Staging liver fibrosis with various diffusion-weighted magnetic resonance imaging models

BACKGROUND Diffusion-weighted imaging(DWI)has been developed to stage liver fibrosis.However,its diagnostic performance is inconsistent among studies.Therefore,it is worth studying the diagnostic value of various diffusion models for liver fibrosis in one cohort.AIM To evaluate the clinical potential of six diffusion-weighted models in liver fibrosis staging and compare their diagnostic performances.METHODS This prospective study enrolled 59 patients suspected of liver disease and scheduled for liver biopsy and 17 healthy participants.All participants underwent multi-b value DWI.The main DWI-derived parameters included Mono-apparent diffusion coefficient(ADC)from mono-exponential DWI,intravoxel incoherent motion model-derived true diffusion coefficient(IVIM-D),diffusion kurtosis imaging-derived apparent diffusivity(DKI-MD),stretched exponential model-derived distributed diffusion coefficient(SEM-DDC),fractional order calculus(FROC)model-derived diffusion coefficient(FROC-D)and FROC model-derived microstructural quantity(FROC-μ),and continuous-time random-walk(CTRW)model-derived anomalous diffusion coefficient(CTRW-D)and CTRW model-derived temporal diffusion heterogeneity index(CTRW-α).The correlations between DWI-derived parameters and fibrosis stages and the parameters’diagnostic efficacy in detecting significant fibrosis(SF)were assessed and compared.RESULTS CTRW-D(r=-0.356),CTRW-α(r=-0.297),DKI-MD(r=-0.297),FROC-D(r=-0.350),FROC-μ(r=-0.321),IVIM-D(r=-0.251),Mono-ADC(r=-0.362),and SEM-DDC(r=-0.263)were significantly correlated with fibrosis stages.The areas under the ROC curves(AUCs)of the combined index of the six models for distinguishing SF(0.697-0.747)were higher than each of the parameters alone(0.524-0.719).The DWI models’ability to detect SF was similar.The combined index of CTRW model parameters had the highest AUC(0.747).CONCLUSION The DWI models were similarly valuable in distinguishing SF in patients with liver disease.The combined index of CTRW parameters had the highest AUC.

Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model:A dual-center study

BACKGROUND Perineural invasion(PNI)has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer(RC).Preoperative prediction of PNI status is helpful for individualized treatment of RC.Recently,several radiomics studies have been used to predict the PNI status in RC,demonstrating a good predictive effect,but the results lacked generalizability.The preoperative prediction of PNI status is still challenging and needs further study.AIM To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients.METHODS This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers.The patients underwent preoperative high-resolution magnetic resonance imaging(MRI)between May 2019 and August 2022.Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging(T2WI)and contrast-enhanced T1WI(T1CE)sequences.The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared(T2WI,T1CE and T2WI+T1CE fusion sequences).A clinical-radiomics(CR)model was established by combining the radiomics features and clinical risk factors.The internal and external validation groups were used to validate the proposed models.The area under the receiver operating characteristic curve(AUC),DeLong test,net reclassification improvement(NRI),integrated discrimination improvement(IDI),calibration curve,and decision curve analysis(DCA)were used to evaluate the model performance.RESULTS Among the radiomics models,the T2WI+T1CE fusion sequences model showed the best predictive performance,in the training and internal validation groups,the AUCs of the fusion sequence model were 0.839[95%confidence interval(CI):0.757-0.921]and 0.787(95%CI:0.650-0.923),which were higher than those of the T2WI and T1CE sequence models.The CR model constructed by combining clinical risk factors had the best predictive performance.In the training and internal and external validation groups,the AUCs of the CR model were 0.889(95%CI:0.824-0.954),0.889(95%CI:0.803-0.976)and 0.894(95%CI:0.814-0.974).Delong test,NRI,and IDI showed that the CR model had significant differences from other models(P<0.05).Calibration curves demonstrated good agreement,and DCA revealed significant benefits of the CR model.CONCLUSION The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively,which facilitates individualized treatment of RC patients.

