Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interfere...Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interference.To address those limitations,this paper proposes a multi-channel contrast prediction coding and complex-valued residuals network(MCPC-MCVResNet)framework.This model employs contrast prediction techniques to directly extract discriminative features from electromagnetic signal sequences,effectively capturing both amplitude and phase information within I/Q data.A core innovation of this approach is the sphere space softmax(SS-softmax)loss,which optimizes intra-class clustering density of while establishing well-defined boundaries between distinct emitters.The SS-softmax mechanism significantly enhances the model's capacity to discern subtle variations among radiation emitters.Experimental results demonstrate superior identification accuracy,rapid convergence,and exceptional robustness in low signal-to-noise ratio environments.展开更多
Rock residual strength,as an important input parameter,plays an indispensable role in proposing the reasonable and scientific scheme about stope design,underground tunnel excavation and stability evaluation of deep ch...Rock residual strength,as an important input parameter,plays an indispensable role in proposing the reasonable and scientific scheme about stope design,underground tunnel excavation and stability evaluation of deep chambers.Therefore,previous residual strength models of rocks established were reviewed.And corresponding related problems were stated.Subsequently,starting from the effects of bedding and whole life-cycle evolution process,series of triaxial mechanical tests of deep bedded sandstone with five bedding angles were conducted under different confining pressures.Then,six residual strength models considering the effects of bedding and whole life-cycle evolution process were established and evaluated.Finally,a cohesion loss model for determining residual strength of deep bedded sandstone was verified.The results showed that the effects of bedding and whole life-cycle evolution process had both significant influences on the evolution characteristic of residual strength of deep bedded sandstone.Additionally,residual strength parameters:residual cohesion and residual internal friction angle of deep bedded sandstone were not constant,which both significantly changed with increasing bedding angle.Besides,the cohesion loss model was the most suitable for determining and estimating the residual strength of bedded rocks,which could provide more accurate theoretical guidance for the stability control of deep chambers.展开更多
In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantl...In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.展开更多
Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated ...Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated in diverse pathological conditions.Accurate prediction of m6A sites is critical for elucidating their regulatory mechanisms and informing drug development.However,traditional experimental methods are time-consuming and costly.Although various computational approaches have been proposed,challenges remain in feature learning,predictive accuracy,and generalization.Here,we present m6A-PSRA,a dual-branch residual-network-based predictor that fully exploits RNA sequence information to enhance prediction performance and model generalization.Methods m6A-PSRA adopts a parallel dual-branch network architecture to comprehensively extract RNA sequence features via two independent pathways.The first branch applies one-hot encoding to transform the RNA sequence into a numerical matrix while strictly preserving positional information and sequence continuity.This ensures that the biological context conveyed by nucleotide order is retained.A bidirectional long short-term memory network(BiLSTM)then processes the encoded matrix,capturing both forward and backward dependencies between bases to resolve contextual correlations.The second branch employs a k-mer tokenization strategy(k=3),decomposing the sequence into overlapping 3-mer subsequences to capture local sequence patterns.A pre-trained Doc2vec model maps these subsequences into fixeddimensional vectors,reducing feature dimensionality while extracting latent global semantic information via context learning.Both branches integrate residual networks(ResNet)and a self-attention mechanism:ResNet mitigates vanishing gradients through skip connections,preserving feature integrity,while self-attention adaptively assigns weights to focus on sequence regions most relevant to methylation prediction.This synergy enhances both feature learning and generalization capability.Results Across 11 tissues from humans,mice,and rats,m6A-PSRA consistently outperformed existing methods in accuracy(ACC)and area under the curve(AUC),achieving>90%ACC and>95%AUC in every tissue tested,indicating strong cross-species and cross-tissue adaptability.Validation on independent datasets—including three human cell lines(MOLM1,HEK293,A549)and a long-sequence dataset(m6A_IND,1001 nt)—confirmed stable performance across varied biological contexts and sequence lengths.Ablation studies demonstrated that the dual-branch architecture,residual network,and self-attention mechanism each contribute critically to performance,with their combination reducing interference between pathways.Motif analysis revealed an enrichment of m6A sites in guanine(G)and cytosine(C),consistent with known regulatory patterns,supporting the model’s biological plausibility.Conclusion m6A-PSRA effectively captures RNA sequence features,achieving high prediction accuracy and robust generalization across tissues and species,providing an efficient computational tool for m6A methylation site prediction.展开更多
Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small targe...Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.展开更多
