Huazhong University of Science and Technology (HUST) is a key research-based comprehensive university in Wuhan, China,under the direct supervision of the Ministry of Education of China. Renowned as the epitome of the ...Huazhong University of Science and Technology (HUST) is a key research-based comprehensive university in Wuhan, China,under the direct supervision of the Ministry of Education of China. Renowned as the epitome of the development of higher education in new China, it is a “211” Project,“985” Project,“Double First-Class” university in China.展开更多
Cross-talk between tumor cells and mechanical stress in the tumor microenvironment has been shown to be involved in carcinogenesis.High mechanical stress in tumors can alter the metabolism and behaviors of cancer cell...Cross-talk between tumor cells and mechanical stress in the tumor microenvironment has been shown to be involved in carcinogenesis.High mechanical stress in tumors can alter the metabolism and behaviors of cancer cells and cause cancer cells to attain cancer stem-like cell properties,thus driving tumor progression and promoting metastasis.The mechanical signal is converted into a biochemical signal that activates tumorigenic signaling pathways through mechanotransduction.Herein,we describe the physical changes occurring during reprogramming of cancer cell metabolism,which regulate cancer stem cell functions and promote tumor progression and aggression.Furthermore,we highlight emerging therapeutic strategies targeting mechanotransduction signaling pathways.展开更多
Evolution of eukaryotes from simple cells to complex multicellular organisms remains a mystery. Our postulate is that cytoskeletal stiffening is a necessary condition for evolution of complex multicellular organisms f...Evolution of eukaryotes from simple cells to complex multicellular organisms remains a mystery. Our postulate is that cytoskeletal stiffening is a necessary condition for evolution of complex multicellular organisms from early simple eukaryotes. Recent findings show that embryonic stem cells are as soft as primitive eukaryotes-amoebae and that differentiated tissue cells can be two orders of magnitude stiffer than embryonic stem cells. Soft embryonic stem cells become stiff as they differentiate into tissue cells of the complex multicellular organisms to match their microenvironment stiffness. We perhaps see in differentiation of embryonic stem cells (derived from inner cell mass cells) the echo of those early evolutionary events. Early soft unicellular organisms might have evolved to stiffen their cytoskeleton to protect their structural integrity from external mechanical stresses while being able to maintain form, to change shape, and to move.展开更多
A brachial plexus injury model was established in rabbits by stretching the C6 nerve root. Imme- diately after the stretching, a suspension of human amniotic epithelial cells was injected into the injured brachial ple...A brachial plexus injury model was established in rabbits by stretching the C6 nerve root. Imme- diately after the stretching, a suspension of human amniotic epithelial cells was injected into the injured brachial plexus. The results of tensile mechanical testing of the brachial plexus showed that the tensile elastic limit strain, elastic limit stress, maximum stress, and maximum strain of the injured brachial plexuses were significantly increased at 24 weeks after the injection. The treatment clearly improved the pathological morphology of the injured brachial plexus nerve, as seen by hematoxylin eosin staining, and the functions of the rabbit forepaw were restored. These data indicate that the injection of human amniotic epithelial cells contributed to the repair of brachial plexus injury, and that this technique may transform into current clinical treatment strategies.展开更多
Zero-emission eco-friendly vehicles with partly or fully electric powertrains have exhibited rapidly increased demand for reducing the emissions of air pollutants and improving the energy efficiency. Advanced catalyti...Zero-emission eco-friendly vehicles with partly or fully electric powertrains have exhibited rapidly increased demand for reducing the emissions of air pollutants and improving the energy efficiency. Advanced catalytic and energy materials are essential as the significant portions in the key technologies of eco-friendly vehicles, such as the exhaust emission control system,power lithium ion battery and hydrogen fuel cell. Precise synthesis and surface modification of the functional materials and electrodes are required to satisfy the efficient surface and interface catalysis, as well as rapid electron/ion transport. Atomic layer deposition(ALD), an atomic and close-to-atomic scale manufacturing method, shows unique characteristics of precise thickness control, uniformity and conformality for film deposition, which has emerged as an important technique to design and engineer advanced catalytic and energy materials. This review has summarized recent process of ALD on the controllable preparation and modification of metal and oxide catalysts, as well as lithium ion battery and fuel cell electrodes. The enhanced catalytic and electrochemical performances are discussed with the unique nanostructures prepared by ALD. Recent works on ALD reactors for mass production are highlighted. The challenges involved in the research and development of ALD on the future practical applications are presented, including precursor and deposition process investigation, practical device performance evaluation, large-scale and efficient production, etc.展开更多
It is well-known that optimizing the wheel system of lunar rovers is essential.However,this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers.In this study,an experi...It is well-known that optimizing the wheel system of lunar rovers is essential.However,this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers.In this study,an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its performance.First,a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension,considering the complex terrain of the moon.Next,the performance was optimized under the limitations of preserving the slope passage and minimizing power consumption.This was achieved via analysis of the wheel force during movement.Finally,the effectiveness of the proposed method was demonstrated by several simulation experiments.The newly designed wheel can protrude on demand and reduce energy consumption;it can be used as a reference for lunar rover development engineering in China.展开更多
The effects of adding alloy element zinc on the static and dynamic mechanical properties of copper-zinc alloy were investigated. Tensile and low cycle fatigue behaviors of the C11000 copper and H63 copper-zinc alloy w...The effects of adding alloy element zinc on the static and dynamic mechanical properties of copper-zinc alloy were investigated. Tensile and low cycle fatigue behaviors of the C11000 copper and H63 copper-zinc alloy were obtained by using a miniature tester that combined the functions of in situ tensile and fatigue testing. A piezoelectric actuator was adopted as the actuator for the fatigue testing, and the feasibility of the fatigue actuator was verified by the transient harmonic response analysis based on static tensile preload and dynamic sinusoidal load. The experimental results show that the yield strength and tensile strength of the C11000 copper are improved after adding 37%(mass fraction) zinc, and H63 copper-zinc alloy presents more obvious cyclic hardening behavior and more consumed irreversible plastic work during each stress cycle compared with C11000 copper for the same strain controlled cycling. Additionally, based on the Manson-Coffin theory, the strain-life equations of the two materials were also obtained. C11000 copper and H63 copper-zinc alloy show transition life of 16832 and 1788 cycles, respectively.展开更多
Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a c...Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a crucial topic of research.With advances in deep learning,researchers have developed numerous methods that combine Transformers and convolutional neural networks(CNNs)to create highly accurate models for medical image segmentation.However,efforts to further enhance accuracy by developing larger and more complex models or training with more extensive datasets,significantly increase computational resource consumption.To address this problem,we propose BiCLIP-nnFormer(the prefix"Bi"refers to the use of two distinct CLIP models),a virtual multimodal instrument that leverages CLIP models to enhance the segmentation performance of a medical segmentation model nnFormer.Since two CLIP models(PMC-CLIP and CoCa-CLIP)are pre-trained on large datasets,they do not require additional training,thus conserving computation resources.These models are used offline to extract image and text embeddings from medical images.These embeddings are then processed by the proposed 3D CLIP adapter,which adapts the CLIP knowledge for segmentation tasks by fine-tuning.Finally,the adapted embeddings are fused with feature maps extracted from the nnFormer encoder for generating predicted masks.This process enriches the representation capabilities of the feature maps by integrating global multimodal information,leading to more precise segmentation predictions.We demonstrate the superiority of BiCLIP-nnFormer and the effectiveness of using CLIP models to enhance nnFormer through experiments on two public datasets,namely the Synapse multi-organ segmentation dataset(Synapse)and the Automatic Cardiac Diagnosis Challenge dataset(ACDC),as well as a self-annotated lung multi-category segmentation dataset(LMCS).展开更多
This paper proposes a non-intrusive computational method for mechanical dynamic systems involving a large-scale of interval uncertain parameters,aiming to reduce the computational costs and improve accuracy in determi...This paper proposes a non-intrusive computational method for mechanical dynamic systems involving a large-scale of interval uncertain parameters,aiming to reduce the computational costs and improve accuracy in determining bounds of system response.The screening method is firstly used to reduce the scale of active uncertain parameters.The sequential high-order polynomials surrogate models are then used to approximate the dynamic system’s response at each time step.To reduce the sampling cost of constructing surrogate model,the interaction effect among uncertain parameters is gradually added to the surrogate model by sequentially incorporating samples from a candidate set,which is composed of vertices and inner grid points.Finally,the points that may produce the bounds of the system response at each time step are searched using the surrogate models.The optimization algorithm is used to locate extreme points,which contribute to determining the inner points producing system response bounds.Additionally,all vertices are also checked using the surrogate models.A vehicle nonlinear dynamic model with 72 uncertain parameters is presented to demonstrate the accuracy and efficiency of the proposed uncertain computational method.展开更多
As large language models(LLMs)continue to demonstrate their potential in handling complex tasks,their value in knowledge-intensive industrial scenarios is becoming increasingly evident.Fault diagnosis,a critical domai...As large language models(LLMs)continue to demonstrate their potential in handling complex tasks,their value in knowledge-intensive industrial scenarios is becoming increasingly evident.Fault diagnosis,a critical domain in the industrial sector,has long faced the dual challenges of managing vast amounts of experiential knowledge and improving human-machine collaboration efficiency.Traditional fault diagnosis systems,which are primarily based on expert systems,suffer from three major limitations:(1)ineffective organization of fault diagnosis knowledge,(2)lack of adaptability between static knowledge frameworks and dynamic engineering environments,and(3)difficulties in integrating expert knowledge with real-time data streams.These systemic shortcomings restrict the ability of conventional approaches to handle uncertainty.In this study,we proposed an intelligent computer numerical control(CNC)fault diagnosis system,integrating LLMs with knowledge graph(KG).First,we constructed a comprehensive KG that consolidated multi-source data for structured representation.Second,we designed a retrievalaugmented generation(RAG)framework leveraging the KG to support multi-turn interactive fault diagnosis while incorporating real-time engineering data into the decision-making process.Finally,we introduced a learning mechanism to facilitate dynamic knowledge updates.The experimental results demonstrated that our system significantly improved fault diagnosis accuracy,outperforming engineers with two years of professional experience on our constructed benchmark datasets.By integrating LLMs and KG,our framework surpassed the limitations of traditional expert systems rooted in symbolic reasoning,offering a novel approach to addressing the cognitive paradox of unstructured knowledge modeling and dynamic environment adaptation in industrial settings.展开更多
Modified ethylene-vinyl acetate copolymer(EVAM)and amino-functionalized nano-silica(NSiO_(2))par-ticles were employed as the base materials for the synthesis of the nanocomposite pour point depressant designated as EV...Modified ethylene-vinyl acetate copolymer(EVAM)and amino-functionalized nano-silica(NSiO_(2))par-ticles were employed as the base materials for the synthesis of the nanocomposite pour point depressant designated as EVAM-g-NSiO_(2).This synthesis involved a chemical grafting process within a solution system,followed by a structural characterization.Moreover,combining macro-rheological performance with microscopic structure observation,the influence of the nanocomposite pour point depressant on the rheological properties of the model waxy oil system was investigated.The results indicate that when the mass ratio of NSiO_(2) to EVAM is 1:100,the prepared EVAM-g-NSiO_(2) nanocomposite pour point depressant exhibits excellent pour point reduction and viscosity reduction properties.Moreover,the nanocomposite pour point depressant obtained through a chemical grafting reaction demonstrates structural stability(the bonding between the polymer and nanoparticles is stable).The pour points of model waxy oils doped with 500 mg/kg ethylene-vinyl acetate copolymer(EVA),EVAM,and EVAM/SiO_(2) were reduced from 34℃ to 23,20,and 21℃,respectively.After adding the same dosage of EVAM-g-NSiO_(2) nanocomposite pour point depressant,the pour point of the model wax oil decreased to 12℃ and the viscosity at 32℃ decreased from 2399 to 2396.9 mPa·s,achieving an impressive viscosity reduction rate of 99.9%.Its performance surpassed that of EVA,EVAM,and EVAM/SiO_(2).The EVAM-g-NSiO_(2) dispersed in the oil phase acts as the crystallization nucleus for wax crystals,resulting in a dense structure of wax crystals.The compact wax crystal blocks are difficult to overlap with each other,pre-venting the formation of a three-dimensional network structure,thereby improving the low-temperature flowability of the model waxy oil.展开更多