Magnetic resonance imaging of extraocular rectus muscles abnormalities in acute acquired concomitant esotropia

AIM:To investigate the difference of medial rectus(MR)and lateral rectus(LR)between acute acquired concomitant esotropia(AACE)and the healthy controls(HCs)detected by magnetic resonance imaging(MRI).METHODS:A case-control study.Eighteen subjects with AACE and eighteen HCs were enrolled.MRI scanning data were conducted in target-controlled central gaze with a 3-Tesla magnetic resonance scanner.Extraocular muscles(EOMs)were scanned in contiguous image planes 2-mm thick spanning the EOM origins to the globe equator.To form posterior partial volumes(PPVs),the LR and MR cross-sections in the image planes 8,10,12,and 14 mm posterior to the globe were summed and multiplied by the 2-mm slice thickness.The data were classified according to the right eye,left eye,dominant eye,and non-dominant eye,and the differences in mean cross-sectional area,maximum cross-sectional area,and PPVs of the MR and LR muscle in the AACE group and HCs group were compared under the above classifications respectively.RESULTS:There were no significant differences between the two groups of demographic characteristics.The mean cross-sectional area of the LR muscle was significantly greater in the AACE group than that in the HCs group in the non-dominant eyes(P=0.028).The maximum cross-sectional area of the LR muscle both in the dominant and non-dominant eye of the AACE group was significantly greater than the HCs group(P=0.009,P=0.016).For the dominant eye,the PPVs of the LR muscle were significantly greater in the AACE than that in the HCs group(P=0.013),but not in the MR muscle(P=0.698).CONCLUSION:The size and volume of muscles dominant eyes of AACE subjects change significantly to overcome binocular diplopia.The LR muscle become larger to compensate for the enhanced convergence in the AACE.

High-grade pancreatic intraepithelial neoplasia diagnosed based on changes in magnetic resonance cholangiopancreatography findings:A case report

BACKGROUND High-grade pancreatic intraepithelial neoplasia(PanIN)exhibits no mass and is not detected by any examination modalities.However,it can be diagnosed by pancreatic juice cytology from indirect findings.Most previous cases were diagnosed based on findings of a focal stricture of the main pancreatic duct(MPD)and caudal MPD dilatation and subsequent pancreatic juice cytology using endoscopic retrograde cholangiopancreatography(ERCP).We experienced a case of high-grade PanIN with an unclear MPD over a 20-mm range,but without caudal MPD dilatation on magnetic resonance cholangiopancreatography(MRCP).CASE SUMMARY A 60-year-old female patient underwent computed tomography for a follow-up of uterine cancer post-excision,which revealed pancreatic cysts.MRCP revealed an unclear MPD of the pancreatic body at a 20-mm length without caudal MPD dilatation.Thus,course observation was performed.After 24 mo,MRCP revealed an increased caudal MPD caliber and a larger pancreatic cyst.We performed ERCP and detected atypical cells suspected of adenocarcinoma by serial pancreatic juice aspiration cytology examination.We performed a distal pancreatectomy and obtained a histopathological diagnosis of high-grade PanIN.Pancreatic parenchyma invasion was not observed,and curative resection was achieved.CONCLUSION High-grade Pan-IN may cause MPD narrowing in a long range without caudal MPD dilatation.

Outcomes and efficacy of magnetic resonance imaging-compatible sacral nerve stimulator for management of fecal incontinence: A multi-institutional study

