that the DC transconductance enhancement does not effectively improve RF characteristics,which is attributed to the ionization-induced transconductance having a time constant significantly larger than that of conventional transconductance components.These findings provide a theoretical foundation for controlling the impact-ionization.展开更多
This paper presents an efficient model reduction technique for linear time-varying systems based on shifted Legendre polynomials.The approach constructs approximate low-rank decomposition factors of finite-time Gramia...This paper presents an efficient model reduction technique for linear time-varying systems based on shifted Legendre polynomials.The approach constructs approximate low-rank decomposition factors of finite-time Gramians directly from the expansion coefficients of impulse responses.Leveraging these factors,we develop two model reduction algorithms that integrate the low-rank square root method with dominant subspace projection.Our method is computationally efficient and flexible,requiring only a few matrix-vector operations and a singular value decomposition of a low-dimensional matrix,thereby avoiding the need to solve differential Lyapunov equations.Numerical experiments confirm the effectiveness of the proposed approach.展开更多
Nonlinear classification models are widely used in various fields due to their excellent performance in handling complex problems.This paper investigates the learning performance of nonlinear classification models bas...Nonlinear classification models are widely used in various fields due to their excellent performance in handling complex problems.This paper investigates the learning performance of nonlinear classification models based on Markov sampling,which builds upon the traditional framework using i.i.d.samples.Subsequently,we introduce a ueMC-NL algorithm,tailored specifically for nonlinear classification models,facilitating the production of ueMC samples from a finite dataset.Numerical investigations on the random forest and the MLP model reveal that nonlinear classification models utilizing ueMC samples yield lower misclassification rates compared to i.i.d.samples.展开更多
This paper focused on the modeling of microbial fermentation processes under varying production environments and proposed a novel approach.Considering that the dynamic characteristics of microorganisms differ across g...This paper focused on the modeling of microbial fermentation processes under varying production environments and proposed a novel approach.Considering that the dynamic characteristics of microorganisms differ across growth stages,we introduced the concept of multi-stage sensitivity analysis,in which each stage was investigated separately.The fuzzy C-means(FCM)algorithm was employed to cluster process data under nominal conditions,thereby dividing the penicillin fermentation process into distinct growth stages.Based on this division,the Latin hypercube sampling with partial rank correlation coefficient(LHS-EPRCC)method was applied to conduct sensitivity analysis for each stage,identifying an importance parameter set(IPS)that corresponds to the stage-specific growth characteristics.Re-estimation and correction of the IPS were then performed to enhance the predictive accuracy of the model.In a penicillin fermentation process deviating from nominal conditions,the proposed method was applied for model correction.Simulation results demonstrate that the corrected model aligns well with the actual process,thereby verifying the effectiveness of the proposed multistage sensitivity analysis approach in addressing complex fermentation processes and environmental uncertainties.展开更多
Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is empl...Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is employed to simulate the ballistic impact of 7.62 mm armor-piercing projectiles on Aluminum AA5083-H116 and Steel Secure 500 armors,focusing on the evaluation of material deformation and penetration resistance at varying impact points.While the D-shaped armor plate is penetrated by the armor-piercing projectiles,the combination of the perforated D-shaped and base armor plates successfully halts penetration.A numerical model based on the finite element method is developed using software such as SolidWorks and ANSYS to analyze the interaction between radiator armor and bullet.The perforated design of radiator armor is to maintain airflow for radiator function,with hole sizes smaller than the bullet core diameter to protect radiator assemblies.Predictions are made regarding the brittle fracture resulting from the projectile core′s bending due to asymmetric impact,and the resulting fragments failed to penetrate the perforated base armor plate.Craters are formed on the surface of the perforated D-shaped armor plate due to the impact of projectile fragments.The numerical model accurately predicts hole growth and projectile penetration upon impact with the armor,demonstrating effective protection of the radiator assemblies by the radiator armor.展开更多
Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple l...Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple label matching.However,previous studies have largely modeled this task as syndrome pattern classification within a fixed label space,which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning,and is also insufficient to capture the openness,semantic variability,and cross-disease reusability of syndrome manifestation expression.This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought(PR-CoT)supervision into large language models(LLMs)could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer.Methods Syndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information(X)→pathogenesis structure(Z)→syndrome pattern output(Y),where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment.Within this framework,a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders.After preprocessing,information extraction,manual proofreading,and data cleaning,the dataset comprised 4800 training cases,400 development cases,and 400 test cases.Each sample was annotated with a structured PR-CoT consisting of three progressive levels:clinical information summarization,comprehensive pathogenesis analysis,and syndrome pattern output.Supervised fine-tuning was conducted on open-source LLMs,with an end-to-end model serving as the baseline.Qwen3-32B was used as the primary experimental model,and Qwen3-14B as the scale comparison model.A progressive multidimensional evaluation framework was further established,comprising a structural parsing level,a semantic similarity level,and an expert blind review level.At the structural parsing level,syndrome pattern expressions were decomposed into structural elements and evaluated using Precision,Recall,F1 score,and Jaccard similarity.At the semantic similarity level,independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns.At the expert blind review level,three TCM experts independently evaluated model outputs on two dimensions:syndrome differentiation consistency and terminology standardization of syndrome patterns.In addition,zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets.Results At the structural parsing level,PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components.Compared with the corresponding baselines,neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision.In contrast,at the semantic similarity level,PR-CoT supervision produced stable positive gains across different model scales and evaluation systems.The average semantic score of Qwen3-32B increased from 6.4258 in the baseline model to 6.5850 after PR-CoT supervision,and that of Qwen3-14B increased from 5.8700 to 5.9642.At the expert blind review level,the overall score of Qwen3-32B(PR-CoT)was 7.0260±0.1077,higher than 6.4163±0.2889 for its baseline.In zero-shot cross-disease testing,the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets,indicating a certain degree of transferability.Conclusion The benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility,rather than in improved hard matching of structural elements.These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures,rather than as a classification task within a traditional fixed label space.By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework,this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment,interpretability,and multi-level evaluation.展开更多
War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient an...War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.展开更多
Weak rock-concrete interfaces significantly affect the stability of underground supporting structures.This study aims to investigate the effects of interface inclination on the creep behavior and failure mechanisms of...Weak rock-concrete interfaces significantly affect the stability of underground supporting structures.This study aims to investigate the effects of interface inclination on the creep behavior and failure mechanisms of composite specimens.Seven rock-concrete composite specimens with different inclination angles under hydrothermal curing at 60℃were prepared,and uniaxial and graded loading creep tests were completed.The results show that with increasing interface inclination,the compressive strength of the composite specimens initially decreases and then increases,with the minimum strength observed at inclinations between 60°and 75°.Three typical failure patterns were identified:axial failure,composite failure,and interface failure.The creep failure strength exceeded the uniaxial compressive strength,indicating improved time-dependent deformation resistance under elevated temperature curing.The instantaneous strain initially decreases and then increases as the interface inclination angle grows.Compared to 0°inclination,specimens with 45°,60°,75°,and 90°inclinations exhibited reductions in instantaneous strain of 2.64%,18.84%,23.29%,and 0.73%,respectively.The steady-state creep rate and creep ratio exhibited a decrease-stabilization-increase trend with increasing stress levels.Creep strain increased with increasing stress levels for all inclinations,with a sharp increase near the failure stage.A nonlinear constitutive model considering interface inclination and creep damage was developed based on damage theory.A nonlinear damage-based constitutive model incorporating interface inclination effects was developed,and its theoretical predictions closely matched the experimental data in all creep stages.These findings provide a quantitative understanding of creep failure mechanisms in rock-concrete interfaces and provide practical references for enhancing the safety of underground support systems.展开更多