The effect of alcoholic polyethylene-vinyl acetate(EVA)product ethylene-vinyl alcohol copolymer(EVAL)on the low-temperature flow properties of model oil containing asphaltene(ASP)was investigated.The change of wax cry...The effect of alcoholic polyethylene-vinyl acetate(EVA)product ethylene-vinyl alcohol copolymer(EVAL)on the low-temperature flow properties of model oil containing asphaltene(ASP)was investigated.The change of wax crystal microscopic morphology of model oil before and after modification were examined,and the influence of asphaltene mass fraction on the rheological improvement effect of EVAL was analyzed.The composite system of EVAL and asphaltene significantly reduced the pour point,gel point,apparent viscosity,storage modulus and loss modulus of waxy oil at low temperatures.When the EVAL concentration is 400 ppm and the asphaltene mass fraction is 0.5 wt%,the synergistic effect of the two is optimal,which can reduce the pour point by 17℃and the modulus value by more than 98%.The introduction of EVAL strengthens the interaction between asphaltenes and wax crystals,forming EVALASP aggregates,which promote the adsorption of wax crystals on asphaltenes to form composite particles,and the polar groups prevent the aggregation of wax crystals and reduce the size of wax crystals,thus greatly improving the fluidity of waxy oils.展开更多
As renewable fuel,alcohol could be added into the fuel with low volatility to trigger flash boiling,which has been considered an effective method to facilitate the atomization of fuel sprays and reduce emissions.Howev...As renewable fuel,alcohol could be added into the fuel with low volatility to trigger flash boiling,which has been considered an effective method to facilitate the atomization of fuel sprays and reduce emissions.However,the competing relationship between volatility and high latent heat leads to a complex atomization process,making it more challenging to investigate the effects of alcohol addition on plume interaction and vortex evolution.To illustrate the influences of alcohol addition on the fuel atomization performance,spray macroscopic and microscopic characteristics under various operating conditions were obtained using Diffuse Backlight Illumination(DBI)and Phase Doppler Anemometry(PDA)methods,and dynamic collapse ratio were used to characterize morphologies variations.The addition of alcohol facilitates the nucleation process,and its effects are affected by heavy components,attributed to the dependence of the energy barrier of nucleation on fuel properties.Parameters were proposed based on the energy barrier,supplementing Rp to predict the plume expansion of fuels with unknown properties.The dynamic collapse ratio is able to reflect the plume evaporation efficiency,plume interaction and vortex movement direction.This study aims to shed more light on the flashing characteristics of multi-component fuels with distinct properties,facilitating the efficient utilization of renewable fuels.展开更多
Although Markov chain Monte Carlo（MCMC） algorithms are accurate, many factors may cause instability when they are utilized in reliability analysis; such instability makes these algorithms unsuitable for widespread e...Although Markov chain Monte Carlo（MCMC） algorithms are accurate, many factors may cause instability when they are utilized in reliability analysis; such instability makes these algorithms unsuitable for widespread engineering applications. Thus, a reliability modeling and assessment solution aimed at small-sample data of numerical control（NC） machine tools is proposed on the basis of Bayes theories. An expert-judgment process of fusing multi-source prior information is developed to obtain the Weibull parameters＇ prior distributions and reduce the subjective bias of usual expert-judgment methods. The grid approximation method is applied to two-parameter Weibull distribution to derive the formulas for the parameters＇ posterior distributions and solve the calculation difficulty of high-dimensional integration. The method is then applied to the real data of a type of NC machine tool to implement a reliability assessment and obtain the mean time between failures（MTBF）. The relative error of the proposed method is 5.8020×10-4 compared with the MTBF obtained by the MCMC algorithm. This result indicates that the proposed method is as accurate as MCMC. The newly developed solution for reliability modeling and assessment of NC machine tools under small-sample data is easy, practical, and highly suitable for widespread application in the engineering field; in addition, the solution does not reduce accuracy.展开更多
Proper meshing of Hy-Vo silent chain and sprocket is important for realizing the transmission of the silent chain with more efficiency and less noise. Based on the study of the meshing theory of the Hy-Vo silent chain...Proper meshing of Hy-Vo silent chain and sprocket is important for realizing the transmission of the silent chain with more efficiency and less noise. Based on the study of the meshing theory of the Hy-Vo silent chain with the sprocket and the roll cutting machining principle of the sprocket with the hob, the proper conditions of the meshing for the Hy-Vo silent chain and the sprocket are put forward with the variable pitch characteristic of the Hy-Vo silent chain taken into consideration, and the proper meshing design method on the condition that the value of the link tooth pressure angle is unequal to the value of the sprocket tooth pressure angle is studied. Experiments show that this new design method is feasible. In addition, the design of the pitch, the sprocket tooth pressure angle and the fillet radius of the sprocket addendum circle are studied. It is crucial for guiding the design of the hob which cuts the Hy-Vo silent chain sprocket.展开更多
Due to the advantages of large workspace,low cost and the integrated vision/force sensing,robotic milling has become an important way for machining of complex parts.In recent years,many scholars have studied the probl...Due to the advantages of large workspace,low cost and the integrated vision/force sensing,robotic milling has become an important way for machining of complex parts.In recent years,many scholars have studied the problems existing in the applications of robotic milling,and lots of results have been made in the dynamics,pose planning,deformation control etc.,which provides theoretical guidance for high precision and high efficiency of robotic milling.From the perspective of complex parts robotic milling,this paper focuses on machining process planning and control techniques including the analysis of the robot-workspace,robot trajectory planning,vibration monitoring and control,deformation monitoring and compensation.As well as the principles of these technologies such as robot stiffness characteristics,dynamic characteristics,chatter mechanisms,and deformation mechanisms.The methods and characteristics related to the theory and technology of robotic milling of complex parts are summarized systematically.The latest research progress and achievements in the relevant fields are reviewed.It is hoped that the challenges,strategies and development related to robotic milling could be clarified through the carding work in this paper,so as to promote the application of related theories and technologies in high efficiency and precision intelligent milling with robot for complex parts.展开更多