Correction:J Cotton Res 8,27(2025)https://doi.org/10.1186/s42397-025-00228-y During the publication process of the original article(Soltani Toularoud et al.2025),the article title has been wrongly captured.Te article ...Correction:J Cotton Res 8,27(2025)https://doi.org/10.1186/s42397-025-00228-y During the publication process of the original article(Soltani Toularoud et al.2025),the article title has been wrongly captured.Te article title should be corrected from:of butisanstar and clopyralid herbicides on Gos-sypium hirsutum L.growth:insights from a pot experiment to:Residual efects of butisanstar and clopyralid herbi-cides on Gossypium hirsutum L.growth:insights from a pot experiment Te original article(Soltani Toularoud et al.2025)has been updated.Te publisher apologizes to the authors and readers for the inconvenience caused.展开更多
Background The intensive use of herbicides in agriculture raises concerns about their residual impacts on non-target crops such as cotton(Gossypium hirsutum L.),which is often rotated with cereals,sugar beet,and canol...Background The intensive use of herbicides in agriculture raises concerns about their residual impacts on non-target crops such as cotton(Gossypium hirsutum L.),which is often rotated with cereals,sugar beet,and canola.Butisanstar(BUT)and clopyralid(CLO)are widely used for broadleaf weed control in these rotations.However,how residual herbicide activity influences cotton growth and development is not well understood.This study evaluated these residual effects by measuring multiple growth parameters in a greenhouse.Cotton was grown for 40 days in soil incubated for 90 days with herbicide treatments arranged in a factorial design(type:BUT,CLO,and their combination;dose:0,1/2,1,2,and 5×recommended field dose[RFD]).Results Herbicide residues reduced cotton growth in a dose-dependent manner,with greater inhibition at higher doses.The combined BUT+CLO treatment produced the strongest negative effects,followed by CLO and then BUT alone.Compared with controls,seedling emergence declined by 12%–83%,root length by 12%–87%,plant height by 10%–84%,and chlorophyll index by 12%–80%across treatments from 1/2×RFD BUT to 5×RFD BUT+CLO.Root and shoot biomass also decreased significantly.Under the 5×RFD combined treatment,shoot N,P,and K concentrations dropped by 48%,78%,and 70%,respectively,relative to the control.Conclusions Even low levels of residual BUT and CLO impair cotton growth.To mitigate these effects,it should avoid planting cotton on recently treated soils,leave sufficient intervals between herbicide application and cotton planting,and apply soil amendments to boost microbial degradation.These measures are essential for sustaining soil health and cotton productivity.展开更多
Nowadays, ultrafine explosives are widely used in military fields. Ultrafine 2,2',4,4',6,6'-hexanitrostilbene(HNS) has emerged as an optimal primer for explosion foil initiators due to its excellent therma...Nowadays, ultrafine explosives are widely used in military fields. Ultrafine 2,2',4,4',6,6'-hexanitrostilbene(HNS) has emerged as an optimal primer for explosion foil initiators due to its excellent thermal stability and high-voltage short-pulse initiation performance. However, the solid phase ripening of ultrafine HNS leads to a degradation in its impact detonation performance. Previous studies have indicated that residual dimethyl formamide(DMF), which is present in ultrafine HNS prepared using the recrystallization method, affects ultrafine HNS ripening. The mechanism of residual solvent effects on solid phase ripening of ultrafine HNS is unclear. In this work, the specific surface area(SSA) derived from small angle X-ray scattering(SAXS) was utilized for kinetic fitting analysis to explore the mechanism by which residual solvents enhance the solid phase ripening of ultrafine HNS. The results of the SSA measured by insitu SAXS under conditions of 150℃ for 40 h revealed that the sample with 0.2% residual DMF exhibited a 21.51% decrease in SSA, whereas the sample with only 0.04% residual DMF showed a decrease of 15.66%.Furthermore, the higher amounts of residual DMF accelerated the reduction in SSA with time. Kinetic fitting analysis demonstrated that reducing residual DMF would lower both the activation energy and the pre-exponential factor, consequently decreasing the rate constant of solid phase ripening. The mechanism was speculated that it primarily facilitated the Ostwald ripening(OR). Additionally, contrast variation small angle X-ray scattering(CV-SAXS) confirmed that coating of ultrafine HNS particles is an effective method for inhibiting ripening, significantly reducing both the rate and extent of ripening of ultrafine HNS. This study predicts how residual solvents impact the solid phase ripening process of ultrafine HNS and proposes strategies for enhancing the long-term stability of ultrafine explosives.展开更多
In this work,the effect of ultrasonic vibration modes on the mechanical properties and relaxation of residual stress in 6061-T6 aluminum alloy was studied.A new ultrasonic vibration Johnson-Cook model was proposed,and...In this work,the effect of ultrasonic vibration modes on the mechanical properties and relaxation of residual stress in 6061-T6 aluminum alloy was studied.A new ultrasonic vibration Johnson-Cook model was proposed,and the relaxation and distribution of residual stress under ultrasonic vibration were predicted and analyzed using the finite element method(FEM).The mechanical properties of 6061-T6 aluminum alloy under different ultrasonic vibration modes were analyzed through experiments involving notched specimen tensile testing and scanning electron microscopy(SEM)analysis.The findings indicate that ultrasonic vibration treatment during deformation,unloading,and load-holding,as well as treatment with its natural ultrasonic frequency,can effectively release residual stress;however,treatment with its natural frequency has the highest rate of release up to 65.4%.Ultrasonic vibration treatment during deformation better inhibits fracture under the same conditions.The FEM results are in good agreement with the experimental results,and it can be used as a valid tool for predicting residual stress release under ultrasonic vibration.展开更多