A machine learning-based APP may quickly and non-destructively evaluate the quality of parameters,such as hardness and anthocyanin content in blue honeysuckle berries(Lonicera caerulea L.,BHB),based on changes in peri...A machine learning-based APP may quickly and non-destructively evaluate the quality of parameters,such as hardness and anthocyanin content in blue honeysuckle berries(Lonicera caerulea L.,BHB),based on changes in pericarp color characteristics.The color feature information of the BHB pericarp was extracted,and the corresponding hardness and anthocyanin content were determined at various growing stages.Correlation analysis of BHB quality indexes was conducted by single and combined components of BHB epidermal color features.The results showed that fruit hardness had a significantly negative correlation with color feature parameter R-G,and its anthocyanin content had a significantly positive correlation with color feature parameter R.Comparing the eight models,random forest(RF)was established to evaluate the hardness and anthocyanin content of BHB according to the correlation between pericarp color features and hardness and anthocyanin content on BHB quality evaluation APP on the WeChat platform.The credibility of APP embedding RF model for evaluating hardness and anthocyanin content in BHB was validated with the determination coefficient of 0.89 and 0.93 in practice.This approach could efficiently and conveniently evaluate the quality indexes of BHB in real time and serve as a technical reference for the detection of quality indicators of other berries using smartphones.展开更多
To predict the ranking of the country’s innovation capability in the world in real-time,this study designs a twophased prediction model based on the pairwise comparison.Data from the global innovation index(GII)repor...To predict the ranking of the country’s innovation capability in the world in real-time,this study designs a twophased prediction model based on the pairwise comparison.Data from the global innovation index(GII)reports are employed in this study.Countries with different income levels have shown different development inertias,the two-phased prediction model is thus proposed.In the first phase,the GII data from the previous year are applied to predict the ranking of innovation capability for high-income countries.In the second phase,more years of historical data are adopted to predict the innovation ranking for other countries.The global innovation rankings for all countries and economies are thus obtained.Experiments have proved that the model requires only a few indicators to get accurate results.The model provides real-time decision support for decision-makers to formulate innovative development policies.展开更多
In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis...In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis nonlinear characteristics of piezo-positioning actuator.The static nonlinear part and dynamic linear part of the Hammerstein model are represented by models obtained through the Prandtl-Ishlinskii(PI)model and Hankel matrix system identification method,respectively.This model demonstrates good generalization capability for typical input frequencies below 200 Hz.A sliding mode inverse compensation tracking control strategy based on P-I inverse model and integral augmentation is proposed.Experimental results show that compared with PID inverse compensation control and sliding mode control without inverse compensation,the sliding mode inverse compensation control has a more ideal step response and no overshoot,moreover,the settling time is only 6.2 ms.In the frequency domain,the system closed-loop tracking bandwidth reaches 119.9 Hz,and the disturbance rejection bandwidth reaches 86.2 Hz.The proposed control strategy can effectively compensate the hysteresis nonlinearity,and improve the tracking accuracy and antidisturbance capability of piezo-positioning system.展开更多
To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conduc...To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.展开更多
To investigate the effects of water and cyclic loading on dolomite’s mechanical properties during deep mining,mechanical experiments on non-pressure water absorption and cyclic loading were conducted.The findings rev...To investigate the effects of water and cyclic loading on dolomite’s mechanical properties during deep mining,mechanical experiments on non-pressure water absorption and cyclic loading were conducted.The findings reveal that the elastic modulus and Poisson ratio of dolomite fluctuate with increasing water content.The mass of water absorption is positively correlated with time and the water absorption stage can be divided into three stages:accelerated,decelerated,and stabilized stages.During this process,the number of pores in dolomite increases,while the pore diameter initially decreases and then fluctuates.Microscopic analysis shows that the proportion of mesopores first increases and then decreases,while micropores exhibit the opposite trend,and the proportion of macropores fluctuates around 0%.A model diagram of structural evolution during water absorption has been developed.Additionally,the softening process of dolomite’s water absorption strength is categorized into three stages:a relatively stable stage,an accelerated softening stage dominated by mesopore water absorption,and a decelerated softening stage characterized by micropore water absorption.A uniaxial damage constitutive model for dolomite under water influence was established based on the Weibull distribution and Mohr-Coulomb strength criterion,and experimental validation indicates its strong applicability.展开更多
Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi...Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.展开更多
基金supported by the National Natural Science Foundation of China(62222117).
