Ruptured hepatoblastoma with massive internal bleeding in an adult

Hepatoblastoma is the most common primary hepatic tumor of children. However, only a very few cases have been reported in adults. Most studies support treatment with chemotherapy followed by surgical resection. We present the first reported case of adult hepatoblastoma in Taiwan. A 52-year-old female suffered from sudden onset of abdominal pain and general weakness for days.Internal bleeding with hemorrhagic shock was suspected and two massive lesions in both lobes of the liver with hemoperitoneum were noted from imaging studies.Surgical resection of the larger left lobe tumor and radio-frequency ablation of the right smaller one were performed. The histopathology diagnosis was of a hepatoblastoma.

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.

Radiofrequency ablation combined with transcatheter arterial chemoembolization for recurrent liver cancer

BACKGROUND The recurrence rate of liver cancer after surgery is high.Radiofrequency ablation(RFA)combined with transcatheter arterial chemoembolization(TACE)is an effective treatment for liver cancer;however,its efficacy in recurrent liver cancer remains unclear.AIM To investigate the clinical effect of TACE combined with RFA in the treatment of recurrent liver cancer.METHODS Ninety patients with recurrent liver cancer were divided into 2 groups according to treatment plan:Control(RFA alone);and experimental[TACE combined with RFA(TACE+RFA)].The incidence of increased alanine aminotransferase levels,complications,and other indices were compared between the two groups before and after the procedures.RESULTS One month after the procedures,the short-term efficacy rate and Karnofsky Performance Status scores of the experimental group were significantly higher than those of the control group(P<0.05).Alpha-fetoprotein(AFP)and total bilirubin levels were lower than those in the control group(P<0.05);The overall response rate was 82.22%and 66.67%in the experimental and control groups,respectively;The disease control rate was 93.33%and 82.22%in the experimental and control groups,respectively,the differences are statistically significant(P<0.05).And there were no statistical differences in complications between the two groups(P>0.05).CONCLUSION TACE+RFA was effective for the treatment of recurrent liver cancer and significantly reduced AFP levels and improved various indices of liver function.

Nucleotide excision repair gene polymorphisms and hepatoblastoma susceptibility in Eastern Chinese children:A five-center case-control study

Objective:Nucleotide excision repair(NER)plays a vital role in maintaining genome stability,and the effect of NER gene polymorphisms on hepatoblastoma susceptibility is still under investigation.This study aimed to evaluate the relationship between NER gene polymorphisms and the risk of hepatoblastoma in Eastern Chinese Han children.Methods:In this five-center case-control study,we enrolled 966 subjects from East China(193 hepatoblastoma patients and 773 healthy controls).The TaqMan method was used to genotype 19 single nucleotide polymorphisms(SNPs)in NER pathway genes,including ERCC1,XPA,XPC,XPD,XPF,and XPG.Then,multivariate logistic regression analysis was performed,and odds ratios(ORs)and 95%confidence intervals(95%CIs)were utilized to assess the strength of associations.Results:Three SNPs were related to hepatoblastoma risk.XPC rs2229090 and XPD rs3810366 significantly contributed to hepatoblastoma risk according to the dominant model(adjusted OR=1.49,95%CI=1.07−2.08,P=0.019;adjusted OR=1.66,95%CI=1.12−2.45,P=0.012,respectively).However,XPD rs238406 conferred a significantly decreased risk of hepatoblastoma under the dominant model(adjusted OR=0.68,95%CI=0.49−0.95;P=0.024).Stratified analysis demonstrated that these significant associations were more prominent in certain subgroups.Moreover,there was evidence of functional implications of these significant SNPs suggested by online expression quantitative trait loci(eQTLs)and splicing quantitative trait loci(sQTLs)analysis.Conclusions:In summary,NER pathway gene polymorphisms(XPC rs2229090,XPD rs3810366,and XPD rs238406)are significantly associated with hepatoblastoma risk,and further research is required to verify these findings.