BACKGROUND Fecal incontinence(FI)is an involuntary passage of fecal matter which can have a significant impact on a patient’s quality of life.Many modalities of treatment exist for FI.Sacral nerve stimulation is a well-established treatment for FI.Given the increased need of magnetic resonance imaging(MRI)for diagnostics,the In-terStim which was previously used in sacral nerve stimulation was limited by MRI incompatibility.Medtronic MRI-compatible InterStim was approved by the United States Food and Drug Administration in August 2020 and has been widely used.AIM To evaluate the efficacy,outcomes and complications of the MRI-compatible InterStim.METHODS Data of patients who underwent MRI-compatible Medtronic InterStim placement at UPMC Williamsport,University of Minnesota,Advocate Lutheran General Hospital,and University of Wisconsin-Madison was pooled and analyzed.Patient demographics,clinical features,surgical techniques,complications,and outcomes were analyzed.Strengthening the Reporting of Observational studies in Epidemiology(STROBE)cross-sectional reporting guidelines were used.RESULTS Seventy-three patients had the InterStim implanted.The mean age was 63.29±12.2 years.Fifty-seven(78.1%)patients were females and forty-two(57.5%)patients had diabetes.In addition to incontinence,overlapping symptoms included diarrhea(23.3%),fecal urgency(58.9%),and urinary incontinence(28.8%).Fifteen(20.5%)patients underwent Peripheral Nerve Evaluation before proceeding to definite implant placement.Thirty-two(43.8%)patients underwent rechargeable InterStim placement.Three(4.1%)patients needed removal of the implant.Migration of the external lead connection was observed in 7(9.6%)patients after the stage I procedure.The explanation for one patient was due to infection.Seven(9.6%)patients had other complications like nerve pain,hematoma,infection,lead fracture,and bleeding.The mean follow-up was 6.62±3.5 mo.Sixty-eight(93.2%)patients reported significant improvement of symptoms on follow-up evaluation.CONCLUSION This study shows promising results with significant symptom improvement,good efficacy and good patient outcomes with low complication rates while using MRI compatible InterStim for FI.Further long-term follow-up and future studies with a larger patient population is recommended.

Potential applications of 7 Tesla magnetic resonance imaging in paediatric neuroimaging:Feasibility and challenges

The integration of 7 Tesla magnetic resonance imaging(7 T MRI)in adult patients has marked a revolutionary stride in radiology.In this article we explore the feasibility of 7 T MRI in paediatric practice,emphasizing its feasibility,applications,challenges,and safety considerations.The heightened resolution and tissue contrast of 7 T MRI offer unprecedented diagnostic accuracy,particularly in neuroimaging.Applications range from neuro-oncology to neonatal brain imaging,showcasing its efficacy in detecting subtle structural abnormalities and providing enhanced insights into neurological conditions.Despite the promise,challenges such as high cost,discomfort,and safety concerns necessitate careful consideration.Research suggests that,with precautions,7 T MRI is feasible in paediatrics,yet ongoing studies and safety assessments are imperative.

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

A Comprehensive Survey on Deep Learning Multi-Modal Fusion:Methods,Technologies and Applications

Multi-modal fusion technology gradually become a fundamental task in many fields,such as autonomous driving,smart healthcare,sentiment analysis,and human-computer interaction.It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities.Under complex scenes,multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions.However,achieving outstanding performance is challenging because of equipment performance limitations,missing information,and data noise.This paper comprehensively reviews existing methods based onmulti-modal fusion techniques and completes a detailed and in-depth analysis.According to the data fusion stage,multi-modal fusion has four primary methods:early fusion,deep fusion,late fusion,and hybrid fusion.The paper surveys the three majormulti-modal fusion technologies that can significantly enhance the effect of data fusion and further explore the applications of multi-modal fusion technology in various fields.Finally,it discusses the challenges and explores potential research opportunities.Multi-modal tasks still need intensive study because of data heterogeneity and quality.Preserving complementary information and eliminating redundant information between modalities is critical in multi-modal technology.Invalid data fusion methods may introduce extra noise and lead to worse results.This paper provides a comprehensive and detailed summary in response to these challenges.