文摘Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to tasks involving medical images.We provide an experimental insight into this phenomenon:CLIP is insensitive to the class names of medical images.For instance,replacing the class name“medulloblastoma”(a type of brain tumor)with“dog”in prompts has minimal impact on performance,a phenomenon not observed with natural images.To realign prompt learning with medical image recognition,we propose a novel prompt learning strategy,termed prompt reverse learning(PeLen).Different from the existing methods that adapt CLIP’s representations to downstream tasks,PeLen adapts task-specific representations to CLIP’s representations.Built upon the insensitivity to the class names of medical images,PeLen designates natural images and their class names to represent a specific class of medical images and class names,e.g.,allowing the image and text of a dog to correspond to the image and text of medulloblastoma.Consequently,PeLen learns prompts to align the representations between the medical images and the visual and textual representations of natural images.Our experiments demonstrate the efficacy of PeLen for medical image recognition.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
基金Supported by the National Natural Science Foundation of China(62474195)。
文摘In this work,100 nm gate-length InP-based high electron mobility transistors(HEMTs)with a composite InGaAs/InAs/InGaAs channel are fabricated.DC measurements indicate that the InAs channel enhances transconductance but shifts the peak point toward lower V_(gs) under high V_(ds) bias.Peak separation analysis reveals the DC transconductance curve is composed of two components:the gate-controlled transconductance and the impact-ionization-induced additional transconductance.Further analysis demonstrates that the anomalous shift originates from the channel impact ionization intensity variation,which is caused by changes in the gate-drain electric field rather than the carrier density in the channel.Two additional current sources are introduced in the small-signal model to characterize the impact-ionization-induced transconductance,and the numerical variation trends of their parameters are consistent with the peak separation results,which validate the mechanism's correctness.RF measurements confirm that the DC transconductance enhancement does not effectively improve RF characteristics,which is attributed to the ionization-induced transconductance having a time constant significantly larger than that of conventional transconductance components.These findings provide a theoretical foundation for controlling the impact-ionization.
文摘This paper presents an efficient model reduction technique for linear time-varying systems based on shifted Legendre polynomials.The approach constructs approximate low-rank decomposition factors of finite-time Gramians directly from the expansion coefficients of impulse responses.Leveraging these factors,we develop two model reduction algorithms that integrate the low-rank square root method with dominant subspace projection.Our method is computationally efficient and flexible,requiring only a few matrix-vector operations and a singular value decomposition of a low-dimensional matrix,thereby avoiding the need to solve differential Lyapunov equations.Numerical experiments confirm the effectiveness of the proposed approach.
文摘Nonlinear classification models are widely used in various fields due to their excellent performance in handling complex problems.This paper investigates the learning performance of nonlinear classification models based on Markov sampling,which builds upon the traditional framework using i.i.d.samples.Subsequently,we introduce a ueMC-NL algorithm,tailored specifically for nonlinear classification models,facilitating the production of ueMC samples from a finite dataset.Numerical investigations on the random forest and the MLP model reveal that nonlinear classification models utilizing ueMC samples yield lower misclassification rates compared to i.i.d.samples.
基金National Natural Science Foundation of China(62471204,62473175,62403215,61833007)Natural Science Foundation of Jiangsu Province(BK20241607,BE2023022-2)Research start-up fund for high-level talent(928201/186)。
文摘This paper focused on the modeling of microbial fermentation processes under varying production environments and proposed a novel approach.Considering that the dynamic characteristics of microorganisms differ across growth stages,we introduced the concept of multi-stage sensitivity analysis,in which each stage was investigated separately.The fuzzy C-means(FCM)algorithm was employed to cluster process data under nominal conditions,thereby dividing the penicillin fermentation process into distinct growth stages.Based on this division,the Latin hypercube sampling with partial rank correlation coefficient(LHS-EPRCC)method was applied to conduct sensitivity analysis for each stage,identifying an importance parameter set(IPS)that corresponds to the stage-specific growth characteristics.Re-estimation and correction of the IPS were then performed to enhance the predictive accuracy of the model.In a penicillin fermentation process deviating from nominal conditions,the proposed method was applied for model correction.Simulation results demonstrate that the corrected model aligns well with the actual process,thereby verifying the effectiveness of the proposed multistage sensitivity analysis approach in addressing complex fermentation processes and environmental uncertainties.
文摘Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is employed to simulate the ballistic impact of 7.62 mm armor-piercing projectiles on Aluminum AA5083-H116 and Steel Secure 500 armors,focusing on the evaluation of material deformation and penetration resistance at varying impact points.While the D-shaped armor plate is penetrated by the armor-piercing projectiles,the combination of the perforated D-shaped and base armor plates successfully halts penetration.A numerical model based on the finite element method is developed using software such as SolidWorks and ANSYS to analyze the interaction between radiator armor and bullet.The perforated design of radiator armor is to maintain airflow for radiator function,with hole sizes smaller than the bullet core diameter to protect radiator assemblies.Predictions are made regarding the brittle fracture resulting from the projectile core′s bending due to asymmetric impact,and the resulting fragments failed to penetrate the perforated base armor plate.Craters are formed on the surface of the perforated D-shaped armor plate due to the impact of projectile fragments.The numerical model accurately predicts hole growth and projectile penetration upon impact with the armor,demonstrating effective protection of the radiator assemblies by the radiator armor.
