The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To...The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.展开更多
Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image dis...Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.展开更多
Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical applicati...Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical application of multi-omics parameters is still restricted by the expensive and less accessible assays,although they accurately reflect immune status.A comprehensive evaluation framework based on“easy-to-obtain”multi-model clinical parameters is urgently required,incorporating clinical features to establish baseline patient profiles and disease staging;routine blood tests assessing systemic metabolic and functional status;immune cell subsets quantifying subcluster dynamics;imaging features delineating tumor morphology,spatial configuration,and perilesional anatomical relationships;immunohistochemical markers positioning qualitative and quantitative detection of tumor antigens from the cellular and molecular level.This integrated phenomic approach aims to improve prognostic stratification and clinical decision-making in hepatocellular carcinoma management conveniently and practically.展开更多
Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters...Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.展开更多
As binary geological media,soil-rock mixtures(SRMs)exhibit a distinct gradational composition,leading to their unique mechanical behaviors.To appraise the stability of SRM slopes,it is essential to determine equivalen...As binary geological media,soil-rock mixtures(SRMs)exhibit a distinct gradational composition,leading to their unique mechanical behaviors.To appraise the stability of SRM slopes,it is essential to determine equivalent parameters of SRMs,which are typically obtained through experimental and numerical methods.In contrasted to other numerical methods,the numerical manifold method(NMM)is more effective in addressing SRM problems.This is because the high-precision regular mathematical meshes in NMM can be used without aligning with the soil-rock interfaces and boundaries of SRMs.In the current research,the equivalent strength parameters of SRMs,i.e.the equivalent cohesion ce and internal friction angleϕ_(e),are determined using NMM.Initially,an NMM triaxial numerical model is established and validated based on triaxial experiments.Subsequently,the soil and rock parameters are derived through parameter inversion.Moreover,the impacts of rock content,size,shape and rock blocks'major-axis orientation on ce andϕ_(e) of SRMs are thoroughly examined using the NMM triaxial numerical model.Additionally,a fitting function is proposed to linkϕ_(e) to the rock content and size of SRMs.When other influencing factors are fixed,the above fitting model leads to the following conclusions:(1)the predictedϕ_(e) of SRMs increase with the increase of rock content;and(2)SRM samples with smaller rocks display a higher predictedϕ_(e).展开更多
The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle...The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that th...A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.展开更多
Accurate measurement of helicopter rotor motion parameters(flap,lead-lag,torsion,and azimuth angles)is essential for rotor blade design,helicopter dynamics modeling,and flight safety and health monitoring.However,the ...Accurate measurement of helicopter rotor motion parameters(flap,lead-lag,torsion,and azimuth angles)is essential for rotor blade design,helicopter dynamics modeling,and flight safety and health monitoring.However,the existing methods face challenges in testing equipment installation,calibration,and data transmission,resulting in limited reports on real-time in-flight measurements of blade motion parameters.This paper proposes a non-contact optoelectronic method based on two-dimensional position-sensitive detectors for in-flight measurement and a ground calibration system to obtain real-time rotor motion parameters during helicopter flight.The proposed method establishes the time evolution relationship of rotor motion parameters and verifies the performance of the in-flight measurement system regarding measurement resolution and accuracy through the construction of a blade motion posture experimental platform.The proposed method has been applied to the flight measurement of a medium-sized single-rotor helicopter,and the obtained results have been compared with theoretical analysis outcomes.Furthermore,this paper examines the characteristics of blade motion parameters during flight and discusses the challenges and potential solutions for measuring rotor motion parameters during helicopter flight using the proposed method.展开更多
In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the...In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the identification of these weak influence parameters in the complex systems and propose a identification model based on the parameter recursion.As an application,three parameters of the steam generator are identified,that is,the valve opening,the valve CV value,and the reference water level,in which the valve opening and the reference water level are weak influence parameters under most operating conditions.Numerical simulation results show that,in comparison with the multi-layer perceptron(MLP),the identification error rate is decreased.Actually,the average identification error rate for the valve opening decreases by 0.96%,for the valve CV decreases by 0.002%,and for the reference water level decreases by 12%after one recursion.After two recursions,the average identification error rate for the valve opening decreases by 11.07%,for the valve CV decreases by 2.601%,and for the reference water level decreases by 95.79%.This method can help to improve the control of the steam generator.展开更多