Pediatric Hepatoblastoma: A Case Report and Review of the Literature

Introduction and Objective: Hepatoblastoma is the most frequent malignant embryonal liver tumor, and its incidence is increasing. Children under 3 years of age are particularly affected. The etiology is largely unknown, but a genetic predisposition exists. This case highlights the clinical and imaging characteristics and management strategies. Case Report: We report the case of an 18 months old male admitted for exploration of an abdominal mass and was diagnosed with hepatoblastoma. Conclusion: Hepatoblastoma is an aggressive tumor that begins locally and eventually metastasizes to the brain, lungs, lymph nodes, and other organs. Tumor stage at presentation and resectability are the most significant prognostic factors.

Computer-assisted three-dimensional individualized extreme liver resection for hepatoblastoma in proximity to the major liver vasculature

BACKGROUND The management of hepatoblastoma(HB)becomes challenging when the tumor remains in close proximity to the major liver vasculature(PMV)even after a full course of neoadjuvant chemotherapy(NAC).In such cases,extreme liver resection can be considered a potential option.AIM To explore whether computer-assisted three-dimensional individualized extreme liver resection is safe and feasible for children with HB who still have PMV after a full course of NAC.METHODS We retrospectively collected data from children with HB who underwent surgical resection at our center from June 2013 to June 2023.We then analyzed the detailed clinical and three-dimensional characteristics of children with HB who still had PMV after a full course of NAC.RESULTS Sixty-seven children diagnosed with HB underwent surgical resection.The age at diagnosis was 21.4±18.8 months,and 40 boys and 27 girls were included.Fifty-nine(88.1%)patients had a single tumor,39(58.2%)of which was located in the right lobe of the liver.A total of 47 patients(70.1%)had PRE-TEXT III or IV.Thirty-nine patients(58.2%)underwent delayed resection.After a full course of NAC,16 patients still had close PMV(within 1 cm in two patients,touching in 11 patients,compressing in four patients,and showing tumor thrombus in three patients).There were 6 patients of tumors in the middle lobe of the liver,and four of those patients exhibited liver anatomy variations.These 16 children underwent extreme liver resection after comprehensive preoperative evaluation.Intraoperative procedures were performed according to the preoperative plan,and the operations were successfully performed.Currently,the 3-year event-free survival of 67 children with HB is 88%.Among the 16 children who underwent extreme liver resection,three experienced recurrence,and one died due to multiple metastases.CONCLUSION Extreme liver resection for HB that is still in close PMV after a full course of NAC is both safe and feasible.This approach not only reduces the necessity for liver transplantation but also results in a favorable prognosis.Individualized three-dimensional surgical planning is beneficial for accurate and complete resection of HB,particularly for assessing vascular involvement,remnant liver volume and anatomical variations.

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.

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.

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.

Recorded recurrent deep reinforcement learning guidance laws for intercepting endoatmospheric maneuvering missiles

This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise.The attack-defense engagement scenario is modeled as a partially observable Markov decision process(POMDP).Given the benefits of recurrent neural networks(RNNs)in processing sequence information,an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with POMDPs.The measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance frequency.During training,the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the agent.The training curves show that the proposed RRTD3 successfully enhances data efficiency,training speed,and training stability.The test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited energy.The main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these systems.Minimizing SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is critical.Our proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration model.The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation.This is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO framework.The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability reduction.Through comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP minimization.This paper compares nine methods for predicting outage probability in wireless-powered communications.The IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance improvement.In contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

Ocular rosacea without facial erythema involvement manifesting as bilateral multiple recurrent chalazions:A case report

