Yes-associated protein-1 may serve as a diagnostic marker and therapeutic target for residual/recurrent hepatocellular carcinoma post-transarterial chemoembolization

Background and aim:The transcriptional co-activator Yes-associated protein-1(YAP1)has been impli-cated as an oncogene and is overexpressed in different kinds of human cancers,especially hepatocellular carcinoma(HCC).However,the role of YAP1 has not been reported in residual/recurrent HCC after transarterial chemoembolization(TACE).Our aim is to determine whether YAP1 is overexpressed in the residual/recurrent HCC after TACE.Methods:A total of 105 tumor tissues from 71 patients including 30 cases of primary HCC without prior treatment,35 cases of residual/recurrent HCC post TACE,and 6 cases of hepatoblastoma were included in the immunohistochemical study.YAP1 immunoreactivity was blindly scored as 0,1+,2+or 3+in density and percentages of positive cells.Results:About 33.3%(10/30)of primary HCC without prior treatment showed 2+of YAP1 immunore-activity.While 82.8%(29/35)of residual/recurrent HCCs after TACE treatment displayed 2-3+of YAP1 immunoreactivity,which was significantly higher compared to primary HCC without prior treatment(P=0.0002).YAP1 immunoreactivity was moderately to strongly positive(2-3+)in 100%of the hep-atoblastoma,particularly in the embryonal components(3+in 100%cases).Conclusions:YAP1 is significantly upregulated in the residual/recurrent HCCs post TACE treatment,suggesting that YAP1 may serve as a sensitive diagnostic marker and a treatment target for residual/recurrent HCC post TACE.

Structural properties of residual carbon in coal gasification fine slag and their influence on flotation separation and resource utilization:A review

Coal gasification fine slag(FS)is a typical solid waste generated in coal gasification.Its current disposal methods of stockpil-ing and landfilling have caused serious soil and ecological hazards.Separation recovery and the high-value utilization of residual carbon(RC)in FS are the keys to realizing the win-win situation of the coal chemical industry in terms of economic and environmental benefits.The structural properties,such as pore,surface functional group,and microcrystalline structures,of RC in FS(FS-RC)not only affect the flotation recovery efficiency of FS-RC but also form the basis for the high-value utilization of FS-RC.In this paper,the characteristics of FS-RC in terms of pore structure,surface functional groups,and microcrystalline structure are sorted out in accordance with gasification type and FS particle size.The reasons for the formation of the special structural properties of FS-RC are analyzed,and their influence on the flotation separation and high-value utilization of FS-RC is summarized.Separation methods based on the pore structural characterist-ics of FS-RC,such as ultrasonic pretreatment-pore-blocking flotation and pore breaking-flocculation flotation,are proposed to be the key development technologies for improving FS-RC recovery in the future.The design of low-cost,low-dose collectors containing polar bonds based on the surface and microcrystalline structures of FS-RC is proposed to be an important breakthrough point for strengthening the flotation efficiency of FS-RC in the future.The high-value utilization of FS should be based on the physicochemical structural proper-ties of FS-RC and should focus on the environmental impact of hazardous elements and the recyclability of chemical waste liquid to es-tablish an environmentally friendly utilization method.This review is of great theoretical importance for the comprehensive understand-ing of the unique structural properties of FS-RC,the breakthrough of the technological bottleneck in the efficient flotation separation of FS,and the expansion of the field of the high value-added utilization of FS-RC.

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.

Increased retinal venule diameter as a prognostic indicator for recurrent cerebrovascular events:a prospective observational study