The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional ch...The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of deep residual shrinkage network (DRSN)neural network-based method that is used to solve this problem.Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then,the DRSN is used to train the noise reduction process based on the results of the least square (LS) channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed signal to noise ratio (SNR) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the spatial-frequency-temporal convolutional neural networks (SF-CNN)with similar computational complexity and achieves better advantages in the full SNR range than the minimum mean squared error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.展开更多
为解决光在水下传播过程中由吸收与散射效应导致的水下图像模糊、对比度低和颜色失真问题,提出一种基于Inception-Residual和生成对抗网络的水下图像增强算法。首先,将退化水下图像缩放至256×256×3大小,以获得用于训练模型的...为解决光在水下传播过程中由吸收与散射效应导致的水下图像模糊、对比度低和颜色失真问题,提出一种基于Inception-Residual和生成对抗网络的水下图像增强算法。首先,将退化水下图像缩放至256×256×3大小,以获得用于训练模型的数据集。接着,将Inception模块、残差思想、编码解码结构和生成对抗网络相结合,构建IRGAN(Generative Adversarial Network with Inception-Residual)模型来增强水下图像。然后,利用全局相似性、内容感知和色彩感知构造多项损失函数,约束生成网络和判别网络的对抗训练。最后,通过训练好的模型对退化水下图像进行处理以获得清晰的水下图像。实验结果表明与现有增强方法相比,所提算法增强的水下图像在PSNR、UIQM和IE指标上的平均值分别比第二名提升13.6%、4.1%和0.9%。在主观感知和客观评估中,增强后的水下图像在清晰度、对比度增强和颜色校正方面均得到改善。展开更多
For the product degradation process with random effect (RE), measurement error (ME) and nonlinearity in step-stress accelerated degradation test (SSADT), the nonlinear Wiener based degradation model with RE and ME is ...For the product degradation process with random effect (RE), measurement error (ME) and nonlinearity in step-stress accelerated degradation test (SSADT), the nonlinear Wiener based degradation model with RE and ME is built. An analytical approximation to the probability density function (PDF) of the product's lifetime is derived in a closed form. The process and data of SSADT are analyzed to obtain the relation model of the observed data under each accelerated stress. The likelihood function for the population-based observed data is constructed. The population-based model parameters and its random coefficient prior values are estimated. According to the newly observed data of the target product in SSADT, an analytical approximation to the PDF of its residual lifetime (RL) is derived in accordance with its individual degradation characteristics. The parameter updating method based on Bayesian inference is applied to obtain the posterior value of random coefficient of the RL model. A numerical example by simulation is analyzed to verify the accuracy and advantage of the proposed model.展开更多
The relationships among compression ratio and stress, compression ratio and residual oil of cake in pressing process of castor beans were studied using the test equipment under different states of oilseeds and ways of...The relationships among compression ratio and stress, compression ratio and residual oil of cake in pressing process of castor beans were studied using the test equipment under different states of oilseeds and ways of pressing manners. The results show that variation of stress increases nonlinearly and residual oil rate decreases with the increase of compression ratio. Lower residual oil of cake was obtained by pressing gently and frequently. Curve fitting on both relationships had been built and parameters for the model were obtained by least square procedure and deepening research on pressing process of the castor beans for castor oil. By assuming that the value of oil production is equivalent to the value of energy consumption, the critical compression ratio of intact seeds is 6.2 while that of crushed seeds is 3.6.展开更多
In this paper, a new one-dimensional phenomenological model is developed for the assessment of the ballistic performance of Adobe. Adobe is a masonry largely spread in areas of the world involved in military operation...In this paper, a new one-dimensional phenomenological model is developed for the assessment of the ballistic performance of Adobe. Adobe is a masonry largely spread in areas of the world involved in military operations. Addressing fundamental ballistic parameters such as residual velocity or penetration depth for this building technology is necessary. The model follows the hypotheses for the ballistic response of concrete targets to high velocity impacts, provided with a dominant contribution of shear friction typical of soils. The hypotheses at the basis of the model are consistent with all experimental evidence collected by authors on Adobe. Adobe brick and mortar belong to the material class of concrete,whereas the overall mechanical parameters are determined by the internal soil mixture, including the percentage of fibre reinforcement. Despite its relative simplicity, the model is capable of well predicting ballistic test results currently available in literature for Adobe, including the data of an experimental campaign recently performed by the authors on real Adobe walls in the field.展开更多
The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illuminati...The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illumination variations and interference. To overcome issues above, a robust detection algorithm with triple constraints for cooperative targets based on spectral residual (TCSR) is proposed. Firstly, by designing an asymmetric cooperative target, which comprises red background, green H and triangle target, the captured original image is converted into a Lab color space, whose saliency map is yielded by constructing the spectral residual. Then, the triple constraints are developed according to the prior knowledge of the cooperative target. Finally, the salient region in saliency map is considered as the cooperative target, and it meets the triple constraints. Experimental results in complex environments show that the proposed TCSR outperforms the standard methods in higher detection accuracy and lower false alarm rate.展开更多