文摘Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple label matching.However,previous studies have largely modeled this task as syndrome pattern classification within a fixed label space,which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning,and is also insufficient to capture the openness,semantic variability,and cross-disease reusability of syndrome manifestation expression.This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought(PR-CoT)supervision into large language models(LLMs)could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer.Methods Syndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information(X)→pathogenesis structure(Z)→syndrome pattern output(Y),where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment.Within this framework,a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders.After preprocessing,information extraction,manual proofreading,and data cleaning,the dataset comprised 4800 training cases,400 development cases,and 400 test cases.Each sample was annotated with a structured PR-CoT consisting of three progressive levels:clinical information summarization,comprehensive pathogenesis analysis,and syndrome pattern output.Supervised fine-tuning was conducted on open-source LLMs,with an end-to-end model serving as the baseline.Qwen3-32B was used as the primary experimental model,and Qwen3-14B as the scale comparison model.A progressive multidimensional evaluation framework was further established,comprising a structural parsing level,a semantic similarity level,and an expert blind review level.At the structural parsing level,syndrome pattern expressions were decomposed into structural elements and evaluated using Precision,Recall,F1 score,and Jaccard similarity.At the semantic similarity level,independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns.At the expert blind review level,three TCM experts independently evaluated model outputs on two dimensions:syndrome differentiation consistency and terminology standardization of syndrome patterns.In addition,zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets.Results At the structural parsing level,PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components.Compared with the corresponding baselines,neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision.In contrast,at the semantic similarity level,PR-CoT supervision produced stable positive gains across different model scales and evaluation systems.The average semantic score of Qwen3-32B increased from 6.4258 in the baseline model to 6.5850 after PR-CoT supervision,and that of Qwen3-14B increased from 5.8700 to 5.9642.At the expert blind review level,the overall score of Qwen3-32B(PR-CoT)was 7.0260±0.1077,higher than 6.4163±0.2889 for its baseline.In zero-shot cross-disease testing,the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets,indicating a certain degree of transferability.Conclusion The benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility,rather than in improved hard matching of structural elements.These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures,rather than as a classification task within a traditional fixed label space.By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework,this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment,interpretability,and multi-level evaluation.
文摘War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.
基金Project(52174141)supported by the National Natural Foundation of ChinaProject(2023A313)supported by the Science and Technology Plan of Huainan City,ChinaProject(2024C943)supported by the Postdoctoral Research of Anhui Province,China。
文摘Weak rock-concrete interfaces significantly affect the stability of underground supporting structures.This study aims to investigate the effects of interface inclination on the creep behavior and failure mechanisms of composite specimens.Seven rock-concrete composite specimens with different inclination angles under hydrothermal curing at 60℃were prepared,and uniaxial and graded loading creep tests were completed.The results show that with increasing interface inclination,the compressive strength of the composite specimens initially decreases and then increases,with the minimum strength observed at inclinations between 60°and 75°.Three typical failure patterns were identified:axial failure,composite failure,and interface failure.The creep failure strength exceeded the uniaxial compressive strength,indicating improved time-dependent deformation resistance under elevated temperature curing.The instantaneous strain initially decreases and then increases as the interface inclination angle grows.Compared to 0°inclination,specimens with 45°,60°,75°,and 90°inclinations exhibited reductions in instantaneous strain of 2.64%,18.84%,23.29%,and 0.73%,respectively.The steady-state creep rate and creep ratio exhibited a decrease-stabilization-increase trend with increasing stress levels.Creep strain increased with increasing stress levels for all inclinations,with a sharp increase near the failure stage.A nonlinear constitutive model considering interface inclination and creep damage was developed based on damage theory.A nonlinear damage-based constitutive model incorporating interface inclination effects was developed,and its theoretical predictions closely matched the experimental data in all creep stages.These findings provide a quantitative understanding of creep failure mechanisms in rock-concrete interfaces and provide practical references for enhancing the safety of underground support systems.