Microvasculature of the retina is considered an alternative marker of cerebral vascular risk in healthy populations.However,the ability of retinal vasculature changes,specifically focusing on retinal vessel diameter,to predict the recurrence of cerebrovascular events in patients with ischemic stroke has not been determined comprehensively.While previous studies have shown a link between retinal vessel diameter and recurrent cerebrovascular events,they have not incorporated this information into a predictive model.Therefore,this study aimed to investigate the relationship between retinal vessel diameter and subsequent cerebrovascular events in patients with acute ischemic stroke.Additionally,we sought to establish a predictive model by combining retinal veessel diameter with traditional risk factors.We performed a prospective observational study of 141 patients with acute ischemic stroke who were admitted to the First Affiliated Hospital of Jinan University.All of these patients underwent digital retinal imaging within 72 hours of admission and were followed up for 3 years.We found that,after adjusting for related risk factors,patients with acute ischemic stroke with mean arteriolar diameter within 0.5-1.0 disc diameters of the disc margin(MAD_(0.5-1.0DD))of≥74.14μm and mean venular diameter within 0.5-1.0 disc diameters of the disc margin(MVD_(0.5-1.0DD))of≥83.91μm tended to experience recurrent cerebrovascular events.We established three multivariate Cox proportional hazard regression models:model 1 included traditional risk factors,model 2 added MAD_(0.5-1.0DD)to model 1,and model 3 added MVD0.5-1.0DD to model 1.Model 3 had the greatest potential to predict subsequent cerebrovascular events,followed by model 2,and finally model 1.These findings indicate that combining retinal venular or arteriolar diameter with traditional risk factors could improve the prediction of recurrent cerebrovascular events in patients with acute ischemic stroke,and that retinal imaging could be a useful and non-invasive method for identifying high-risk patients who require closer monitoring and more aggressive management.

A gated recurrent unit model to predict Poisson’s ratio using deep learning

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other models.The GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,respectively.The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.

Potential value of detection of minimal residual disease in colorectal cancer following radical resection

Although there has been significant advancement in the identification and management of colorectal cancer(CRC)in recent years,there is still room for improvement in the current standard treatment regimen.One area of concern is the lack of reliable tumor markers to predict treatment efficacy and guide tailored care.Due to its dynamic,effective,and non-invasive benefits over tissue biopsy,the detection of minimal or molecular residual lesions(MRD)based on circulating tumor DNA(ctDNA)is beneficial to the clinical development of drugs for patients with CRC after radical treatment,as well as for continuous monitoring of tumor recurrence and malignancy molecular gene evolution.The detection of ctDNA can currently be used to guide individual postoperative auxiliary treatment decisions(upgrade or downgrade treatment)in CRC,stratify the risk of clinical recurrence more precisely,and predict the risk of recurrence in advance of imaging examination,according to a large number of observational or prospective clinical studies.With increasing clarity comes the possibility of selecting a regimen of treatment based on postoperative ctDNA,which also improves the accuracy of clinical recurrence risk assessment for CRC.Therefore,it is anticipated that the identification of ctDNA would alter the current framework for dealing with CRC and lead to individualized,stratified precision therapy;however,additional confirmation will require subsequent high-quality,prospective,large-scale randomized controlled studies.This article will provide an overview of the definition and clinical significance of MRD,the primary indications and technological challenges for MRD detection,along with the advancement in clinical research about ctDNA detection following radical resection of the CRC.

Residual stress modeling of mitigated fused silica damage sites with CO_(2)laser annealing

A numerical model based on measured fictive temperature distributions is explored to evaluate the residual stress fields of CO_(2)laser-annealed mitigated fused silica damage sites.The proposed model extracts the residual strain from the differences in thermoelastic contraction of fused silica with different fictive temperatures from the initial frozen-in temperatures to ambient temperature.The residual stress fields of mitigated damage sites for the CO_(2)laser-annealed case are obtained by a finite element analysis of equilibrium equations and constitutive equations.The simulated results indicate that the proposed model can accurately evaluate the residual stress fields of laser-annealed mitigated damage sites with a complex thermal history.The calculated maximum hoop stress is in good agreement with the reported experimental result.The estimated optical retardance profiles from the calculated radial and hoop stress fields are consistent with the photoelastic measurements.These results provide sufficient evidence to demonstrate the suitability of the proposed model for describing the residual stresses of mitigated fused silica damage sites after CO_(2)laser annealing.