With the gradual depletion of mineral resources in the shallow part of the earth,resource exploitation continues to move deeper into the earth,it becomes a hot topic to simulate the whole process of rock strain soften...With the gradual depletion of mineral resources in the shallow part of the earth,resource exploitation continues to move deeper into the earth,it becomes a hot topic to simulate the whole process of rock strain softening,deformation and failure in deep environment,especially under high temperature and high pressure.On the basis of Lemaitre’s strain-equivalent principle,combined with statistics and damage theory,a statistical constitutive model of rock thermal damage under triaxial compression condition is established.At the same time,taking into account the existing damage model is difficult to reflect residual strength after rock failure,the residual strength is considered in this paper by introducing correction factor of damage variable,the model rationality is also verified by experiments.Analysis of results indicates that the damage evolution curve reflects the whole process of rock micro-cracks enclosure,initiation,expansion,penetration,and the formation of macro-cracks under coupled effect of temperature and confining pressure.Rock thermal damage shows logistic growth function with the increase of temperature.Under the same strain condition,rock total damage decreases with the rise of confining pressure.By studying the electron microscope images(SEM)of rock fracture,it is inferred that 35.40 MPa is the critical confining pressure of brittle to plastic transition for this granite.The model parameter F reflects the average strength of rock,and M reflects the morphological characteristics of rock stress–strain curves.The physical meanings of model parameters are clear and the model is suitable for complex stress states,which provides valuable references for the study of rock deformation and stability in deep engineering.展开更多
A small problem about soil particle regularization and contacts but essential to geotechnical engineering was studied.The soils sourced from Guangzhou and Xiamen were sieved into five different particle scale ranges(d...A small problem about soil particle regularization and contacts but essential to geotechnical engineering was studied.The soils sourced from Guangzhou and Xiamen were sieved into five different particle scale ranges(d<0.075 mm,0.075 mm≤d<0.1 mm,0.1 mm≤d<0.2 mm,0.2 mm≤d<0.5 mm and 0.5 mm≤d<1.0 mm)to study the structures and particle contacts of granite residual soil.The X-ray micro computed tomography method was used to reconstruct the microstructure of granite residual soil.The particle was identified and regularized using principal component analysis(PCA).The particle contacts and geometrical characteristics in 3D space were analyzed and summarized using statistical analyses.The results demonstrate that the main types of contact among the particles are face-face,face-angle,face-edge,edge-edge,edge-angle and angle-angle contacts for particle sizes less than 0.2 mm.When the particle sizes are greater than 0.2 mm,the contacts are effectively summarized as face-face,face-angle,face-edge,edge-edge,edge-angle,angle-angle,sphere-sphere,sphere-face,sphere-edge and sphere-angle contacts.The differences in porosity among the original sample,reconstructed sample and regularized sample are closely related to the water-swelling and water-disintegrable characteristics of granite residual soil.展开更多
In low signal-to-noise ratio(SNR)environments,the traditional radar emitter recognition(RER)method struggles to recognize multiple radar emitter signals in parallel.This paper proposes a multi-label classification and...In low signal-to-noise ratio(SNR)environments,the traditional radar emitter recognition(RER)method struggles to recognize multiple radar emitter signals in parallel.This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network.This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs.First,we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform(STFT).The time-frequency distribution image is then denoised using a deep normalized convolutional neural network(DNCNN).Secondly,the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established,and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model.Finally,time-frequency image is recognized and classified through the model,thus completing the automatic classification and recognition of the time-domain aliasing signal.Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.展开更多
An adaptive method of residual life estimation for deteriorated products with two performance characteristics (PCs) was proposed, which was sharply different from existing work that only utilized one-dimensional degra...An adaptive method of residual life estimation for deteriorated products with two performance characteristics (PCs) was proposed, which was sharply different from existing work that only utilized one-dimensional degradation data. Once new degradation information was available, the residual life of the product being monitored could be estimated in an adaptive manner. Here, it was assumed that the degradation of each PC over time was governed by a Wiener degradation process and the dependency between them was characterized by the Frank copula function. A bivariate Wiener process model with measurement errors was used to model the degradation measurements. A two-stage method and the Markov chain Monte Carlo (MCMC) method were combined to estimate the unknown parameters in sequence. Results from a numerical example about fatigue cracks show that the proposed method is valid as the relative error is small.展开更多
基金supported by the National Natural Science Foundation of China(62201602)。
文摘Despite the superior advantages of specific emitter identification in extracting emitter features from in-phase and quadrature(I/Q)signals,challenges persist due to signal-type confusion and background noise interference.To address those limitations,this paper proposes a multi-channel contrast prediction coding and complex-valued residuals network(MCPC-MCVResNet)framework.This model employs contrast prediction techniques to directly extract discriminative features from electromagnetic signal sequences,effectively capturing both amplitude and phase information within I/Q data.A core innovation of this approach is the sphere space softmax(SS-softmax)loss,which optimizes intra-class clustering density of while establishing well-defined boundaries between distinct emitters.The SS-softmax mechanism significantly enhances the model's capacity to discern subtle variations among radiation emitters.Experimental results demonstrate superior identification accuracy,rapid convergence,and exceptional robustness in low signal-to-noise ratio environments.