Lower limb injures are frequently observed in passenger car traffic accidents.Previous studies of the injuries focus on long bone fractures by using either cadaver component tests or simulations of the long bone kinem...Lower limb injures are frequently observed in passenger car traffic accidents.Previous studies of the injuries focus on long bone fractures by using either cadaver component tests or simulations of the long bone kinematics,which lack in-depth study on the fractures in stress analysis.This paper aims to investigate lower limb impact biomechanics in real-world car to pedestrian accidents and to predict fractures of long bones in term of stress parameter for femur,tibia,and fibula.For the above purposes,a 3D finite element(FE) model of human body lower limb(HBM-LL) is developed based on human anatomy.The model consists of the pelvis,femur,tibia,fibula,patella,foot bones,primary tendons,knee joint capsule,meniscus,and ligaments.The FE model is validated by comparing the results from a lateral impact between simulations and tests with cadaver lower limb specimens.Two real-world accidents are selected from an in-depth accident database with detailed information about the accident scene,car impact speed,damage to the car,and pedestrian injuries.Multi-body system(MBS) models are used to reconstruct the kinematics of the pedestrians in the two accidents and the impact conditions are calculated for initial impact velocity and orientations of the car and pedestrian during the collision.The FE model is used to perform injury reconstructions and predict the fractures by using physical parameters,such as von Mises stress of long bones.The calculated failure level of the long bones is correlated with the injury outcomes observed from the two accident cases.The reconstruction result shows that the HBM-LL FE model has acceptable biofidelity and can be applied to predict the risk of long bone fractures.This study provides an efficient methodology to investigate the long bone fracture suffered from vehicle traffic collisions.展开更多
Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design r...Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design reasonable values of gas metal arc welding parameters and sequences of Q345D T-joints. The optimized factors included continuous variables （welding current （I）, welding voltage （U） ahd welding speed （V）） and discrete variables （welding sequence （S） and welding direc- tion （D））. The concepts of the pointer and stack in Visual Basic （VB） and the interpolation method were introduced to optimize the variables. The optimization objectives included the different combina- tions of the angular distortion and transverse welding stress along the transverse and longitudinal dis- tributions. Based on the design of experiments （DOE） and the polynomial regression （PR） model, the finite element （FE） results of the T-joint were used to establish the mathematical models. The Pareto front and the compromise solutions were obtained by using a multi-objective particle swarm optimization （MOPSO） algorithm. The optimal results were validated by the corresponding results of the FE method, and the error between the FE results and the two-objective results as well as that be-tween the FE results and the three-objective optimization results were less than 17.2% and 21.5%, respectively. The influence and setting regularity of different factors were discussed according to the compromise solutions.展开更多
To predict the fatigue life for oblique hyperbola-and bilinear-mode S-N curves of metallic materials with various strengths,a machine-learning approach for direct analysis was employed.Additionally,to determine the fa...To predict the fatigue life for oblique hyperbola-and bilinear-mode S-N curves of metallic materials with various strengths,a machine-learning approach for direct analysis was employed.Additionally,to determine the fatigue limit of the utilized materials(AISI 316,AISI 4140 and CA6 NM series)with different S-N curve modes using finite-fatigue life data,a Bayesian optimization-based inverse analysis was performed.The results indicated that predictions of the fatigue life for the utilized datasets via the random forest(RF)algo rithm for AISI 4140 and CA6 NM,and artificial neural network(ANN)for AISI 316,distribute within 2 factor error lines for most data.In the Bayesian optimization-based inverse analysis,the specific explanatory variables corresponding to the optimized maximum fatigue life were treated as the fatigue limits.The predicted fatigue limits either approximated to or slightly underestimated the experimental results,except for several cases with large errors.Using the inverse analysis to predict the fatigue limit for both S-N curve modes is applicable for current employed data-set.However,the explored maximum fatigue lives via BO corresponding to the predicted fatigue limit were underestimated for AISI 4140 and CA6 NM,and was overestimated for AISI 316 because of effect of shape of S-N curves.By combining the ANN or RF direct and BO inverse algorithms,whole S-N curves(including the fatigue limit)were evaluated for the S-N curve shapes of the oblique hyperbola and bilinear modes.展开更多
Understanding the behaviors of heat transfer and fluid flow in weld pool and their effects on the solidification microstructure are significant for performance improvement of laser welds.This paper develops a three-di...Understanding the behaviors of heat transfer and fluid flow in weld pool and their effects on the solidification microstructure are significant for performance improvement of laser welds.This paper develops a three-dimensional numerical model to understand the multi-physical processes such as heat transfer,melt convection and solidification behavior in full-penetration laser welding of thin 5083 aluminum sheet.Solidification parameters including temperature gradient G and solidification rate R,and their combined forms are evaluated to interpret solidification microstructure.The predicted weld dimensions and the microstructure morphology and scale agree well with experiments.Results indicate that heat conduction is the dominant mechanism of heat transfer in weld pool,and melt convection plays a critical role in microstructure scale.The mushy zone shape/size and solidification parameters can be modulated by changing process parameters.Dendritic structures form because of the low G/R value.The scale of dendritic structures can be reduced by increasing GR via decreasing heat input.The columnar to equiaxed transition is predicted quantitatively via the process related G^3/R.These findings illustrate how heat transfer and fluid flow affect the solidification parameters and hence the microstructure,and show how to improve microstructure by optimizing the process.展开更多
文摘Huazhong University of Science and Technology (HUST) is a key research-based comprehensive university in Wuhan, China,under the direct supervision of the Ministry of Education of China. Renowned as the epitome of the development of higher education in new China, it is a “211” Project,“985” Project,“Double First-Class” university in China.
基金the National Natural Science Foundation of China(Grant No.11832008 and 11772073)by the Program of the Postgraduate Tutor Team,Chongqing Education Commission(2018).