Molecular simulation study on the evolution process of hydrate residual structures into hydrate

The clathrate hydrate memory effect is a fascinating phenomenon with potential applications in carbon capture,utilization and storage(CCUS),gas separation,and gas storage as it can accelerate the secondary formation of clathrate hydrate.However,the underlying mechanism of this effect remains unclear.To gain a better understanding of the mechanism,we conducted molecular dynamic simulations to simulate the initial formation and reformation processes of methane hydrate.In this work,we showed the evolution process of hydrate residual structures into hydrate cages.The simulation results indicate that the residual structures are closely related to the existence of hydrate memory effect,and the higher the contribution of hydrate dissociated water to the hydrate nucleation process,the faster the hydrate nucleation.After hydrate dissociation,the locally ordered structures still exist after hydrate dissociation and can promote the formation of cluster structures,thus accelerating hydrate nucleation.Additionally,the nucleation process of hydrate and the formation process of clusters are inseparable.The size of clusters composed of cup-cage structures is critical for hydrate nucleation.The residence time at high temperature after hydrate decomposition will affect the strength of the hydrate memory effect.Our simulation results provide microscopic insights into the occurrence of the hydrate memory effect and shed light on the hydrate reformation process at the molecular scale.

Rationale of a Cross Sectional Analytic Study on Determinants of Recurrent Preeclampsia at University Clinics of Kinshasa (Democratic Republic of Congo) and at Victor Dupouy Hospital Center (France)

Research Background: Pre-eclampsia is one of main causes of materno-foetal mortality and morbidity worldwide, with a prevalence of 3% - 7%. Although considered a primiparous condition, it can nevertheless recur. Several factors appear to be associated with risk of recurrence of pre-eclampsia, such as the term of delivery of previous pregnancy, severity of disease, the existence of co-morbidities and the inter-genital space. Purpose: The aim of our study will be to analyse and identify in a population of pregnant women with a history of preeclampsia risk factors associated with occurrence of recurrent preeclampsia at University clinics of Kinshasa (Democratic Republic of Congo) and at Victor Dupouy Hospital Center (France). Methods: In this study, pregnant women with an history of preeclampsia who will give birth between November 2018 and October 2024 at University Clinics of Kinshasa (UCK) and Victor Dupouy Hospital Center (VDHC) will be included. This will be a cross-sectional analytical study, data from previous and subsequent pregnancies will be studied. Expected Result: The prevalence of recurrent preeclampsia in the study population will be determined. And we will highlight the factors that will determine the recurrence of preeclampsia by analysing the risk factors. Conclusion: Knowledge of the factors associated with recurrent preeclampsia could be an excellent tool for predicting and preventing the disease.

Investigation of risk factors in the development of recurrent urethral stricture after internal urethrotomy

BACKGROUND Urethral stricture is a condition that often develops with trauma and results in narrowing of the urethral lumen.Although endoscopic methods are mostly used in its treatment,it has high recurrence rates.Therefore,open urethroplasty is recommended after unsuccessful endoscopic treatments.AIM To investigate the risk factors associated with urethral stricture recurrence.METHODS The data of male patients who underwent internal urethrotomy for urethral stricture between January 2017 and January 2023 were retrospectively analyzed.Demographic data,comorbidities,preoperative haemogram,and biochemical values obtained from peripheral blood and operative data were recorded.Patients were divided into two groups in terms of recurrence development;recurrence and non-recurrence.Initially recorded data were compared between the two groups.RESULTS A total of 303 patients were included in the study.The mean age of the patients was 66.6±13.6 years.The mean duration of recurrence development was 9.63±9.84(min-max:1-39)months in the recurrence group.Recurrence did not occur in non-recurrence group throughout the follow-up period with an average time of 44.15±24.07(min-max:12-84)months.In the comparison of both groups,the presence of diabetes mellitus(DM),hypertension(HT),and multiple comorbidi-ties were significantly higher in the recurrence(+)group(P=0.038,P=0.012,P=0.013).Blood group,postoperative use of non-steroidal anti-inflammatory drugs,preoperative cystostomy,cause of stricture,iatrogenic cause of stricture,location and length of stricture,indwelling urinary cathater size and day of catheter removal did not differ between the two groups.No statistically significant difference was observed between the two groups in terms of age,uroflowmetric maximum flow rate value,hemo-gram parameters,aspartate aminotransferase(AST),alanine aminotransferase(ALT),fasting blood sugar,creati-nine,glomerular filtration rate,neutrophil-lymphocyte ratio,platelet-lymphocyte ratio,lymphocyte-monocyte ratio,monocyte-lymphocyte ratio and AST/ALT ratios.CONCLUSION In patients with urethral stricture recurrence,only the frequency of DM and HT was high,while inflammation marker levels and stricture-related parameters were similar between the groups.