基金Projects(2024YFC3013801,2022YFC3004602)supported by the National Key R&D Program of ChinaProjects(U23B2093,52034009)supported by the National Natural Science Foundation of China。
文摘Rock residual strength,as an important input parameter,plays an indispensable role in proposing the reasonable and scientific scheme about stope design,underground tunnel excavation and stability evaluation of deep chambers.Therefore,previous residual strength models of rocks established were reviewed.And corresponding related problems were stated.Subsequently,starting from the effects of bedding and whole life-cycle evolution process,series of triaxial mechanical tests of deep bedded sandstone with five bedding angles were conducted under different confining pressures.Then,six residual strength models considering the effects of bedding and whole life-cycle evolution process were established and evaluated.Finally,a cohesion loss model for determining residual strength of deep bedded sandstone was verified.The results showed that the effects of bedding and whole life-cycle evolution process had both significant influences on the evolution characteristic of residual strength of deep bedded sandstone.Additionally,residual strength parameters:residual cohesion and residual internal friction angle of deep bedded sandstone were not constant,which both significantly changed with increasing bedding angle.Besides,the cohesion loss model was the most suitable for determining and estimating the residual strength of bedded rocks,which could provide more accurate theoretical guidance for the stability control of deep chambers.
文摘In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.
基金supported by grants from The National Natural Science Foundation of China(12361104)Yunnan Fundamental Research Projects(202301AT070016,202401AT070036)+2 种基金the Youth Talent Program of Xingdian Talent Support Plan(XDYC-QNRC-2022-0514)the Yunnan Province International Joint Laboratory for Intelligent Integration and Application of Ethnic Multilingualism(202403AP140014)the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University(SMDAYB2023004)。
文摘Objective N6-methyladenosine(m6A),the most prevalent epigenetic modification in eukaryotic RNA,plays a pivotal role in regulating cellular differentiation and developmental processes,with its dysregulation implicated in diverse pathological conditions.Accurate prediction of m6A sites is critical for elucidating their regulatory mechanisms and informing drug development.However,traditional experimental methods are time-consuming and costly.Although various computational approaches have been proposed,challenges remain in feature learning,predictive accuracy,and generalization.Here,we present m6A-PSRA,a dual-branch residual-network-based predictor that fully exploits RNA sequence information to enhance prediction performance and model generalization.Methods m6A-PSRA adopts a parallel dual-branch network architecture to comprehensively extract RNA sequence features via two independent pathways.The first branch applies one-hot encoding to transform the RNA sequence into a numerical matrix while strictly preserving positional information and sequence continuity.This ensures that the biological context conveyed by nucleotide order is retained.A bidirectional long short-term memory network(BiLSTM)then processes the encoded matrix,capturing both forward and backward dependencies between bases to resolve contextual correlations.The second branch employs a k-mer tokenization strategy(k=3),decomposing the sequence into overlapping 3-mer subsequences to capture local sequence patterns.A pre-trained Doc2vec model maps these subsequences into fixeddimensional vectors,reducing feature dimensionality while extracting latent global semantic information via context learning.Both branches integrate residual networks(ResNet)and a self-attention mechanism:ResNet mitigates vanishing gradients through skip connections,preserving feature integrity,while self-attention adaptively assigns weights to focus on sequence regions most relevant to methylation prediction.This synergy enhances both feature learning and generalization capability.Results Across 11 tissues from humans,mice,and rats,m6A-PSRA consistently outperformed existing methods in accuracy(ACC)and area under the curve(AUC),achieving>90%ACC and>95%AUC in every tissue tested,indicating strong cross-species and cross-tissue adaptability.Validation on independent datasets—including three human cell lines(MOLM1,HEK293,A549)and a long-sequence dataset(m6A_IND,1001 nt)—confirmed stable performance across varied biological contexts and sequence lengths.Ablation studies demonstrated that the dual-branch architecture,residual network,and self-attention mechanism each contribute critically to performance,with their combination reducing interference between pathways.Motif analysis revealed an enrichment of m6A sites in guanine(G)and cytosine(C),consistent with known regulatory patterns,supporting the model’s biological plausibility.Conclusion m6A-PSRA effectively captures RNA sequence features,achieving high prediction accuracy and robust generalization across tissues and species,providing an efficient computational tool for m6A methylation site prediction.