In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and l...To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and low energy consumption was proposed.But the unreasonable process parameters may lead to the inferior consistency of the forming quality of single-channel multilayer in Joule heat additive manufacturing process,and it is difficult to reach the condition for forming thinwalled parts.Orthogonal experiments were designed to fabricate single-channel multilayer samples with varying numbers of layers,and their forming quality was evaluated.The influence of printing current,forming speed,and contact pressure on the forming quality of the single-channel multilayer was analyzed.The optimal process parameters were obtained and the quality characterization of the experiment results was conducted.Results show that the printing current has the most significant influence on the forming quality of the single-channel multilayer.Under the optimal process parameters,the forming section is well fused and the surface is continuously smooth.The surface roughness of a single-channel 3-layer sample is 0.16μm,and the average Vickers hardness of cross section fusion zone is 317 HV,which lays a foundation for the subsequent use of Joule heat additive manufacturing technique to form thinwall parts.展开更多
BACKGROUND Sagittal spinopelvic alignment(SSA)is essential for preserving a stable and effective upright posture and locomotion.Although alterations in the SSA are recognised to induce compensatory modifications in th...BACKGROUND Sagittal spinopelvic alignment(SSA)is essential for preserving a stable and effective upright posture and locomotion.Although alterations in the SSA are recognised to induce compensatory modifications in the pelvis,hips,and knees,the inverse relationship concerning knee pathology undergoing total knee arthroplasty(TKA)has been examined by a limited number of studies,yielding inconclusive results.AIM To generate evidence of the effect of TKA on the SSA from existing literature.METHODS Databases like PubMed,EMBASE,and Scopus were used to identify articles related to the“knee spine syndrome”phenomenon using a combination of subject terms and keywords such as“spinopelvic parameters”,“sagittal spinal balance”,and“total knee arthroplasty”were used with appropriate Boolean operators.Studies measuring the SSA following TKA were included,and research was conducted as per preferred reporting items for systematic review and metaanalysis guidelines.RESULTS A total of 475 participants had undergone TKA,and six studies measuring SSA were analysed.Following TKA,pelvic tilt was the only parameter that showed significant changes,while lumbar lordosis(LL),pelvic incidence,and sacral slope were non-significant,as evident from the forest plots.CONCLUSION The body's sagittal alignment is a complex balance between pelvic,spine,and lower extremity parameters.TKA,while having the potential to correct the flexion contracture,can also correct it.Still,the primary SSA for spinal pathology,i.e.,LL,may not be corrected in patients with co-existent spinal degenerative disease.展开更多
This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data a...This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.展开更多
The study of the morphometric parameters of the three most abundant species in the lower course of the Kouilou River (Chrysichthys auratus, Liza falcipinnis and Pellonula vorax) was carried out. The standard length of...The study of the morphometric parameters of the three most abundant species in the lower course of the Kouilou River (Chrysichthys auratus, Liza falcipinnis and Pellonula vorax) was carried out. The standard length of Chrysichthys auratus varies between 43.57 and 210 mm, for an average of 96.70 ± 28.63 mm;the weight varies between 2.92 and 140.83 mg, an average of 73.03 ± 21.62 mg. The condition coefficient is equal to 4.42 ± 1.52. Liza falcipinnis has a standard length which varies between 59.9 mm and 158.08 mm for an average of 88.15 ± 29.74 mm;its weight varies between 4.77 and 76.21 mg, an average of 18.61 ± 11.82 mg. The condition coefficient is equal to 2.47 ± 1.57. Pellonula vorax has a standard length which varies between 60.33 mm and 117.72 mm;for an average of 80.48 ± 17.75 mm;the weight varies between 3.61 and 25.17 mg, an average of 9.03 ± 3.61 mg. The condition coefficient is equal to 2.17 ± 0.57. These three species have a minor allometric growth.展开更多
The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland im...The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.展开更多
The current investigation focuses on intertwined relationships of ecology and aquaculture for the benefit of farmers involved in fish farming practices.The study evaluated glucosinolate reduction in black,brown,and wh...The current investigation focuses on intertwined relationships of ecology and aquaculture for the benefit of farmers involved in fish farming practices.The study evaluated glucosinolate reduction in black,brown,and white mustard meals as fish feed ingredients for Indian Major Carps.Fish were fed with 10%mustard meal-supplemented diets in three forms:Raw(R),Anti-nutritional Rich(AR),and Anti-nutritional Lowered(AL),alongside a control group using floating feed.The three-month indoor experiment(September-November 2023)was conducted in FRP tanks with triplicate treatments.Blood analysis revealed compromised health in AR-fed carps,with reduced hemoglobin levels in rohu,catla and mrigal and elevated total leukocyte counts indicating inflammation in all the three carps studied here.Liver function was impaired in AR-fed fish,shown by increased alanine transaminase levels,highest in rohu followed by mrigal and catla.Histopathological examination of AR-fed carps liver tissue revealed necrotic spots,deformed hepatocytes,and significant vacuolation.In contrast,AL-fed fish demonstrated improved health parameters through Complete Blood Count analysis,liver function tests,and histo-pathological observations,suggesting successful reduction of anti-nutritional factors in the processed mustard meals.In near future,replacement of unprocessed seed meal with processed seed meal will lead to economic gains in fish farming.展开更多