Residual alkali-evoked cross-linked polymer layer for anti-air-sensitivity LiNi_(0.89)Co_(0.06)Mn_(0.05)O_(2)cathode

High-energy density lithium-ion batteries(LIBs)with layered high-nickel oxide cathodes(LiNi_(x)Co_(y)Mn_(1-x-y)O_(2),x≥0.8)show great promise in consumer electronics and vehicular applications.However,LiNi_(x)Co_(y)Mn_(1-x-y)O_(2)faces challenges related to capacity decay caused by residual alkalis owing to high sensitivity to air.To address this issue,we propose a hazardous substances upcycling method that fundamentally mitigates alkali content and concurrently induces the emergence of an anti-air-sensitive layer on the cathode surface.Through the neutralization of polyacrylic acid(PAA)with residual alkalis and then coupling it with 3-aminopropyl triethoxysilane(KH550),a stable and ion-conductive cross-linked polymer layer is in situ integrated into the LiNi_(0.89)Co_(0.06)Mn_(0.05)O_(2)(NCM)cathode.Our characterization and measurements demonstrate its effectiveness.The NCM material exhibits impressive cycling performance,retaining 88.4%of its capacity after 200 cycles at 5 C and achieving an extraordinary specific capacity of 170.0 mA h g^(-1) at 10 C.Importantly,this layer on the NCM efficiently suppresses unfavorable phase transitions,severe electrolyte degradation,and CO_(2)gas evolution,while maintaining commendable resistance to air exposure.This surface modification strategy shows widespread potential for creating air-stable LiNi_(x)Co_(y)Mn_(1-x-y)O_(2)cathodes,thereby advancing high-performance LIBs.

Recurrent Transient Ischemic Attacks Revealing Cerebral Amyloid Angiopathy: A Comprehensive Case

This case report investigates the manifestation of cerebral amyloid angiopathy (CAA) through recurrent Transient Ischemic Attacks (TIAs) in an 82-year-old patient. Despite initial diagnostic complexities, cerebral angiography-MRI revealed features indicative of CAA. Symptomatic treatment resulted in improvement, but the patient later developed a fatal hematoma. The discussion navigates the intricate therapeutic landscape of repetitive TIAs in the elderly with cardiovascular risk factors, emphasizing the pivotal role of cerebral MRI and meticulous bleeding risk management. The conclusion stresses the importance of incorporating SWI sequences, specifically when suspecting a cardioembolic TIA, as a diagnostic measure to explore and exclude CAA in the differential diagnosis. This case report provides valuable insights into these challenges, highlighting the need to consider CAA in relevant cases.

Effects of soil crust on the collapsing erosion of colluvial deposits with granite residual soil

Collapsing erosion is a unique phenomenon commonly observed on the granite residue hillslopes in the tropical and subtropical regions of southern China,characterized by its abrupt occurrence and significant erosion volumes.However,the impacts of soil crust conditions on the erosion of colluvial deposits with granite residual soils have only been studied to a limited extent.To address this issue,this study investigates the impacts of three soil crust conditions(i.e.,without crust,10-minute crust,and 20-minute crust)on gully morphology,rainfall infiltration,and runoff and sediment yield during slope erosion of colluvial deposits with granite residues(classified as Acrisols)in Yudu County,Ganzhou City,Jiangxi Province,China,using simulated rainfall tests and photographic methods.The results showed that as the strength of the soil crust increased,the capacity of moisture infiltration and the width and depth of the gully as well as the sediment concentration and yield ratio decreased;at the same time,the runoff ratio increased.The sediment yield in the without-crust test was found to be 1.24 and 1.43 times higher than that observed in the 10-minute crust and 20-minute crust tests,respectively.These results indicate that soil crusts can effectively prevent slope erosion and moisture infiltration,while providing valuable insights for the management of soil erosion in natural environments.