基金Supported by the Key Laboratory Fund for Equipment Pre-Research(6142207210202)。
文摘Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.
文摘Correction:J Cotton Res 8,27(2025)https://doi.org/10.1186/s42397-025-00228-y During the publication process of the original article(Soltani Toularoud et al.2025),the article title has been wrongly captured.Te article title should be corrected from:of butisanstar and clopyralid herbicides on Gos-sypium hirsutum L.growth:insights from a pot experiment to:Residual efects of butisanstar and clopyralid herbi-cides on Gossypium hirsutum L.growth:insights from a pot experiment Te original article(Soltani Toularoud et al.2025)has been updated.Te publisher apologizes to the authors and readers for the inconvenience caused.
文摘Background The intensive use of herbicides in agriculture raises concerns about their residual impacts on non-target crops such as cotton(Gossypium hirsutum L.),which is often rotated with cereals,sugar beet,and canola.Butisanstar(BUT)and clopyralid(CLO)are widely used for broadleaf weed control in these rotations.However,how residual herbicide activity influences cotton growth and development is not well understood.This study evaluated these residual effects by measuring multiple growth parameters in a greenhouse.Cotton was grown for 40 days in soil incubated for 90 days with herbicide treatments arranged in a factorial design(type:BUT,CLO,and their combination;dose:0,1/2,1,2,and 5×recommended field dose[RFD]).Results Herbicide residues reduced cotton growth in a dose-dependent manner,with greater inhibition at higher doses.The combined BUT+CLO treatment produced the strongest negative effects,followed by CLO and then BUT alone.Compared with controls,seedling emergence declined by 12%–83%,root length by 12%–87%,plant height by 10%–84%,and chlorophyll index by 12%–80%across treatments from 1/2×RFD BUT to 5×RFD BUT+CLO.Root and shoot biomass also decreased significantly.Under the 5×RFD combined treatment,shoot N,P,and K concentrations dropped by 48%,78%,and 70%,respectively,relative to the control.Conclusions Even low levels of residual BUT and CLO impair cotton growth.To mitigate these effects,it should avoid planting cotton on recently treated soils,leave sufficient intervals between herbicide application and cotton planting,and apply soil amendments to boost microbial degradation.These measures are essential for sustaining soil health and cotton productivity.
基金the Presidential Foundation of CAEP(Grant No.YZJJZQ2023005)the National Natural Science Foundation of China(Grant No.22375191).
文摘Nowadays, ultrafine explosives are widely used in military fields. Ultrafine 2,2',4,4',6,6'-hexanitrostilbene(HNS) has emerged as an optimal primer for explosion foil initiators due to its excellent thermal stability and high-voltage short-pulse initiation performance. However, the solid phase ripening of ultrafine HNS leads to a degradation in its impact detonation performance. Previous studies have indicated that residual dimethyl formamide(DMF), which is present in ultrafine HNS prepared using the recrystallization method, affects ultrafine HNS ripening. The mechanism of residual solvent effects on solid phase ripening of ultrafine HNS is unclear. In this work, the specific surface area(SSA) derived from small angle X-ray scattering(SAXS) was utilized for kinetic fitting analysis to explore the mechanism by which residual solvents enhance the solid phase ripening of ultrafine HNS. The results of the SSA measured by insitu SAXS under conditions of 150℃ for 40 h revealed that the sample with 0.2% residual DMF exhibited a 21.51% decrease in SSA, whereas the sample with only 0.04% residual DMF showed a decrease of 15.66%.Furthermore, the higher amounts of residual DMF accelerated the reduction in SSA with time. Kinetic fitting analysis demonstrated that reducing residual DMF would lower both the activation energy and the pre-exponential factor, consequently decreasing the rate constant of solid phase ripening. The mechanism was speculated that it primarily facilitated the Ostwald ripening(OR). Additionally, contrast variation small angle X-ray scattering(CV-SAXS) confirmed that coating of ultrafine HNS particles is an effective method for inhibiting ripening, significantly reducing both the rate and extent of ripening of ultrafine HNS. This study predicts how residual solvents impact the solid phase ripening process of ultrafine HNS and proposes strategies for enhancing the long-term stability of ultrafine explosives.
基金Project(51775480)supported by the National Natural Science Foundation of ChinaProjects(E2018203143,E2022203050)supported by the Natural Science Foundation of Hebei Province,China。
文摘In this work,the effect of ultrasonic vibration modes on the mechanical properties and relaxation of residual stress in 6061-T6 aluminum alloy was studied.A new ultrasonic vibration Johnson-Cook model was proposed,and the relaxation and distribution of residual stress under ultrasonic vibration were predicted and analyzed using the finite element method(FEM).The mechanical properties of 6061-T6 aluminum alloy under different ultrasonic vibration modes were analyzed through experiments involving notched specimen tensile testing and scanning electron microscopy(SEM)analysis.The findings indicate that ultrasonic vibration treatment during deformation,unloading,and load-holding,as well as treatment with its natural ultrasonic frequency,can effectively release residual stress;however,treatment with its natural frequency has the highest rate of release up to 65.4%.Ultrasonic vibration treatment during deformation better inhibits fracture under the same conditions.The FEM results are in good agreement with the experimental results,and it can be used as a valid tool for predicting residual stress release under ultrasonic vibration.