基金Supported by the National Natural Science Foundation of China(32072352)the National Key Research and Development Program Project of China(2022YFD1600500)。
文摘A machine learning-based APP may quickly and non-destructively evaluate the quality of parameters,such as hardness and anthocyanin content in blue honeysuckle berries(Lonicera caerulea L.,BHB),based on changes in pericarp color characteristics.The color feature information of the BHB pericarp was extracted,and the corresponding hardness and anthocyanin content were determined at various growing stages.Correlation analysis of BHB quality indexes was conducted by single and combined components of BHB epidermal color features.The results showed that fruit hardness had a significantly negative correlation with color feature parameter R-G,and its anthocyanin content had a significantly positive correlation with color feature parameter R.Comparing the eight models,random forest(RF)was established to evaluate the hardness and anthocyanin content of BHB according to the correlation between pericarp color features and hardness and anthocyanin content on BHB quality evaluation APP on the WeChat platform.The credibility of APP embedding RF model for evaluating hardness and anthocyanin content in BHB was validated with the determination coefficient of 0.89 and 0.93 in practice.This approach could efficiently and conveniently evaluate the quality indexes of BHB in real time and serve as a technical reference for the detection of quality indicators of other berries using smartphones.
基金supported by the National Natural Science Foundation of China(71901212,72071206)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘To predict the ranking of the country’s innovation capability in the world in real-time,this study designs a twophased prediction model based on the pairwise comparison.Data from the global innovation index(GII)reports are employed in this study.Countries with different income levels have shown different development inertias,the two-phased prediction model is thus proposed.In the first phase,the GII data from the previous year are applied to predict the ranking of innovation capability for high-income countries.In the second phase,more years of historical data are adopted to predict the innovation ranking for other countries.The global innovation rankings for all countries and economies are thus obtained.Experiments have proved that the model requires only a few indicators to get accurate results.The model provides real-time decision support for decision-makers to formulate innovative development policies.
文摘In order to enhance the control performance of piezo-positioning system,the influence of hysteresis characteristics and its compensation method are studied.Hammerstein model is used to represent the dynamic hysteresis nonlinear characteristics of piezo-positioning actuator.The static nonlinear part and dynamic linear part of the Hammerstein model are represented by models obtained through the Prandtl-Ishlinskii(PI)model and Hankel matrix system identification method,respectively.This model demonstrates good generalization capability for typical input frequencies below 200 Hz.A sliding mode inverse compensation tracking control strategy based on P-I inverse model and integral augmentation is proposed.Experimental results show that compared with PID inverse compensation control and sliding mode control without inverse compensation,the sliding mode inverse compensation control has a more ideal step response and no overshoot,moreover,the settling time is only 6.2 ms.In the frequency domain,the system closed-loop tracking bandwidth reaches 119.9 Hz,and the disturbance rejection bandwidth reaches 86.2 Hz.The proposed control strategy can effectively compensate the hysteresis nonlinearity,and improve the tracking accuracy and antidisturbance capability of piezo-positioning system.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0714).
文摘To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.
基金Project(IMRI23005)supported by Ordos Science and Technology Bureau,ChinaProjects(52174096,52304110)supported by the National Natural Science Foundation of China。
文摘To investigate the effects of water and cyclic loading on dolomite’s mechanical properties during deep mining,mechanical experiments on non-pressure water absorption and cyclic loading were conducted.The findings reveal that the elastic modulus and Poisson ratio of dolomite fluctuate with increasing water content.The mass of water absorption is positively correlated with time and the water absorption stage can be divided into three stages:accelerated,decelerated,and stabilized stages.During this process,the number of pores in dolomite increases,while the pore diameter initially decreases and then fluctuates.Microscopic analysis shows that the proportion of mesopores first increases and then decreases,while micropores exhibit the opposite trend,and the proportion of macropores fluctuates around 0%.A model diagram of structural evolution during water absorption has been developed.Additionally,the softening process of dolomite’s water absorption strength is categorized into three stages:a relatively stable stage,an accelerated softening stage dominated by mesopore water absorption,and a decelerated softening stage characterized by micropore water absorption.A uniaxial damage constitutive model for dolomite under water influence was established based on the Weibull distribution and Mohr-Coulomb strength criterion,and experimental validation indicates its strong applicability.
基金National Natural Science Foundation of China(71690233,71971213,71901214)。
文摘Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.