Towards trustworthy multi-modal motion prediction:Holistic evaluation and interpretability of outputs

Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

Parametric resonance of axially functionally graded pipes conveying pulsating fluid

Based on the generalized Hamilton's principle,the nonlinear governing equation of an axially functionally graded(AFG)pipe is established.The non-trivial equilibrium configuration is superposed by the modal functions of a simply supported beam.Via the direct multi-scale method,the response and stability boundary to the pulsating fluid velocity are solved analytically and verified by the differential quadrature element method(DQEM).The influence of Young's modulus gradient on the parametric resonance is investigated in the subcritical and supercritical regions.In general,the pipe in the supercritical region is more sensitive to the pulsating excitation.The nonlinearity changes from hard to soft,and the non-trivial equilibrium configuration introduces more frequency components to the vibration.Besides,the increasing Young's modulus gradient improves the critical pulsating flow velocity of the parametric resonance,and further enhances the stability of the system.In addition,when the temperature increases along the axial direction,reducing the gradient parameter can enhance the response asymmetry.This work further complements the theoretical analysis of pipes conveying pulsating fluid.

Enhanced resonance frequency in Co2FeAl thin film with different thicknesses grown on flexible graphene substrate

The flexible materials exhibit more favorable properties than most rigid substrates in flexibility,weight saving,mechanical reliability,and excellent environmental toughness.Particularly,flexible graphene film with unique mechanical properties was extensively explored in high frequency devices.Herein,we report the characteristics of structure and magnetic properties at high frequency of Co2FeAl thin film with different thicknesses grown on flexible graphene substrate at room temperature.The exciting finding for the columnar structure of Co2FeAl thin film lays the foundation for excellent high frequency property of Co2FeAl/flexible graphene structure.In-plane magnetic anisotropy field varying with increasing thickness of Co2FeAl thin film can be obtained by measurement of ferromagnetic resonance,which can be ascribed to the enhancement of crystallinity and the increase of grain size.Meanwhile,the resonance frequency which can be achieved by the measurement of vector network analyzer with the microstrip method increases with increasing thickness of Co2FeAl thin film.Moreover,in our case with graphene film,the resonance magnetic field is quite stable though folded for twenty cycles,which demonstrates that good flexibility of graphene film and the stability of high frequency magnetic property of Co2FeAl thin film grown on flexible graphene substrate.These results are promising for the design of microwave devices and wireless communication equipment.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Research on Multi-modal In-Vehicle Intelligent Personal Assistant Design

Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.

Inter-well internal resonance analysis of rectangular asymmetric cross-ply bistable composite laminated cantilever shell under transverse foundation excitation

The chaotic dynamic snap-through and complex nonlinear vibrations are investigated in a rectangular asymmetric cross-ply bistable composite laminated cantilever shell,in cases of 1:2 inter-well internal resonance and primary resonance.The transverse foundation excitation is applied to the fixed end of the structure,and the other end is in a free state.The first-order approximate multiple scales method is employed to perform the perturbation analysis on the dimensionless two-degree-of-freedom ordinary differential motion control equation.The four-dimensional averaged equations are derived in both polar and rectangular coordinate forms.Deriving from the obtained frequency-amplitude and force-amplitude response curves,a detailed analysis is conducted to examine the impacts of excitation amplitude,damping coefficient,and tuning parameter on the nonlinear internal resonance characteristics of the system.The nonlinear softening characteristic is exhibited in the upper stable-state,while the lower stable-state demonstrates the softening and linearity characteristics.Numerical simulation is carried out using the fourth-order Runge-Kutta method,and a series of nonlinear response curves are plotted.Increasing the excitation amplitude further elucidates the global bifurcation and chaotic dynamic snap-through characteristics of the bistable cantilever shell.

Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

The breaking of spin symmetry in the single-particle resonances in deformed nuclei

The exploration of spin symmetry (SS) in nuclear physics has been instrumental in identifying atomic nucleus structures.In this study,we solve the Dirac equation from the relativistic mean field (RMF) in complex momentum representation.We investigated SS and its breaking in single-particle resonant states within deformed nuclei,with a focus on the illustrative nucleus168Er.This was the initial discovery of a resonant spin doublet in a deformed nucleus,with the expectation of the SS approaching the continuum threshold.With increasing single-particle energy,the splitting of the resonant spin doublets widened significantly.This escalating splitting implies diminishing adherence to the SS,indicating a departure from the expected behavior as the energy levels increase.We also analyzed the width of the resonant states,showing that lower orbital angular momentum resonances possess shorter decay times and that SS is preserved within broad resonant doublets,as opposed to narrow resonant doublets.Comparing the radial density of the upper components for the bound-state and resonant-state doublets,it becomes evident that while SS is well-preserved in the bound states,it deteriorates in the resonant states.The impact of nuclear deformation (β_(2)) on SS was examined,demonstrating that an increase in β_(2) resulted in higher energy and width splitting in the resonant spin doublets,which is attributed to increased component mixing.Furthermore,the sensitivity of spin doublets to various potential parameters such as surface diffuseness (a),radius (R),and depth (Σ0) is discussed,emphasizing the role of these parameters in SS.This study provides valuable insights into the behavior of spin doublets in deformed nuclei and their interplay with the nuclear structure,thereby advancing our understanding of SS in the resonance state.

Numerical Investigation on the Harbour Resonance Inside A Harbour with Lateral Cavities

Wave-induced harbour resonance is numerically investigated inside a harbour with lateral cavities.The theoretical solutions for the amplification parameter are compared with the simulated results under varying dimensionless wave numbers in order to verify the simulation model in a rectangular harbour at a constant depth.The results indicate that the numerical model can correctly calculate the natural frequency and the natural wave height.A range of calculations are performed for harbour resonance with one pair of lateral cavities,two pairs of lateral cavities and three pairs of lateral cavities,respectively.The simulated results indicate that the amplitude of the amplification parameter decreases both at the primary natural oscillation and the secondary natural oscillation,as the number of lateral cavities increases.The dimensionless wave number reduces as the number of lateral cavities increases both at the primary natural oscillation and the secondary natural oscillation as well.

An underdamped and delayed tri-stable model-based stochastic resonance

Stochastic resonance(SR) is investigated in an underdamped tri-stable potential system driven by Gaussian colored noise and a periodic excitation, where both displacement and velocity time-delayed states feedback are considered. It is challenging to study SR in a second-order delayed multi-stable system analytically. In this paper, the improved energy envelope stochastic average method is developed to derive the analytical expressions of stationary probability density(SPD)and spectral amplification. The effects of noise intensity, damping coefficient, and time delay on SR are analyzed. The results show that the shapes of joint SPD can be adjusted to the desired structure by choosing the time delay and feedback gains. For fixed time delay, the SR peak is increased for negative displacement or velocity feedback gain. Meanwhile, the SR peak is decreased while the optimal noise intensity increases with increasing correlation time of noise. The Monte Carlo simulations(MCS) confirm the effectiveness of the theoretical results.

Influence of exchange bias on spin torque ferromagnetic resonance for quantification of spin–orbit torque efficiency

Antiferromagnet(AFM)/ferromagnet(FM)heterostructure is a popular system for studying the spin–orbit torque(SOT)of AFMs.However,the interfacial exchange bias field induces that the magnetization in FM layer is noncollinear to the external magnetic field,namely the magnetic moment drag effect,which further influences the characteristic of SOT efficiency.In this work,we study the SOT efficiencies of IrMn/NiFe bilayers with strong interfacial exchange bias by using spin-torque ferromagnetic resonance(ST-FMR)method.A full analysis on the AFM/FM systems with exchange bias is performed,and the angular dependence of magnetization on external magnetic field is determined through the minimum rule of free energy.The ST-FMR results can be well fitted by this model.We obtained the relative accurate SOT efficiencyξ_(DL)=0.058 for the IrMn film.This work provides a useful method to analyze the angular dependence of ST-FMR results and facilitates the accurate measurement of SOT efficiency for the AFM/FM heterostructures with strong exchange bias.