文摘Cross-talk between tumor cells and mechanical stress in the tumor microenvironment has been shown to be involved in carcinogenesis.High mechanical stress in tumors can alter the metabolism and behaviors of cancer cells and cause cancer cells to attain cancer stem-like cell properties,thus driving tumor progression and promoting metastasis.The mechanical signal is converted into a biochemical signal that activates tumorigenic signaling pathways through mechanotransduction.Herein,we describe the physical changes occurring during reprogramming of cancer cell metabolism,which regulate cancer stem cell functions and promote tumor progression and aggression.Furthermore,we highlight emerging therapeutic strategies targeting mechanotransduction signaling pathways.
文摘Evolution of eukaryotes from simple cells to complex multicellular organisms remains a mystery. Our postulate is that cytoskeletal stiffening is a necessary condition for evolution of complex multicellular organisms from early simple eukaryotes. Recent findings show that embryonic stem cells are as soft as primitive eukaryotes-amoebae and that differentiated tissue cells can be two orders of magnitude stiffer than embryonic stem cells. Soft embryonic stem cells become stiff as they differentiate into tissue cells of the complex multicellular organisms to match their microenvironment stiffness. We perhaps see in differentiation of embryonic stem cells (derived from inner cell mass cells) the echo of those early evolutionary events. Early soft unicellular organisms might have evolved to stiffen their cytoskeleton to protect their structural integrity from external mechanical stresses while being able to maintain form, to change shape, and to move.
基金financially supported by a grant from the Science and Technology Development Project of Jilin Province of China,No.20110492
文摘A brachial plexus injury model was established in rabbits by stretching the C6 nerve root. Imme- diately after the stretching, a suspension of human amniotic epithelial cells was injected into the injured brachial plexus. The results of tensile mechanical testing of the brachial plexus showed that the tensile elastic limit strain, elastic limit stress, maximum stress, and maximum strain of the injured brachial plexuses were significantly increased at 24 weeks after the injection. The treatment clearly improved the pathological morphology of the injured brachial plexus nerve, as seen by hematoxylin eosin staining, and the functions of the rabbit forepaw were restored. These data indicate that the injection of human amniotic epithelial cells contributed to the repair of brachial plexus injury, and that this technique may transform into current clinical treatment strategies.
基金supported by the National Key R&D Program of China (2020YFB2010401 and 2022YFF1500400)National Natural Science Foundation of China (51835005and 52271216)+2 种基金Hubei Province Natural Science Foundation for Innovative Research Group (2020CFA030)Fundamental Research Funds for the Central Universities,HUST(2020kfy XJJS100)Tencent Foundation。
文摘Zero-emission eco-friendly vehicles with partly or fully electric powertrains have exhibited rapidly increased demand for reducing the emissions of air pollutants and improving the energy efficiency. Advanced catalytic and energy materials are essential as the significant portions in the key technologies of eco-friendly vehicles, such as the exhaust emission control system,power lithium ion battery and hydrogen fuel cell. Precise synthesis and surface modification of the functional materials and electrodes are required to satisfy the efficient surface and interface catalysis, as well as rapid electron/ion transport. Atomic layer deposition(ALD), an atomic and close-to-atomic scale manufacturing method, shows unique characteristics of precise thickness control, uniformity and conformality for film deposition, which has emerged as an important technique to design and engineer advanced catalytic and energy materials. This review has summarized recent process of ALD on the controllable preparation and modification of metal and oxide catalysts, as well as lithium ion battery and fuel cell electrodes. The enhanced catalytic and electrochemical performances are discussed with the unique nanostructures prepared by ALD. Recent works on ALD reactors for mass production are highlighted. The challenges involved in the research and development of ALD on the future practical applications are presented, including precursor and deposition process investigation, practical device performance evaluation, large-scale and efficient production, etc.
文摘It is well-known that optimizing the wheel system of lunar rovers is essential.However,this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers.In this study,an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its performance.First,a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension,considering the complex terrain of the moon.Next,the performance was optimized under the limitations of preserving the slope passage and minimizing power consumption.This was achieved via analysis of the wheel force during movement.Finally,the effectiveness of the proposed method was demonstrated by several simulation experiments.The newly designed wheel can protrude on demand and reduce energy consumption;it can be used as a reference for lunar rover development engineering in China.
基金Projects(51275198,51422503)supported by the National Natural Science Foundation of ChinaProject(2012YQ030075)supported by Special Funds for Development of National Major Scientific Instruments and Equipments,China+1 种基金Project(NECT-12-0238)supported by Program for New Century Excellent Talents in University,ChinaProject(20150520108JH)supported by Young Scientist Fund of Jilin Province of China
文摘The effects of adding alloy element zinc on the static and dynamic mechanical properties of copper-zinc alloy were investigated. Tensile and low cycle fatigue behaviors of the C11000 copper and H63 copper-zinc alloy were obtained by using a miniature tester that combined the functions of in situ tensile and fatigue testing. A piezoelectric actuator was adopted as the actuator for the fatigue testing, and the feasibility of the fatigue actuator was verified by the transient harmonic response analysis based on static tensile preload and dynamic sinusoidal load. The experimental results show that the yield strength and tensile strength of the C11000 copper are improved after adding 37%(mass fraction) zinc, and H63 copper-zinc alloy presents more obvious cyclic hardening behavior and more consumed irreversible plastic work during each stress cycle compared with C11000 copper for the same strain controlled cycling. Additionally, based on the Manson-Coffin theory, the strain-life equations of the two materials were also obtained. C11000 copper and H63 copper-zinc alloy show transition life of 16832 and 1788 cycles, respectively.
基金funded by the National Natural Science Foundation of China(Grant No.6240072655)the Hubei Provincial Key Research and Development Program(Grant No.2023BCB151)+1 种基金the Wuhan Natural Science Foundation Exploration Program(Chenguang Program,Grant No.2024040801020202)the Natural Science Foundation of Hubei Province of China(Grant No.2025AFB148).