The high-speed winding spindle employs a flexible support system incorporating rubber O-rings.By precisely configuring the structural parameters and the number of the O-rings,the spindle can stably surpass its critica...The high-speed winding spindle employs a flexible support system incorporating rubber O-rings.By precisely configuring the structural parameters and the number of the O-rings,the spindle can stably surpass its critical speed points and maintain operational stability across the entire working speed range.However,the support stiffness and damping of rubber O-rings exhibit significant nonlinear frequency dependence.Conventional experimental methods for deriving equivalent stiffness and damping,based on the principle of the forced non-resonance method,require fabricating custom setups for each O-ring specification and conducting vibration tests at varying frequencies,resulting in low efficiency and high costs.This study proposes a hybrid simulation-experimental method for dynamic parameter identification.Firstly,the frequency-dependent dynamic parameters of a specific O-ring support system are experimentally obtained.Subsequently,a corresponding parametric finite element model is established to simulate and solve the equivalent elastic modulus and equivalent stiffness-damping coefficient of this O-ring support system.Ultimately,after iterative simulation,the simulated and experimental results achieve a 99.7%agreement.The parametric finite element model developed herein can directly simulate and inversely estimate frequency-dependent dynamic parameters for O-rings of different specifications but identical elastic modulus.展开更多
The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars ref...The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars reflects sea surface dynamics, to which ocean waves make important contributions. Low-incidence-angle real aperture radar(RAR)demonstrates great potential for independently observing vectorial Doppler information on the ocean surface. To systematically characterize and accurately estimate the wave-induced Doppler frequency shift(WVF) from lowincidence-angle RAR, this study conducts comprehensive influencing factor analysis and establishes sea-stateparameterized WVF models. First, a simulated WVF dataset is generated under a rotating low-incidence-angle RAR.The feature parameters of WVF are then determined by analysing contributing factors including wind waves, swells,and sea state parameters. Furthermore, two WVF models(WVF_Ku P9 with 9 inputs and WVF_Ku P4 with 4 inputs) are constructed by the Transformer encoder for different application scenarios. Both models achieve high accuracy for WVF estimation with root mean square errors(RMSE) of 1.874 Hz and 2.716 Hz, respectively. The reliability and superiority of the proposed models are validated through comparisons with the Ka DOP, which is a typical geophysical model function(GMF). The findings in this paper advance the understanding of WVF characteristics and generation mechanisms. The proposed estimation models can provide reliable estimates, offering critical references for lowincidence-angle RAR applications such as ocean surface current retrieval.展开更多
基金supported by the China Railway Corporation Science and Technology Research and Development Program(Grant Nos.K2020G035 and K2021G024)the National Natural Science Foundation of China(Grant No.52378411).
文摘The suddenness, uncertainty, and randomness of rockbursts directly affect the safety of tunnel construction. The prediction of rockbursts is a fundamental aspect of mitigating or even eliminating rockburst hazards. To address the shortcomings of the current rockburst prediction models, which have a limited number of samples and rely on manual test results as the majority of their input features, this paper proposes rockburst prediction models based on multi-featured drilling parameters of rock drilling jumbo. Firstly, four original drilling parameters, namely hammer pressure (Ph), feed pressure (Pf), rotation pressure (Pr), and feed speed (VP), together with the rockburst grades, were collected from 1093 rockburst cases. Then, a feature expansion investigation was performed based on the four original drilling parameters to establish a drilling parameter feature system and a rockburst prediction database containing 42 features. Furthermore, rockburst prediction models based on multi-featured drilling parameters were developed using the extreme tree (ET) algorithm and Bayesian optimization. The models take drilling parameters as input parameters and rockburst grades as output parameters. The effects of Bayesian optimization and the number of drilling parameter features on the model performance were analyzed using the accuracy, precision, recall and F1 value of the prediction set as the model performance evaluation indices. The results show that the Bayesian optimized model with 42 drilling parameter features as inputs performs best, with an accuracy of 91.89%. Finally, the reliability of the models was validated through field tests.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.SJCX24_1332)Jiangsu Province Education Science Planning Project in 2024(Grant No.B-b/2024/01/122)High-Level Talent Scientific Research Foundation of Jinling Institute of Technology,China(Grant No.jit-b-201918).