基金supported by the National Key Scientific Instrument and Equipment Development Project(61827801).
文摘The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI)to take advantage of the massive multiple-input multiple-output(MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of deep residual shrinkage network (DRSN)neural network-based method that is used to solve this problem.Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then,the DRSN is used to train the noise reduction process based on the results of the least square (LS) channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed signal to noise ratio (SNR) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the spatial-frequency-temporal convolutional neural networks (SF-CNN)with similar computational complexity and achieves better advantages in the full SNR range than the minimum mean squared error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.
文摘为解决光在水下传播过程中由吸收与散射效应导致的水下图像模糊、对比度低和颜色失真问题,提出一种基于Inception-Residual和生成对抗网络的水下图像增强算法。首先,将退化水下图像缩放至256×256×3大小,以获得用于训练模型的数据集。接着,将Inception模块、残差思想、编码解码结构和生成对抗网络相结合,构建IRGAN(Generative Adversarial Network with Inception-Residual)模型来增强水下图像。然后,利用全局相似性、内容感知和色彩感知构造多项损失函数,约束生成网络和判别网络的对抗训练。最后,通过训练好的模型对退化水下图像进行处理以获得清晰的水下图像。实验结果表明与现有增强方法相比,所提算法增强的水下图像在PSNR、UIQM和IE指标上的平均值分别比第二名提升13.6%、4.1%和0.9%。在主观感知和客观评估中,增强后的水下图像在清晰度、对比度增强和颜色校正方面均得到改善。
基金supported by the National Defense Foundation of China(71601183)
文摘For the product degradation process with random effect (RE), measurement error (ME) and nonlinearity in step-stress accelerated degradation test (SSADT), the nonlinear Wiener based degradation model with RE and ME is built. An analytical approximation to the probability density function (PDF) of the product's lifetime is derived in a closed form. The process and data of SSADT are analyzed to obtain the relation model of the observed data under each accelerated stress. The likelihood function for the population-based observed data is constructed. The population-based model parameters and its random coefficient prior values are estimated. According to the newly observed data of the target product in SSADT, an analytical approximation to the PDF of its residual lifetime (RL) is derived in accordance with its individual degradation characteristics. The parameter updating method based on Bayesian inference is applied to obtain the posterior value of random coefficient of the RL model. A numerical example by simulation is analyzed to verify the accuracy and advantage of the proposed model.
基金Project(201304608)supported by the State Forestry Administration of ChinaProject(31470594)supported by the National Natural Science Foundation of China
文摘The relationships among compression ratio and stress, compression ratio and residual oil of cake in pressing process of castor beans were studied using the test equipment under different states of oilseeds and ways of pressing manners. The results show that variation of stress increases nonlinearly and residual oil rate decreases with the increase of compression ratio. Lower residual oil of cake was obtained by pressing gently and frequently. Curve fitting on both relationships had been built and parameters for the model were obtained by least square procedure and deepening research on pressing process of the castor beans for castor oil. By assuming that the value of oil production is equivalent to the value of energy consumption, the critical compression ratio of intact seeds is 6.2 while that of crushed seeds is 3.6.
文摘In this paper, a new one-dimensional phenomenological model is developed for the assessment of the ballistic performance of Adobe. Adobe is a masonry largely spread in areas of the world involved in military operations. Addressing fundamental ballistic parameters such as residual velocity or penetration depth for this building technology is necessary. The model follows the hypotheses for the ballistic response of concrete targets to high velocity impacts, provided with a dominant contribution of shear friction typical of soils. The hypotheses at the basis of the model are consistent with all experimental evidence collected by authors on Adobe. Adobe brick and mortar belong to the material class of concrete,whereas the overall mechanical parameters are determined by the internal soil mixture, including the percentage of fibre reinforcement. Despite its relative simplicity, the model is capable of well predicting ballistic test results currently available in literature for Adobe, including the data of an experimental campaign recently performed by the authors on real Adobe walls in the field.