文摘Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a crucial topic of research.With advances in deep learning,researchers have developed numerous methods that combine Transformers and convolutional neural networks(CNNs)to create highly accurate models for medical image segmentation.However,efforts to further enhance accuracy by developing larger and more complex models or training with more extensive datasets,significantly increase computational resource consumption.To address this problem,we propose BiCLIP-nnFormer(the prefix"Bi"refers to the use of two distinct CLIP models),a virtual multimodal instrument that leverages CLIP models to enhance the segmentation performance of a medical segmentation model nnFormer.Since two CLIP models(PMC-CLIP and CoCa-CLIP)are pre-trained on large datasets,they do not require additional training,thus conserving computation resources.These models are used offline to extract image and text embeddings from medical images.These embeddings are then processed by the proposed 3D CLIP adapter,which adapts the CLIP knowledge for segmentation tasks by fine-tuning.Finally,the adapted embeddings are fused with feature maps extracted from the nnFormer encoder for generating predicted masks.This process enriches the representation capabilities of the feature maps by integrating global multimodal information,leading to more precise segmentation predictions.We demonstrate the superiority of BiCLIP-nnFormer and the effectiveness of using CLIP models to enhance nnFormer through experiments on two public datasets,namely the Synapse multi-organ segmentation dataset(Synapse)and the Automatic Cardiac Diagnosis Challenge dataset(ACDC),as well as a self-annotated lung multi-category segmentation dataset(LMCS).
基金supported by the National Natural Science Foundation of China(Grant No.12272142)Fundamental Research Funds for the Central Universities(Grant No.2172021XXJS048)。
文摘This paper proposes a non-intrusive computational method for mechanical dynamic systems involving a large-scale of interval uncertain parameters,aiming to reduce the computational costs and improve accuracy in determining bounds of system response.The screening method is firstly used to reduce the scale of active uncertain parameters.The sequential high-order polynomials surrogate models are then used to approximate the dynamic system’s response at each time step.To reduce the sampling cost of constructing surrogate model,the interaction effect among uncertain parameters is gradually added to the surrogate model by sequentially incorporating samples from a candidate set,which is composed of vertices and inner grid points.Finally,the points that may produce the bounds of the system response at each time step are searched using the surrogate models.The optimization algorithm is used to locate extreme points,which contribute to determining the inner points producing system response bounds.Additionally,all vertices are also checked using the surrogate models.A vehicle nonlinear dynamic model with 72 uncertain parameters is presented to demonstrate the accuracy and efficiency of the proposed uncertain computational method.
基金funded by the National Natural Science Foundation of China(72104224,L2424237,71974107,L2224059,L2124002,and 91646102)the Beijing Natural Science Foundation(9232015)+4 种基金the Beijing Social Science Foundation(24GLC058)the Construction Project of China Knowledge Center for Engineering Sciences and Technology(CKCEST-2023-1-7)the MOE(Ministry of Education in China)Project of Humanities and Social Sciences(16JDGC011)the Tsinghua University Initiative Scientific Research Program(2019Z02CAU)the Tsinghua University Project of Volvo-Supported Green Economy and Sustainable Development(20183910020)。
文摘As large language models(LLMs)continue to demonstrate their potential in handling complex tasks,their value in knowledge-intensive industrial scenarios is becoming increasingly evident.Fault diagnosis,a critical domain in the industrial sector,has long faced the dual challenges of managing vast amounts of experiential knowledge and improving human-machine collaboration efficiency.Traditional fault diagnosis systems,which are primarily based on expert systems,suffer from three major limitations:(1)ineffective organization of fault diagnosis knowledge,(2)lack of adaptability between static knowledge frameworks and dynamic engineering environments,and(3)difficulties in integrating expert knowledge with real-time data streams.These systemic shortcomings restrict the ability of conventional approaches to handle uncertainty.In this study,we proposed an intelligent computer numerical control(CNC)fault diagnosis system,integrating LLMs with knowledge graph(KG).First,we constructed a comprehensive KG that consolidated multi-source data for structured representation.Second,we designed a retrievalaugmented generation(RAG)framework leveraging the KG to support multi-turn interactive fault diagnosis while incorporating real-time engineering data into the decision-making process.Finally,we introduced a learning mechanism to facilitate dynamic knowledge updates.The experimental results demonstrated that our system significantly improved fault diagnosis accuracy,outperforming engineers with two years of professional experience on our constructed benchmark datasets.By integrating LLMs and KG,our framework surpassed the limitations of traditional expert systems rooted in symbolic reasoning,offering a novel approach to addressing the cognitive paradox of unstructured knowledge modeling and dynamic environment adaptation in industrial settings.
基金supported by the National Natural Science Foundation of China(No.52076036)supported by the National Natural Science Foundation of China(No.52174020).
文摘Modified ethylene-vinyl acetate copolymer(EVAM)and amino-functionalized nano-silica(NSiO_(2))par-ticles were employed as the base materials for the synthesis of the nanocomposite pour point depressant designated as EVAM-g-NSiO_(2).This synthesis involved a chemical grafting process within a solution system,followed by a structural characterization.Moreover,combining macro-rheological performance with microscopic structure observation,the influence of the nanocomposite pour point depressant on the rheological properties of the model waxy oil system was investigated.The results indicate that when the mass ratio of NSiO_(2) to EVAM is 1:100,the prepared EVAM-g-NSiO_(2) nanocomposite pour point depressant exhibits excellent pour point reduction and viscosity reduction properties.Moreover,the nanocomposite pour point depressant obtained through a chemical grafting reaction demonstrates structural stability(the bonding between the polymer and nanoparticles is stable).The pour points of model waxy oils doped with 500 mg/kg ethylene-vinyl acetate copolymer(EVA),EVAM,and EVAM/SiO_(2) were reduced from 34℃ to 23,20,and 21℃,respectively.After adding the same dosage of EVAM-g-NSiO_(2) nanocomposite pour point depressant,the pour point of the model wax oil decreased to 12℃ and the viscosity at 32℃ decreased from 2399 to 2396.9 mPa·s,achieving an impressive viscosity reduction rate of 99.9%.Its performance surpassed that of EVA,EVAM,and EVAM/SiO_(2).The EVAM-g-NSiO_(2) dispersed in the oil phase acts as the crystallization nucleus for wax crystals,resulting in a dense structure of wax crystals.The compact wax crystal blocks are difficult to overlap with each other,pre-venting the formation of a three-dimensional network structure,thereby improving the low-temperature flowability of the model waxy oil.