文摘Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.
文摘Hepatocellular carcinoma presents with three distinct immune phenotypes,including immune-desert,immune-excluded,and immune-inflamed,indicating various treatment responses and prognostic outcomes.The clinical application of multi-omics parameters is still restricted by the expensive and less accessible assays,although they accurately reflect immune status.A comprehensive evaluation framework based on“easy-to-obtain”multi-model clinical parameters is urgently required,incorporating clinical features to establish baseline patient profiles and disease staging;routine blood tests assessing systemic metabolic and functional status;immune cell subsets quantifying subcluster dynamics;imaging features delineating tumor morphology,spatial configuration,and perilesional anatomical relationships;immunohistochemical markers positioning qualitative and quantitative detection of tumor antigens from the cellular and molecular level.This integrated phenomic approach aims to improve prognostic stratification and clinical decision-making in hepatocellular carcinoma management conveniently and practically.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12274348 and 12004335)the National Key Research and Development Program of China(Grant No.2024YFC2813800)。
文摘Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272393 and 52130905).
文摘As binary geological media,soil-rock mixtures(SRMs)exhibit a distinct gradational composition,leading to their unique mechanical behaviors.To appraise the stability of SRM slopes,it is essential to determine equivalent parameters of SRMs,which are typically obtained through experimental and numerical methods.In contrasted to other numerical methods,the numerical manifold method(NMM)is more effective in addressing SRM problems.This is because the high-precision regular mathematical meshes in NMM can be used without aligning with the soil-rock interfaces and boundaries of SRMs.In the current research,the equivalent strength parameters of SRMs,i.e.the equivalent cohesion ce and internal friction angleϕ_(e),are determined using NMM.Initially,an NMM triaxial numerical model is established and validated based on triaxial experiments.Subsequently,the soil and rock parameters are derived through parameter inversion.Moreover,the impacts of rock content,size,shape and rock blocks'major-axis orientation on ce andϕ_(e) of SRMs are thoroughly examined using the NMM triaxial numerical model.Additionally,a fitting function is proposed to linkϕ_(e) to the rock content and size of SRMs.When other influencing factors are fixed,the above fitting model leads to the following conclusions:(1)the predictedϕ_(e) of SRMs increase with the increase of rock content;and(2)SRM samples with smaller rocks display a higher predictedϕ_(e).
基金supported by the National Key Research and Development Plan(Grant No.2022YFB3401901)the National Natural Science Foundation of China(Grant Nos.12192210,12192214,12072295,and 12222209)+1 种基金Independent Project of State Key Laboratory of Rail Transit Vehicle System(Grant No.2023TPL-T03)Fundamental Research Funds for the Central Universities(Grant No.2682023CG004).
文摘The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金financially supported by the National Natural Science Foundation of China(Grant No.42172292)Taishan Scholars Project Special Funding,and Shandong Energy Group(Grant No.SNKJ 2022A01-R26).
文摘A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.
基金the funding provided by the National Helicopter Development Project of China。
文摘Accurate measurement of helicopter rotor motion parameters(flap,lead-lag,torsion,and azimuth angles)is essential for rotor blade design,helicopter dynamics modeling,and flight safety and health monitoring.However,the existing methods face challenges in testing equipment installation,calibration,and data transmission,resulting in limited reports on real-time in-flight measurements of blade motion parameters.This paper proposes a non-contact optoelectronic method based on two-dimensional position-sensitive detectors for in-flight measurement and a ground calibration system to obtain real-time rotor motion parameters during helicopter flight.The proposed method establishes the time evolution relationship of rotor motion parameters and verifies the performance of the in-flight measurement system regarding measurement resolution and accuracy through the construction of a blade motion posture experimental platform.The proposed method has been applied to the flight measurement of a medium-sized single-rotor helicopter,and the obtained results have been compared with theoretical analysis outcomes.Furthermore,this paper examines the characteristics of blade motion parameters during flight and discusses the challenges and potential solutions for measuring rotor motion parameters during helicopter flight using the proposed method.