Recurrent neural network decoding of rotated surface codes based on distributed strategy

Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding algorithms.Quantum error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise models.In this paper,we use a distributed decoding strategy,which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes increases.Our decoding algorithm is based on renormalization group decoding and recurrent neural network decoder.The recurrent neural network is trained through the ResNet architecture to improve its decoding accuracy.Then we test the decoding performance of our distributed strategy decoder,recurrent neural network decoder,and the classic minimum weight perfect matching(MWPM)decoder for rotated surface codes with different code distances under the circuit noise model,the thresholds of these three decoders are about 0.0052,0.0051,and 0.0049,respectively.Our results demonstrate that the distributed strategy decoder outperforms the other two decoders,achieving approximately a 5%improvement in decoding efficiency compared to the MWPM decoder and approximately a 2%improvement compared to the recurrent neural network decoder.

A modified smoothed particle hydrodynamics method considering residual stress for simulating failure and its application in layered rock mass

Residual strength is an indispensable factor in evaluating rock fracture,yet the current Smoothed Particle Hydrodynamics(SPH)framework rarely considers its influence when simulating fracture.An improved cracking strategy considering residual stress in the base bond SPH method was proposed to simulate failures in layered rocks and slopes and verified by experimental results and other simulation methods(i.e.,the discrete element method).Modified Mohr–Coulomb failure criterion was applied to distinguish the mixed failure of tensile and shear.Bond fracture markψwas introduced to improve the kernel function after tensile damage,and the calculation of residual stress after the damage was derived after shear damage.Numerical simulations were carried out to evaluate its performance under different stress and scale conditions and to verify its effectiveness in realistically reproducing crack initiation and propagation and coalescence,even fracture and separation.The results indicate that the improved cracking strategy precisely captures the fracture and failure pattern in layered rocks and rock slopes.The residual stress of brittle tock is correctly captured by the improved SPH method.The improved SPH method that considers residual strength shows an approximately 13%improvement in accuracy for the safety factor of anti-dip layered slopes compared to the method that does not consider residual strength,as validated against analytical solutions.We infer that the improved SPH method is effective and shows promise for applications to continuous and discontinuous rock masses.

Recurrent multisystem Langerhans cell histiocytosis involving the female genitalia: A case report

BACKGROUND Langerhans cell histiocytosis(LCH)is a histiocytic proliferative disease caused by clonal proliferation of Langerhans cells,which is currently defined as an inflam-matory myeloid tumor.It is rare in adults,with an incidence of 1–2 per million,and is highly heterogeneous in clinical presentation,with unpredictable disease progression and outcome.CASE SUMMARY A 52-year-old postmenopausal female patient presented to the gynecology department in July 2023 with bilateral vulvar masses.She was diagnosed with recurrent multisystem LCH.The patient had previously been diagnosed with a single-system and single-focal LCH in October 2021 due to a right maxillofacial mass,which resolved after surgical treatment.A chemotherapy regimen was developed after multidisciplinary consultation.Six cycles of chemotherapy resulted in partial remission,and maintenance chemotherapy is currently being administered.CONCLUSION Recurrent LCH involving the bilateral vulva has been poorly reported.Compre-hensive imaging and pathological evaluation is important for diagnosis.The model of joint multidisciplinary specialist diagnosis and treatment is worthy of clinical application.

Attention-Based Residual Dense Shrinkage Network for ECG Denoising

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac diagnosis.However,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological analysis.Therefore,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic research.Aiming at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this paper.Firstly,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise suppression.Finally,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG signal.Experiments on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic fields.The introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network security.Insufficient feature extraction leads to less accurate classification results.In abnormal traffic detection,the data of network traffic is high-dimensional and complex.This data not only increases the computational burden of model training but also makes information extraction more difficult.To address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection.To fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is introduced.This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive field.The proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual block.This module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information redundancy.Experimental results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

Radar Signal Intra-Pulse Modulation Recognition Based on Deep Residual Network

In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio(SNR),the paper proposes an automatic recog-nition method of complex radar intra-pulse modulation signal type based on deep residual network.The basic principle of the recognition method is to obtain the transformation relationship between the time and frequency of complex radar intra-pulse modulation signal through short-time Fourier transform(STFT),and then design an appropriate deep residual network to extract the features of the time-frequency map and complete a variety of complex intra-pulse modulation signal type recognition.In addition,in order to improve the generalization ability of the proposed method,label smoothing and L2 regularization are introduced.The simulation results show that the proposed method has a recognition accuracy of more than 95%for complex radar intra-pulse modulation sig-nal types under low SNR(2 dB).