BACKGROUND In addition to the non-specific symptomatology of ocular rosacea,currently,there are no reliable diagnostic tests for the disease,which may lead to its misdiagnosis.Here,we report a case of ocular rosacea presenting with multiple recurrent chalazion on both eyelids.CASE SUMMARY A 63-year-old female patient presented with multiple chalazion and dry eyes in both eyes,with no facial erythema.Initial management done were application of steroid eye ointment on both eyelids,hot compresses,and eyelid margin cleaning;noting that there was no relief of symptoms.Surgical excision of the chalazion was done on both eyes,however,bilateral recurrence occurred post-operatively.The pathological studies showed infiltration of a small amount of fibrous tissue with many chronic inflammatory cells.Immunohistochemistry studies were positive for LL-37.Resolution of the chalazion occurred after oral administration of doxycycline and azithromycin.CONCLUSION Our findings show that ophthalmologists should recognize the ocular manifestations of skin diseases.

Embryo Transfer Strategies for Women with Recurrent Implantation Failure During the Frozen-thawed Embryo Transfer Cycles:Sequential Embryo Transfer or Double-blastocyst Transfer?

Objective Both sequential embryo transfer(SeET)and double-blastocyst transfer(DBT)can serve as embryo transfer strategies for women with recurrent implantation failure(RIF).This study aims to compare the effects of SeET and DBT on pregnancy outcomes.Methods Totally,261 frozen-thawed embryo transfer cycles of 243 RIF women were included in this multicenter retrospective analysis.According to different embryo quality and transfer strategies,they were divided into four groups:group A,good-quality SeET(GQ-SeET,n=38 cycles);group B,poor-quality or mixed-quality SeET(PQ/MQ-SeET,n=31 cycles);group C,good-quality DBT(GQ-DBT,n=121 cycles);and group D,poor-quality or mixed-quality DBT(PQ/MQ-DBT,n=71 cycles).The main outcome,clinical pregnancy rate,was compared,and the generalized estimating equation(GEE)model was used to correct potential confounders that might impact pregnancy outcomes.Results GQ-DBT achieved a significantly higher clinical pregnancy rate(aOR 2.588,95%CI 1.267–5.284,P=0.009)and live birth rate(aOR 3.082,95%CI 1.482–6.412,P=0.003)than PQ/MQ-DBT.Similarly,the clinical pregnancy rate was significantly higher in GQ-SeET than in PQ/MQ-SeET(aOR 4.047,95%CI 1.218–13.450,P=0.023).The pregnancy outcomes of GQ-SeET were not significantly different from those of GQ-DBT,and the same results were found between PQ/MQ-SeET and PQ/MQ-DBT.Conclusion SeET relative to DBT did not seem to improve pregnancy outcomes for RIF patients if the embryo quality was comparable between the two groups.Better clinical pregnancy outcomes could be obtained by transferring good-quality embryos,no matter whether in SeET or DBT.Embryo quality plays a more important role in pregnancy outcomes for RIF patients.

An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

Recurrent acute pancreatitis as an initial presentation of primary hyperparathyroidism:A case report

BACKGROUND With 4.9 to 35 instances per 100000 cases,hyperparathyroidism is one of the rarest causes of acute pancreatitis.The major cause of primary hyperparathyroidism is a parathyroid adenoma,which can manifest clinically in various ways.CASE SUMMARY We discuss the unusual case of a 13-year-old boy with recurrent pancreatitis as the initial presentation of primary hyperparathyroidism.The cause of his recurrent pancreatitis remained unknown,and the patient had multiple admissions with acute pancreatitis over 3 years.His diagnosis was delayed due to the initial normal levels of parathyroid hormone,which were later reported elevated in a subsequent episode where ultrasound neck and thyroid scintigraphy revealed a parathyroid adenoma as the underlying cause.After the diagnosis was made,he underwent surgical resection of the adenoma.CONCLUSION This case study stresses the importance of considering uncommon causes for recurrent pancreatitis.

Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 attributes.The WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation process.The results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

Effects of data smoothing and recurrent neural network(RNN)algorithms for real-time forecasting of tunnel boring machine(TBM)performance

Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.

Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.