基金supported by the National Natural Science Foundation of China(61135001)the Scientific Research Program of Shaanxi Provincial Department of Education(16JK1499)+2 种基金the Doctoral Fund of Xi’an University of Science and Technology(2015QDJ007)the Cultivation of Xi’an University of Science and Technology(2014015)the Ministry of Education Key Laboratory of Information Fusion Technology(LIFT2015-G-1)
文摘The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illumination variations and interference. To overcome issues above, a robust detection algorithm with triple constraints for cooperative targets based on spectral residual (TCSR) is proposed. Firstly, by designing an asymmetric cooperative target, which comprises red background, green H and triangle target, the captured original image is converted into a Lab color space, whose saliency map is yielded by constructing the spectral residual. Then, the triple constraints are developed according to the prior knowledge of the cooperative target. Finally, the salient region in saliency map is considered as the cooperative target, and it meets the triple constraints. Experimental results in complex environments show that the proposed TCSR outperforms the standard methods in higher detection accuracy and lower false alarm rate.
基金Projects(51604260,11802145)supported by the National Natural Science Foundation of ChinaProject(SKLGDUEK1204)supported by the State Key Laboratory for Geomechanics and Deep Underground Engineering,ChinaProject(BK20160416)supported by the Natural Science Foundation of Jiangsu Province of China
文摘With the gradual depletion of mineral resources in the shallow part of the earth,resource exploitation continues to move deeper into the earth,it becomes a hot topic to simulate the whole process of rock strain softening,deformation and failure in deep environment,especially under high temperature and high pressure.On the basis of Lemaitre’s strain-equivalent principle,combined with statistics and damage theory,a statistical constitutive model of rock thermal damage under triaxial compression condition is established.At the same time,taking into account the existing damage model is difficult to reflect residual strength after rock failure,the residual strength is considered in this paper by introducing correction factor of damage variable,the model rationality is also verified by experiments.Analysis of results indicates that the damage evolution curve reflects the whole process of rock micro-cracks enclosure,initiation,expansion,penetration,and the formation of macro-cracks under coupled effect of temperature and confining pressure.Rock thermal damage shows logistic growth function with the increase of temperature.Under the same strain condition,rock total damage decreases with the rise of confining pressure.By studying the electron microscope images(SEM)of rock fracture,it is inferred that 35.40 MPa is the critical confining pressure of brittle to plastic transition for this granite.The model parameter F reflects the average strength of rock,and M reflects the morphological characteristics of rock stress–strain curves.The physical meanings of model parameters are clear and the model is suitable for complex stress states,which provides valuable references for the study of rock deformation and stability in deep engineering.
基金Projects(41572277,41877229)supported by the National Natural Science Foundation of ChinaProject(2015A030313118)supported by the Natural Science Foundation of Guangdong Province,ChinaProject(201607010023)supported by the Science and Technology Program of Guangzhou,China
文摘A small problem about soil particle regularization and contacts but essential to geotechnical engineering was studied.The soils sourced from Guangzhou and Xiamen were sieved into five different particle scale ranges(d<0.075 mm,0.075 mm≤d<0.1 mm,0.1 mm≤d<0.2 mm,0.2 mm≤d<0.5 mm and 0.5 mm≤d<1.0 mm)to study the structures and particle contacts of granite residual soil.The X-ray micro computed tomography method was used to reconstruct the microstructure of granite residual soil.The particle was identified and regularized using principal component analysis(PCA).The particle contacts and geometrical characteristics in 3D space were analyzed and summarized using statistical analyses.The results demonstrate that the main types of contact among the particles are face-face,face-angle,face-edge,edge-edge,edge-angle and angle-angle contacts for particle sizes less than 0.2 mm.When the particle sizes are greater than 0.2 mm,the contacts are effectively summarized as face-face,face-angle,face-edge,edge-edge,edge-angle,angle-angle,sphere-sphere,sphere-face,sphere-edge and sphere-angle contacts.The differences in porosity among the original sample,reconstructed sample and regularized sample are closely related to the water-swelling and water-disintegrable characteristics of granite residual soil.
基金The authors would like to acknowledge National Natural Science Foundation of China under Grant 61973037 and Grant 61673066 to provide fund for conducting experiments.
文摘In low signal-to-noise ratio(SNR)environments,the traditional radar emitter recognition(RER)method struggles to recognize multiple radar emitter signals in parallel.This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network.This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs.First,we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform(STFT).The time-frequency distribution image is then denoised using a deep normalized convolutional neural network(DNCNN).Secondly,the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established,and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model.Finally,time-frequency image is recognized and classified through the model,thus completing the automatic classification and recognition of the time-domain aliasing signal.Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.
基金Project(60904002)supported by the National Natural Science Foundation of China
文摘An adaptive method of residual life estimation for deteriorated products with two performance characteristics (PCs) was proposed, which was sharply different from existing work that only utilized one-dimensional degradation data. Once new degradation information was available, the residual life of the product being monitored could be estimated in an adaptive manner. Here, it was assumed that the degradation of each PC over time was governed by a Wiener degradation process and the dependency between them was characterized by the Frank copula function. A bivariate Wiener process model with measurement errors was used to model the degradation measurements. A two-stage method and the Markov chain Monte Carlo (MCMC) method were combined to estimate the unknown parameters in sequence. Results from a numerical example about fatigue cracks show that the proposed method is valid as the relative error is small.