文摘In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the identification of these weak influence parameters in the complex systems and propose a identification model based on the parameter recursion.As an application,three parameters of the steam generator are identified,that is,the valve opening,the valve CV value,and the reference water level,in which the valve opening and the reference water level are weak influence parameters under most operating conditions.Numerical simulation results show that,in comparison with the multi-layer perceptron(MLP),the identification error rate is decreased.Actually,the average identification error rate for the valve opening decreases by 0.96%,for the valve CV decreases by 0.002%,and for the reference water level decreases by 12%after one recursion.After two recursions,the average identification error rate for the valve opening decreases by 11.07%,for the valve CV decreases by 2.601%,and for the reference water level decreases by 95.79%.This method can help to improve the control of the steam generator.
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
基金Shaanxi Province Qin Chuangyuan“Scientist+Engineer”Team Construction Project(2022KXJ-071)2022 Qin Chuangyuan Achievement Transformation Incubation Capacity Improvement Project(2022JH-ZHFHTS-0012)+1 种基金Shaanxi Province Key Research and Development Plan-“Two Chains”Integration Key Project-Qin Chuangyuan General Window Industrial Cluster Project(2023QCY-LL-02)Xixian New Area Science and Technology Plan(2022-YXYJ-003,2022-XXCY-010)。
文摘To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and low energy consumption was proposed.But the unreasonable process parameters may lead to the inferior consistency of the forming quality of single-channel multilayer in Joule heat additive manufacturing process,and it is difficult to reach the condition for forming thinwalled parts.Orthogonal experiments were designed to fabricate single-channel multilayer samples with varying numbers of layers,and their forming quality was evaluated.The influence of printing current,forming speed,and contact pressure on the forming quality of the single-channel multilayer was analyzed.The optimal process parameters were obtained and the quality characterization of the experiment results was conducted.Results show that the printing current has the most significant influence on the forming quality of the single-channel multilayer.Under the optimal process parameters,the forming section is well fused and the surface is continuously smooth.The surface roughness of a single-channel 3-layer sample is 0.16μm,and the average Vickers hardness of cross section fusion zone is 317 HV,which lays a foundation for the subsequent use of Joule heat additive manufacturing technique to form thinwall parts.
文摘BACKGROUND Sagittal spinopelvic alignment(SSA)is essential for preserving a stable and effective upright posture and locomotion.Although alterations in the SSA are recognised to induce compensatory modifications in the pelvis,hips,and knees,the inverse relationship concerning knee pathology undergoing total knee arthroplasty(TKA)has been examined by a limited number of studies,yielding inconclusive results.AIM To generate evidence of the effect of TKA on the SSA from existing literature.METHODS Databases like PubMed,EMBASE,and Scopus were used to identify articles related to the“knee spine syndrome”phenomenon using a combination of subject terms and keywords such as“spinopelvic parameters”,“sagittal spinal balance”,and“total knee arthroplasty”were used with appropriate Boolean operators.Studies measuring the SSA following TKA were included,and research was conducted as per preferred reporting items for systematic review and metaanalysis guidelines.RESULTS A total of 475 participants had undergone TKA,and six studies measuring SSA were analysed.Following TKA,pelvic tilt was the only parameter that showed significant changes,while lumbar lordosis(LL),pelvic incidence,and sacral slope were non-significant,as evident from the forest plots.CONCLUSION The body's sagittal alignment is a complex balance between pelvic,spine,and lower extremity parameters.TKA,while having the potential to correct the flexion contracture,can also correct it.Still,the primary SSA for spinal pathology,i.e.,LL,may not be corrected in patients with co-existent spinal degenerative disease.
基金This work is supported by the Ministry of Education of Humanities and Social Science projects in China(No.20YJCZH124)Guangdong Province Education and Teaching Reform Project No.640:Research on the Teaching Practice and Application of Online Peer Assessment Methods in the Context of Artificial Intelligence.
文摘This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.
文摘The study of the morphometric parameters of the three most abundant species in the lower course of the Kouilou River (Chrysichthys auratus, Liza falcipinnis and Pellonula vorax) was carried out. The standard length of Chrysichthys auratus varies between 43.57 and 210 mm, for an average of 96.70 ± 28.63 mm;the weight varies between 2.92 and 140.83 mg, an average of 73.03 ± 21.62 mg. The condition coefficient is equal to 4.42 ± 1.52. Liza falcipinnis has a standard length which varies between 59.9 mm and 158.08 mm for an average of 88.15 ± 29.74 mm;its weight varies between 4.77 and 76.21 mg, an average of 18.61 ± 11.82 mg. The condition coefficient is equal to 2.47 ± 1.57. Pellonula vorax has a standard length which varies between 60.33 mm and 117.72 mm;for an average of 80.48 ± 17.75 mm;the weight varies between 3.61 and 25.17 mg, an average of 9.03 ± 3.61 mg. The condition coefficient is equal to 2.17 ± 0.57. These three species have a minor allometric growth.