基金financially supported by the National Natural Science Foundation of China(No.52076036)。
文摘The effect of alcoholic polyethylene-vinyl acetate(EVA)product ethylene-vinyl alcohol copolymer(EVAL)on the low-temperature flow properties of model oil containing asphaltene(ASP)was investigated.The change of wax crystal microscopic morphology of model oil before and after modification were examined,and the influence of asphaltene mass fraction on the rheological improvement effect of EVAL was analyzed.The composite system of EVAL and asphaltene significantly reduced the pour point,gel point,apparent viscosity,storage modulus and loss modulus of waxy oil at low temperatures.When the EVAL concentration is 400 ppm and the asphaltene mass fraction is 0.5 wt%,the synergistic effect of the two is optimal,which can reduce the pour point by 17℃and the modulus value by more than 98%.The introduction of EVAL strengthens the interaction between asphaltenes and wax crystals,forming EVALASP aggregates,which promote the adsorption of wax crystals on asphaltenes to form composite particles,and the polar groups prevent the aggregation of wax crystals and reduce the size of wax crystals,thus greatly improving the fluidity of waxy oils.
基金financially supported by the National Natural Science Foundation of China(52104183)Natural Science Fundation of Chongqing(cstc2021jcyj-bshX0091)+2 种基金Graduate Research and Innovation Foundation of Chongqing(CYB22024)the Fundamental Research Funds for the Central Universities(2021CDJQY-031)the Cheung Kong Scholars and Innovation Team Development Program(IRT17R112).
文摘As renewable fuel,alcohol could be added into the fuel with low volatility to trigger flash boiling,which has been considered an effective method to facilitate the atomization of fuel sprays and reduce emissions.However,the competing relationship between volatility and high latent heat leads to a complex atomization process,making it more challenging to investigate the effects of alcohol addition on plume interaction and vortex evolution.To illustrate the influences of alcohol addition on the fuel atomization performance,spray macroscopic and microscopic characteristics under various operating conditions were obtained using Diffuse Backlight Illumination(DBI)and Phase Doppler Anemometry(PDA)methods,and dynamic collapse ratio were used to characterize morphologies variations.The addition of alcohol facilitates the nucleation process,and its effects are affected by heavy components,attributed to the dependence of the energy barrier of nucleation on fuel properties.Parameters were proposed based on the energy barrier,supplementing Rp to predict the plume expansion of fuels with unknown properties.The dynamic collapse ratio is able to reflect the plume evaporation efficiency,plume interaction and vortex movement direction.This study aims to shed more light on the flashing characteristics of multi-component fuels with distinct properties,facilitating the efficient utilization of renewable fuels.
基金Supported by Research on Reliability Assessment and Test Methods of Heavy Machine Tools,China(State Key Science&Technology Project High-grade NC Machine Tools and Basic Manufacturing Equipment,Grant No.2014ZX04014-011)Reliability Modeling of Machining Centers Considering the Cutting Loads,China(Science&Technology Development Plan for Jilin Province,Grant No.3D513S292414)Graduate Innovation Fund of Jilin University,China(Grant No.2014053)
文摘Although Markov chain Monte Carlo（MCMC） algorithms are accurate, many factors may cause instability when they are utilized in reliability analysis; such instability makes these algorithms unsuitable for widespread engineering applications. Thus, a reliability modeling and assessment solution aimed at small-sample data of numerical control（NC） machine tools is proposed on the basis of Bayes theories. An expert-judgment process of fusing multi-source prior information is developed to obtain the Weibull parameters＇ prior distributions and reduce the subjective bias of usual expert-judgment methods. The grid approximation method is applied to two-parameter Weibull distribution to derive the formulas for the parameters＇ posterior distributions and solve the calculation difficulty of high-dimensional integration. The method is then applied to the real data of a type of NC machine tool to implement a reliability assessment and obtain the mean time between failures（MTBF）. The relative error of the proposed method is 5.8020×10-4 compared with the MTBF obtained by the MCMC algorithm. This result indicates that the proposed method is as accurate as MCMC. The newly developed solution for reliability modeling and assessment of NC machine tools under small-sample data is easy, practical, and highly suitable for widespread application in the engineering field; in addition, the solution does not reduce accuracy.
基金This project is supported by National Natural Science Foundation of China(No.50575089).
文摘Proper meshing of Hy-Vo silent chain and sprocket is important for realizing the transmission of the silent chain with more efficiency and less noise. Based on the study of the meshing theory of the Hy-Vo silent chain with the sprocket and the roll cutting machining principle of the sprocket with the hob, the proper conditions of the meshing for the Hy-Vo silent chain and the sprocket are put forward with the variable pitch characteristic of the Hy-Vo silent chain taken into consideration, and the proper meshing design method on the condition that the value of the link tooth pressure angle is unequal to the value of the sprocket tooth pressure angle is studied. Experiments show that this new design method is feasible. In addition, the design of the pitch, the sprocket tooth pressure angle and the fillet radius of the sprocket addendum circle are studied. It is crucial for guiding the design of the hob which cuts the Hy-Vo silent chain sprocket.
基金supported by National Science Fund for Distinguished Young Scholars of China(No.51625502)Innovative Group Project of National Natural Science Foundation of China(No.51721092)Innovative Group Project of Hubei Province of China(No.2017CFA003)。
文摘Due to the advantages of large workspace,low cost and the integrated vision/force sensing,robotic milling has become an important way for machining of complex parts.In recent years,many scholars have studied the problems existing in the applications of robotic milling,and lots of results have been made in the dynamics,pose planning,deformation control etc.,which provides theoretical guidance for high precision and high efficiency of robotic milling.From the perspective of complex parts robotic milling,this paper focuses on machining process planning and control techniques including the analysis of the robot-workspace,robot trajectory planning,vibration monitoring and control,deformation monitoring and compensation.As well as the principles of these technologies such as robot stiffness characteristics,dynamic characteristics,chatter mechanisms,and deformation mechanisms.The methods and characteristics related to the theory and technology of robotic milling of complex parts are summarized systematically.The latest research progress and achievements in the relevant fields are reviewed.It is hoped that the challenges,strategies and development related to robotic milling could be clarified through the carding work in this paper,so as to promote the application of related theories and technologies in high efficiency and precision intelligent milling with robot for complex parts.