基金supported by National Natural Science Foundation of China(No.61761027)Gansu Young Doctor’s Fund for Higher Education Institutions(No.2021QB-053)。
文摘The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts’hand-drawing,which could obtain a high accuracy segmentation and extraction result.
文摘The current investigation focuses on intertwined relationships of ecology and aquaculture for the benefit of farmers involved in fish farming practices.The study evaluated glucosinolate reduction in black,brown,and white mustard meals as fish feed ingredients for Indian Major Carps.Fish were fed with 10%mustard meal-supplemented diets in three forms:Raw(R),Anti-nutritional Rich(AR),and Anti-nutritional Lowered(AL),alongside a control group using floating feed.The three-month indoor experiment(September-November 2023)was conducted in FRP tanks with triplicate treatments.Blood analysis revealed compromised health in AR-fed carps,with reduced hemoglobin levels in rohu,catla and mrigal and elevated total leukocyte counts indicating inflammation in all the three carps studied here.Liver function was impaired in AR-fed fish,shown by increased alanine transaminase levels,highest in rohu followed by mrigal and catla.Histopathological examination of AR-fed carps liver tissue revealed necrotic spots,deformed hepatocytes,and significant vacuolation.In contrast,AL-fed fish demonstrated improved health parameters through Complete Blood Count analysis,liver function tests,and histo-pathological observations,suggesting successful reduction of anti-nutritional factors in the processed mustard meals.In near future,replacement of unprocessed seed meal with processed seed meal will lead to economic gains in fish farming.
基金National Key R&D Program of China(No.2017YFB1304000)Fundamental Research Funds for the Central Universities,China(No.2232023G-05-1)。
文摘The high-speed winding spindle employs a flexible support system incorporating rubber O-rings.By precisely configuring the structural parameters and the number of the O-rings,the spindle can stably surpass its critical speed points and maintain operational stability across the entire working speed range.However,the support stiffness and damping of rubber O-rings exhibit significant nonlinear frequency dependence.Conventional experimental methods for deriving equivalent stiffness and damping,based on the principle of the forced non-resonance method,require fabricating custom setups for each O-ring specification and conducting vibration tests at varying frequencies,resulting in low efficiency and high costs.This study proposes a hybrid simulation-experimental method for dynamic parameter identification.Firstly,the frequency-dependent dynamic parameters of a specific O-ring support system are experimentally obtained.Subsequently,a corresponding parametric finite element model is established to simulate and solve the equivalent elastic modulus and equivalent stiffness-damping coefficient of this O-ring support system.Ultimately,after iterative simulation,the simulated and experimental results achieve a 99.7%agreement.The parametric finite element model developed herein can directly simulate and inversely estimate frequency-dependent dynamic parameters for O-rings of different specifications but identical elastic modulus.
基金The National Natural Science Foundation of China under contract No. 42274159the Project supported by Key Laboratory of Space Ocean Remote Sensing and Application,MNR under contract No.2023CFO016。
文摘The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars reflects sea surface dynamics, to which ocean waves make important contributions. Low-incidence-angle real aperture radar(RAR)demonstrates great potential for independently observing vectorial Doppler information on the ocean surface. To systematically characterize and accurately estimate the wave-induced Doppler frequency shift(WVF) from lowincidence-angle RAR, this study conducts comprehensive influencing factor analysis and establishes sea-stateparameterized WVF models. First, a simulated WVF dataset is generated under a rotating low-incidence-angle RAR.The feature parameters of WVF are then determined by analysing contributing factors including wind waves, swells,and sea state parameters. Furthermore, two WVF models(WVF_Ku P9 with 9 inputs and WVF_Ku P4 with 4 inputs) are constructed by the Transformer encoder for different application scenarios. Both models achieve high accuracy for WVF estimation with root mean square errors(RMSE) of 1.874 Hz and 2.716 Hz, respectively. The reliability and superiority of the proposed models are validated through comparisons with the Ka DOP, which is a typical geophysical model function(GMF). The findings in this paper advance the understanding of WVF characteristics and generation mechanisms. The proposed estimation models can provide reliable estimates, offering critical references for lowincidence-angle RAR applications such as ocean surface current retrieval.