基金supported by National Hi-tech Research and Development Program of China (863 Program,Grant No. 2006AA110101)"111 Program" of Ministry of Education and State Administration of Foreign Experts Affairs of China (Grant No. 111-2-11)+1 种基金General Motors Research and Development Center (Grant No. RD-209)Project of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,China (Grant No. 60870004)
文摘Lower limb injures are frequently observed in passenger car traffic accidents.Previous studies of the injuries focus on long bone fractures by using either cadaver component tests or simulations of the long bone kinematics,which lack in-depth study on the fractures in stress analysis.This paper aims to investigate lower limb impact biomechanics in real-world car to pedestrian accidents and to predict fractures of long bones in term of stress parameter for femur,tibia,and fibula.For the above purposes,a 3D finite element(FE) model of human body lower limb(HBM-LL) is developed based on human anatomy.The model consists of the pelvis,femur,tibia,fibula,patella,foot bones,primary tendons,knee joint capsule,meniscus,and ligaments.The FE model is validated by comparing the results from a lateral impact between simulations and tests with cadaver lower limb specimens.Two real-world accidents are selected from an in-depth accident database with detailed information about the accident scene,car impact speed,damage to the car,and pedestrian injuries.Multi-body system(MBS) models are used to reconstruct the kinematics of the pedestrians in the two accidents and the impact conditions are calculated for initial impact velocity and orientations of the car and pedestrian during the collision.The FE model is used to perform injury reconstructions and predict the fractures by using physical parameters,such as von Mises stress of long bones.The calculated failure level of the long bones is correlated with the injury outcomes observed from the two accident cases.The reconstruction result shows that the HBM-LL FE model has acceptable biofidelity and can be applied to predict the risk of long bone fractures.This study provides an efficient methodology to investigate the long bone fracture suffered from vehicle traffic collisions.
基金financially sponsored by National Natural Science Foundation of China(No.50975121)Changchun Science and Technology Plan Projects(No.10KZ03)the Plan for Scientific and Technology Development of Jilin Province(No.20150520106JH)
文摘Q345D high-quality low-carbon steel has been extensively employed in structures with stringent weld- ing quality requirements. A multi-objective optimization of welding stress and deformation was presented to design reasonable values of gas metal arc welding parameters and sequences of Q345D T-joints. The optimized factors included continuous variables （welding current （I）, welding voltage （U） ahd welding speed （V）） and discrete variables （welding sequence （S） and welding direc- tion （D））. The concepts of the pointer and stack in Visual Basic （VB） and the interpolation method were introduced to optimize the variables. The optimization objectives included the different combina- tions of the angular distortion and transverse welding stress along the transverse and longitudinal dis- tributions. Based on the design of experiments （DOE） and the polynomial regression （PR） model, the finite element （FE） results of the T-joint were used to establish the mathematical models. The Pareto front and the compromise solutions were obtained by using a multi-objective particle swarm optimization （MOPSO） algorithm. The optimal results were validated by the corresponding results of the FE method, and the error between the FE results and the two-objective results as well as that be-tween the FE results and the three-objective optimization results were less than 17.2% and 21.5%, respectively. The influence and setting regularity of different factors were discussed according to the compromise solutions.
文摘To predict the fatigue life for oblique hyperbola-and bilinear-mode S-N curves of metallic materials with various strengths,a machine-learning approach for direct analysis was employed.Additionally,to determine the fatigue limit of the utilized materials(AISI 316,AISI 4140 and CA6 NM series)with different S-N curve modes using finite-fatigue life data,a Bayesian optimization-based inverse analysis was performed.The results indicated that predictions of the fatigue life for the utilized datasets via the random forest(RF)algo rithm for AISI 4140 and CA6 NM,and artificial neural network(ANN)for AISI 316,distribute within 2 factor error lines for most data.In the Bayesian optimization-based inverse analysis,the specific explanatory variables corresponding to the optimized maximum fatigue life were treated as the fatigue limits.The predicted fatigue limits either approximated to or slightly underestimated the experimental results,except for several cases with large errors.Using the inverse analysis to predict the fatigue limit for both S-N curve modes is applicable for current employed data-set.However,the explored maximum fatigue lives via BO corresponding to the predicted fatigue limit were underestimated for AISI 4140 and CA6 NM,and was overestimated for AISI 316 because of effect of shape of S-N curves.By combining the ANN or RF direct and BO inverse algorithms,whole S-N curves(including the fatigue limit)were evaluated for the S-N curve shapes of the oblique hyperbola and bilinear modes.
基金the National Natural Science Foundation of China under Grant No.5181101756,51861165202 and No.51721092the Major Project of Science and Technology Innovation Special for Hubei Province under Grant No.2018AAA027+3 种基金the Fundamental Research Funds for the Central Universities,HUST:No.2018JYCXJJ034 and No.2019JYCXJJ025the Postdoctoral Science Foundation of China under Grant No.2018M632837the opening project of State Key Laboratory of Digital Manufacturing Equipment and Technology(HUST)under grant No.DMETKF2018001supported by the China Scholarship Council as a visiting scholar at the University of Virginia。
文摘Understanding the behaviors of heat transfer and fluid flow in weld pool and their effects on the solidification microstructure are significant for performance improvement of laser welds.This paper develops a three-dimensional numerical model to understand the multi-physical processes such as heat transfer,melt convection and solidification behavior in full-penetration laser welding of thin 5083 aluminum sheet.Solidification parameters including temperature gradient G and solidification rate R,and their combined forms are evaluated to interpret solidification microstructure.The predicted weld dimensions and the microstructure morphology and scale agree well with experiments.Results indicate that heat conduction is the dominant mechanism of heat transfer in weld pool,and melt convection plays a critical role in microstructure scale.The mushy zone shape/size and solidification parameters can be modulated by changing process parameters.Dendritic structures form because of the low G/R value.The scale of dendritic structures can be reduced by increasing GR via decreasing heat input.The columnar to equiaxed transition is predicted quantitatively via the process related G^3/R.These findings illustrate how heat transfer and fluid flow affect the solidification parameters and hence the microstructure,and show how to improve microstructure by optimizing the process.
